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

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker

Connectivity networks in gambling disorder: a resting-state fMRI study

Gambling disorder (GD) is characterized by an inability to stop or control gambling behaviour and is often accompanied by gambling- related cognitive distortions. Task-based functional Magnetic Resonance Imaging (fMRI) studies have revealed abnormal responses within the prefrontal and insular cortex, and mesolimbic reward regions. Studies examining resting-state functional connectivity in GD, although limited in number, have so far applied seed-based analysis approaches which revealed altered brain functioning. Here, we applied data-driven Independent Components Analysis to resting-state multi-echo fMRI data. Networks of interest were selected by spatially correlating them to independently derived network templates. Using dual regression, we compared connectivity strength between 20 GD patients and 20 healthy controls within 4 well-known networks (the ventral attention, limbic, frontoparietal control, and default mode network) and an additional basal ganglia component. Compared to controls, GD patients showed increased integration of the right middle insula within the ventral attention network, an area suggested to play an important role in addiction- related drive. Moreover, our findings indicate that gambling-related cognitive distortions – a hallmark of GD – were positively related to stronger integration of the amygdala, medial prefrontal cortex and insula within various resting-state networks

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker

Predicting the naturalistic course in anxiety disorders using clinical and biological markers: a machine learning approach

Background Disease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients would enable personalized management of these patients. This study aimed to predict recovery from anxiety disorders within 2 years applying a machine learning approach. Methods In total, 887 patients with anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia, or social phobia) were selected from a naturalistic cohort study. A wide array of baseline predictors (N = 569) from five domains (clinical, psychological, sociodemographic, biological, lifestyle) were used to predict recovery from anxiety disorders and recovery from all common mental disorders (CMDs: anxiety disorders, major depressive disorder, dysthymia, or alcohol dependency) at 2-year follow-up using random forest classifiers (RFCs). Results At follow-up, 484 patients (54.6%) had recovered from anxiety disorders. RFCs achieved a cross-validated area-under-the-receiving-operator-characteristic-curve (AUC) of 0.67 when using the combination of all predictor domains (sensitivity: 62.0%, specificity 62.8%) for predicting recovery from anxiety disorders. Classification of recovery from CMDs yielded an AUC of 0.70 (sensitivity: 64.6%, specificity: 62.3%) when using all domains. In both cases, the clinical domain alone provided comparable performances. Feature analysis showed that prediction of recovery from anxiety disorders was primarily driven by anxiety features, whereas recovery from CMDs was primarily driven by depression features. Conclusions The current study showed moderate performance in predicting recovery from anxiety disorders over a 2-year follow-up for individual patients and indicates that anxiety features are most indicative for anxiety improvement and depression features for improvement in general.Stress-related psychiatric disorders across the life spa

Predicting Trauma-Focused Therapy Outcome From Resting-State Functional Magnetic Resonance Imaging in Veterans With Posttraumatic Stress Disorder

Background Trauma-focused psychotherapy is the first-line treatment for posttraumatic stress disorder (PTSD) but 30-50% of patients do not benefit sufficiently. Here, we tested whether resting-state functional magnetic imaging (rs-fMRI) can predict treatment response for individual patients. Methods 44 male veterans with PTSD underwent baseline rs-fMRI scanning followed by trauma-focused therapy (EMDR or TF-CBT). Resting-state networks (RSN) were obtained using independent component analysis with 70 components on the basis of 28 trauma-exposed healthy controls, matched for age and gender. Dual regression was used to obtain subject-specific RSNs for the PTSD patients. All RSNs were individually included in a machine learning classification analysis using Gaussian process classifiers. Classifier performance was assessed using 10 times repeated 10-fold cross-validation. Results Patients were grouped into treatment responders (n = 24) and non-responders (n = 20), based on a 30% decrease in total clinician-administered PTSD scale for the DSM-IV (CAPS) score from pre- to post-treatment assessment. A network centered around the pre-supplementary motor area achieved an average accuracy of 81% (p < 0.001, based on a permutation test, corrected for multiple comparisons across 44 signal components), with a sensitivity of 84.5%, specificity of 77.5%, and area under receiver-operator curve (AUC) of 0.93. Conclusions Rs-fMRI recordings are capable of providing personalized predictions of treatment response in a sample of veterans with PTSD. It therefore has the potential to be useful as a biomarker of treatment response and should be validated in larger independent studies. Supported By ZonMw; AMC; Dutch Ministry of Defens