Imaging-genomic spatial-modality attentive fusion for studying neuropsychiatric disorders.

Multimodal learning has emerged as a powerful technique that leverages diverse data sources to enhance learning and decision-making processes. Adapting this approach to analyzing data collected from different biological domains is intuitive, especially for studying neuropsychiatric disorders. A complex neuropsychiatric disorder like schizophrenia (SZ) can affect multiple aspects of the brain and biologies.

An interpretable generative multimodal neuroimaging-genomics framework for decoding alzheimer's disease.

Alzheimer's disease (AD) is the most prevalent form of dementia, affecting millions worldwide with a progressive decline in cognitive abilities. The AD continuum encompasses a prodromal stage known as Mild Cognitive Impairment (MCI), where patients may either progress to AD (MCIc) or remain stable (MCInc). Understanding the underlying mechanisms of AD requires complementary analyses relying on different data sources, leading to the development of multimodal deep learning models.

SpaDE: Semantic Locality Preserving Biclustering for Neuroimaging Data.

The most discriminative and revealing patterns in the neuroimaging population are often confined to smaller subdivisions of the samples and features. Especially in neuropsychiatric conditions, symptoms are expressed within micro subgroups of individuals and may only underly a subset of neurological mechanisms. As such, running a whole-population analysis yields suboptimal outcomes leading to reduced specificity and interpretability.

A Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and Age.

To develop an approach to evaluate multiple overlapping brain functional change patterns (FCPs) in functional network connectivity (FNC) and apply to study developmental changes in brain function. FNC, the network analog of functional connectivity (FC), is commonly used to capture the intrinsic functional relationships among brain networks. Ongoing research on longitudinal changes of intrinsic FC across whole-brain functional networks has proven useful for characterizing age-related changes, but to date, there has been little focus on capturing multivariate patterns of FNC change with brain development.

Deep multimodal predictome for studying mental disorders.

Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders.

Statelets: Capturing recurrent transient variations in dynamic functional network connectivity.

Dynamic functional network connectivity (dFNC) analysis is a widely used approach for capturing brain activation patterns, connectivity states, and network organization. However, a typical sliding window plus clustering (SWC) approach for analyzing dFNC models the system through a fixed sequence of connectivity states. SWC assumes connectivity patterns span throughout the brain, but they are relatively spatially constrained and temporally short-lived in practice.

Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness.

Neuropsychiatric disorders such as schizophrenia are very heterogeneous in nature and typically diagnosed using self-reported symptoms. This makes it difficult to pose a confident prediction on the cases and does not provide insight into the underlying neural and biological mechanisms of these disorders. Combining neuroimaging and genomic data with a multi-modal 'predictome' paves the way for biologically informed markers and may improve prediction reliability.

Shared sets of correlated polygenic risk scores and voxel-wise grey matter across multiple traits identified via bi-clustering.

Neuropsychiatric disorders involve complex polygenic determinants as well as brain alterations. The combination of genetic inheritance and neuroimaging approaches could advance our understanding of psychiatric disorders. However, cross-disorder overlap is a current issue since psychiatric conditions share some neurogenetic correlates, symptoms, and brain effects.

Tri-Clustering Dynamic Functional Network Connectivity Identifies Significant Schizophrenia Effects Across Multiple States in Distinct Subgroups of Individuals.

Brain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e.

NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders.

Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders.

N-BiC: A Method for Multi-Component and Symptom Biclustering of Structural MRI Data: Application to Schizophrenia.

Objective: We propose and develop a novel biclustering (N-BiC) approach for performing N-way biclustering of neuroimaging data. Our approach is applicable to an arbitrary number of features from both imaging and behavioral data (e.g.

Epidemiological predictors of metabolic syndrome in urban West Bengal, India.

Introduction: Metabolic syndrome is one of the emerging health problems of the world. Its prevalence is high in urban areas. Though pathogenesis is complex, but the interaction of obesity, sedentary lifestyle, dietary, and genetic factors are known as contributing factors.