Biology subjects

Rahaman, M. A.

Publications and source records attributed to Rahaman, M. A..

5 recordsLinked to original sources

bioRxiv2025

aNy-way ICA and its application to estimate cortico-thalamo-cerebellar functional links in schizophrenia

Disrupted large-scale brain network interactions are a hallmark of schizophrenia, yet the circuit-level mechanisms underlying cognitive deficits remain incompletely understood. In particular, cortico-thalamo-cerebellar circuitry has been implicated in "cognitive dysmetria," but its functional organization across subdomains has not been fully characterized. Here, we apply a flexible multimodal data fusion framework (aNy-way independent component analysis) to integrate 4D domain-specific resting-state fMRI data from the cortex, thalamus, and cerebellum. Using a large discovery cohort (FBIRN, N=311) and two independent replication datasets (COBRE, N=148; MPRC, N=364), we identify a robust cortico-thalamo-cerebellar circuit involving higher-order thalamic nuclei, visual cortex, default mode network, and cerebellar posterior regions. This circuit shows altered connectivity in schizophrenia, characterized by reduced anticorrelation between thalamus and cortex, and is consistently associated with cognitive impairments, including processing speed, working memory, and reasoning deficits across cohorts. Importantly, these findings are largely replicated across independent cohorts. Together, these results provide data-driven evidence for a cortico-thalamo-cerebellar mechanism underlying cognitive dysfunction in schizophrenia, supporting the cognitive dysmetria hypothesis. This work also demonstrates the utility of aNy-way independent component analysis for identifying clinically relevant brain circuits in psychiatric disorders. HighlightsO_LIIdentified cortico-thalamo-cerebellar circuits in schizophrenia C_LIO_LIAltered thalamocortical interactions differentiated schizophrenia C_LIO_LICircuit alterations were associated with cognitive impairment C_LIO_LIFindings are replicated across three independent multisite datasets C_LIO_LIMultidomain fusion enabled subdomain-specific circuit characterization C_LI

neuroscience↗

bioRxiv2024

SpaDE: Semantic Locality Preserving Biclusteringfor 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. Biclustering is a potential solution since subject heterogeneity makes one-dimensional clustering less effective in this realm. Yet, high dimensional sparse input space and semantically incoherent grouping of attributes make post hoc analysis challenging. Therefore, we propose a deep neural network called semantic locality preserving auto decoder (SpaDE), for unsupervised feature learning and biclustering. SpaDE produces coherent subgroups of subjects and neural features preserving semantic locality and enhancing neurobiological interpretability. Also, it regularizes for sparsity to improve representation learning. We employ SpaDE on human brain connectome collected from schizophrenia (SZ) and healthy control (HC) subjects. The model outperforms several state-of-the-art biclustering methods. Our method extracts modular neural communities showing significant (HC/SZ) group differences in distinct brain networks including visual, sensorimotor, and subcortical. Moreover, these biclustered connectivity substructures exhibit substantial relations with various cognitive measures such as attention, working memory, and visual learning.

neuroscience↗

bioRxiv2024

Multi-modal Spatial-modality Attentive Fusion for Studying Neurodegenerative Disorders

Multi-modal 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. These biological sources each present distinct yet correlated expressions of subjects underlying physiological processes. Joint learning from these data sources can improve our understanding of the disorder. However, combining these biological sources is challenging for several reasons: (i) observations are domains-specific, leading to data being represented in dissimilar subspaces, and (ii) fused data is often noisy and high-dimensional, making it challenging to identify relevant information. To address these challenges, we propose a multi-modal artificial intelligence (AI) model with a novel fusion module inspired by a bottleneck attention module (BAM). We use deep neural networks (DNN) to learn latent space representations of the input streams. Next, we introduce a two-dimensional (spatio-modality) attention module to regulate the intermediate fusion for SZ classification. We implement spatial attention via a dilated convolutional neural network that creates large receptive fields for extracting significant contextual patterns. The resulting joint learning framework maximizes complementarity allowing us to explore the correspondence among the modalities. We test our model on a multi-modal imaging-genetic dataset and achieve an SZ prediction accuracy of 94.10% (P < 0.0001), outperforming state-of-the-art unimodal and multi-modal models for the task. Moreover, the model provides inherent interpretability that helps identify concepts significant for the neural networks decision and explains the underlying physiopathology of the disorder. Results also show that functional connectivity among subcortical, sensorimotor, and cognitive control domains plays an important role in characterizing SZ. Analysis of the spatio-modality attention scores suggests that structural components like the supplementary motor area, caudate, and insula play a significant role in SZ. Biclustering the attention scores discover a multi-modal cluster that includes genes CSMD1, ATK3, MOB4, and HSPE1, all of which have been identified as relevant to schizophrenia. In summary, feature attribution appears to be especially useful for probing the transient and confined but decisive patterns of complex disorders, and it shows promise for extensive applicability in future studies.

neuroscience↗

bioRxiv2020

Statelets: high dimensional predominant shapes 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) approaches for analyzing dFNC continuously models the system through a fixed set of connectivity patterns or states. It assumes these patterns are span throughout the brain, but in practice, they are more spatially constrained and temporally short-lived. Thus, SWC is not designed to capture transient dynamic changes nor heterogeneity across subjects/time. Here, we adapt time series motifs to model the temporal dynamics of functional connectivity. We propose a state-space data mining approach that combines a probabilistic pattern summarization framework called Statelets -- a subset of high dimensional state-shape prototypes capturing the dynamics. We handle scale differences using the earth mover distance and utilize kernel density estimation to build a probability density profile for local motifs. We apply the framework to study dFNC collected from patients with schizophrenia (SZ) and healthy control (HC). Results demonstrate SZ subjects exhibit reduced modularity in their brain network organization relative to HC. These statelets in the HC group show more recurrence across the dFNC time-course compared to the SZ. An analysis of the consistency of the connections across time reveals significant differences within visual, sensorimotor, and default mode regions where HC subjects show higher consistency than SZ. The introduced statelet-approach also enables the handling of dynamic information in cross-modal applications to study healthy and disordered brains and multi-modal fusion within a single dataset.

neuroscience↗

bioRxiv2020

A novel method for tri-clustering dynamic functional network connectivity (dFNC) identifies significant schizophrenia effects across multiple states in distinct subgroups of individuals

BackgroundBrain 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., sliding window clusters). Though, such an approach does not factor in the homogeneity of underlying data and may end up with a less meaningful subgrouping of the dataset. MethodsDynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more apples-to-apples comparison between groups within analogous subsets of time-space and subjects. dNTiC sorts the dFNC states by maximizing similarity across individuals and minimizing variance among the pairs of components within a state. ResultsResulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared to HC, SZ in most tri-clusters show hypoconnectivity (low positive) among subcortical, default mode, cognitive control but hyper-connectivity (high positive) between sensory networks. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks compared to SZ. Results also provide statistically significant difference in reoccurrence time between SZ and HC subjects for two distinct dFNC states. ConclusionsOutcomes emphasize the utility of the proposed method for characterizing and leveraging variance within high-dimensional data to enhance the interpretability and sensitivity of measurements in the study of a heterogeneous disorder like schizophrenia and in unconstrained experimental conditions such as resting fMRI.

neuroscience↗