Poster No:
583
Submission Type:
Abstract Submission
Authors:
Lukas Sempach1, Sarah Ulrich1, Laura Han2,3, Elena Pozzi2,3, Dick Veltman4, Lianne Schmaal2,3, Paul Thompson5, André Schmidt1, for the ENIGMA Major Depressive Disorder Working Group6
Institutions:
1Department of Clinical Research, University Psychiatric Clinics, University of Basel, Basel, Switzerland, 2Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia, 3Orygen, Parkville, VIC, Australia, 4Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam, North Netherlands, 5Imaging Genetics Center, Keck School of Medicine of University of Southern California, Los Angeles, CA, 6https://enigma.ini.usc.edu/ongoing/enigma-mdd-working-group/, International
First Author:
Lukas Sempach
Department of Clinical Research, University Psychiatric Clinics, University of Basel
Basel, Switzerland
Co-Author(s):
Sarah Ulrich
Department of Clinical Research, University Psychiatric Clinics, University of Basel
Basel, Switzerland
Laura Han
Centre for Youth Mental Health, The University of Melbourne, Parkville|Orygen, Parkville
VIC, Australia|VIC, Australia
Elena Pozzi
Centre for Youth Mental Health, The University of Melbourne, Parkville|Orygen, Parkville
VIC, Australia|VIC, Australia
Dick Veltman
Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Psychiatry
Amsterdam, North Netherlands
Lianne Schmaal
Centre for Youth Mental Health, The University of Melbourne, Parkville|Orygen, Parkville
VIC, Australia|VIC, Australia
Paul Thompson, PhD
Imaging Genetics Center, Keck School of Medicine of University of Southern California
Los Angeles, CA
André Schmidt
Department of Clinical Research, University Psychiatric Clinics, University of Basel
Basel, Switzerland
Introduction:
Major depressive disorder (MDD) is a disabling disorder characterized by a heterogeneous phenotype encompassing a spectrum of symptom profiles. MDD varies in clinical characteristics such as frequency and duration of episodes, response to medication, and stability of remission (Cuijpers et al. 2020). This clinical heterogeneity also implies heterogeneity in the underlying pathophysiology. In terms of neurostructural hallmarks of MDD, large-scale studies have provided evidence for the presence of several structural differences in MDD patients compared to healthy individuals (Schmaal et al. 2017; 2016). These differences, albeit detectable, are small in terms of effect size, a common observation to neuroanatomical differences between MDD patients and healthy individuals (Winter et al. 2022). Heightened neurostructural heterogeneity in MDD patients might be a factor contributing to these subtle group differences. Thus, decomposing variance across neurostructural measures and distinct brain regions in MDD has the potential to map different neuroanatomical phenotypes onto different clinical phenotypes. This study aims to investigate and quantify the neurostructural heterogeneity in MDD patients, providing insights into variance differences and linking them to clinical measures.
Methods:
This collaborative effort by the ENIGMA MDD consortium quantified neurostructural heterogeneity in MDD patients (N=3641) and healthy individuals (N=4876) from 40 international sites. Heterogeneity was computed as region-specific and global variance, based on structural magnetic resonance imaging (MRI) measures of subcortical volume (SV), cortical surface area (SA), and cortical thickness (CT). Neuroimaging data were preprocessed following standardized ENIGMA protocols and harmonized using neuroComBat (Fortin et al. 2018) and all analyses were performed on data matched for age and sex. Region-specific differences in variance were measured using the Coefficient of Variation (CV) and the Variability Ratio (VR). To assess variance globally, the Person-Based Similarity Index (PBSI) was calculated (Doucet et al. 2019). A lower PBSI score indicates higher neurostructural variance in the subject's brain profile. Lastly, a normative modelling approach was applied computing the norm-PBSI indicating the 'normativeness' of neuroanatomical brain profiles of MDD patients. Deviating norm-PBSI scores were identified (> -2 SD) and clinical features of non-deviating and deviating MDD patients were compared.
Results:
MDD patients demonstrated increased variance (CV > 1) in the CT of 6 temporal lobe brain regions (p < .05). No region-specific variance differences were observed for SA and SV. The PBSI revealed increased heterogeneity in the CT profiles of MDD patients (PBSI-MDD = 0.801, PBSI-HC = 0.807, p < .001, d = 0.21). Again, no differences in heterogeneity between the groups were observed for SA and SV profiles. The norm-PBSI analysis identified deviating MDD patients across all three domains: 193 (5.87%) for SV, 173 (5.25%) for CT, and 153 (4.63%) for SA. Compared to non-deviators in CT, MDD patients with deviating CT profiles reported higher severity of depressive symptoms (p < .03). And MDD patients with deviating SA profiles experienced less episodes than non-deviators in SA (p < .02).
Conclusions:
This examination of both regional and global neuroanatomical heterogeneity demonstrates an increase in CT variance among MDD patients. Notably, MDD patients with a neuroanatomical phenotype reflecting most pronounced heterogeneity in CT (norm deviators) exhibited the highest symptom burden. Together, these findings serve as an initial benchmark, shedding light on the clinical significance of neuroanatomical heterogeneity in MDD patients, which may foster stratification and treatment guidance in the future. Further longitudinal investigations are required to confirm the effect of CT heterogeneity on symptomatology.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Classification and Predictive Modeling
Other Methods
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Keywords:
Affective Disorders
Modeling
NORMAL HUMAN
Psychiatric Disorders
Statistical Methods
STRUCTURAL MRI
Other - Heterogeneity
1|2Indicates the priority used for review
Provide references using author date format
Cuijpers, P. (2020), “A Network Meta-Analysis of the Effects of Psychotherapies, Pharmacotherapies and Their Combination in the Treatment of Adult Depression.” World Psychiatry : Official Journal of the World Psychiatric Association (WPA) 19 (1): 92–107.
Doucet, G.E. (2019), “Person-Based Brain Morphometric Similarity Is Heritable and Correlates With Biological Features.” Cerebral Cortex 29 (2): 852–62.
Fortin, J.P. (2018), “Harmonization of Cortical Thickness Measurements across Scanners and Sites.” NeuroImage 167 (February): 104–20.
Schmaal, L. (2017), “Cortical Abnormalities in Adults and Adolescents with Major Depression Based on Brain Scans from 20 Cohorts Worldwide in the ENIGMA Major Depressive Disorder Working Group.” Molecular Psychiatry 22 (6): 900–909.
Schmaal, L. (2016), “Subcortical Brain Alterations in Major Depressive Disorder: Findings from the ENIGMA Major Depressive Disorder Working Group.” Molecular Psychiatry 21 (6): 806–12.
Winter, N.R. (2022), “Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities.” JAMA Psychiatry 79 (9).