Defining Cross-Disorder Structural Connectome Variation using Morphometric INverse Divergence
Poster No:
1206
Submission Type:
Abstract Submission
Authors:
Benjamin Chidiac1, Sarah Morgan2, Petra Vértes1
Institutions:
1University of Cambridge, Cambridge, Cambridgeshire, 2King's College London, London, London
First Author:
Co-Author(s):
Introduction:
Understanding how brain structure varies through health and disease is an essential goal of modern neuroscience as it can help uncover new pathways for prevention and treatment. A popular method used to achieve this is the construction of morphometric brain connectomes from structural neuroimaging data, which can provide a picture of disease expression at the global level (Wang & He, 2024). Recently, a novel method of morphometric connectome construction has been proposed that improves on key technical constraints of previous mapping techniques. Morphometric INverse Divergence (MIND) uses Kullback-Leibler divergence to estimate the probability density function of one or more morphological features in a brain region and by comparing feature distributions across regions, creates a map of brain-wide structural variation (Sebenius et al., 2023). MIND has been shown to provide a more reliable and informative measure of structural similarity (a proxy of connectivity and shared genetic basis) compared to alternative methods (Sebenius et al., 2023; Seidlitz et al., 2018; Sebenius et al., 2024). However, the cross-validity of this method and its ability to detect case-control differences is yet to be thoroughly examined.
Methods:
This study applied the MIND method to multi-site data from the US-based Bipolar and Schizophrenia Network on Immediate Phenotypes (B-SNIP) study. Data included demographics and T1-weighted MRI images of participants aged 15-65 years, including 281 cases with a diagnosis of either schizophrenia, schizoaffective and bipolar I disorder, 352 first-degree relatives and 182 healthy controls after quality control (N = 815). Images were acquired on 3T scanners and MPRAGE or IR-SPGR sequences were used as appropriate for the scanner brand, which varied across site. Scans were pre-processed using FreeSurfer 5.3 (Fischl et al., 1999) and MIND was calculated following the pipeline detailed at https://github.com/isebenius/MIND. Statistical analyses subsequently examined patterns of MIND variation across participant subgroups, including different disorder diagnoses, using two-tailed Student's t-tests and multivariate regression (controlling for site, age, sex, Euler number and regional brain volume, where appropriate).
Results:
Analyses showed a strong positive correlation between regional MIND scores from the B-SNIP study and those from the Adolescent Brain Cognitive Development (ABCD) study, which were reported in the original MIND validation study (Pearson's r = 0.97; Sebenius et al., 2023). Global (i.e., whole brain averaged) MIND scores in this study were not found to be predictive of age, but did significantly predict case and relative status compared to controls (p < 0.001). Visualised in Figure 1, subjects with schizophrenia displayed highest mean regional MIND scores relative to controls (MIND difference = 0.890), followed by those with schizoaffective disorder (MIND difference = 0.338); case-control differences for both these subgroups were most pronounced in somatosensory and visual cortices. In contrast, subjects with bipolar I disorder exhibited lower mean regional MIND compared to controls (MIND difference = -1.356), most evidently in auditory and language processing areas. Patterns in mean case-control differences observed across schizophrenia, schizoaffective and bipolar I disorder subgroups were all found to be significantly distinct from each other (p < 0.001).
Conclusions:
Results suggest MIND to be a reliable and insightful measure of case-control differences in structural similarity. It appears MIND is sensitive to variation in case-control differences across disorders, with decreasing mean regional similarity observed from schizophrenia to schizoaffective disorder to bipolar I disorder, relative to healthy controls. Future investigations of these cross-disorder differences at a more granular level may facilitate better understanding of the distinct structural profiles that characterise such disorders.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Novel Imaging Acquisition Methods:
Imaging Methods Other
Keywords:
Morphometrics
Psychiatric Disorders
Schizophrenia
STRUCTURAL MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Sebenius, I., Seidlitz, J., Warrier, V., Bethlehem, R. A. I., Alexander-Bloch, A., Mallard, T. T., Garcia, R. R., Bullmore, E. T., & Morgan, S. E. (2023). Robust estimation of cortical similarity networks from brain MRI. Nature Neuroscience, 26(8), 1461–1471. https://doi.org/10.1038/s41593-023-01376-7
Seidlitz, J., Váša, F., Shinn, M., Romero-Garcia, R., Whitaker, K. J., Vértes, P. E., Wagstyl, K., Kirkpatrick Reardon, P., Clasen, L., Liu, S., Messinger, A., Leopold, D. A., Fonagy, P., Dolan, R. J., Jones, P. B., Goodyer, I. M., NSPN Consortium, Raznahan, A., & Bullmore, E. T. (2018). Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation. Neuron, 97(1), 231–247.e7. https://doi.org/10.1016/j.neuron.2017.11.039
Wang, J., & He, Y. (2024). Toward individualized connectomes of brain morphology. Trends in Neurosciences, 47(2), 106–119. https://doi.org/10.1016/j.tins.2023.11.011