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

General and specific effects of early-life psychosocial adversities on adolescent grey matter volume

AbstractExposure to childhood adversities (CA) is associated with subsequent alterations in regional brain grey matter volume (GMV). Prior studies have focused mainly on severe neglect and maltreatment. The aim of this study was to determine in currently healthy adolescents if exposure to more common forms of CA results in reduced GMV. Effects on brain structure were investigated using voxel-based morphometry in a cross-sectional study of youth recruited from a population-based longitudinal cohort. 58 participants (mean age=18.4) with (n=27) or without (n=31) CA exposure measured retrospectively from maternal interview were included in the study. Measures of recent negative life events (RNLE) recorded at 14 and 17years, current depressive symptoms, gender, participant/parental psychiatric history, current family functioning perception and 5-HTTLPR genotype were covariates in analyses. A multivariate analysis of adversities demonstrated a general association with a widespread distributed neural network consisting of cortical midline, lateral frontal, temporal, limbic, and cerebellar regions. Univariate analyses showed more specific associations between adversity measures and regional GMV: CA specifically demonstrated reduced vermis GMV and past psychiatric history with reduced medial temporal lobe volume. In contrast RNLE aged 14 was associated with increased lateral cerebellar and anterior cingulate GMV. We conclude that exposure to moderate levels of childhood adversities occurring during childhood and early adolescence exerts effects on the developing adolescent brain. Reducing exposure to adverse social environments during early life may optimize typical brain development and reduce subsequent mental health risks in adult life

An overview of the first 5 years of the ENIGMA obsessive–compulsive disorder working group: The power of worldwide collaboration

Abstract Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive?compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA

The impact of early-life stress in the development and course of bipolar disorder: Mechanisms and implications

Traumatic events experienced throughout the different stages of childhood and adolescence are frequent circumstances with a detrimental impact on the physical and psychological health of the individual. A growing body of evidence shows the trauma-related effects on the hypothalamic-pituitary-adrenal axis, the sympathetic nervous system, the serotonin system, the immune system, on brain development, structure, and connectivity. Interestingly, a relation was found between early life stress and Bipolar Disorder: the patients who were exposed to childhood trauma showed a worsened course of the disorder with poor clinical and psychopathological factors. According to the kindling hypothesis, early environmental stressors interact with the genetic susceptibility through epigenetic mechanisms, making the subject more vulnerable to milder stressors, and lowering the threshold for the occurrence of subsequent mood episodes. Understanding these processes is crucial to the discovery of new targets of treatment to reduce or, possibly, revert the effect of early life stress on bipolar disorder

Using neurobiological measures to predict and assess trauma-focused psychotherapy outcome in youth with posttraumatic stress disorder

In this thesis we examined different predictive neurobiological measures of traumafocused psychotherapy response and investigated the biological mechanisms underlying trauma-focused psychotherapy response in youth with PTSD. Our results suggest that activity of the major neuroendocrine stress response systems and brain functional connectivity before treatment are indeed associated with trauma-focused treatment response. Moreover, trauma-focused psychotherapy response seems to be related to longitudinal changes in autonomic nervous system activity during stress and brain structure. Together, these findings improve our understanding of the relationship between neurobiological measures and traumafocused psychotherapy response in youth with PTSD. However, these insights have currently limited to no clinical value because the current state of evidence does not support implementation of neurobiological biomarkers for treatment selection and necessary trials of (augmentation) treatments targeting neurobiological mechanisms related to treatment response have not been performed yet. The way forward now, is to perform individual prediction studies in less heterogeneous patient samples and to perform developmentally informed long-term studies examining (neuro) developmental trajectories related to PTSD and treatment response. These studies are necessary to address whether neurobiological measures can eventually improve treatment outcome and reduce the burden of PTSD in affected youth

Developing neuroimaging biomarkers of blast-induced traumatic brain injury

In the past two decades, the awareness of the physical and emotional effects and sequalae of traumatic brain injuries (TBI) has grown considerably, especially in the case of soldiers returning from their deployment in Iraq and Afghanistan, after sustaining blast-induced TBI (bTBI). While the understanding of bTBI and how it compares to civilian non-blast TBI is essential for proper prevention, diagnosis and treatment, it is currently limited, especially in human in-vivo studies. Developing neuroimaging biomarkers of bTBI is key in understanding primary blast injury mechanism. I therefore investigated the patterns of white matter and grey matter injuries that are specific to bTBI and aren¶t commonl\ seen in civilians Zho suffered from head trauma using advanced neuroimaging techniques. However, because of significant methodological issues and limitations, I developed and tested a new pipeline capable of running the analysis of white matter abnormalities in soldiers, called subject-specific diffusion segmentation (SSDS). I also used standard methodologies to investigate changes at the level of the grey matter structures, and more particularly the limbic system. Finally, I trained a machine learning algorithm that builds decision trees with the aim of classifying between patients with TBI and controls, and between different TBI mechanisms as an example of what could potentially be applied in the context of bTBI. I found three main neuroimaging biomarkers specific to bTBI. The first one is a microstructural white matter abnormality at the level of the middle cerebellar peduncle, characterized by a decrease of diffusivity measures. The second is also a decrease in diffusivity properties, at the level of the white matter boundary, and the third one is a loss of hippocampal volume, with no association to post-traumatic stress disorder. Finally, I demonstrated that SSDS can be used in tandem with a machine learning algorithm for potential diagnosis of TBI with high accuracy. These findings provide mechanistic insights into bTBI and the effect of primary blast injuries on the human brain. This work also identifies important neuroimaging biomarkers that might facilitate prevention and diagnosis in soldiers who suffered from bTBI.Open Acces

Viewing the personality traits through a cerebellar lens. A focus on the constructs of novelty seeking, harm avoidance, and alexithymia

The variance in the range of personality trait expression appears to be linked to structural variance in specific brain regions. In evidencing associations between personality factors and neurobiological measures, it seems evident that the cerebellum has not been up to now thought as having a key role in personality. This paper will review the most recent structural and functional neuroimaging literature that engages the cerebellum in personality traits, as novelty seeking and harm avoidance, and it will discuss the findings in the context of contemporary theories of affective and cognitive cerebellar function. By using region of interest (ROI)- and voxel-based approaches, we recently evidenced that the cerebellar volumes correlate positively with novelty seeking scores and negatively with harm avoidance scores. Subjects who search for new situations as a novelty seeker does (and a harm avoiding does not do) show a different engagement of their cerebellar circuitries in order to rapidly adapt to changing environments. The emerging model of cerebellar functionality may explain how the cerebellar abilities in planning, controlling, and putting into action the behavior are associated to normal or abnormal personality constructs. In this framework, it is worth reporting that increased cerebellar volumes are even associated with high scores in alexithymia, construct of personality characterized by impairment in cognitive, emotional, and affective processing. On such a basis, it seems necessary to go over the traditional cortico-centric view of personality constructs and to address the function of the cerebellar system in sustaining aspects of motivational network that characterizes the different temperamental trait

Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches:A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.</p

Staging evaluation of posttraumatic stress disorder : a machine learning study

Os transtornos de estresse relacionados a um evento traumático, como o transtorno de estresse agudo (TEA) e o transtorno de estresse pós-traumático (TEPT), são caracterizados por alta morbidade e prejuízo social significativo. No Brasil, estima-se que 80% da população já foi exposta a pelo menos um evento traumático ao longo da vida em grandes centros urbanos, como São Paulo e Rio de Janeiro; o crescente problema da violência urbana mostra-se fator importante para a gênese dos transtornos relacionados ao trauma. Devido à etiologia do TEPT ser multicausal e complexa, técnicas de Machine Learning (Aprendizado de Máquina – ML) tem sido usadas para desenvolver escores de risco, para predição diagnóstica e para definição de tratamento. Contudo, considerando sua heterogeneidade clínica e etiológica, realizar o diagnóstico e definir um tratamento adequado pode ser muitas vezes desafiador. O uso do estadiamento clínico surge como um método mais refinado de diagnóstico, procurando definir a progressão do transtorno em momentos específicos durante o continuum da enfermidade. Esta abordagem pode auxiliar em um diagnóstico mais aprimorado, conhecer melhor o prognóstico e escolher o melhor tratamento de acordo com o estágio do transtorno. Assim, o TEPT aparece como um exemplo importante de como um método de estadiamento pode trazer benefícios. O objetivo desta tese é avaliar como os aspectos pessoais, clínicos e relacionados ao trauma dos pacientes atendidos em ambulatórios especializados em trauma psíquico podem estar relacionados à predição do estadiamento clínico de TEPT usando técnicas de ML.Stress disorders related to a traumatic event, such as acute stress disorder (ASD) and posttraumatic stress disorder (PTSD), are characterized by high morbidity and significant social impairment. In Brazil, it is estimated that 80% of the population has already been exposed to at least one traumatic event throughout life in large urban centers, such as São Paulo and Rio de Janeiro; the growing problem of urban violence proves to be an important factor in the genesis of trauma-related disorders. The etiology of PTSD is multicausal and complex; techniques of Machine Learning (ML) have been used to develop PTSD risk scores, to predict its diagnosis and to choose better treatments. However, considering its clinical and etiological heterogeneity, making the diagnosis and defining an appropriate treatment can often be challenging. The use of clinical staging appears as a refined method of diagnosis, aiming to define the progression of the disorder at specific times during the continuum of the illness. This approach may provide improved diagnosis, better understand the prognosis and choose the best treatment according to the stage of the disorder. Thus, PTSD appears as an important example of how a staging method can bring benefits. The objective of this thesis is to evaluate how the personal, clinical and trauma-related aspects of patients who sought care at outpatient clinics specialized in emotional trauma can be related to the prediction of the PTSD staging using ML techniques