Background
Major depressive disorder (MDD) has been shown to be associated with a disrupted topological
organization of functional brain networks. However, little is known regarding whether
these changes have a structural basis. Diffusion tensor imaging (DTI) enables comprehensive
whole-brain mapping of the white matter tracts that link regions distributed throughout
the entire brain, the so-called human connectome.
Methods
We examined whole-brain structural networks in a cohort of 95 MDD outpatients and
102 matched control subjects. Structural networks were represented by an 84 × 84 connectivity
matrix representing probabilistic white matter connections between 84 parcellated
cortical and subcortical regions using DTI tractography. Network-based statistics
were used to assess differences in the interregional connectivity matrix between the
two groups, and graph theory was used to examine overall topological organization.
Results
Our network-based statistics analysis demonstrates lowered structural connectivity
within two distinct brain networks that are present in depression: the first primarily
involves the regions of the default mode network and the second comprises the frontal
cortex, thalamus, and caudate regions that are central in emotional and cognitive
processing. These two altered networks were observed in the context of an overall
preservation of topology as reflected as no significant group differences for the
graph-theory measures.
Conclusions
This is the first report to use DTI to show the structural connectomic alterations
present in MDD. Our findings highlight that altered structural connectivity between
nodes of the default mode network and the frontal-thalamo-caudate regions are core
neurobiological features associated with MDD.
Key Words
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Biological PsychiatryAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Orbitofrontal cortex function and structure in depression.Ann N Y Acad Sci. 2007; 1121: 499-527
- Functional neuroimaging of major depressive disorder: A meta-analysis and new integration of baseline activation and neural response data.Am J Psychiatry. 2012; 169: 693-704
- Brain structural and functional abnormalities in mood disorders: Implications for neurocircuitry models of depression.Brain Struct Funct. 2008; 213: 93-118
- Neuroimaging and depression: Current status and unresolved issues.Curr Dir Psychol Sci. 2008; 17: 159-163
- Modulating dysfunctional limbic-cortical circuits in depression: Towards development of brain-based algorithms for diagnosis and optimised treatment.Br Med Bull. 2003; 65: 193-207
- The ’resting-state hypothesis’ of major depressive disorder-a translational subcortical-cortical framework for a system disorder.Neurosci Biobehav Rev. 2011; 35: 1929-1945
- The default mode network and self-referential processes in depression.Proc Natl Acad Sci U S A. 2009; 106: 1942-1947
- Loss of white matter integrity in major depressive disorder: Evidence using tract-based spatial statistical analysis of diffusion tensor imaging.Hum Brain Mapp. 2011; 32: 2161-2171
- Human connectomics.Curr Opin Neurobiol. 2012; 22: 144-153
- Mapping the structural core of human cerebral cortex.PLoS Biol. 2008; 6: e159
- Complex brain networks: Graph theoretical analysis of structural and functional systems.Nat Rev Neurosci. 2009; 10: 186-198
- Graph analysis of the human connectome: Promise, progress, and pitfalls.Neuroimage. 2013; 80: 426-444
- Network-based statistic: Identifying differences in brain networks.Neuroimage. 2010; 53: 1197-1207
- Connectomic intermediate phenotypes for psychiatric disorders.Front Psychiatry. 2012; 3: 32
- Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder.Biol Psychiatry. 2011; 70: 334-342
- Changes in community structure of resting state functional connectivity in unipolar depression.PloS One. 2012; 7: e41282
- Resting-state functional connectivity in late-life depression: Higher global connectivity and more long distance connections.Front Psychiatry. 2013; 3: 116
- Anomalous gray matter structural networks in major depressive disorder.Biol Psychiatry. 2013; 74: 777-785
- Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment.J Neurosci. 2012; 32: 4307-4318
- Abnormal brain anatomical topological organization of the cognitive-emotional and the frontoparietal circuitry in major depressive disorder [published online ahead of print November 22].Magn Reson Med. 2013; (doi:10.1002/mrm.25036)
- Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging.PloS One. 2012; 7: e45972
- Mapping inter-regional connectivity of the entire cortex to characterize major depressive disorder: A whole-brain diffusion tensor imaging tractography study.Neuroreport. 2012; 23: 566-571
- Brain imaging predictors and the international study to predict optimized treatment for depression: Study protocol for a randomized controlled trial.Trials. 2013; 14: 224
- International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: Rationale and protocol.Trials. 2011; 12: 4
- Cortical surface-based analysis. I. Segmentation and surface reconstruction.Neuroimage. 1999; 9: 179-194
- Regional heterogeneity in limbic maturational changes: Evidence from integrating cortical thickness, volumetric and diffusion tensor imaging measures.Neuroimage. 2011; 55: 868-879
- An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.Neuroimage. 2006; 31: 968-980
- Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain.Neuron. 2002; 33: 341-355
- Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?.Neuroimage. 2007; 34: 144-155
- BrainNet Viewer: A network visualization tool for human brain connectomics.PLoS One. 2013; 8: e68910
- Complex network measures of brain connectivity: Uses and interpretations.Neuroimage. 2010; 52: 1059-1069
- Functional connectivity in the resting brain: A network analysis of the default mode hypothesis.Proc Natl Acad Sci U S A. 2003; 100: 253-258
- Widespread reductions in grey matter volume in depression.Neuroimage Clin. 2013; 3: 332-339
- An analysis of functional neuroimaging studies of dorsolateral prefrontal cortical activity in depression.Psychiatry Res. 2006; 148: 33-45
- The functional neuroanatomy of depression: Distinct roles for ventromedial and dorsolateral prefrontal cortex.Behav Brain Res. 2009; 201: 239-243
- Using standardized fMRI protocols to identify patterns of prefrontal circuit dysregulation that are common and specific to cognitive and emotional tasks in major depressive disorder: First wave results from the iSPOT-D study.Neuropsychopharmacology. 2013; 38: 863-871
- Impaired prefronto-thalamic functional connectivity as a key feature of treatment-resistant depression: A combined MEG, PET and rTMS study.PloS One. 2013; 8: e70089
- The subgenual anterior cingulate cortex in mood disorders.CNS Spectr. 2008; 13: 663-681
- Resting-state functional connectivity in major depression: Abnormally increased contributions from subgenual cingulate cortex and thalamus.Biol Psychiatry. 2007; 62: 429-437
- Subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression.Biol Psychiatry. 2008; 64: 461-467
- Resting-state cortico-thalamic-striatal connectivity predicts response to dorsomedial prefrontal rTMS in major depressive disorder.Neuropsychopharmacology. 2014; 39: 488-498
- Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers.Neuroimage. 2008; 41: 1267-1277
- The parcellation-based connectome: Limitations and extensions.Neuroimage. 2013; 80: 397-404
- Concepts and principles in the analysis of brain networks.Ann N Y Acad Sci. 2011; 1224: 126-146
- Whole-brain anatomical networks: Does the choice of nodes matter?.Neuroimage. 2010; 50: 970-983
- Network scaling effects in graph analytic studies of human resting-state FMRI data.Front Syst Neurosci. 2010; 4: 22
Article info
Publication history
Published online: March 10, 2014
Accepted:
February 13,
2014
Received in revised form:
February 12,
2014
Received:
December 10,
2013
Identification
Copyright
© 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
ScienceDirect
Access this article on ScienceDirectLinked Article
- Connectomics Reveals Faulty Wiring Patterns for Depressed BrainBiological PsychiatryVol. 76Issue 7
- PreviewUncovering the neural basis of psychiatric and neurological disorders is the foundation for the development of diagnosis and treatment programs. While disorder-related changes in focal brain areas and specific brain connections have been scrutinized, a recently developed research framework—human brain connectomics (1)—offers the opportunity to study the brain as a complex, integrative network. In a nutshell, a brain network can be constructed on the basis of connections (edges) among brain regions (nodes) derived from a variety of imaging data.
- Full-Text
- Preview