Convolutional neural networks for the segmentation of small rodent brain MRI

Image segmentation is a common step in the analysis of preclinical brain MRI, often performed manually. This is a time-consuming procedure subject to inter- and intra- rater variability. A possible alternative is the use of automated, registration-based segmentation, which suffers from a bias owed to the limited capacity of registration to adapt to pathological conditions such as Traumatic Brain Injury (TBI). In this work a novel method is developed for the segmentation of small rodent brain MRI based on Convolutional Neural Networks (CNNs). The experiments here presented show how CNNs provide a fast, robust and accurate alternative to both manual and registration-based methods. This is demonstrated by accurately segmenting three large datasets of MRI scans of healthy and Huntington disease model mice, as well as TBI rats. MU-Net and MU-Net-R, the CCNs here presented, achieve human-level accuracy while eliminating intra-rater variability, alleviating the biases of registration-based segmentation, and with an inference time of less than one second per scan. Using these segmentation masks I designed a geometric construction to extract 39 parameters describing the position and orientation of the hippocampus, and later used them to classify epileptic vs. non-epileptic rats with a balanced accuracy of 0.80, five months after TBI. This clinically transferable geometric approach detects subjects at high-risk of post-traumatic epilepsy, paving the way towards subject stratification for antiepileptogenesis studies

Predicting the future:Clinical outcome prediction with machine learning in neuropsychiatry

Treatment of psychiatric disorders relies on subjective measures of symptoms to establish diagnoses and lacks an objective way to determine which treatments might work best for an individual patient. To improve the current state-of-the-art and to be able to help a growing number of patients with mental health disorders more efficiently, the discovery of biomarkers predictive of treatment outcome and prognosis is needed. In addition, the application of machine learning methods provides an improvement over the standard group-level analysis approach since it allows for individualized predictions. Machine learning models can also be tested for their generalization capabilities to new patients which would quantify their potential for clinical applicability. In this thesis, these approaches were combined and investigated across a set of different neuropsychiatric disorders. The investigated applications included the prediction of disease course in patients with anxiety disorders, early detection of behavioural frontotemporal dementia in at-risk individuals using structural magnetic resonance imaging (MRI), prediction of deep-brain stimulation treatment-outcome in patients with therapy-resistant obsessive compulsive disorder using structural MRI and prediction of treatment-response for adult and youth patients with posttraumatic stress disorder using resting-state functional MRI scans. Across all studies this thesis showed that machine learning methods combined with neuroimaging data can be utilized to identify biomarkers predictive of future clinical outcomes in neuropsychiatric disorders. Promising as it seems, this can only be the first step for the inclusion of these new approaches into clinical practice as further studies utilizing larger sample sizes are necessary to validate the discovered biomarkers

Concussion classification via deep learning using whole-brain white matter fiber strains

Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based deep learning and machine learning classifiers consistently outperformed all scalar injury metrics across all performance categories in cross-validation (e.g., average accuracy of 0.844 vs. 0.746, and average area under the receiver operating curve (AUC) of 0.873 vs. 0.769, respectively, based on the testing dataset). Nevertheless, deep learning achieved the best cross-validation accuracy, sensitivity, and AUC (e.g., accuracy of 0.862 vs. 0.828 and 0.842 for SVM and RF, respectively). These findings demonstrate the superior performances of deep learning in concussion prediction, and suggest its promise for future applications in biomechanical investigations of traumatic brain injury.Comment: 18 pages, 7 figures, and 4 table

Machine learning for the prediction of psychosocial outcomes in acquired brain injury

Acquired brain injury (ABI) can be a life changing condition, affecting housing, independence, and employment. Machine learning (ML) is increasingly used as a method to predict ABI outcomes, however improper model evaluation poses a potential bias to initially promising findings (Chapter One). This study aimed to evaluate, with transparent reporting, three common ML classification methods. Regularised logistic regression with elastic net, random forest and linear kernel support vector machine were compared with unregularised logistic regression to predict good psychosocial outcomes after discharge from ABI inpatient neurorehabilitation using routine cognitive, psychometric and clinical admission assessments. Outcomes were selected on the basis of decision making for care packages: accommodation status, functional participation, supervision needs, occupation and quality of life. The primary outcome was accommodation (n = 164), with models internally validated using repeated nested cross-validation. Random forest was statistically superior to logistic regression for every outcome with areas under the receiver operating characteristic curve (AUC) ranging from 0.81 (95% confidence interval 0.77-0.85) for the primary outcome of accommodation, to its lowest performance for predicting occupation status with an AUC of 0.72 (0.69-0.76). The worst performing ML algorithm was support vector machine, only having statistically superior performance to logistic regression for one outcome, supervision needs, with an AUC of 0.75 (0.71-0.80). Unregularised logistic regression models were poorly calibrated compared to ML indicating severe overfitting, unlikely to perform well in new samples. Overall, ML can predict psychosocial outcomes using routine psychosocial admission data better than other statistical methods typically used by psychologists

Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease

Damage to specific brain circuits can cause specific neuropsychiatric symptoms. Therapeutic stimulation to these same circuits may modulate these symptoms. To determine whether these circuits converge, we studied depression severity after brain lesions (n = 461, five datasets), transcranial magnetic stimulation (n = 151, four datasets) and deep brain stimulation (n = 101, five datasets). Lesions and stimulation sites most associated with depression severity were connected to a similar brain circuit across all 14 datasets (P < 0.001). Circuits derived from lesions, deep brain stimulation and transcranial magnetic stimulation were similar (P < 0.0005), as were circuits derived from patients with major depression versus other diagnoses (P < 0.001). Connectivity to this circuit predicted out-of-sample antidepressant efficacy of transcranial magnetic stimulation and deep brain stimulation sites (P < 0.0001). In an independent analysis, 29 lesions and 95 stimulation sites converged on a distinct circuit for motor symptoms of Parkinson’s disease (P < 0.05). We conclude that lesions, transcranial magnetic stimulation and DBS converge on common brain circuitry that may represent improved neurostimulation targets for depression and other disorders

Neural correlates of post-traumatic brain injury (TBI) attention deficits in children

Traumatic brain injury (TBI) in children is a major public health concern worldwide. Attention deficits are among the most common neurocognitive and behavioral consequences in children post-TBI which have significant negative impacts on their educational and social outcomes and compromise the quality of their lives. However, there is a paucity of evidence to guide the optimal treatment strategies of attention deficit related symptoms in children post-TBI due to the lack of understanding regarding its neurobiological substrate. Thus, it is critical to understand the neural mechanisms associated with TBI-induced attention deficits in children so that more refined and tailored strategies can be developed for diagnoses and long-term treatments and interventions. This dissertation is the first study to investigate neurobiological substrates associated with post-TBI attention deficits in children using both anatomical and functional neuroimaging data. The goals of this project are to discover the quantitatively measurable markers utilizing diffusion tensor imaging (DTI), structural magnetic resonance imaging (MRI), and functional MRI (fMRI) techniques, and to further identify the most robust neuroimaging features in predicting severe post-TBI attention deficits in children, by utilizing machine learning and deep learning techniques. A total of 53 children with TBI and 55 controls from age 9 to 17 are recruited. The results show that the systems-level topological properties in left frontal regions, parietal regions, and medial occipitotemporal regions in structural and functional brain network are significantly associated with inattentive and/or hyperactive/impulsive symptoms in children post-TBI. Semi-supervised deep learning modeling further confirms the significant contributions of these brain features in the prediction of elevated attention deficits in children post-TBI. The findings of this project provide valuable foundations for future research on developing neural markers for TBI-induced attention deficits in children, which may significantly assist the development of more effective and individualized diagnostic and treatment strategies

Enhanced pre-frontal functional-structural networks to support postural control deficits after traumatic brain injury in a pediatric population

Traumatic brain injury (TBI) affects the structural connectivity, triggering the re-organization of structural-functional circuits in a manner that remains poorly understood. We focus here on brain networks re-organization in relation to postural control deficits after TBI. We enrolled young participants who had suffered moderate to severeTBI, comparing them to young typically developing control participants. In comparison to control participants, TBI patients (but not controls) recruited prefrontal regions to interact with two separated networks: 1) a subcortical network including part of the motor network, basal ganglia, cerebellum, hippocampus, amygdala, posterior cingulum and precuneus; and 2) a task-positive network, involving regions of the dorsal attention system together with the dorsolateral and ventrolateral prefrontal regions

Using machine learning to predict individual severity estimates of alcohol withdrawal syndrome in patients with alcohol dependence

Despite its high prevalence in diverse clinical settings, treatment of alcohol withdrawal syndrome (AWS) is mainly based on subjective clinical opinion. Without reliable predictors of potential harmful AWS outcomes at the individual patient’s level, decisions like provision of pharmacotherapy rely on resource-intensive in-patient monitoring. By contrast, an accurate risk prognosis would enable timely preemptive treatment, open up possibilities for safe out-patient care and lead to a more efficient use of health care resources. The aim of this project was to develop such tools using clinical and patient-reported information easily attainable at patient’s admission. To this end, a machine learning framework incorporating nested cross-validation, ensemble learning, and external validation was developed to retrieve accurate, generalizable prediction models for three meaningful AWS outcomes: (1) Separating mild and more severe AWS as defined by the established AWS scale, and directly identifying patients at risk of (2) delirium tremens as well as (3) withdrawal seizures. Based on 121 sociodemographic, clinical and laboratory-based variables, that were retrieved retrospectively from the patients’ charts, this classification paradigm was used to build predictive models in two cohorts of AWS patients at major detoxification wards in Munich (Ludwig-Maximilian-Universität München, n=389; Technische Universität München, n=805). Moderate to severe AWS cases were predicted with significant balanced accuracy (BAC) in both cohorts (LMU, BAC = 69.4%; TU, BAC = 55.9%). A post-hoc association between the models’ poor outcome predictions and higher clomethiazole doses further added to their clinical validity. While delirium tremens cases were accurately identified in the TU cohort (BAC = 75%), the framework yielded no significant model for withdrawal seizures. Variable importance analyses revealed that predictive patterns highly varied between both treatment sites and withdrawal outcomes. Besides several previously described variables (most notably, low platelet count and cerebral brain lesions), several new predictors were identified (history of blood pressure abnormalities, positive urine-based benzodiazepine screening and years of schooling), emphasizing the utility of data-driven, hypothesis-free prediction approaches. Due to limitations of the datasets as well as site-specific patient characteristics, the models did not generalize across treatment sites, highlighting the need to conduct strict validation procedures before implementing prediction tools in clinical care. In conclusion, this dissertation provides evidence on the utility of machine learning methods to enable personalized risk predictions for AWS severity. More specifically, nested-cross validation and ensemble learning could be used to ensure generalizable, clinically applicable predictions in future prospective research based on multi-center collaboration.Die prädiktive Einschätzung der Ausprägung von Entzugssymptomen bei Patient*innen mit Alkoholabhängigkeit beruht trotz jahrzehntelanger wissenschaftlicher Bemühungen weiterhin auf subjektiver klinischer Einschätzung. Entgiftungsbehandlungen werden daher weltweit vorwiegend im stationären Rahmen durchgeführt, um eine engmaschige klinische Überwachung zu gewährleisten. Da über 90 % der Entzugssyndrome mit lediglich milder vegetativer Symptomatik verlaufen, bindet dieses Vorgehen wertvolle Ressourcen. Datenbasierte Prädiktionstools könnten einen wichtigen Beitrag in Richtung einer individualisierten, akkuraten und verlässlichen Verlaufsbeurteilung leisten. Diese würde sichere ambulante Behandlungskonzepte, prophylaktische medikamentöse Behandlungen von Risikopatient*innen, sowie innovative Behandlungsforschung basierend auf stratifizierten Risikogruppen ermöglichen. Das Ziel dieser Arbeit war die Entwicklung solcher prädiktiven Tools für Patient*innen mit Alkoholentzugssyndrom (AES). Hierfür wurde ein innovatives Machine Learning Paradigma unter Verwendung von strikten Validierungsmethoden (Nested Cross-Validation und Out-of-Sample External Validation) verwendet, um generalisierbare, akkurate Prädiktionsmodelle für drei bedeutsame klinische Endpunkte des AES zu entwickeln: (1) die Klassifikation von milden in Abgrenzung zu moderat bis schwer ausgeprägten AES Verläufen, definiert nach einer hierfür etablierten klinischen Skala (AES Skala), sowie die direkte Identifikation der Komplikationen (2) Delirium tremens (DT) sowie von (3) zerebralen Entzugsanfällen (WS). Dieses Paradigma wurde unter Verwendung von 121 retrospektiv erfassten klinischen, laborbasierten, sowie soziodemographischen Variablen auf 1194 Patient*innen mit Alkoholabhängigkeit an zwei großen Entgiftungsstationen in München angewandt (Ludwig-Maximilian-Universität München, n=389; Technische Universität München, n=805). Moderate bis schwere AES Verläufe konnten an beiden Behandlungszentren mit einer signifikanten Genauigkeit (balanced accuracy [BAC]) prädiziert werden (LMU, BAC = 69.4%; TU, BAC = 55.9%). In einer post-hoc Analyse war die Prädiktion moderater bis schwerer Verläufe zudem mit höheren kumulativen Clomethiazol-Dosen assoziiert, was für die klinische Validität der Modelle spricht. Während DT in der TU Kohorte mit einer hohen Genauigkeit (BAC = 75%) identifiziert werden konnte, war die Prädiktion von Entzugsanfällen nicht erfolgreich. Eine explorative Analyse konnte zeigen, dass die prädiktive Bedeutsamkeit einzelner Variable sowohl zwischen den Behandlungszentren als auch den einzelnen Endpunkten deutlich variierte. Neben mehreren bereits in früheren wissenschaftlichen Arbeiten beschriebenen prädiktiv wertvollen Variablen (insbesondere einer durchschnittlich niedrigeren Thrombozytenzahl im Blut sowie von strukturellen zerebralen Läsionen) konnten hierbei mehrere neue Prädiktoren identifiziert werden (Blutdruckauffälligkeiten in der Vorgeschichte; positives Urinscreening auf Benzodiazepine; Anzahl der Schuljahre). Diese Ergebnisse unterstreichen den Wert von datenbasierten, hypothesen-freien Prädiktionsansätzen. Aufgrund von Limitationen des retrospektiven Datensatzes, wie der fehlenden zentrumsübergreifenden Verfügbarkeit einiger Variablen, sowie klinischen Besonderheiten der beiden Kohorten, ließen sich die Modelle am jeweils anderen Behandlungszentrum nicht validieren. Dieses Ergebnis unterstreicht die Notwendigkeit, die Generalisierbarkeit von Prädiktionsergebnissen adäquat zu testen, bevor hierauf basierende Tools für die klinische Praxis empfohlen werden. Solche Methoden wurden im Rahmen dieser Arbeit erstmalig in einem Forschungsprojekt zum AES verwendet. Zusammenfassend, zeigen die Ergebnisse dieser Dissertation erstmalig einen Nutzen von Machine Learning Ansätzen zur individualisierten Risikoprädiktion schwerer AES Verläufe an. Das hierbei verwendete cross-validierte Machine Learning Paradigma wäre ein mögliches Analyseverfahren, um in künftigen prospektiven Multi-Center-Studien verlässliche Prädikationsergebnisse mit hohem klinischen Anwendungspotential zu erreichen