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

Phenotyping functional brain dynamics:A deep learning prespective on psychiatry

This thesis explores the potential of deep learning (DL) techniques combined with multi-site functional magnetic resonance imaging (fMRI) to enable automated diagnosis and biomarker discovery for psychiatric disorders. This marks a shift from the convention in the field of applying standard machine learning techniques on hand-crafted features from a single cohort.To enable this, we have focused on three main strategies: utilizing minimally pre-processed data to maintain spatio-temporal dynamics, developing sample-efficient DL models, and applying emerging DL training techniques like self-supervised and transfer learning to leverage large population-based datasets.Our empirical results suggest that DL models can sometimes outperform existing machine learning methods in diagnosing Autism Spectrum Disorder (ASD) and Major Depressive Disorder (MDD) from resting-state fMRI data, despite the smaller datasets and the high data dimensionality. Nonetheless, the generalization performance of these models is currently insufficient for clinical use, raising questions about the feasibility of applying supervised DL for diagnosis or biomarker discovery due to the highly heterogeneous nature of the disorders. Our findings suggest that normative modeling on functional brain dynamics provides a promising alternative to the current paradigm

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

Decoding non-invasive brain activity with novel deep-learning approaches

This thesis delves into the world of non-invasive electrophysiological brain signals like electroencephalography (EEG) and magnetoencephalography (MEG), focusing on modelling and decoding such data. The research aims to investigate what happens in the brain when we perceive visual stimuli or engage in covert speech (inner speech) and enhance the decoding performance of such stimuli. The findings have significant implications for the development of brain-computer interfaces (BCIs), leading to assistive communication technologies for paralysed individuals. The thesis is divided into two main sections, methodological and experimental work. A central concern in both sections is the large variability present in electrophysiological recordings, whether it be within-subject or between-subject variability, and to a certain extent between-dataset variability. In the methodological sections, we explore the potential of deep learning for brain decoding. The research acknowledges the urgent need for more sophisticated models and larger datasets to improve the decoding and modelling of EEG and MEG signals. We present advancements in decoding visual stimuli using linear models at the individual subject level. We then explore how deep learning techniques can be employed for group decoding, introducing new methods to deal with between-subject variability. Finally, we also explores novel forecasting models of MEG data based on convolutional and Transformer-based architectures. In particular, Transformer-based models demonstrate superior capabilities in generating signals that closely match real brain data, thereby enhancing the accuracy and reliability of modelling the brain’s electrophysiology. In the experimental section, we present a unique dataset containing high-trial inner speech EEG, MEG, and preliminary optically pumped magnetometer (OPM) data. We highlight the limitations of current BCI systems used for communication, which are either invasive or extremely slow. While inner speech decoding from non-invasive brain signals has great promise, it has been a challenging goal in the field with limited decoding approaches, indicating a significant gap that needs to be addressed. Our aim is to investigate different types of inner speech and push decoding performance by collecting a high number of trials and sessions from a few participants. However, the decoding results are found to be mostly negative, underscoring the difficulty of decoding inner speech. In conclusion, this thesis provides valuable insight into the challenges and potential solutions in the field of electrophysiology, particularly in the decoding of visual stimuli and inner speech. The findings could pave the way for future research and advancements in the field, ultimately improving communication capabilities for paralysed individuals