Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Brain networks under attack : robustness properties and the impact of lesions

A growing number of studies approach the brain as a complex network, the so-called ‘connectome’. Adopting this framework, we examine what types or extent of damage the brain can withstand—referred to as network ‘robustness’—and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer’s disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions—and especially those connecting different subnetworks—was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research

What does the amygdala contribute to social cognition?

The amygdala has received intense recent attention from neuroscientists investigating its function at the molecular, cellular, systems, cognitive, and clinical level. It clearly contributes to processing emotionally and socially relevant information, yet a unifying description and computational account have been lacking. The difficulty of tying together the various studies stems in part from the sheer diversity of approaches and species studied, in part from the amygdala's inherent heterogeneity in terms of its component nuclei, and in part because different investigators have simply been interested in different topics. Yet, a synthesis now seems close at hand in combining new results from social neuroscience with data from neuroeconomics and reward learning. The amygdala processes a psychological stimulus dimension related to saliency or relevance; mechanisms have been identified to link it to processing unpredictability; and insights from reward learning have situated it within a network of structures that include the prefrontal cortex and the ventral striatum in processing the current value of stimuli. These aspects help to clarify the amygdala's contributions to recognizing emotion from faces, to social behavior toward conspecifics, and to reward learning and instrumental behavior

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data

In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrap$632+$and k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead to$sim 96$correct classification rates with less than 10% of the original features

Doctor of Philosophy

dissertationAtrial fibrillation (AF) is the leading cause of ischemic stroke and is the most commonly observed arrhythmia in clinical cardiology. Catheter ablation of AF, in which specific regions of cardiac anatomy associated with AF are intenionally injured to create scar tissue, has been honed over the last 15 years to become a relatively common and safe treatment option. However, the success of these anatomically driven ablation strategies, particularly in hearts that have been exposed to AF for extended periods, remains poor. AF induces changes in the electrical and structural properties of the cardiac tissue that further promotes the permanence of AF. In a process known as electroanatomical (EAM) mapping, clinicians record time signals known as electrograms (EGMs) from the heart and the locations of the recording sites to create geometric representations, or maps, of the electrophysiological properties of the heart. Analysis of the maps and the individual EGM morphologies can indicate regions of abnormal tissue, or substrates that facilitate arrhythmogenesis and AF perpetuation. Despite this progress, limitations in the control of devices currently used for EAM acquisition and reliance on suboptimal metrics of tissue viability appear to be hindering the potential of treatment guided by substrate mapping. In this research, we used computational models of cardiac excitation to evaluate param- eters of EAM that affect the performance of substrate mapping. These models, which have been validated with experimental and clinical studies, have yielded new insights into the limitations of current mapping systems, but more importantly, they guided us to develop new systems and metrics for robust substrate mapping. We report here on the progress in these simulation studies and on novel measurement approaches that have the potential to improve the robustness and precision of EAM in patients with arrhythmias. Appropriate detection of proarrhythmic substrates promises to improve ablation of AF beyond rudimentary destruction of anatomical targets to directed targeting of complicit tissues. Targeted treatment of AF sustaining tissues, based on the substrate mapping approaches described in this dissertation, has the potential to improve upon the efficacy of current AF treatment options