Auditory event-related potentials in separating patients with depressive disorders and non-depressed controls : A narrative review

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Auditory event-related potentials in separating patients with depressive disorders and non-depressed controls : A narrative review © The Author(s), 2022. Published version Kangas, Elina S.; Vuoriainen, Elisa; Lindeman, Sari; Astikainen, Piia Kangas, E. S., Vuoriainen, E., Lindeman, S., & Astikainen, P. (2022). Auditory event-related potentials in separating patients with depressive disorders and non-depressed controls : A narrative review. International Journal of Psychophysiology, 179, 119-142. https://doi.org/10.1016/j.ijpsycho.2022.07.003 2022 International Journal of Psychophysiology 179 (2022) 119–142 Available online 15 July 2022 0167-8760/© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Review Auditory event-related potentials in separating patients with depressive disorders and non-depressed controls: A narrative review Elina S. Kangas a , * , Elisa Vuoriainen b , Sari Lindeman c , d , Piia Astikainen a a Department of Psychology, University of Jyvaskyla, Jyv¨ askyl¨ a, Finland b Human Information Processing Laboratory, Faculty of Social Sciences / Psychology, Tampere University, Tampere, Finland c Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland d Central Finland Health Care District, Jyv¨ askyl¨ a, Finland ARTICLE INFO Keywords: Auditory P3 Biomarker Depressive disorder Loudness dependence of auditory evoked potentials Mismatch negativity ABSTRACT This narrative review brings together the findings regarding the differences in the auditory event-related potentials (ERPs) between patients with depressive disorder and non-depressed control subjects. These studies' results can inform us of the possible alterations in sensory-cognitive processing in depressive disorders and the potential of using these ERPs in clinical applications. Auditory P3, mismatch negativity (MMN) and loudness dependence of auditory evoked potentials (LDAEP) were the subjects of the investigation. A search in PubMed yielded 84 studies. The findings of the reviewed studies were not highly consistent, but some patterns could be identified. For auditory P3b, the common findings were attenuated amplitude and prolonged latency among depressed patients. Regarding auditory MMN, especially the amplitude of duration deviance MMN was commonly attenuated, and the amplitude of frequency deviance MMN was increased in depressed patients. In LDAEP studies, generally, no differences between depressed patients and non-depressed controls were reported, although some group differences concerning specific depression subtypes were found. This review posits that future research should investigate whether certain stimulus conditions are particularly efficient at separating depressed and non-depressed participant groups. Future studies should contrast responses in different subpopulations of depressed patients, as well as different clinical groups (e.g., depressive disorder and anxiety disorder patients), to investigate the specificity of the auditory ERP alterations for depressive disorders. 1. Introduction Major depressive disorder (MDD) is one of the most common mental illnesses with an estimated 3.8 % of the world's population affected (Institute of Health Metrics and Evaluation, 2019). Furthermore, MDD is expected to be the leading cause of the burden of disease worldwide by 2030 (World Federation for Mental Health, 2012). In addition to the huge economic and social burden, MDD entails a lot of suffering and has a strong impact on the quality of life of depressed individuals. Therefore, efficient tools for comprehensive MDD diagnostics and treatment are needed. MDD is a heterogeneous syndrome with a complex spectrum of possible symptoms that are often shared with other disorders (Bilello, 2016; Fried and Nesse, 2015), all of which also complicate comprehensive diagnostics. Unfortunately, there are currently no objective methods for diagnosing/characterising MDD, which may delay the start of treatment and negatively affect optimal treatment outcomes (Gaynes et al., 2009). In clinical practice, the diagnosis of MDD is currently based on structured clinical interviews to determine the presence of symptoms (typically, the Structured Clinical Interview for DSM; First et al., 2015). Hence, similar to the diagnoses of other psychiatric disorders, the diagnosis of MDD is determined by descriptive diagnostic criteria based on the symptoms reported by the patient. More objective ways to diagnose depression based on biomarkers have been the subject of research. According to Atkinson et al. (2001), a biomarker is a characteristic that can be objectively measured and evaluated as an indicator of normal or pathogenic biological processes. Biomarkers can be used, for instance, as diagnostic tools for identifying individuals with a disease (Atkinson, 2001). Important performance characteristics of biomarkers include sensitivity (the fraction of people with a disease who test positive) and specificity (the fraction of people without the disease who test negative, e.g., FDA-NIH Biomarker Working Group, 2016). It would be * Corresponding author at: Department of Psychology, University of Jyv¨ askyl¨ a, P.O. Box 35, 40014 Jyv¨ askyl¨ a, Finland. E-mail address: [email protected] (E.S. Kangas). Contents lists available at ScienceDirect International Journal of Psychophysiology journal homepage: www.elsevier.com/locate/ijpsycho https://doi.org/10.1016/j.ijpsycho.2022.07.003 Received 29 October 2021; Received in revised form 30 June 2022; Accepted 7 July 2022 International Journal of Psychophysiology 179 (2022) 119–142 120 important to identify biomarkers that could provide more objective assessment techniques for MDD diagnostics. Aside from clinical interviews, biomarkers could be used as tools to differentiate between patients with MDD and non-depressed subjects, as well as to define more homogenous subtypes of MDD and to develop more personalised approaches for treatment in the future. Previous studies indicated that markers reflecting the activity of the inflammatory, neurotransmitter, neurotrophic, neuroendocrine and metabolic systems, as well as genetic and epigenetic features might be potential candidates for MDD biomarkers, but the findings are still quite inconsistent (for reviews, see e.g., Hacimusalar and Es¸el, 2018; Strawbridge et al., 2017). In biomarker research, event-related potentials (ERPs) derived from electroencephalography (EEG) in the context of MDD have also been investigated. This narrative review summarises the findings on auditory ERPs reflecting sensory-cognitive processing, which have been widely investigated in patients with MDD and which might have the potential to be developed as biomarkers for MDD. This review addresses the first phase of the research on possible future biomarkers focusing on sensitivity by investigating whether auditory ERPs could be used to differentiate between depressed and non-depressed participant groups. Furthermore, this review also discusses whether alterations in auditory ERP components are specific to some subtypes of depressive disorder, whether they are related to the severity of depression and whether there are some confounding factors (e.g., comorbid disorders and antidepressant medication) that might have an impact on the results on ERPs. This narrative review does not provide a quantitative synthesis of the findings; instead, it presents an overall picture of current knowledge comprised of four decades of research on alterations in auditory ERPs in patients with depressive disorder, providing a foundation for future research. This review focuses on ERPs from the auditory modality. There is abundant research on auditory sensory-cognitive ERPs in the context of MDD. Nonetheless, research on these auditory ERPs for MDD has not yet been comprehensively reviewed, but these ERPs have biomarker potential for other psychiatric disorders. For instance, auditory sensorycognitive ERPs can differentiate between schizophrenia patients and control subjects (for meta-analyses for MMN, see Erickson et al., 2016; Umbricht and Krljes, 2005; for P3, see Bramon et al., 2004; Jeon and Polich, 2003). Furthermore, pre-attentive auditory responses (e.g., N1, P2, mismatch negativity [MMN] and P3a) can be examined in the absence of participants' attention. Thus, they may be beneficial for studies investigating clinical patient groups, including MDD patients, because problems related to motivational and attentional factors in measurement can be avoided. Visual ERPs in patients with MDD have also been investigated. These studies are beyond the scope of the present review that focuses only on auditory ERPs. MDD is characterised by deficits in emotional processing, reactivity and regulation (Bylsma, 2021). Visual ERPs elicited, for instance, by emotional pictures or in reward-related tasks can be utilised to investigate these emotion-related deficits in MDD patients. Studies on visual ERPs related to affective cognition in MDD patients have been widely reviewed, and the results indicate that ERP components reflecting emotional and motivational processing (late positive potential [LPP]) and reward processing (reward positivity [RewP]) are attenuated in MDD patients and persons who are at risk of depression (for reviews, see Hajcak and Foti, 2020; Kujawa and Burkhouse, 2017; Kujawa et al., 2020; Proudfit, 2015; Proudfit et al., 2015). Since MDD includes affective and motivational symptoms (American Psychiatric Association, 2013), these visual ERPs may also be potential future biomarkers for MDD. However, in the present narrative review, the focus on ERP research on MDD patients is different, addressing auditory ERPs reflecting basic sensory-cognitive processing. More specifically, whereas the aforementioned reviews on visual ERP components related to affective cognition provide insight into alterations in the emotional and motivational information processing in MDD patients, in the present review, the theoretical framework for the connection between auditory sensory-cognitive ERPs and MDD is mostly based on deficits in more basic cognitive functions in MDD patients (e.g., Rock et al., 2014). ERPs are neural responses time locked to the onset of sensory stimuli or cognitive or motor functions, and they are derived from EEG measurement of neural activity by signal averaging (e.g., Luck, 2014). EEG provides a method for studying information streams in the human brain; therefore, it can also be utilised to assess pathological states, including those related to neuropsychiatric disorders (Maekawa et al., 2012). EEG may be a promising resource for the identification of MDD biomarkers because MDD is associated with changes in brain activity and neurocognitive processes (Mumtaz et al., 2015). EEG directly measures ongoing neural activity in the brain with a high temporal resolution (in a time scale of milliseconds, e.g., Sanei and Chambers, 2007). It is also a non-invasive and cost-effective resource for discovering MDD biomarkers. These advantages of the EEG method may facilitate possible future implementation in clinical practice. There are many possible reasons for the assumption that ERPs reflecting sensory and cognitive functions can be considered potential tools for differentiating between patients with MDD and non-depressed control participants. Studies that utilised cognitive tests have shown that MDD patients exhibit deficits in attention, learning, memory, executive functions, etc. (Austin et al., 2001; Burt et al., 1995; Lee et al., 2012; Rock et al., 2014; Veiel, 1997). Information processing deficits underlying these overt cognitive dysfunctions detected with cognitive tests can be examined with a precise temporal resolution using ERPs. The high temporal resolution of ERPs allows for investigations into the serial stages of information processing at pre-attentive and attentive levels. Thus, ERP components can be used as tools to examine the differences in the specific information processing stages between MDD patients and non-depressed control participants, contributing to a better understanding of the neurophysiological underpinnings of the cognitive dysfunctions in MDD. Furthermore, ERPs are modulated by neurotransmitters, such as serotonin, dopamine, noradrenaline and glutamate (e.g., Hegerl and Juckel, 1993; Liu et al., 2009; Pogarell et al., 2011; Swick et al., 1994; Umbricht et al., 2000), that have been implicated in the pathophysiology of depression (e.g., Belujon and Grace, 2017; Malhi et al., 2005; Moret and Briley, 2011; Moriguchi et al., 2019). In this narrative review, we bring together findings about whether there are deficits in patients with MDD in the early pre-attentive cognitive processing stage reflecting automatic change detection (MMN) and the later attentive cognitive processing stage reflecting attentional and working memory operations (P3). MMN has traditionally been studied by utilising an ignore oddball condition in which a rare deviant stimulus is interspersed with a repetitive standard stimulus (N¨ a¨ at¨ anen et al., 1978; for reviews, see N¨ a¨ at¨ anen et al., 2005, 2007). Several aspects suggest that MMN may be altered in MDD patients. MMN is associated with predictive coding theory, according to which the brain predicts future events based on previous sensory input (Friston, 2005). When the input does not match the prediction, a prediction error occurs, and the error signal is projected upward in the hierarchical neural network to update the predictive model (Friston, 2005). MMN has been suggested to reflect prediction errors (e.g., Chennu et al., 2013; Wacongne et al., 2011; for reviews, see Carbajal and Malmierca, 2018; Denham and Winkler, 2020). Predictive coding, as a fundamental information processing mechanism that enables adaptive behaviour, is theorised to be aberrant in psychiatric conditions, including depressive disorders (for reviews, see Kube et al., 2020; Smith et al., 2021). Moreover, at the neurochemical level, MMN is suggested to reflect the functioning of glutaminergic N-methyl-D-aspartate (NMDA) receptors (e. g., Javitt et al., 1996; Umbricht et al., 2000, 2002), which has been suggested to be dysfunctional in MDD patients (e.g., Adell, 2020; Inoshita et al., 2018; Sanacora et al., 2008). P3 is a later-occurring component than MMN in the information processing chain (for a review, see Friedman et al., 2001; Kok, 2001). It is most commonly studied utilising an attend oddball task in which the E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 121 target stimuli are rarely presented among repetitive standard stimuli, and the subject is required to respond to the targets (Friedman et al., 2001; Polich and Criado, 2006; Polich, 2007). It is feasible that P3 reflecting attentional and working memory operations might differ between patients with MDD and non-depressed controls since studies investigating cognitive functions utilising cognitive tests have indicated that selective attention and working memory are especially impaired among MDD patients (e.g., Christopher and MacDonald, 2005; Landrø et al., 2001; for a meta-analysis, see Rock et al., 2014). Furthermore, dopamine and noradrenaline are neurotransmitters that contribute to the presence of depressive symptoms (e.g., Belujon and Grace, 2017; Malhi et al., 2005; Moret and Briley, 2011), and the dopaminergic and noradrenergic systems have been suggested to be associated with P3 response (for reviews, see Nieuwenhuis et al., 2005, 2011; Polich, 2007). In this review, the findings regarding whether patients with MDD and non-depressed controls differ in terms of a sensory ERP component called the loudness dependence of auditory evoked potentials (LDAEP), also known as the intensity dependence of auditory evoked potentials, are summarised. LDAEP has primarily been defined as the change in the amplitude of the auditory N1/P2 component (a difference between N1 and P2) in response to different stimulus intensities, and it has been regarded as an indirect indicator of central serotonin function (Hegerl et al., 2001; Hegerl and Juckel, 1993). Serotonergic dysfunction in the central nervous system is suggested to be one of the major pathophysiological factors of depression (e.g., Coppen, 1967; Hasler, 2010; Kraus et al., 2017; Meltzer, 1990), which is why LDAEP in MDD patients is being investigated. However, it is obvious that in addition to serotonin, other neurotransmitters, especially other monoamines (noradrenaline and dopamine), play a role in the development of depressive disorders (for reviews, see Belujon and Grace, 2017; Malhi et al., 2005; Moret and Briley, 2011; Shao and Zhu, 2020), and various subtypes of depression may differ in terms of neurotransmitter function (Malhi et al., 2005). Thus, LDAEP might also have the potential to differentiate subgroups of depressive disorder. The present review did not include studies in which treatment responses were predicted based on auditory ERPs, since such reviews already exist; there is growing evidence that LDAEP can act as a predictor of SSRI treatment response in MDD patients (for reviews, see Bruder et al., 2012; Leuchter et al., 2009; Mumtaz et al., 2015; Olbrich and Arns, 2013; Park, 2020; Wade and Iosifescu, 2016; for a metaanalysis, see Yoon et al., 2021). Some previous reviews have also discussed the effects of the illness stage on auditory ERPs, suggesting that, by now, neither auditory MMN nor auditory P3 has shown the potential to be a marker of a stage of a depressive disorder (Lavoie et al., 2019; McGorry et al., 2014). However, only a handful of reviews have responded to the question of whether auditory ERPs can discriminate between patients with MDD and non-depressed control subjects (e.g., Bruder et al., 2012; Mumtaz et al., 2015; N¨ a¨ at¨ anen et al., 2012; Olbrich and Arns, 2013; for a meta-analysis on MMN, see Tseng et al., 2021), even though this is an essential question for the development of biomarkers for MDD diagnostics. In these previous reviews, the newest findings were naturally missing, and in some of them, the main focus was neither depressive disorders (N¨ a¨ at¨ anen et al., 2012) nor auditory ERPs (Mumtaz et al., 2015; Olbrich and Arns, 2013). In the metaanalysis by Tseng et al. (2021), the main focus was on the findings of frequency deviance and duration deviance MMN responses in adults with MDD. This review extends this work by comprehensively reviewing the MMN literature, including also other deviant types. The aim of the present work is to summarise the available literature on P3, MMN and LDAEP components, focusing specifically on the comparison between MDD patients and non-depressed controls. Next, these ERP components are briefly introduced. 1.1. P3 The P3 ERP component (also known as P3b or P300) first reported by Sutton et al. (1965) is a commonly studied ERP component that is related to attention, decision-making and working memory updating during cognitive task performance (for reviews, see Kok, 2001; Polich, 2007). The latency of P3 is thought to be related to cognitive efficiency, reflecting the information processing speed (Polich, 2007). Hence, it provides a valuable tool for investigating these cognitive processes in the healthy and diseased human brain (Wronka et al., 2012). Therefore, P3 alterations in MDD patients have also been investigated. P3 is traditionally elicited in the attend two-stimulus oddball condition, in which the target stimuli are rarely presented among repetitive standard stimuli (Friedman et al., 2001; Polich and Criado, 2006; Polich, 2007). Sometimes, a three-stimulus oddball condition is used in which there are infrequent distracter stimuli in addition to rare targets and repetitive standard stimuli (Fabiani and Friedman, 1995). In both oddball conditions, the subject is required to respond to the targets (e.g., by pressing a button). Different experimental designs can be used to study the subcomponents of P3, including P3b, P3a and novelty P3 (Friedman et al., 2001; Polich and Criado, 2006; Polich, 2007). In an attend oddball condition, the target stimuli elicit a P3b response that peaks around 300–500 ms after the onset of the task-relevant target stimulus and that has a centro-parietal scalp distribution (Polich, 2007). The P3b elicited by the target stimuli, to which the subject is required to respond, reflects the allocation of attentional resources, stimulus classification and working memory operations, as well as decision-making (Kok, 2001). While P3b reflects top-down controlled processing (e.g., Debener et al., 2002g; Wronka et al., 2012), P3a and novelty P3 reflect the bottom-up capture of attention and orienting (e.g., Escera et al., 2000; Friedman et al., 2001; Polich, 2007). P3a is elicited by deviant stimuli in an ignore oddball condition where participants are not attending to the stimuli. A novelty P3 is elicited by novel infrequent distracter stimuli in the threestimulus oddball condition. These stimuli include the standard stimuli, rare target stimuli and distractor stimuli. Both P3a and novelty P3 peak earlier than P3b, around 250–300 ms after the stimulus onset, and they have a fronto-central scalp distribution (Knight and Scabini, 1998). Most research on P3 activity in MDD patients has focused on the P3b subcomponent. P3 components have been suggested to reflect the neuromodulatory effects of the noradrenergic system (Liu et al., 2009; Swick et al., 1994; for reviews, see Nieuwenhuis et al., 2005, 2011; Polich and Criado, 2006; Polich, 2007) and dopaminergic system (e.g., Liu et al., 2009; Mulert et al., 2006; Pogarell et al., 2011; for reviews, see Polich and Criado, 2006; Polich, 2007; for studies showing no such effect, see Oranje et al., 2006, 2009; Spronk et al., 2013). According to the theory connecting P3 and noradrenaline proposed by Nieuwenhuis et al. (2005), P3 components may reflect upstream activity originating from the locus coeruleus–noradrenaline system. Motivationally significant stimuli (either intrinsically motivational or task-related stimuli) increase locus coeruleus activation, leading to the release of noradrenaline in cortical projection areas. This activity is indexed by the P3 amplitude. Given the P3 response's association with noradrenergic and dopaminergic neurotransmitter systems relevant to depression (for reviews, see Belujon and Grace, 2017; Malhi et al., 2005; Moret and Briley, 2011) and motivational and attentional functions (for reviews, see Friedman et al., 2001; Kok, 2001; Polich, 2007), P3 provides a feasible target in the search for MDD biomarkers. 1.2. MMN Pre-attentive information processing is an essential part of perception and cognition (N¨ a¨ at¨ anen et al., 2010). Pre-attentive information processing can be studied utilising MMN, which reflects automatic auditory change detection (Kujala et al., 2007; N¨ a¨ at¨ anen et al., 1978, 2007, 2011h). MMN can be used as an objective indicator of auditory discrimination accuracy and auditory sensory–memory duration, accuracy and capacity (N¨ a¨ at¨ anen et al., 2011g, 2012). It is typically elicited E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 122 at a latency of 150–250 ms after the onset of deviance in an ignore oddball condition, in which a rare deviant stimulus is interspersed with a repetitive standard stimulus at fronto-central electrode sites in EEG recordings (Garrido et al., 2009; N¨ a¨ at¨ anen et al., 1978, 2005, 2007). MMN can be produced in experimental designs that employ changes in several different stimulus properties, for example, in stimulus frequency, duration and intensity, as well as in feature combinations and abstract features (e.g., N¨ a¨ at¨ anen et al., 1978, 1989; Tervaniemi et al., 1994; for reviews, see e.g., N¨ a¨ at¨ anen et al., 2010, 2011h; Paavilainen, 2013; Winkler and Cowan, 2005). MMN amplitude and latency reflect variations in cognitive abilities in healthy individuals (Bazana and Stelmack, 2002: the general factor of intelligence, g; Light et al., 2007: global functioning; Str¨ ommer et al., 2017: working memory capacity and psychomotor speed), and MMN abnormality is associated with cognitive deficits (N¨ a¨ at¨ anen et al., 2011g, 2012, 2014). At the neurochemical level, MMN is thought to reflect the functioning of NMDA receptors (e.g., Javitt et al., 1996; Umbricht et al., 2000; Umbricht et al., 2002), and therefore, MMN not only indexes the local neural mechanism of auditory discrimination but also cognitive performance more generally (N¨ a¨ at¨ anen et al., 2012, 2014). There is an abundance of research on MMN regarding schizophrenia (for meta-analyses, see Erickson et al., 2016; Umbricht and Krljes, 2005). Impaired NMDA receptors among schizophrenia patients have been linked to reduced MMN amplitude (e.g., Lavoie et al., 2007; for reviews, see N¨ a¨ at¨ anen et al., 2012, 2014). MMN alterations in MDD have been much less studied, even though the NMDA receptor system is also suggested to be dysfunctional in MDD patients (Adell, 2020; Inoshita et al., 2018; Sanacora et al., 2008). 1.3. LDAEP Serotonergic dysfunction is postulated to be one of the main pathophysiological factors in depression (e.g., Coppen, 1967; Hasler, 2010; Kraus et al., 2017; Meltzer, 1990). High concentrations of cortical serotonin have been found in the primary auditory cortex in which serotonin behaves as a neuromodulator (Hegerl et al., 1998, 2001; Juckel et al., 1997), and auditory ERPs indirectly reflect the modulatory effects of serotonin on cortical functioning (Hegerl et al., 2001). The LDAEP is an auditory ERP component that can be used as a tool to obtain information on central serotonergic activity (Hegerl and Juckel, 1993; Hegerl et al., 2001). LDAEP is a measure that assesses the amplitude changes of the auditory N1/P2 component (a difference between N1 and P2) in response to varying loudness levels of auditory stimulation; a low LDAEP indicates high serotonergic activity, and vice versa (Hegerl and Juckel, 1993; Hegerl et al., 2001). LDAEP can be used in investigations of serotonergic dysregulation in patients with MDD (e.g., Hegerl et al., 1998). There is an abundance of research in which LDAEP has been measured to predict the treatment response of selective serotonin reuptake inhibitors (SSRIs) in MDD patients. The findings suggest that stronger baseline LDAEP values predict a favourable response to SSRIs (for reviews, see Leuchter et al., 2009; Wade and Iosifescu, 2016; for a meta-analysis, see Yoon et al., 2021). However, whether LDAEP can discriminate between MDD patients and non-depressed controls has been less studied. 2. Methods A literature search until June 2021 was performed by entering the following keywords into the PubMed database: (‘depression’ or ‘depressive’ or ‘depressed’) and (‘P300’ or ‘P3’ or ‘P3a’ or ‘P3b’ or ‘novelty P3’ or ‘mismatch negativity’ or ‘MMN’ or ‘loudness dependence’ or ‘LDAEP’ or ‘intensity dependence’ or ‘IDAEP’). The following limits were applied: title/abstract. In addition, three studies were identified through reference lists. Original experimental studies were included in the review if they contribute to the field of auditory ERP biomarker research concerning the discrimination between clinically diagnosed depressive disorder patients and non-depressed control participants. Studies that investigated visual and somatosensory and olfactory ERPs were excluded. The following diagnoses were included: major depressive disorder, major depression, depressive disorder, major depressive episode, depressive episode and dysthymia. Hereafter, all diagnoses are referred to as depressive disorder. Studies examining only bipolar disorder were excluded because we wanted to focus on unipolar depressive disorder, and including bipolar disorder might also cause more variability in findings. However, in five studies included, a small number of participants had been diagnosed as having a bipolar disorder (currently depressed), while a vast majority of the participants in these few studies were diagnosed as having a unipolar depressive disorder (Ancy et al., 1996; Bruder et al., 2009; Gallinat et al., 2000; Gordon et al., 1986; Tenke et al., 2010). A study that did not report the number of bipolar disorder participants was excluded (Linka et al., 2007). Only studies published in peer-reviewed journals and written in English were included. Regarding P3b, only the studies in which an oddball task had been applied were included. 3. Results In total, 84 articles met the inclusion criteria. In the following chapters, all diagnoses are referred to as depressive disorder. The exact diagnoses are reported in Table 1, in which auditory P3, MMN and LDAEP studies that are discussed in the following sections are listed. 3.1. P3 Most ERP research on depressive disorder focused on P3b utilising a frequency deviance auditory oddball task with rare target stimuli and repetitive non-target stimuli. The participants were required to respond to the target stimuli. There are also a few studies on auditory P3a and novelty P3. 3.1.1. P3b An attenuated auditory P3b amplitude among patients with depressive disorder was found in multiple studies (Ancy et al., 1996; Blackwood et al., 1987; Gangadhar et al., 1993; Iv et al., 2010; Jaworska et al., 2013; Karaaslan et al., 2003; Kawasaki et al., 2004; Kemp et al., 2009, 2010; Li et al., 2011; Muir et al., 1991; Murthy et al., 1997; Pfefferbaum et al., 1984; R¨ oschke et al., 1996; R¨ oschke and Wagner, 2003; Singh et al., 2000; Tenke et al., 2010; Urretavizcaya et al., 2003; van Dinteren et al., 2015; Wagner et al., 1997; Yanai et al., 1997; Zhou et al., 2019; difference only between suicidal depressed patients and controls: Hansenne et al., 1996). Additionally, an increased P3b amplitude in patients with depressive disorder was reported in one study (Li et al., 2014), and in one study, patients with comorbid depressive and anxiety disorders had an increased P3b amplitude compared to depressive disorder patients without anxiety disorder and control participants without depressive disorder and anxiety disorder (Bruder et al., 2002). However, several studies did not find any differences in P3b amplitude between depressed patients and controls (Barreiros et al., 2020; Bruder et al., 1998, 2002, 2009; Feldmann et al., 2018; Giedke et al., 1981; Greimel et al., 2015; Himani et al., 1999; Houston et al., 2004; Kalayam and Alexopoulos, 1999; Kaustio et al., 2002; Nan et al., 2018; Roth et al., 1981; Sara et al., 1994; Shim et al., 2019; Swanwick et al., 1996; Vandoolaeghe et al., 1998; Weir et al., 1998). Regarding auditory P3b latency, there are many studies in which prolonged P3 latency was found in patients with depressive disorder (Himani et al., 1999; Kalayam et al., 1998; Kalayam and Alexopoulos, 1999; Karaaslan et al., 2003; Kemp et al., 2009, 2010; Kindermann et al., 2000; Nan et al., 2018; Singh et al., 2000; Tripathi et al., 2015; Urretavizcaya et al., 2003; Vandoolaeghe et al., 1998; Zhou et al., 2019). In addition, a shorter latency was reported in one study (Sumi et al., 2000). Numerous studies have found no differences in P3b latency between E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 123 Table 1 Auditory ERP studies comparing patients with depressive disorder and nondepressed control subjects in amplitude and/or latency of P3, MMN and LDAEP components. For P3 and MMN, the analysis has compared deviant and standard responses, but for LDAEP, the analyses differ between the studies. Therefore, the analysis is briefly described for LDAEP studies. Study Sample Stimulus condition/ analysis Amplitude results Latency results P3b Ancy et al. (1996) 17 melancholic depression: An attend oddball DD <CONT No difference between recurrent DD/ single condition with DD and CONT episode/ bipolar disorder frequency deviance currently depressed, 15 CONT Barreiros et al. (2020) Cohort 1: An attend oddball No difference No difference 20 symptomremitted MDD, condition with between MDD and between MDD and 23 CONT frequency deviance CONT either in cohort CONT either in cohort Cohort 2: 1 or cohort 2 1 or cohort 2 19 symptomremitted MDD, 19 CONT Blackwood et al. (1987) 16 MDD (med. free), An attend oddball MDD < CONT No difference between 59 CONT condition with MDD and CONT frequency deviance Bruder et al. (1998) 40 MDD/DYS (med. free), An attend oddball No difference Not analysed 22 CONT condition with between MDD/DYS frequency deviance and CONT Bruder et al. (2002) 58 MDD/DYS (med. free), Attend oddball P3b (averaged over Not analysed 18 MDD/DYS + anxiety conditions with conditions): disord. (med. free), frequency deviance No difference 49 CONT and syllable deviance between MDD/DYS and CONT, MDD/DYS +anxiety >MDD/ DYS, MDD/DYS +anxiety >CONT Feldmann et al. (2018) 22 MD adolescents, An attend oddball No differences No differences 20 remitted MD condition with between MD, between MD, adolescents, frequency deviance remitted MD and CONT remitted MD and CONT 32 CONT adolescents Gangadhar et al. (1993) 17 MDD mel. (med. free), An attend oddball MDD < CONT No difference between 22 CONT condition with MDD and CONT Table 1 (continued) Study Sample Stimulus condition/ analysis Amplitude results Latency results frequency deviance Giedke et al. (1981) 13 primary depressive An attend oddball No difference No difference between inpatients, condition with between depressive depressive patients 13 CONT frequency deviance patients and CONT and CONT Gordon et al. (1986) 17 MDD/bipolar depressive An attend oddball Not analysed No difference between phase, condition with MDD/ bipolar depressive 55 CONT frequency deviance phase and CONT Greimel et al. (2015) 24 MD (med. free) An attend oddball No difference No difference between children/ adolescents, condition with between MD and CONT MD and CONT 21 CONT children/ adolescents frequency deviance Hansenne et al. (1996) 10 MDD with suic., An attend oddball MDD with suic. < No difference between 10 MD without suic., condition with CONT, MDD with suic. and 20 CONT frequency deviance No difference CONT, between MDD No difference between without suic. and CONT MDD without suic. and CONT Himani et al. (1999) 20 MD, An attend oddball No difference between MD > CONT 20 CONT condition with MD and CONT frequency deviance Kalayam et al. (1998) 43 MD older adults, An attend oddball Not analysed MD > CONT 24 CONT older adults condition with frequency deviance Kalayam and Alexopoulos (1999) 25 MDDpr older adults An attend oddball No differences MDDpr > CONT, (poor response to condition with between the groups MDDpr > MDDgr, antidepressants), frequency deviance No difference between 24 MDDgr older adults MDDgr and CONT (good response to antidepressants), 22 CONT older adults Karaaslan et al. (2003) 16 MDD with psyc. (med. An attend oddball MDD with psych. < MDD with psyc. > CONT, free), condition with CONT, MDD without psyc. > CONT (continued on next page) E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 124 Table 1 (continued) Study Sample Stimulus condition/ analysis Amplitude results Latency results 20 MDD without psyc. frequency deviance No differences between med free), MDD without psyc. 20 CONT and CONT Kaustio et al. (2002) 22 MD/DYS (med. free), An attend oddball No difference between No difference between 22 CONT condition with MD/DYS and CONT MD/DYS and CONT frequency deviance Kawasaki et al. (2004) 22 MDD (med. free), An attend oddball MDD < CONT No difference between 22 CONT condition with MDD and CONT frequency deviance Kemp et al. (2009) 78 MDD (med. free), An attend oddball MDD < CONT, MDD > CONT, 127 depressed mood (DM), condition with MDD <DM, No difference between 116 CONT frequency deviance No difference between MDD and DM, DM and CONT No difference between DM and CONT Kemp et al. (2010) 57 MDD mel. (med. free), An attend oddball MDD mel. < CONT, MDD mel. >CONT, 48 MDD nonmel. (med. condition with MDD mel < No difference between free, frequency deviance MDD nonmel., MDD nonmel. and CONT, 116 CONT No difference between No difference between MDD nonmel. and MDD mel. and MDD CONT non-mel. Kindermann et al. (2000) 25 MD older adults, An attend oddball Not analysed MD > CONT 20 CONT older adults condition with frequency deviance Kraiuhin et al. (1990) 11 depression older adults, An attend oddball Not analysed No difference between 15 CONT older adults condition with depression and CONT frequency deviance Li et al. (2011) 32 MDD/DYS (med. free), An attend oddball MDD/DYS <CONT Not analysed 30 CONT condition with frequency deviance Li et al. (2014) 30 MDE (med. free), An attend oddball MDE > CONT No difference between 30 CONT condition with (frontal P3b) MDE and CONT frequency deviance Table 1 (continued) Study Sample Stimulus condition/ analysis Amplitude results Latency results Muir et al. (1991) 46 MDD, An attend oddball MDD < CONT No difference between 212 CONT condition with MDD and CONT frequency deviance Murthy et al. (1997) 15 DE with somatic An attend oddball DE <CONT, No differences between symptoms (med. free), condition with DYS < CONT, the groups 15 DYS (med. free), frequency deviance No difference between 15 CONT DE and DYS Nan et al. (2018) 45 MDD, An attend oddball No difference between MDD > CONT 45 CONT condition with MDD and CONT frequency and intensity deviance Pfefferbaum et al. (1984) 17 MD (med. free), An attend oddball MD (med. free) < CONT, No differences between 17 MD (medicated), condition with No difference between the groups 115 CONT frequency deviance MD (medicated) and CONT R¨ oschke et al. (1996) 11 MDE (med. free), An attend oddball MDE < CONT Not analysed 10 CONT condition with frequency deviance R¨ oschke and Wagner (2003) 21 MDE (med. free), An attend oddball MDE < CONT Not analysed 21 CONT condition with frequency deviance Roth et al. (1981) 21 MDD, An attend oddball No difference between No difference between 28 CONT condition with MDD and CONT MDD and CONT frequency deviance Sara et al. (1994) 27 MD, An attend oddball No difference between No difference between 27 CONT condition with MD and CONT MD and CONT frequency deviance Shim et al. (2019) 67 MDD, An attend oddball No difference between No difference between 39 CONT condition with MDD and CONT MDD and CONT frequency deviance Singh et al. (2000) 40 MDD, An attend oddball MDD < CONT MDD > CONT 40 CONT condition with (continued on next page) E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 125 Table 1 (continued) Study Sample Stimulus condition/ analysis Amplitude results Latency results frequency deviance Sumi et al. (2000) 35 MDD/DYS older adults, An attend oddball Not analysed MDD/DYS <CONT 39 CONT older adults condition with frequency deviance Swanwick et al. (1996) 15 MDE older adults, An attend oddball No difference between No difference between 21 CONT older adults condition with MDE and CONT MDE and CONT frequency deviance Tripathi et al. (2015) 30 MDD, An attend oddball Not analysed MDD > CONT 30 CONT condition Urretavizcaya et al. (2003) 50 MD mel. (med. free), An attend oddball MD <CONT MD > CONT 31 CONT condition with frequency deviance van Dinteren et al. (2015) 1008 MDD (med. free), An attend oddball MDD < CONT No difference between 336 CONT condition with MDD and CONT frequency deviance Vandoolaeghe et al. (1998) 35 MD (med. free), An attend oddball No difference between MD > CONT 11 CONT condition with MD and CONT frequency deviance Wagner et al. (1997) 11 MDE (med. free), An attend oddball MDE < CONT Not analysed 10 CONT condition with frequency deviance Weir et al. (1998) 14 MDD, An attend oddball No difference between No difference between 31 CONT condition with MDD and CONT MDD and CONT frequency deviance Yanai et al. (1997) 16 MD older adults (med. An attend oddball MD <CONT No difference between free), condition with MD and CONT 17 CONT older adults frequency deviance Zhou et al. (2019) 30 DE, An attend oddball DE <CONT DE >CONT 30 CONT condition with frequency and intensity deviance P3a Chen et al. (2015) 45 MDDf (first episode) mel., An ignore oddball MDDr < CONT, MDDr > CONT, 40 MDDr (recurrent) mel., condition with MDDr < MDDf, MDDr > MDDf, 46 CONT Table 1 (continued) Study Sample Stimulus condition/ analysis Amplitude results Latency results duration deviance No difference between No difference between MDDf and CONT MDDf and CONT K¨ ahk¨ onen et al. (2007) 13 MDD (med. free), An ignore oddball No difference between No difference between 12 CONT condition with MDD and CONT in P3a MDD and CONT in P3a frequency deviance elicited by elicited by and with novel novel stimuli novel stimuli stimuli Lepist¨ o et al. (2004) 10 MD children (med. free), An ignore oddball MD >CONT No difference between 10 CONT children condition with MD and CONT /ka/ and /ta/ syllables Xu et al. (2014) 32 MDD (treatment An ignore MDD < CONT No difference between resistant) (med. free), condition with 60 dB MDD and CONT 31 CONT deviant stimuli and 0 dB standard stimuli (no sound) Novelty P3 Bruder et al. (2009) 20 MDD/DYS/ bipolar disorder An attend oddball Novelty P3: Not analysed (med. free), condition with MDD < CONT, 20 CONT frequency deviance Target P3 (P3b): and novel sounds No difference between MDD and CONT Houston et al. (2004) 28 remitted MDD female Task 1: Only CONT group (not No difference between adolescents, An attend oddball MDD group) MDD and CONT 96 CONT female adolescents condition with exhibited smaller target P3 frequency deviance amplitudes during the Task 2: task 2 as compared to An attend oddball task 1. Otherwise, condition with no differences in the frequency deviance novelty P3 or target P3 and novel sounds between MDD and CONT Iv et al. (2010) 14 MDD first episode (med. An attend oddball Novelty P3: Not analysed free), (continued on next page) E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 126 Table 1 (continued) Study Sample Stimulus condition/ analysis Amplitude results Latency results condition with MDD < CONT, 17 CONT frequency deviance Target P3 (P3b): and novel sounds MDD < CONT at the right hemisphere Jaworska et al. (2013) 25 MDD (NDRI, SSRI nonAn attend oddball Novelty P3: MDD No differences between responders), condition with (non-res.) < CONT, the groups 26 MDD (NDRI, SSRI frequency deviance No difference between responders), and novel sounds MDD (res.) and CONT, 43 CONT No difference between MDD (res.) and MDD (non-res.) Target P3 (P3b): MDD (nonres.) < CONT, MDD (nonres.) <MDD (res.), No difference between MDD (res.) and CONT Tenke et al. (2010) 49 MDD/DYS/ bipolar An attend oddball Novelty P3: Not analysed depressive phase (med. free), condition with depressed patients < CONT 49 CONT frequency deviance Target P3 (P3b): and novel sounds depressed patients < CONT MMN Bissonnette et al. (2020) 16 MDD, An ignore multiIntensity deviance MMN: Frequency deviance MMN: 26 CONT feature paradigm MDD > CONT, MDD > CONT, with 5 deviant tone Location deviance MMN: No differences in the types (frequency, MDD > CONT, intensity, location, duration, intensity, No differences in the duration, or continuity location, continuity) frequency, duration, or deviance MMN between continuity deviance MMN MDD and CONT between MDD and CONT Chen et al. (2015) 45 MDDf (first episode) mel., An ignore oddball MDDf < CONT, Table 1 (continued) Study Sample Stimulus condition/ analysis Amplitude results Latency results No differences between 40 MDDr (recurrent) mel., condition with MDDr < CONT, the groups 46 CONT duration deviance No differences between MDDf and MDDr He et al. (2010) 22 treatmentresistant MDD An ignore oddball MDD > CONT, No differences between (med. free), condition with MDD > MDD +BPD, the groups 22 treatmentresistant MDD frequency deviance No difference between +borderline personality MDD +BPD and CONT disorder (BPD) (med. free), 32 CONT Hirakawa et al. (2017) 20 MDD, An ignore oddball MDD < CONT in the MDD < CONT (MEG study) 36 CONT condition with right hemisphere, frequency deviance No differences between MDD and CONT in the left hemisphere K¨ ahk¨ onen et al. (2007) 13 MDD (med. free), An ignore oddball EEG: 10 % frequency No difference in MMN or (EEG and MEG study) 12 CONT condition with deviance MMN MMNm latencies 10 % and 20 % MDD > CONT, No between MDD and CONT frequency deviance group difference in 20 % condition MEG: MMNm No differences between MDD and CONT Kim et al. (2020) 27 MDD, An ignore oddball No difference between Not analysed 32 CONT condition with MDD and CONT duration deviance Kuang et al. (2016) 60 MDD (med. free), An ignore oddball Not analysed MDD > CONT 30 CONT condition with intensity and frequency deviance Lepist¨ o et al. (2004) 10 MD children (med. free), An ignore oddball No difference between MD < CONT 10 CONT children condition with MD and CONT (continued on next page) E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 133 findings on P3b within these studies could be at least partly explained by the diagnostic homogeneity of the samples (compared to studies in which depressive disorder as a wide diagnostic group was studied). The inconsistency in the results regarding auditory P3b between depressed patients and non-depressed controls in general may also be related to some comorbid factors. For instance, Bruder et al. (2002) investigated the P3b amplitude in patients with depression only, anxiety only, a comorbid group (depression and anxiety) and a control group. The amplitude of P3b did not differ between the depression-only group and control group, but the comorbid group exhibited an increased P3b amplitude when compared to the other groups. Thus, the need to take comorbidities into account when studying P3b amplitude is obvious. However, in most of the reviewed P3b studies, anxiety was not considered even if anxiety symptoms occur commonly during the course of a depressive disorder (Rosellini et al., 2018), and anxiety disorders are common comorbid disorders among depressive disorder patients (Hasin et al., 2018). In the reviewed studies, anxiety symptoms were not included as a covariant in the analyses. Furthermore, 11 studies excluded anxiety disorder, and only one study excluded anxiety symptoms for depressed participants. Regarding antidepressant medication, in 24 of the reviewed P3b studies, the participants with depressive disorder were medication-free; in 16 studies, the depressed participants were on medication; and in 8 studies, the medication condition was not reported. Importantly, in most of the studies in which an attenuated amplitude was found, the depressed patients were unmedicated. Instead, in most of the studies in which no difference in the P3b amplitude between depressed patients and non-depressed controls was found, the depressed patients were on medication, or the current medication condition was not reported. Therefore, medication might be able to recover attenuated P3b amplitude in depression. Some studies indicated that attenuated P3b amplitude indeed normalises with successful antidepressant treatments (Blackwood et al., 1987; Karaaslan et al., 2003; Yanai et al., 1997). However, there were also some contradictory findings related to P3b and antidepressants. In the study by Vandoolaeghe et al. (1998), no differences between pre-treatment and post-treatment P3b amplitudes were found, and in the study by Sara et al. (1994), there were no differences in the amplitude of P3b between unmedicated and medicated depressed patients. SSRIs are commonly used antidepressants. In healthy adults, neither the administration of SSRIs (Oranje et al., 2008; Wienberg et al., 2010) nor an acute depletion of serotonin (Ahveninen et al., 2002) had an impact on P3b amplitude. However, it is possible that antidepressants used by depressed participants had an impact on the results reviewed here, but more research is still needed to address this issue. An important question related to the potential clinical utility of auditory P3b is whether P3b amplitude provides a trait or state marker of depressive disorder. In addition to the aforementioned studies indicating that altered P3b amplitude normalises with successful antidepressant treatment (Blackwood et al., 1987; Karaaslan et al., 2003; Yanai et al., 1997), altered P3b amplitude normalises with electroconvulsive therapy treatment (Ancy et al., 1996; Gangadhar et al., 1993; Nurminen et al., 2005). Additionally, some other results are in line with the suggestion of P3b amplitude as a state-dependent phenomenon. Barreiros et al. (2020) and Houston et al. (2004) found no differences in P3b amplitude between symptom-remitted depressive disorder patients and never-depressed controls, and Gangadhar et al. (1993) and van Dinteren et al. (2015) reported a negative correlation between P3b amplitude and depression severity. In contrast, several studies showed that P3b amplitude does not correlate with depression severity (Barreiros et al., 2020; Blackwood et al., 1987; Hansenne et al., 1996; Karaaslan et al., 2003; Murthy et al., 1997; Roth et al., 1981; Sara et al., 1994; Urretavizcaya et al., 2003; Vandoolaeghe et al., 1998; Yanai et al., 1997) suggesting that P3b might rather be a trait marker of depression. Thus, the results are conflicting, and more research is required. In the P3b studies reviewed in this work, a frequency deviance auditory oddball task was most frequently utilised. However, these studies varied in the frequencies of the tones, interstimulus intervals, stimulus duration, number of trials and probability for target tones. There were also differences in the sample sizes between the studies. Furthermore, the inconsistencies in the P3b latency findings may partly be due to the difficulty in defining latencies accurately because P3 responses usually have a plateau shape, and clear peaks may have been lacking. In future studies on P3b, it would be important to manipulate different parameters (e.g., stimulus parameters) in oddball tasks to find the best options for studies investigating differences between patients with depressive disorder and controls. It is unclear whether the twostimuli oddball task is suitable for investigations into P3b differences between depressed patients and controls. According to Bruder et al. (2012), the inconsistent findings regarding P3b among depressed patients may have been due to the use of a simple two-stimuli task that is not demanding enough to reveal a depression-related decrease in the P3b amplitude. Bruder et al. (2012) suggested measuring P3b during more challenging cognitive tasks that allow the evaluation of hypotheses concerning specific cognitive deficits in depression. 4.2. MMN The auditory MMN was examined in the absence of participants' attention and task performance (e.g., N¨ a¨ at¨ anen et al., 1978, 2012), which avoided some problems related to motivational and attentional factors in measurement (He et al., 2010; K¨ ahk¨ onen et al., 2007; Takei et al., 2009). However, the studies that investigated auditory MMN alterations in depressive disorder reviewed in this work showed inconsistent results regarding both amplitudes and latencies, with some reporting attenuated MMN amplitudes or shorter latencies, and others reporting increased amplitudes or prolonged latencies. In some studies, no differences between depressed patients and non-depressed controls were found. The heterogeneity of the research paradigm and stimuli, as well as the heterogeneity of depressive disorder as an illness, may partly explain this inconsistency. In contrast to studies on P3b in which an attend oddball task with sound frequency as a deviating stimulus feature has been most frequently applied, in MMN studies, an ignore oddball condition with multiple different deviating stimulus features (e.g., duration, frequency, intensity, vowel, syllable and multi-feature paradigms) has been employed. According to the findings of the auditory MMN studies, the amplitude of the duration deviance MMN might be mostly attenuated in patients with depressive disorder compared to controls (Chen et al., 2015; Naismith et al., 2012; Qiao et al., 2013, 2015), while frequency deviance MMN amplitude was found to be mostly increased in depressed patients (He et al., 2010; K¨ ahk¨ onen et al., 2007; Restuccia et al., 2016). However, there are also duration deviance MMN studies (Bissonnette et al., 2020; Kim et al., 2020; Takei et al., 2009; Umbricht et al., 2003) and frequency deviance MMN studies (Bissonnette et al., 2020; Mu et al., 2016; Takei et al., 2009; Umbricht et al., 2003) in which no difference between depressed patients and non-depressed controls were found, and one frequency deviance MMNm study (Hirakawa et al., 2017) discovered an attenuated MMNm amplitude among depressed patients. According to a meta-analysis by Tseng et al. (2021), only duration deviance MMN but not frequency deviance MMN amplitude was significantly altered in depressed patients compared to non-depressed controls. In the present work, findings regarding MMN responses elicited by some other stimulus types were also reviewed; for instance, Bissonnette et al. (2020) found an increased intensity deviance MMN amplitude in depressed patients, while Mu et al. (2016), Ruohonen and Astikainen (2017) and Ruohonen et al. (2020) did not find any differences in intensity deviance MMN between depressed patients and non-depressed controls. Regarding vowel and syllable deviance conditions, no differences in MMN amplitudes between depressed patients and controls were found (Lepist¨ o et al., 2004; Takei et al., 2009). MMN is associated with predictive coding theory, which is regarded E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 134 as a general information processing mechanism of the brain (Friston, 2005). Predictive coding mechanism has been suggested to be altered in patients with depressive disorder (for reviews, see Kube et al., 2020; Smith et al., 2021). However, the auditory MMN studies reviewed in this work showed inconsistent findings, varying in terms of deviating stimulus features. For example, duration MMN was usually attenuated, while frequency MMN was increased in amplitude in depressed patients, and the results of intensity deviance MMN were conflicting. The reason why the frequency deviance prediction error signal indexed by MMN is commonly increased while the duration deviance prediction error signal is attenuated is unknown and requires further studies in which both of these deviance types are investigated in the same subject groups. In addition to variable stimulus features, there are also other methodological differences in auditory MMN studies investigating patients with depressive disorder. For instance, the locations of the electrodes applied in the analysis (fronto-central/temporal/midline) varied across the studies. In addition, both EEG and MEG were used in these studies. MMN research in schizophrenia patients is abundant, and the research paradigm has become standardised over the years; duration and frequency deviance oddball conditions have been used in most studies, and the findings of attenuated MMN amplitude have been consistently replicated (for meta-analyses, see Erickson et al., 2016; Umbricht and Krljes, 2005). In the future, it would be important to study MMN in depression by applying a more equal research methodology. Based on their meta-analysis, Tseng et al. (2021) suggested continuing MMN research by simultaneously applying both duration and frequency deviance conditions to investigate whether depressed patients show more impaired duration deviance MMN than frequency deviance MMN. Another interesting question related to stimulus types is whether MMN responses elicited by neutral auditory stimuli or those elicited by emotional auditory stimuli are better for differentiating between depressed patients and non-depressed control subjects. Because depression is associated with dysfunction in emotional information processing (e.g., Phillips et al., 2003), auditory MMN elicited by emotional stimuli may also be suitable to discriminate between depressed patients and controls. However, there was only one study in which auditory MMN elicited by emotional stimuli was investigated; in an MMN study by Pang et al. (2014), meaningless syllables were spoken with either neutral or emotional (happy, angry and sad) prosodies. Sad stimuli were processed differently by depressed patients and controls. Sad MMN was absent among depressed patients, while it was elicited in the non-depressed control group (Pang et al., 2014). Differences between depressed patients and non-depressed controls in the processing of emotional stimuli have also been found in early visual ERP (MMN, P1 and N170) studies, in which emotional face stimuli were utilised (Chang et al., 2010; Ruohonen et al., 2020; Wu et al., 2016; Xu et al., 2018; Zhang et al., 2016; Zhao et al., 2015) and in later visual ERPs reflecting various aspects of affective cognition (for reviews, see Hajcak and Foti, 2020; Kujawa and Burkhouse, 2017; Kujawa et al., 2020; Proudfit, 2015; Proudfit et al., 2015). More research comparing depressed and nondepressed groups in auditory MMN responses elicited by emotional stimuli is required. In addition to different methodological issues, heterogeneity of depressive disorder diagnoses may contribute to the varying results of auditory MMN studies. In this review, for instance, first-episode depression (e.g., Chen et al., 2015; Qiao et al., 2013, 2015), lifetime depression in older adults (Naismith et al., 2012), treatment-resistant depression (TRD) (He et al., 2010) and melancholic depression (Chen et al., 2015) were investigated. In addition, comorbidities may be confounding factors in the studies that investigated MMN in depressive disorder. He et al. (2010) investigated MMN in patients with TRD, patients with borderline personality disorder (BPD), patients with TRD comorbid with BPD (TRD +BPD) and in control participants without TRD and BPD. The MMN amplitude was higher in the TRD group than in the other three groups, but there were no differences in MMN amplitude between the TRD +BPD group and controls or between the TRD +BPD group and the BPD-only group. The use of antidepressant medication may contribute to the varying results of MMN studies. The medication condition is important to take into account in the studies that investigate MMN in patients with depressive disorder. In half of the reviewed MMN studies, depressed participants were on antidepressants. SSRIs are frequently used to treat depressive disorders. The serotonergic modulation of MMN amplitude has been investigated in depressed patients (e.g., Kuang et al., 2016) and in healthy participants (e.g., Ahveninen et al., 2002; K¨ ahk¨ onen et al., 2005; Leung et al., 2010; Oranje et al., 2008; Wienberg et al., 2010). Also, animal models have been utilised (e.g., Pan et al., 2020). However, these studies showed inconsistent results; in some, SSRIs increased MMN amplitude (Oranje et al., 2008; Wienberg et al., 2010), whereas in other studies, SSRIs decreased MMN amplitude (Pan et al., 2020). In the studies by Ahveninen et al. (2002) and K¨ ahk¨ onen et al. (2005), acute serotonin depletion led to increased MMN amplitude. It has also been suggested that the acute depletion of serotonin does not affect MMN (Leung et al., 2010). Regarding depressed participants, Kuang et al. (2016) showed that the pre-treatment latency of MMN was prolonged, but it decreased following the SSRI treatment. Antidepressants may be a confounding factor in studies investigating MMN in patients with depressive disorder. In future studies, it would be important to be able to distinguish the effects of antidepressants from the effects of depressive disorder on MMN. For instance, studies comparing unmedicated and medicated depressed patients would provide new information about this issue. In some of the reviewed MMN studies, the relationship between MMN amplitude and the severity of depressive disorder was investigated. It seems that MMN amplitude is not related to depressive symptom severity (He et al., 2010; Hirakawa et al., 2017; K¨ ahk¨ onen et al., 2007; Mu et al., 2016; Naismith et al., 2012; Pang et al., 2014; Qiao et al., 2013, 2015; Takei et al., 2009; Umbricht et al., 2003). In the study by Naismith et al. (2012), patients with remitted depression demonstrated a reduced MMN amplitude, showing that the amplitude was not related to depressive symptom severity, and in a study by Bonetti et al. (2017), a relation between depression risk and MMN amplitude was found. These studies suggest that MMN may provide a trait marker of depression, but more research is still needed to confirm this, because in many studies, the conclusion was based on a null result (no correlation was found between severity and MMN amplitude). Future studies could use longitudinal designs to address the question of whether MMN is a state or trait marker of depression. This is an essential issue regarding the possible clinical use of MMN in depressive disorder diagnostics in the future. Regarding the recurrence of depressive episodes, no correlation between MMN amplitude and the number of depressive episodes was found (Naismith et al., 2012). In addition, in the studies by Chen et al. (2015), Ruohonen and Astikainen (2017) and Umbricht et al. (2003), MMN amplitude values did not differ significantly between depressed patients with one episode of illness and those with multiple episodes. These results indicate that recurrence of depressive episodes may not cause an accumulation of impairment in the MMN amplitude. MMN is suggested to reflect the functioning of NMDA receptors (e.g., Javitt et al., 1996; Umbricht et al., 2000, 2002), which has been suggested to be dysfunctional in depressive disorder (Adell, 2020; Inoshita et al., 2018; Sanacora et al., 2008). Therefore, in depressed patients, MMN may be an index of more general cognitive performance than auditory change detection alone (N¨ a¨ at¨ anen et al., 2012, 2014). The reviewed studies showed that reduced MMN amplitude in depressed patients was associated with reduced semantic fluency and greater selfrated functional disability (Naismith et al., 2012), poor social functioning (Kim et al., 2020) and deficits in attention switching functions (Chen et al., 2015). In contrast, Ruohonen et al.'s (2020) study showed no relationship between MMN amplitude and cognitive performance, and Umbricht et al. (2003) showed MMN amplitude did not correlate with the intelligence quotient (IQ). Regarding MMN latency, Bissonnette E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 135 et al. (2020) showed that prolonged MMN latency following the duration deviant was associated with deficits in cognitive functions, such as working memory and attention. 4.3. LDAEP LDAEP has been regarded as an indirect indicator of central serotonin function; a low LDAEP indicates high serotonergic activity and vice versa (Hegerl et al., 2001; Hegerl and Juckel, 1993). Since serotonergic dysfunction is suggested to be one of the major pathophysiological factors in depression (e.g., Coppen, 1967; Hasler, 2010; Kraus et al., 2017; Meltzer, 1990), it can be assumed that patients with depressive disorder and non-depressed control participants differ in LDAEP. However, studies comparing LDAEP between depressed patients and nondepressed controls have produced contradictory findings. Several studies have not found any differences in LDAEP between patients with depressive disorder and controls. However, an increased LDAEP in depressed patients compared to controls and attenuated LDAEP among depressed patients have been reported in a few studies. The inconsistent findings regarding LDAEP as a tool to distinguish between depressed patients and non-depressed controls may reflect the heterogeneity of depression diagnoses with multiple different symptom profiles and underlying neural mechanisms. Regarding the neurotransmitters, previous studies indicated that not only serotonin but also other neurotransmitters, especially other monoamines (noradrenaline and dopamine), contribute to the development and maintenance of depressive disorder (for reviews, see Belujon and Grace, 2017; Malhi et al., 2005; Moret and Briley, 2011; Shao and Zhu, 2020), and different subtypes of depression may differ in terms of the function of the neurotransmitters (Malhi et al., 2005). Depression is suggested to be related to functional interactions between neurotransmitter systems (Malhi et al., 2005). Regarding subtypes of depressive disorder, melancholic depression and atypical depression have different and partly contrasting clinical symptoms. Melancholic depression is associated with a lack of reactivity of mood, psychomotor slowing, anxiety, appetite loss and insomnia, whereas atypical depression is a disorder with mood reactivity, metabolic abnormalities, increased appetite, hypersomnia, leaden paralysis and interpersonal rejection sensitivity (American Psychiatric Association, 2013). In addition to different symptom profiles, melancholic depression and atypical depression seem to have opposite characteristics in terms of LDAEP strength. Fitzgerald et al. (2009) found that the melancholic depression group had a weaker LDAEP than the nondepressed controls, whereas no difference in LDAEP existed between the non-melancholic group and control group. Lee et al. (2014) showed that patients with atypical depression had stronger LDAEP than those with non-atypical depression, which may suggest that there is deficient serotonergic activity in patients with atypical depression. This is in line with the findings that atypical depression symptoms, such as mood, sleep and appetite disturbances, are related to serotonergic dysfunction (Malhi et al., 2005). On the contrary, melancholic depression patients may have abundant serotonergic activity, as indicated by a weaker LDAEP. Several neurotransmitter systems, including dopaminergic and noradrenergic systems, are involved in melancholic depression (Malhi et al., 2005), and melancholic depression symptoms (e.g., psychomotor slowing, anhedonia and disturbances in drive, energy and volition) are attributable to dopamine and noradrenaline dysfunction (Malhi et al., 2005). According to Fitzgerald et al. (2009), noradrenergic abnormalities may explain an increase in serotonin tone in melancholic depression patients. The aforementioned studies by Fitzgerald et al. (2009) and Lee et al. (2014) suggest that melancholic and atypical depression may have different biological bases that could be separated utilising LDAEP. However, to confirm this, studies that directly compare LDAEP between melancholic depression and atypical depression are needed. To the best of our knowledge, no studies have compared LDAEP between different depression severity groups, but the relationship between LDAEP and depression severity has been studied. The findings regarding the correlation between LDAEP and depression severity have been inconsistent (no correlation: Chen et al., 2005; Gallinat et al., 2000; Park et al., 2010; a trend toward a negative correlation: Fitzgerald et al., 2009; Jaworska et al., 2012; a negative correlation; Park and Lee, 2013; a positive correlation: Kim et al., 2019; Ostermann et al., 2012). More research on the relationship between LDAEP and depression severity is needed. Numerous studies have investigated the relationship between LDAEP and suicidality in patients with depressive disorder (for a review, see Park, 2015) because serotonergic functions are suggested to be associated with suicidal behaviour (e.g., Miller et al., 2013; for a review, see Mann, 2013). A study by Graßnickel et al. (2015) showed no differences in LDAEP between suicidal depressed patients and non-suicidal depressed patients. In addition, in the study by Park et al. (2014), LDAEP did not differ between depressed patients with suicidal ideation and those without suicide ideation. Kim et al. (2021) demonstrated that there was no difference in LDAEP between depressed patients with suicide attempts and those with suicidal ideation. Some studies also found a sharper slope of LDAEP in depressed patients with suicidality compared to patients without suicidality (e.g., Chen et al., 2005; Hwang et al., 2021; Kim and Park, 2013; Uhl et al., 2012;). In contrast, in the study by Lee et al. (2014), a negative correlation between the LDAEP value and the suicidal ideation score was found in patients with atypical depression. Thus, the findings regarding the relationship between suicidality and LDAEP are highly inconsistent. The heterogeneity regarding the subtypes of depression and regarding how suicidality is defined (e.g., acute suicide attempts/suicide ideation) may explain the variation in the findings. Similar to P3 and MMN studies, in studies that investigated LDAEP in patients with depressive disorder, the comorbid factors must be considered. For instance, Kim et al. (2019) demonstrated that LDAEP was attenuated in the depressed patients with ADHD symptoms compared to depressed patients without ADHD symptoms and controls without depression and ADHD symptoms, but there were no differences between the depressed patients without ADHD symptoms and controls. Manjarrez-Gutierrez et al. (2009) showed that LDAEP was increased in the patients with depression comorbid with diabetes compared to the control group without depression and diabetes, depression-only group and diabetes-only group, whereas there was no difference between the depression-only group and controls. This finding is in line with the suggestion that a deficit of serotonin is related to both the diabetic state (for a review, see Prabhakar et al., 2015) and depressive disorder (e.g., Coppen, 1967; Hasler, 2010; Kraus et al., 2017; Meltzer, 1990). The comorbidity of diabetes and depressive disorder may cause an accumulation of increase in the LDAEP. Even if it seems that LDAEP cannot be used as a tool to differentiate between depressed patients and non-depressed controls, certain features in depressive disorder that are affected by a potentially higher degree of dysfunction in serotonergic functions, such as melancholy (e.g., Fitzgerald et al., 2009) and suicidality (e.g., Chen et al., 2005; Kim and Park, 2013), might be associated with LDAEP modulations. In the future, LDAEP can be investigated as a potential tool for identifying biologically different subgroups of depressive disorder. However, conclusions regarding the relationship between LDAEP and serotonin-related depressive symptoms and subtypes should be made cautiously. Most of the evidence regarding the association between LDAEP and serotonin came from animal studies (e.g., Juckel et al., 1997, 1999). Instead, studies investigating humans have provided more inconsistent findings (e.g., Debener et al., 2002h; Dierks et al., 1999; Guille et al., 2008; K¨ ahk¨ onen et al., 2002; Nathan et al., 2006; Segrave et al., 2006; Simmons et al., 2011; Uhl et al., 2006). Furthermore, in addition to serotonin, LDAEP is suggested to be sensitive to some other neurotransmitter systems (e.g., glutamatergic system; O'Neill et al., 2007). Thus, more research on the relationship between LDAEP and serotonin in healthy humans, as well as on LDAEP in different subgroups of depressive disorder, is needed. E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 136 In the reviewed LDAEP studies, some potential confounding factors were related to the heterogeneity of the samples. They could partly explain the inconsistent findings in the differences in LDAEP between depressed patients and non-depressed controls. An essential potential confounder is the use of antidepressants, especially SSRIs, because LDAEP is suggested to reflect central serotonergic functions (e.g., Hegerl et al., 2001). Even if there is growing evidence of LDAEP as a predictor of SSRI treatment response (e.g., Gallinat et al., 2000; Jaworska et al., 2013; Lee et al., 2005, 2015; Mulert et al., 2002; Paige et al., 1994; for a review, see e.g., Leuchter et al., 2009; for a meta-analysis, see Yoon et al., 2021), the findings of the effects of SSRIs on LDAEP are contradictory. Some studies showed no difference between pre-treatment LDAEP and post-treatment LDAEP in depressed patients (Gallinat et al., 2000; Linka et al., 2009; Paige et al., 1994). Furthermore, Gopal et al. (2004) showed that no difference in LDAEP was found between SSRI medicated and unmedicated depressed patients, and Ostermann et al. (2012) found no differences in LDAEP between depressed patients treated with SSRIs and those treated with other antidepressants. Regarding healthy adults, neither an acute administration of SSRIs (Guille et al., 2008; Uhl et al., 2006) nor an acute depletion of serotonin (Debener et al., 2002h; Dierks et al., 1999) had an impact on LDAEP. However, in a few studies investigating healthy adults, decreased LDAEP after the administration of a single dose of the SSRI (Nathan et al., 2006; Segrave et al., 2006) and chronic SSRI administration (Simmons et al., 2011) was found. Thus, more research addressing the effects of SSRIs on LDAEP is needed. Regarding methodological issues, the procedure for measuring LDAEP varied between the studies reviewed in this work. In most studies, LDAEP was defined as an amplitude change in the auditory N1/ P2 component in response to different stimulus intensities. However, there are also other ways to investigate LDAEP. For instance, in addition to N1/P2 amplitude change, amplitude change in N1, P1, P2, and P1/N1 components in response to different stimulus intensities have also been studied (Jaworska et al., 2012; Linka et al., 2009; Ruohonen et al., 2020). In the study by Gopal et al. (2004), amplitude change in response to different stimulus intensities in the brainstem ERP was also investigated. Linka et al. (2009) found associations between the LDAEP amplitude slopes and somatic symptoms of depression for N1 but not for P1/N1 nor N1/P2. Thus, the results may vary for different ERP components. Additionally, the stimulus intensities varied between the studies. However, in the reviewed studies, no systematic differences in the parameters in the study design were identified between the studies in which no between-group differences were found and the studies in which some differences between patients with depressive disorder and non-depressed controls were reported. In future research, it would be important to manipulate different parameters in LDAEP study designs to find the most effective method to separate depressed patients and controls. 5. Conclusion, limitations and future directions This narrative review addressed the first step into researching possible future biomarkers for depressive disorder, focusing on sensitivity by investigating whether auditory P3, MMN and LDAEP components could be used to separate depressed and non-depressed participant groups. The results showed that the findings of the reviewed studies were inconsistent, but some patterns could be identified. For auditory P3b, the common finding was attenuated amplitude and prolonged latency among depressed patients. Regarding auditory MMN, especially the amplitude of duration deviance MMN was commonly attenuated, and the amplitude of frequency deviance MMN was increased in depressed patients. In LDAEP studies, commonly, no differences between depressed patients and non-depressed controls were reported, although group differences concerning specific depression subtypes were found. Still, the inconsistency in the results of the reviewed studies shows that, in general, these auditory ERPs do not have a high sensitivity in distinguishing the presence or absence of a depressive disorder. The narrative approach enabled us to review the findings of a large range of methodologically different studies on auditory P3, MMN and LDAEP alterations in patients with depressive disorder, presenting an overall picture of the current knowledge. The broad focus of a narrative review is a strength that allows for comprehensive coverage of a topic, providing the foundation for future research. In the present review, the literature search performed using systematic selection criteria in a PubMed database yielded studies from four decades, from 1981 to 2021. However, the comprehensive approach of narrative review is also a weakness. It is obvious that compared to meta-analyses in which pooled data can be analysed statistically, narrative reviews that cannot provide a quantitative synthesis of the literature are a much less objective research design. Thus, even if differences in auditory ERPs between depressed and non-depressed groups were recognised, the magnitude of the differences is not known. Regarding research on possible future biomarkers for depressive disorder, the effect sizes are of high importance. Hence, the rigour of meta-analyses is needed to synthesise the results of studies on auditory ERP biomarkers for depression. Based on the results of the present narrative review, meta-analytic research could be continued by focusing on narrower topics. This narrative review indicated that there are some methodological issues that should be considered in future studies on auditory P3, MMN and LDAEP in patients with depressive disorder. In many of the reviewed studies, the sample sizes were relatively small and not estimated to ensure the sufficient power of the study. There was also a lack of standardisation regarding experimental paradigms. Regarding auditory MMN studies investigating depressive disorder, duration deviance and frequency deviance oddball conditions were most often applied. Replications of these studies are still needed to draw clear conclusions regarding a suitable experimental paradigm. Additionally, in future studies on auditory MMN, as well as P3b and LDAEP, it would be important to manipulate different parameters (e.g., stimulus parameters) in experimental designs to find the best options for studies investigating differences between depressed patients and controls. Additionally, the inclusion criteria for depressed patients differed between the reviewed studies, for instance, regarding the use of antidepressant medication, the stage of illness and the subtypes of depressive disorder. These differences may partly explain the inconsistent results regarding auditory P3, MMN and LDAEP in depressive disorder in the present narrative review. Standardisation of methodological issues and inclusion criteria, as well as replications of the studies, would better enable a meta-analytic research approach in the future. This narrative review yielded some conclusions that provide suggestions for future studies. P3b is the most frequently studied auditory ERP component in depressed patients. In the reviewed studies, attenuated auditory P3b amplitude and prolonged P3b latency among depressed patients were common findings. However, in P3b studies, and especially in LDAEP studies, the findings were more consistent when investigating some subgroups of depressive disorder instead of a heterogeneous group of depressed patients. This suggests that it would be important to study auditory ERPs systematically in clinical subgroups rather than in heterogeneous samples of depressed patients. Furthermore, comorbid factors are important to consider. For instance, anxiety disorders and anxiety symptoms are common among depressive disorder patients. In future studies, in addition to excluding depressed patients with a comorbid anxiety disorder, it would be important to control for the effect of anxiety symptoms in the analysis. Regarding the possible clinical utility of auditory P3, MMN and LDAEP as tools to differentiate between depressed patients and nondepressed subjects in the future, the question of whether these ERPs could be conceptualised as a trait or state marker of depressive disorder is essential. Longitudinal designs with multiple assessments could be useful to address this issue, but these kinds of studies are still very rare (for visual ERPs, see Ruohonen et al., 2020). Another essential question regarding clinical utility is related to the specificity of possible future E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 137 auditory ERP biomarkers; it would be important to investigate whether they could differentiate depressive disorder from other psychiatric disorders. For instance, while P3b is commonly found to be attenuated and prolonged in depression, these P3b findings may not be specific to depression (e.g., Blackwood et al., 1987; Roth et al., 1981; Wagner et al., 1997; Weir et al., 1998; Xu et al., 2014). Currently, research comparing auditory ERPs between depressive disorders and other psychiatric conditions is quite rare, and the findings are variable (e.g., Blackwood et al., 1987; Bruder et al., 2002; He et al., 2010; Kaur et al., 2012; Kim and Park, 2020; Kim et al., 2020; Li et al., 2011; Muir et al., 1991; Park and Lee, 2013; R¨ oschke et al., 1996; Roth et al., 1981; Shim et al., 2019; Umbricht et al., 2003; Wagner et al., 1997; Xu et al., 2014). For this reason, more research is required. The present review focused on auditory ERPs. In the future, it would be important to study ERP alterations in different sensory modalities in patients with depressive disorder to determine which sensory modality can best separate depressed and non-depressed groups. Regarding the visual modality, there is already growing literature indicating that visual ERPs can differentiate between depressed and non-depressed groups (for reviews, see Hajcak and Foti, 2020; Kujawa and Burkhouse, 2017; Kujawa et al., 2020; Proudfit, 2015; Proudfit et al., 2015). In addition, an essential question would be which sensory modality is the most suitable for studies comparing ERPs between groups of depressive disorder patients and groups of patients with other psychiatric disorders. Furthermore, depressive disorder is associated with dysfunction in emotional processing (for reviews, see Bylsma, 2021; Phillips et al., 2003). However, the alterations in the information processing are usually more diverse in depressed patients (for a meta-analysis, Rock et al., 2014). The findings of the present narrative review suggest alterations in the basic sensory-cognitive information processing by showing some differences between depressed and non-depressed groups in the auditory ERPs elicited by neutral stimuli. Thus, not only emotional information processing but also the processing of basic sensory information may be altered in depressed patients. Future studies should investigate ERPs elicited by both emotional and neutral stimuli to find out whether ERP components reflecting emotional and basic sensory information processing are differently related to depressive symptoms or symptom profiles. An important question would be whether these ERP components together could have utility in separating subgroups of depressive disorder and developing more personalised approaches for treatment in the future. In the studies reviewed in the present work, ERPs reflecting sensory-cognitive processing were elicited mostly by neutral auditory stimuli. In only one ERP study (Pang et al., 2014), emotional stimuli were applied to elicit the auditory MMN component. Regarding the visual modality, ERP research investigating affective cognition in depressive disorder is already abundant; previous studies have shown that visual ERPs reflecting emotional, motivational and reward processing can differentiate between depressed and non-depressed groups (for a review, see e.g., Proudfit et al., 2015). Auditory ERPs elicited by emotional stimuli might also differ between depressed patients and nondepressed controls. Hence, research on auditory ERPs in depressed patients conducted by applying emotional stimuli is required. In sum, the conclusions drawn from the findings of the studies reviewed in this work suggest that future research should investigate whether certain stimulus conditions are particularly efficient at separating depressed and non-depressed participant groups. Future studies should also compare responses in different subpopulations of depressed patients and different clinical groups (e.g., depressive disorder and anxiety disorder patients) to investigate the specificity of the auditory ERP alterations for depressive disorder. Funding This work was supported by The Finnish Cultural Foundation (grant number 00200466 to Elina S. Kangas). Declaration of competing interest The authors declare no conflict of interest. Data availability No data was used for the research described in the article. References 1 *Ancy, J., Gangadhar, B.N., Janakiramaiah, N., 1996. “Normal” P300 amplitude predicts rapid response to ECT in melancholia. J. Affect. Disord. 41 (3), 211–215. https://doi. org/10.1016/s0165-0327(96)00090-0. *Barreiros, A.R., Breukelaar, I.A., Chen, W., Erlinger, M., Antees, C., Medway, M., Boyce, P., Hazell, P., Williams, L.W., Malhi, G.S., Harris, A.W.F., Korgaonkar, M.S., 2020. Neurophysiological markers of attention distinguish bipolar disorder and unipolar depression. J. Affect. Disord. 274, 411–419. https://doi.org/10.1016/j. jad.2020.05.048. *Bissonnette, J.N., Francis, A.M., Hull, K.M., Leckey, J., Pimer, L., Berrigan, L.I., Fisher, D.J., 2020. MMN-indexed auditory change detection in major depressive disorder. In: Clinical EEG and Neuroscience, 51(6), pp. 365–372. https://doi.org/ 10.1177/155005942091420. *Blackwood, D.H., Whalley, L.J., Christie, J.E., Blackburn, I.M., St Clair, D.M., McInnes, A., 1987. Changes in auditory P3 event-related potential in schizophrenia and depression. Br. J. Psychiatry 150, 154–160. https://doi.org/10.1192/ bjp.150.2.154. *Bruder, G.E., Tenke, C.E., Towey, J.P., Leite, P., Fong, R., Stewart, J.E., McGrath, P.J., Quitkin, F.M., 1998. Brain ERPs of depressed patients to complex tones in an oddball task: relation of reduced P3 asymmetry to physical anhedonia. Psychophysiology 35 (19), 54–63. *Bruder, G.E., Kayser, J., Tenke, C.E., Leite, P., Schneier, F.R., Stewart, J.W., Quitkin, F. M., 2002. Cognitive ERPs in depressive and anxiety disorders during tonal and phonetic oddball tasks. Clin. Electroencephalogr. 33 (3), 119–124. https://doi.org/ 10.1177/155005940203300308. *Bruder, G.E., Kroppmann, C.J., Kayser, J., Stewart, J.W., McGrath, P.J., Tenke, C.E., 2009. Reduced brain responses to novel sounds in depression: P3 findings in a novelty oddball task. Psychiatry Res. 170 (2–3), 218–223. https://doi.org/10.1016/ j.psychres.2008.10.023. *Chen, J., Zhang, Y., Wei, D., Wu, X., Fu, Q., Xu, F., Wang, H., Ye, M., Ma, W., Yang, L., Zhang, Z., 2015. Neurophysiological handover from MMN to P3a in first-episode and recurrent major depression. J. Affect. Disord. 174, 173–179. https://doi.org/ 10.1016/j.jad.2014.11.049. *Feldmann, L., Piechaczek, C.E., Pehl, C., Bartling, J., Bakos, S., Schulte-K¨ orne, G., Greimel, E., 2018. State or trait? Auditory event-related potentials in adolescents with current and remitted major depression. Neuropsychologia 113, 95–103. https://doi.org/10.1016/j.neuropsychologia.2018.03.035. *Gallinat, J., Bottlender, R., Juckel, G., Munke-Puchner, A., Stotz, G., Kuss, H.-J., Mavrogiorgou, P., Hegerl, U., 2000. The loudness dependency of the auditory evoked N1/P2-component as a predictor of the acute SSRI response in depression. Psychopharmacology 148 (4), 404–411. https://doi.org/10.1007/s002130050070. *Gangadhar, B.N., Ancy, J., Janakiranaiah, N., Umapathy, C., 1993. P300 amplitude in non-bipolar, melancholic depression. J. Affect. Disord. 28 (1), 57–60. https://doi. org/10.1016/0165-0327(93)90077-w. *Giedke, H., Thier, P., Bolz, J., 1981. The relationship between P3-latency and reaction time in depression. Biol. Psychol. 13, 31–49. https://doi.org/10.1016/0301-0511 (81)90026-0. *Gopal, K.V., Bishop, C.E., Carney, L., 2004. Auditory measures in clinically depressed individuals. Int. J. Audiol. 43 (9), 499–505. https://doi.org/10.1080/ 14992020400050064. *Gordon, E., Kraiuhin, C., Harris, A., Meares, R., Howson, A., 1986. The differential diagnosis of dementia using P300 latency. Biol. Psychiatry 21 (12), 1123–1132. https://doi.org/10.1016/0006-3223(86)90220-9. *Graßnickel, V., Illes, F., Juckel, G., Uhl, I., 2015. Loudness dependence of auditory evoked potentials (LDAEP) in clinical monitoring of suicidal patients with major depression in comparison with non-suicidal depressed patients and healthy volunteers: a follow-up-study. J. Affect. Disord. 184, 299–304. https://doi.org/ 10.1016/j.jad.2015.06.007 0165-0327. *Greimel, E., Trinkl, M., Bartling, J., Bakos, S., Grossheinrich, N., Schulte-K¨ orne, G., 2015. Auditory selective attention in adolescents with major depression: an eventrelated potential study. J. Affect. Disord. 172, 445–452. https://doi.org/10.1016/j. jad.2014.10.022. *Hansenne, M., Pitchot, W., Gonzalez Moreno, A., Urcelay Zaldua, I., Ansseau, M., 1996. Suicidal behavior in depressive disorder: an event-related potential study. Biol. Psychiatry 40 (2), 116–122. https://doi.org/10.1016/0006-3223(95)00372-x. *He, W., Chai, H., Zheng, L., Yu, W., Chen, W., Li, J., Chen, W., Wang, W., 2010. Mismatch negativity in treatment-resistant depression and borderline personality disorder. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 34 (2), 366–371. https:// doi.org/10.1016/j.pnpbp.2009. 12.021. 1 *articles included in Table 1. E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 138 *Hirakawa, N., Hirano, Y., Nakamura, I., Hirano, S., Sato, J., Oribe, N., Ueno, T., Kanba, S., Onitsuka, T., 2017. Right hemisphere pitch-mismatch negativity reduction in patients with major depression: an MEG study. J. Affect. Disord. 215, 225–229. https://doi.org/10.1016/j.jad.2017.03.046. *Houston, R.J., Bauer, L.O., Hesselbrock, V.M., 2004. P300 evidence of cognitive inflexibility in female adolescents at risk for recurrent depression. Prog. NeuroPsychopharmacol. Biol. Psychiatry 28 (3), 529–536. https://doi.org/10.1016/j. pnpbp.2004.01.004. *Hwang, M., Lee, Y.J., Lee, M., Kang, B., Lee, Y.S., Hwang, J., Woo, S.I., Hahn, S.W., 2021. Relationship between the loudness dependence of the auditory evoked potential and the severity of suicidal ideation in patients with major depressive disorder. Clin. Psychopharmacol. Neurosci. 19 (2), 323–333. https://doi.org/ 10.9758/cpn.2021.19.2.323. *Iv, J., Zhao, L., Gong, J., Chen, C., Miao, D., 2010. Event-related potential based evidence of cognitive dysfunction in patients during the first episode of depression using a novelty oddball task. Psychiatry Res. 182 (1), 58–66. https://doi.org/ 10.1016/j.pscychresns.2010.02.005. *Kalayam, B., Alexopoulos, G.S., 1999. Prefrontal dysfunction and treatment response in geriatric depression. Arch. Gen. Psychiatry 56 (8), 713–718. https://doi.org/ 10.1001/archpsyc.56.8.713. *Kalayam, B., Alexopoulos, G.S., Kindermann, S., Kakuma, T., Brown, G.G., Young, R.C., 1998. P300 latency in geriatric depression. Am. J. Psychiatr. 155 (3), 425–427. https://doi.org/10.1176/ajp.155.3.425. *Karaaslan, F., Gonul, A.S., Oguz, A., Erdinc, E., Esel, E., 2003. P300 changes in major depressive disorders with and without psychotic features. J. Affect. Disord. 73 (3), 283–287. https://doi.org/10.1016/s0165-0327(01)00477-3. *Kaustio, O., Partanen, J., Valkonen-Korhonen, M., Viinam¨ aki, H., Lehtonen, J., 2002. Affective and psychotic symptoms relate to different types of P300 alteration in depressive disorder. J. Affect. Disord. 71 (1), 43–50. https://doi.org/10.1016/ s0165-0327(01)00410-4. *Kawasaki, T., Tanaka, S., Wang, J., Hokama, H., Hiramatsu, K., 2004. Abnormalities of P300 cortical current density in unmedicated depressed patients revealed by LORETA analysis of event-related potentials. Psychiatry Clin. Neurosci. 58 (1), 68–75. https://doi.org/10.1111/j.1440-1819.2004.01195.x. *Kemp, A.H., Hopkinson, P.J., Hermens, D.F., Rowe, D.L., Sumich, A.L., Clark, C.R., Drinkenburg, W., Abdi, N., Penrose, R., McFarlane, A., Boyce, P., Gordon, E., Williams, L.M., 2009. Fronto-temporal alterations within the first 200ms during an attentional task distinguish major depression, non-clinical participants with depressed mood and healthy controls: a potential biomarker? Hum. Brain Mapp. 30 (2), 602–614. https://doi.org/10.1016/j.jad.2009.08.010. *Kemp, A.H., Benito, L.P., Quintana, D.S., Clark, C.R., McFarlane, A., Mayur, P., Harris, A., Boyce, P., Williams, L.M., 2010. Impact of depression heterogeneity on attention: an auditory oddball event related potential study. J. Affect. Disord. 123 (1–3), 202–207. https://doi.org/10.1016/j.jad.2009.08.010. *Kim, J.S., Kim, D.-W., Kwon, Y.J., Lee, H.Y., Kim, S., Shim, S.H., 2019. The relationship between auditory evoked potentials and symptoms of attention-deficit/hyperactivity disorder in adult patients with major depressive disorder. Int. J. Psychophysiol. 142, 50–56. https://doi.org/10.1016/j.ijpsycho.2019.06.008. *Kim, S., Baek, J.H., Shim, S., Kwon, Y.J., Lee, H.Y., Yoo, J.H., Kim, J.S., 2020. Mismatch negativity indices and functional outcomes in unipolar and bipolar depression. Sci. Rep. 10 (1), 12831. https://doi.org/10.1038/s41598-020-69776-4. *Kim, J.S., Kim, S., Lee, H.S., Kwon, Y.J., Lee, H.Y., Shim, S.H., 2021. Auditory evoked potentials and suicidal behaviors in patients with major depressive disorders. Sci. Rep. 11 (1), 7255. https://doi.org/10.1038/s41598-021-86602-7. *Kindermann, S.S., Kalayam, B., Brown, G.G., Burdick, K.E., Alexopoulos, G.S., 2000. Executive functions and P300 latency in elderly depressed patients and control subjects. Am. J. Geriatr. Psychiatr. 8 (1), 57–65. https://doi.org/10.1097/ 00019442-200002000-00008. *Kraiuhin, C., Gordon, E., Coyle, S., Sara, G., Rennie, C., Howson, A., Landau, P., Meares, R., 1990. Normal latency of the P300 event-related potential in mild-tomoderate Alzheimer's disease and depression. Biol. Psychiatry 28 (5), 372–386. https://doi.org/10.1016/0006-3223(90)90406-r. *Kuang, W., Tian, L., Yue, L., Li, J., 2016. Effects of escitalopram with a Chinese traditional compound Jiuweizhenxin-keli on mismatch negativity and P50 in patients with major depressive disorders. Neuropsychiatr. Dis. Treat. 12, 1935–1941. https://doi.org/10.2147/NDT.S104020. *Lepist¨ o, T., Soininen, M., Ceponiene, R., Almqvist, F., N¨ a¨ at¨ anen, R., Aronen, E., 2004. Auditory event-related potential indices of increased distractibility in children with major depression. Clin. Neurophysiol. 115 (3), 620–627. https://doi.org/10.1016/j. clinph.2003.10.020. *Li, Y., Hu, Y., Liu, T., Wu, D., 2011. Dipole source analysis of auditory P300 response in depressive and anxiety disorders. Cogn. Neurodyn. 5 (2), 221–229. https://doi.org/ 10.1007/s11571-011-9156-y. *Li, X., Wu, H., Lou, C., Xing, B., Yu, E., 2014. Study on the executive function of attention in depression patients based on SPECT technology. Int. J. Clin. Exp. Med. 7 (4), 1110–1115. *Linka, T., Sartory, G., Wiltfang, J., Müller, B.W., 2009. Treatment effects of serotonergic and noradrenergic antidepressants on the intensity dependence of auditory ERP components in major depression. Neurosci. Lett. 463 (1), 26–30. https://doi.org/ 10.1016/j.neulet.2009.07.038. *Manjarrez-Gutierrez, G., Marquez, R.H., Mejenes-Alvarez, S.A., Godinez-Lopez, T., Hernandez-R, J., 2009. Functional change of the auditory cortex related to brain serotonergic neurotransmission in type 1 diabetic adolescents with and without depression. World J. Biol. Psychiatry 10 (4 Pt 3), 877–883. https://doi.org/10.1080/ 15622970902717032. *Mu, Z., Chang, Y., Xu, J., Pang, X., Zhang, H., Liu, X., Zheng, Y., Liu, X., Liu, X., Wan, Y., 2016. Preattentive dysfunction of musical processing in major depressive disorder: a mismatch negativity study. J. Affect. Disord. 194, 50–56. https://doi.org/10.1016/j. jad.2016.01.028. *Muir, W.J., St Clair, D.M., Blackwood, D.H., 1991. Long-latency auditory event-related potentials in schizophrenia and in bipolar and unipolar affective disorder. Psychol. Med. 21 (4), 867–879. https://doi.org/10.1017/s003329170002986x. *Murthy, P.J., Gangadhar, B.N., Janakiramaiah, N., Subbakrishna, D.K., 1997. Normalization of P300 amplitude following treatment in dysthymia. Biol. Psychiatry 42 (8), 740–743. https://doi.org/10.1016/s0006-3223(97)00296-5. *Naismith, S.L., Mowszowski, L., Ward, P.B., Diamond, K., Paradise, M., Kaur, M., Lewis, S.J.G., Hickie, I.B., Hermens, D.F., 2012. Reduced temporal mismatch negativity in late-life depression: an event-related potential index of cognitive deficit and functional disability? J. Affect. Disord. 138 (1–2), 71–78. https://doi.org/ 10.1016/j.jad.2011.12.028. *Nan, C., Wang, G., Wang, H., Wang, X., Liu, Z., Xiao, L., Bai, H., Wu, S., 2018. The P300 component decreases in a bimodal oddball task in individuals with depression: an event-related potentials study. Clin. Neurophysiol. 129 (12), 2525–2533. https:// doi.org/10.1016/j.clinph.2018.09.012. *Ostermann, J., Uhl, I., K¨ ohler, E., Juckel, G., Norra, C., 2012. The loudness dependence of auditory evoked potentials and effects of psychopathology and psychopharmacotherapy in psychiatric inpatients. Hum. Psychopharmacol. Clin. Exp. 27 (6), 595–604. https://doi.org/10.1002/hup.2269. *Pang, X., Xu, J., Chang, Y., Tang, D., Zheng, Y., Liu, Y., Sun, Y., 2014. Mismatch negativity of sad syllables is absent in patients with major depressive disorder. PLoS One 9 (3), e91995. https://doi.org/10.1371/journal.pone.0091995. *Park, Y.M., Lee, S.H., Kim, S., Bae, S.M., 2010. The loudness dependence of the auditory evoked potential (LDAEP) in schizophrenia, bipolar disorder, major depressive disorder, anxiety disorder, and healthy controls. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 34 (2), 313–316. https://doi.org/10.1016/j.pnpbp.2009.12.004. *Pfefferbaum, A., Wenegrat, B.G., Ford, J.M., Roth, W.T., Kopell, B.S., 1984. Clinical application of P3 component of event-related potentials II. Dementia, depression and schizophrenia. Electroencephalogr. Clin. Neurophysiol. 59 (2), 104–124. https://doi. org/10.1016/0168-5597(84)90027-3. *Qiao, Z., Yu, Y., Wang, L., Yang, X., Qiu, X., Zhang, C., Ning, N., Shi, J., Chen, l., Li, Z., Liu, J., Xu, J., Zhao, L., Yang, Y., 2013. Impaired pre-attentive change detection in major depressive disorder patients revealed by auditory mismatch negativity. Psychiatry Res. Neuroimaging 211 (1), 78–84. https://doi.org/10.1016/j. pscychresns.2012.07.006. *Qiao, Z., Yang, A., Qiu, X., Yang, X., Zhang, C., Zhu, X., He, J., Wang, L., Bai, B., Sun, H., Zhao, L., Yang, Y., 2015. Gender effect on pre-attentive change detection in major depressive disorder patients revealed by auditory MMN. Psychiatry Res. Neuroimaging 234 (1), 7–14. https://doi.org/10.1016/j.pscychresns.2015.05.011 0925-4927. *Restuccia, D., Vollono, C., Scaloni, L., Buccelletti, F., Camardese, G., 2016. Abnormality of auditory mismatch negativity in depression and its dependence on stimulus intensity. Clin. EEG Neurosci. 47 (2), 105–112. https://doi.org/10.1177/ 1550059415584704. *R¨ oschke, J., Wagner, P., 2003. A confirmatory study on the mechanisms behind reduced P300 waves in depression. Neuropsychopharmacology 28 (Suppl. 1), S9–S12. https://doi.org/10.1038/sj.npp.1300139. *R¨ oschke, J., Wagner, P., Mann, K., Fell, J., Gr¨ ozinger, M., Frank, C., 1996. Single trial analysis of event related potentials: a comparison between schizophrenics and depressives. Biol. Psychiatry 40 (9), 844–852. https://doi.org/10.1016/0006-3223 (95)00652-4. *Roth, W.T., Pfefferbaum, A., Kelly, A.F., Berger, P.A., Kopell, B.S., 1981. Auditory event-related potentials in schizophrenia and depression. Psychiatry Res. 4 (2), 199–212. https://doi.org/10.1016/0165-1781(81)90023-8. *Ruohonen, E.M., Astikainen, P., 2017. Brain responses to sound intensity changes dissociate depressed participants and healthy controls. Biol. Psychol. 127, 74–81. https://doi.org/10.1016/j.biopsycho.2017.05.008. *Ruohonen, E.M., Kattainen, S., Li, X., Taskila, A.-E., Ye, C., Astikainen, P., 2020. Eventrelated potentials to changes in sound intensity demonstrate alterations in brain function related to depression and aging. Front. Hum. Neurosci. 14, 98. https://doi. org/10.3389/fnhum.2020.00098. *Sara, G., Gordon, E., Kraiuhin, C., Coyle, S., Howson, A., Meares, R., 1994. The P300 ERP component: an index of cognitive dysfunction in depression? J. Affect. Disord. 31 (1), 29–38. https://doi.org/10.1016/0165-0327(94)90124-4. *Shim, M., Jin, J.M., Im, C.-H., Lee, S.-H., 2019. Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features. NeuroImage 24, 102001. https://doi.org/10.1016/j.nicl.2019.102001. *Singh, R., Shukla, R., Dalal, P.K., Sinha, P.K., Trivedi, J.K., 2000. P300 event related potential in depression. Indian J. Psychiatry 42 (4), 402–409. *Sumi, N., Nan’no, H., Fujimoto, O., Ohta, Y., Takeda, M., 2000. Interpeak latency of auditory event-related potentials (P300) in senile depression and dementia of the Alzheimer type. Psychiatry Clin. Neurosci. 54 (6), 679–684. https://doi.org/ 10.1046/j.1440-1819.2000.00769.x. *Swanwick, G.R., Rowan, M., Coen, R.F., O’Mahony, D., Lee, H., Lawlor, B.A., Walsh, J. B., Coakley, D., 1996. Clinical application of electrophysiological markers in the differential diagnosis of depression and very mild Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 60 (1), 82–86. https://doi.org/10.1136/jnnp.60.1.82. *Takei, Y., Kumano, S., Hattori, S., Uehara, T., Kawakubo, Y., Kasai, K., Fukuda, M., Mikuni, M., 2009. Preattentive dysfunction in major depression: a magnetoencephalography study using auditory mismatch negativity. Psychophysiology 46 (1), 52–61. https://doi.org/10.1111/j.1469-8986. 2008.00748.x. E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 139 *Tenke, C.E., Kayser, J., Stewart, J.W., Bruder, G.E., 2010. Novelty P3 reductions in depression: characterization using principal components analysis (PCA) of current source density (CSD) waveforms. Psychophysiology 47 (1), 133–146. https://doi. org/10.1111/j.1469-8986.2009.00880.x. *Tripathi, S.M., Mishra, N., Tripathi, R.K., Gurnani, K.C., 2015. P300 latency as an indicator of severity in major depressive disorder. Ind. Psychiatry J. 24 (2), 163–167. https://doi.org/10.4103/0972-6748.181726. *Uhl, I., Krumova, E.K., Regeniter, S., B¨ ar, K.-J., Norra, C., Richter, H., Assion, H.-J., Westermann, A., Juckel, G., Maier, C., 2011. Association between wind-up ratio and central serotonergic function in healthy subjects and depressed patients. Neurosci. Lett. 504 (2), 176–180. https://doi.org/10.1016/j.neulet.2011.09.033. *Umbricht, D., Koller, R., Schmid, L., Skrabo, A., Grübel, C., Huber, T., Stassen, H., 2003. How specific are deficits in mismatch negativity generation to schizophrenia? Biol. Psychiatry 53 (12), 1120–1131. https://doi.org/10.1016/S0006-3223(02)01642-6. *Urretavizcaya, M., Moreno, I., Benlloch, L., Cardon, N., Serrallonga, J., Menchon, J.M., Vallejo, J., 2003. Auditory event-related potentials in 50 melancholic patients: increased N100, N200 and P300 latencies and diminished P300 amplitude. J. Affect. Disord. 74 (3), 293–297. https://doi.org/10.1016/s0165-0327(02)00016-2. *van Dinteren, R., Arns, M., Kenemans, L., Jongsma, M.L., Kessels, R.P., Fitzgerald, P., Fallahpour, K., Debattista, C., Gordon, E., Williams, L.M., 2015. Utility of eventrelated potentials in predicting antidepressant treatment response: an iSPOT-D report. Eur. Neuropsychopharmacol. 25 (11), 1981–1990. https://doi.org/10.1016/ j.euroneuro.2015.07.022. *Vandoolaeghe, E., van Hunsel, F., Nuyten, D., Maes, M., 1998. Auditory event related potentials in major depression: prolonged P300 latency and increased P200 amplitude. J. Affect. Disord. 48 (2–3), 105–113. https://doi.org/10.1016/s01650327(97)00165-1. *Wagner, P., R¨ oschke, J., Fell, J., Frank, C., 1997. Differential pathophysiological mechanisms of reduced P300 amplitude in schizophrenia and depression: a single trial analysis. Schzophrenia Res. 25 (3), 221–229. https://doi.org/10.1016/s09209964(97)00027-3. *Weir, N., Fiaschi, K., Machin, D., 1998. The distribution and latency of the auditory P300 in schizophrenia and depression. Schizophr. Res. 31 (2–3), 151–158. https:// doi.org/10.1016/s0920-9964(98)00017-6. *Xu, S., Chai, H., Hu, J., Xu, Y., Chen, W., Wang, W., 2014. Passive event-related potentials to a single tone in treatment-resistant depression, generalized anxiety disorder, and borderline personality disorder patients. J. Clin. Neurophysiol. 31 (5), 488–492. https://doi.org/10.1097/WNP.0000000000000091. *Yanai, I., Fujikawa, T., Osada, M., Yamawaki, S., Touhouda, Y., 1997. Changes in auditory P300 in patients with major depression and silent cerebral infarction. J. Affect. Disord. 46 (3), 263–271. https://doi.org/10.1016/s0165-0327(97)001006. *Zhou, L., Wang, G., Nan, C., Wang, H., Liu, Z., Bai, H., 2019. Abnormalities in P300 components in depression: an ERP-sLORETA study. Nordic J. Psychiatry 73 (1), 1–8. https://doi.org/10.1080/08039488.2018.1478991. Adell, A., 2020. Brain NMDA receptors in schizophrenia and depression. Biomolecules 10 (6), 947. https://doi.org/10.3390/biom10060947. Ahveninen, J., K¨ ahk¨ onen, S., Pennanen, S., Liesivuori, J., Ilmoniemi, R.J., J¨ a¨ askel¨ ainen, I.P., 2002. Tryptophan depletion effects on EEG and MEG responses suggest serotonergic modulation of auditory involuntary attention in humans. Neuroimage 16 (4), 1052–1061. https://doi.org/10.1006/nimg.2002.1142. American Psychiatric Association, 2013. Diagnostic and Statistical Manual of Mental Disorders, 5th ed. (Washington, DC). Atkinson, A.J., Biomarkers Definitions Working Group, et al., 2001. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 69 (3), 89–95. https://doi.org/10.1067/mcp.2001.113989. Austin, M.-P., Mitchell, P., Goodwin, G.M., 2001. Cognitive deficits in depression: possible implications for functional neuropathology. Br. J. Psychiatry 178, 200–206. https://doi.org/10.1192/bjp.178.3.200. Bazana, P.G., Stelmack, R.M., 2002. Intelligence and information processing during an auditory discrimination task with backward masking: an event-related potential analysis. J. Pers. Soc. Psychol. 83, 998–1008. https://doi.org/10.1037//00223514.83.4.998. Belujon, P., Grace, A.A., 2017. Dopamine system dysregulation in major depressive disorders. Int. J. Neuropsychopharmacol. 20 (12), 1036–1046. https://doi.org/ 10.1093/ijnp/pyx056. Bilello, J.A., 2016. Seeking an objective diagnosis of depression. Biomark. Med 10 (8), 861–875. https://doi.org/10.2217/bmm-2016-0076. Bonetti, L., Haumann, N.T., Vuust, P., Kliuchko, M., Brattico, E., 2017. Risk of depression enhances auditory pitch discrimination in the brain as indexed by the mismatch negativity. Clin. Neurophysiol. 128 (10), 1923–1936. https://doi.org/10.1016/j. clinph.2017.07.004. Bosaipo, N.B., Foss, M.P., Young, A.H., Juruena, M.F., 2017. Neuropsychological changes in melancholic and atypical depression: a systematic review. Neurosci. Biobehav. Rev. 73, 309–325. https://doi.org/10.1016/j.neubiorev.2016.12.014. Bramon, E., Rabe-Hesketh, S., Sham, P., Murray, R.M., Frangou, S., 2004. Meta-analysis of the P300 and P50 waveforms in schizophrenia. Schizophr. Res. 70 (2–3), 315–329. https://doi.org/10.1016/j.schres.2004.01.004. Bruder, G., Kayser, J., Tenke, C., 2012. Event-related brain potentials in depression: Clinical, cognitive and neurophysiologic implications. In: Luck, S.J., Kappenman, E. S. (Eds.), The Oxford Handbook of Event-Related Potential Components. Oxford University Press, New York, pp. 563–592. https://doi.org/10.1093/oxfordhb/ 9780195374148.013.0257. Burt, D.B., Zembar, M.J., Niederehe, G., 1995. Depression and memory impairment: a meta-analysis of the association, its pattern, and specificity. Psychol. Bull. 117 (2), 285–305. https://doi.org/10.1037/0033-2909.117.2.285. Bylsma, L.M., 2021. Emotion context insensitivity in depression: toward an integrated and contextualized approach. Psychophysiology 58 (2), e13715. https://doi.org/ 10.1111/psyp.13715. Carbajal, G.V., Malmierca, M.S., 2018. The neuronal basis of predictive coding along the auditory pathway: from the subcortical roots to cortical deviance detection. Trends Hear. 22, 1–33. https://doi.org/10.1177/2331216518784822. Chang, Y., Xu, J., Shi, N., Zhang, B., Zhao, L., 2010. Dysfunction of processing task irrelevant emotional faces in major depressive disorder patients revealed by expressionrelated visual MMN. Neurosci. Lett. 472 (1), 33–37. https://doi.org/ 10.1016/j.neulet.2010.01.050. Chen, T.-J., Yu, Y.W.-Y., Chen, M.-C., Wang, S.-Y., Tsai, S.-J., Lee, T.-W., 2005. Serotonin dysfunction and suicide attempts in major depressives: an auditory event-related potential study. Neuropsychobiology 52 (1), 28–36. https://doi.org/10.1159/ 000086175. Chennu, S., Noreika, V., Gueorguiev, D., Blenkmann, A., Kochen, S., Ibanez, A., Owen, A. M., Bekinschtein, T.A., 2013. Expectation and attention in hierarchical auditory prediction. J. Neurosci. 33 (27), 11194–11205. https://doi.org/10.1523/ JNEUROSCI.0114-13.2013. Christopher, G., MacDonald, J., 2005. The impact of clinical depression on working memory. Cogn. Neuropsychiatry 10 (5), 379–399. https://doi.org/10.1080/ 13546800444000128. Coppen, A., 1967. The biochemistry of affective disorders. Br. J. Psychiatry 113 (504), 1237–1264 (doi:4169954). Debener, S., Kranczioch, C., Herrmann, C.S., Engel, A.K., 2002g. Auditory novelty oddball allows reliable distinction of top-down and bottom-up processes of attention. Int. J. Psychophysiol. 46 (1), 77–84. https://doi.org/10.1016/s0167-8760(02) 00072-7. Debener, S., Strobel, A., Kürschner, K., Kranczioch, C., Hebenstreit, J., Maercker, A., Beauducel, A., Brocke, B., 2002h. Is auditory evoked potential augmenting/reducing affected by acute tryptophan depletion? Biol. Psychol. 59 (2), 121–133. https://doi. org/10.1016/s0301-0511(01)00132-6. Denham, S.L., Winkler, I., 2020. Predictive coding in auditory perception: challenges and unresolved questions. Eur. J. Neurosci. 51 (5), 1151–1160. https://doi.org/10.1111/ ejn.13802. Dierks, T., Barta, S., Demisch, L., Schmeck, K., Englert, E., Kewitz, A., Maurer, K., Poutska, F., 1999. Intensity dependence of auditory evoked potentials (AEPs) as biological marker for cerebral serotonin levels: effects of tryptophan depletion in healthy subjects. Psychopharmacology 146 (1), 101–107. https://doi.org/10.1007/ s002130051094. Erickson, M.A., Ruffle, A., Gold, J.M., 2016. A meta-analysis of mismatch negativity in schizophrenia: from clinical risk to disease specificity and progression. Biol. Psychiatry 79 (12), 980–987. https://doi.org/10.1016/j.biopsych.2015.08.025. Escera, C., Alho, K., Schr¨ oger, E., Winkler, I., 2000. Involuntary attention and distractibility as evaluated with event-related brain potentials. Audiol. Neuro Otol. 5 (3–4), 151–166. https://doi.org/10.1159/000013877. Fabiani, M., Friedman, D., 1995. Changes in brain activity patterns in aging: the novelty oddball. Psychophysiology 32 (6), 579–594. https://doi.org/10.1111/j.14698986.1995.tb01234.x. FDA-NIH Biomarker Working Group. BEST (biomarkers, EndpointS, and other tools) resource [internet]. Silver spring (MD): Food and Drug Administration (US); 2016-. Diagnostic biomarker. 2016 Dec 22 [updated 2020 Nov 16]. Available from: https: //www.ncbi.nlm.nih.gov/books/NBK402285/ co-published by National Institutes of Health (US), Bethesda (MD). First, M.B., Williams, J.B.W., Karg, R.S., Spitzer, R.L., 2015. Structured Clinical Interview for DSM-5 -Research Version (SCID-5 for DSM-5, Research Version; SCID-5-RV). American Psychiatric Association, Arlington, VA. Fitzgerald, P.B., Mellow, T.B., Hoy, K.E., Segrave, R., Cooper, N.R., Upton, D.J., Croft, R. J., 2009. A study of intensity dependence of the auditory evoked potential (IDAEP) in medicated melancholic and non-melancholic depression. J. Affect. Disord. 117 (3), 212–216. https://doi.org/10.1016/j.jad.2009.01.009. Fleming, S.K., Blasey, C., Schatzberg, A.F., 2004. Neuropsychological correlates of psychotic features in major depressive disorders: a review and meta-analysis. J. Psychiatr. Res. 38 (1), 27–35. https://doi.org/10.1016/s0022-3956(03)00100-6. Fried, E.I., Nesse, R.M., 2015. Depression is not a consistent syndrome: an investigation of unique symptom patterns in the STAR*D study. J. Affect. Disord. 172, 96–102. https://doi.org/10.1016/j.jad.2014.10.010. Friedman, D., Cycowicz, Y.M., Gaeta, H., 2001. The novelty P3: an event-related brain potential (ERP) sign of the brain’s evaluation of novelty. Neurosci. Biobehav. Rev. 25 (4), 355–373. https://doi.org/10.1016/s0149-7634(01)00019-7. Friston, K., 2005. A theory of cortical responses. Philos. Trans. R. Soc. B 360 (1456), 815–836. https://doi.org/10.1098/rstb.2005.1622. Garrido, M.I., Kilner, J.M., Stephan, K.E., Friston, K.J., 2009. The mismatch negativity: a review of underlying mechanisms. Clin. Neurophysiol. 120 (3), 453–463. https:// doi.org/10.1016/j.clinph.2008.11.029. Gaynes, B.N., Warde, D., Trivedi, M.H., Wisniewski, S.R., Fava, M., Rush, A.J., 2009. What did STAR*D teach us? Results from a large-scale, practical, clinical trial for patients with depression. Psychiatr. Serv. 60 (11), 1439–1445. https://doi.org/ 10.1176/ps.2009.60.11.1439. Guille, V., Croft, R.J., O’Neill, B.V., Illic, S., Phan, K.L., Nathan, P.J., 2008. An examination of acute changes in serotonergic neurotransmission using the loudness dependence measure of auditory cortex evoked activity: effects of citalopram, escitalopram and sertraline. Hum. Psychopharmacol. 23 (3), 231–241. https://doi. org/10.1002/hup.922. Hacimusalar, Y., Es¸el, E., 2018. Suggested biomarkers for major depressive disorder. Arch. Neuropsychiat. 55 (3), 280–290. https://doi.org/10.5152/npa.2017.19482. E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 140 Hajcak, G., Foti, D., 2020. Significance?... Significance! Empirical, methodological, and theoretical connections between the late positive potential and P300 as neural responses to stimulus significance: an integrative review. Psychophysiology 57 (7), e13570. https://doi.org/10.1111/psyp.13570. Hasin, D.S., Sarvet, A.L., Meyers, J.L., Saha, T.D., Ruan, W.J., Stohl, M., Grant, B.F., 2018. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatry 75 (4), 336–346. https://doi.org/10.1001/ jamapsychiatry.2017.4602. Hasler, G., 2010. Pathophysiology of depression: do we have any solid evidence of interest to clinicians? World Psychiatry 9 (3), 155–161. https://doi.org/10.1002/ j.2051-5545. 2010.tb00298.x. Hegerl, U., Juckel, G., 1993. Intensity dependence of auditory evoked potentials as an indicator of central serotonergic neurotransmission: a new hypothesis. Biol. Psychiatry 33 (3), 173–187. https://doi.org/10.1016/0006-3223(93)90137-3. Hegerl, U., Bottlender, R., Gallinat, J., Kuss, H.J., Ackenheil, M., Moller, H.J., 1998. The serotonin syndrome scale: first results on validity. Eur. Arch. Psychiatry Clin. Neurosci. 248 (2), 96–103. https://doi.org/10.1007/s004060050024. Hegerl, U., Gallinat, J., Juckel, G., 2001. Event-related potentials. Do they reflect central serotonergic neurotransmission and do they predict clinical response to serotonin agonists? J. Affect. Disord. 62 (1–2), 93–100. https://doi.org/10.1016/s0165-0327 (00)00353-0. Himani, A., Tandon, O.P., Bhatia, M.S., 1999. A study of P300-event related evoked potential in the patients of major depression. Indian J. Physiol. Pharmacol. 43 (3), 367–372. Inoshita, M., Umehara, H., Watanabe, S.Y., Nakataki, M., Kinoshita, M., Tomioka, Y., Tajima, A., Numata, S., Ohmori, T., 2018. Elevated peripheral blood glutamate levels in major depressive disorder. Neuropsychiatr. Dis. Treat. 14, 945–953. https://doi. org/10.2147/NDT.S159855. Institute of Health Metrics and Evaluation, 2019. Global Health Data Exchange (GHDx). http://ghdx.healthdata.org/gbd-results-tool?params=gbd-api-2019-permalink/d78 0dffbe8a381b25e1416884959e88b (Accessed 26 March 2022). Javitt, D.C., Steinschneider, M., Schroeder, C.E., Arezzo, J.C., 1996. Role of cortical Nmethyl-Daspartate receptors in auditory sensory memory and mismatch negativity generation: implications for schizophrenia. Proc. Natl. Acad. Sci. U. S. A. 93 (21), 11962–11967. https://doi.org/10.1073/pnas.93.21.11962. Jaworska, N., Blier, P., Fusee, W., Knott, V., 2012. Scalpand sLORETA derived loudness dependence of auditory evoked potentials (LDAEPs) in unmedicated depressed males and females and healthy controls. Clin. Neurophysiol. 123 (9), 1769–1778. https:// doi.org/10.1016/j.clinph.2012.02.076. Jaworska, N., De Somma, E., Blondeau, C., Tessier, P., Norris, S., Fusee, W., Smith, D., Blier, P., Knott, V., 2013. Auditory P3 in antidepressant pharmacotherapy treatment responders, non-responders and controls. Eur. Neuropsychopharmacol. 23 (11), 1561–1569. https://doi.org/10.1016/j.euroneuro.2013.03.003. Jeon, Y.W., Polich, J., 2003. Meta-analysis of P300 and schizophrenia: patients, paradigms, and practical implications. Psychophysiology 40 (5), 684–701. https:// doi.org/10.1111/1469-8986.00070. Juckel, G., Molnn´ ar, M., Hegerl, U., Cs´ epe, V., Karmos, G., 1997. Auditory-evoked potentials as indicator of brain serotonergic activity—first evidence in behaving cats. Biol. Psychiatry 41 (12), 1181–1195. https://doi.org/10.1016/s0006-3223(96) 00240-5. Juckel, G., Hegerl, U., Moln´ ar, M., Cs´ epe, V., Karmos, G., 1999. Auditory evoked potentials reflect serotonergic neuronal activity—a study in behaving cats administered drugs acting on 5-HT1A autoreceptors in the dorsal raphe nucleus. Neuropsychopharmacology 21 (6), 710–716. https://doi.org/10.1016/s0893-133x (99)00074-3. K¨ ahk¨ onen, S., J¨ a¨ askel¨ ainen, I.P., Pennanen, S., Liesivuori, J., Ahveninen, J., 2002. Acute trytophan depletion decreases intensity dependence of auditory evoked magnetic N1/P2 dipole source activity. Psychopharmacology 164 (2), 221–227. https://doi. org/10.1007/s00213-002-1194-z. K¨ ahk¨ onen, S., M¨ akinen, V., J¨ a¨ askel¨ ainen, I.P., Pennanen, S., Liesivuori, J., Ahveninen, J., 2005. Serotonergic modulation of mismatch negativity. Psychiatry Res. 138 (1), 61–74. https://doi.org/10.1016/j.pscychresns.2004.09.006. K¨ ahk¨ onen, S., Yamashita, H., Ryts¨ al¨ a, H., Suominen, K., Ahveninen, J., Isomets¨ a, E., 2007. Dysfunction in early auditory processing in major depressive disorder revealed by combined MEG and EEG. J. Psychiatry Neurosci. 32 (5), 316–322. Kaur, M., Lagopoulos, J., Ward, P.B., Watson, T.L., Naismith, S.L., Hickie, I.B., Hermens, D.F., 2012. Mismatch negativity/P3a complex in young people with psychiatric disorders: a cluster analysis. PLoS One 7 (12), e51871. https://doi.org/ 10.1371/journal.pone.0051871. Khan, A.Y., Carrithers, J., Preskorn, S.H., Lear, R., Wisniewski, S.R., Rush, A.J., Stegman, D., Kelley, C., Kreiner, K., Nierenberg, A.A., Fava, M., 2006. Clinical and demographic factors associated with DSM-IV melancholic depression. Ann. Clin. Psychiatry 18 (2), 91–98. https://doi.org/10.1080/10401230600614496. Kim, D.H., Park, Y.M., 2013. The association between suicidality and serotonergic dysfunction in depressed patients. J. Affect. Disord. 148 (1), 72–76. https://doi.org/ 10.1016/j.jad.2012.11.051. Kim, Y.R., Park, Y.M., 2020. Mismatch negativity and loudness dependence of auditory evoked potentials among patients with major depressive disorder, bipolar II disorder, and bipolar I disorder. Brain Sci. 10 (11), 789. https://doi.org/10.3390/ brainsci10110789. Knight, R.T., Scabini, D., 1998. Anatomic bases of event-related potentials and their relationship to novelty detection in humans. J. Clin. Neurophysiol. 15 (1), 3–13. https://doi.org/10.1097/00004691-199801000-00003. Kok, A., 2001. On the utility of P3 amplitude as a measure of processing capacity. Psychophysiology 38 (3), 557–577. https://doi.org/10.1017/s0048577201990559. Kraus, C., Castr´ en, E., Kasper, S., Lanzenberger, R., 2017. Serotonin and neuroplasticity - links between molecular, functional and structural pathophysiology in depression. Neurosci. Biobehav. Rev. 77, 317–326. https://doi.org/10.1016/j. neubiorev.2017.03.007. Kube, T., Schwarting, R., Rozenkrantz, L., Glombiewski, J.A., Rief, W., 2020. Distorted cognitive processes in major depression: a predictive processing perspective. Biol. Psychiatry 87 (5), 388–398. https://doi.org/10.1016/j.biopsych.2019.07.017. Kujala, T., Tervaniemi, M., Schr¨ oger, E., 2007. The mismatch negativity in cognitive and clinical neuroscience: theoretical and methodological considerations. Biol. Psychol. 74 (1), 1–19. https://doi.org/10.1016/j.biopsycho.2006.06.001. Kujawa, A., Burkhouse, K.L., 2017. Vulnerability to depression in youth: advances from affective neuroscience. Biol. Psychiatry 2 (1), 28–37. https://doi.org/10.1016/j. bpsc.2016.09.006. Kujawa, A., Klein, D.N., Pegg, S., Weinberg, A., 2020. Developmental trajectories to reduced activation of positive valence systems: a review of biological and environmental contributions. Dev. Cogn. Neurosci. 43, 100791 https://doi.org/ 10.1016/j.dcn.2020.100791. Landrø, N.I., Stiles, T.C., Sletvold, H., 2001. Neuropsychological function in nonpsychotic unipolar major depression. Neuropsychiatry Neuropsychol. Behav. Neurol. 14 (4), 233–240. Lavoie, S., Murray, M.M., Deppen, P., Knyazeva, M.G., Berk, M., Boulat, O., Bove, P., Bush, A.I., Conus, P., Copolov, D., Fornari, E., Meuli, R., Solida, A., Vianin, P., Cuenod, M., Buclin, T., Do, K.Q., 2007. Glutathione precursor, N-acetyl-cysteine, improves mismatch negativity in schizophrenia patients. Neuropsychopharmacology 33 (9), 2187–2199. https://doi.org/10.1038/sj.npp.1301624. Lavoie, S., Polari, A.R., Goldstone, S., Nelson, B., McGorry, P.D., 2019. Staging model in psychiatry: review of the evolution of electroencephalography abnormalities in major psychiatric disorders. Early Interv. Psychiatry 13 (6), 1319–1328. https://doi. org/10.1111/eip.12792. Lee, T.W., Yu, Y.W., Chen, T.J., Tsai, S.J., 2005. Loudness dependence of the auditory evoked potential and response to antidepressants in Chinese patients with major depression. J. Psychiatry Neurosci. 30 (3), 202–205. Lee, R.S., Hermens, D.F., Porter, M.A., Redoblado-Hodge, M.A., 2012. A meta-analysis of cognitive deficits in first-episode major depressive disorder. J. Affect. Disord. 140 (2), 113–124. https://doi.org/10.1016/j.jad.2011.10.023. Lee, S.-H., Park, Y.-C., Yoon, S., Kim, J.-I., Hahn, S.W., 2014. Clinical implications of loudness dependence of auditory evoked potentials in patients with atypical depression. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 54, 7–12. https://doi. org/10.1016/j.pnpbp.2014.05.010. Lee, B.H., Park, Y.M., Lee, S.H., Shim, M., 2015. Prediction of long-term treatment response to selective serotonin reuptake inhibitors (SSRIs) using scalp and source loudness dependence of auditory evoked potentials (LDAEP) analysis in patients with major depressive disorder. Int. J. Mol. Sci. 16 (3), 6251–6265. https://doi.org/ 10.3390/ijms16036251. Leuchter, A.F., Cook, I.A., Hunter, A., Korb, A., 2009. Use of clinical neurophysiology for the selection of medication in the treatment of major depressive disorder: the state of the evidence. Clin. EEG Neurosci. 40 (2), 78–83. https://doi.org/10.1177/ 155005940904000207. Leung, S., Croft, R.J., Guille, V., Scholes, K., O’Neill, B.V., Phan, K.L., Nathan, P.J., 2010. Acute dopamine and/or serotonin depletion does not modulate mismatch negativity (MMN) in healthy human participants. Psychopharmacology 208 (2), 233–244. https://doi.org/10.1007/s00213-009-1723-0. Light, G.A., Swerdlow, N.R., Braff, D.L., 2007. Preattentive sensory processing as indexed by the MMN and P3a brain responses is associated with cognitive and psychosocial functioning in healthy adults. J. Cogn. Neurosci. 19 (10), 1624–1632. https://doi. org/10.1162/jocn.2007.19.10.1624. Linka, T., Sartory, G., Bender, S., Gastpar, M., Müller, B.W., 2007. The intensity dependence of auditory ERP components in unmedicated patients with major depression and healthy controls, an analysis of group differences. J. Affect. Disord. 103 (1–3), 139–145. https://doi.org/10.1016/j.jad.2007.01.018. Linka, T., Sartory, G., Gastpar, M., Scherbaum, N., Müller, B.W., 2009. Clinical symptoms of major depression are associated with the intensity-dependence of auditory event related potential components. Psychiatry Res. 169 (2), 139–143. https://doi.org/ 10.1016/j.psychres.2008.06.009. Liu, J., Kiehl, K.A., Pearlson, G., Perrone-Bizzozero, N.I., Eichele, T., Calhoun, V.D., 2009. Genetic determinants of target and novelty-related event-related potentials in the auditory oddball response. Neuroimage 46 (3), 809–816. https://doi.org/ 10.1016/j.neuroimage.2009.02.045. Luck, S.J., 2014. An Introduction to the Event-Related Potential Technique, 2 ed. MIT Press, Cambridge, Mass. Maekawa, T., Hirano, S., Onitsuka, T., 2012. Auditory and visual mismatch negativity in psychiatric disorders: a review. Curr. Psychiatr. Rev. 8 (2), 97–105. https://doi.org/ 10.2174/1573400511208020097. Malhi, G.S., Parker, G.B., Greenwood, J., 2005. Structural and functional models of depression: from sub-types to substrates. Acta Psychiatr. Scand. 111 (2), 94–105. https://doi.org/10.1111/j.1600-0447.2004.00475.x. Mann, J.J., 2013. The serotonergic system in mood disorders and suicidal behaviour. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 368 (1615), 20120537. https://doi.org/ 10.1098/rstb.2012.0537. McGorry, P., Keshavan, M., Goldstone, S., Amminger, P., Allott, K., Berk, M., Lavoie, S., Pantelis, C., Yung, A., Wood, S., Hickie, I., 2014. Biomarkers and clinical staging in psychiatry. World Psychiatry 13 (3), 211–223. https://doi.org/10.1002/wps.20144. Meltzer, H.Y., 1990. Role of serotonin in depression. Ann. N. Y. Acad. Sci. 600, 486–499. https://doi.org/10.1111/j.1749-6632.1990.tb16904.x. Miller, J.M., Hesselgrave, N., Ogden, R.T., Sullivan, G.M., Oquendo, M.A., Mann, J.J., Parsey, R.V., 2013. Positron emission tomography quantification of serotonin E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 141 transporter in suicide attempters with major depressive disorder. Biol. Psychiatry 74 (4), 287–295. https://doi.org/10.1016/j.biopsych.2013.01.024. Moret, C., Briley, M., 2011. The importance of norepinephrine in depression. Neuropsychiatr. Dis. Treat. 7 (Suppl. 1), 9–13. https://doi.org/10.2147/NDT. S19619. Moriguchi, S., Takamiya, A., Noda, Y., Horita, N., Wada, M., Tsugawa, S., Plitman, E., Sano, Y., Tarumi, R., ElSalhy, M., Katayama, N., Ogyu, K., Miyazaki, T., Kishimoto, T., Graff-Guerrero, A., Meyer, J.H., Blumberger, D.M., Daskalakis, Z.J., Mimura, M., Nakajima, S., 2019. Glutamatergic neurometabolite levels in major depressive disorder: a systematic review and meta-analysis of proton magnetic resonance spectroscopy studies. Mol. Psychiatry 24 (7), 952–964. https://doi.org/ 10.1038/s41380-018-0252-9. Mulert, C., Juckel, G., Augustin, H., Hegerl, U., 2002. Comparison between the analysis of the loudness dependency of the auditory N1/P2 component with LORETA and dipole source analysis in the prediction of treatment response to the selective serotonin reuptake inhibitor citalopram in major depression. Clin. Neurophysiol. 113 (10), 1566–1572. https://doi.org/10.1016/s1388-2457(02)00252-3. Mulert, C., Juckel, G., Giegling, I., Pogarell, O., Leicht, G., Karch, S., Mavrogiorgou, P., M¨ oller, H.J., Hegerl, U., Rujescu, D., 2006. A Ser9Gly polymorphism in the dopamine D3 receptor gene (DRD3) and event-related P300 potentials. Neuropsychopharmacology 31 (6), 1335–1344. https://doi.org/10.1038/sj. npp.1300984. Mumtaz, W., Malik, A.S., Yasin, M.A.M., Xia, L., 2015. Review on EEG and ERP predictive biomarkers for major depressive disorder. Biomed. Signal Process. Control 22, 85–98. https://doi.org/10.1016/j.bspc.2015.07.003. N¨ a¨ at¨ anen, R., Gaillard, A.W.K., M¨ antysalo, S., 1978. Early selective-attention effect on evoked potential reinterpreted. Acta Psychol. 42 (4), 313–329. https://doi.org/ 10.1016/0001-6918(78)90006-9. N¨ a¨ at¨ anen, R., Paavilainen, P., Reinikainen, K., 1989. Do event-related potentials to infrequent decrements in duration of auditory stimuli demonstrate a memory trace in man? Neurosci. Lett. 107 (1–3), 347–352. https://doi.org/10.1016/0304-3940 (89)90844-6. N¨ a¨ at¨ anen, R., Jacobsen, T., Winkler, I., 2005. Memory-based or afferent process in mismatch negativity (MMN): a review of the evidence. Psychophysiology 42 (1), 25–32. https://doi.org/10.1111/j.1469-8986.2005.00256.x. N¨ a¨ at¨ anen, R., Paavilainen, P., Rinne, T., Alho, K., 2007. The mismatch negativity (MMN) in basic research of central auditory processing: a review. Clin. Neurophysiol. 118 (12), 2544–2590. https://doi.org/10.1016/j.clinph.2007.04.026. N¨ a¨ at¨ anen, R., Astikainen, P., Ruusuvirta, T., Huotilainen, M., 2010. Automatic auditory intelligence: an expression of the sensory-cognitive core of cognitive processes. Brain Res. 64 (1), 123–136. https://doi.org/10.1016/j.brainresrev.2010.03.001. N¨ a¨ at¨ anen, R., Kujala, T., Kreegipuu, K., Carlson, S., Escera, C., Baldeweg, T., Ponton, C., 2011g. The mismatch negativity: an index of cognitive decline in neuropsychiatric and neurological diseases and in ageing. Brain 134 (Pt 12), 3435–3453. https://doi. org/10.1093/brain/awr064. N¨ a¨ at¨ anen, R., Kujala, T., Winkler, I., 2011h. Auditory processing that leads to conscious perception: a unique window to central auditory processing opened by the mismatch negativity and related responses. Psychophysiology 48 (1), 4–22. https://doi.org/ 10.1111/j.1469-8986.2010.01114.x. N¨ a¨ at¨ anen, R., Kujala, T., Escera, C., Baldeweg, T., Kreegipuu, K., Carlson, S., Ponton, C., 2012. The mismatch negativity (MMN)—a unique window to disturbed central auditory processing in ageing and different clinical conditions. Clin. Neurophysiol. 123 (3), 424–458. https://doi.org/10.1016/j.clinph.2011.09.020. N¨ a¨ at¨ anen, R., Sussman, E.S., Salisbury, D., Shafer, V.L., 2014. Mismatch negativity (MMN) as an index of cognitive dysfunction. Brain Topogr. 27 (4), 451–466. https:// doi.org/10.1007/s10548-014-0374-6. Naismith, S.L., Hickie, I.B., Turner, K., Little, C.L., Winter, V., Ward, P.B., Wilhelm, K., Mitchell, P., Parker, G., 2003. Neuropsychological performance in patients with depression is associated with clinical, etiological and genetic risk factors. J. Clin. Exp. Neuropsychol. 25 (6), 866–877. https://doi.org/10.1076/jcen.25.6.866.16472. Nathan, P.J., Segrave, R., Phan, K.L., O’Neill, B., Croft, R.J., 2006. Direct evidence that acutely enhancing serotonin with the selective serotonin reuptake inhibitor citalopram modulates the loudness dependence of the auditory evoked potential (LDAEP) marker of central serotonin function. Human Psychopharmacol. 21 (1), 47–52. https://doi.org/10.1002/hup.740. Nieuwenhuis, S., Aston-Jones, G., Cohen, J.D., 2005. Decision making, the P3, and the locus coeruleus-norepinephrine system. Psychol. Bull. 131 (4), 510–532. https://doi. org/10.1037/0033-2909.131.4.510. Nieuwenhuis, S., De Geus, E.J., Aston-Jones, G., 2011. The anatomical and functional relationship between the P3 and autonomic components of the orienting response. Psychophysiology 48 (2), 162–175. https://doi.org/10.1111/j.14698986.2010.01057.x. Nurminen, M., Valkonen-Korhonen, M., Mervaala, E., P¨ a¨ akk¨ onen, A., Partanen, J., Viinam¨ aki, H., Lehtonen, J., 2005. Enhanced attention-dependent auditory processing by electroconvulsive therapy in psychotic depression. J. ECT 21 (1), 19–24. https://doi.org/10.1097/01.yct.0000158015.88677.bc. Olbrich, S., Arns, M., 2013. EEG biomarkers in major depressive disorder: discriminative power and prediction of treatment response. Int. Rev. Psychiatry 25 (5), 604–618. https://doi.org/10.3109/09540261.2013.816269. O’Neill, B.V., Croft, R.J., Leung, S., Oliver, C., Phan, K.L., Nathan, P.J., 2007. High-dose glycine inhibits the loudness dependence of the auditory evoked potential (LDAEP) in healthy humans. Psychopharmacology 195 (1), 85–93. https://doi.org/10.1007/ s00213-007-0870-4. Oranje, B., Gispen-de Wied, C.C., Westenberg, H.G., Kemner, C., Verbaten, M.N., Kahn, R.S., 2006. No effects of l-dopa and bromocriptine on psychophysiological parameters of human selective attention. J. Psychopharmacol. 20 (6), 789–798. https://doi.org/10.1177/0269881106061712. Oranje, B., Jensen, K., Wienberg, M., Glenthøj, B.Y., 2008. Divergent effects of increased serotonergic activity on psychophysiological parameters of human attention. Int. J. Neuropsychopharmacol. 11 (4), 453–463. https://doi.org/10.1017/ S1461145707008176. Oranje, B., Gispen-de Wied, C.C., Westenberg, H.G., Kemner, C., Verbaten, M.N., Kahn, R.S., 2009. Haloperidol counteracts the ketamine-induced disruption of processing negativity, but not that of the P300 amplitude. Int. J. Neuropsychopharmacol. 12 (6), 823–832. https://doi.org/10.1017/ S1461145708009814. Paavilainen, P., 2013. The mismatch-negativity (MMN) component of the auditory event-related potential to violations of abstract regularities: a review. Int. J. Psychophysiol. 88 (2), 109–123. https://doi.org/10.1016/j.ijpsycho.2013.03.015. Paige, S.R., Fitzpatrick, D.F., Kline, J.P., Balogh, S.E., Hendricks, S.E., 1994. Eventrelated potential amplitude/intensity slopes predict response to antidepressants. Neuropsychobiology 30 (4), 197–201. https://doi.org/10.1159/000119161. Pan, W., Lyu, K., Zhang, H., Li, C., Chen, P., Ying, M., Chen, F., Tang, J., 2020. Attenuation of auditory mismatch negativity in serotonin transporter knockout mice with anxiety-related behaviors. Behav. Brain Res. 379, 112387 https://doi.org/ 10.1016/j.bbr.2019.112387. Park, Y.M., 2015. Relationship between serotonergic dysfunction based on loudness dependence of auditory-evoked potentials and suicide in patients with major depressive disorder. Psychiatry Investig. 12 (4), 421–424. https://doi.org/10.4306/ pi.2015.12.4.421. Park, Y.M., 2020. The hypothesis on the prediction of treatment response with buspirone augmentation along with serotonergic antidepressant in patients with major depressive disorder using loudness dependence of auditory evoked potentials: two cases and review of the literature for evidence. Psychiatry Investig. 17 (3), 222–224. https://doi.org/10.30773/pi.2019.0293. Park, Y.-M., Lee, S.-H., 2013. Can the loudness dependence of auditory evoked potentials and suicidality be used to differentiate between depressive patients with and without bipolarity. Psychiatry Investig. 10 (2), 143–147. https://doi.org/10.4306/ pi.2013.10.2.143. Park, Y.-M., Lee, B.-H., Lee, S.-H., 2014. The association between serum lipid levels, suicide ideation, and central serotonergic activity in patients with major depressive disorder. J. Affect. Disord. 159, 62–65. https://doi.org/10.1016/j.jad.2014.01.016. Parker, G., McCraw, S., Blanch, B., Hadzi-Pavlovic, D., Synnott, H., Rees, A.M., 2013. Discriminating melancholic and non-melancholic depression by prototypic clinical features. J. Affect. Disord. 144 (3), 199–207. https://doi.org/10.1016/j. jad.2012.06.042. Phillips, M.L., Drevets, W.C., Rauch, S.L., Lane, R., 2003. Neurobiology of emotion perception II: implications for major psychiatric disorders. Biol. Psychiatry 54, 515e528. https://doi.org/10.1016/S0006-3223(03)00171-9. Pogarell, O., Padberg, F., Karch, S., Segmiller, F., Juckel, G., Mulert, C., Hegerl, U., Tatsch, K., Koch, W., 2011. Dopaminergic mechanisms of target detection - P300 event related potential and striatal dopamine. Psychiatry Res. 194 (3), 212–218. https://doi.org/10.1016/j.pscychresns.2011.02.002. Polich, J., 2007. Updating P300: an integrative theory of P3a and P3b. Clin. Neurophysiol. 118 (10), 2128–2148. https://doi.org/10.1016/j.clinph.2007.04.019. Polich, J., Criado, J.R., 2006. Neuropsychology and neuropharmacology of P3a and P3b. Int. J. Psychophysiol. 60 (2), 172–185. https://doi.org/10.1016/j. ijpsycho.2005.12.012. Prabhakar, V., Gupta, D., Kanade, P., Radhakrishnan, M., 2015. Diabetes-associated depression: the serotonergic system as a novel multifunctional target. Indian J. Pharm. 47 (1), 4–10. https://doi.org/10.4103/0253-7613.150305. Proudfit, G.H., 2015. The reward positivity: from basic research on reward to a biomarker for depression. Psychophysiology 52 (4), 449–459. https://doi.org/ 10.1111/psyp.12370. Proudfit, G.H., Bress, J.N., Foti, D., Kujawa, A., Klein, D.N., 2015. Depression and eventrelated potentials: emotional disengagement and reward insensitivity. Curr. Opin. Psychol. 4, 110–113. https://doi.org/10.1016/j.copsyc.2014.12.018. Rock, P.L., Roiser, J.P., Riedel, W.J., Blackwell, A.D., 2014. Cognitive impairment in depression: a systematic review and meta-analysis. Psychol. Med. 44 (10), 2029–2040. https://doi.org/10.1017/S0033291713002535. Rosellini, A.J., Bourgeois, M.L., Correa, J., Tung, E.S., Goncharenko, S., Brown, T.A., 2018. Anxious distress in depressed outpatients: prevalence, comorbidity, and incremental validity. J. Psychiatr. Res. 103, 54–60. https://doi.org/10.1016/j. jpsychires.2018.05.006. Ruohonen, E.M., Alhainen, V., Astikainen, P., 2020. Event-related potentials to taskirrelevant sad faces as a state marker of depression. Biol. Psychol. 149, 107806 https://doi.org/10.1016/j.biopsycho.2019.107806. Sanacora, G., Zarate, C.A., Krystal, J.H., Manji, H.K., 2008. Targeting the glutamatergic system to develop novel, improved therapeutics for mood disorders. Nat. Rev. Drug Discov. 7, 426–437. https://doi.org/10.1038/nrd24 62. Sanei, S., Chambers, J., 2007. EEG signal processing. John Wiley & Sons, Chichester, England; Hoboken, NJ. Santosh, P.J., Malhotra, S., Raghunathan, M., Mehra, Y.N., 1994. A study of P300 in melancholic depression: correlation with psychotic features. Biol. Psychiatry 35 (7), 474–479. https://doi.org/10.1016/0006-3223(94)90046-9. Schlegel, S., Nieber, D., Herrmann, C., Bakauski, E., 1991. Latencies of the P300 component of the auditory event-related potential in depression are related to the Bech-Rafaelsen melancholia scale but not to the Hamilton rating scale for depression. Acta Psychiatr. Scand. 83 (6), 438–440. https://doi.org/10.1111/j.16000447.1991.tb05571.x. E.S. Kangas et al. International Journal of Psychophysiology 179 (2022) 119–142 142 Segrave, R., Croft, R.J., Illic, S., Luan Phan, K., Nathan, P.J., 2006. Pindolol does not augment central serotonin function increases to citalopram in humans: an auditory evoked potential investigation. Pharmacol. Biochem. Behav. 85 (1), 82–90. https:// doi.org/10.1016/j.pbb.2006.07.013. Shao, X., Zhu, G., 2020. Associations among monoamine neurotransmitter pathways, personality traits, and major depressive disorder. Front. Psychiatry 11, 381. https:// doi.org/10.3389/fpsyt.2020.00381. Simmons, J.G., Nathan, P.J., Berger, G., Allen, N.B., 2011. Chronic modulation of serotonergic neurotransmission with sertraline attenuates the loudness dependence of the auditory evoked potential in healthy participants. Psychopharmacology 217 (1), 101–110. https://doi.org/10.1007/s00213-011-2265-9. Smith, R., Badcock, P., Friston, K.J., 2021. Recent advances in the application of predictive coding and active inference models within clinical neuroscience. Psychiatry Clin. Neurosci. 75 (1), 3–13. https://doi.org/10.1111/pcn.13138. Spronk, D.B., Veth, C.P., Arns, M., Schofield, P.R., Dobson-Stone, C., Ramaekers, J.G., Franke, B., de Bruijn, E.R., Verkes, R.J., 2013. DBH -1021C>T and COMT Val108/ 158Met genotype are not associated with the P300 ERP in an auditory oddball task. Clin. Neurophysiol. 124 (5), 909–915. https://doi.org/10.1016/j. clinph.2012.11.008. Strawbridge, R., Young, A.H., Cleare, A.J., 2017. Biomarkers for depression: recent insights, current challenges and future prospects. Neuropsychiatr. Dis. Treat. 13, 1245–1262. https://doi.org/10.2147/NDT.S114542. Str¨ ommer, J.M., P˜ oldver, N., Waselius, T., Kirjavainen, V., J¨ arvel¨ ainen, S., Bj¨ orksten, S., Tarkka, I.M., Astikainen, P., 2017. Automatic auditory and somatosensory brain responses in relation to cognitive abilities and physical fitness in older adults. Sci. Rep. 7 (1), 13699. https://doi.org/10.1038/s41598-017-14139-9. Sutton, S., Braren, M., Zubin, J., John, E., 1965. Evoked potential correlates of stimulus uncertainty. Science 150 (3700), 1187–1188. https://doi.org/10.1126/ science.150.3700.1187. Swick, D., Pineda, J.A., Foote, S.L., 1994. Effects of systemic clonidine on auditory eventrelated potentials in squirrel monkeys. Brain Res. Bull. 33 (1), 79–86. https://doi. org/10.1016/0361-9230(94)90051-5. Tervaniemi, M., Maury, S., N¨ a¨ at¨ anen, R., 1994. Neural representations of abstract stimulus features in the human brain as reflected by the mismatch negativity. Neuroreport 5 (7), 844–846. https://doi.org/10.1097/00001756-19940300000027. Tseng, Y.J., Nouchi, R., Cheng, C.H., 2021. Mismatch negativity in patients with major depressive disorder: a meta-analysis. Clin. Neurophysiol. 132 (10), 2654–2665. https://doi.org/10.1016/j.clinph.2021.06.019. Uhl, I., Gorynia, I., Gallinat, J., Mulert, C., Wutzler, A., Heinz, A., Juckel, G., 2006. Is the loudness dependence of auditory evoked potentials modulated by the selective serotonin reuptake inhibitor citalopram in healthy subjects? Human Psychopharmacol. 21 (7), 463–471. https://doi.org/10.1002/hup.803. Uhl, I., Illes, F., Graßnickel, V., Echterhoff, S., Norra, C., Juckel, G., 2012. Loudness dependence of auditory evoked potentials (LDAEP) in clinical monitoring of suicidal patients with major depression: a pilot study. Eur. Arch. Psychiatry Clin. Neurosci. 262 (6), 487–492. https://doi.org/10.1007/s00406-012-0297-8. Umbricht, D., Krljes, S., 2005. Mismatch negativity in schizophrenia: a meta-analysis. Schizophr. Res. 76 (1), 1–23. https://doi.org/10.1016/j.schres.2004.12.002. Umbricht, D., Schmid, L., Koller, R., Vollenweider, F.X., Hell, D., Javitt, D.C., 2000. Ketamine-induced deficits in auditory and visual context-dependent processing in healthy volunteers. Arch. Gen. Psychiatry 57 (12), 1139–1147. https://doi.org/ 10.1001/archpsyc.57.12.1139. Umbricht, D., Koller, R., Vollenweider, F.X., Schmid, L., 2002. Mismatch negativity predicts psychotic experiences induced by NMDA receptor antagonist in healthy volunteers. Biol. Psychiatry 51 (5), 400–406. https://doi.org/10.1016/s0006-3223 (01)01242-2. Veiel, H.O.F., 1997. A preliminary profile of neuropsychological deficits associated with major depression. J. Clin. Exp. Neuropsychol. 19 (4), 587–603. https://doi.org/ 10.1080/01688639708403745. Wacongne, C., Labyt, E., van Wassenhove, V., Bekinschtein, T., Naccache, L., Dehaene, S., 2011. Evidence for a hierarchy of predictions and prediction errors in human cortex. Proc. Natl. Acad. Sci. U. S. A. 108 (51), 20754–20759. https://doi. org/10.1073/pnas.1117807108. Wade, E.C., Iosifescu, D.V., 2016. Using electroencephalography for treatment guidance in major depressive disorder. Biol. Psychiatry 1 (5), 411–422. https://doi.org/ 10.1016/j.bpsc.2016.06.002. Wienberg, M., Glenthoj, B.Y., Jensen, K.S., Oranje, B., 2010. A single high dose of escitalopram increases mismatch negativity without affecting processing negativity or P300 amplitude in healthy volunteers. J. Psychopharmacol. 24 (8), 1183–1192. https://doi.org/10.1177/0269881109102606. Winkler, I., Cowan, N., 2005. From sensory to long-term memory: evidence from auditory memory reactivation studies. Exp. Psychol. 52 (1), 3–20. https://doi.org/ 10.1027/1618-3169.52.1.3. Withall, A., Harris, L.M., Cumming, S.R., 2010. A longitudinal study of cognitive function in melancholic and non-melancholic subtypes of major depressive disorder. J. Affect. Disord. 123 (1–3), 150–157. https://doi.org/10.1016/j.jad.2009.07.012. World Federation for Mental Health, 2012. Depression: A Global Crisis. World Federation for Mental Health, Occoquan. Wronka, E., Kaiser, J., Coenen, A.M.L., 2012. Neural generators of the auditory evoked potential components P3a and P3b. Acta Neurobiol. Exp. 72 (1), 51–64. Wu, X., Chen, J., Jia, T., Ma, W., Zhang, Y., Deng, Z., Yang, L., 2016. Cognitive Bias by gender interaction on N170 response to emotional facial expressions in major and minor depression. Brain Topogr. 29 (2), 232–242. https://doi.org/10.1007/s10548015-0444-4. Xu, Q., Ruohonen, E.M., Ye, C., Li, X., Kreegipuu, K., Stefanics, G., Luo, W., Astikainen, P., 2018. Automatic processing of changes in facial emotions in dysphoria: a magnetoencephalography study. Front. Hum. Neurosci. 12, 186. https://doi.org/10.3389/fnhum.2018.00186. Yoon, S., Kim, Y., Lee, S.H., 2021. Does the loudness dependence of auditory evoked potential predict response to selective serotonin reuptake inhibitors?: a Metaanalysis. Clin. Psychopharmacol. Neurosci. 19 (2), 254–261. https://doi.org/ 10.9758/cpn.2021.19.2.254. Zhang, D., He, Z., Chen, Y., Wei, Z., 2016. Deficits of unconscious emotional processing in patients with major depression: an ERP study. J. Affect. Disord. 199, 13–20. https://doi.org/10.1016/j.jad.2016.03.056. Zhao, Q., Tang, Y., Chen, S., Lyu, Y., Curtin, A., Wang, J., Sun, J., Tong, S., 2015. Early perceptual anomaly of negative facial expression in depression: an event-related potential study. Neurophysiol. Clin. 45 (6), 435–443. https://doi.org/10.1016/J. NEUCLI.2015.09.01. E.S. Kangas et al.