UC Irvine

The present thesis is dedicated to studying the human language connectome by combining diffusion-weighted magnetic resonance imaging (dMRI) and high-dimensional predictive algorithms to isolate its relevant connections amongst the broader white matter feature space. According to the classical “Broca-Wernicke” and contemporary “Dual-Stream” neurobiological models of language, the fluent production and repetition of speech relies on a distinct and lateralized neuroanatomical circuit. Where these models diverge is in their neuroanatomical explanations, such that the former attributes these functions to the posterior inferior frontal gyrus (i.e., Broca’s area) and its disconnection from the arcuate fasciculus while the latter emphasizes a cortico-centric “dorsal stream” network where repetition processing occurs by way of a sensorimotor node in sylvian parieto-temporal cortex (called “area Spt”). Experiment 1 of this thesis tested the assumption that the speech production system is scaffolded by a lateralized set of connections by performing whole-brain inter-hemispheric white matter comparisons between left- and right-hemisphere dominant subjects as determined by a clinically-indicated Intracarotid Amytal (Wada) Test. Indeed, this semi-automated analysis reconstructed a lateralized network for speech production that was underscored by its convergence of pathways to Broca’s area and whose white matter properties successfully enabled Wada-concordant classifications using a ridge-logistic regression model. Experiment 2 used dMRI collected by the Human Connectome Project to perform multi-shell fiber tractography of Broca’s area in order to segment the underlying sub-circuits connecting to this classical region. Experiment 3 linked connectivity directly with behavior using a connectome-based lesion-symptom mapping approach in a large dMRI sample acquired from stroke patients. Over two-thousand distinct connections were mapped and a LASSO-regularized latent projection-based regression algorithm was implemented to automatically isolate the subset of connections predictive of speech repetition performance. We found that the embedded feature selection algorithm identified a local set of sylvian parieto-temporal connections that could make accurate out-of-sample predictions of repetition performance - a result which fuses the claims made by the “Broca-Wernicke” and “Dual-Stream” models regarding the anatomic delineation of the repetition circuit. Together, these studies demonstrate the utility in using dMRI and machine-learning algorithms to understand the anatomy of eloquent functional networks while accommodating the collinearities that are characteristic of brain data.