Machine learning and DWI brain communicability networks for Alzheimer's disease detection

Signal processing and machine learning techniques are changing the clinical practice based on medical imaging from many perspectives. A major topic is related to (i) the development of computer aided diagnosis systems to provide clinicians with novel, non-invasive and low-cost support-tools, and (ii) to the development of new methodologies for the analysis of biomedical data for finding new disease biomarkers. Advancements have been recently achieved in the context of Alzheimer's disease (AD) diagnosis through the use of diffusionweighted imaging (DWI) data. When combinedwith tractography algorithms, this imaging modality enables the reconstruction of the physical connections of the brain that can be subsequently investigated through a complex network-based approach. A graph metric particularly suited to describe the disruption of the brain connectivity due to AD is communicability. In this work, we develop a machine learning framework for the classification and feature importance analysis of AD based on communicability at the whole brain level. We fairly compare the performance of three state-of-the-art classification models, namely support vector machines, random forests and artificial neural networks, on the connectivity networks of a balanced cohort of healthy control subjects and AD patients from the ADNI database. Moreover, we clinically validate the information content of the communicabilitymetric by performing a feature importance analysis. Both performance comparison and feature importance analysis provide evidence of the robustness of the method. The results obtained confirm that the whole brain structural communicability alterations due to AD are a valuable biomarker for the characterization and investigation of pathological conditions

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

Hierarchical Information-Based Clustering for Connectivity-Based Cortex Parcellation

One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area (“the functional fingerprint”) is closely related to its anatomical connections (“the connectional fingerprint”) and, hence, a segregated cortical area may be characterized by a highly coherent connectivity pattern. Diffusion tractography can be used to identify borders between such cortical areas. Each cortical area is defined based upon a unique probabilistic tractogram and such a tractogram is representative of a group of tractograms, thereby forming the cortical area. The underlying methodology is called connectivity-based cortex parcellation and requires clustering or grouping of similar diffusion tractograms. Despite the relative success of this technique in producing anatomically sensible results, existing clustering techniques in the context of connectivity-based parcellation typically depend on several non-trivial assumptions. In this paper, we embody an unsupervised hierarchical information-based framework to clustering probabilistic tractograms that avoids many drawbacks offered by previous methods. Cortex parcellation of the inferior frontal gyrus together with the precentral gyrus demonstrates a proof of concept of the proposed method: The automatic parcellation reveals cortical subunits consistent with cytoarchitectonic maps and previous studies including connectivity-based parcellation. Further insight into the hierarchically modular architecture of cortical subunits is given by revealing coarser cortical structures that differentiate between primary as well as premotoric areas and those associated with pre-frontal areas

Towards Precision Psychiatry: gray Matter Development And Cognition In Adolescence

Precision Psychiatry promises a new era of optimized psychiatric diagnosis and treatment through comprehensive, data-driven patient stratification. Among the core requirements towards that goal are: 1) neurobiology-guided preprocessing and analysis of brain imaging data for noninvasive characterization of brain structure and function, and 2) integration of imaging, genomic, cognitive, and clinical data in accurate and interpretable predictive models for diagnosis, and treatment choice and monitoring. In this thesis, we shall touch on specific aspects that fit under these two broad points. First, we investigate normal gray matter development around adolescence, a critical period for the development of psychopathology. For years, the common narrative in human developmental neuroimaging has been that gray matter declines in adolescence. We demonstrate that different MRI-derived gray matter measures exhibit distinct age and sex effects and should not be considered equivalent, as has often been done in the past, but complementary. We show for the first time that gray matter density increases from childhood to young adulthood, in contrast with gray matter volume and cortical thickness, and that females, who are known to have lower gray matter volume than males, have higher density throughout the brain. A custom preprocessing pipeline and a novel high-resolution gray matter parcellation were created to analyze brain scans of 1189 youths collected as part of the Philadelphia Neurodevelopmental Cohort. This work emphasizes the need for future studies combining quantitative histology and neuroimaging to fully understand the biological basis of MRI contrasts and their derived measures. Second, we use the same gray matter measures to assess how well they can predict cognitive performance. We train mass-univariate and multivariate models to show that gray matter volume and density are complementary in their ability to predict performance. We suggest that parcellation resolution plays a big role in prediction accuracy and that it should be tuned separately for each modality for a fair comparison among modalities and for an optimal prediction when combining all modalities. Lastly, we introduce rtemis, an R package for machine learning and visualization, aimed at making advanced data analytics more accessible. Adoption of accurate and interpretable machine learning methods in basic research and medical practice will help advance biomedical science and make precision medicine a reality

Understanding Individual Differences within Large-scale Brain Networks across Cognitive Contexts

Historically, human neuroimaging has studied brain regions “activated” during behavior and how they differ between groups of people. This approach has improved our understanding of healthy and disordered brain function, but has two key shortcomings. First, focusing on brain activation restricts how we understand the brain, ignoring vital, behind-the-scenes processing. In the past decade, the focus has shifted to communication between brain regions, or connectivity, revealing networks that exhibit subtle, consistent differences across behaviors and diagnoses. Without activation-focused research’s constraints, connectivity-focused neuroimaging research more comprehensively assesses brain function. Second, focusing on group differences ignores substantial within-group heterogeneity and often imposes false dichotomies. Recent findings show that brain network variability within an individual is nearly as great as across a group. Altogether, this illustrates a need for understanding individual variability in brain networks and how it relates to behavior. Therefore, I have developed a pipeline for investigating individual differences in brain connectivity, adapting robust statistical methods to address unique challenges of neuroimaging data analysis. Here, I describe this pipeline and apply it to two datasets. First, I explore between-individual variability in brain connectivity underlying intelligence and academic performance to better understand factors contributing to student success. Second, I assess the relative contributions of stress, sleep, and hormones to within-individual variability in brain connectivity across the menstrual cycle to illuminate little-studied phenomena affecting the everyday lives of half the population. Finally, I introduce a novel signal processing workflow for cleaning electrophysiological measures of bodily stress and arousal in neuroimaging research

Atypical Integration of Sensory-to-Transmodal Functional Systems Mediates Symptom Severity in Autism.

A notable characteristic of autism spectrum disorder (ASD) is co-occurring deficits in low-level sensory processing and high-order social interaction. While there is evidence indicating detrimental cascading effects of sensory anomalies on the high-order cognitive functions in ASD, the exact pathological mechanism underlying their atypical functional interaction across the cortical hierarchy has not been systematically investigated. To address this gap, here we assessed the functional organisation of sensory and motor areas in ASD, and their relationship with subcortical and high-order trandmodal systems. In a resting-state fMRI data of 107 ASD and 113 neurotypical individuals, we applied advanced connectopic mapping to probe functional organization of primary sensory/motor areas, together with targeted seed-based intrinsic functional connectivity (iFC) analyses. In ASD, the connectopic mapping revealed topological anomalies (i.e., excessively more segregated iFC) in the motor and visual areas, the former of which patterns showed association with the symptom severity of restricted and repetitive behaviors. Moreover, the seed-based analysis found diverging patterns of ASD-related connectopathies: decreased iFCs within the sensory/motor areas but increased iFCs between sensory and subcortical structures. While decreased iFCs were also found within the higher-order functional systems, the overall proportion of this anomaly tends to increase along the level of cortical hierarchy, suggesting more dysconnectivity in the higher-order functional networks. Finally, we demonstrated that the association between low-level sensory/motor iFCs and clinical symptoms in ASD was mediated by the high-order transmodal systems, suggesting pathogenic functional interactions along the cortical hierarchy. Findings were largely replicated in the independent dataset. These results highlight that atypical integration of sensory-to-high-order systems contributes to the complex ASD symptomatology

Parcellation of the human cerebral cortex using diffusion MRI

Histological methods have long been used to segment the cerebral cortex into structurally distinct cortical areas that have served as a basis for research into brain structure and function and remain in use today. There is great interest in adapting and extending these methods to be able to use non-invasive imaging, so that tighter structure-function relationships can be measured in living subjects. Whilst diffusion neuroimaging methods have been widely applied to white matter, the reduced anisotropy in the thin, complexly folded grey matter of the cortex has so far limited its study. In vivo parcellation pipelines have instead focussed on T1 and T2 weighted MRI. Recent advances in imaging hardware have reignited interest in grey matter diffusion MRI as a viable candidate for characterising architectonic domains. This Thesis explores the capabilities of dMRI as a measure of cortical microstructure using in vivo datasets from healthy adult participants. A cortical parcellation pipeline was developed in which both unsupervised and supervised algorithms were explored. Results were presented at both the group level and single subject level across the entire cortical sheet. The diffusion-based feature space characterised the known variation in cellular composition and fibre density relative to the local cortical surface normal. Thus they remain invariant to the confounding orientation changes associated with cortical folding, which usually inhibit studies of cortical microstructure. The features were compared to the alternative T1w/T2w myelin mapping methods to demonstrate that the diffusion MRI signal provides a complementary mode of contrast. A series of classification experiments were used to determine the most effective methods for utilising diffusion in grey matter applications. Several additional methods from the dMRI literature were compared to highlight the benefit of higher-order tissue representations. Similarly, classification tasks were used to corroborate the benefits of sampling multiple b-values in cortical studies. The experimental chapters provide strong evidence in favour of the future use of diffusion MRI as a measure of the varying microstructure that defines cortical areas

Machine learning based computational models with permeability for white matter microstructure imaging

Characterising tissue microstructure is of paramount importance for understanding neurological conditions such as Multiple Sclerosis. Therefore, there is a growing interest in imaging tissue microstructure non-invasively. One way to achieve this is by developing tissue models and fitting them to the diffusion-MRI signal. Nevertheless, some microstructure parameters, such as permeability, remain elusive because analytical models that incorporate them are intractable. Machine learning based computational models offer a promising alternative as they bypass the need for analytical expressions. The aim of this thesis is to develop the first machine learning based computational model for white matter microstructure imaging using two promising approaches: random forests and neural networks. To test the feasibility of this new approach, we provide for the first time a direct comparison of machine learning parameter estimates with histology. In this thesis, we demonstrate the idea by estimating permeability via the intra-axonal exchange time τ_i, a potential imaging biomarker for demyelinating pathologies. We use simulations of the diffusion-MRI signal to construct a mapping between signals and microstructure parameters including τ_i. We show for the first time that clinically viable diffusion-weighted sequences can probe exchange times up to approximately 1000 ms. Using healthy in-vivo human and mouse data, we show that our model's estimates are within the plausible range for white matter tissue and display well known trends such as the high-low-high intra-axonal volume fraction f across the corpus callosum. Using human and mouse data from demyelinated tissue, we show that our model detects trends in line with the expected MS pathology: a significant decrease in f and τ_i. Moreover, we show that our random forest estimates of f and τ_i correlate very strongly with histological measurements of f and myelin thickness. This thesis demonstrates that machine learning based computational models are a feasible approach for white matter microstructure imaging. The continually improving SNR in the clinical scanners and the availability of more realistic simulations open up possibilities of using such models as imaging biomarkers for demyelinating diseases such as Multiple Sclerosis