Machine learning for classification of ADHD

Attention Deficit Hyperactive Disorder (ADHD) is well-known common causation of childhood behavioural disorders. It is estimated that around 5-10% of children globally are affected with this disorder. ADHD is attributed to problematic behaviours that include inattention and impulsivity. Children find it extremely difficult to focus, be attentive and to organise themselves. It contributes to a lifetime of impairment, poor quality of life and long-term burden on affected families. Since there is no single cause found in the prevalence or absence of ADHD. The usual method of diagnosis is merely dependent on behavioural analysis which are all subjective. Clinicians usually take months to diagnose the condition. To date, there are no biological markers that exist for ADHD. To measure neurobiological data objectively, an assessment of the brain behaviour relationship is essential to transform the method of diagnosis. Automatic diagnosis is a profound way for an effective cure. In this dissertation, we aim to solve the problem of automatic diagnosis of ADHD using machine learning methods based on functional MRI (fMRI) data. The proposed methods begin with classical machine learning and move to deep learning as a way to improve the classification performance. Interpretability of results is an important aspect, so functional connectivity is a central theme in the work and the proposed methods utilise functional connectivity in increasingly more complex ways. In the first method, we have evaluated a clustering based novel method to calculate functional connectivity. After calculating functional connectivity, we employ Elastic Net feature selection to select the discriminant features and integrate non-imaging data. Finally, a Support Vector Machine (SVM) classifier is trained to classify ADHD. The second method presents a deep learning based novel method, called FCNet, that calculates functional connectivity from fMRI time-series signals. The FCNet consists of two networks, i) a convolutional neural network in a Siamese architecture that extracts abstract features from a pair of time-series signals and, ii) a similarity measure network that computes the strength of similarity between the extracted features which serves as functional connectivity. Similar to the previous method, an Elastic Net and SVM is applied to classify ADHD. In the third method, we have proposed an end-to-end trainable model to classify ADHD from preprocessed fMRI time-series data. The model takes fMRI time-series signals as input and outputs the predicted labels, and is trained end to-end using back-propagation. The proposed model is comprised of three networks, namely i) a feature extractor, ii) a functional connectivity network, and iii) a classification network. Our findings highlight that functional connectivity serves as an important biomarker towards classification of ADHD and the frontal lobe is altered the most in the case of ADHD. The frontal lobe is known to be associated with cognitive functions like attention, memory, planning and mood. Our findings of the frontal lobe anomalies in ADHD support findings of the earlier studies. Our results reveal that an end to-end trainable deep network incorporating functional connectivity yields higher detection rates

In vivo characterization of cerebral networks with functional and structural magnetic resonance techniques

2012/2013Lo studio delle connessioni anatomiche e funzionali del cervello risulta essere un passo essenziale per comprendere i meccanismi alla base del funzionamento cerebrale. Diverse tecniche di Neuroimmagini sono state sviluppate negli ultimi anni al fine di approfondire la conoscenza della connettività cerebrale umana in vivo. Il presente studio si articola in quattro differenti esperimenti condotti su gruppi di soggetti sani e non, per valutare la validità di differenti tecniche e della loro combinazione nella caratterizzazione della connettività anatomica e funzionale e delle alterazioni che essa subisce nell'ambito di differenti patologie.XXVI Ciclo198

Developing multidimensional metrics for evaluating paediatric neurodevelopmental disorders

Healthy brain functioning depends on efficient communication of information between brain regions, forming complex networks. By quantifying synchronisation between brain regions, a functionally connected brain network can be articulated. In neurodevelopmental disorders, where diagnosis is based on measures of behaviour and tasks, a measure of the underlying biological mechanisms holds promise as a potential clinical tool. Graph theory provides a tool for investigating the neural correlates of neuropsychiatric disorders, where there is disruption of efficient communication within and between brain networks. This research aimed to use recent conceptualisation of graph theory, along with measures of behaviour and cognitive functioning, to increase understanding of the neurobiological risk factors of atypical development. Using magnetoencephalography to investigate frequency-specific temporal dynamics at rest, the research aimed to identify potential biological markers derived from sensor-level whole-brain functional connectivity. Whilst graph theory has proved valuable for insight into network efficiency, its application is hampered by two limitations. First, its measures have hardly been validated in MEG studies, and second, graph measures have been shown to depend on methodological assumptions that restrict direct network comparisons. The first experimental study (Chapter 3) addressed the first limitation by examining the reproducibility of graph-based functional connectivity and network parameters in healthy adult volunteers. Subsequent chapters addressed the second limitation through adapted minimum spanning tree (a network analysis approach that allows for unbiased group comparisons) along with graph network tools that had been shown in Chapter 3 to be highly reproducible. Network topologies were modelled in healthy development (Chapter 4), and atypical neurodevelopment (Chapters 5 and 6). The results provided support to the proposition that measures of network organisation, derived from sensor-space MEG data, offer insights helping to unravel the biological basis of typical brain maturation and neurodevelopmental conditions, with the possibility of future clinical utility

Myelin imaging and characterization by magnetic resonance imaging

280 p.Los axones neuronales están recubiertos de una membrana lipídica llamada mielina, que protege a los axones y posibilita una transmisión rápida y eficiente del impulso eléctrico. En ciertas patologías como la lesión cerebral traumática, la isquemia o principalmente, en la esclerosis múltiple, la pérdida de mielina o desmielinización da lugar a la muerte neuronal y por consiguiente a la pérdida de capacidades cognitivas. Este estado puede ser revertido por medio de la remielinización, en la que los oligodendrocitos mielinizantes del sistema nervioso central regeneran la vaina de mielina, evitando la degeneración de las neuronas. En los últimos años se ha realizado un esfuerzo considerable en el desarrollo de terapias remielinizantes. Para ello, es imprescindible el desarrollo de técnicas para la evaluación no-invasiva de estas terapias y una caracterización profunda de los procesos de desmielinización y remielinización. En este contexto, la imagen por resonancia magnética (IRM) juega un papel fundamental por su carácter no-invasivo, alta resolución y versatilidad.Los principales objetivos de esta tesis han sido el desarrollo de protocolos de IRM para la cuantificación de mielina y la caracterización de los procesos de remielinización y desmielinización a través de resonancia magnética funcional en reposo. Para ello se ha utilizado como base el modelo murinocuprizona, en la que la administración del tóxico da lugar a la desmielinización en el cerebro, seguido por la remielinización. Los datos y conclusiones obtenidas se han contrastado en otros modelos de ratón, como en modelos de Alzheimer o en ratones sanos envejecidos.A grandes rasgos, hemos podido concluir que la imagen ponderada en peso T2 es la más específica y sensible para la cuantificación de mielina en el modelo cuprizona. Por ello, en este trabajo se propone la utilización de la imagen ponderada en peso T2 para la evaluación de terapias remielinizantes en el modelo cuprizona. Sin embargo, el interés de realizar imagen multiparamétríca ha quedado al descubierto al realizar imagen de modelos de ratón de Alzheimer, pudiendo detectar patología no relacionada con pérdida de mielina en zonas de materia blanca.Así mismo, hemos podido comprobar como la desmielinización conlleva la pérdida de la conectividad y función cerebral y la remielinización posibilita la recuperación por medio de la resonancia magnética funcional en reposo. Además, el potencial agente remielinizante clemastina, ha demostrado su capacidad de promover la remielinización a nivel anatómico y funcional tras 2 semanas de tratamiento. Finalmente, se ha realizado un estudio para determinar el efecto del envejecimiento en la conectividad del cerebro. Hemos podido observar que en ratones sanos, se ha observado un incremento de la conectividad cerebral hasta el mes 8, seguido de un descenso hasta el mes 13, probablemente debido a la neurodegeneración.En este trabajo hemos contribuido al desarrollo de terapias remielinizantes, por un lado, desarrollando protocolos de imagen para la cuantificación de mielina en modelos animales y por otro lado, caracterizando la desmielinización y remielinización a nivel funcional y anatómico

The neurophysiologic landscape of the sleep onset: a systematic review

Background: The sleep onset process is an ill-defined complex process of transition from wakefulness to sleep, characterized by progressive modifications at the subjective, behavioural, cognitive, and physiological levels. To this date, there is no international consensus which could aid a principled characterisation of this process for clinical research purposes. The current review aims to systemise the current knowledge about the underlying mechanisms of the natural heterogeneity of this process. Methods: In this systematic review, studies investigating the process of the sleep onset from 1970 to 2022 were identified using electronic database searches of PsychINFO, MEDLINE, and Embase. Results: A total of 139 studies were included; 110 studies in healthy participants and 29 studies in participants with sleep disorders. Overall, there is a limited consensus across a body of research about what distinct biomarkers of the sleep onset constitute. Only sparse data exists on the physiology, neurophysiology and behavioural mechanisms of the sleep onset, with majority of studies concentrating on the non-rapid eye movement stage 2 (NREM 2) as a potentially better defined and a more reliable time point that separates sleep from the wake, on the sleep wake continuum. Conclusions: The neurophysiologic landscape of sleep onset bears a complex pattern associated with a multitude of behavioural and physiological markers and remains poorly understood. The methodological variation and a heterogenous definition of the wake-sleep transition in various studies to date is understandable, given that sleep onset is a process that has fluctuating and ill-defined boundaries. Nonetheless, the principled characterisation of the sleep onset process is needed which will allow for a greater conceptualisation of the mechanisms underlying this process, further influencing the efficacy of current treatments for sleep disorders.This paper represents independent research in part funded by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London

Advances in Analysis and Exploration in Medical Imaging

With an ever increasing life expectancy, we see a concomitant increase in diseases capable of disrupting normal cognitive processes. Their diagnoses are difficult, and occur usually after daily living activities have already been compromised. This dissertation proposes machine learning methods for the study of the neurological implications of brain lesions. It addresses the analysis and exploration of medical imaging data, with particular emphasis to (f)MRI. Two main research directions are proposed. In the first, a brain tissue segmentation approach is detailed. In the second, a document mining framework, applied to reports of neuroscientific studies, is described. Both directions are based on retrieving consistent information from multi-modal data. A contribution in this dissertation is the application of a semi-supervised method, discriminative clustering, to identify different brain tissues and their partial volume information. The proposed method relies on variations of tissue distributions in multi-spectral MRI, and reduces the need for a priori information. This methodology was successfully applied to the study of multiple sclerosis and age related white matter diseases. It was also showed that early-stage changes of normal-appearing brain tissue can already predict decline in certain cognitive processes. Another contribution in this dissertation is in neuroscience meta-research. One limitation in neuroimage processing relates to data availability. Through document mining of neuroscientific reports, using images as source of information, one can harvest research results dealing with brain lesions. The context of such results can be extracted from textual information, allowing for an intelligent categorisation of images. This dissertation proposes new principles, and a combination of several techniques to the study of published fMRI reports. These principles are based on a number of distance measures, to compare various brain activity sites. Application to studies of the default mode network validated the proposed approach. The aforementioned methodologies rely on clustering approaches. When dealing with such strategies, most results depend on the choice of initialisation and parameter settings. By defining distance measures that search for clusters of consistent elements, one can estimate a degree of reliability for each data grouping. In this dissertation, it is shown that such principles can be applied to multiple runs of various clustering algorithms, allowing for a more robust estimation of data agglomeration

Functional network correlates of language and semiology in epilepsy

Epilepsy surgery is appropriate for 2-3% of all epilepsy diagnoses. The goal of the presurgical workup is to delineate the seizure network and to identify the risks associated with surgery. While interpretation of functional MRI and results in EEG-fMRI studies have largely focused on anatomical parameters, the focus of this thesis was to investigate canonical intrinsic connectivity networks in language function and seizure semiology. Epilepsy surgery aims to remove brain areas that generate seizures. Language dysfunction is frequently observed after anterior temporal lobe resection (ATLR), and the presurgical workup seeks to identify the risks associated with surgical outcome. The principal aim of experimental studies was to elaborate understanding of language function as expressed in the recruitment of relevant connectivity networks and to evaluate whether it has value in the prediction of language decline after anterior temporal lobe resection. Using cognitive fMRI, we assessed brain areas defined by parameters of anatomy and canonical intrinsic connectivity networks (ICN) that are involved in language function, specifically word retrieval as expressed in naming and fluency. fMRI data was quantified by lateralisation indices and by ICN_atlas metrics in a priori defined ICN and anatomical regions of interest. Reliability of language ICN recruitment was studied in 59 patients and 30 healthy controls who were included in our language experiments. New and established language fMRI paradigms were employed on a three Tesla scanner, while intellectual ability, language performance and emotional status were established for all subjects with standard psychometric assessment. Patients who had surgery were reinvestigated at an early postoperative stage of four months after anterior temporal lobe resection. A major part of the work sought to elucidate the association between fMRI patterns and disease characteristics including features of anxiety and depression, and prediction of postoperative language outcome. We studied the efficiency of reorganisation of language function associated with disease features prior to and following surgery. A further aim of experimental work was to use EEG-fMRI data to investigate the relationship between canonical intrinsic connectivity networks and seizure semiology, potentially providing an avenue for characterising the seizure network in the presurgical workup. The association of clinical signs with the EEG-fMRI informed activation patterns were studied using the data from eighteen patients’ whose seizures and simultaneous EEG-fMRI activations were reported in a previous study. The accuracy of ICN_atlas was validated and the ICN construct upheld in the language maps of TLE patients. The ICN construct was not evident in ictal fMRI maps and simulated ICN_atlas data. Intrinsic connectivity network recruitment was stable between sessions in controls. Amodal linguistic processing and the relevance of temporal intrinsic connectivity networks for naming and that of frontal intrinsic connectivity networks for word retrieval in the context of fluency was evident in intrinsic connectivity networks regions. The relevance of intrinsic connectivity networks in the study of language was further reiterated by significant association between some disease features and language performance, and disease features and activation in intrinsic connectivity networks. However, the anterior temporal lobe (ATL) showed significantly greater activation compared to intrinsic connectivity networks – a result which indicated that ATL functional language networks are better studied in the context of the anatomically demarked ATL, rather than its functionally connected intrinsic connectivity networks. Activation in temporal lobe networks served as a predictor for naming and fluency impairment after ATLR and an increasing likelihood of significant decline with greater magnitude of left lateralisation. Impairment of awareness served as a significant classifying feature of clinical expression and was significantly associated with the inhibition of normal brain functions. Canonical intrinsic connectivity networks including the default mode network were recruited along an anterior-posterior anatomical axis and were not significantly associated with clinical signs