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More than just statics: temporal dynamics of intrinsic brain activity predicts the suicidal ideation in depressed patients

Published online by Cambridge University Press:  18 June 2018

Jiao Li ,
Xujun Duan ,
Qian Cui ,
Huafu Chen  and
Wei Liao Open the ORCID record for Wei Liao [Opens in a new window]
Jiao Li
Affiliation:
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
Xujun Duan
Affiliation:
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
Qian Cui
Affiliation:
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
Huafu Chen
Affiliation:
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
Wei Liao*
Affiliation:
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
*
Author for correspondence: Wei Liao, E-mail: [email protected]
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Abstract

Background

Major depressive disorder (MDD) is associated with high risk of suicide. Conventional neuroimaging works showed abnormalities of static brain activity and connectivity in MDD with suicidal ideation (SI). However, little is known regarding alterations of brain dynamics. More broadly, it remains unclear whether temporal dynamics of the brain activity could predict the prognosis of SI.

Methods

We included MDD patients (n = 48) with and without SI and age-, gender-, and education-matched healthy controls (n = 30) who underwent resting-state functional magnetic resonance imaging. We first assessed dynamic amplitude of low-frequency fluctuation (dALFF) – a proxy for intrinsic brain activity (iBA) – using sliding-window analysis. Furthermore, the temporal variability (dynamics) of iBA was quantified as the variance of dALFF over time. In addition, the prediction of the severity of SI from temporal variability was conducted using a general linear model.

Results

Compared with MDD without SI, the SI group showed decreased brain dynamics (less temporal variability) in the dorsal anterior cingulate cortex, the left orbital frontal cortex, the left inferior temporal gyrus, and the left hippocampus. Importantly, these temporal variabilities could be used to predict the severity of SI (r = 0.43, p = 0.03), whereas static ALFF could not in the current data set.

Conclusions

These findings suggest that alterations of temporal variability in regions involved in executive and emotional processing are associated with SI in MDD patients. This novel predictive model using the dynamics of iBA could be useful in developing neuromarkers for clinical applications.

Keywords

Amplitude of low-frequency fluctuationsdynamicsintrinsic brain activitymajor depressionpredictive modelsuicidal ideation
Type
Original Articles
Information
Psychological Medicine , Volume 49 , Issue 5 , April 2019 , pp. 852 - 860
Copyright
Copyright © Cambridge University Press 2018 

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References

Allen, EA, Damaraju, E, Plis, SM, Erhardt, EB, Eichele, T and Calhoun, VD (2014) Tracking whole-brain connectivity dynamics in the resting state. Cerebral Cortex 24, 663–676.Google Scholar
Angst, F, Stassen, HH, Clayton, PJ and Angst, J (2002) Mortality of patients with mood disorders: follow-up over 34–38 years. Journal of Affective Disorders 68, 167–181.Google Scholar
Ballard, ED, Lally, N, Nugent, AC, Furey, ML, Luckenbaugh, DA and Zarate, CA Jr. (2015) Neural correlates of suicidal ideation and its reduction in depression. International Journal of Neuropsychopharmacology 18, 1–6.Google Scholar
Bassett, DS and Sporns, O (2017) Network neuroscience. Nature Neuroscience 20, 353–364.Google Scholar
Bremner, JD, Vythilingam, M, Vermetten, E, Vaccarino, V and Charney, DS (2004) Deficits in hippocampal and anterior cingulate functioning during verbal declarative memory encoding in midlife major depression. American Journal of Psychiatry 161, 637–645.Google Scholar
Bush, G, Luu, P and Posner, MI (2000) Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences 4, 215–222.Google Scholar
Carter, CS, Braver, TS, Barch, DM, Botvinick, MM, Noll, D and Cohen, JD (1998) Anterior cingulate cortex, error detection, and the online monitoring of performance. Science 280, 747–749.Google Scholar
Chase, HW, Segreti, AM, Keller, TA, Cherkassky, VL, Just, MA, Pan, LA and Brent, DA (2017) Alterations of functional connectivity and intrinsic activity within the cingulate cortex of suicidal ideators. Journal of Affective Disorders 212, 78–85.Google Scholar
Chen, X, Lu, B and Yan, CG (2018) Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes. Human Brain Mapping 39, 300–318.Google Scholar
Christoff, K, Irving, ZC, Fox, KC, Spreng, RN and Andrews-Hanna, JR (2016) Mind-wandering as spontaneous thought: a dynamic framework. Nature Reviews Neurosciences 17, 718–731.Google Scholar
Cochrane-Brink, KA, Lofchy, JS and Sakinofsky, I (2000) Clinical rating scales in suicide risk assessment. General Hospital Psychiatry 22, 445–451.Google Scholar
Damaraju, E, Allen, EA, Belger, A, Ford, JM, McEwen, S, Mathalon, DH, Mueller, BA, Pearlson, GD, Potkin, SG, Preda, A, Turner, JA, Vaidya, JG, van Erp, TG and Calhoun, VD (2014) Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. NeuroImage: Clinical 5, 298–308.Google Scholar
Davidson, RJ, Pizzagalli, D, Nitschke, JB and Putnam, K (2002) Depression: perspectives from affective neuroscience. Annual Review of Psychology 53, 545–574.Google Scholar
Diamond, A (2013) Executive functions. Annual Review of Psychology 64, 135–168.Google Scholar
Du, L, Zeng, J, Liu, H, Tang, D, Meng, H, Li, Y and Fu, Y (2017) Fronto-limbic disconnection in depressed patients with suicidal ideation: a resting-state functional connectivity study. Journal of Affective Disorders 215, 213–217.Google Scholar
Eklund, A, Nichols, TE and Knutsson, H (2016) Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences of the USA 113, 7900–7905.Google Scholar
Finn, ES, Shen, X, Scheinost, D, Rosenberg, MD, Huang, J, Chun, MM, Papademetris, X and Constable, RT (2015) Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nature Neuroscience 18, 1664–1671.Google Scholar
Fried, EI and Nesse, RM (2015) Depression sum-scores don't add up: why analyzing specific depression symptoms is essential. BMC Medicine 13, 72.Google Scholar
Friston, KJ, Williams, S, Howard, R, Frackowiak, RS and Turner, R (1996) Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine 35, 346–355.Google Scholar
Frodl, T, Bokde, AL, Scheuerecker, J, Lisiecka, D, Schoepf, V, Hampel, H, Moller, HJ, Bruckmann, H, Wiesmann, M and Meisenzahl, E (2010) Functional connectivity bias of the orbitofrontal cortex in drug-free patients with major depression. Biological Psychiatry 67, 161–167.Google Scholar
Fu, CH, Williams, SC, Brammer, MJ, Suckling, J, Kim, J, Cleare, AJ, Walsh, ND, Mitterschiffthaler, MT, Andrew, CM, Pich, EM and Bullmore, ET (2007) Neural responses to happy facial expressions in major depression following antidepressant treatment. American Journal of Psychiatry 164, 599–607.Google Scholar
Fu, Z, Tu, Y, Di, X, Du, Y, Pearlson, GD, Turner, JA, Biswal, BB, Zhang, Z and Calhoun, VD (2017) Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: an application to schizophrenia. Neuroimage. In press, doi: 10.1016/j.neuroimage.2017.09.035.Google Scholar
Hutchison, RM, Womelsdorf, T, Allen, EA, Bandettini, PA, Calhoun, VD, Corbetta, M, Della Penna, S, Duyn, JH, Glover, GH, Gonzalez-Castillo, J, Handwerker, DA, Keilholz, S, Kiviniemi, V, Leopold, DA, de Pasquale, F, Sporns, O, Walter, M and Chang, C (2013) Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360–378.Google Scholar
Jia, Z, Wang, Y, Huang, X, Kuang, W, Wu, Q, Lui, S, Sweeney, JA and Gong, Q (2014) Impaired frontothalamic circuitry in suicidal patients with depression revealed by diffusion tensor imaging at 3.0 T. Journal of Psychiatry & Neuroscience 39, 170–177.Google Scholar
Johnston, JAY, Wang, F, Liu, J, Blond, BN, Wallace, A, Liu, J, Spencer, L, Cox Lippard, ET, Purves, KL, Landeros-Weisenberger, A, Hermes, E, Pittman, B, Zhang, S, King, R, Martin, A, Oquendo, MA and Blumberg, HP (2017) Multimodal neuroimaging of frontolimbic structure and function associated with suicide attempts in adolescents and young adults with bipolar disorder. American Journal of Psychiatry 174, 667–675.Google Scholar
Joiner, TE Jr., Brown, JS and Wingate, LR (2005) The psychology and neurobiology of suicidal behavior. Annual Review of Psychology 56, 287–314.Google Scholar
Kaiser, RH, Whitfield-Gabrieli, S, Dillon, DG, Goer, F, Beltzer, M, Minkel, J, Smoski, M, Dichter, G and Pizzagalli, DA (2016) Dynamic resting-state functional connectivity in major depression. Neuropsychopharmacology 41, 1822–1830.Google Scholar
Kerns, JG, Cohen, JD, MacDonald, AW III, Cho, RY, Stenger, VA and Carter, CS (2004) Anterior cingulate conflict monitoring and adjustments in control. Science 303, 1023–1026.Google Scholar
Kim, J, Criaud, M, Cho, SS, Diez-Cirarda, M, Mihaescu, A, Coakeley, S, Ghadery, C, Valli, M, Jacobs, MF, Houle, S and Strafella, AP (2017a) Abnormal intrinsic brain functional network dynamics in Parkinson's disease. Brain 140, 2955–2967.Google Scholar
Kim, K, Kim, SW, Myung, W, Han, CE, Fava, M, Mischoulon, D, Papakostas, GI, Seo, SW, Cho, H, Seong, JK and Jeon, HJ (2017b) Reduced orbitofrontal-thalamic functional connectivity related to suicidal ideation in patients with major depressive disorder. Scientific Reports 7, 15772.Google Scholar
Klonsky, ED and May, AM (2014) Differentiating suicide attempters from suicide ideators: a critical frontier for suicidology research. Suicide and Life-Threatening Behavior 44, 1–5.Google Scholar
Kohavi, R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence. Montreal: Morgan Kaufmann Publishers Inc., pp. 1137–1145.Google Scholar
Kucyi, A, Hove, MJ, Esterman, M, Hutchison, RM and Valera, EM (2017) Dynamic brain network correlates of spontaneous fluctuations in attention. Cerebral Cortex 27, 1831–1840.Google Scholar
Leonardi, N and Van De Ville, D (2015) On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage 104, 430–436.Google Scholar
Li, R, Liao, W, Yu, Y, Chen, H, Guo, X, Tang, YL and Chen, H (2018) Differential patterns of dynamic functional connectivity variability of striato-cortical circuitry in children with benign epilepsy with centrotemporal spikes. Human Brain Mapping 39, 1207–1217.Google Scholar
Li, XY, Phillips, MR, Tong, YS, Li, KJ, Zhang, YP and Xu, D (2010) Reliability and validity of the Chinese version of Beck suicide ideation scale (BSI-CV) in adult community residents (in Chinese). Chinese Mental Health Journal 24, 250–255.Google Scholar
Liao, W, Wu, GR, Xu, Q, Ji, GJ, Zhang, Z, Zang, YF and Lu, G (2014a) DynamicBC: a MATLAB toolbox for dynamic brain connectome analysis. Brain Connectivity 4, 780–790.Google Scholar
Liao, W, Zhang, Z, Mantini, D, Xu, Q, Ji, GJ, Zhang, H, Wang, J, Wang, Z, Chen, G, Tian, L, Jiao, Q, Zang, YF and Lu, G (2014b) Dynamical intrinsic functional architecture of the brain during absence seizures. Brain Structure & Function 219, 2001–2015.Google Scholar
Lieberman, MD and Eisenberger, NI (2015) The dorsal anterior cingulate cortex is selective for pain: results from large-scale reverse inference. Proceedings of the National Academy of Sciences of the USA 112, 15250–15255.Google Scholar
Liu, J, Ren, L, Womer, FY, Wang, J, Fan, G, Jiang, W, Blumberg, HP, Tang, Y, Xu, K and Wang, F (2014) Alterations in amplitude of low frequency fluctuation in treatment-naive major depressive disorder measured with resting-state fMRI. Human Brain Mapping 35, 4979–4988.Google Scholar
Marchand, WR, Lee, JN, Johnson, S, Thatcher, J, Gale, P, Wood, N and Jeong, EK (2012) Striatal and cortical midline circuits in major depression: implications for suicide and symptom expression. Progress in Neuro-Psychopharmacology & Biological Psychiatry 36, 290–299.Google Scholar
Marzuk, PM, Hartwell, N, Leon, AC and Portera, L (2005) Executive functioning in depressed patients with suicidal ideation. Acta Psychiatrica Scandinavica 112, 294–301.Google Scholar
Matsuo, K, Nicoletti, M, Nemoto, K, Hatch, JP, Peluso, MA, Nery, FG and Soares, JC (2009) A voxel-based morphometry study of frontal gray matter correlates of impulsivity. Human Brain Mapping 30, 1188–1195.Google Scholar
Mayberg, HS (2003) Modulating dysfunctional limbic-cortical circuits in depression: towards development of brain-based algorithms for diagnosis and optimised treatment. British Medical Bulletin 65, 193–207.Google Scholar
Mesulam, MM (2002) The human frontal lobes: transcending the default mode through contingent encoding. In Stuss, DT and Knight, RT (eds), Principles of Frontal Lobe Function. Oxford: Oxford University Press, pp. 8–30.Google Scholar
Monkul, ES, Hatch, JP, Nicoletti, MA, Spence, S, Brambilla, P, Lacerda, AL, Sassi, RB, Mallinger, AG, Keshavan, MS and Soares, JC (2007) Fronto-limbic brain structures in suicidal and non-suicidal female patients with major depressive disorder. Molecular Psychiatry 12, 360–366.Google Scholar
Myung, W, Han, CE, Fava, M, Mischoulon, D, Papakostas, GI, Heo, JY, Kim, KW, Kim, ST, Kim, DJ, Kim, DK, Seo, SW, Seong, JK and Jeon, HJ (2016) Reduced frontal-subcortical white matter connectivity in association with suicidal ideation in major depressive disorder. Translational Psychiatry 6, e835.Google Scholar
Nock, MK, Borges, G, Bromet, EJ, Alonso, J, Angermeyer, M, Beautrais, A, Bruffaerts, R, Chiu, WT, de Girolamo, G, Gluzman, S, de Graaf, R, Gureje, O, Haro, JM, Huang, Y, Karam, E, Kessler, RC, Lepine, JP, Levinson, D, Medina-Mora, ME, Ono, Y, Posada-Villa, J and Williams, D (2008) Cross-national prevalence and risk factors for suicidal ideation, plans and attempts. British Journal of Psychiatry 192, 98–105.Google Scholar
O'Connor, RC and Nock, MK (2014) The psychology of suicidal behaviour. The Lancet. Psychiatry 1, 73–85.Google Scholar
Olson, IR, Plotzker, A and Ezzyat, Y (2007) The enigmatic temporal pole: a review of findings on social and emotional processing. Brain 130, 1718–1731.Google Scholar
Pan, LA, Hassel, S, Segreti, AM, Nau, SA, Brent, DA and Phillips, ML (2013) Differential patterns of activity and functional connectivity in emotion processing neural circuitry to angry and happy faces in adolescents with and without suicide attempt. Psychological Medicine 43, 2129–2142.Google Scholar
Pannekoek, JN, van der Werff, SJ, Meens, PH, van den Bulk, BG, Jolles, DD, Veer, IM, van Lang, ND, Rombouts, SA, van der Wee, NJ and Vermeiren, RR (2014) Aberrant resting-state functional connectivity in limbic and salience networks in treatment-naive clinically depressed adolescents. Journal of Child Psychology and Psychiatry 55, 1317–1327.Google Scholar
Posner, MI and DiGirolamo, GJ (1998) Executive attention conflict, target detection, and cognitive control. In Parasuraman, R (ed.), The Attentive Brain. Cambridge, MA, USA: The MIT Press, pp. 401–423.Google Scholar
Power, JD, Barnes, KA, Snyder, AZ, Schlaggar, BL and Petersen, SE (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154.Google Scholar
Preti, MG, Bolton, TA and Van De Ville, D (2017) The dynamic functional connectome: state-of-the-art and perspectives. Neuroimage 160, 41–54.Google Scholar
Pu, S, Nakagome, K, Yamada, T, Yokoyama, K, Matsumura, H, Yamada, S, Sugie, T, Miura, A, Mitani, H, Iwata, M, Nagata, I and Kaneko, K (2015) Suicidal ideation is associated with reduced prefrontal activation during a verbal fluency task in patients with major depressive disorder. Journal of Affective Disorders 181, 9–17.Google Scholar
Raichle, ME and Snyder, AZ (2007) A default mode of brain function: a brief history of an evolving idea. Neuroimage 37, 1083–1090.Google Scholar
Rashid, B, Arbabshirani, MR, Damaraju, E, Cetin, MS, Miller, R, Pearlson, GD and Calhoun, VD (2016) Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity. Neuroimage 134, 645–657.Google Scholar
Rogers, MA, Kasai, K, Koji, M, Fukuda, R, Iwanami, A, Nakagome, K, Fukuda, M and Kato, N (2004) Executive and prefrontal dysfunction in unipolar depression: a review of neuropsychological and imaging evidence. Neuroscience Research 50, 1–11.Google Scholar
Schoenbaum, G, Roesch, MR and Stalnaker, TA (2006) Orbitofrontal cortex, decision-making and drug addiction. Trends in Neuroscience 29, 116–124.Google Scholar
Schwarzkopf, DS, De Haas, B and Rees, G (2012) Better ways to improve standards in brain-behavior correlation analysis. Frontiers in Human Neuroscience 6, 200.Google Scholar
Sheline, YI, Barch, DM, Price, JL, Rundle, MM, Vaishnavi, SN, Snyder, AZ, Mintun, MA, Wang, S, Coalson, RS and Raichle, ME (2009) The default mode network and self-referential processes in depression. Proceedings of the National Academy of Sciences of the USA 106, 1942–1947.Google Scholar
Shen, X, Finn, ES, Scheinost, D, Rosenberg, MD, Chun, MM, Papademetris, X and Constable, RT (2017) Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols 12, 506–518.Google Scholar
Szanto, K, Mulsant, BH, Houck, P, Dew, MA and Reynolds, CF III (2003) Occurrence and course of suicidality during short-term treatment of late-life depression. Archives of General Psychiatry 60, 610–617.Google Scholar
Tagliazucchi, E, Carhart-Harris, R, Leech, R, Nutt, D and Chialvo, DR (2014) Enhanced repertoire of brain dynamical states during the psychedelic experience. Human Brain Mapping 35, 5442–5456.Google Scholar
Tomasi, D, Shokri-Kojori, E and Volkow, ND (2016) Temporal changes in local functional connectivity density reflect the temporal variability of the amplitude of low frequency fluctuations in gray matter. PLoS ONE 11, e0154407.Google Scholar
van Tol, MJ, Li, M, Metzger, CD, Hailla, N, Horn, DI, Li, W, Heinze, HJ, Bogerts, B, Steiner, J, He, H and Walter, M (2014) Local cortical thinning links to resting-state disconnectivity in major depressive disorder. Psychological Medicine 44, 2053–2065.Google Scholar
Viard, A, Piolino, P, Desgranges, B, Chetelat, G, Lebreton, K, Landeau, B, Young, A, De La Sayette, V and Eustache, F (2007) Hippocampal activation for autobiographical memories over the entire lifetime in healthy aged subjects: an fMRI study. Cerebral Cortex 17, 2453–2467.Google Scholar
Wang, L, Shen, Y, Liang, Z, Luo, Z and Zhang, K (2012) Reliability and validity of BSI-CV in evaluating the depression patients (in Chinese). China Journal of Health Psychology 20, 159–160.Google Scholar
Wang, L, Xia, M, Li, K, Zeng, Y, Su, Y, Dai, W, Zhang, Q, Jin, Z, Mitchell, PB, Yu, X, He, Y and Si, T (2015) The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder. Human Brain Mapping 36, 768–778.Google Scholar
Westheide, J, Quednow, BB, Kuhn, KU, Hoppe, C, Cooper-Mahkorn, D, Hawellek, B, Eichler, P, Maier, W and Wagner, M (2008) Executive performance of depressed suicide attempters: the role of suicidal ideation. European Archives of Psychiatry and Clinical Neuroscience 258, 414–421.Google Scholar
Whittle, S, Chanen, AM, Fornito, A, McGorry, PD, Pantelis, C and Yucel, M (2009) Anterior cingulate volume in adolescents with first-presentation borderline personality disorder. Psychiatry Research 172, 155–160.Google Scholar
Willeumier, K, Taylor, DV and Amen, DG (2011) Decreased cerebral blood flow in the limbic and prefrontal cortex using SPECT imaging in a cohort of completed suicides. Translational Psychiatry 1, e28.Google Scholar
Winkler, AM, Ridgway, GR, Douaud, G, Nichols, TE and Smith, SM (2016) Faster permutation inference in brain imaging. Neuroimage 141, 502–516.Google Scholar
Woo, CW, Chang, LJ, Lindquist, MA and Wager, TD (2017) Building better biomarkers: brain models in translational neuroimaging. Nature Neuroscience 20, 365–377.Google Scholar
Xia, M, Wang, J and He, Y (2013) Brainnet Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8, e68910.Google Scholar
Yan, CG, Cheung, B, Kelly, C, Colcombe, S, Craddock, RC, Di Martino, A, Li, Q, Zuo, XN, Castellanos, FX and Milham, MP (2013) A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76, 183–201.Google Scholar
Yan, CG, Wang, XD, Zuo, XN and Zang, YF (2016) DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics 14, 339–351.Google Scholar
Yan, CG, Yang, Z, Colcombe, SJ, Zuo, XN and Milham, MP (2017) Concordance among indices of intrinsic brain function: insights from inter-individual variation and temporal dynamics. Science Bulletin 62, 1572–1584.Google Scholar
Zang, YF, He, Y, Zhu, CZ, Cao, QJ, Sui, MQ, Liang, M, Tian, LX, Jiang, TZ and Wang, YF (2007) Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain and Development 29, 83–91.Google Scholar
Zhou, M, Hu, X, Lu, L, Zhang, L, Chen, L, Gong, Q and Huang, X (2017) Intrinsic cerebral activity at resting state in adults with major depressive disorder: a meta-analysis. Progress in Neuro-Psychopharmacology & Biological Psychiatry 75, 157–164.Google Scholar
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