| dc.description.abstract | Intact structural connectivity among brain regions is critical to cognition. Structural connectivity forms the substratum for information flow between brain regions, and its plasticity is a hallmark of learning in the brain. Moreover, structural connectivity markers constitute a heritable phenotype. Investigating neuroanatomical connectivity in the human brain is, therefore, critical not only for uncovering the neural underpinnings of behavior but also for understanding connectomic bases of neurodevelopmental and neurodegenerative disorders, such as autism and Alzheimer’s Disease. Diffusion magnetic resonance imaging (dMRI) and tractography are among the only techniques, at present, that enable estimation of anatomical connectivity in the human brain, in-vivo. By tracking the anisotropic diffusion of water molecules in white matter, dMRI and tractography enable post hoc reconstruction of contiguous fascicles between distal brain regions. How accurately can dMRI and tractography track these connections to match ground-truth in the brain? Are structural connections between specific pairs of brain regions informative about subjects’ cognitive capacities, like attention? Could changes in these connections indicate mechanisms of cognitive decline, both in healthy and pathologically aging populations?
In this thesis, I report results from three studies, each of which addresses one of these key questions. In the first study, I explored how the midbrain contributes to attention, by combining model-based analysis of behavior with dMRI-tractography. Specifically, I examined the role of the superior colliculus (SC), a vertebrate midbrain structure, in attention. Does the SC control perceptual sensitivity to attended information, does it enable biasing choices toward attended information, or both? I mapped structural connections of the human SC with neocortical regions and found that the strengths of these connections correlated with, and were strongly predictive of, individuals’ choice bias, but not sensitivity. Taken together with previous studies, these results indicate that the human SC may play an evolutionarily conserved role in controlling choice bias during visual attention.
In the second study, I developed a novel approach, implemented on GPUs, for pruning structural connectomes, at scale. First, I identified key limitations of a state-of-the-art connectome pruning technique, Linear Fascicle Evaluation (LiFE), and introduced a GPU-based implementation that achieves >100x speedups over conventional CPU-based implementations. Leveraging these speedups, I advanced LiFE’s algorithm by imposing a regularization constraint on estimated fiber weights. This regularized, accelerated, LiFE algorithm (“ReAl-LiFE”) estimates sparser connectomes that also provide more accurate fits to the underlying diffusion signal, and enables rapid and accurate connectome evaluation at scale.
In the third study, I demonstrated several real-world applications of the ReAl-LiFE technique for analysis of large datasets. First, I showed that structural connectivity estimated with ReAl-LiFE predicts behavioral scores across a range of cognitive tasks in a cohort with 200 healthy human volunteers from the Human Connectome Project database. Moreover, ReAl-LiFE pruned connection weights provided a more reliable marker for structural connectivity strength than the number of fibers in the unpruned connectome. Second, ReAl-LiFE connection weights effectively predicted both chronological age, as well as age-related decline in cognitive factor scores in a cohort of 101 healthy, aged volunteers whose data were acquired as part of the Tata longitudinal study on aging at IISc. Finally, analyzing nearly 100 dMRI scans from the ADNI database, I showed that ReAl-LiFE outperformed competing approaches in terms of its accuracy with classifying patients with Alzheimer’s Dementia from healthy, age-matched controls, based on cortico-hippocampal connection weights.
In summary, these findings show that diffusion MRI and tractography can serve as powerful tools for addressing key questions regarding brain-behavior relationships. In this thesis, I developed a technique to reliably estimate structural connectivity between distal brain regions, identified the role of subcortical structural connections in attention, and showed that cortical connectivity can be used to predict behavioral scores and cognitive decline. Broadly, these results will be relevant for understanding the connectomic basis of various cognitive processes, like attention, in healthy populations, and its dysfunction in diseased patients. | en_US |
