Dynamic Resting-State Functional Connectivity in Major Depression

Major depressive disorder (MDD) is characterized by abnormal resting-state functional connectivity (RSFC), especially in medial prefrontal cortical (MPFC) regions of the default network. However, prior research in MDD has not examined dynamic changes in functional connectivity as networks form, interact, and dissolve over time. We compared unmedicated individuals with MDD (n=100) to control participants (n=109) on dynamic RSFC (operationalized as SD in RSFC over a series of sliding windows) of an MPFC seed region during a resting-state functional magnetic resonance imaging scan. Among participants with MDD, we also investigated the relationship between symptom severity and RSFC. Secondary analyses probed the association between dynamic RSFC and rumination. Results showed that individuals with MDD were characterized by decreased dynamic (less variable) RSFC between MPFC and regions of parahippocampal gyrus within the default network, a pattern related to sustained positive connectivity between these regions across sliding windows. In contrast, the MDD group exhibited increased dynamic (more variable) RSFC between MPFC and regions of insula, and higher severity of depression was related to increased dynamic RSFC between MPFC and dorsolateral prefrontal cortex. These patterns of highly variable RSFC were related to greater frequency of strong positive and negative correlations in activity across sliding windows. Secondary analyses indicated that increased dynamic RSFC between MPFC and insula was related to higher levels of recent rumination. These findings provide initial evidence that depression, and ruminative thinking in depression, are related to abnormal patterns of fluctuating communication among brain systems involved in regulating attention and self-referential thinking.

[1]  Kenneth A. Bollen,et al.  Regression Diagnostics , 1985 .

[2]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[3]  A. Beck,et al.  Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. , 1996, Journal of personality assessment.

[4]  Karl J. Friston,et al.  Detecting Activations in PET and fMRI: Levels of Inference and Power , 1996, NeuroImage.

[5]  L. Parsons,et al.  Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. , 1999, The American journal of psychiatry.

[6]  N. Cohen,et al.  Prefrontal regions play a predominant role in imposing an attentional 'set': evidence from fMRI. , 2000, Brain research. Cognitive brain research.

[7]  R. Dennis Cook,et al.  Detection of Influential Observation in Linear Regression , 2000, Technometrics.

[8]  J. Cohen,et al.  Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. , 2000, Science.

[9]  V. Haughton,et al.  Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data. , 2001, AJNR. American journal of neuroradiology.

[10]  Andrew M. Busch,et al.  The Behavioral Activation for Depression Scale (BADS): Psychometric Properties and Factor Structure , 2007 .

[11]  Thomas T. Liu,et al.  A component based noise correction method (CompCor) for BOLD and perfusion based fMRI , 2007, NeuroImage.

[12]  V. Menon,et al.  A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks , 2008, Proceedings of the National Academy of Sciences.

[13]  Edward T. Bullmore,et al.  Neuroinformatics Original Research Article , 2022 .

[14]  H. Pashler,et al.  Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition 1 , 2009, Perspectives on psychological science : a journal of the Association for Psychological Science.

[15]  Vince D. Calhoun,et al.  Convergent Approaches for Defining Functional Imaging Endophenotypes in Schizophrenia , 2009, Front. Hum. Neurosci..

[16]  A. Craig,et al.  How do you feel — now? The anterior insula and human awareness , 2009, Nature Reviews Neuroscience.

[17]  S. Petersen,et al.  Role of the anterior insula in task-level control and focal attention , 2010, Brain Structure and Function.

[18]  Catie Chang,et al.  Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.

[19]  V. Menon,et al.  Saliency, switching, attention and control: a network model of insula function , 2010, Brain Structure and Function.

[20]  R. Buckner,et al.  Functional-Anatomic Fractionation of the Brain's Default Network , 2010, Neuron.

[21]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[22]  G. D. de Araujo,et al.  Interaction between Advanced Glycation End Products Formation and Vascular Responses in Femoral and Coronary Arteries from Exercised Diabetic Rats , 2012, PloS one.

[23]  Susan L. Whitfield-Gabrieli,et al.  Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks , 2012, Brain Connect..

[24]  Martin A. Lindquist,et al.  Dynamic connectivity regression: Determining state-related changes in brain connectivity , 2012, NeuroImage.

[25]  E. Koster,et al.  The Default Mode Network and Recurrent Depression: A Neurobiological Model of Cognitive Risk Factors , 2012, Neuropsychology Review.

[26]  David T. Jones,et al.  Non-Stationarity in the “Resting Brain’s” Modular Architecture , 2012, PloS one.

[27]  R. Kessler,et al.  Twelve‐month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States , 2012, International journal of methods in psychiatric research.

[28]  J. Ford,et al.  Default mode network activity and connectivity in psychopathology. , 2012, Annual review of clinical psychology.

[29]  Ravi S. Menon,et al.  Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques , 2013, Human brain mapping.

[30]  G. Freedman,et al.  Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010 , 2013, PLoS medicine.

[31]  Fenna M. Krienen,et al.  Opportunities and limitations of intrinsic functional connectivity MRI , 2013, Nature Neuroscience.

[32]  Hillary D. Schwarb,et al.  Short‐time windows of correlation between large‐scale functional brain networks predict vigilance intraindividually and interindividually , 2013, Human brain mapping.

[33]  Hans-Jochen Heinze,et al.  Association between heart rate variability and fluctuations in resting-state functional connectivity , 2013, NeuroImage.

[34]  Theodore P. Zanto,et al.  Fronto-parietal network: flexible hub of cognitive control , 2013, Trends in Cognitive Sciences.

[35]  B. Bogerts,et al.  Local cortical thinning links to resting-state disconnectivity in major depressive disorder , 2013, Psychological Medicine.

[36]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[37]  Luke J. Chang,et al.  Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference. , 2013, Cerebral cortex.

[38]  R. N. Spreng,et al.  The default network and self‐generated thought: component processes, dynamic control, and clinical relevance , 2014, Annals of the New York Academy of Sciences.

[39]  Richard J. Davidson,et al.  Tai chi training reduces self-report of inattention in healthy young adults , 2014, Front. Hum. Neurosci..

[40]  V. Calhoun,et al.  Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects , 2014, Front. Hum. Neurosci..

[41]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.

[42]  P. Gasquoine,et al.  Contributions of the Insula to Cognition and Emotion , 2014, Neuropsychology Review.

[43]  P. Falkai,et al.  The impact of environmental factors in severe psychiatric disorders , 2014, Front. Neurosci..

[44]  Laura C. Buchanan,et al.  The spatial structure of resting state connectivity stability on the scale of minutes , 2014, Front. Neurosci..

[45]  Vince D. Calhoun,et al.  Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis , 2014, NeuroImage.

[46]  Stefan Everling,et al.  Network Structure Shapes Spontaneous Functional Connectivity Dynamics , 2015, The Journal of Neuroscience.

[47]  Dimitri Van De Ville,et al.  On spurious and real fluctuations of dynamic functional connectivity during rest , 2015, NeuroImage.

[48]  Chris Petty,et al.  Resting-State Connectivity Predictors of Response to Psychotherapy in Major Depressive Disorder , 2015, Neuropsychopharmacology.

[49]  Aiden E. G. F. Arnold,et al.  Spatial and temporal functional connectivity changes between resting and attentive states , 2015, Human brain mapping.

[50]  J. Andrews-Hanna,et al.  Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. , 2015, JAMA psychiatry.

[51]  Marie T Banich,et al.  Distracted and down: neural mechanisms of affective interference in subclinical depression. , 2015, Social cognitive and affective neuroscience.