Neurobehavioral Strategies of Skill Acquisition in Left and Right Hand Dominant Individuals

The brain consists of vast networks of connected pathways communicating through synchronized electrochemical activity propagated along fiber tracts. The current understanding is that the brain has a modular organization where regions of specialized processes are dynamically coupled through long-range projections of dense axonal networks connecting spatially distinct regions enabling signal transfer necessary for all complex thought and behavior, including regulation of movement. The central objective of the dissertation was to understand how sensorimotor information is integrated, allowing for adaptable motor behavior and skill acquisition in the left-and right-hand dominant populations. To this end participants, of both left- and right-hand dominance, repeatedly completed a visually guided, force matching task while neurobiological and neurobehavioral outcome measurements were continuously recorded via EEG and EMG. Functional connectivity and graph theoretical measurements were derived from EEG. Cortico-cortical coherence patterns were used to infer neurostrategic discrepancies employed in the execution of a motor task for each population. EEG activity was also correlated with neuromuscular activity from EMG to calculate cortico-muscular connectivity. Neurological patterns and corresponding behavioral changes were used to express how hand dominance influenced the developing motor plan, thereby increasing understanding of the sensorimotor integration process. The cumulative findings indicated fundamental differences in how left- and right-hand dominant populations interact with the world. The right-hand dominant group was found to rely on visual information to inform motor behavior where the left-hand dominant group used visual information to update motor behavior. The left-hand group was found to have a more versatile motor plan, adaptable to both dominant, nondominant, and bimanual tasks. Compared to the right-hand group it might be said that they were more successful in encoding the task, however behaviorally they performed the same. The implications of the findings are relevant to both clinical and performance applications providing insight as to potential alternative methods of information integration. The inclusion of the left-hand dominant population in the growing conceptualization of the brain will generate a more complete, stable, and accurate understanding of our complex biology

Visual processing speed in the aging brain

Either reading a text in the office or looking for an apple in the supermarket, we are continuously flooded with visual stimuli. But how does the human brain support the efficient processing of those stimuli? And, if pathological changes occur in the brain, how do these changes lead to reductions in such efficient processing? In the present dissertation, aging is used as a model to address these two questions. First, individual differences in visual processing speed are examined in association with the coherence of the brain’s spontaneous activity and how this coherence is affected by normal aging. Second, individual differences in visual processing speed are studied in association with behavior in tasks that measure complex visual object perception in patients at risk of Alzheimer’s dementia and healthy aging adults. Based on these two approaches, evidence will be presented for an association of a slowed visual processing with (a) decreased coherent activity of a frontoinsular network in healthy aging and (b) simultaneous object perception deficits in patients at risk of Alzheimer’s dementia. This evidence provides critical insights into the particular link between visual processing speed and the coherence of the brain’s spontaneous activity and reveals perceptual deficits in patients whose clinically most apparent impairments lie in memory

The cognitive neuroscience of visual working memory

Visual working memory allows us to temporarily maintain and manipulate visual information in order to solve a task. The study of the brain mechanisms underlying this function began more than half a century ago, with Scoville and Milner’s (1957) seminal discoveries with amnesic patients. This timely collection of papers brings together diverse perspectives on the cognitive neuroscience of visual working memory from multiple fields that have traditionally been fairly disjointed: human neuroimaging, electrophysiological, behavioural and animal lesion studies, investigating both the developing and the adult brain

Pyramidal Cells in Prefrontal Cortex of Primates: Marked Differences in Neuronal Structure Among Species

The most ubiquitous neuron in the cerebral cortex, the pyramidal cell, is characterized by markedly different dendritic structure among different cortical areas. The complex pyramidal cell phenotype in granular prefrontal cortex (gPFC) of higher primates endows specific biophysical properties and patterns of connectivity, which differ from those in other cortical regions. However, within the gPFC, data have been sampled from only a select few cortical areas. The gPFC of species such as human and macaque monkey includes more than 10 cortical areas. It remains unknown as to what degree pyramidal cell structure may vary among these cortical areas. Here we undertook a survey of pyramidal cells in the dorsolateral, medial, and orbital gPFC of cercopithecid primates. We found marked heterogeneity in pyramidal cell structure within and between these regions. Moreover, trends for gradients in neuronal complexity varied among species. As the structure of neurons determines their computational abilities, memory storage capacity and connectivity, we propose that these specializations in the pyramidal cell phenotype are an important determinant of species-specific executive cortical functions in primates

Anatomical Correlates of Working Memory Deficits in Schizophrenia

La mémoire de travail — c’est-à-dire la capacité limitée de retenir et de manipuler temporairement l’information — est un déficit cognitif central en schizophrénie. La perturbation de cette fonction possède un fort impact dans la vie quotidienne des patients. Des travaux récents de notre laboratoire ont pu mettre en évidence que ces troubles de mémoire de travail ne sont pas homogènes et que certains processus sont plus perturbés que d’autres. Par exemple, une méta-analyse du laboratoire a démontré que l’encodage volontaire d’information est une des fonctions spécifiquement affectée en schizophrénie (Grot, Potvin et al. 2014). Plus spécifiquement, l’association volontaire d’informations distinctes en un ensemble cohérent (par exemple, un objet et sa position spatiale) est déficitaire chez les patients. Ce déficit spécifique est notamment sous-tendu par une hypoactivation du cortex préfrontal et pariétal chez les patients (Grot, Légaré et al. 2017). Ces deux régions sont liées à l’attention, à la manipulation d’information, et aux stratégies d’encodage, ce qui confère l’habilité et la flexibilité nécessaire à la mémoire de travail (Kane and Engle2002, Baddeley2003). Il est intéressant de noter que de nombreuses études rapportent aussi une réduction de l’épaisseur corticale de ces régions chez les patients, ainsi qu’une altération des fibres blanches les interconnectant (Goldman, Pezawas et al. 2009). En ce sens, notre étude a montré qu’une modification anatomique du réseau préfrontal-pariétal pourrait expliquer le déficit spécifique de mémoire de travail en schizophrénie. Plus spécifiquement, la latéralisation gauche de ce réseau serait atténuée en schizophrénie, et engendrerait le déficit observé en mémoire de travail.Working memory, which is the limited capacity to temporarily maintain and manipulate information, is a core cognitive deficit in schizophrenia. This impairment has a strong impact on the daily lives of patients. A previous study of our laboratory observed that working memory deficits are not homogeneous and that some processes are more disturbed than others (Grot, Potvin et al., 2014). This was supported by a subsequent study, which showed that the voluntary association of distinct information into a coherent whole (i.e. an object and its spatial position) was specifically impaired in patients with schizophrenia (Grot, Légaré et al., 2017). This specific deficit, which is referred to as active binding, is underpinned by a hypoactivation of the left prefrontal and parietal cortex in patients (Grot, Légaré et al., 2017). These two regions are related to attentional processes, manipulation, and encoding strategies, which confer the skills and flexibility required for working memory (Kane and Engle 2002, Baddeley 2003). Interestingly, numerous studies report a cortical thickness reduction in these regions, as well as an alteration of the white fibres interconnecting them in patients with schizophrenia (Goldman, Pezawas et al., 2009). Accordingly, our study showed that anatomical modifications of this network could underpin the specific active binding deficit observed in schizophrenia patients. More specifically, a reduced leftward lateralization of the prefrontal-parietal network could contribute to this specific working memory deficit in patients

MULTIVARIATE MODELING OF COGNITIVE PERFORMANCE AND CATEGORICAL PERCEPTION FROM NEUROIMAGING DATA

State-of-the-art cognitive-neuroscience mainly uses hypothesis-driven statistical testing to characterize and model neural disorders and diseases. While such techniques have proven to be powerful in understanding diseases and disorders, they are inadequate in explaining causal relationships as well as individuality and variations. In this study, we proposed multivariate data-driven approaches for predictive modeling of cognitive events and disorders. We developed network descriptions of both structural and functional connectivities that are critical in multivariate modeling of cognitive performance (i.e., fluency, attention, and working memory) and categorical perceptions (i.e., emotion, speech perception). We also performed dynamic network analysis on brain connectivity measures to determine the role of different functional areas in relation to categorical perceptions and cognitive events. Our empirical studies of structural connectivity were performed using Diffusion Tensor Imaging (DTI). The main objective was to discover the role of structural connectivity in selecting clinically interpretable features that are consistent over a large range of model parameters in classifying cognitive performances in relation to Acute Lymphoblastic Leukemia (ALL). The proposed approach substantially improved accuracy (13% - 26%) over existing models and also selected a relevant, small subset of features that were verified by domain experts. In summary, the proposed approach produced interpretable models with better generalization.Functional connectivity is related to similar patterns of activation in different brain regions regardless of the apparent physical connectedness of the regions. The proposed data-driven approach to the source localized electroencephalogram (EEG) data includes an array of tools such as graph mining, feature selection, and multivariate analysis to determine the functional connectivity in categorical perceptions. We used the network description to correctly classify listeners behavioral responses with an accuracy over 92% on 35 participants. State-of-the-art network description of human brain assumes static connectivities. However, brain networks in relation to perception and cognition are complex and dynamic. Analysis of transient functional networks with spatiotemporal variations to understand cognitive functions remains challenging. One of the critical missing links is the lack of sophisticated methodologies in understanding dynamics neural activity patterns. We proposed a clustering-based complex dynamic network analysis on source localized EEG data to understand the commonality and differences in gender-specific emotion processing. Besides, we also adopted Bayesian nonparametric framework for segmentation neural activity with a finite number of microstates. This approach enabled us to find the default network and transient pattern of the underlying neural mechanism in relation to categorical perception. In summary, multivariate and dynamic network analysis methods developed in this dissertation to analyze structural and functional connectivities will have a far-reaching impact on computational neuroscience to identify meaningful changes in spatiotemporal brain activities