Structural changes induced by daily music listening in the recovering brain after middle cerebral artery stroke: a voxel-based morphometry study
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This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Author(s): Title: Year: Version: Please cite the original version: All material supplied via JYX is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Structural changes induced by daily music listening in the recovering brain after middle cerebral artery stroke: a voxel-based morphometry study Särkämö, Teppo; Ripollés, Pablo; Vepsäläinen, Henna; Autti, Taina; Silvennoinen, Heli M.; Salli, Eero; Laitinen, Sari; Forsblom, Anita; Soinila, Seppo; Rodríguez-Fornells, Antoni Särkämö, T., Ripollés, P., Vepsäläinen, H., Autti, T., Silvennoinen, H. M., Salli, E., Laitinen, S., Forsblom, A., Soinila, S., & Rodríguez-Fornells, A. (2014). Structural changes induced by daily music listening in the recovering brain after middle cerebral artery stroke: a voxel-based morphometry study. Frontiers in Human Neuroscience, 8, Article 245. https://doi.org/10.3389/fnhum.2014.00245 2014
HUMAN NEUROSCIENCE ORIGINAL RESEARCH ARTICLE published: 17 April 2014 doi: 10.3389/fnhum.2014.00245 Structural changes induced by daily music listening in the recovering brain after middle cerebral artery stroke: a voxel-based morphometry study Teppo Särkämö1,2*, Pablo Ripollés3,4, HennaVepsäläinen1,Taina Autti5, Heli M. Silvennoinen5, Eero Salli5, Sari Laitinen6,Anita Forsblom7, Seppo Soinila8and Antoni Rodríguez-Fornells3,4,9 1Cognitive Brain Research Unit, Cognitive Science, Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland 2Finnish Centre of Interdisciplinary Music Research, University of Helsinki, Helsinki, Finland 3Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain 4Department of Basic Psychology, University of Barcelona, Barcelona, Spain 5Department of Radiology, HUS Medical Imaging Center, Helsinki University Central Hospital, University of Helsinki, Helsinki, Finland 6Miina Sillanpää Foundation, Helsinki, Finland 7Department of Music, University of Jyväskylä, Jyväskylä, Finland 8Department of Neurology,Turku University Hospital,Turku, Finland 9Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain Edited by: Eckart Altenmüller, University of Music and Drama Hannover, Germany Reviewed by: Jens Dieter Rollnik, BDH-Klinik Hessisch Oldendorf, Germany Bernhard Haslinger,Technische Universität München, Germany *Correspondence: Teppo Särkämö, Cognitive Brain Research Unit, Cognitive Science, Institute of Behavioural Sciences, University of Helsinki, Siltavuorenpenger 1B, P.O. Box 9, Helsinki FI-00014, Finland e-mail: [email protected] Music is a highly complex and versatile stimulus for the brain that engages many temporal, frontal, parietal, cerebellar, and subcortical areas involved in auditory, cognitive, emotional, and motor processing. Regular musical activities have been shown to effectively enhance the structure and function of many brain areas, making music a potential tool also in neurological rehabilitation. In our previous randomized controlled study, we found that listening to music on a daily basis can improve cognitive recovery and improve mood after an acute middle cerebral artery stroke. Extending this study, a voxel-based morphometry (VBM) analysis utilizing cost function masking was performed on the acute and 6-month post-stroke stage structural magnetic resonance imaging data of the patients (n=49) who either listened to their favorite music [music group (MG), n=16] or verbal material [audio book group (ABG), n=18] or did not receive any listening material [control group (CG), n=15] during the 6month recovery period. Although all groups showed significant gray matter volume (GMV) increases from the acute to the 6-month stage, there was a specific network of frontal areas [left and right superior frontal gyrus (SFG), right medial SFG] and limbic areas [left ventral/subgenual anterior cingulate cortex (SACC) and right ventral striatum (VS)] in patients with left hemisphere damage in which the GMV increases were larger in the MG than in the ABG and in the CG. Moreover, the GM reorganization in the frontal areas correlated with enhanced recovery of verbal memory, focused attention, and language skills, whereas the GM reorganization in the SACC correlated with reduced negative mood.This study adds on previous results, showing that music listening after stroke not only enhances behavioral recovery, but also induces fine-grained neuroanatomical changes in the recovering brain. Keywords: music, speech, stroke, magnetic resonance imaging, voxel-based morphometry, environmental enrichment, neuroplasticity, rehabilitation INTRODUCTION During the past 10 years, advanced magnetic resonance imaging (MRI) analysis methods, such as voxel-based morphometry (VBM) and diffusion tensor imaging (DTI), have provided novel information about the dynamics of the structural neuroplastic changes underlying spontaneous recovery and rehabilitation after stroke. Based on longitudinal VBM and DTI studies of stroke patients, the recovery of cognitive and motor deficits is associated with gray matter volume (GMV) changes in many frontal, temporal, cerebellar, and subcortical (e.g., hippocampus) areas (Grau-Olivares et al., 2010;Dang et al., 2013;Fan et al., 2013) as well as changes in the integrity of the white matter (WM) tracts connecting and projecting from these areas (Liang et al., 2008;van Meer et al., 2012;Thiebaut de Schotten et al., 2014). In longitudinal intervention studies, intensive motor rehabilitation using constraint-induced movement therapy (CIMT) has been shown to increase GMV in frontal and parietal sensory–motor areas and in the hippocampus (Gauthier et al.,2008) and intensive aphasia rehabilitation using constraint-induced language therapy (CILT) or melodic intonation therapy (MIT) has been observed to enhance the integrity of the WM tracks connecting frontal and temporal regions (arcuate fasciculus) in the left (Breier et al., 2011) and right (Schlaug et al., 2009) hemispheres, respectively. All in all, these findings suggest that both behavioral recovery and active rehabilitation after stroke are closely linked to fine-grained neuroanatomical changes in the recovering brain. However, very Frontiers in Human Neuroscience www.frontiersin.org April 2014 | Volume 8 | Article 245 | 1
Särkämö et al. Music listening enhances neuroplasticity after stroke little is known about the wider potential effects of the recovery environment on structural brain plasticity after stroke in humans. Converging evidence from both animal (Johansson, 2004; Nithianantharajah and Hannan, 2006) and human studies (Johansson, 2012;Janssen et al., 2014) indicates that an environmental enrichment (EE), which provides additional sensory, cognitive,motor,and/or social stimulation compared to a standard environment, plays an important role in enhancing behavioral recovery after an acute stroke. In addition, evidence from animal studies suggests that the post-stroke EE can induce a number of cellular and molecular neuroplastic effects in the brain, including increase in dendritic complexity (Biernaskie and Corbett, 2001; Johansson and Belichenko,2002),neural stem and progenitor cells (Komitova et al., 2005;Matsumori et al., 2006), and neurotrophic and neural growth factor levels (Gobbo and O’Mara, 2004;Söderström et al.,2009),and that these changes are associated with better cognitive or motor recovery. Interestingly, especially a multisensory EE, which includes auditory, visual, and olfactory stimuli, has been found to be effective in improving cognitive and motor recovery and reducing lesion volume (Maegele et al., 2005a,b). Also evidence from developmental animal studies shows that a purely auditory EE, which contains complex sounds or music, can enhance the structure and function of the auditory cortex (Engineer et al., 2004;Bose et al., 2010) as well as improve learning and memory and upregulate various neurotransmitters (e.g., dopamine, glutamate) and neurotrophins associated with them (Sutoo and Akiyama, 2004;Angelucci et al., 2007;Nichols et al., 2007). Overall, these findings indicate that auditory enrichment can be beneficial for the brain and suggest that it could potentially contribute to better cognitive and neural recovery also after stroke. In the human brain, music and speech constitute the two most complex and versatile auditory stimuli in terms of their acoustic richness and the breadth of the neural networks involved in their perception and learning (Zatorre, 2013). Neuroimaging studies of healthy subjects have demonstrated that music processing engages a vast bilateral network of temporal, frontal, parietal, cerebellar, and limbic/paralimbic areas associated with the perception of complex acoustic features (e.g., melody, rhythm), syntactic and semantic processing, attention and working memory, episodic and semantic memory, motor and rhythm processing, and experiencing emotions and reward (Blood and Zatorre, 2001;Janata et al., 2002a,b;Platel et al., 2003;Koelsch et al., 2004, 2005, 2006;Menon and Levitin, 2005;Bengtsson et al., 2009;Salimpoor et al., 2011, 2013;Alluri et al., 2012;Herdener et al., 2014; for recent reviews see, Koelsch, 2010, 2011;Zatorre, 2013). Evidence from VBM and DTI studies also indicates that frequent musical activities, such as playing an instrument or singing, can lead to long-term structural changes in the brain, especially in frontal, temporal, and parietal areas and in the WM pathways (e.g., corpus callosum, arcuate fasciculus) connecting them (Gaser and Schlaug, 2003;Hyde et al., 2009;Halwani et al., 2011;James et al., 2014). Improvements in attention and executive functioning have also been reported in healthy older adults after regular music playing activities, such as piano playing (Bugos et al., 2007), and one longitudinal study also highlighted the role of playing musical instruments and dancing as leisure activities associated with a reduced risk of developing dementia (Verghese et al., 2003). Regarding the potential rehabilitative use of music after stroke, results from recent clinical studies suggest that active music-based interventions that utilize singing (MIT) or instrument playing (music-supported therapy, MST), can be effective in improving speech and motor recovery through enhancing the functioning and connectivity of temporal auditory and frontal motor areas (Schlaug et al., 2008, 2009;Altenmüller et al., 2009;Rojo et al., 2011;Rodríguez-Fornells et al., 2012; Grau-Sánchez et al., 2013). Very little, however, is known about the potential neuroplastic changes induced by everyday musical activities, such as music listening, after stroke. Previously, we performed a randomized controlled trial (RCT) concerning the potential rehabilitative effects of an enriched sound environment on stroke recovery. Sixty patients with an acute left (n=29) or right (n=31) hemisphere middle cerebral artery (MCA) brain infarction were randomized to a music group (MG) (daily listening to self-selected music), an audio book group (ABG) (daily listening to self-selected audio books), and a control group (CG) (standard care only) and their recovery was followed for 6 months using behavioral measures (neuropsychological tests and questionnaires on mood), an auditory magnetoencephalography (MEG) measurement, and structural MRI. Fifty-four patients completed the whole 6-month followup. Behavioral results showed that verbal memory and focused attention improved more in the MG than in the ABG or CG after the intervention period at the 3-month follow-up and also remained better at the longitudinal 6-month follow-up (Särkämö et al., 2008), suggesting that regular music listening enhanced cognitive recovery. Compared to the CG,the MG also experienced less depressed and confused mood at the 3-month follow-up (Särkämö et al., 2008). MEG results showed that the mismatch negativity (MMN) response to frequency changes strengthened more in the MG and ABG compared to the CG at the 6-month follow-up,indicating that regular exposure to both music and speech enhanced early auditory encoding in the recovering brain (Särkämö et al., 2010a). In the present study, our aim was to determine with a VBM analysis of the longitudinal structural MRI data (baseline acute stage and 6-month stage) from the same patient sample whether daily music listening could also lead to structural GM and WM reorganization in the brain and if this change would also be related to the previously found positive effects of music on cognitive and emotional recovery after stroke. MATERIALS AND METHODS SUBJECTS AND STUDY DESIGN Sixty stroke patients were recruited during 2004–2006 from the Department of Neurology of the Helsinki University Central Hospital (HUCH). All patients had an acute ischemic MCA stroke in the left (n=29) or right (n=31) temporal, frontal, parietal, or subcortical brain regions. Additional inclusion criteria were: no prior neurological/psychiatric disease, drug/alcohol abuse, or hearing deficit; right-handed; ≤75 years old; Finnish-speaking; and able to co-operate. Recruited patients were randomly assigned to one of three groups (n=20 in each): an MG, an ABG, or a CG. Randomization was performed with a random number generator by a researcher not involved in the patient enrollment. The Frontiers in Human Neuroscience www.frontiersin.org April 2014 | Volume 8 | Article 245 | 2
Särkämö et al. Music listening enhances neuroplasticity after stroke study was approved by the HUCH Ethics Committee, and all patients signed an informed consent.All patients received standard treatment for stroke in terms of medical care and rehabilitation. During the follow-up, the patients underwent a neuropsychological assessment (including cognitive tests and questionnaires) and an auditory MEG measurement 1 week (baseline), 3 months, and 6 months post-stroke, and a structural MRI within 2 weeks of the stroke onset and 6 months post-stroke. Details regarding the methodology and results of the neuropsychological assessments and the MEG experiment are available in the previous published articles (Särkämö et al., 2008, 2010a). Of the 60 patients originally recruited into the study, 55 completed the study up to the 3-month stage and 54 up to the 6-month stage. For the purpose of the longitudinal VBM analyses, appropriate MRI data were unavailable in three patients and the image quality was insufficient in two further patients. Thus, data from 49 patients were used in the present study. Demographic and clinical characteristics as well as the musical and linguistic activities of the patients are shown in Tables 1 and 2, presented separately for the patients with left hemisphere damage (LHD, n=23) and right hemisphere damage (RHD, n=26). There were no significant differences between the MG, ACG, and CG on any demographic or clinical variables, prior musical or linguistic activities, or in other rehabilitation received during the 6-month follow-up whereas the frequency of listening to music and audio books differed highly significantly between the groups both at the 3-month and the 6-month stage. However, there were no statistically significant differences between the MG andABG on how many hours per day the patients listened to the provided material (music in the MG, audio books in the ABG) on average, although within the RHD patients the daily listening amounts were slightly higher in the MG than in the ABG. Overall, these results indicate that the groups were comparable and that the intervention protocol worked well. INTERVENTION As soon as possible after their enrollment to the study (mean 8.8 days post-stroke, range 3–21 days), the MG and ABG patients were individually contacted by a music therapist. In the MG, the therapist provided the patients with portable CD players and CDs of their own favorite music in any musical genre (mostly popular music with lyrics but also jazz, folk, or classical music). Similarly, the therapist provided the ABG with portable players and self-selected narrated audio books. The patients were trained in using the players and were instructed to listen to the material by themselves daily (for a minimum of 1 h per day) for the following 2 months in addition to standard care and rehabilitation. After this intervention period (3-month stage), they were encouraged to continue listening to the material on their own. In order to ensure that the patients were able to engage in the listening protocol, the therapist kept close weekly contact with the patients and the nurses and/or relatives of the patients were asked to help. Frequency of listening was verified from the listening diaries, which the patients kept during the intervention period and from questionnaires at the 3and 6-month stages. The CG was not given any listening material and received only the standard care and rehabilitation during the follow-up. MRI DATA ACQUISITION Structural MRI was performed within 2 weeks of stroke onset and 6 months post-stroke using the 1.5 T Siemens Vision scanner of Table 1 | Demographic and clinical characteristics of the patients (n=49). Left hemisphere damage (n=23) Right hemisphere damage (n=26) MG (n=7) ABG (n=8) CG (n=8) p-value MG (n=9) ABG (n=10) CG (n=7) p-value DEMOGRAPHICAL CHARACTERISTICS Age (years) 55.3 (11.0) 57.9 (7.4) 60.0 (8.9) 0.615 (F) 59.6 (7.9) 59.4 (9.0) 63.4 (4.7) 0.519 (F) Gender (male/female) 3/4 6/2 5/3 0.439 (χ2) 6/3 2/8 3/4 0.111 (χ2) Education (years) 10.9 (4.4) 12.9 (3.1) 9.9 (3.4) 0.262 (F) 11.1 (4.5) 11.3 (2.9) 9.3 (3.9) 0.518 (F) CLINICAL CHARACTERISTICS Time from stroke onset to acute MRI (days) 7.0 (3.2) 7.6 (3.7) 7.6 (2.8) 0.922 (F) 6.9 (1.6) 9.1 (3.4) 8.2 (4.3) 0.366 (F) Time from stroke onset to 6-month MRI (days) 184.4 (6.2) 184.6 (15.9) 192.4 (16.2) 0.449 (F) 189.0 (5.9) 182.7 (8.8) 193.9 (22.0) 0.228 (F) Hemiparesis (yes/no) 5/2 4/4 3/5 0.409 (χ2) 9/0 10/0 7/0 – Aphasia (yes/no) 4/3 6/2 6/2 0.701 (χ2) Lesion size (max. diameter in cm) 47.0 (17.8) 39.7 (18.8) 52.5 (17.6) 0.384 (F) 57.3 (28.9) 58.5 (20.5) 62.1 (25.3) 0.924 (F) OTHER REHABILITATION DURINGTHE 6-MONTH FOLLOW-UPa Physical therapy 19.7 (30.5) 11.3 (30.0) 4.0 (9.4) 0.732 (K) 26.9 (41.6) 30.2 (37.7) 20.1 (26.5) 0.991 (K) Occupational therapy 14.3 (22.7) 0.4 (0.8) 8.0 (20.3) 0.627 (K) 9.8 (12.9) 10.0 (14.7) 5.9 (6.4) 0.886 (K) Speech therapy 15.3 (20.5) 3.7 (9.4) 10.9 (11.5) 0.340 (K) 15.3 (20.5) 3.7 (9.4) 10.9 (11.5) 0.340 (K) Neuropsychological rehabilitation 4.0 (10.9) 2.3 (4.3) 1.0 (1.9) 0.851 (K) 2.9 (5.1) 2.5 (4.9) 0.7 (1.0) 0.976 (K) Data are mean (SD) unless otherwise stated. MG, music group; ABG, audio book group; CG, control group; F, one-way ANOVA; χ2, chi-square test (likelihood ratio); K, Kruskal–Wallis test. aNumber of therapy sessions. Frontiers in Human Neuroscience www.frontiersin.org April 2014 | Volume 8 | Article 245 | 3
Särkämö et al. Music listening enhances neuroplasticity after stroke Table 2 | Musical and linguistic activities of the patients (n=49). Left hemisphere damage (n=23) Right hemisphere damage (n=26) MG (n=7) ABG (n=8) CG (n=8) p-value MG (n=9) ABG (n=10) CG (n=7) p-value BEFORE STROKE Listening to music 0.592 (K) 0.617 (K) Never 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (14.3) Rarely 1 (14.3) 1 (12.5) 2 (25) 2 (22.2) 2 (20) 0 (0) Once a month 0 (0) 0 (0) 1 (12.5) 0 (0) 1 (10) 0 (0) Once a week 1 (14.3) 3 (37.5) 1 (12.5) 0 (0) 1 (10) 2 (28.6) 2–3 Times a week 3 (42.9) 4 (50) 2 (25) 2 (22.2) 3 (30) 0 (0) Daily 2 (28.6) 0 (0) 2 (25) 5 (55.6) 3 (30) 4 (57.1) Listening to radio 0.991 (K) 0.163 (K) Never 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) Rarely 1 (14.3) 0 (0) 1 (12.5) 0 (0) 1 (10) 0 (0) Once a month 0 (0) 0 (0) 1 (12.5) 0 (0) 1 (10) 0 (0) Once a week 1 (14.3) 2 (25) 0 (0) 0 (0) 1 (10) 1 (14.3) 2–3 Times a week 0 (0) 1 (12.5) 0 (0) 1 (11.1) 1 (10) 3 (42.9) Daily 5 (71.4) 5 (62.5) 6 (75) 8 (88.9) 6 (60) 3 (42.9) Reading 0.686 (K) 0.220 (K) Never 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) Rarely 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) Once a month 0 (0) 0 (0) 1 (12.5) 1 (11.1) 0 (0) 0 (0) Once a week 0 (0) 1 (12.5) 1 (12.5) 4 (44.4) 4 (40) 0 (0) 2–3 Times a week 3 (42.9) 1 (12.5) 1 (12.5) 2 (22.2) 2 (20) 3 (42.9) Daily 4 (57.1) 4 (50) 5 (62.5) 2 (22.2) 4 (40) 4 (57.1) DURINGTHE FIRST 3 MONTHS POST-STROKE Listening to music 0.003 (K) 0.001 (K) Data missing 1 (14.3) 0 (0) 0 (0) 1 (11.1) 0 (0) 0 (0) Never 0 (0) 6 (75) 5 (62.5) 0 (0) 2 (20) 3 (42.9) Rarely 0 (0) 1 (12.5) 1 (12.5) 0 (0) 3 (30) 1 (14.3) Once a month 0 (0) 0 (0) 0 (0) 0 (0) 1 (10) 0 (0) Once a week 0 (0) 0 (0) 0 (0) 0 (0) 1 (10) 0 (0) 2–3 Times a week 0 (0) 0 (0) 1 (12.5) 0 (0) 2 (20) 2 (28.6) Daily 6 (85.7) 1 (12.5) 1 (12.5) 8 (88.9) 1 (10) 1 (14.3) Listening to audio books <0.001 (K) <0.001 (K) Data missing 1 (14.3) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) Never 5 (71.4) 0 (0) 6 (75) 9 (100) 1 (10) 6 (85.7) Rarely 1 (14.3) 0 (0) 2 (25) 0 (0) 0 (0) 0 (0) Once a month 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) Once a week 0 (0) 1 (12.5) 0 (0) 0 (0) 0 (0) 0 (0) 2–3 Times a week 0 (0) 2 (25) 0 (0) 0 (0) 0 (0) 0 (0) Daily 0 (0) 5 (62.5) 0 (0) 0 (0) 9 (90) 1 (14.3) Hours per day listening to group materiala1.6 (0.7) 1.3 (0.5) – 0.484 (T) 2.1 (0.6) 1.4 (0.7) – 0.076 (T) DURING 3–6 MONTHS POST-STROKE Listening to music 0.010 (K) 0.018 (K) Data missing 0 (0) 0 (0) 1 (12.5) 1 (11.1) 0 (0) 0 (0) Never 0 (0) 0 (0) 2 (25) 0 (0) 2 (20) 2 (28.6) Rarely 0 (0) 1 (12.5) 2 (25) 0 (0) 1 (10) 1 (14.3) Once a month 0 (0) 2 (25) 0 (0) 0 (0) 3 (30) 1 (14.3) Once a week 0 (0) 1 (12.5) 2 (25) 1 (11.1) 2 (20) 0 (0) 2–3 Times a week 3 (42.9) 0 (0) 1 (12.5) 3 (33.3) 1 (10) 2 (28.6) Daily 4 (57.1) 4 (50) 0 (0) 4 (44.4) 1 (10) 1 (14.3) (Continued) Frontiers in Human Neuroscience www.frontiersin.org April 2014 | Volume 8 | Article 245 | 4
Särkämö et al. Music listening enhances neuroplasticity after stroke Table 2 | Continued Left hemisphere damage (n=23) Right hemisphere damage (n=26) MG (n=7) ABG (n=8) CG (n=8) p-value MG (n=9) ABG (n=10) CG (n=7) p-value Listening to audio books <0.001 (K) 0.004 (K) Data missing 0 (0) 0 (0) 1 (12.5) 0 (0) 0 (0) 0 (0) Never 6 (85.7) 0 (0) 5 (62.5) 8 (88.9) 2 (20) 6 (85.7) Rarely 1 (14.3) 0 (0) 2 (25) 0 (0) 1 (10) 0 (0) Once a month 0 (0) 2 (25) 0 (0) 0 (0) 0 (0) 0 (0) Once a week 0 (0) 0 (0) 0 (0) 0 (0) 2 (20) 0 (0) 2–3 Times a week 0 (0) 3 (37.5) 0 (0) 1 (11.1) 1 (10) 1 (14.3) Daily 0 (0) 3 (37.5) 0 (0) 0 (0) 4 (40) 0 (0) Data shown as frequency (percentage) unless otherwise stated. MG, music group; ABG, audio book group; CG, control group; K, Kruskal–Wallis test;T, t-test. aMusic listening in the MG and audio book listening in the ABG [data are mean (SD)]. the HUCH Department of Radiology. Clinically, the MRI was used by two experienced neuroradiologists (authors Taina Autti and Heli M. Silvennoinen) to verify the stroke diagnosis and to evaluate the size and location of the lesion. The MRI sequence included a 3D set of high-resolution T1 images (TE=3.68 ms, TR=1900 ms, TI=1100 ms, flip angle 15°, isotropic voxel size of 1 mm3), which were used in the present VBM analysis. In addition, also a smaller set of fluid-attenuated inversion recovery (2D FLAIR) images, which are sensitive to acute infarcts, were acquired and used in accurately locating the lesion area, especially in the acute stage. VOXEL-BASED MORPHOMETRY ANALYSIS Morphometric analysis was carried out using VBM (Ashburner and Friston, 2000) and Statistical Parametric Mapping software (SPM8; The Welcome Department of Imaging Neuroscience,London) under MATLAB 7.8.0 (The MathWorks Inc., Natick, MA, USA). The normalization of brain images is a prerequisite in any multi-subject voxel-wise MRI data analysis and especially important when dealing with abnormal brains. In order to achieve an accurate segmentation and normalization of lesioned GM and WM tissue, Unified Segmentation (Ashburner and Friston, 2005) with medium regularization and cost function masking (CFM) was applied to the structural T1-weighted images of each subject (Brett et al., 2001). The cost function masks were defined by manually depicting for each patient at each time (acute and 6month stage) binary lesion masks of the lesioned tissue using the MRIcron software package1(Rorden and Brett, 2000). This technique has been widely used with patients suffering from stroke (Crinion et al., 2007;Andersen et al., 2010;Ripollés et al., 2012), achieving optimal normalization with no post-registration lesion shrinkage or out-of-brain distortion (Ripollés et al., 2012). During normalization, the GM and WM images were modulated in order to preserve the total amount of the signal. The resulting normalized GM and WM tissue probability maps were smoothed by using an isotropic spatial filter (FWHM =6 mm) to reduce residual inter-individual variability. 1http://www.mccauslandcenter.sc.edu/mricro/mricron/index.html All normalized and smoothed GM and WM images were further analyzed in order to compare the differences in the GMV or white matter volume (WMV). Because the processing of music and speech are generally known to involve the left and right hemispheres to a different degree (e.g., Zatorre et al., 2002;Tervaniemi and Hugdahl, 2003) and they are therefore differentially affected by lesion laterality, we performed separate analyses for the LHD patients (MG: n=7, ABG: n=8, CG: n=8) and the RHD patients (MG: n=9, ABG: n=10, CG: n=7). Thus, four separate mixed-design analysis of variance (ANOVA) models (GMV–LHD, GMV–RHD, WMV–LHD, WMV–RHD) were built with Group (MG/ABG/CG) as a between-subjects variable and Time (acute stage/6-month stage) as a within-subjects variable (thereby ensuring that each subject acted as its own control). Total intracranial volume (TIV) was included as a nuisance variable in order to correct for global differences for head size. Three different Group ×Time interactions were calculated: MG >CG and ABG, ABG >CG and MG,CG >MG andABG. In other words,we tested if the increments in post–pre GMV in one group (e.g., MG) were greater than in the other two groups (e.g., CG and ABG). In addition, post hoc paired t-tests were planned to check the direction of the effect of Time within each Group (6 months >acute). It has been suggested that combined intensity and cluster size thresholds such as p<0.005 with a 10 voxel extent produce a desirable balance between Type I and Type II errors (Lieberman and Cunningham, 2009). Taking a slightly more stringent approach, the results are reported in tables at p<0.001 (uncorrected threshold) with a cluster size of ≥50 voxels of spatial extent. For the sake of visual clarity, results are shown in figures at p<0.01 (uncorrected threshold), although only clusters reported in the tables are labeled and commented throughout the text. Anatomical and cytoarchitectonical areas were identified using the Automated Anatomical Labeling (Tzourio-Mazoyer et al.,2002) and the Talairach Daemon database atlases (Lancaster et al., 2000) included in the xjView toolbox2. Finally, for any cluster of voxels where a significant Group ×Time interaction was found, mean GMV or WMV increase (6 months −acute stage) was calculated for each patient and correlated with behavioral changes (also 6 months −acute 2http://www.alivelearn.net/xjview8/ Frontiers in Human Neuroscience www.frontiersin.org April 2014 | Volume 8 | Article 245 | 5
Särkämö et al. Music listening enhances neuroplasticity after stroke stage) in cognitive tests and mood scales. For the cognitive measures, changes in the summary scores of the tests measuring the following cognitive domains were included: verbal memory, short-term and working memory,language skills, visuospatial cognition, executive functions, focused attention (correct responses and reactions times), and sustained attention (correct responses and reactions times; for details,see Särkämö et al.,2008). Similarly, for the mood measures,changes in the eight Profile of Mood States (POMS) scales (tension,depression,irritability,vigor,fatigue,inertia, confusion, and forgetfulness) were included (for details, see Särkämö et al., 2008). RESULTS GRAY AND WHITE MATTER VOLUME CHANGES DURING RECOVERY Significant GMV increases were found post-intervention (6 months −acute) for all three groups of LHD patients (see Table 3;Figure 1) and RHD patients (see Table 4;Figure 2). Areas identified were mostly located in the temporal, frontal,motor, limbic, and cerebellar brain regions, especially in the contralesional hemisphere, with the largest and most extensive volume increases occurring in the MG. In LHD patients, significant Group ×Time interactions in GMV were found for the MG >ABG and CG contrast (see Table 5; Figure 3) in five different clusters: three in frontal areas [left and right superior frontal gyrus (SFG) and right medial SFG] and two in limbic areas [left ventral/subgenual anterior cingulate cortex (SACC) and right ventral striatum (VS) / globus pallidum). The reversed contrasts (ABG >CG and MG, CG >MG and ABG) did not yield any significant regions. In RHD patients,there were no significant Group ×Time interactions in GMV in any area at the selected threshold (p<0.001 uncorrected). However, when using a slightly more lenient threshold (p<0.005 uncorrected), a single cluster emerged in the left insula (MNI −33 −6−8; 73 voxels of extent; t(22) =3.36) for the MG >ABG and CG contrast (see Figure 4). Again, no other clusters were found using the reversed contrasts (ABG >CG and MG, CG >MG and ABG) at this same threshold. There were no significant Time effects or Group ×Time interactions in the WMV in LHD or RHD patients. CORRELATION BETWEEN GRAY MATTER CHANGES AND BEHAVIORAL RECOVERY In order to determine the functional relevance of the observed GMV increases induced by the music listening intervention, we performed correlation analyses with the longitudinal behavioral data (also 6 months −acute). In LHD patients, the increase in GMV in the identified frontal areas correlated significantly with improvement in verbal memory, language skills, and focused attention (see Table 6 for individual cluster correlations; in Figure 5 the frontal clusters are pooled together for illustrative Table 3 | GMV increases (6-month−acute) in LHD patients (n=23). Anatomical area MNI coordinates Cluster size t-value CG Left cerebellum −14 −59 −40 189 4.75 Right temporal pole (BA 38) 34 5 −20 111 4.46 Right cerebellum 14 −59 −42 59 4.27 Left pons −7−29 −35 203 4.08 Right posterior cingulate gyrus 12 −40 24 83 3.96 ABG Right calcarine/cuneus (BA 17, 18) 17 −77 12 1084 5.14 Left cerebellum −7−53 −17 166 4.75 Right pons −18 −35 −42 85 4.71 Right calcarine (BA 17) 9 −82 4 239 4.66 Right precentral gyrus (BA 6) 47 0 31 110 4.18 MG Left ventral/subgenual anterior cingulate cortex (BA 10) −10 34 −3 185 5.75 Right superior frontal gyrus (BA 32, 6) 19 4 51 588 5.54 Right middle frontal gyrus (BA 32, 9) 21 24 39 1367 5.39 Right inferior frontal gyrus 31 13 21 153 5.35 Right ventral striatum 12 15 −11 484 4.85 Right fusiform gyrus (BA 19) 32 −45 −9 130 4.82 Right orbitofrontal cortex (BA 11) 28 44 −6 264 4.80 Right superior frontal gyrus (BA 10) 22 51 2 490 4.74 Right superior medial frontal gyrus (BA 8) 8 32 51 76 4.67 Right precuneus (BA 7) 19 −53 46 166 4.60 Right posterior cingulate gyrus 14 −40 25 471 4.46 Right ventral striatum/globus pallidum 14 6 −2 252 4.43 Left supplementary motor area −11 10 50 54 4.07 Results are reported at a p<0.001 (uncorrected threshold) with 50 voxels of spatial extent. CG, control group; ABG, audio book group; MG, music group; BA, Brodmann area. Frontiers in Human Neuroscience www.frontiersin.org April 2014 | Volume 8 | Article 245 | 6
Särkämö et al. Music listening enhances neuroplasticity after stroke FIGURE 1 | GMV increases (6-month −acute) in LHD patients (n=23). Lesion overlap indicating the number of patients with damage at a particular voxel and GMV increases within the three groups are shown in blue–green–red and red–yellow, respectively. Neurological convention is used. Results are shown at p<0.01 (uncorrected) with ≥50 voxels of spatial extent and overlaid over a canonical template with MNI coordinates at the bottom right of each slice. Only clusters surviving a p<0.001 threshold are labeled (see alsoTable 3). TP, temporal pole; PCG, posterior cingulate gyrus; Cr, cerebellum; PrCG, precentral gyrus; Calc, calcarine; Prec, precuneus; SFG, superior frontal gyrus; VS, ventral striatum; IFG, inferior frontal gyrus; SACC, ventral/subgenual anterior cingulate cortex; OFC, orbitofrontal cortex; FfG, fusiform gyrus; L, left hemisphere; R, right hemisphere. purposes). Similarly, increase in GMV in the limbic regions (left SACC) was significantly correlated with a decrease in selfreported depression, tension, fatigue, forgetfulness, and irritability and marginally correlated also with a decrease in self-reported confusion. In RHD patients, the GMV increases in the left insula cluster were also found to correlate with the improvement of language skills (r=0.63, p<0.002; Figure 4). There were no other significant correlations. DISCUSSION The novel key finding of the present VBM study was that regular music listening during the 6-month post-stroke stage can lead to structural reorganization in the recovering brain. Specifically, compared with patients who listened daily to audio books (ABG) or who did not receive any additional listening material (CG), the patients who listened daily to their own favorite music (MG) showed more increase in GMV from the acute to Frontiers in Human Neuroscience www.frontiersin.org April 2014 | Volume 8 | Article 245 | 7
Särkämö et al. Music listening enhances neuroplasticity after stroke Table 4 | GMV increases (6-month−acute) in RHD patients (n=26). Anatomical area MNI coordinates Cluster size t-value CG Left supramarginal gyrus (BA 40) −44 −28 28 1597 6.13 Left thalamus −15 −6 6 946 5.97 Left brainstem −4−20 −10 695 5.27 Left inferior temporal lobe (BA 20, 21) −44 −5−35 208 4.42 Right cerebellum 14 −42 −25 93 4.19 Left sup./mid. occipital gyrus −21 −86 12 405 4.18 Left cuneus (BA 18) −5−85 22 340 4.18 Left orbitofrontal cortex (BA 47) −16 25 −21 85 3.97 ABG Left cerebellum −9−52 −40 874 6.42 Left posterior cingulum −12 −43 21 322 5.85 Right posterior cingulum 20 −43 26 79 4.83 Left middle cingulum −12 −11 35 546 4.78 Right orbitofrontal cortex (BA 10) 15 53 1 185 4.66 Right cerebellum 16 −71 −51 268 4.64 Left thalamus −15 −6 11 105 4.40 Left insula −33 −14 10 61 4.21 Left precuneus −20 −50 14 59 4.19 Left precentral gyrus (BA 6) −29 −11 45 102 4.18 Left postcentral gyrus (BA 4) −43 −17 51 169 4.13 MG Right precuneus (BA 31) 16 −45 21 1544 6.35 Left post/middle/ant cingulate gyrus; left sup./mid. frontal gyrus; left supp. motor area; left inferior frontal gyrus; pars triangularis (BA 32, 31, 24, 9) −10 −12 34 11173 6.16 Left supramarginal gyrus; left postcentral gyrus (BA 40) −48 −23 27 1472 6.09 Left inferior frontal gyrus; left precentral gyrus (BA 6) −42 9 17 799 5.92 Right middle cingulate gyrus (BA 24) 18 30 26 970 4.90 Left orbitofrontal cortex (BA 47, 11) −25 23 −16 1661 4.85 Left inf./mid. temporal gyrus (BA 20) −46 −5−31 717 4.56 Left fusiform gyrus −34 −17 −27 266 4.42 Left insula −34 −12 −7 387 4.39 Left parahippocampal gyrus 26 −65 −38 191 4.21 Right cerebellum 11 31 −11 280 4.18 Right anterior cingulate (BA 32) 19 43 4 72 4.14 Left precentral gyrus −34 5 35 188 4.12 Left middle temporal gyrus −56 −46 −8 372 4.06 Right fusiform gyrus 36 −42 −16 246 4.05 Results are reported at a p<0.001 (uncorrected threshold) with 50 voxels of spatial extent. CG, control group; ABG, audio book group; MG, music group; BA, Brodmann area. the 6-month stage in a network of frontolimbic areas, primarily in the healthy contralesional side but also perilesionally. Importantly, the observed GMV increases in this network were directly associated with the behavioral improvement in cognitive functioning and reduction in negative mood shown previously for music listening (Särkämö et al., 2008;Forsblom et al., 2012). The areaspecific correlations obtained (attention, memory, and language for frontal areas; mood for limbic regions), the lack of differences in the reversed contrasts (ABG >CG and MG, CG >MG and ABG), and the fact that effects emerge in areas that have previously been found to be closely associated with music processing and cognitive/emotional processing (see below), argue against our results being false positives. Moreover, given that the patient groups were comparable at baseline and the potential effects of other types of rehabilitation (standard stroke rehabilitation) and activities (audio book listening) were controlled for, these findings suggest that a musically enriched environment can be beneficial for acute stroke recovery and that neuroplastic changes in the frontolimbic network may underlie its efficacy. In the present study, the frontal GMV increases associated with music listening in LHD patients were located in the left and right SFG and the right medial SFG and correlated with the improvement of verbal memory,language skills,and focused attention over the 6-month follow-up. These correlations are well in line with the previous findings of the study showing that music listening enhanced the recovery of verbal memory and focused attention Frontiers in Human Neuroscience www.frontiersin.org April 2014 | Volume 8 | Article 245 | 8
Särkämö et al. Music listening enhances neuroplasticity after stroke vascular mild cognitive impairment. Cerebrovasc. Dis. 30, 157–166. doi:10.1159/ 000316059 Grau-Sánchez, J., Amengual, J. L., Rojo, N., Veciana de Las Heras, M., Montero, J., Rubio, F., et al. (2013). Plasticity in the sensorimotor cortex induced by musicsupported therapy in stroke patients: a TMS study. Front. Hum. Neurosci. 7:494. doi:10.3389/fnhum.2013.00494 Green, A. C., Baerentsen, K. B., Stødkilde-Jørgensen, H., Wallentin, M., Roepstorff, A., and Vuust, P. (2008). Music in minor activates limbic structures: a relationship with dissonance? Neuroreport 19, 711–775. doi:10.1097/WNR. 0b013e3282fd0dd8 Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H., et al. (2007). Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol. Psychiatry 62, 429–437. doi:10.1016/j.biopsych.2006.09.020 Grieve, S. M., Korgaonkar, M. S., Koslow, S. H., Gordon, E., and Williams, L. M. (2013). Widespread reductions in gray matter volume in depression. Neuroimage Clin. 3, 332–339. doi:10.1016/j.nicl.2013.08.016 Halwani, G. F., Loui, P., Rüber, T., and Schlaug, G. (2011). Effects of practice and experience on the arcuate fasciculus: comparing singers, instrumentalists, and non-musicians. Front. Psychol. 2:156. doi:10.3389/fpsyg.2011.00156 Herdener, M., Humbel, T., Esposito, F., Habermeyer, B., Cattapan-Ludewig, K., and Seifritz, E. (2014). Jazz drummers recruit language-specific areas for the processing of rhythmic structure. Cereb. Cortex 24, 836–843. doi:10.1093/cercor/bhs367 Hoekzema, E., Carmona, S., Tremols, V., Gispert, J. D., Guitart, M., Fauquet, J., et al. (2010). Enhanced neural activity in frontal and cerebellar circuits after cognitive training in children with attention-deficit/hyperactivity disorder. Hum. Brain Mapp. 31, 1942–1950. doi:10.1002/hbm.20988 Huang, S., Seidman, L. J., Rossi, S., and Ahveninen, J. (2013). Distinct cortical networks activated by auditory attention and working memory load. Neuroimage 83, 1098–1108. doi:10.1016/j.neuroimage.2013.07.074 Hyde, K. L., Lerch, J., Norton, A., Forgeard, M., Winner, E., Evans, A. C., et al. (2009). Musical training shapes structural brain development. J. Neurosci. 29, 3019–3025. doi:10.1523/jneurosci.5118-08.2009 James, C. E., Oechslin, M. S., Van De Ville, D., Hauert, C. A., Descloux, C., and Lazyras, F. (2014). Musical training yields opposite effects on grey matter density in cognitive versus sensorimotor networks. Brain Struct. Funct. 219, 353–366. doi:10.1007/s00429-013-0504-z Janata, P. (2009). The neural architecture of music-evoked autobiographical memories. Cereb. Cortex 19, 2579–2594. doi:10.1093/cercor/bhp008 Janata, P., Birk, J. L., Van Horn, J. D., Leman, M., Tillmann, B., and Bharucha, J. J. (2002a). The cortical topography of tonal structures underlying Western music. Science 298, 2167–2170. doi:10.1126/science.1076262 Janata, P., Tillmann, B., and Bharucha, J. J. (2002b). Listening to polyphonic music recruits domain-general attention and working memory circuits. Cogn. Aff. Behav. Neurosci. 2, 121–140. doi:10.3758/CABN.2.2.121 Janssen,H.,Ada,L.,Bernhardt, J., McElduff,P.,Pollack,M.,Nilsson,M., et al. (2014). An enriched environment increases activity in stroke patients undergoing rehabilitation in a mixed rehabilitation unit: a pilot non-randomized controlled trial. Disabil. Rehabil. 36, 255–262. doi:10.3109/09638288.2013.788218 Jerde, T. A., Childs, S. K., Handy, S. T., Nagode, J. C., and Pardo, J. V. (2011). Dissociable systems of working memory for rhythm and melody. Neuroimage 57, 1572–1579. doi:10.1016/j.neuroimage.2011.05.061 Johansson,B. B. (2004). Functional and cellular effects of environmental enrichment after experimental brain infarcts. Restor. Neurol. Neurosci. 22, 163–174. Johansson, B. B. (2012). Multisensory stimulation in stroke rehabilitation. Front. Hum. Neurosci. 6:60. doi:10.3389/fnhum.2012.00060 Johansson, B. B., and Belichenko, P. V. (2002). Neuronal plasticity and dendritic spines: effect of environmental enrichment on intact and postischemic rat brain. J. Cereb. Blood Flow. Metab. 22, 89–96. doi:10.1097/00004647-200201000-00011 Kang, D. H.,Jo,H. J.,Jung,W. H.,Kim,S. H., Jung,Y. H., Choi,C. H.,et al. (2013). The effect of meditation on brain structure: cortical thickness mapping and diffusion tensor imaging. Soc. Cogn. Affect. Neurosci. 8, 27–33. doi:10.1093/scan/nss056 Kitayama, S., Chua, H. F., Tompson, S., and Han, S. (2013). Neural mechanisms of dissonance: an fMRI investigation of choice justification. Neuroimage 69, 206–212. doi:10.1016/j.neuroimage.2012.11.034 Kleber, B., Birbaumer, N., Veit, R., Trevorrow, T., and Lotze, M. (2007). Overt and imagined singing of an Italian aria. Neuroimage 36, 889–900. doi:10.1016/j. neuroimage.2007.02.053 Koelsch, S. (2010). Towards a neural basis of music-evoked emotions. Trends Cogn. Sci. 14, 131–137. doi:10.1016/j.tics.2010.01.002 Koelsch, S. (2011). Toward a neural basis of music perception – a review and updated model. Front. Psychol. 2:110. doi:10.3389/fpsyg.2011.00110 Koelsch, S., Fritz, T., Schulze, K., Alsop, D., and Schlaug, G. (2005). Adults and children processing music: an fMRI study. Neuroimage 25, 1068–1076. doi:10.1016/j.neuroimage.2004.12.050 Koelsch, S., Fritz, T., V Cramon, D. Y., Müller, K., and Friederici, A. D. (2006). Investigating emotion with music: an fMRI study. Hum. Brain Mapp. 27, 239–250. doi:10.1002/hbm.20180 Koelsch, S., Kasper, E., Sammler, D., Schulze, K., Gunter, T., and Friederici, A. D. (2004). Music, language and meaning: brain signatures of semantic processing. Nat. Neurosci. 7, 302–307. doi:10.1038/nn1197 Komitova, M., Mattsson, B., Johansson, B. B., and Eriksson, P. S. (2005). Enriched environment increases neural stem/progenitor cell proliferation and neurogenesis in the subventricular zone of stroke-lesioned adult rats. Stroke 36, 1278–1282. doi:10.1161/01.str.0000166197.94147.59 Lai, C. H. (2013). Gray matter volume in major depressive disorder: a metaanalysis of voxel-based morphometry studies. Psychiatry Res. 211, 37–46. doi:10.1016/j.pscychresns.2012.06.006 Lancaster, J. L., Woldorff, M. G., Parsons, L. M., Liotti, M., Freitas, C. S., Rainey, L., et al. (2000). Automated Talairach atlas labels for functional brain mapping. Hum. Brain Mapp. 10, 120–131. doi:10.1002/1097-0193(200007) Lappe, C., Steinsträter, O., and Pantev, C. (2013). Rhythmic and melodic deviations in musical sequences recruit different cortical areas for mismatch detection. Front. Hum. Neurosci. 7:260. doi:10.3389/fnhum.2013.00260 Lee, Y. S., Janata, P., Frost, C., Hanke, M., and Granger, R. (2011). Investigation of melodic contour processing in the brain using multivariate pattern-based fMRI. Neuroimage 57, 293–300. doi:10.1016/j.neuroimage.2011.02.006 Liang, Z., Zeng, J., Zhang, C., Liu, S., Ling, X., Xu, A., et al. (2008). Longitudinal investigations on the anterograde and retrograde degeneration in the pyramidal tract following pontine infarction with diffusion tensor imaging. Cerebrovasc. Dis. 25, 209–216. doi:10.1159/000113858 Lieberman, M. D., and Cunningham,W. A. (2009). Type I and Type II error concerns in fMRI research: re-balancing the scale. Soc. Cogn. Affect. Neurosci. 4, 423–428. doi:10.1093/scan/nsp052 Maegele, M., Lippert-Gruener, M., Ester-Bode, T., Garbe, J., Bouillon, B., Neugebauer, E., et al. (2005a). Multimodal early onset stimulation combined with enriched environment is associated with reduced CNS lesion volume and enhanced reversal of neuromotor dysfunction after traumatic brain injury in rats. Eur. J. Neurosci. 21, 2406–2418. doi:10.1111/j.1460-9568.2005.04070.x Maegele, M., Lippert-Gruener, M., Ester-Bode, T., Sauerland, S., Schäfer, U., Molcanyi, M., et al. (2005b). Reversal of neuromotor and cognitive dysfunction in an enriched environment combined with multimodal early onset stimulation after traumatic brain injury in rats. J. Neurotrauma 22, 772–782. doi:10.1089/neu.2005.22.772 Matsumori, Y., Hong, S. M., Fan, Y., Kayama, T., Hsu, C. Y., Weinstein, P. R., et al. (2006). Enriched environment and spatial learning enhance hippocampal neurogenesis and salvages ischemic penumbra after focal cerebral ischemia. Neurobiol. Dis. 22, 187–198. doi:10.1016/j.nbd.2005.10.015 Menon, V., and Levitin, D. J. (2005). The rewards of music listening: response and physiological connectivity of the mesolimbic system. Neuroimage 28, 175–184. doi:10.1016/j.neuroimage.2005.05.053 Mitterschiffthaler, M. T., Fu, C. H., Dalton, J. A., Andrew, C. M., and Williams, S. C. (2007). A functional MRI study of happy and sad affective states induced by classical music. Hum. Brain Mapp. 28, 1150–1162. doi:10.1002/hbm.20337 Montag, C., Reuter, M., and Axmacher, N. (2011). How one’s favorite song activates the reward circuitry of the brain: personality matters! Behav. Brain Res. 225, 511–514. doi:10.1016/j.bbr.2011.08.012 Nichols, J. A., Jakkamsetti, V. P., Salgado, H., Dinh, L., Kilgard, M. P., and Atzori, M. (2007). Environmental enrichment selectively increases glutamatergic responses in layer II/III of the auditory cortex of the rat. Neuroscience 145, 832–840. doi:10.1016/j.neuroscience.2006.12.061 Nithianantharajah,J.,and Hannan,A. J. (2006). Enriched environments,experiencedependent plasticity and disorders of the nervous system. Nat. Rev. Neurosci. 7, 697–709. doi:10.1038/nrn1970 Northoff, G., and Bermpohl, F. (2004). Cortical midline structures and the self. Trends Cogn. Sci. 8, 102–107. doi:10.1016/j.tics.2004.01.004 Frontiers in Human Neuroscience www.frontiersin.org April 2014 | Volume 8 | Article 245 | 15
Särkämö et al. Music listening enhances neuroplasticity after stroke Omar, R., Henley, S. M., Bartlett, J. W., Hailstone, J. C., Gordon, E., Sauter, D. A., et al. (2011). The structural neuroanatomy of music emotion recognition: evidence from frontotemporal lobar degeneration. Neuroimage 56, 1814–1821. doi:10.1016/j.neuroimage.2011.03.002 Osuch, E. A., Bluhm, R. L., Williamson, P. C., Théberge, J., Densmore, M., and Neufeld, R. W. (2009). Brain activation to favorite music in healthy controls and depressed patients. Neuroreport 20, 1204–1208. doi:10.1097/wnr. 0b013e32832f4da3 Platel, H., Baron, J. C., Desgranges, B., Bernard, F., and Eustache, F. (2003). Semantic and episodic memory of music are subserved by distinct neural networks. Neuroimage 20, 244–256. doi:10.1016/S1053-8119(03)00287-8 Price,C. J. (2010). The anatomy of language: a review of 100 fMRI studies published in 2009. Ann. N.Y. Acad. Sci. 1191,62–88. doi:10.1111/j.1749-6632.2010.05444.x Ripollés, P., Marco-Pallarés, J., de Diego-Balaguer, R., Miró, J., Falip, M., Juncadella, M., et al. (2012). Analysis of automated methods for spatial normalization of lesioned brains. Neuroimage 60, 1296–1306. doi:10.1016/j.neuroimage.2012. 01.094 Rodríguez-Fornells, A., Rojo, N., Amengual, J. L., Ripollés, P., Altenmüller, E., and Münte, T. F. (2012). The involvement of audio-motor coupling in the musicsupported therapy applied to stroke patients. Ann. N. Y. Acad. Sci. 1252, 282–293. doi:10.1111/j.1749-6632.2011.06425.x Rojo, N., Amengual, J. L., Juncadella, M., Rubio, F., Camara, E., Marco-Pallarés, J., et al. (2011). Music-supported therapy induces plasticity in the sensorimotor cortex in chronic stroke: a single-case study using multimodal imaging (fMRITMS). Brain Inj. 25, 787–793. doi:10.3109/02699052.2011.576305 Rorden, C., and Brett, M. (2000). Stereotaxic display of brain lesions. Behav. Neurol. 12, 191–200. doi:10.1155/2000/421719 Salimpoor, V. N., Benovoy, M., Larcher, K., Dagher, A., and Zatorre, R. J. (2011). Anatomically distinct dopamine release during anticipation and experience of peak emotion to music. Nat. Neurosci. 14, 257–262. doi:10.1038/nn.2726 Salimpoor, V. N., van den Bosch, I., Kovacevic, N., McIntosh, A. R., Dagher, A., and Zatorre, R. J. (2013). Interactions between the nucleus accumbens and auditory cortices predict music reward value. Science 340, 216–219. doi:10.1126/science. 1231059 Särkämö, T., Pihko, E., Laitinen, S., Forsblom, A., Soinila, S., Mikkonen, M., et al. (2010a). Music and speech listening enhance the recovery of early sensory processing after stroke. J. Cogn. Neurosci. 22, 2716–2727. doi:10.1162/jocn.2009. 21376 Särkämö, T., Tervaniemi, M., Soinila, S., Autti, T., Silvennoinen, H. M., Laine, M., et al. (2010b). Auditory and cognitive deficits associated with acquired amusia after stroke: a magnetoencephalography and neuropsychological follow-up study. PLoS ONE 5:e15157. doi:10.1371/journal.pone.0015157 Särkämö, T., Tervaniemi, M., Laitinen, S., Forsblom, A., Soinila, S., Mikkonen, M., et al. (2008). Music listening enhances cognitive recovery and mood after middle cerebral artery stroke. Brain 131, 866–876. doi:10.1093/brain/awn013 Särkämö, T., Tervaniemi, M., Soinila, S., Autti, T., Silvennoinen, H. M., Laine, M., et al. (2009). Cognitive deficits associated with acquired amusia after stroke: a neuropsychological follow-up study. Neuropsychologia 47, 2642–2651. doi:10.1016/j.neuropsychologia.2009.05.015 Schlaug, G., Marchina, S., and Norton, A. (2008). From singing to speaking: why singing may lead to recovery of expressive language function in patients with Broca’s aphasia. Music Percept. 25, 315–323. doi:10.1525/mp.2008.25.4.315 Schlaug, G., Marchina, S., and Norton, A. (2009). Evidence for plasticity in whitematter tracts of patients with chronic Broca’s aphasia undergoing intense intonation-based speech therapy. Ann. N. Y. Acad. Sci. 2009, 385–394. doi:10. 1111/j.1749-6632.2009.04587.x Söderström, I., Strand, M., Ingridsson, A. C., Nasic, S., and Olsson, T. (2009). 17beta-estradiol and enriched environment accelerate cognitive recovery after focal brain ischemia. Eur. J. Neurosci. 29, 1215–1224. doi:10.1111/j.1460-9568. 2009.06662.x Sutoo, D., and Akiyama, K. (2004). Music improves dopaminergic neurotransmission: demonstration based on the effect of music on blood pressure regulation. Brain Res. 1016, 255–262. doi:10.1016/j.brainres.2004.05.018 Tervaniemi, M., and Hugdahl, K. (2003). Lateralization of auditory-cortex functions. Brain Res. Rev. 43, 231–246. doi:10.1016/j.brainresrev.2003.08.004 Thiebaut de Schotten, M., Tomaiuolo, F., Aiello, M., Merola, S., Silvetti, M., Lecce, F., et al. (2014). Damage to white matter pathways in subacute and chronic spatial neglect: a group study and 2 single-case studies with complete virtual “in vivo” tractography dissection. Cereb. Cortex 24, 691–706. doi:10.1093/cercor/bhs351 Trost, W., Ethofer, T., Zentner, M., and Vuilleumier, P. (2012). Mapping aesthetic musical emotions in the brain. Cereb. Cortex 22, 2769–2783. doi:10.1093/cercor/ bhr353 Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289. doi:10.1006/nimg.2001.0978 Vago, D. R., and Silbersweig, D. A. (2012). Self-awareness, self-regulation, and selftranscendence (S-ART): a framework for understanding the neurobiological mechanisms of mindfulness. Front. Hum. Neurosci. 6:296. doi:10.3389/fnhum. 2012.00296 van Meer, M. P., Otte,W. M., van derMarel, K., Nijboer, C. H., Kavelaars,A., van derSprenkel, J. W., et al. (2012). Extent of bilateral neuronal network reorganization and functional recovery in relation to stroke severity. J. Neurosci. 32, 4495–4507. doi:10.1523/jneurosci.3662-11.2012 Verghese, J., Lipton, R. B., Katz, M. J., Hall, C. B., Derby, C. A., Kuslansky, G., et al. (2003). Leisure activities and the risk of dementia in the elderly. N. Engl. J. Med. 348, 2508–2516. doi:10.1056/NEJMoa022252 Villarreal, M. F., Cerquetti, D., Caruso, S., SchwarczLópezAranguren, V., Gerschcovich, E. R., Frega, A. L., et al. (2013). Neural correlates of musical creativity: differences between high and low creative subjects. PLoS ONE 8:e75427. doi:10.1371/journal.pone.0075427 Wehrum, S., Degé, F., Ott, U., Walter, B., Stippekohl, B., Kagerer, S., et al. (2011). Can you hear a difference? Neuronal correlates of melodic deviance processing in children. Brain Res. 1402, 80–92. doi:10.1016/j.brainres.2011.05.057 Yoshimura, S., Okamoto,Y., Onoda, K., Matsunaga, M., Okada, G., Kunisato,Y., et al. (in press). Cognitive behavioral therapy for depression changes medial prefrontal and ventral anterior cingulate cortex activity associated with self-referential processing. Soc. Cogn. Affect. Neurosci. doi:10.1093/scan/nst009 Zarate, J. M., and Zatorre, R. J. (2008). Experience-dependent neural substrates involved in vocal pitch regulation during singing. Neuroimage 40, 1871–1887. doi:10.1016/j.neuroimage.2008.01.026 Zatorre, R. J. (2013). Predispositions and plasticity in music and speech learning: neural correlates and implications. Science 342, 585–589. doi:10.1126/science. 1238414 Zatorre, R. J., Belin, P., and Penhune, V. B. (2002). Structure and function of auditory cortex: music and speech. Trends Cogn. Sci. 6, 37–46. doi:10.1016/S13646613(00)01816-7 Zatorre, R. J., Fields, R. D., and Johansen-Berg, H. (2012). Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat. Neurosci. 15, 528–536. doi:10.1038/nn.3045 Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Received: 30 January 2014; accepted: 03 April 2014; published online: 17 April 2014. Citation: Särkämö T, Ripollés P, Vepsäläinen H, Autti T, Silvennoinen HM, Salli E, Laitinen S, Forsblom A, Soinila S and Rodríguez-Fornells A (2014) Structural changes induced by daily music listening in the recovering brain after middle cerebral artery stroke: a voxel-based morphometry study. Front. Hum. Neurosci. 8:245. doi: 10.3389/fnhum.2014.00245 This article was submitted to the journal Frontiers in Human Neuroscience. Copyright © 2014 Särkämö, Ripollés,Vepsäläinen, Autti, Silvennoinen, Salli, Laitinen, Forsblom, Soinila and Rodríguez-Fornells. 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