Altered cerebral activities and functional connectivity in depression: a systematic review of fMRI studies

Abstract

Background: As one of the leading causes of global disability, major depressive disorder (MDD) places a noticeable burden on individuals and society. Despite the great expectation on finding accurate biomarkers and effective treatment targets of MDD, studies in applying functional magnetic resonance imaging (fMRI) are still faced with challenges, including the representational ambiguity, small sample size, low statistical power, relatively high false positive rates, etc. Thus, reviewing studies with solid methodology may help achieve a consensus on the pathology of MDD.

Methods: In this systematic review, we screened fMRI studies on MDD through strict criteria to focus on reliable studies with sufficient sample size, adequate control of head motion, and a proper multiple comparison control strategy.

Results: We found consistent evidence regarding the dysfunction within and among the default mode network (DMN), the frontoparietal network (FPN), and other brain regions. However, controversy remains, probably due to the heterogeneity of participants and data processing strategies.

Conclusion: Future studies are recommended to apply a comprehensive set of neuro-behavioral measurements, consider the heterogeneity of MDD patients and other potentially confounding factors, apply surface-based neuroscientific network fMRI approaches, and advance research transparency and open science by applying state-of-the-art pipelines along with open data sharing.

Author summary

Major depressive disorder (MDD) is a prevalent disorder and places noticeable societal burdens, however, findings of objective biomarkers in previous researches were inconsistent. In this systematic review, to detect more reproducible MDD-specific brain circuits, fMRI studies on MDD are screened by strict criteria that carefully minimize the effect of fMRI methodology issues. Though dysfunction in the default mode network (DMN) and the frontoparietal network (FPN) was repeatedly reported, heterogeneity remains in the included studies. Based on these findings, we highlight the necessity of considering some specific potentially confounding factors in future fMRI studies on MDD.

Graphical abstract

Keywords

depression / resting-state fMRI / task-based fMRI / default mode network / frontoparietal network

References

[1] Friedrich,M. (2017). Depression is the leading cause of disability around the world. JAMA, 317: 1517

[2] Vos,T., Lim,S. S., Abbafati,C., Abbas,K. M., Abbasi,M., Abbasifard,M., Abbasi-Kangevari,M., Abbastabar,H., Abd-Allah,F., Abdelalim,A. . (2020). Global burden of 369 diseases and injuries in 204 countries and territories, 1990‒2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet, 396: 1204–1222

[3] rez-Stable,E. J., Miranda,J., oz,R. F. Ying,Y. (1990). Depression in medical outpatients. Underrecognition and misdiagnosis. Arch. Intern. Med., 150: 1083–1088

[4] Bandettini,P. (2012). Twenty years of functional MRI: the science and the stories. Neuroimage, 62: 575–588

[5] Bijsterbosch,J., Harrison,S. J., Jbabdi,S., Woolrich,M., Beckmann,C., Smith,S. Duff,E. (2020). Challenges and future directions for representations of functional brain organization. Nat. Neurosci., 23: 1484–1495

[6] Ioannidis,J. (2005). Why most published research findings are false. PLoS Med., 2: e124

[7] Button,K. S., Ioannidis,J. P., Mokrysz,C., Nosek,B. A., Flint,J., Robinson,E. S. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci., 14: 365–376

[8] Zuo,X. N., Xu,T. Milham,M. (2019). Harnessing reliability for neuroscience research. Nat. Hum. Behav., 3: 768–771

[9] Ciric,R., Wolf,D. H., Power,J. D., Roalf,D. R., Baum,G. L., Ruparel,K., Shinohara,R. T., Elliott,M. A., Eickhoff,S. B., Davatzikos,C. . (2017). Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage, 154: 174–187

[10] Yan,C. Cheung,B., Kelly,C., Colcombe,S., Craddock,R. C., Di Martino,A., Li,Q., Zuo,X. N., Castellanos,F. X. Milham,M. (2013). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage, 76: 183–201

[11] Ciric,R., Rosen,A. F. G., Erus,G., Cieslak,M., Adebimpe,A., Cook,P. A., Bassett,D. S., Davatzikos,C., Wolf,D. H. Satterthwaite,T. (2018). Mitigating head motion artifact in functional connectivity MRI. Nat. Protoc., 13: 2801–2826

[12] Eklund,A., Nichols,T. E. (2016). Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proc. Natl. Acad. Sci. USA, 113: 7900–7905

[13] Algermissen,J. Mehler,D. M. (2018). May the power be with you: are there highly powered studies in neuroscience, and how can we get more of them? J. Neurophysiol., 119: 2114–2117

[14] Chen,X., Lu,B. Yan,C. (2018). Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes. Hum. Brain Mapp., 39: 300–318

[15] Van Dijk,K. R., Sabuncu,M. R. Buckner,R. (2012). The influence of head motion on intrinsic functional connectivity MRI. Neuroimage, 59: 431–438

[16] Power,J. D., Barnes,K. A., Snyder,A. Z., Schlaggar,B. L. Petersen,S. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59: 2142–2154

[17] Eklund,A., Nichols,T. E. (2016). Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proc. Natl. Acad. Sci. USA, 113: 7900–7905

[18] Nichols,T. (2003). Controlling the familywise error rate in functional neuroimaging: a comparative review. Stat. Methods Med. Res., 12: 419–446

[19] Genovese,C. R., Lazar,N. A. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage, 15: 870–878

[20] Grimes,D. A. Schulz,K. (1996). Determining sample size and power in clinical trials: the forgotten essential. Semin. Reprod. Endocrinol., 14: 125–131

[21] Demenescu,L. R., Renken,R., Kortekaas,R., van Tol,M. J., Marsman,J. B., van Buchem,M. A., van der Wee,N. J., Veltman,D. J., den Boer,J. A. (2011). Neural correlates of perception of emotional facial expressions in out-patients with mild-to-moderate depression and anxiety. A multicenter fMRI study. Psychol. Med., 41: 2253–2264

[22] van Tol,M. J., van der Wee,N. J., Demenescu,L. R., Nielen,M. M., Aleman,A., Renken,R., van Buchem,M. A., Zitman,F. G. Veltman,D. (2011). Functional MRI correlates of visuospatial planning in out-patient depression and anxiety. Acta Psychiatr. Scand., 124: 273–284

[23] Bermingham,R., Carballedo,A., Lisiecka,D., Fagan,A., Morris,D., Fahey,C., Meaney,J., Gill,M. (2012). Effect of genetic variant in BICC1 on functional and structural brain changes in depression. Neuropsychopharmacology, 37: 285–2862

[24] Yang,X., Ma,X., Li,M., Liu,Y., Zhang,J., Huang,B., Zhao,L., Deng,W., Li,T. (2015). Anatomical and functional brain abnormalities in unmedicated major depressive disorder. Neuropsychiatr. Dis. Treat., 11: 2415–2423

[25] Gollier-Briant,F., re-Martinot,M. L., Lemaitre,H., Miranda,R., Vulser,H., Goodman,R., Struve,M., Fadai,T., Kappel,V. . (2016). Neural correlates of three types of negative life events during angry face processing in adolescents. Soc. Cogn. Affect. Neurosci., 11: 1961–1969

[26] Posner,J., Cha,J., Wang,Z., Talati,A., Warner,V., Gerber,A., Peterson,B. (2016). Increased default mode network connectivity in individuals at high familial risk for depression. Neuropsychopharmacology, 41: 1759–1767

[27] Casement,M. D., Keenan,K. E., Hipwell,A. E., Guyer,A. E. Forbes,E. (2016). Neural reward processing mediates the relationship between insomnia symptoms and depression in adolescence. Sleep, 39: 439–447

[28] Hermesdorf,M., Sundermann,B., Feder,S., Schwindt,W., Minnerup,J., Arolt,V., Berger,K., Pfleiderer,B. (2016). Major depressive disorder: Findings of reduced homotopic connectivity and investigation of underlying structural mechanisms. Hum. Brain Mapp., 37: 1209–1217

[29] Davey,C. G., Breakspear,M., Pujol,J. Harrison,B. (2017). A brain model of disturbed self-appraisal in depression. Am. J. Psychiatry, 174: 895–903

[30] ksel,D., Dietsche,B., Forstner,A. J., Witt,S. H., Maier,R., Rietschel,M., Konrad,C., then,M. M., Dannlowski,U., Baune,B. T. . (2017). Polygenic risk for depression and the neural correlates of working memory in healthy subjects. Prog. Neuropsychopharmacol. Biol. Psychiatry, 79: 67–76

[31] Ye,J., Shen,Z., Xu,X., Yang,S., Chen,W., Liu,X., Lu,Y., Liu,F., Lu,J., Li,N. . (2017). Abnormal functional connectivity of the amygdala in first-episode and untreated adult major depressive disorder patients with different ages of onset. Neuroreport, 28: 214–221

[32] Pan,P. M., Sato,J. R., Salum,G. A., Rohde,L. A., Gadelha,A., Zugman,A., Mari,J., Jackowski,A., Picon,F., Miguel,E. C. . (2017). Ventral striatum functional connectivity as a predictor of adolescent depressive disorder in a longitudinal community-based sample. Am. J. Psychiatry, 174: 1112–1119

[33] Admon,R., Kaiser,R. H., Dillon,D. G., Beltzer,M., Goer,F., Olson,D. P., Vitaliano,G. Pizzagalli,D. (2017). Dopaminergic enhancement of striatal response to reward in major depression. Am. J. Psychiatry, 174: 378–386

[34] Lopez,K. C., Luby,J. L., Belden,A. C. Barch,D. (2018). Emotion dysregulation and functional connectivity in children with and without a history of major depressive disorder. Cogn. Affect. Behav. Neurosci., 18: 232–248

[35] Mehta,N. D., Haroon,E., Xu,X., Woolwine,B. J., Li,Z. Felger,J. (2018). Inflammation negatively correlates with amygdala-ventromedial prefrontal functional connectivity in association with anxiety in patients with depression: Preliminary results. Brain Behav. Immun., 73: 725–730

[36] Qi,S., Yang,X., Zhao,L., Calhoun,V. D., Perrone-Bizzozero,N., Liu,S., Jiang,R., Jiang,T., Sui,J. (2018). MicroRNA132 associated multimodal neuroimaging patterns in unmedicated major depressive disorder. Brain, 141: 916–926

[37] Tokuda,T., Yoshimoto,J., Shimizu,Y., Okada,G., Takamura,M., Okamoto,Y., Yamawaki,S. (2018). Identification of depression subtypes and relevant brain regions using a data-driven approach. Sci. Rep., 8: 14082

[38] Tu,Z., Jia,Y. Y., Wang,T., Qu,H., Pan,J. X., Jie,J., Xu,X. Y., Wang,H. Y. (2018). Modulatory interactions of resting-state brain functional connectivity in major depressive disorder. Neuropsychiatr. Dis. Treat., 14: 2461–2472

[39] Wang,D., Xue,S. W., Tan,Z., Wang,Y., Lian,Z. (2019). Altered hypothalamic functional connectivity patterns in major depressive disorder. Neuroreport, 30: 1115–1120

[40] Fitzgerald,J. M., Klumpp,H., Langenecker,S. Phan,K. (2019). Transdiagnostic neural correlates of volitional emotion regulation in anxiety and depression. Depress. Anxiety, 36: 453–464

[41] Xia,M., Si,T., Sun,X., Ma,Q., Liu,B., Wang,L., Meng,J., Chang,M., Huang,X., Chen,Z. . (2019). Reproducibility of functional brain alterations in major depressive disorder: Evidence from a multisite resting-state functional MRI study with 1,434 individuals. Neuroimage, 189: 700–714

[42] Yao,Z., Zou,Y., Zheng,W., Zhang,Z., Li,Y., Yu,Y., Zhang,Z., Fu,Y., Shi,J., Zhang,W. . (2019). Structural alterations of the brain preceded functional alterations in major depressive disorder patients: Evidence from multimodal connectivity. J. Affect. Disord., 253: 107–117

[43] Chin Fatt,C. R., Jha,M. K., Cooper,C. M., Fonzo,G., South,C., Grannemann,B., Carmody,T., Greer,T. L., Kurian,B., Fava,M. . (2020). Effect of intrinsic patterns of functional brain connectivity in moderating antidepressant treatment response in major depression. Am. J. Psychiatry, 177: 143–154

[44] Zhu,D. M., Zhang,C., Yang,Y., Zhang,Y., Zhao,W., Zhang,B., Zhu,J. (2020). The relationship between sleep efficiency and clinical symptoms is mediated by brain function in major depressive disorder. J. Affect. Disord., 266: 327–337

[45] Hilland,E., Harmer,C. J., Browning,M., Maglanoc,L. A. (2020). Attentional bias modification is associated with fMRI response toward negative stimuli in individuals with residual depression: a randomized controlled trial. J. Psychiatry Neurosci., 45: 23–33

[46] Korgaonkar,M. S., Goldstein-Piekarski,A. N., Fornito,A. Williams,L. (2020). Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder. Mol. Psychiatry, 25: 1537–1549

[47] Rupprechter,S., Romaniuk,L., Series,P., Hirose,Y., Hawkins,E., Sandu,A. L., Waiter,G. D., McNeil,C. J., Shen,X., Harris,M. A. . (2020). Blunted medial prefrontal cortico-limbic reward-related effective connectivity and depression. Brain, 143: 1946–1956

[48] Yang,Y., Zhu,D. M., Zhang,C., Zhang,Y., Wang,C., Zhang,B., Zhao,W., Zhu,J. (2020). Brain structural and functional alterations specific to low sleep efficiency in major depressive disorder. Front. Neurosci., 14: 50

[49] GrahamJ.,Salimi-Khorshidi G.,HaganC.,WalshN.,GoodyerI., LennoxB.. (2013) Meta-analytic evidence for neuroimaging models of depression: state or trait? J. Affect. Disord., 151, 423–431

[50] Groenewold,N. A., Opmeer,E. M., de Jonge,P., Aleman,A. Costafreda,S. (2013). Emotional valence modulates brain functional abnormalities in depression: evidence from a meta-analysis of fMRI studies. Neurosci. Biobehav. Rev., 37: 152–163

[51] Zhang,W. N., Chang,S. H., Guo,L. Y., Zhang,K. L. (2013). The neural correlates of reward-related processing in major depressive disorder: a meta-analysis of functional magnetic resonance imaging studies. J. Affect. Disord., 151: 531–539

[52] Iwabuchi,S. J., Krishnadas,R., Li,C., Auer,D. P., Radua,J. (2015). Localized connectivity in depression: a meta-analysis of resting state functional imaging studies. Neurosci. Biobehav. Rev., 51: 77–86

[53] Wang,X. L., Du,M. Y., Chen,T. L., Chen,Z. Q., Huang,X. Q., Luo,Y., Zhao,Y. J., Kumar,P. Gong,Q. (2015). Neural correlates during working memory processing in major depressive disorder. Prog. Neuropsychopharmacol. Biol. Psychiatry, 56: 101–108

[54] Zhong,X., Pu,W. (2016). Functional alterations of fronto-limbic circuit and default mode network systems in first-episode, drug-naïve patients with major depressive disorder: A meta-analysis of resting-state fMRI data. J. Affect. Disord., 206: 280–286

[55] Wang,W., Zhao,Y., Hu,X., Huang,X., Kuang,W., Lui,S., Kemp,G. J. (2017). Conjoint and dissociated structural and functional abnormalities in first-episode drug-naive patients with major depressive disorder: a multimodal meta-analysis. Sci. Rep., 7: 10401

[56] Kambeitz,J., Cabral,C., Sacchet,M. D., Gotlib,I. H., Zahn,R., Serpa,M. H., Walter,M., Falkai,P. (2017). Detecting neuroimaging biomarkers for depression: A meta-analysis of multivariate pattern recognition studies. Biol. Psychiatry, 82: 330–338

[57] Zhou,M., Hu,X., Lu,L., Zhang,L., Chen,L., Gong,Q. (2017). Intrinsic cerebral activity at resting state in adults with major depressive disorder: A meta-analysis. Prog. Neuropsychopharmacol. Biol. Psychiatry, 75: 157–164

[58] Keren,H., Callaghan,G., Vidal-Ribas,P., Buzzell,G. A., Brotman,M. A., Leibenluft,E., Pan,P. M., Meffert,L., Kaiser,A., Wolke,S. . (2018). Reward processing in depression: A conceptual and meta-analytic review across fMRI and EEG Studies. Am. J. Psychiatry, 175: 1111–1120

[59] Sha,Z., Xia,M., Lin,Q., Cao,M., Tang,Y., Xu,K., Song,H., Wang,Z., Wang,F., Fox,P. . (2018). Meta-connectomic analysis reveals commonly disrupted functional architectures in network modules and connectors across brain disorders. Cereb. Cortex, 28: 4179–4194

[60] Wu,J. C., Buchsbaum,M. S., Johnson,J. C., Hershey,T. G., Wagner,E. A., Tung,C. (1993). Magnetic resonance and positron emission tomography imaging of the corpus callosum: size, shape and metabolic rate in unipolar depression. J. Affect. Disord., 28: 15–25

[61] Biswal,B., Yetkin,F. Z., Haughton,V. M. Hyde,J. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med., 34: 537–541

[62] Friston,K. J., Ashburner,J., Frith,C. D., Poline,J. Heather,J. D. Frackowiak,R. S. (1995). Spatial registration and normalization of images. Hum. Brain Mapp., 3: 165–189

[63] Yan,C. G., Wang,X. D., Zuo,X. N. Zang,Y. (2016). DPABI: data processing & analysis for (resting-state) brain imaging. Neuroinformatics, 14: 339–351

[64] Jenkinson,M., Bannister,P., Brady,M. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17: 825–841

[65] Cox,R. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res., 29: 162–173

[66] Yan,C. G., Chen,X., Li,L., Castellanos,F. X., Bai,T. J., Bo,Q. J., Cao,J., Chen,G. M., Chen,N. X., Chen,W. . (2019). Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc. Natl. Acad. Sci. USA, 116: 9078–9083

[67] Castellanos,F. X., Margulies,D. S., Kelly,C., Uddin,L. Q., Ghaffari,M., Kirsch,A., Shaw,D., Shehzad,Z., Di Martino,A., Biswal,B. . (2008). Cingulate-precuneus interactions: a new locus of dysfunction in adult attention-deficit/hyperactivity disorder. Biol. Psychiatry, 63: 332–337

[68] Burstein,R., Noseda,R. (2015). Migraine: multiple processes, complex pathophysiology. J. Neurosci., 35: 6619–6629

[69] Zhou,H. X., Chen,X., Shen,Y. Q., Li,L., Chen,N. X., Zhu,Z. C., Castellanos,F. X. Yan,C. (2020). Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depression. Neuroimage, 206: 116287

[70] Chen,X., Chen,N. Shen,Y. Li,H. Li,L., Lu,B., Zhu,Z. C., Fan,Z. Yan,C. (2020). The subsystem mechanism of default mode network underlying rumination: A reproducible neuroimaging study. Neuroimage, 221: 117185

[71] Greicius,M. D., Flores,B. H., Menon,V., Glover,G. H., Solvason,H. B., Kenna,H., Reiss,A. L. Schatzberg,A. (2007). Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol. Psychiatry, 62: 429–437

[72] Hamilton,J. P., Farmer,M., Fogelman,P. Gotlib,I. (2015). Depressive rumination, the default-mode network, and the dark matter of clinical neuroscience. Biol. Psychiatry, 78: 224–230

[73] Silvers,J. A., Weber,J., Wager,T. D. Ochsner,K. (2015). Bad and worse: neural systems underlying reappraisal of high- and low-intensity negative emotions. Soc. Cogn. Affect. Neurosci., 10: 172–179

[74] George,M. S., Taylor,J. J. Short,E. (2013). The expanding evidence base for rTMS treatment of depression. Curr. Opin. Psychiatry, 26: 13–18

[75] Ochsner,K. N., Silvers,J. A. Buhle,J. (2012). Functional imaging studies of emotion regulation: a synthetic review and evolving model of the cognitive control of emotion. Ann. N. Y. Acad. Sci., 1251: E1–E24

[76] EysenckM.. (2020) Cognitive Psychology: A Student’s Handbook. London: Taylor & Francis Group

[77] GrossJ. J.. (2007) Emotion regulation: conceptual foundations. In: Handbook of Emotion Regulation, Gross, J. J. (Ed.), pp. 3–24. New York: The Guilford Press

[78] Wang,L., Zhao,Y., Edmiston,E. K., Womer,F. Y., Zhang,R., Zhao,P., Jiang,X., Wu,F., Kong,L., Zhou,Y. . (2020). Structural and functional abnormities of amygdala and prefrontal cortex in major depressive disorder with suicide attempts. Front. Psychiatry, 10: 923

[79] Peterson,B. (2015). Editorial: Research Domain Criteria (RDoC): a new psychiatric nosology whose time has not yet come. J. Child Psychol. Psychiatry, 56: 719–722

[80] Sanislow,C. A., Ferrante,M., Pacheco,J., Rudorfer,M. V. Morris,S. (2019). Advancing translational research using NIMH research domain criteria and computational methods. Neuron, 101: 779–782

[81] Tozzi,L., Staveland,B., Holt-Gosselin,B., Chesnut,M., Chang,S. E., Choi,D., Shiner,M., Wu,H., Lerma-Usabiaga,G., Sporns,O. . (2020). The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression. Neuroimage, 214: 116715

[82] Association,A. (2013). Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). American Psychiatric Publishing, Inc

[83] Gotlib,I. H. (2010). Cognition and depression: current status and future directions. Annu. Rev. Clin. Psychol., 6: 285–312

[84] ller,V. I., Cieslik,E. C., Serbanescu,I., Laird,A. R., Fox,P. T. Eickhoff,S. (2017). Altered brain activity in unipolar depression revisited: Meta-analyses of neuroimaging studies. JAMA Psychiatry, 74: 47–55

[85] Cesario,J. (2014). Priming, replication, and the hardest science. Perspect. Psychol. Sci., 9: 40–48

[86] Maxwell,S. E., Lau,M. Y. Howard,G. (2015). Is psychology suffering from a replication crisis? What does “failure to replicate” really mean? Am. Psychol., 70: 487–498

[87] Tackett,J. L., Lilienfeld,S. O., Patrick,C. J., Johnson,S. L., Krueger,R. F., Miller,J. D., Oltmanns,T. F. Shrout,P. (2017). It’s time to broaden the replicability conversation: Thoughts for and from clinical psychological Science. Perspect. Psychol. Sci., 12: 742–756

[88] Tackett,J. L., Brandes,C. M., King,K. M. Markon,K. (2019). Psychology’s Replication Crisis and Clinical Psychological Science. Annu. Rev. Clin. Psychol., 15: 579–604

[89] Chao-Gan,Y. (2010). DPARSF: A MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front. Syst. Neurosci., 4: 13

[90] Ahmed,A. T., Frye,M. A., Rush,A. J., Biernacka,J. M., Craighead,W. E., McDonald,W. M., Bobo,W. V., Riva-Posse,P., Tye,S. J., Mayberg,H. S. . (2018). Mapping depression rating scale phenotypes onto research domain criteria (RDoC) to inform biological research in mood disorders. J. Affect. Disord., 238: 1–7

[91] Bullmore,E. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci., 10: 186–198

[92] Dale,A. M., Fischl,B. Sereno,M. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage, 9: 179–194

[93] Bassett,D. S. (2017). Network neuroscience. Nat. Neurosci., 20: 353–364

[94] Long,Y., Cao,H., Yan,C., Chen,X., Li,L., Castellanos,F. X., Bai,T., Bo,Q., Chen,G., Chen,N. . (2020). Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium. Neuroimage Clin., 26: 102163

[95] Ye,M., Yang,T., Qing,P., Lei,X., Qiu,J. (2015). Changes of functional brain networks in major depressive disorder: A graph theoretical analysis of resting-state fMRI. PLoS One, 10: e0133775

[96] Coalson,T. S., Van Essen,D. C. Glasser,M. (2018). The impact of traditional neuroimaging methods on the spatial localization of cortical areas. Proc. Natl. Acad. Sci. USA, 115: E6356–E6365

ACKNOWLEDGMENTS

This work was supported by the National Key R&D Program of China (2017YFC1309902 to CY), the National Natural Science Foundation of China (81671774, 81630031 to CY), the 13th Five-year Informatization Plan of Chinese Academy of Sciences (XXH13505 to CY), the Key Research Program of the Chinese Academy of Sciences (ZDBS-SSW-JSC006 to CY), Beijing Nova Program of Science and Technology (Z191100001119104 to CY), Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (Y9CX422005 to XC), China Postdoctoral Science Foundation (2019M660847 to XC), China National Postdoctoral Program for Innovative Talents (BX20200360 to XC).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Xue-Ying Li, Xiao Chen and Chao-Gan Yan declare that they have no conflict of interest.

This article does not contain any studies with human or animal materials performed by any of the authors.

OPEN ACCESS

This article is licensed by the CC By under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

RIGHTS & PERMISSIONS

2022 The Authors (2022). Published by Higher Education Press.