Functional connectivity of major depression disorder using ongoing EEG during music perception

This is a sel -a chi ed e sion o an o iginal a icle. This e sion may di e om he o iginal in pagina ion and ypog aphic de ails. Au ho (s): Ti le: Yea : Ve sion: Copy igh : Righ s: Righ s u l: Please ci e he o iginal e sion: CC BY-NC-ND 4.0 h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0/ Func ional connec i i y o majo dep ession diso de using ongoing EEG du ing music pe cep ion © 2020 Else ie Accep ed e sion (Final d a ) Liu, Wenya; Zhang, Chi; Wang, Xiaoyu; Xu, Jing; Chang, Yi; Ris aniemi, Tapani; Cong, Fengyu Liu, W., Zhang, C., Wang, X., Xu, J., Chang, Y., Ris aniemi, T., & Cong, F. (2020). Func ional connec i i y o majo dep ession diso de using ongoing EEG du ing music pe cep ion. Clinical Neu ophysiology, 131(10), 2413-2422. h ps://doi.o g/10.1016/j.clinph.2020.06.031 2020 Jou nal P e-p oo s Func ional connec i i y o majo dep ession diso de using ongoing EEG du - ing music pe cep ion Wenya Liu, Chi Zhang, Xiaoyu Wang, Jing Xu, Yi Chang, Tapani Ris aniemi, Fengyu Cong PII: S1388-2457(20)30416-8 DOI: h ps://doi.o g/10.1016/j.clinph.2020.06.031 Re e ence: CLINPH 2009314 To appea in: Clinical Neu ophysiology Recei ed Da e: 25 No embe 2019 Re ised Da e: 7 May 2020 Accep ed Da e: 29 June 2020 Please ci e his a icle as: Liu, W., Zhang, C., Wang, X., Xu, J., Chang, Y., Ris aniemi, T., Cong, F., Func ional connec i i y o majo dep ession diso de using ongoing EEG du ing music pe cep ion, Clinical Neu ophysiology (2020), doi: h ps://doi.o g/10.1016/j.clinph.2020.06.031 This is a PDF ile o an a icle ha has unde gone enhancemen s a e accep ance, such as he addi ion o a co e page and me ada a, and o ma ing o eadabili y, bu i is no ye he de ini i e e sion o eco d. This e sion will unde go addi ional copyedi ing, ypese ing and e iew be o e i is published in i s inal o m, bu we a e p o iding his e sion o gi e ea ly isibili y o he a icle. Please no e ha , du ing he p oduc ion p ocess, e o s may be disco e ed which could a ec he con en , and all legal disclaime s ha apply o he jou nal pe ain. © 2020 Published by Else ie B.V. on behal o In e na ional Fede a ion o Clinical Neu ophysiology. Func ional connec i i y o majo dep ession diso de using ongoing EEG du ing music pe cep ion Wenya Liua,b, Chi Zhanga, Xiaoyu Wanga, Jing Xuc, Yi Changc, Tapani Ris aniemib, Fengyu Conga,b,d,e aSchool o Biomedical Enginee ing, Facul y o Elec onic and Elec ical Enginee ing, Dalian Uni e si y o Technology, 116024, Dalian, China bFacul y o In o ma ion Technology, Uni e si y o Jy äskylä, 40014, Jy äskylä, Finland cDepa men o Neu ology and Psychia y, Fi s A ilia ed Hospi al, Dalian Medical Uni e si y, 116011, Dalian, China dSchool o A i icial In elligence, Facul y o Elec onic In o ma ion and Elec ical Enginee ing, Dalian Uni e si y o Technology, 116024, Dalian, China eKey Labo a o y o In eg a ed Ci cui and Biomedical Elec onic Sys em, Liaoning P o ince. Dalian Uni e si y o Technology, 116024, Dalian, China Co esponding au ho : 1. Fengyu Cong, School o Biomedical Enginee ing, Facul y o Elec onic and Elec ical Enginee ing, Dalian Uni e si y o Technology, 116024, Dalian, China Email: [email p o ec ed] 2. Jing Xu, Depa men o Neu ology and Psychia y, Fi s A ilia ed Hospi al, Dalian Medical Uni e si y, 116011, Dalian, China Email: [email p o ec ed] 3. Yi Chang, Depa men o Neu ology and Psychia y, Fi s A ilia ed Hospi al, Dalian Medical Uni e si y, 116011, Dalian, China Email: [email p o ec ed] Highligh s: Majo dep ession causes al e ed connec i i y in del a and be a bands du ing music pe cep ion. Be a band connec i i y is a p omising bioma ke o he diagnosis o majo dep ession diso de . Na u alis ic music s imuli lead o equency-speci ic unc ional connec i i y. Abs ac Objec i e: The unc ional connec i i y (FC) o majo dep ession diso de (MDD) has no been well s udied unde na u alis ic and con inuous s imuli condi ions. In his s udy, we in es iga ed he equency-speci ic FC o MDD pa ien s exposed o condi ions o music pe cep ion using ongoing elec oencephalog am (EEG). Me hods: Fi s , we applied phase lag index (PLI) me hod o calcula e he connec i i y ma ices and g aph heo y-based me hods o measu e he opology o b ain ne wo ks ac oss di e en equency bands. Then, classi ica ion me hods we e adop ed o iden i y he mos disc imina e equency band o he diagnosis o MDD. Resul s: Du ing music pe cep ion, MDD pa ien s exhibi ed a dec eased connec i i y pa e n in he del a band bu an inc eased connec i i y pa e n in he be a band. Heal hy people showed a le hemisphe e- dominan phenomenon, bu MDD pa ien s did no show such a la e alized e ec . Suppo ec o machine (SVM) achie ed he bes classi ica ion pe o mance in he be a equency band wi h an accu acy o 89.7%, sensi i i y o 89.4% and speci ici y o 89.9%. Conclusions: MDD pa ien s exhibi ed an al e ed FC in del a and be a bands, and he be a band showed a supe io i y in he diagnosis o MDD. Signi icance: Ou s udy p o ided a p omising e e ence o he diagnosis o MDD, and e ealed a new pe spec i e o unde s anding he opology o MDD b ain ne wo ks du ing music pe cep ion. Keywo ds: unc ional connec i i y, ongoing EEG, majo dep ession diso de , music pe cep ion, na u alis ic s imuli. 1. In oduc ion Majo dep ession diso de (MDD) is cu en ly one o he mos p e alen psychia ic diso de s, and i subs an ially dis up s pa ien s’ li es. MDD pa ien s a e usually cha ac e ized by de ici s o a ec i e and cogni i e unc ions (Kaise e al. 2015; Li e al. 2018; Xia e al. 2018). Al hough many esea che s ha e dedica ed hemsel es o he explo a ion o he pa hophysiology o MDD, he neu al mechanisms o i s e iology and pa hogenesis a e s ill no ully unde s ood. Cu en ly, he e a e no bioma ke s o he clinical diagnosis o MDD (Fingelku s and Fingelku s 2015; Gao e al. 2018; Nugen e al. 2019). Con en ionally, he clinical diagnosis o MDD equen ly depends on some public c i e ia, such as Diagnos ic and S a is ical Manual o Men al Diso de s V (DSM-5), which makes he diagnosis o MDD e y subjec i e due o human ac o s and causes aul y diagnos ic esul s (Mum az e al. 2015; Nugen e al. 2019). Fo his eason, nonin asi e neu oimaging echniques, such as elec oencephalog am (EEG), magne oencephalog aphy (MEG) and unc ional magne ic esonance imaging ( MRI), a e u gen ly needed as mo e e ec i e and in elligen diagnos ic ools. EEG is an inexpensi e echnique ha bene i s om high empo al esolu ion. EEG is able o eco d elec ical ac i i y a equencies ela ed o neu onal ac i i y and o cap u e he dynamic changes a a millisecond scale. These ad an ages make EEG a e y p omising echnique o commonly use in he diagnosis o MDD (Baska an e al. 2012; Mum az e al. 2015, 2017). Many MRI s udies ha e demons a ed ha he pa hogenesis o MDD is he abno mali y o la ge-scale b ain ne wo ks, such as de aul mode ne wo k (DMN) (Zhu e al. 2012; Wu e al. 2013) and a ec i e ne wo k (AN) (A e y e al. 2014), o he dysconnec i i y o some b ain egions, such as co icolimbic pa hways (Nugen e al. 2019), a he han he dys unc ion o an indi idual b ain egion. So, unc ional connec i i y (FC) has p o en o be e ec i e o in es iga e ne wo k dys unc ion in MDD. FC p o ides a new line o hough o he diagnosis o MDD pa ien s, and many s udies, especially MRI and EEG s udies, ha e ocused on he classi ica ion o MDD based on FC analysis (Wang e al. 2017; Gao e al. 2018; Sakai and Yamada 2019). Howe e , FC analysis and MDD classi ica ion always ocus on es ing- s a e o highly con olled and epea ed s imuli, bu he di e ences in FC unde na u alis ic and con inuous s imuli be ween heal hy people and MDD pa ien s ha e no been well s udied. Compa ed wi h es ing s a e, lis ening o con inuous music is mo e closely ela ed o eal-wo ld expe ience (Wang e al. 2020), and emo ional a ousal can be induced o a ec i e p ocessing (Miku a e al. 2012). Music he apy has become an a ac i e ool o MDD ea men , so unde s anding he mechanism o he b ain esponse du ing lis ening o music is he basis o he diagnosis and ea men o MDD (Michael e al. 2005; Ma a os e al. 2008). An inc easing amoun o li e a u e has demons a ed ha human b ain ne wo ks a e di e en ac oss equency bands in bo h es ing-s a e and ask condi ions, and ne wo ks in speci ic equency bands may e eal di e en b ain unc ions (B ookes e al. 2012, 2016; Hilleb and e al. 2012, 2016). P e ious s udies ha e demons a ed al e ed FC in MDD in di e en equency bands, so FC analysis ac oss di e en equency bands is impo an o he diagnosis o MDD (Mum az e al. 2015; Kno e al. 2001; Whi on e al. 2018). Some s udies ha e ound ha equency-speci ic and la ge-scale b ain ne wo ks will eme ge du ing music pe cep ion o sus ain ongoing cogni i e asks (Allu i e al. 2012; Cong e al. 2013; Wang e al. 2020). A e iew by Ma a os e al. emphasized ha music he apy was associa ed wi h imp o emen s in mood o ea dep ession (Ma a os e al. 2008). Some esea che s ha e al eady ocused on equency-speci ic b ain esponses o music in dep ession pa ien s and o he psychia ic diso de s and ha e ound ha music he apy can al e FC and modula e b ain esponses (Michael e al. 2005; Rami ez e al. 2015; Dha madhika i e al. 2018). These p e ious s udies suppo ou assump ion ha al e ed FC exis s in di e en equency bands du ing music pe cep ion in MDD pa ien s. Howe e , ew s udies ha e in es iga ed he mechanism o dysconnec i i y and b ain esponses o MDD pa ien s du ing music pe cep ion. Fo elec ophysiological neu oimaging echniques, like EEG, he collec ed signals om one scalp senso a e ac ually om he whole b ain due o he olume conduc ion e ec (Van Den B oek e al. 1998; Scho elen and G oss 2009; B unne e al. 2016). B ain connec i i y in senso space is usually con ounded by olume conduc ion, and e en wi h he conduc ion o sou ce econs uc ion me hods, sou ce leakage s ill exis s due o he ill-posed na u e o he in e se p oblem (O’Neill e al. 2018). An inc easing numbe o s udies ha e demons a ed ha he communica ion o b ain egions o neu al popula ions depends on phase in e ac ions (Womelsdo e al. 2007; Pal a and Pal a 2012; He e al. 2019). A ze o-lag in e ac ion is conside ed o be he consequence o olume conduc ion because signal leakage is ins an aneous. Among he phase synch oniza ion me hods, phase lag index (PLI) disca ds he in e ac ions esul ing om phase di e ences o ze o, so PLI is no sensi i e o he olume conduc ion e ec ; hus, i is commonly used in he FC analysis o EEG and MEG s udies (S am e al. 2007; Vinck e al. 2011; Wu e al. 2012). Ruiz-Gómez e al ha e demons a ed ha PLI could educe he bias in oduced by he spu ious in luence o olume conduc ion and was supe io o he o he se en FC synch oniza ion measu es (Ruiz-Gómez e al. 2019). Ne wo k analysis me hods based on g aph heo y a e widely used o e eal he opology o b ain ne wo ks (Spo ns 2018; Ren e al. 2019). In EEG senso space, he b ain ne wo ks a e cons i u ed by nodes ep esen ing elec odes and edges ep esen ing FC s eng h be ween e e y pai o nodes. The a ious ne wo k p ope ies a e e icien measu es used o quan i y b ain unc ional in eg a ion and unc ional seg ega ion (Rubino and Spo ns 2010; Liao e al. 2017). Deg ee, which is a measu e o in luence, clus e ing coe icien , which is a measu e o unc ional seg ega ion, and cha ac e is ic pa h leng h, which is a measu e o unc ional in eg a ion, a e ne wo k p ope ies ha a e commonly used o quan i y he e iciency o in o ma ion p ocessing (Acha d e al. 2006; He e al. 2007; Gong and He 2015). In his s udy, we applied deg ee, clus e ing coe icien and cha ac e is ic pa h leng h o quan i y he di e ences be ween heal hy people and MDD pa ien s. In his s udy, we collec ed EEG da a om heal hy people and MDD pa ien s unde condi ions o music pe cep ion, and used he PLI me hod o calcula e FC ac oss i e ypically analyzed equency bands: del a, he a, alpha, be a and gamma bands. A e s a is ical analysis using he ne wo k-based-s a is ic (NBS) me hod, we compa ed he wo g oups h ough connec i i y ma ices and g aph- heo y based ne wo k p ope ies in del a and be a equency bands, which exhibi ed signi ican di e ences. Finally, machine lea ning me hods we e used o pe o m he classi ica ion. 2. Me hods 2.1 Da a acquisi ion Nine een heal hy adul s ( ou een emales and i e males) aged 24 o 65 yea s in he con ol (CON) g oup and wen y adul s ( ou een emales and six males) wi h MDD aged 23 o 58 yea s in he MDD g oup we e ec ui ed o his expe imen . All he pa ien s we e om he Fi s A ilia ed Hospi al o Dalian Medical Uni e si y in China. This s udy was app o ed by he e hics commi ee o he hospi al, and all he pa icipan s signed he in o med consen be o e hei en ollmen . None o he pa icipan s epo ed hea ing loss o o mal aining in music. MDD pa ien s we e p ima ily diagnosed by a clinical expe , and he cou se o he disease a ied om 2 and 36 mon hs. All he pa icipan s we e es ed acco ding o Hamil on Ra ing Scale o Dep ession (HRSD), Hamil on Anxie y Ra ing Scale (HAMA) and Mini-Men al S a e Examina ion (MMSE). The means and s anda d de ia ions (SD) o age, gende , educa ion and clinical measu es o bo h g oups a e lis ed in Table 1. Du ing he expe imen , pa icipan s we e old o si com o ably in a chai and lis en o a piece o music. An 8.5-minu e long musical piece o mode n ango by As o Piazzolla was used as he s imulus due o i s ich musical s uc u e and high ange o a ia ion in musical ea u es, such as dynamics, imb e, onali y and hy hm (Allu i e al. 2012, 2013). The EEG da a we e eco ded by he Neu oscan Quik-cap de ice wi h 64 elec odes a anged acco ding o he in e na ional 10-20 sys em. Elec odes placed a he le and igh ea lobes we e used as he e e ences. The da a we e down-sampled o 256 Hz o u he p ocessing and isually checked o emo e ob ious a i ac s om head mo emen s. Eye mo emen s a i ac s we e ejec ed by independen componen analysis (ICA), and 50-Hz a i ac s we e emo ed by sho ime Fou ie ans o m (STFT). STFT was applied o il e he da a in o i e ypically analyzed equency bands, namely, he del a (0.5-4 Hz), he a (4-8 Hz), alpha (8-13 Hz), be a (13-30 Hz) and gamma (30-80 Hz) bands, o u he analysis. 2.2 Phase synch oniza ion In his s udy, phase synch oniza ion was measu ed be ween all he pai s o channels by he PLI me hod, which is an asymme y index ha measu es he dis ibu ion o phase di e ences (S am e al. 2007; Vinck e al. 2011). Due o he ins an aneous sp ead o cu en , he same sou ces collec ed by wo elec odes a e conside ed o cause a ze o-lag phase di e ence, which is ejec ed by PLI. The e o e, PLI is less sensi i e o he olume conduc ion e ec , and i can e eal he ue coupling s eng h be ween pai s o channels. Fo an EEG signal om one channel, he analy ical signal can be cons uc ed 𝑥 ( 𝑡 ) , 𝑡 = 1,2,3,⋯,𝑇 𝑧(𝑡) by Hilbe ans o m, 𝑧 ( 𝑡 ) = 𝑥 ( 𝑡 ) + 𝑖 𝑥 ( 𝑡 ) = 1 𝜋 𝑃𝑉 ∫ ∞ ― 𝑥 𝑥 ( 𝜏 ) 𝑡 ― 𝜏 𝑑𝜏, # ( 1 ) whe e is he imagina y pa , and e e s o he Cauchy p incipal alue. Then, he ins an aneous 𝑥 ( 𝑡 ) 𝑃𝑉 ampli ude and he ins an aneous phase can be compu ed as ollows: 𝐴(𝑡) φ( 𝑡 ) { 𝐴 ( 𝑡 ) = [ 𝑥 ( 𝑡 ) ] 2 + [ 𝑥 ( 𝑡 ) ] 2 𝜑 ( 𝑡 ) = 𝑎𝑟𝑐𝑡𝑎𝑛 𝑋 ( 𝑡 ) 𝑥 ( 𝑡 ) . # ( 2 ) The e o e, he phase di e ence o wo signals and a ime can be o mula ed as: ∆𝜑(𝑡) 𝑥 𝑎 (𝑡) 𝑥 𝑏 (𝑡) 𝑡 ∆𝜑 ( 𝑡 ) = 𝜑 𝑎 ( 𝑡 ) ― 𝜑 𝑏 ( 𝑡 ) . # ( 3 ) Then, he PLI index can be de ined ia 𝑃𝐿𝐼 = | < 𝑠𝑖𝑔𝑛[∆𝜑(𝑡)] > |, 𝑡 = 1…𝑇. # ( 4 ) The alue o PLI index a ies be ween 0 and 1. A alue o 0 indica es no coupling o coupling wi h a phase di e ence cen e ed a ound 0 mod , and a alue o 1 indica es pe ec phase synch oniza ion 𝜋 be ween wo signals a a cons an lag excep 0 o . 𝜋 In his s udy, o he 8.5-minu e EEG da a wi h a sampling equency o 256 Hz, we i s emo ed ou unusable elec odes. Then, we emo ed he i s and las 10 seconds o he EEG da a o a oid ansi ion e ec s, and we segmen ed he EEG da a in o non-o e lapping epochs by a ime window o 10 seconds, so he e we e a o al o 49 epochs. Then, an adjacency ma ix o was calcula ed by PLI o 60 × 60 each epoch and each equency band. 2.3 Ne wo k analysis G aph heo y is no mally used a e he calcula ion o he adjacency ma ix o quan i y he opology o b ain ne wo ks. In his s udy, we used h ee commonly used ne wo k measu es o quan i y in luence, unc ional seg ega ion and unc ional in eg a ion, including deg ee, clus e ing coe icien and cha ac e is ic pa h leng h. All he ne wo k measu es men ioned abo e we e compu ed using he B ain Connec i i y Toolbox (Rubino and Spo ns 2010) (h p://www.b ain-connec i i y- oolbox.ne ). Fo an adjacency ma ix , wi h nodes, ep esen s he connec ion s eng h be ween node and 𝑮 𝑁 𝑤 𝑖𝑗 𝑖 node , whe e . The diagonal elemen s mean sel -connec ions o nodes, so 𝑗 0 ≤ 𝑤 𝑖𝑗 ≤ 1 𝑤 𝑖𝑖 = 0, 𝑖 = 1 , . 2,⋯,𝑁 2.3.1 Deg ee Deg ee is conside ed an impo an ma ke o ne wo k de elopmen and esilience, and o a weigh ed ne wo k, he deg ee o node can be de ined as ollows: 𝑖 𝑘 𝑖 = ∑ 𝑗 ∈ 𝑁 𝑤 𝑖𝑗 # ( 5 ) 2.3.2 Clus e ing coe icien Clus e ing coe icien is a measu e o unc ional seg ega ion which is a e lec ion o he local o ganiza ion o a ne wo k by depic ing he endency o a node o ming local iangles (Rubino and Spo ns 2010), and i s de ini ion o a weigh ed ne wo k o node is desc ibed as ollows: 𝑖 𝐶 𝑖 = 2 𝑡 𝑖 𝑘 𝑖 ( 𝑘 𝑖 ― 1 ) , # ( 6 ) whe e is he geome ic mean o iangles a ound . The clus e ing coe icien 𝑡 𝑖 = 1 2 ∑ 𝑗,ℎ ∈ 𝑁 ( 𝑤 𝑖𝑗 𝑤 𝑖ℎ 𝑤 𝑗ℎ ) 1 3 𝑖 o he whole ne wo k is de ined as he mean o clus e ing coe icien o all nodes, 𝐶 = 1 𝑁 ∑ 𝑖 ∈ 𝑁 𝐶 𝑖 . # ( 7 ) 2.3.3 Cha ac e is ic pa h leng h Cha ac e is ic pa h leng h is he a e age o sho es pa h leng h be ween all pai s o nodes and is commonly used o measu e unc ional in eg a ion. Cha ac e is ic pa h leng h is a e lec ion o he e iciency o a ne wo k (Bullmo e and Spo ns 2009). The de ini ion is desc ibed as ollows: 𝐿 = 1 𝑁 ∑ 𝑖 ∈ 𝑁 ∑ 𝑗 ∈ 𝑁, 𝑗 ≠ 𝑖 𝑑 𝑖𝑗 𝑁 ― 1 , # ( 8 ) whe e is he sho es pa h leng h be ween node and node , and is he sho es 𝑑 𝑖𝑗 = ∑ 𝑎 𝑢𝑣 ∈ 𝑔 𝑤 𝑖⟷𝑗 1 𝑤 𝑢𝑣 𝑖 𝑗 𝑔 𝑤 𝑖⟷𝑗 weigh ed pa h be ween and . 𝑖 𝑗 2.4 S a is ical analysis To de e mine in which equency band a signi ican di e ence exis s be ween he CON g oup and MDD g oup, Ne wo k Based S a is ic Toolbox was applied in his s udy (Zalesky e al. 2010). The NBS me hod can con ol he amily-wise e o when mul iple uni a ia e es ing is pe o med a each connec ion o a ne wo k. NBS me hod is used o iden i y signi ican b ain ne wo k subs uc u es o med by some sup a h eshold links bu no o iden i y indi idual links as being signi ican . The h eshold is used on he es s a is ic compu ed o each pai wise connec ion, and di e en h esholds can cons uc di e en le el o spa se g aphs. A e a e aging he adjacency ma ices ac oss ime windows o each subjec , s a is ical analysis was pe o med be ween he CON g oup and MDD g oup o each equency band. A signi icance le el o co ec ed and a nonpa ame ic pe mu a ion es o 5000 𝑃 < 0.05 pe mu a ions we e used in his s udy. T- es was selec ed o he s a is ical es , and di e en es s a is ic h esholds ( -s a is ic) we e es ed o iden i y he mos signi ican b ain ne wo k subs uc u es. 2.5 Classi ica ion Conside ing he limi a ions o using sliding windows wi hou o e lapping, which will lead o he p oblem ha FC opology may no been well desc ibed wi hin one ixed ime window (Liuzzi e al. 2019), we a e aged e e y six ime windows ( he connec i i y ma ices wi hin one minu e) o gene a e one classi ica ion sample o highligh he main connec i i y pa e ns du ing music pe cep ion. To imp o e classi ica ion pe o mances, we cons uc ed spa se ne wo ks based on he no ion o connec ed g aphs o emo e edundan in o ma ion, which can ensu e ha e e y node has a connec ion o ano he node o a spa se ne wo k. The de ailed me hod o h eshold selec ion can be ound in e e ence (A ay and Biyikoǧlu 2005). In his s udy, we used he adjacency ma ices ob ained by PLI o pe o m classi ica ion, and we compa ed he classi ica ion pe o mance using o iginal ne wo ks and spa se ne wo ks be ween del a and be a equency bands and six classi ie s, including decision ee (DT), Gaussian mix u e model (GMM), k-nea es neighbo (KNN), naï e Bayes (NB), andom o es (RF) and suppo ec o machine (SVM). We un olded he adjacency ma ix o a ec o as one sample. Because o he symme y p ope y o he adjacency ma ix, we can ob ain a iables o each sample. 𝑁 ( 𝑁 ― 1)/2 = 60(60 ― 1)/2 = 1770 The e o e, we can ge 152 samples o he CON g oup and 160 samples o he MDD g oup. To a oid o e i ing, p incipal componen analysis (PCA) was applied o dimension educ ion be o e classi ica ion. To assess he pe o mance o classi ica ion, we calcula ed some s a is ical e alua ion measu emen s including accu acy, sensi i i y, and speci ici y (Yan e al. 2019), which can be calcula ed by: accu acy = 𝑇𝑃 + 𝑇𝑁 𝑇𝑃 + 𝐹𝑃 + 𝑇𝑁 + 𝐹𝑁 , # ( 9 ) sensi i i y = 𝑇𝑃 𝑇𝑃 + 𝐹𝑁 , # ( 10 ) speci ici y = 𝑇𝑁 𝑇𝑁 + 𝐹𝑃 , # ( 11 ) whe e TP, TN, FP and FN ep esen ue posi i e, ue nega i e, alse posi i e and alse nega i e, espec i ely. To ob ain a eliable classi ica ion esul , we shu led he da a o de , used 10- old c oss alida ion, and an 10 imes o each classi ie . Then, we a e aged he classi ica ion esul s o calcula e he inal pe o mance o each classi ie and each equency band. 3. Resul s 3.1 Phase synch oniza ion A e s a is ical analysis by NBS o each equency band, a signi ican di e ence only exis ed in wo equency bands: del a and be a bands (del a: , he a: , alpha: , be a: 𝑃 = 0.0450 𝑃 = 0. 2386 𝑃 = 0. 3447 , gamma: ). The adjacency ma ices o hese wo equency bands o he CON 𝑃 = 0.03 44 𝑃 = 0 .0649 g oup and MDD g oup a e shown in Figu e 1. Fo he del a equency band, he connec i i y s eng h inc eased in he MDD g oup (del a: , ) compa ed o he CON g oup (del a: mean = 0.0867 SD = 0.0197 , ;). Howe e , o he be a equency band, he connec i i y s eng h o mean = 0.0853 SD = 0.0178 he MDD g oup ( , ) dec eased compa ed wi h ha o he CON g oup mean = 0.0408 SD = 0.0133 ( , ). F om Figu e 1, we can see ha sho -dis ance synch oniza ion was mean = 0.0485 SD = 0.0143 s onge han long-dis ance synch oniza ion, and he whole b ain connec i i y was o med by many small modules. The signi ican b ain ne wo k connec ions be ween he CON g oup and he MDD g oup in del a and be a equency bands a e shown in Figu e 2. We can see ha he e we e 13 signi ican connec ions in he del a band dis ibu ed wi hin igh cen al b ain a eas and be ween igh empo al and le pa ie al b ain egions. While in he be a band, he e we e 43 signi ican connec ions cha ac e ized mos ly by long-dis ance edges, which we e dis ibu ed mos ly wi hin on al b ain a eas and be ween on al and pa ie a-occipi al b ain a eas. The subs uc u es we e conside ed o be impo an indica o s o he Mum az W, Xia L, Ali SSA, Yasin MAM, Hussain M, Malik AS. Elec oencephalog am (EEG)-based compu e - aided echnique o diagnose majo dep essi e diso de (MDD). Biomed Signal P ocess Con ol. 2017;31:108–115. Nugen AC, Fa me C, E ans JW, Snide SL, Bane jee D, Za a e CA. Mul imodal imaging e eals a complex pa e n o dys unc ion in co icolimbic pa hways in majo dep essi e diso de . Hum B ain Mapp. 2019;40:3940–3950. O’Neill GC, Tewa ie P, Vidau e D, Liuzzi L, Wool ich MW, B ookes MJ. Dynamics o la ge-scale elec ophysiological ne wo ks: A echnical e iew. Neu oimage. 2018;180:559–576. Ohnishi T. Func ional Ana omy o Musical Pe cep ion in Musicians. Ce eb Co ex. 2001;11:754–760. Olb ich S, T änkne A, Chi ka T, Hege l U, Schönknech P. Func ional connec i i y in majo dep ession: Inc eased phase synch oniza ion be ween on al co ical EEG-sou ce es ima es. Psychia y Res - Neu oimaging. 2014;222:91–99. Pal a S, Pal a JM. Disco e ing oscilla o y in e ac ion ne wo ks wi h M/EEG: Challenges and b eak h oughs. T ends Cogn. Sci. 2012;16:219–230. Pa k H-J, F is on K. S uc u al and unc ional b ain ne wo ks: om connec ions o cogni ion. Science. 2013;342:1238411. Punkanen M, Ee ola T, E kkilä J. Biased emo ional ecogni ion in dep ession: Pe cep ion o emo ions in music by dep essed pa ien s. J A ec Diso d. 2011;130:118–126. Rami ez R, Palencia-Le le M, Gi aldo S, Vam akousis Z. Musical neu o eedback o ea ing dep ession in elde ly people. F on Neu osci. 2015;9:354. Ren Y, Cong F, Ris aniemi T, Wang Y, Li X, Zhang R. T ansien seizu e onse ne wo k o localiza ion o epilep ogenic zone: e ec i e connec i i y and g aph heo y-based analyses o ECoG da a in empo al lobe epilepsy. J Neu ol. 2019;266:844–859. Rubino M, Spo ns O. Complex ne wo k measu es o b ain connec i i y: Uses and in e p e a ions. Neu oimage. 2010;52:1059–1069. Ruiz-Gómez SJ, Ho ne o R, Poza J, Ma u ana-Candelas A, Pin o N, Gómez C. Compu a ional modeling o he e ec s o EEG olume conduc ion on unc ional connec i i y me ics. Applica ion o Alzheime ’s disease con inuum. J Neu al Eng. 2019;16: 066019. Ruiz MH, Koelsch S, Bha acha ya J. Dec ease in ea ly igh alpha band phase synch oniza ion and la e gamma band oscilla ions in p ocessing syn ax in music. Hum B ain Mapp. 2009;30:1207–1225. Sakai K, Yamada K. Machine lea ning s udies on majo b ain diseases: 5-yea ends o 2014–2018. Jpn J Radiol. 2019;37:34–72. Scho elen JM, G oss J. Sou ce connec i i y analysis wi h MEG and EEG. Hum B ain Mapp. 2009;30:1857–1865. Singh MK, Kesle SR, Hadi Hosseini SM, Kelley RG, Ama ya D, Hamil on JP, e al. Anomalous g ay ma e s uc u al ne wo ks in majo dep essi e diso de . Biol Psychia y. 2013;74:777–785. Spo ns O. G aph heo y me hods: applica ions in b ain ne wo ks. Dialogues Clin Neu osci. 2018;20:111–121. S am CJ, Nol e G, Da e sho e A. Phase lag index: Assessmen o unc ional connec i i y om mul i channel EEG and MEG wi h diminished bias om common sou ces. Hum B ain Mapp. 2007;28:1178–1193. Toi iainen P, Allu i V, B a ico E, Wallen in M, Vuus P. Cap u ing he musical b ain wi h Lasso: Dynamic decoding o musical ea u es om MRI da a. Neu oimage. 2014;88:170–180. Uy denhoe P, Po elange P, Jacquy J, Cha les G, Linkowski P, Mendlewicz J. Regional ce eb al blood low and la e alized hemisphe ic dys unc ion in dep ession. B J Psychia y. 1983;143:128–132. Van Den B oek SP, Reinde s F, Donde winkel M, Pe e s MJ. Volume conduc ion e ec s in EEG and MEG. Elec oencephalog Clin Neu ophysiol. 1998;106:522–534. Vee IM, Beckmann C, Van Tol M-J, Fe a ini L, Milles J, Vel man D, e al. Whole b ain es ing-s a e analysis e eals dec eased unc ional connec i i y in majo dep ession. F on Sys Neu osci. 2010;4:41. Vinck M, Oos en eld R, Van Winge den M, Ba aglia F, Penna z CMA. An imp o ed index o phase- synch oniza ion o elec ophysiological da a in he p esence o olume-conduc ion, noise and sample-size bias. Neu oimage. 2011;55:1548–1565. Wang X, Liu W, Toi iainen P, Ris aniemi T, Cong F. G oup analysis o ongoing EEG da a based on as double- coupled nonnega i e enso decomposi ion. J Neu osci Me hods. 2020;330: 108502. Wang X, Ren Y, Zhang W. Dep ession Diso de Classi ica ion o MRI Da a Using Spa se Low-Rank Func ional B ain Ne wo k and G aph-Based Fea u es. Compu Ma h Me hods Med. 2017;2017:1–11. Wa anabe T, Yagishi a S, Kikyo H. Memo y o music: Roles o igh hippocampus and le in e io on al gy us. Neu oimage. 2008;39:483–491. Wheele RE, Da idson RJ, Toma ken AJ. F on al b ain asymme y and emo ional eac i i y: A biological subs a e o a ec i e s yle. Psychophysiology. 1993;30:82–89. Whi on AE, Deccy S, I onside ML, Kuma P, Bel ze M, Pizzagalli DA. Elec oencephalog aphy Sou ce Func ional Connec i i y Re eals Abno mal High-F equency Communica ion Among La ge-Scale Func ional Ne wo ks in Dep ession. Biol Psychia y Cogn Neu osci Neu oimaging. 2018;3:50–58. Womelsdo T, Scho elen JM, Oos en eld R, Singe W, Desimone R, Engel AK, e al. Modula ion o neu onal in e ac ions h ough neu onal synch oniza ion. Science. 2007;316:1609–1612. Wu D, Yuan Y, Bai F, You J, Li L, Zhang Z. Abno mal unc ional connec i i y o he de aul mode ne wo k in emi ed la e-onse dep ession. J A ec Diso d. 2013;147:277–287. Wu J, Zhang J, Liu C, Liu D, Ding X, Zhou C. G aph heo e ical analysis o EEG unc ional connec i i y du ing music pe cep ion. B ain Res. 2012;1483:71–81. Xia MR, Si TM, He Y. Imaging connec omics in dep ession. CNS Neu osci The . 2018;24:991. Yan R, Zhang C, Sp uy K, Wei L, Wang Z, Tian L, e al. Mul i-modali y o polysomnog aphy signals’ usion o au oma ic sleep sco ing. Biomed Signal P ocess Con ol. 2019;49:14–23. Ye M, Yang T, Qing P, Lei X, Qiu J, Liu G. Changes o unc ional b ain ne wo ks in majo dep essi e diso de : A g aph heo e ical analysis o es ing-s a e MRI. PLoS One. 2015;10:e0133775. Zalesky A, Fo ni o A, Bullmo e ET. Ne wo k-based s a is ic: Iden i ying di e ences in b ain ne wo ks. Neu oimage. 2010;53:1197–1207. Zhu X, Wang X, Xiao J, Liao J, Zhong M, Wang W, e al. E idence o a dissocia ion pa e n in es ing-s a e de aul mode ne wo k connec i i y in i s -episode, ea men -nai e majo dep ession pa ien s. Biol Psychia y. 2012;71:611–617. Figu e legends Figu e 1. A e aged adjacency ma ices o he CON g oup and MDD g oup ac oss ime windows o del a and be a equency bands. Each adjacency ma ix is o med by a ma ix wi h ze o alues 60 × 60 in he diagonal. CON, con ol; MDD, majo dep ession diso de . Figu e 2. The signi ican b ain ne wo k connec ions in del a and be a equency bands o he CON g oup and MDD g oup. The esul s we e conduc ed by he NBS me hod using 5000 pe mu a ions, co ec ed alue o , and maximum componen h eshold o he del a band and 𝑝 𝑝 < 0.05 𝑡 > 3.1 o he be a band. The e a e 13 signi ican connec ions in he del a band and 43 signi ican 𝑡 > 2.3 connec ions in he be a band. CON, con ol; MDD, majo dep ession diso de ; NBS, ne wo k based s a is ic. Figu e 3. The deg ee o each node in del a and be a equency bands o he CON g oup and MDD g oup. CON, con ol; MDD, majo dep ession diso de . Figu e 4. Boxplo o clus e ing coe icien and cha ac e is ic pa h leng h o he CON g oup and MDD g oup in del a and be a equency bands. The uppe and lowe black lines ep esen he maximum alue and he minimum alue, espec i ely, and he ed c oss indica es ou lie s. The bo om and op edges o he blue box indica e he 25 h and 75 h pe cen iles, and he ed line and hombus in he box indica e he median alue and he mean alue, espec i ely. CON, con ol; MDD, majo dep ession diso de . Table 1. Means and s anda d de ia ions o age, gende , educa ion and clinical measu es o he CON g oup and MDD g oup. CON g oup MDD g oup Analysis Mean SD(Range) Mean SD(Range) - alue 𝑝 Age 38.4 11.8(24-65) 42.9 11.0(23-58) >0.05 Educa ion 13.6 3.8(6-20) 12.8 3.4(6-16) >0.05 HRSD 2.4 1.3(0-4) 23.3 3.6(16-28) <0.01 HAMA 2.4 1.3(0-5) 19.2 3.0(15-25) <0.01 MMSE 28.2 0.9(27-30) 28.1 1.1(26-30) >0.05 Du a ion 0 0 12.8 8.5(2-36) - Gende 14 emales, 5 males 14 emales, 6 males - Abb e ia ions: CON, con ol; MDD, majo dep ession diso de ; SD, s anda d de ia ions; HRSD, Hamil on Ra ing Scale o Dep ession; HAMA, Hamil on Anxie y Ra ing Scale; MMSE, Mini-Men al S a e Examina ion. Table 2. The classi ica ion accu acy o six classi ie s in del a and be a equency bands. Ne wo k DT GMM KNN NB RF SVM O iginal 54.7% 49.7% 65.7% 61.2% 62.0% 66.9% Del a Spa se 60.9% 53.2% 68.7% 62.5% 69.7% 72.9% O iginal 61.4% 49.5% 77.4% 55.2% 70.9% 78.2% Be a Spa se 68.5% 54.8% 85.6% 56.7% 82.3% 89.7% Abb e ia ions: DT, decision ee; GMM, Gaussian mix u e model; KNN, k-nea es neighbo ; NB, naï e Bayes; RF, andom o es ; SVM, suppo ec o machine.