Abstract
Background
Altered brain structural connectivity has been implicated in the pathophysiology of
psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major
depressive disorder (MDD). However, it is unknown which part of these connectivity
abnormalities are disorder specific and which are shared across the spectrum of psychotic
and affective disorders. We investigated common and distinct brain connectivity alterations
in a large sample (N = 1743) of patients with SZ, BD, or MDD and healthy control (HC) subjects.
Methods
This study examined diffusion-weighted imaging-based structural connectome topology
in 720 patients with MDD, 112 patients with BD, 69 patients with SZ, and 842 HC subjects
(mean age of all subjects: 35.7 years). Graph theory–based network analysis was used
to investigate connectome organization. Machine learning algorithms were trained to
classify groups based on their structural connectivity matrices.
Results
Groups differed significantly in the network metrics global efficiency, clustering,
present edges, and global connectivity strength with a converging pattern of alterations
between diagnoses (e.g., efficiency: HC > MDD > BD > SZ, false discovery rate–corrected
p = .028). Subnetwork analysis revealed a common core of edges that were affected across
all 3 disorders, but also revealed differences between disorders. Machine learning
algorithms could not discriminate between disorders but could discriminate each diagnosis
from HC. Furthermore, dysconnectivity patterns were found most pronounced in patients
with an early disease onset irrespective of diagnosis.
Conclusions
We found shared and specific signatures of structural white matter dysconnectivity
in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results
showed a compromised brain communication across a spectrum of major psychiatric disorders.
Keywords
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
- Textbook of Psychiatry.The Macmillan Company, New York1924
- Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders.Nat Hum Behav. 2019; 3: 988-998
- Associated microscale spine density and macroscale connectivity disruptions in schizophrenia.Biol Psychiatry. 2016; 80: 293-301
- The human connectome: A complex network.Ann N Y Acad Sci. 2011; 1224: 109-125
- Complex brain networks: Graph theoretical analysis of structural and functional systems [published correction appears in Nat Rev Neurosci 2009; 10:312.Nat Rev Neurosci. 2009; 10: 186-198
- A cross-disorder connectome landscape of brain dysconnectivity.Nat Rev Neurosci. 2019; 20: 435-446
- Analysis of shared heritability in common disorders of the brain.Science. 2018; 360eaap8757
- The p factor: One general psychopathology factor in the structure of psychiatric disorders?.Clin Psychol Sci. 2014; 2: 119-137
- Abnormal rich club organization and functional brain dynamics in schizophrenia.JAMA Psychiatry. 2013; 70: 783-792
- Anatomical insights into disrupted small-world networks in schizophrenia.Neuroimage. 2012; 59: 1085-1093
- Bipolar disorder and gender are associated with frontolimbic and basal ganglia dysconnectivity: A study of topological variance using network analysis.Brain Connect. 2019; 9: 745-759
- Brain network analysis reveals affected connectome structure in bipolar I disorder.Hum Brain Mapp. 2016; 37: 122-134
- Diffusion tensor imaging brain structural clustering patterns in major depressive disorder.Hum Brain Mapp. 2021; 42: 5023-5036
- Abnormal structural networks characterize major depressive disorder: A connectome analysis.Biol Psychiatry. 2014; 76: 567-574
- Structural alterations of the brain preceded functional alterations in major depressive disorder patients: Evidence from multimodal connectivity.J Affect Disord. 2019; 253: 107-117
- Differences in network properties of the structural connectome in bipolar and unipolar depression.Psychiatry Res Neuroimaging. 2022; 321111442
- Neurobiological commonalities and distinctions among 3 major psychiatric disorders: A graph theoretical analysis of the structural connectome.J Psychiatry Neurosci. 2020; 45: 15-22
- Anatomical dysconnectivity in bipolar disorder compared with schizophrenia: A selective review of structural network analyses using diffusion MRI.J Affect Disord. 2017; 209: 217-228
- A structural brain network of genetic vulnerability to psychiatric illness.Mol Psychiatry. 2021; 26: 2089-2100
- The Marburg-Münster Affective Disorders Cohort Study (MACS): A quality assurance protocol for MR neuroimaging data.Neuroimage. 2018; 172: 450-460
- Neurobiology of the major psychoses: A translational perspective on brain structure and function—The FOR2107 consortium.Eur Arch Psychiatry Clin Neurosci. 2018; 269: 949-962
- Strukturiertes Klinisches Interview für DSM-VI (SKID).Hogrefe, Göttingen, Germany1997
- Structural and functional connectivity reconstruction with CATO-A Connectivity Analysis TOolbox.bioRxiv. 2021; https://doi.org/10.1101/2021.05.31.446012
- A model-based method for retrospective correction of geometric distortions in diffusion-weighted EPI.Neuroimage. 2002; 16: 177-199
- Mapping the structural core of human cerebral cortex.PLoS Biol. 2008; 6: e159
- Mapping the human connectome at multiple scales with diffusion spectrum MRI.J Neurosci Methods. 2012; 203: 386-397
- Fiber tracking: Principles and strategies – A technical review.NMR Biomed. 2002; 15: 468-480
- Mapping connectomes with diffusion MRI: Deterministic or probabilistic tractography?.Magn Reson Med. 2019; 81: 1368-1384
- Connectome sensitivity or specificity: Which is more important?.Neuroimage. 2016; 142: 407-420
- Estimating false positives and negatives in brain networks.Neuroimage. 2013; 70: 402-409
- Complex network measures of brain connectivity: Uses and interpretations.Neuroimage. 2010; 52: 1059-1069
- 10kin1day: A bottom-up neuroimaging initiative.Front Neurol. 2019; 10: 425
- Python 3 Reference Manual.CA, Scotts Valley2009
- Controlling the false discovery rate: A practical and powerful approach to multiple testing.J R Stat Soc B: Stat Methodol. 1995; 57: 289-300
- Network-based statistic: Identifying differences in brain networks.Neuroimage. 2010; 53: 1197-1207
- Age of onset of mental disorders: A review of recent literature.Curr Opin Psychiatry. 2007; 20: 359-364
- Age at onset and the outcomes of schizophrenia: A systematic review and meta-analysis.Early Interv Psychiatry. 2017; 11: 453-460
- Making individual prognoses in psychiatry using neuroimaging and machine learning.Biol Psychiatry Cogn Neurosci Neuroimaging. 2018; 3: 798-808
- Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.Neuroimage. 2017; 145: 137-165
- Detecting neuroimaging biomarkers for schizophrenia: A meta-analysis of multivariate pattern recognition studies.Neuropsychopharmacology. 2015; 40: 1742-1751
- Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: A systematic review.Front Aging Neurosci. 2017; 9: 329
- PHOTONAI-A Python API for rapid machine learning model development.PLoS One. 2021; 16e0254062
- Detecting neuroimaging biomarkers for psychiatric disorders: Sample size matters.Front Psychiatry. 2016; 7: 50
- Systematic misestimation of machine learning performance in neuroimaging studies of depression.Neuropsychopharmacology. 2021; 46: 1510-1517
- Using structural MRI to identify bipolar disorders −13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.Mol Psychiatry. 2020; 25: 2130-2143
- Topological properties of brain structural networks represent early predictive characteristics for the occurrence of bipolar disorder in patients with major depressive disorder: A 7-year prospective longitudinal study.Front Psychiatry. 2018; 9: 704
- Connectome organization is related to longitudinal changes in general functioning, symptoms and IQ in chronic schizophrenia.Schizophr Res. 2016; 173: 166-173
- Early onset of depression and treatment outcome in patients with major depressive disorder.J Psychiatr Res. 2021; 139: 150-158
- Raincloud plots: A multi-platform tool for robust data visualization.Wellcome Open Res. 2019; 4: 63
- NeuroMArVL.Monash University, 2017 (Available at:)https://immersive.erc.monash.edu/neuromarvl/Date accessed: March 30, 2022
- BrainNet Viewer: A network visualization tool for human brain connectomics.PLoS One. 2013; 8e68910
Article Info
Publication History
Published online: June 21, 2022
Accepted:
May 31,
2022
Received in revised form:
May 27,
2022
Received:
November 15,
2021
Publication stage
In Press Journal Pre-ProofFootnotes
JR and MG contributed equally to this work as joint first authors.
MvdH and UD contributed equally to this work as joint last authors.
Identification
Copyright
© 2022 Society of Biological Psychiatry.
