Redefine Trans-Neuropsychiatric Disorder Brain Patterns through Big-Data and Machine Learning
Description: PROJECT NARRATIVE/RELEVANCE The Connectomes Related to Human Disease/Human Connectome Project (CRHD) and Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) are two large scale NIH-funded initiatives exploring disorder-related patterns in the human brain. The two projects are complementary: CRHD focuses on in-depth, high-resolution multimodal phenotyping of the brain connectome in modest samples, whereas ENIGMA's analyses harness the statistical power of large and inclusive, worldwide samples. We propose combining the strengths of CRHD and ENIGMA to test novel biomarkers based on replicable and consistent illness deficit patterns provided by Big Data studies. We posit that major brain illnesses do not impact an isolated region or brain circuit but rather lead to deficit patterns across multiple brain regions and circuits that are shared or unique to an illness; such deficit patterns are highly replicable and linked to severity, duration and treatment resistance. We will develop and evaluate a multivariate vulnerability model based on (1) the degree of similarity between an individual's brain patterns and expected disorder patterns defined as a linear combination of deficits - conceptually similar to polygenic risk scores - and (2) a vulnerability metric computed using machine learning. We will test the specificity and sensitivity of these models against the single most discriminative neuroimaging traits in six diagnostic categories shared by CRHD and ENIGMA: schizophrenia-spectrum disorders (SSD) and psychosis, major depressive disorder (MDD), bipolar disorders (BP), epilepsy (EP), mild cognitive impairment (MCI), and Alzheimer's disease (AD). We will use multivariate analyses to compare sensitivity and specificity of the regional vulnerability index (RVI) and machine learning vulnerability (MVI) in separating patients and controls in the CHRD samples, mapping unique and shared deficit patterns across six diagnostic categories. We will link vulnerability metrics to cognitive outcomes and diagnosis using mediation analyses. Finally, we will enrich vulnerability models with CHRD's connectomics based phenotypes including cortical myelination to advance white matter integrity measurements for future large-scale ENIGMA studies.
Funding type: research
Funder scheme: RF1
Start date: 2021-04-01
End date: 2024-03-31
Investigator: PETER V. KOCHUNOV
Institution: UNIVERSITY OF MARYLAND BALTIMORE
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Funded outputs count: 168
Funder award ID: RF1MH123163
OpenAlex ID: G7341836837