Scalable Subcortical Shape Analysis from the ENIGMA Bipolar Disorder Working Group (N=2,995)
Poster No:
426
Submission Type:
Abstract Submission
Authors:
Yanghee Im1, Melody Kang1, Sophia Thomopoulos1, Ole Andreasson2, Paul Thompson1, Chris Ching1, ENIGMA BD Working Group1
Institutions:
1University of Southern California, Los Angeles, CA, 2Oslo University Hospital, Oslo, Norway
First Author:
Co-Author(s):
Introduction:
Bipolar disorder (BD) is characterized by deficits in emotional and reward processing (Merikangas, 2021; Strakowski, 2012; Phillips, 2014) that may be related to altered subcortical volumes in regions such as the hippocampus and thalamus (Hibar, 2016). The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group (ENIGMA-BD) is pooling data and resources from around the world to create the largest global neuroimaging BD dataset. Following our study of gross subcortical volumes (Hibar, 2016), we applied a high-resolution subcortical shape analysis technique to this large, multicenter cohort to evaluate localized patterns of morphometric variation. We hypothesized that those with BD would have locally thinner and contracted hippocampal, amygdala and thalamic volumes compared to healthy controls (HC), and that shape analysis would reveal more spatially complex morphometric alterations associated with treatment than were detected in our previous gross volumetric analysis (Hibar, 2016). Finally, we evaluated a novel method to enhance model fitting efficiency when analyzing large-scale, multisite data.
Methods:
T1-weighted brain MRI data from 19 retrospective study samples (BD=1,218, HC=1,777; ages 8.0 - 86.5, 56% female) (http://enigma.ini.usc.edu/ongoing/enigma-bipolar-working-group/) were processed using the ENIGMA Shape Analysis Protocols (http://enigma.ini.usc.edu/protocols/imaging-protocols/) to derive two shape metrics: 1) radial distance (thickness), which measures the distance from surface points to a medial curve (Gutman, 2012), and 2) the Jacobian determinant, which indicates local surface dilation and contraction of the surface mesh template (Gutman, 2015). These measures were computed for the left and right nucleus accumbens, amygdala, caudate, hippocampus, putamen, pallidum and thalamus models. A linear mixed effects model was fit at each homologous thickness and Jacobian value across the surface to assess BD versus HC group differences and model associations with treatment at time of scan (lithium, antiepileptic, 1st/2nd generation antipsychotic, antidepressant) while accounting for site as a random effect and adjusting for age, sex, and intracranial volume (ICV). The fast and efficient mixed‐effects algorithm (FEMA) (Parekh, 2024) was tested to improve computational efficiency compared to traditional linear mixed models (fitlme). All results were corrected for multiple comparisons (FDR q<0.05).
Results:
Compared to HC, those with BD had smaller gross hippocampal and amygdala volumes. Subcortical shape analysis revealed complex differences between groups, including differentially thicker/thinner and dilated/contracted subregions across all structures in those with BD (Figure 1A). Cohen's D effect sizes from shape analysis were higher than those from subcortical volume analysis (Figure 2). Those with BD taking antiepileptic medications had patterns of lower thickness and contracted surface area compared to those not taking antiepileptics (Figure 1B). FEMA significantly accelerated model fitting, providing a 14-fold increase in speed compared to the traditional approaches (Figure 1C).
Conclusions:
Our shape analysis findings extend our prior analyses documenting smaller overall hippocampal, thalamic and amygdala volumes in BD (Hibar, 2016). Shape analysis provided more detailed maps of localized morphometric variations across these structures. Notably, the caudate and putamen showed patterns of local surface dilation, which were not detected in our earlier gross volumetric studies. The extent to which these shape variations correspond to specific subfields may provide new insights into the distinct neuronal populations affected by BD. Finally, FEMA significantly boosted computational performance (Parekh, 2024), which is essential for powering large-scale, vertex-wise analysis such as these.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Other Methods
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
Data analysis
Machine Learning
MRI
Psychiatric Disorders
Statistical Methods
STRUCTURAL MRI
Other - Big Data Analysis
1|2Indicates the priority used for review
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Provide references using APA citation style.
Strakowski SM et al. (2012) The functional neuroanatomy of bipolar disorder: a consensus model. Bipolar Disorders 14(4):313-325.
Phillips ML et al. (2014) A critical appraisal of neuroimaging studies of bipolar disorder: toward a new conceptualization of underlying neural circuitry and a road map for future research. Am J Psychiatry 171(8):829-43.
Hibar DP, et al. (2016) Subcortical volumetric abnormalities in bipolar disorder. Mol Psychiatry 21(12):1710-1716. doi: 10.1038/mp.2015.227
http://enigma.ini.usc.edu/ongoing/enigma-bipolar-working-group/
http://enigma.ini.usc.edu/protocols/imaging-protocols/
Gutman, B.A. et al. (2012) Shape matching with medial curves and 1-D group-wise registration. 9th IEEE International Symposium on Biomedical Imaging, 716–719.
Gutman, B.A. et al. (2015) Medial Demons Registration Localizes the Degree of Genetic Influence over Subcortical Shape Variability: An N=1480 Meta-analysis. IEEE International Symposium on Biomedical Imaging, 1402-1406.
Parekh, P. et al. (2024). FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data. Human Brain Mapping, 45(2), e26579.
Wang L. et al. (2008) Progressive deformation of deep brain nuclei and hippocampal-amygdala formation in schizophrenia. Biol Psychiatry 64:1060-1068.