A Machine-Learning-Based Investigation of Schizophrenia Using Structural MRI

openSchizophrenia is a serious mental health concerns that affects 1% of the population (Jones et al., 2005). This study aimed to create objective tools that can correctly classify people with schizophrenia according to their diagnosis, predominant symptoms, illness duration, and illness severity based on their structural brain imaging variables. 1087 brain images (700=healthy controls, 387=people with schizophrenia) included in the analysis. Support Vector Machines, random forests, logistic regression, and XGBoost were used for diagnostic classification and reached 71% of maximum accuracy. Sulcal width was found to be the most important brain imaging variable that differed between groups. Support vector machines and random forests were used to classify patients according to their predominant symptoms and these classifications reached a maximum accuracy of 66%. Support vector machines could correctly classify people with schizophrenia according to their illness duration with a 75% accuracy and according to their illness severity with 69%. The result of the study shows that using machine learning methods, it is possible to create objective tools for schizophrenia that can be later used in clinics. Keywords: Schizophrenia, Structural MRI, Machine Learning ClassificationSchizophrenia is a serious mental health concerns that affects 1% of the population (Jones et al., 2005). This study aimed to create objective tools that can correctly classify people with schizophrenia according to their diagnosis, predominant symptoms, illness duration, and illness severity based on their structural brain imaging variables. 1087 brain images (700=healthy controls, 387=people with schizophrenia) included in the analysis. Support Vector Machines, random forests, logistic regression, and XGBoost were used for diagnostic classification and reached 71% of maximum accuracy. Sulcal width was found to be the most important brain imaging variable that differed between groups. Support vector machines and random forests were used to classify patients according to their predominant symptoms and these classifications reached a maximum accuracy of 66%. Support vector machines could correctly classify people with schizophrenia according to their illness duration with a 75% accuracy and according to their illness severity with 69%. The result of the study shows that using machine learning methods, it is possible to create objective tools for schizophrenia that can be later used in clinics. Keywords: Schizophrenia, Structural MRI, Machine Learning Classificatio

Ensemble Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Complex Graph Measures from Diffusion Tensor Images

The human brain is a complex network of interacting regions. The gray matter regions of brain are interconnected by white matter tracts, together forming one integrative complex network. In this article, we report our investigation about the potential of applying brain connectivity patterns as an aid in diagnosing Alzheimer's disease and Mild Cognitive Impairment (MCI). We performed pattern analysis of graph theoretical measures derived from Diffusion Tensor Imaging (DTI) data representing structural brain networks of 45 subjects, consisting of 15 patients of Alzheimer's disease (AD), 15 patients of MCI, and 15 healthy subjects (CT). We considered pair-wise class combinations of subjects, defining three separate classification tasks, i.e., AD-CT, AD-MCI, and CT-MCI, and used an ensemble classification module to perform the classification tasks. Our ensemble framework with feature selection shows a promising performance with classification accuracy of 83.3% for AD vs. MCI, 80% for AD vs. CT, and 70% for MCI vs. CT. Moreover, our findings suggest that AD can be related to graph measures abnormalities at Brodmann areas in the sensorimotor cortex and piriform cortex. In this way, node redundancy coefficient and load centrality in the primary motor cortex were recognized as good indicators of AD in contrast to MCI. In general, load centrality, betweenness centrality, and closeness centrality were found to be the most relevant network measures, as they were the top identified features at different nodes. The present study can be regarded as a “proof of concept” about a procedure for the classification of MRI markers between AD dementia, MCI, and normal old individuals, due to the small and not well-defined groups of AD and MCI patients. Future studies with larger samples of subjects and more sophisticated patient exclusion criteria are necessary toward the development of a more precise technique for clinical diagnosis

Large-scale inference in the focally damaged human brain

Clinical outcomes in focal brain injury reflect the interactions between two distinct anatomically distributed patterns: the functional organisation of the brain and the structural distribution of injury. The challenge of understanding the functional architecture of the brain is familiar; that of understanding the lesion architecture is barely acknowledged. Yet, models of the functional consequences of focal injury are critically dependent on our knowledge of both. The studies described in this thesis seek to show how machine learning-enabled high-dimensional multivariate analysis powered by large-scale data can enhance our ability to model the relation between focal brain injury and clinical outcomes across an array of modelling applications. All studies are conducted on internationally the largest available set of MR imaging data of focal brain injury in the context of acute stroke (N=1333) and employ kernel machines at the principal modelling architecture. First, I examine lesion-deficit prediction, quantifying the ceiling on achievable predictive fidelity for high-dimensional and low-dimensional models, demonstrating the former to be substantially higher than the latter. Second, I determine the marginal value of adding unlabelled imaging data to predictive models within a semi-supervised framework, quantifying the benefit of assembling unlabelled collections of clinical imaging. Third, I compare high- and low-dimensional approaches to modelling response to therapy in two contexts: quantifying the effect of treatment at the population level (therapeutic inference) and predicting the optimal treatment in an individual patient (prescriptive inference). I demonstrate the superiority of the high-dimensional approach in both settings

Developmental Changes in the Organization of Functional Connections between the Basal Ganglia and Cerebral Cortex

The basal ganglia (BG) comprise a set of subcortical nuclei with sensorimotor, cognitive, and limbic subdivisions, indicative of functional organization. BG dysfunction in several developmental disorders suggests the importance of the healthy maturation of these structures. However, few studies have investigated the development of BG functional organization. Using resting-state functional connectivity MRI (rs-fcMRI), we compared human child and adult functional connectivity of the BG with rs-fcMRI-defined cortical systems. Because children move more than adults, customized preprocessing, including volume censoring, was used to minimize motion-induced rsfcMRI artifact. Our results demonstrated functional organization in the adult BG consistent with subdivisions previously identified in anatomical tracing studies. Group comparisons revealed a developmental shift in bilateral posterior putamen/pallidum clusters from preferential connectivity with the somatomotor “face” system in childhood to preferential connectivity with control/attention systems (frontoparietal, ventral attention) in adulthood. This shift was due to a decline in the functional connectivity of these clusters with the somatomotor face system over development, and no change with control/attention systems. Applying multivariate pattern analysis, we were able to reliably classify individuals as children or adults based on BG–cortical system functional connectivity. Interrogation of the features driving this classification revealed, in addition to the somatomotor face system, contributions by the orbitofrontal, auditory, and somatomotor hand systems. These results demonstrate that BG–cortical functional connectivity evolves over development, and may lend insight into developmental disorders that involve BG dysfunction, particularly those involving motor systems (e.g., Tourette syndrome)

Využití strojového učení v analýze dat z magnetické rezonance za účelem zlepšení diagnostiky časné schizofrenie

Background: Early diagnosis of schizophrenia could improve the outcomes and limit the negative effects of untreated illness. Although participants with schizophrenia show structural/functional alterations on the group level, these findings have a limited diagnostic utility. Novel methods of MRI analyses, such as machine learning (ML), may help bring neuroimaging from bench to the bedside. Here, we used ML to differentiate participants with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls (HC) based on neuroimaging data and compared the diagnostic utility of such approach with the utility of between group comparisons using classical statistical methods. Method: Firstly, we performed a classical fMRI experiment in FES using a self/other- agency task (SA/OA) and compared FES (N=35) versus controls (N=35) using conventional statistics. We than classified FES and healthy controls (HC) using linear kernel support vector machine (SVM) from the resting-state functional connectivity (rsFC) and fractional anisotropy (FA) in 63/63 and 77/77 age- and sex-matched FES and HC participants. We also investigated the between-group differences in rsFC and FA using classical between-group comparisons. Results: FES group exhibited a decreased activation during the emergent SA experience...Úvod: Včasná diagnóza schizofrenie může omezit negativní dopad neléčené nemoci. Progresivní funkční a strukturální změny byly opakovaně detekovány metodami skupinové statistiky, avšak kvůli nízké senzitivitě a specificitě nenašly v klinické praxi dosud využití. Nové metody analýzy, jako například strojové učení, mají v kombinaci s neurozobrazovacími metodami v psychiatrii diagnostický potenciál. Provedli jsme klasifikaci pacientů s první epizodou schizofrenie a zdravých dobrovolníků založenou na neurozobrazovacích datech a srovnali možnosti jejího klinického využití s přístupy klasické skupinové statistiky. Metody: V prvním kroku jsme provedli analýzu klasického fMRI experimentu v blokovém designu s využitím ''self-agency'' paradigmatu (SA) pomocí klasické skupinové statistiky. Následně jsme klasifikovali pacienty s FES a zdravé dobrovolníky pomocí linear support vector machine (SVM) z dat klidové funkční konektivity (rsFC) a frakční anizotropie (FA) pomocí strojového učení na souborech 63/63 (rsFC) a 77/77 (FA) pacientů/zdravých dobrovolníků, kteří byli jednotlivě matchováni podle věku a pohlaví. Výsledky: U FES jsme detekovali nižší aktivaci během SA prožitku v centrálních mediálních strukturách (CMS). SVM byl schopen rozlišit pacienty od zdravých dobrovolníků s přesností 73.0% (p=0.001) (rsFC) a...Department of Psychiatry and Medical Psychology - Department of PsychiatryKlinika psychiatrie a lékařské psychologie - klinika psychiatrie3. lékařská fakultaThird Faculty of Medicin

Contributions to the study of Austism Spectrum Brain conectivity

164 p.Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines

Impact of Machine Learning Pipeline Choices in Autism Prediction from Functional Connectivity Data

Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction, and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this paper, we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines. Specifically, we consider six brain parcellation definitions, five methods for functional connectivity matrix construction, six feature extraction/selection approaches, and nine classifier building algorithms. We report the prediction performance sensitivity to each of these choices, as well as the best results that are comparable with the state of the art.This work has been partially supported by theFEDER funds through MINECO project TIN2017-85827-P. This project has received funding from theEuropean Union’s Horizon 2020 research and inno-vation program under the Marie Sklodowska-Curiegrant agreement No 77772

A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder