Abstract
Previous investigations have revealed performance deficits and altered neural processes during working-memory (WM) tasks in major depressive disorder (MDD). While most of these studies used task-based functional magnetic resonance imaging (fMRI), there is an increasing interest in resting-state fMRI to characterize aberrant network dynamics involved in this and other MDD-associated symptoms. It has been proposed that activity during the resting-state represents characteristics of brain-wide functional organization, which could be highly relevant for the efficient execution of cognitive tasks. However, the dynamics linking resting-state properties and task-evoked activity remain poorly understood. Therefore, the present study investigated the association between spontaneous activity as indicated by the amplitude of low frequency fluctuations (ALFF) at rest and activity during an emotional n-back task. 60 patients diagnosed with an acute MDD episode, and 52 healthy controls underwent the fMRI scanning procedure. Within both groups, positive correlations between spontaneous activity at rest and task-activation were found in core regions of the central-executive network (CEN), whereas spontaneous activity correlated negatively with task-deactivation in regions of the default mode network (DMN). Compared to healthy controls, patients showed a decreased rest-task correlation in the left prefrontal cortex (CEN) and an increased negative correlation in the precuneus/posterior cingulate cortex (DMN). Interestingly, no significant group-differences within those regions were found solely at rest or during the task. The results underpin the potential value and importance of resting-state markers for the understanding of dysfunctional network dynamics and neural substrates of cognitive processing.
Introduction
The symptomatology associated with major depressive disorder (MDD) can be roughly divided into affective, vegetative and cognitive dimensions [1]. The cognitive domain comprises deficits in attention [2], visual learning and memory [3], processing speed [4] and executive functioning [5], such as working-memory (WM) impairments [6]. WM-deficits can be observed even after clinical remission [7, 8] and may have substantial impact on (psychosocial) functioning [9, 10]. Furthermore, WM deficits have been found to negatively predict MDD-treatment outcome [11,12,13], which underlines the relevance of WM-function as a diagnostic biomarker and potential therapeutic target for individuals with acute or remitted MDD. These findings emphasize the importance of better understanding altered WM-processes in MDD and their underlying neurobiological mechanisms.
Functional magnetic resonance imaging (fMRI) studies of healthy individuals have revealed numerous networks activated during WM, highlighting the crucial involvement of prefrontal and parietal regions, which constitute important nodes of the central executive network (CEN; [14,15,16]). Furthermore, in healthy controls (HC) significant deactivation of regions within the default mode network (DMN) during WM performance were reported [17, 18]. This is in support of the theory that suppression of these regions, associated with internally-directed and self-referential cognition during periods of task absence [19], is necessary for effective execution of cognitive tasks [20]. A recent meta-analysis of fMRI findings in MDD revealed stronger activation of DMN regions in patients during WM performance [21], which in line with the above reported findings in HC can be interpreted as the failure to adaptively suppress internally-directed cognition for the effective processing of external information [22]. Another meta-analysis of MDD studies [23] reported stronger activation specifically in the left dorsolateral prefrontal cortex (DLPFC) of MDD subjects compared to performance-matched HC, which is in support of the hypothesis that frontal hyperactivation represents a compensatory mechanism to counteract dysfunctional neural activation in other regions to preserve WM performance. Despite these trends, results from studies aimed at delineating differences in WM-related (de-)activation patterns between MDD-patients and healthy control subjects show considerable heterogeneity with various reported regional differences suggesting that our understanding of the neural mechanisms underlying WM deficits in MDD remains incomplete.
In another, hitherto largely unrelated stream of research resting-state fMRI is increasingly employed for the identification of altered neural mechanisms in MDD. Implying involvement of similar regions as in task-based studies, a recent meta-analysis reported large disruptions of resting-state functional connectivity (FC) within and between nodes of the DMN and CEN in MDD patients [24]. Other resting state studies have investigated the amplitude of low frequency fluctuations (ALFF), which quantifies changes of the BOLD signal as a marker of spontaneous neural activity [25]. In MDD increased spontaneous neural activity can be found in the medial prefrontal cortex (mPFC), a core hub of the DMN, and in the insula, which is associated with a coordinative role of switching between the CEN and DMN [26]. Basic research on WM-processes has repeatedly demonstrated associations between WM-performance and functional connectivity between [27, 28] and within these networks [29, 30]. In a machine-learning-based investigation, **. Mol Psychiatry. 2019;24:888–900." href="#ref-CR92" id="ref-link-section-d6654420e2743_1">92,93]. The fact that we allowed patients with concurrent antidepressant medication into the study may have further increased between-subject variability. It is possible that increased heterogeneity may have contributed to the lack of replication of previous findings, such as the increased frontal activity during WM-performance in MDD subjects [23, 71, 72]. Application of regression models with resting-state parameters and clinical- and demographical covariates as predictors and the task-activation parameters as the dependent variable within a sample of MDD subjects could further advance our presented approach and counteract problems associated with heterogeneity of MDD samples. Adding symptom-based clustering methods of MDD patients might help to reveal symptom-specific alterations of the rest-task relationship. Third, due to our decision to use a whole-brain voxel-wise approach, we were only able to evaluate regional coherence between resting-state fluctuations and task-evoked activity. The question of whether the mechanisms underlying the rest-task relationship (and their alterations) are region-inherent or driven by large-scale network dynamics and top-down processes therefore had to remain unanswered. Since significant rest-task correlations and neural alterations in MDD mainly emerged in regions associated with the CEN or DMN, modulatory effects within or between different networks, as factors causing the rest-task relationship, seem highly likely. For example, future research may want to investigate the coordinative role of the anterior insula in DMN and CEN (de)activation [94] and between-network dynamics by exploring connectivity-based resting-state indices and their relation to task-evoked activation.
Conclusion
Taken together, these findings suggest that resting-state activity reflects important properties of WM processes and their neural representations. The fact that a consistent pattern of correlations was found across HC and MDD-patients underlines the applicability and relevance of resting-state data for the understanding of brain functionality. Most importantly, analysis of rest-task relationships identified meaningful MDD-associated differences involving main hubs of the CEN and DMN that would have remained unnoticed in analyses of separate parameters. In conclusion, the integration of rest- and task data with parameters of their relationship offers an avenue to gain a more comprehensive understanding of the processes underlying cognitive deficits and network mechanisms altered in MDD.
Data availability
All data analyzed during the current study are available from the corresponding author upon reasonable request.
References
Otte C, Gold SM, Penninx BW, Pariante CM, Etkin A, Fava M, et al. Major depressive disorder. Nat Rev Dis Primers. 2016;2:1–20.
Wang X, Zhou H, Zhu X. Attention deficits in adults with Major depressive disorder: a systematic review and meta-analysis. Asian J Psychiatr. 2020;53:102359.
Lee RS, Hermens DF, Porter MA, Redoblado-Hodge MA. A meta-analysis of cognitive deficits in first-episode major depressive disorder. J Affect Disord. 2012;140:113–24.
Nuño L, Gómez-Benito J, Carmona VR, Pino O. A systematic review of executive function and information processing speed in major depression disorder. Brain Sci. 2021;11:147.
Snyder HR. Major depressive disorder is associated with broad impairments on neuropsychological measures of executive function: a meta-analysis and review. Psychol Bull. 2013;139:81.
Nikolin S, Tan YY, Schwaab A, Moffa A, Loo CK, Martin D. An investigation of working memory deficits in depression using the n-back task: A systematic review and meta-analysis. J Affect Disord. 2021;284:1–8.
Roca M, Monzón S, Vives M, López-Navarro E, Garcia-Toro M, Vicens C, et al. Cognitive function after clinical remission in patients with melancholic and non-melancholic depression: a 6 month follow-up study. J Affect Disord. 2015;171:85–92.
Semkovska M, Quinlivan L, O’Grady T, Johnson R, Collins A, O’Connor J, et al. Cognitive function following a major depressive episode: a systematic review and meta-analysis. Lancet Psychiatry. 2019;6:851–61.
Lam RW, Kennedy SH, McIntyre RS, Khullar A. Cognitive dysfunction in major depressive disorder: effects on psychosocial functioning and implications for treatment. Can J Psychiatry. 2014;59:649–54.
Kaser M, Deakin JB, Michael A, Zapata C, Bansal R, Ryan D, et al. Modafinil improves episodic memory and working memory cognition in patients with remitted depression: a double-blind, randomized, placebo-controlled study. Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2:115–22.
Gorlyn M, Keilp JG, Grunebaum MF, Taylor BP, Oquendo MA, Bruder GE, et al. Neuropsychological characteristics as predictors of SSRI treatment response in depressed subjects. J Neural Transm. 2008;115:1213–9.
Etkin A, Patenaude B, Song YJC, Usherwood T, Rekshan W, Schatzberg AF, et al. A cognitive–emotional biomarker for predicting remission with antidepressant medications: a report from the iSPOT-D trial. Neuropsychopharmacology. 2015;40:1332–42.
Talarowska M, Zboralski K, Gałecki P. Correlations between working memory effectiveness and depression levels after pharmacological therapy. Psychiatr Pol. 2013;47:255–67.
Emch M, Von Bastian CC, Koch K. Neural correlates of verbal working memory: An fMRI meta-analysis. Front Hum Neurosci. 2019;13:180.
Nee DE, Brown JW, Askren MK, Berman MG, Demiralp E, Krawitz A, et al. A meta-analysis of executive components of working memory. Cereb Cortex. 2013;23:264–82.
Yaple ZA, Stevens WD, Arsalidou M. Meta-analyses of the n-back working memory task: fMRI evidence of age-related changes in prefrontal cortex involvement across the adult lifespan. Neuroimage. 2019;196:16–31.
Čeko M, Gracely JL, Fitzcharles M-A, Seminowicz DA, Schweinhardt P, Bushnell MC. Is a responsive default mode network required for successful working memory task performance? J Neurosci. 2015;35:11595–605.
Qin S, Basak C. Age-related differences in brain activation during working memory updating: an fMRI study. Neuropsychologia. 2020;138:107335.
Raichle ME. The brain’s default mode network. Annu Rev Neurosci. 2015;38:433–47.
Anticevic A, Cole MW, Murray JD, Corlett PR, Wang X-J, Krystal JH. The role of default network deactivation in cognition and disease. Trends Cogn Sci. 2012;16:584–92.
Wang X, Cheng B, Roberts N, Wang S, Luo Y, Tian F, et al. Shared and distinct brain fMRI response during performance of working memory tasks in adult patients with schizophrenia and major depressive disorder. Human Brain Mapp. 2021;42:5458–76.
Gärtner M, Ghisu ME, Scheidegger M, Bönke L, Fan Y, Stippl A, et al. Aberrant working memory processing in major depression: evidence from multivoxel pattern classification. Neuropsychopharmacology. 2018;43:1972–9.
Wang X-L, Du M-Y, Chen T-L, Chen Z-Q, Huang X-Q, Luo Y, et al. Neural correlates during working memory processing in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2015;56:101–8.
Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA. Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiatry. 2015;72:603–11.
Yang H, Long X-Y, Yang Y, Yan H, Zhu C-Z, Zhou X-P, et al. Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. Neuroimage. 2007;36:144–52.
Gong J, Wang J, Qiu S, Chen P, Luo Z, Wang J, et al. Common and distinct patterns of intrinsic brain activity alterations in major depression and bipolar disorder: voxel-based meta-analysis. Transl Psychiatry. 2020;10:353.
Sala-Llonch R, Pena-Gomez C, Arenaza-Urquijo EM, Vidal-Piñeiro D, Bargallo N, Junque C, et al. Brain connectivity during resting state and subsequent working memory task predicts behavioural performance. Cortex. 2012;48:1187–96.
Osaka M, Kaneda M, Azuma M, Yaoi K, Shimokawa T, Osaka N. Capacity differences in working memory based on resting state brain networks. Sci Rep. 2021;11:1–11.
Esposito F, Aragri A, Latorre V, Popolizio T, Scarabino T, Cirillo S, et al. Does the default-mode functional connectivity of the brain correlate with working-memory performances? Arch Ital Biol. 2009;147:11–20.
Jockwitz C, Caspers S, Lux S, Eickhoff SB, Jütten K, Lenzen S, et al. Influence of age and cognitive performance on resting-state brain networks of older adults in a population-based cohort. Cortex. 2017;89:28–44.
**ao Y, Lin Y, Ma J, Qian J, Ke Z, Li L, et al. Predicting visual working memory with multimodal magnetic resonance imaging. Hum Brain Mapp. 2021;42:1446–62.
Zou Q, Ross TJ, Gu H, Geng X, Zuo XN, Hong LE, et al. Intrinsic resting‐state activity predicts working memory brain activation and behavioral performance. Hum Brain Mapp. 2013;34:3204–15.
Northoff G, Wiebking C, Feinberg T, Panksepp J. The ‘resting-state hypothesis’ of major depressive disorder—A translational subcortical–cortical framework for a system disorder. Neurosci Biobehav Rev. 2011;35:1929–45.
Cole MW, Bassett DS, Power JD, Braver TS, Petersen SE. Intrinsic and task-evoked network architectures of the human brain. Neuron. 2014;83:238–51.
Mennes M, Zuo X-N, Kelly C, Di Martino A, Zang Y-F, Biswal B, et al. Linking inter-individual differences in neural activation and behavior to intrinsic brain dynamics. Neuroimage. 2011;54:2950–9.
Tavor I, Jones OP, Mars RB, Smith S, Behrens T, Jbabdi S. Task-free MRI predicts individual differences in brain activity during task performance. Science. 2016;352:216–20.
Mennes M, Kelly C, Zuo X-N, Di Martino A, Biswal BB, Castellanos FX, et al. Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity. Neuroimage. 2010;50:1690–701.
Cole MW, Ito T, Bassett DS, Schultz DH. Activity flow over resting-state networks shapes cognitive task activations. Nat Neurosci. 2016;19:1718–26.
Zhou Y, Wang Z, Zuo X-N, Zhang H, Wang Y, Jiang T, et al. Hyper-coupling between working memory task-evoked activations and amplitude of spontaneous fluctuations in first-episode schizophrenia. Schizophr Res. 2014;159:80–89.
Mennes M, Kelly C, Colcombe S, Castellanos FX, Milham MP. The extrinsic and intrinsic functional architectures of the human brain are not equivalent. Cereb Cortex. 2013;23:223–9.
Erdman A, Abend R, Jalon I, Artzi M, Gazit T, Avirame K, et al. Ruminative tendency relates to ventral striatum functionality: evidence from task and resting-state fMRI. Front Psychiatry. 2020;11:67.
Satterthwaite TD, Kable JW, Vandekar L, Katchmar N, Bassett DS, Baldassano CF, et al. Common and dissociable dysfunction of the reward system in bipolar and unipolar depression. Neuropsychopharmacology. 2015;40:2258–68.
Loeffler LA, Radke S, Habel U, Ciric R, Satterthwaite TD, Schneider F, et al. The regulation of positive and negative emotions through instructed causal attributions in lifetime depression–A functional magnetic resonance imaging study. Neuroimage Clin. 2018;20:1233–45.
Anand A, Li Y, Wang Y, Wu J, Gao S, Bukhari L, et al. Activity and connectivity of brain mood regulating circuit in depression: a functional magnetic resonance study. Biol Psychiatry. 2005;57:1079–88.
Davey CG, Yücel M, Allen NB, Harrison BJ. Task-related deactivation and functional connectivity of the subgenual cingulate cortex in major depressive disorder. Front Psychiatry. 2012;3:14.
Yang Y, Zhong N, Imamura K, Lu S, Li M, Zhou H, et al. Task and resting-state fMRI reveal altered salience responses to positive stimuli in patients with major depressive disorder. PLoS One. 2016;11:e0155092.
Ho TC, Connolly CG, Blom EH, LeWinn KZ, Strigo IA, Paulus MP, et al. Emotion-dependent functional connectivity of the default mode network in adolescent depression. Biol Psychiatry. 2015;78:635–46.
Domke A-K, Hempel M, Hartling C, Stippl A, Carstens L, Gruzman R et al. Functional connectivity changes between amygdala and prefrontal cortex after ECT are associated with improvement in distinct depressive symptoms. Eur Arch Psychiatry Clin Neurosci. 2023;273:1489–99.
Gruzman R, Hartling C, Domke A-K, Stippl A, Carstens L, Bajbouj M, et al. Investigation of Neurofunctional Changes Over the Course of Electroconvulsive Therapy. Int J Neuropsychopharmacol. 2023;26:20–31.
Lifshitz M, Sacchet MD, Huntenburg JM, Thiery T, Fan Y, Gärtner M, et al. Mindfulness-based therapy regulates brain connectivity in major depression. Psychother and Psychosom. 2019;88:375–7.
Grimm S, Weigand A, Kazzer P, Jacobs AM, Bajbouj M. Neural mechanisms underlying the integration of emotion and working memory. Neuroimage. 2012;61:1188–94.
Võ ML, Conrad M, Kuchinke L, Urton K, Hofmann MJ, Jacobs AM. The Berlin affective word list reloaded (BAWL-R). Behav Res Methods. 2009;41:534–8.
Beck AT, Steer RA, Brown GK. Beck depression inventory-II. San Antonio. 1996;78:490–8.
Zhang Z, Peng P, Eickhoff SB, Lin X, Zhang D, Wang Y. Neural substrates of the executive function construct, age‐related changes, and task materials in adolescents and adults: ALE meta‐analyses of 408 fMRI studies. Dev Sci. 2021;24:e13111.
Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA. 2005;102:9673–8.
Zhou H-X, Chen X, Shen Y-Q, Li L, Chen N-X, Zhu Z-C, et al. Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depression. Neuroimage. 2020;206:116287.
Sheline YI, Barch DM, Price JL, Rundle MM, Vaishnavi SN, Snyder AZ, et al. The default mode network and self-referential processes in depression. Proc Natl Acad Sci USA. 2009;106:1942–7.
Andrews‐Hanna JR, Smallwood J, Spreng RN. The default network and self‐generated thought: Component processes, dynamic control, and clinical relevance. Ann N Y Acad Sc Sciences. 2014;1316:29–52.
Yan C, Liu D, He Y, Zou Q, Zhu C, Zuo X, et al. Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load. PLoS one. 2009;4:e5743.
Niendam TA, Laird AR, Ray KL, Dean YM, Glahn DC, Carter CS. Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn Affect Behav Neurosci. 2012;12:241–68.
Zanto TP, Gazzaley A. Fronto-parietal network: flexible hub of cognitive control. Trends Cogn Sci. 2013;17:602–3.
Koenigs M, Barbey AK, Postle BR, Grafman J. Superior parietal cortex is critical for the manipulation of information in working memory. J Neurosci. 2009;29:14980–6.
Öztekin I, McElree B, Staresina BP, Davachi L. Working memory retrieval: contributions of the left prefrontal cortex, the left posterior parietal cortex, and the hippocampus. J Cogn Neurosci. 2009;21:581–93.
Awh E, Jonides J, Smith EE, Schumacher EH, Koeppe RA, Katz S. Dissociation of storage and rehearsal in verbal working memory: Evidence from positron emission tomography. Psychol Sci. 1996;7:25–31.
Fitzgerald PB, Srithiran A, Benitez J, Daskalakis ZZ, Oxley TJ, Kulkarni J, et al. An fMRI study of prefrontal brain activation during multiple tasks in patients with major depressive disorder. Hum Brain Mapp. 2008;29:490–501.
Barch DM, Sheline YI, Csernansky JG, Snyder AZ. Working memory and prefrontal cortex dysfunction: specificity to schizophrenia compared with major depression. Biol Psychiatry. 2003;53:376–84.
Ravizza SM, Delgado MR, Chein JM, Becker JT, Fiez JA. Functional dissociations within the inferior parietal cortex in verbal working memory. Neuroimage. 2004;22:562–73.
Nyberg L, Dahlin E, Stigsdotter Neely A, Bäckman L. Neural correlates of variable working memory load across adult age and skill: Dissociative patterns within the fronto‐parietal network. Scand J Psychol. 2009;50:41–46.
Darki F, Klingberg T. The role of fronto-parietal and fronto-striatal networks in the development of working memory: a longitudinal study. Cereb Cortex. 2015;25:1587–95.
DeYoung CG, Shamosh NA, Green AE, Braver TS, Gray JR. Intellect as distinct from Openness: differences revealed by fMRI of working memory. J Pers Soc Psychol. 2009;97:883.
Harvey P-O, Fossati P, Pochon J-B, Levy R, LeBastard G, Lehéricy S, et al. Cognitive control and brain resources in major depression: an fMRI study using the n-back task. Neuroimage. 2005;26:860–9.
Matsuo K, Glahn D, Peluso M, Hatch J, Monkul E, Najt P, et al. Prefrontal hyperactivation during working memory task in untreated individuals with major depressive disorder. Mol Psychiatry. 2007;12:158–66.
Cromheeke S, Mueller SC. Probing emotional influences on cognitive control: an ALE meta-analysis of cognition emotion interactions. Brain Struct Funct. 2014;219:995–1008.
Li J, Liu J, Zhong Y, Wang H, Yan B, Zheng K, et al. Causal interactions between the default mode network and central executive network in patients with major depression. Neurosci. 2021;475:93–102.
Svoboda E, McKinnon MC, Levine B. The functional neuroanatomy of autobiographical memory: a meta-analysis. Neuropsychologia. 2006;44:2189–208.
Summerfield JJ, Hassabis D, Maguire EA. Cortical midline involvement in autobiographical memory. Neuroimage. 2009;44:1188–1200.
Maddock RJ, Garrett AS, Buonocore MH. Remembering familiar people: the posterior cingulate cortex and autobiographical memory retrieval. Neurosci. 2001;104:667–76.
Wolff S, Brechmann A. Dorsal posterior cingulate cortex responds to negative feedback information supporting learning and relearning of response policies. Cereb Cortex. 2023;33:5947–56.
Pearson JM, Heilbronner SR, Barack DL, Hayden BY, Platt ML. Posterior cingulate cortex: adapting behavior to a changing world. Trends Cogn Sci. 2011;15:143–51.
Pearson JM, Hayden BY, Raghavachari S, Platt ML. Neurons in posterior cingulate cortex signal exploratory decisions in a dynamic multioption choice task. Curr Biol. 2009;19:1532–7.
Leech R, Kamourieh S, Beckmann CF, Sharp DJ. Fractionating the default mode network: distinct contributions of the ventral and dorsal posterior cingulate cortex to cognitive control. J Neurosci. 2011;31:3217–24.
Wen X, Liu Y, Yao L, Ding M. Top-down regulation of default mode activity in spatial visual attention. J Neurosci. 2013;33:6444–53.
Hu Y, Chen X, Gu H, Yang Y. Resting-state glutamate and GABA concentrations predict task-induced deactivation in the default mode network. J Neurosci. 2013;33:18566–73.
Rajan A, Meyyappan S, Walker H, Samuel IBH, Hu Z, Ding M. Neural mechanisms of internal distraction suppression in visual attention. Cortex. 2019;117:77–88.
Garrison KA, Santoyo JF, Davis JH, Thornhill IVTA, Kerr CE, Brewer JA. Effortless awareness: using real time neurofeedback to investigate correlates of posterior cingulate cortex activity in meditators’ self-report. Front Hum Neurosci. 2013;7:440.
Brewer JA, Garrison KA, Whitfield-Gabrieli S. What about the “self” is processed in the posterior cingulate cortex? Front Hum Neurosci. 2013;7:647.
Scalabrini A, Vai B, Poletti S, Damiani S, Mucci C, Colombo C, et al. All roads lead to the default-mode network—global source of DMN abnormalities in major depressive disorder. Neuropsychopharmacology. 2020;45:2058–69.
Lu X, Zhang J-F, Gu F, Zhang H-X, Zhang M, Song R-Z, et al. Altered task modulation of global signal topography in the default-mode network of unmedicated major depressive disorder. J Affect Disord. 2022;297:53–61.
Northoff G. Spatiotemporal psychopathology I: no rest for the brain’s resting state activity in depression? Spatiotemporal psychopathology of depressive symptoms. J Affect Disord. 2016;190:854–66.
Mayer JS, Bittner RA, Nikolić D, Bledowski C, Goebel R, Linden DE. Common neural substrates for visual working memory and attention. Neuroimage. 2007;36:441–53.
Fried E. Moving forward: how depression heterogeneity hinders progress in treatment and research. Expert Rev Neurother. 2017;17:423–5.
Beijers L, Wardenaar KJ, van Loo HM, Schoevers RA. Data-driven biological subtypes of depression: systematic review of biological approaches to depression subty**. Mol Psychiatry. 2019;24:888–900.
Van Loo HM, De Jonge P, Romeijn J-W, Kessler RC, Schoevers RA. Data-driven subtypes of major depressive disorder: a systematic review. BMC Med. 2012;10:1–12.
Sridharan D, Levitin DJ, Menon V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci USA. 2008;105:12569–74.
Acknowledgements
Support from the staff of the Center for Cognitive Neuroscience at the Free University of Berlin, as well as the participation of all patients and volunteers in the current study is gratefully acknowledged. This research was funded by German Research Foundation (Deutsche Forschungsgemeinschaft) Grant BA2255 3-1 and GR 4510/2-1, awarded to Thorsten Barnhofer and Simone Grimm, respectively. The funders had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article.
Funding
Open Access funding enabled and organized by Projekt DEAL.
Author information
Authors and Affiliations
Contributions
TB, SG and MG made substantial contributions to the design and conception of the study and revised the manuscript critically. MH, MG and SG contributed substantially to the analysis and interpretation of the data. MH wrote the manuscript. AD, CH, AS and LC contributed substantially to the data acquisition and revised the manuscript critically. All authors approved the final version of the manuscript.
Corresponding author
Ethics declarations
Competing interests
SG has served as a consultant to and received research support from Boehringer Ingelheim Pharma. The authors declare no conflict of interest.
Ethics approval
The study was carried out in compliance with the declaration of Helsinki and approved by the Charité University Medicine Berlin, Campus Mitte (EA4/055/13 & EA4/053/16 & EA4/130/15). Written informed consent was given by all subjects after detailed information about the purpose and the study routines were provided [Clinical Trials Registration number: NCT02871141 & NCT02801513].
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed 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/.
About this article
Cite this article
Hempel, M., Barnhofer, T., Domke, AK. et al. Aberrant associations between neuronal resting-state fluctuations and working memory-induced activity in major depressive disorder. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-024-02647-w
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41380-024-02647-w
- Springer Nature Limited