Multivariate pattern analysis of DTI reveals differential white matter in individuals with obsessive-compulsive disorder

Prediction of brain maturity in infants using machine-learning algorithms

Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23-29weeks of gestation and without moderate-severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p<0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants.

Integrating Genetic, Neuropsychological and Neuroimaging Data to Model Early-Onset Obsessive Compulsive Disorder Severity

We propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete information were used in the present analysis. First, we performed a multivariate genetic association analysis of OCD severity with 266 genetic polymorphisms. This association analysis was used to select and prioritize the SNPs that would be included in the model. Second, we split the sample into a training set (N = 38) and a validation set (N = 18). Third, entropy-based measures of information gain were used for feature selection with the training subset. Fourth, the selected features were fed into two supervised methods of class prediction based on machine learning, using the leave-one-out procedure with the training set. Finally, the resulting model was validated with the validation set. Nine variables were used for the creation of the OCD severity predictor, including six genetic polymorphisms and three variables from the neuropsychological data. The developed model classified child and adolescent patients with OCD by disease severity with an accuracy of 0.90 in the testing set and 0.70 in the validation sample. Above its clinical applicability, the combination of particular neuropsychological, neuroimaging, and genetic characteristics could enhance our understanding of the neurobiological basis of the disorder.

Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach

Developmental dyslexia has been hypothesized to result from multiple causes and exhibit multiple manifestations, implying a distributed multidimensional effect on human brain. The disruption of specific white-matter (WM) tracts/regions has been observed in dyslexic children. However, it remains unknown if developmental dyslexia affects the human brain WM in a multidimensional manner. Being a natural tool for evaluating this hypothesis, the multivariate machine learning approach was applied in this study to compare 28 school-aged dyslexic children with 33 age-matched controls. Structural magnetic resonance imaging (MRI) and diffusion tensor imaging were acquired to extract five multitype WM features at a regional level: white matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) classifier achieved an accuracy of 83.61% using these MRI features to distinguish dyslexic children from controls. Notably, the most discriminative features that contributed to the classification were primarily associated with WM regions within the putative reading network/system (e.g., the superior longitudinal fasciculus, inferior fronto-occipital fasciculus, thalamocortical projections, and corpus callosum), the limbic system (e.g., the cingulum and fornix), and the motor system (e.g., the cerebellar peduncle, corona radiata, and corticospinal tract). These results were well replicated using a logistic regression classifier. These findings provided direct evidence supporting a multidimensional effect of developmental dyslexia on WM connectivity of human brain, and highlighted the involvement of WM tracts/regions beyond the well-recognized reading system in dyslexia. Finally, the discriminating results demonstrated a potential of WM neuroimaging features as imaging markers for identifying dyslexic individuals.

Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy

Magnetic resonance imaging (MRI) studies have revealed brain structural abnormalities in obsessive-compulsive disorder (OCD) patients, involving both gray matter (GM) and white matter (WM). However, the results of previous publications were based on average differences between groups, which limited their usages in clinical practice. Therefore, the aim of this study was to examine whether the application of multivariate pattern analysis (MVPA) to high-dimensional structural images would allow accurate discrimination between OCD patients and healthy control subjects (HCS). High-resolution T1-weighted images were acquired from 33 OCD patients and 33 demographically matched HCS in a 3.0 T scanner. Differences in GM and WM volume between OCD and HCS were examined using two types of well-established MVPA techniques: support vector machine (SVM) and Gaussian process classifier (GPC). We also drew a receiver operating characteristic (ROC) curve to evaluate the performance of each classifier. The classification accuracies for both classifiers using GM and WM anatomy were all above 75%. The highest classification accuracy (81.82%, P<0.001) was achieved with the SVM classifier using WM information. Regional brain anomalies with high discriminative power were based on three distributed networks including the fronto-striatal circuit, the temporo-parieto-occipital junction and the cerebellum. Our study illustrated that both GM and WM anatomical features may be useful in differentiating OCD patients from HCS. WM volume using the SVM approach showed the highest accuracy in our population for revealing group differences, which suggested its potential diagnostic role in detecting highly enriched OCD patients at the level of the individual.

Neural Correlates of Response to Pharmacotherapy in Obsessive-Compulsive Disorder: Individualized Cortical Morphology-Based Structural Covariance

BACKGROUND

Primary pharmacotherapy regimen for obsessive-compulsive disorder (OCD) named Serotonin reuptake inhibitors (SRIs) does not attain sufficient symptom improvement in 40-60% of OCD. We aimed to decode the differential profile of OCD-related brain pathology per subject in the context of cortical surface area (CSA) or thickness (CT)-based individualized structural covariance (ISC) and to demonstrate the potential of which as a biomarker of treatment response to SRI-based pharmacotherapy in OCD using the support vector machine (SVM).

METHODS

T1-weighted magnetic resonance imaging was obtained at 3T from 56 unmedicated OCD subjects and 75 healthy controls (HCs) at baseline. After 4months of SRI-based pharmacotherapy, the OCD subjects were classified as responders (OCD-R,N=25; ≥35% improvement) or nonresponders (OCD-NR,N=31; <35% improvement) according to the percentage change in the Yale-Brown Obsessive Compulsive Scale total score. Cortical ISCs sustaining between-group difference (p<.001) for every run of leave-one-out group-comparison were packaged as feature set for group classification using the SVM.

RESULTS

An optimal feature set of the top 12 ISCs including a CT-ISC between the dorsolateral prefrontal cortex versus precuneus, a CSA-ISC between the anterior insula versus intraparietal sulcus, as well as perisylvian area-related ISCs predicted the initial prognosis of OCD as OCD-R or OCD-NR with an accuracy of 89.0% (sensitivity 88.4%, specificity 90.1%). Extended sets of ISCs distinguished the OCD subjects from the HCs with 90.7-95.6% accuracy (sensitivity 90.8-96.2%, specificity 91.1-95.0%).

CONCLUSION

We showed the potential of cortical morphology-based ISCs, which reflect dysfunctional cortical maturation process, as a possible biomarker that predicts the clinical treatment response to SRI-based pharmacotherapy in OCD.

Discriminative Structured Feature Engineering for Macroscale Brain Connectomes

Neuroimaging techniques can measure structural and functional brain connectivity with unprecedented detail in vivo. This so-called brain connectome can be represented as high dimensional matrices corresponding to edge weights in graphs. After measuring the matrices of two cohorts (i.e., patients and healthy controls), one is often required to formulate computational network models for effective feature engineering to draw discriminative distinctions between the cohorts, as well as estimate the associated statistical significance. We designed a novel method to reveal the intrinsic features of functional matrices of discriminative power for group comparison. More specifically, by encouraging co-selection of edges connected to the same node, we preserved the discriminative edges to maximum extent. To reduce the false positive rate of the extracted discriminative edges, an optimization procedure was developed to evaluate the significance of these edges and remove trivial ones. We validated the proposed method using both synthetic data and real benchmarks, and compared it to ℓ1 regularized logistic regression, univariate t-test and stability selection. The experimental results clearly showed that the proposed approach outperformed the three competing methods under various settings. In addition to increasing the F-measure of feature selection, our approach captured the endogenous, discriminative connectivity patterns consistent with recent findings in biomedical literature. This data-driven method paves a new avenue of enquiry into the inherent nature of network models for functional brain connectomes.

Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders

Multivariate analysis (MVA) is a class of statistical and pattern recognition methods that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of medical neuroimaging-related challenges including identifying variables associated with a measure of clinical importance (i.e. patient outcome), creating diagnostic tests, assisting in characterizing developmental disorders, understanding disease etiology, development and progression, assisting in treatment monitoring and much more. Compared to adults, imaging of developing immature brains has attracted less attention from MVA researchers. However, remarkable MVA research growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to neurodevelopmental disorders in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. The goal of this manuscript is to provide a concise review of the state of the scientific literature on studies employing brain MRI and MVA in a pre-adult population. Neurological developmental disorders addressed in the MVA research contained in this review include autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia and more. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in pediatric/neonatal/fetal brain MRI, the field is still young and considerable research growth remains ahead of us.

Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging

Attention-deficit/hyperactivity disorder (ADHD) is a common disabling psychiatric disorder associated with consistent deficits in error processing, inhibition and regionally decreased grey matter volumes. The diagnosis is based on clinical presentation, interviews and questionnaires, which are to some degree subjective and would benefit from verification through biomarkers. Here, pattern recognition of multiple discriminative functional and structural brain patterns was applied to classify adolescents with ADHD and controls. Functional activation features in a Flanker/NoGo task probing error processing and inhibition along with structural magnetic resonance imaging data served to predict group membership using support vector machines (SVMs). The SVM pattern recognition algorithm correctly classified 77.78% of the subjects with a sensitivity and specificity of 77.78% based on error processing. Predictive regions for controls were mainly detected in core areas for error processing and attention such as the medial and dorsolateral frontal areas reflecting deficient processing in ADHD (Hart et al., in Hum Brain Mapp 35:3083-3094, 2014), and overlapped with decreased activations in patients in conventional group comparisons. Regions more predictive for ADHD patients were identified in the posterior cingulate, temporal and occipital cortex. Interestingly despite pronounced univariate group differences in inhibition-related activation and grey matter volumes the corresponding classifiers failed or only yielded a poor discrimination. The present study corroborates the potential of task-related brain activation for classification shown in previous studies. It remains to be clarified whether error processing, which performed best here, also contributes to the discrimination of useful dimensions and subtypes, different psychiatric disorders, and prediction of treatment success across studies and sites.

From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics

Psychiatric disorders are increasingly being recognised as having a biological basis, but their diagnosis is made exclusively behaviourally. A promising approach for 'biomarker' discovery has been based on pattern recognition methods applied to neuroimaging data, which could yield clinical utility in future. In this review we survey the literature on pattern recognition for making diagnostic predictions in psychiatric disorders, and evaluate progress made in translating such findings towards clinical application. We evaluate studies on many criteria, including data modalities used, the types of features extracted and algorithm applied. We identify problems common to many studies, such as a relatively small sample size and a primary focus on estimating generalisability within a single study. Furthermore, we highlight challenges that are not widely acknowledged in the field including the importance of accommodating disease prevalence, the necessity of more extensive validation using large carefully acquired samples, the need for methodological innovations to improve accuracy and to discriminate between multiple disorders simultaneously. Finally, we identify specific clinical contexts in which pattern recognition can add value in the short to medium term.

The use of dynamic susceptibility contrast (DSC) MRI to automatically classify patients with first episode psychosis

Hemodynamic changes in the brain have been reported in major psychosis in respect to healthy controls, and could unveil the basis of structural brain modifications happening in patients. The study of first episode psychosis is of particular interest because the confounding role of chronicity and medication can be excluded. The aim of this work is to automatically discriminate first episode psychosis patients and normal controls on the basis of brain perfusion employing a support vector machine (SVM) classifier. 35 normal controls and 35 first episode psychosis underwent dynamic susceptibility contrast magnetic resonance imaging, and cerebral blood flow and volume, along with mean transit time were obtained. We investigated their behavior in the whole brain and in selected regions of interest, in particular the left and right frontal, parietal, temporal and occipital lobes, insula, caudate and cerebellum. The distribution of values of perfusion indexes were used as features in a support vector machine classifier. Mean values of blood flow and volume were slightly lower in patients, and the difference reached statistical significance in the right caudate, left and right frontal lobes, and in left cerebellum. Linear SVM reached an accuracy of 83% in the classification of patients and normal controls, with the highest accuracy associated with the right frontal lobe and left parietal lobe. In conclusion, we found evidence that brain perfusion could be used as a potential marker to classify patients with psychosis, who show reduced blood flow and volume in respect to normal controls.

Subcallosal Cingulate Connectivity in Anorexia Nervosa Patients Differs From Healthy Controls: A Multi-tensor Tractography Study

BACKGROUND

Anorexia nervosa is characterized by extreme low body weight and alterations in affective processing. The subcallosal cingulate regulates affect through wide-spread white matter connections and is implicated in the pathophysiology of anorexia nervosa.

OBJECTIVES

We examined whether those with treatment refractory anorexia nervosa undergoing deep brain stimulation (DBS) of the subcallosal white matter (SCC) show: (1) altered anatomical SCC connectivity compared to healthy controls, (2) white matter microstructural changes, and (3) microstructural changes associated with clinically-measured affect.

METHODS

Diffusion magnetic resonance imaging (dMRI) and deterministic multi-tensor tractography were used to compare anatomical connectivity and microstructure in SCC-associated white matter tracts. Eight women with treatment-refractory anorexia nervosa were compared to 8 age- and sex-matched healthy controls. Anorexia nervosa patients also completed affect-related clinical assessments presurgically and 12 months post-surgery.

RESULTS

(1) Higher (e.g., left parieto-occipital cortices) and lower (e.g., thalamus) connectivity in those with anorexia nervosa compared to controls. (2) Decreases in fractional anisotropy, and alterations in axial and radial diffusivities, in the left fornix crus, anterior limb of the internal capsule (ALIC), right anterior cingulum and left inferior fronto-occipital fasciculus. (3) Correlations between dMRI metrics and clinical assessments, such as low pre-surgical left fornix and right ALIC fractional anisotropy being related to post-DBS improvements in quality-of-life and depressive symptoms, respectively.

CONCLUSIONS

We identified widely-distributed differences in SCC connectivity in anorexia nervosa patients consistent with heterogenous clinical disruptions, although these results should be considered with caution given the low number of subjects. Future studies should further explore the use of affect-related connectivity and behavioral assessments to assist with DBS target selection and treatment outcome.

Meta-analytic investigations of structural grey matter, executive domain-related functional activations, and white matter diffusivity in obsessive compulsive disorder: an integrative review

Obsessive-compulsive disorder (OCD) is a debilitating disorder. However, existing neuroimaging findings involving executive function and structural abnormalities in OCD have been mixed. Here we conducted meta-analyses to investigate differences in OCD samples and controls in: Study 1 - grey matter structure; Study 2 - executive function task-related activations during (i) response inhibition, (ii) interference, and (iii) switching tasks; and Study 3 - white matter diffusivity. Results showed grey matter differences in the frontal, striatal, thalamus, parietal and cerebellar regions; task domain-specific neural differences in similar regions; and abnormal diffusivity in major white matter regions in OCD samples compared to controls. Our results reported concurrence of abnormal white matter diffusivity with corresponding abnormalities in grey matter and task-related functional activations. Our findings suggested the involvement of other brain regions not included in the cortico-striato-thalamo-cortical network, such as the cerebellum and parietal cortex, and questioned the involvement of the orbitofrontal region in OCD pathophysiology. Future research is needed to clarify the roles of these brain regions in the disorder.