Inter and intra-hemispheric structural imaging markers predict depression relapse after electroconvulsive therapy: a multisite study.

Relapse of depression following treatment is high. Biomarkers predictive of an individual's relapse risk could provide earlier opportunities for prevention. Since electroconvulsive therapy (ECT) elicits robust and rapidly acting antidepressant effects, but has a >50% relapse rate, ECT presents a valuable model for determining predictors of relapse-risk. Although previous studies have associated ECT-induced changes in brain morphometry with clinical response, longer-term outcomes have not been addressed. Using structural imaging data from 42 ECT-responsive patients obtained prior to and directly following an ECT treatment index series at two independent sites (UCLA: n = 17, age = 45.41±12.34 years; UNM: n = 25; age = 65.00±8.44), here we test relapse prediction within 6-months post-ECT. Random forests were used to predict subsequent relapse using singular and ratios of intra and inter-hemispheric structural imaging measures and clinical variables from pre-, post-, and pre-to-post ECT. Relapse risk was determined as a function of feature variation. Relapse was well-predicted both within site and when cohorts were pooled where top-performing models yielded balanced accuracies of 71-78%. Top predictors included cingulate isthmus asymmetry, pallidal asymmetry, the ratio of the paracentral to precentral cortical thickness and the ratio of lateral occipital to pericalcarine cortical thickness. Pooling cohorts and predicting relapse from post-treatment measures provided the best classification performances. However, classifiers trained on each age-disparate cohort were less informative for prediction in the held-out cohort. Post-treatment structural neuroimaging measures and the ratios of connected regions commonly implicated in depression pathophysiology are informative of relapse risk. Structural imaging measures may have utility for devising more personalized preventative medicine approaches

Modern Views of Machine Learning for Precision Psychiatry

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research

The Examination of White Matter Microstructure, Autism Traits, and Social Cognitive Abilities in Neurotypical Adults

The purpose of this study was to examine the relationships among mentalizing abilities, self-reported autism traits, and two white matter tracts, uncinate fasciculus (UF) and inferior longitudinal fasciculus (ILF), in neurotypical adults. UF and ILF were hypothesized to connect brain regions implicated in a neuroanatomical model of mentalizing. Data were available for 24 neurotypical adults (mean age = 21.92 (4.72) years; 15 women). Tract-based spatial statistics (TBSS) was used to conduct voxelwise cross-participant comparisons of fractional anisotropy (FA) values in UF and ILF as predicted by mentalizing abilities and self-reported autism traits. Self-reported autism traits were positively related to FA values in left ILF. Results suggest that microstructural differences in left ILF are specifically involved in the expression of subclinical autism traits in neurotypical individuals

Predicting Weight-Related Outcomes in Healthy Adolescents: Clinical Applications of fMRI and Machine Learning

Obesity and obesity-related diseases have increased dramatically worldwide in recent years; however, previous studies have shown that weight loss interventions are largely ineffective in the long term. As a result, focus has shifted to determining objective predictors of weight gain, including neural correlates of such weight behaviors. Previous imaging studies have investigated the brain using univariate methods that do not enable detection of the multivariate complex patterns that may separate those prone to weight gain from those who are not. The present study used a supervised machine learning method (SVM; support vector machine) to classify adolescents (N = 135) into those who would gain weight or become weight variable over a 3-year period. Whole brain SVM analyses were performed for a) structural MRI, b) fMRI during milkshake tasting, c) fMRI during an inhibitory control go/no-go task, d) fMRI during a food image task and e) a combination of modalities. Structural scans did not significantly predict weight gain or weight variability. For functional scans in the milkshake and food image paradigms, SVMs significantly predicted weight gain using a linear mixed-effects method. Predictive accuracy increased when these two paradigms were concatenated in a single model. SVMs did not reach significance for classification of weight variability in any of the paradigms. These results support that weight gain proneness can be characterized by different neural activation to food stimuli and that these differences precede weight gain. The findings suggest that SVM may be useful for identifying neural markers of weight gain proneness.Ph.D., Psychology -- Drexel University, 201

Évaluation électrophysiologique auditive et examen du langage et de l’attention chez l’enfant né prématurément et l’enfant né à terme

L’objectif de cette thèse est l’étude du développement de l’attention auditive et des capacités de discrimination langagière chez l’enfant né prématurément ou à terme. Les derniers mois de grossesse sont particulièrement importants pour le développement cérébral de l’enfant et les conséquences d’une naissance prématurée sur le développement peuvent être considérables. Les enfants nés prématurément sont plus à risque de développer une variété de troubles neurodéveloppementaux que les enfants nés à terme. Même en l’absence de dommages cérébraux visibles, de nombreux enfants nés avant terme sont à risque de présenter des troubles tels que des retards langagiers ou des difficultés attentionnelles. Dans cette thèse, nous proposons donc une méthode d’investigation des processus préattentionnels auditifs et de discrimination langagière, à l’aide de l’électrophysiologie à haute densité et des potentiels évoqués auditifs (PEAs). Deux études ont été réalisées. La première visait à mettre sur pied un protocole d’évaluation de l’attention auditive et de la discrimination langagière chez l’enfant en santé, couvrant différents stades de développement (3 à 7 ans, 8 à 13 ans, adultes ; N = 40). Pour ce faire, nous avons analysé la composante de Mismatch Negativity (MMN) évoquée par la présentation de sons verbaux (syllabes /Ba/ et /Da/) et non verbaux (tons synthétisés, Ba : 1578 Hz/2800 Hz ; Da : 1788 Hz/2932 Hz). Les résultats ont révélé des patrons d’activation distincts en fonction de l’âge et du type de stimulus présenté. Chez tous les groupes d’âge, la présentation des stimuli non verbaux a évoqué une MMN de plus grande amplitude et de latence plus rapide que la présentation des stimuli verbaux. De plus, en réponse aux stimuli verbaux, les deux groupes d’enfants (3 à 7 ans, 8 à 13 ans) ont démontré une MMN de latence plus tardive que celle mesurée dans le groupe d’adultes. En revanche, en réponse aux stimuli non verbaux, seulement le groupe d’enfants de 3 à 7 ans a démontré une MMN de latence plus tardive que le groupe d’adulte. Les processus de discrimination verbaux semblent donc se développer plus tardivement dans l’enfance que les processus de discrimination non verbaux. Dans la deuxième étude, nous visions à d’identifier les marqueurs prédictifs de déficits attentionnels et langagiers pouvant découler d’une naissance prématurée à l’aide des PEAs et de la MMN. Nous avons utilisé le même protocole auprès de 74 enfants âgés de 3, 12 et 36 mois, nés prématurément (avant 34 semaines de gestation) ou nés à terme (au moins 37 semaines de gestation). Les résultats ont révélé que les enfants nés prématurément de tous les âges démontraient un délai significatif dans la latence de la réponse MMN et de la P150 par rapport aux enfants nés à terme lors de la présentation des sons verbaux. De plus, les latences plus tardives de la MMN et de la P150 étaient également corrélées à des performances langagières plus faibles lors d’une évaluation neurodéveloppementale. Toutefois, aucune différence n’a été observée entre les enfants nés à terme ou prématurément lors de la discrimination des stimuli non verbaux, suggérant des capacités préattentionnelles auditives préservées chez les enfants prématurés. Dans l’ensemble, les résultats de cette thèse indiquent que les processus préattentionnels auditifs se développent plus tôt dans l'enfance que ceux associés à la discrimination langagière. Les réseaux neuronaux impliqués dans la discrimination verbale sont encore immatures à la fin de l'enfance. De plus, ceux-ci semblent être particulièrement vulnérables aux impacts physiologiques liés à la prématurité. L’utilisation des PEAs et de la MMN en réponse aux stimuli verbaux en bas âge peut fournir des marqueurs prédictifs des difficultés langagières fréquemment observées chez l’enfant prématuré.The aim of this thesis is to investigate early auditory attention and language development in full-term and preterm children. The last months of pregnancy are particularly important for the child’s cerebral development, and the impacts of a premature birth on his/her neurodevelopment can be substantial. Prematurely born children are at higher risk of developing a variety of neurodevelopmental disorders compared to full-terms. Even without visible brain injury, many premature children are at risk of presenting disorders such as language delays and attentional difficulties. In this thesis, we suggest an approach to investigate pre-attentional processes and early language discrimination abilities in infants using high-density electrophysiology and auditory event-related potentials (AERPs). We conducted two studies. The first one aimed at establishing a paradigm to evaluate auditory attention and language discrimination development in healthy full-term children, over different developmental stages (3 to 7 years, 8 to 13 years, adults; N = 40). To do so, we analyzed the Mismatch Negativity (MMN) component in response to speech (spoken syllables /Ba/ and /Da/) and non-speech stimuli (frequency-synthesized tones, Ba: 1578 Hz/2800 Hz; Da: 1788 Hz/2932 Hz). Distinct patterns of activation were revealed according to stimulus type and age. In all groups, non-speech stimuli elicited an MMN of larger amplitude and earlier latency than did the presentation of speech stimuli. Moreover, in response to speech stimuli, both children groups (3 to 7 years, 8 to 13 years) showed a significantly delayed MMN response compared to the adults group. In contrast, in response to non-speech stimuli, only the youngest group (3 to 7 years) showed a significantly delayed MMN compared to the adults. Age-related differences for tone discrimination therefore appear to occur earlier in children’s development than do the discriminative processes for speech sounds. In the second study, we aimed at identifying the electrophysiological markers of auditory attention and language deficits often incurred by a premature birth. We thus presented this paradigm to 74 infants born preterm (before 34 gestational weeks) or full-term (at least 37 gestational weeks), aged 3, 12 and 36 months old. Our results indicated that preterm children of all age groups showed a significantly delayed MMN and P150 responses to speech stimuli compared to full-terms. Moreover, significant correlations were found between the delayed MMN and P150 responses to speech sounds and lower language scores on a neurodevelopmental assessment. However, no significant differences were found between full-term and preterm children for the MMN in response to non-speech stimuli, suggesting preserved pre-attentional auditory discrimination abilities in these children. Altogether, the findings from this thesis indicate that the neurodevelopmental processes associated with auditory pre-attentional skills occur earlier in childhood compared to language discrimination processes. Cerebral networks involved in speech discrimination are still immature in late childhood. Furthermore, neural networks involved in speech discrimination and language development also appear to be particularly vulnerable to the impacts of prematurity. The use of AERPs and the MMN response to speech stimuli in infancy can thus provide predictive markers of language difficulties commonly seen in premature infants

Catecholamines and cognition after traumatic brain injury

Cognitive problems are one of the main causes of ongoing disability after traumatic brain injury. The heterogeneity of the injuries sustained and the variability of the resulting cognitive deficits makes treating these problems difficult. Identifying the underlying pathology allows a targeted treatment approach aimed at cognitive enhancement. For example, damage to neuromodulatory neurotransmitter systems is common after traumatic brain injury and is an important cause of cognitive impairment. Here, we discuss the evidence implicating disruption of the catecholamines (dopamine and noradrenaline) and review the efficacy of catecholaminergic drugs in treating post-traumatic brain injury cognitive impairments. The response to these therapies is often variable, a likely consequence of the heterogeneous patterns of injury as well as a non-linear relationship between catecholamine levels and cognitive functions. This individual variability means that measuring the structure and function of a person’s catecholaminergic systems is likely to allow more refined therapy. Advanced structural and molecular imaging techniques offer the potential to identify disruption to the catecholaminergic systems and to provide a direct measure of catecholamine levels. In addition, measures of structural and functional connectivity can be used to identify common patterns of injury and to measure the functioning of brain ‘networks’ that are important for normal cognitive functioning. As the catecholamine systems modulate these cognitive networks, these measures could potentially be used to stratify treatment selection and monitor response to treatment in a more sophisticated manner

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