Granular factoring into neuroimaging dynamic across space, time, and modality
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Rahaman, Md Abdur
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Abstract
The proliferation of data across scientific, industrial, and biomedical fields has ushered in an era of increasingly complex, high-dimensional, and heterogeneous datasets. These datasets span multiple modalities, temporal dynamics, and spatial hierarchies, rendering conventional analytical methods insufficient. Informative patterns often remain obscured in large-scale datasets due to inherent complexity, including noise, high dimensionality, and confounding variables that mask true underlying signals. Therefore, a central challenge in modern data science is to extract meaningful, interpretable signatures from this layered complexity, particularly when variability across individuals is substantial and multifaceted. Unlocking these hidden structures requires methods that can adapt to heterogeneity, preserve fine-grained information, and operate effectively across multiple levels of abstraction.
In computational neuroscience, the focus is on decoding the human brain through diverse neuroimaging technologies such as magnetic resonance imaging (MRI), positron emission tomography (PET), and electroencephalography (EEG). These technologies generate large-scale data aimed at understanding brain structure, function, and the neural basis of brain disorders. However, individual variability, demographics, and biological underpinnings introduce heterogeneity in the biological population. This heterogeneity is heightened further in neuropsychiatric disorders such as schizophrenia, autism, and Alzheimer's due to diversity in disease effects, progression, and symptom manifestation. Therefore, the explanatory signals, such as features, trends, and biomarkers, are often temporally transient, spatially constrained, and perceptible only within specific subpopulations. Consequently, traditional approaches that rely on population-level averaging often fail to capture the localized or subgroup-specific trends that are critical for understanding complex systems.
This dissertation introduces a comprehensive computational framework for granular factoring into large-scale, heterogeneous data, such as neuroimaging, medical informatics, and genomics. The central idea is to navigate complex data landscapes by stratifying them into smaller, homogeneous substructures, thereby enabling localized exploration and targeted knowledge extraction. Rather than flattening or averaging across differences, the proposed models leverage heterogeneity as a source of insight-preserving local structure, revealing subgroup-specific patterns, and improving generalization across applications.
To operationalize this vision, novel algorithms are developed for spatial, temporal, and modality-aware clustering, biclustering, and subgroup discovery. These methods are complemented by summarization strategies that distill massive datasets into lower-dimensional, information-rich representations while retaining critical trends and associations. Deep neural architectures for multi-modal data fusion further enable the integration of disparate sources, supporting holistic analysis and cross-domain reasoning in environments with high signal complexity and strong domain interdependence. The effectiveness of the proposed methodologies is demonstrated in high-stakes application areas, particularly in neuroimaging and biomedical informatics. Here, the models show promise in characterizing population heterogeneity, identifying subtypes in neuropsychiatric disorders, and uncovering clinically relevant biomarkers in imaging, behavioral, and genomic modalities.
By providing a unified computational toolkit for analyzing large, complex, and heterogeneous datasets, this work contributes to the broader field of data science. Notably, the thesis advances computational neuroscience by introducing scalable modeling techniques that enable fine-grained analysis of brain function and dysfunction across healthy and clinical populations. It bridges algorithmic innovation with domain-driven insight, advancing the design of interpretable and adaptive models capable of addressing real-world challenges across scientific and societal domains. The framework has broader relevance for fields such as bioinformatics, healthcare analytics, environmental monitoring, and beyond, where the ability to decode structure from complexity is essential to translate data into knowledge.
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Date
2025-04-28
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Dissertation