The goal of this study is to determine the intentional movement direction based on the neural signals recorded from the motor cortex using optical brain imaging techniques. Towards this goal, we developed a cross-correlation analysis technique to determine the movement direction from the hemodynamic signals recorded from the motor cortex. Healthy human subjects were asked to perform a two-dimensional hand movement in two orthogonal directions while the hemodynamic signals were recorded from the motor cortex simultaneously with the movements. The movement directions were correlated with the hemodynamic signals to establish the cross-correlation patterns of firings among these neurons. Based on the specific cross-correlation patterns with respect to the different movement directions, we can distinguish the different intentional movement directions between front-back and right-left movements. This is based on the hypothesis that different movement directions can be determined by different cooperative firings among various groups of neurons. By identifying the different correlation patterns of brain activities with each group of neurons for each movement, we can decode the specific movement direction based on the hemodynamic signals. By developing such a computational method to decode movement direction, it can be used to control the direction of a wheelchair for paralyzed patients based on the changes in hemodynamic signals recorded using non-invasive optical imaging techniques.

The theme of this dissertation is to improve the exploration of patient subgroups with a precision medicine lens, specifically using repeated measures data to evaluate longitudinal trajectories of clinical, biological, and lifestyle measures. Our proposed methodological contributions fall into two branches of statistical methodology: undirected graphical models and functional data analysis. In the first part of this dissertation, our goal was to study longitudinal networks of brain imaging biomarkers and clinical symptoms during the time leading up to manifest Huntington's disease diagnosis among patients with known genetic risk of disease. Understanding the interrelationships between measures may improve our ability to identify patients who are nearing disease onset and who therefore might be ideal patients for clinical trial recruitment. Gaussian graphical models are a powerful approach for network modeling, and several extensions to these models have been developed to estimate time-varying networks. We propose a time-varying Gaussian graphical model specifically for a time scale that is centered on an anchoring event such as disease diagnosis. Our method contains several novel components intended to 1) reduce bias known to stem from 𝑙₁ penalization, and 2) improve temporal smoothness in network edge strength and structure. These novel components include time-varying adaptive lasso weights, as well as a combination of 𝑙₁, 𝑙₂, and 𝑙₀ penalization. We demonstrated via simulation studies that our proposed approach, as well as more computationally efficient subsets of our full proposed approach, have superior performance compared to existing methods. We applied our proposed approach to the PREDICT-HD study and found that the network edges did change with time leading up to and beyond diagnosis, with change points occurring at different times for different edges. For clinical symptoms, bradykinesia became well-connected with symptoms from several other domains. For imaging measures, we observed a loss of connection over time among gray matter regions, white matter regions, and the hippocampus. In the second part of this dissertation, we consider time-varying network models for settings in which data are not all Gaussian. We sought to compare longitudinal clinical symptom networks between patients with neuropathologically-defined Alzheimer's disease (AD) vs. neuropathologically-defined Lewy body dementia (LBD), two common types of dementia which can often be clinically misdiagnosed. Given that the clinical measures of interest were largely non-Gaussian, we examined the literature for undirected graphical models for mixed data types. We then proposed an extension to the existing time-varying mixed graphical model by adding time-varying adaptive lasso weights, modeling time in reverse in order to treat neuropathological diagnoses as baseline covariates. The proposed adaptive lasso extension serves a two-fold purpose: they alleviate well-known bias of 𝑙₁ penalization and they encourage temporal smoothness in edge estimation. We demonstrated the improved performance of our extension in simulations studies. Applying our method to the National Alzheimer's Coordinating Center database, we found that the edge structure surrounding the Wechsler Memory Scale Revised (WMS-R) Logical Memory parts IA (immediate recall) and IIA (delayed recall) may contain important markers for discriminant analysis of AD and LBD populations. In the third part of this dissertation, we explored a methodologically distinct area of research from the first two parts, moving from graphical models to functional data analysis. Our goal was to extract meaningful chronotypes, or phenotypes of circadian rhythms, from activity count data collected from accelerometers. Existing approaches for analyzing diurnal patterns using these data, including the cosinor model and functional principal components analysis, have revealed and quantified population-level diurnal patterns, but considerable subject-level variability remained uncaptured in features such as wake/sleep times and activity intensity. This remaining informative variability could provide a better understanding of chronotypes, or behavioral manifestations of one’s underlying 24-hour rhythm. Curve registration, or alignment, is a technique in functional data analysis that separates "vertical" variability in activity intensity from "horizontal" variability in time-dependent markers like wake and sleep times. We developed a parametric registration framework for 24-hour accelerometric rest-activity profiles that are represented as dichotomized into epoch-level states of activity or rest. Specifically, we estimated subject-specific piecewise linear time-warping functions parametrized with a small set of parameters. We applied this method to data from the Baltimore Longitudinal Study of Aging and illustrated how estimated parameters can give a more flexible quantification of chronotypes compared to traditional approaches.

L’observation du système nerveux, de son métabolisme et de certaines de ses structures est possible grâce à la neuroimagerie. Une littérature importante issue du « neurodroit » véhicule des imaginaires et des fantasmes relatifs aux possibilités judiciaires qu’offriraient ces technologies.Qu’il s’agisse de détection du mensonge, d’identification cérébrale des individus dangereux ou encore de prédiction de comportements déviants, la neuroimagerie, en l’état actuel des technologies, ne peut pourtant être sérieusement conçue comme pouvant faire l’objet de telles applications.L’utilisation de la neuroimagerie dans le cadre d’expertises est néanmoins une réalité, dans les tribunaux canadiens comme dans la loi française.Cette thèse souligne que les conceptions des technologies dont témoignent les deux systèmes juridiques étudiés s’avèrent lacunaires, ce qui engendre des risques. Elle évoque les conditions du recours à une normativité extra-juridique, la normalisation technique, qui pourrait s’élaborer dans ce contexte controversé, et esquisse les traits d’un dialogue amélioré entre les normativités juridique et technologique. / Neuroimaging allows the observation of the nervous system, of both its metabolism and some of its structures. An important literature in “neurolaw” conveys illusions and fantaisies about the judicial possibilities that imaging technologies would contain.Whether it is about lies detection, cerebral identifications of dangerous individuals through their neurobiology or predictions of criminal behaviors, neuroimaging, in the current state of technologies, can not be seriously conceived as being able to offer such applications.Judicial uses of neuroimaging through expertise are a reality nonetheless, in Canadian courts as in French law.This thesis emphasizes that the conceptions of imaging technologies integrated in the two legal systems studied are incomplete, which creates an important amount of risks. It discusses the conditions for the use of an extra-legal normativity, the international technical standardization, which could be elaborated in this particular and controversial context, and outlines several features of an increased dialogue between legal and technological norms.

No description available.

No description available.

The limbic system is hypothesized to play a critical role in pathophysiology of schizophrenia, with abnormalities thought to contribute to the expression of various aspects of the cognitive deficits and clinical symptoms. Psychosis is understood as highly heritable and family members, specifically non-affected siblings, while not displaying overt signs of the disorder, often exhibit features similar to those observed in patients, though to a lesser degree. The overarching aim of this project was to investigate the integrity of limbic circuitry in a sample of patients with schizophrenia and their non-affected siblings and examine its potential relationship with various clinical features of the illness. Cortical thickness of the entorhinal, parahippocampal, cingulate, and orbitofrontal cortices; as well as subcortical surface shape of the hippocampus and amygdala were the focus of this study. Findings from this study reveal relative similarity in limbic integrity between individuals with schizophrenia and theirnon-affected siblings, which are both disparate from healthy individuals. This suggests aspects of the neurobiological underpinnings of psychosis, particularly limbic regions, are genetically influenced regardless of symptom expression and are latent features in non-affected family members. Relationships between positive symptomatology and shape abnormalities of subcortical structures suggest a potential substrate for clinical characteristics in psychosis not evident in non-ill siblings.

Schizophrenia is a severe mental disorder that can manifest in various ways and is often characterized by the appearance of positive symptoms (hallucinations, delusions), negative symptoms (social and attention impairment) and cognitive dysfunction (thought disorders, memory and executive function impairments). Traditional treatment methodologies involve blocking the dopamine receptor by binding to the same site as dopamine. These treatments are largely inadequate and lead to an array of adverse side effects. Side effects include weight gain, diabetes, and movement disorders; which critically limit the therapeutic value of antipsychotic drug treatment. Limited symptom control and severe adverse effects have led to poor drug adherence and a deprived quality of life for patients suffering from schizophrenia. The complex etiology of schizophrenia combined with a lack of effective translational models and tests to represent and assess the illness have hindered drug development. Evidently, there is a strong demand for a new generation of pharmaceuticals and an improved translational pipeline for the treatment of schizophrenia. The collection of studies presented here contribute to the advancement of translational tools for drug discovery, the establishment of pre-clinical models to embody the various symptoms, and the development of a novel drug candidate for schizophrenia. Allosteric modulation of G-protein coupled receptors is evolving as a new wave of therapy with promising implications for various CNS disorders. Allosteric compounds regulate binding without blocking the receptor. PAOPA, a dopamine D2 receptor allosteric modulator, prevents and treats schizophrenia-like symptoms in pre-clinical animal models of schizophrenia with no apparent adverse effects. The studies outlined in this thesis further categorize PAOPA as a novel therapeutic candidate for schizophrenia. Moreover, the findings presented here provide further insight into the potential therapeutic mechanism of action of PAOPA and set the foundation for the development of a new generation of antipsychotic drugs. These studies constitute an innovative approach to expanding research in the field of drug development for schizophrenia. / Thesis / Doctor of Philosophy (PhD)

Understanding how the brain represents and processes information is crucial for advancing neuroscience and artificial intelligence. Representational similarity analysis (RSA) has been instrumental in characterizing neural representations by comparing multivariate response patterns elicited by sensory stimuli. However, traditional RSA relies solely on geometric properties, overlooking crucial topological information. This thesis introduces topological RSA (tRSA), a novel framework that combines geometric and topological properties of neural representations. tRSA applies nonlinear monotonic transforms to representational dissimilarities, emphasizing local topology while retaining intermediate-scale geometry. The resulting geo-topological matrices enable model comparisons that are robust to noise and individual idiosyncrasies. This thesis introduces several key methodological advances: (1) Topological RSA (tRSA) identifies computational signatures as accurately as RSA while compressing unnecessary variation with capabilities to test topological hypotheses; (2) Adaptive Geo-Topological Dependence Measure (AGTDM) provides a robust statistical test for detecting complex multivariate relationships; (3) Procrustes-aligned Multidimensional Scaling (pMDS) aligns time-resolved representational geometries to illuminate processing stages in neural computation; (4) Temporal Topological Data Analysis (tTDA) applies spatio-temporal filtration techniques to reveal developmental trajectories in biological systems; and (5) Single-cell Topological Simplicial Analysis (scTSA) characterizes higher-order cell population complexity across different stages of development. Through analyses of neural recordings, biological data, and simulations of neural network models, this thesis demonstrates the power and versatility of these new methods. By advancing RSA with topological techniques, this work provides a powerful new lens for understanding brains, computational models, and complex biological systems. These methods not only offer robust approaches for adjudicating among competing models but also reveal novel theoretical insights into the nature of neural computation. This thesis lays the foundation for future investigations at the intersection of topology, neuroscience, and time series data analysis, promising to deepen our understanding of how information is represented and processed in biological and artificial neural networks. The methods developed here have potential applications in fields ranging from cognitive neuroscience to clinical diagnosis and AI development, paving the way for more nuanced understanding of brain function and dysfunction.

Over 30 years of functional imaging studies have demonstrated that the human brain operates as a complex and interconnected system, with distinct functional networks and long-range coordination of neural activity. Yet, how our brains coordinate our behavior from moment to moment, permitting us to think, talk, and move at the same time, has been almost impossible to decode (Chapter 1). The invasive, long-term, and often multi-regional iEEG monitoring utilized for epilepsy surgery evaluation presents a valuable opportunity for studying brain-wide dynamic neural activity in behaving human subjects. In this study, we analyzed over 93 hours of iEEG recordings along with simultaneously acquired video recordings from 10 patients with drug-resistant focal epilepsy syndromes, who underwent invasive iEEG with broadly distributed bilateral depth electrodes for clinical evaluation. Initially, we explored the dynamic connectivity patterns quantified from band-limited neural activities using metrics from previous literature in a subset of subjects. These metrics can characterize long-range connectivity across brain regions and reveal variations over time. They have shown success in identifying state differences using controlled task presentations and trial-averaged data. However, we found that replicating this success with naturalistic, complex behaviors in our subjects is challenging. Although they demonstrate differences across wake and sleep states, they are less sensitive in differentiating more complicated and subtle state transitions during wakefulness. In addition, patterns identified from individual frequency bands exhibit patient-to-patient differences, making it difficult to generalize results across frequency bands and subjects. (Chapter 2). Inspired by clinical electrocortical stimulation mapping studies, which seek to identify critical brain sites for language and motor function, and the frequency gradient observed from human scalp and intracranial EEG recordings, we developed a new approach to meet the requirements for real-time analysis and frequency band selection. It is worth mentioning that detecting state transitions in naturalistic behavior requires analyzing raw EEG during individual transitions. We refer to this as "real-time analysis," to distinguish it from formal task performance and trial-averaging techniques. Rather than representing data as time-varying signals within specific frequency bands, we incorporated all frequencies (2-55 Hz) into our analysis by calculating the power spectral density (PSD) at each electrode. This analysis confirmed that the human brain’s neural activity PSD is heterogenous, exhibiting a distinct topography with bilateral symmetry, consistent with prior resting-state MEG and iEEG studies. However, investigating the variability of each region’s PSD over time (within a 2-second moving window), we discovered the tendency of individual electrode channel to switch back and forth between 2 distinct power spectral densities (PSDs, 2-55Hz) (Chapter 3). We further recognized that this ‘spectral switching’ occurs synchronously between distant sites, even between regions with differing baseline PSDs, revealing long-range functional networks that could be obscured in the analysis of individual frequency bands. Moreover, the real-time PSD-switching dynamics of specific networks exhibited striking alignment with activities such as conversation, hand movements, and eyes open versus closed, revealing a multi-threaded functional network representation of concurrent naturalistic behaviors. These network-behavior relationships were stable over multiple days but were altered during sleep, suggesting state-dependent plasticity of brain-wide network organization (Chapter 4). Our results provide robust evidence for the presence of multiple synchronous neuronal networks across the human brain. The real-time PSD switching dynamics of these networks provide physiologically interpretable read-outs, demonstrating the parallel engagement of multiple brain regions in a range of concurrent naturalistic behaviors (Chapter 5).

Mental and neurological disorders account for a large part of the total global disease burden, yet there is a severe lack of effective treatments for reducing the associated disability and mortality. Brain dysfunctions are caused by a large variety of factors, such as pathological network connectivity, altered cellular and physiological properties, and neurotransmitter imbalances that act together or alone to result in profound behavioral impacts. Thus, there is an urgent need for integrative tools that allow an unbiased whole-brain understanding of the underlying pathophysiology of complex brain disorders. Recent advances in tissue clearing, labeling, and high-resolution light sheet microscopy, are enabling mapping and comparative analysis of large intact brain samples in normal and diseased states. However, multiple challenges remain, specifically in achieving uniform labeling of specific molecular targets in large tissues, scalable microscopy platforms for high-resolution whole-brain imaging, and multi-scale high-accuracy comparative data analysis tools. Here, I present my work in the development of a set of novel methods to address some of these challenges. The first aim focuses on developing a rapid and uniform deep tissue molecular labeling method by utilizing modified DNA aptamers to significantly reduce the staining times (e.g., less than 4 days for an intact mouse brain, as opposed to several weeks). The second aim introduces a cost-effective (~20x cheaper) and scalable light sheet fluorescence microscopy (LSFM) implementation, so-called projected Light sheet microscopy (pLSM), for rapid high-resolution imaging of large biological samples. The third aim is focused on developing a suite of large data analysis methods (suiteWB) for high-resolution whole-brain comparative phenotyping – both at the level of neuron densities and their brain-wide projection patterns. Through this pipeline, we systematically investigated the brain-wide dopaminergic modulatory pathway alterations resulting from chronic ketamine exposure. Altogether, these sets of highly integrative labeling, imaging, and analysis tools will facilitate a comprehensive understanding of the pathophysiology of complex brain disorders and the discovery of novel therapeutic targets.