Multi-Kernel Capsule Network for Schizophrenia Identification

Schizophrenia seriously affects the quality of life. To date, both simple (e.g., linear discriminant analysis) and complex (e.g., deep neural network) machine learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e., feature extraction and classification) to achieve the identification, which disables simultaneous tuning for the best feature extraction and classifier training. The complex methods integrate two steps and can be simultaneously tuned to achieve optimal performance, but these methods require a much larger amount of data for model training. To overcome the aforementioned drawbacks, we proposed a multi-kernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match with partition sizes of brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of widely-used dropout strategy in deep learning, we developed capsule dropout in the capsule layer to prevent overfitting of the model. The comparison results showed that the proposed method outperformed the state-of-the-art methods. Besides, we compared performances using different parameters and illustrated the routing process to reveal characteristics of the proposed method. MKCapsnet is promising for schizophrenia identification. Our study first utilized capsule neural network for analyzing functional connectivity of magnetic resonance imaging (MRI) and proposed a novel multi-kernel capsule structure with consideration of brain anatomical parcellation, which could be a new way to reveal brain mechanisms. In addition, we provided useful information in the parameter setting, which is informative for further studies using a capsule network for other neurophysiological signal classification

The role of MRI in diagnosing autism: a machine learning perspective.

There is approximately 1 in every 44 children in the United States suffers from autism spectrum disorder (ASD), a disorder characterized by social and behavioral impairments. Communication difficulties, interpersonal difficulties, and behavioral difficulties are the top common symptoms. Even though symptoms can begin as early as infancy, it may take multiple visits to a pediatric specialist before an accurate diagnosis can be made. In addition, the diagnosis can be subjective, and different specialists may give different scores. There is a growing body of research suggesting differences in brain development and/or environmental and/or genetic factors contribute to autism development, but scientists have yet to identify exactly the pathology of this disorder. ASD can currently be diagnosed by a set of diagnostic evaluations, regarded as the gold standard, such as the Autism Diagnostic Observation Schedule (ADOS) or the Autism Diagnostic Interview-Revised (ADI-R). A team of qualified clinicians is needed for performing the behavioral and communication tests as well as clinical history information, hence a considerable amount of time, effort, and subjective judgment is involved in using these gold-standard diagnostic instruments. In addition to standard observational assessment, recent advancements in neuroimaging and machine learning suggest a rapid and objective alternative, using brain imaging. An investigation of the employment of different imaging modalities, namely Diffusion Tensor Imaging (DTI), and resting state functional MRI (rs-fMRI) for autism diagnosis is presented in this comprehensive work. A detailed study of the implementation of feature engineering tools to find discriminant insights from different brain imaging modalities, including the use of novel feature representations, and the use of a machine learning framework to assist in the accurate classification of autistic individuals is introduced in this dissertation. Based on three large publicly available datasets, this extensive research highlights different decisions along the pipeline and their impact on diagnostic accuracy. It also identifies potentially impacted brain regions that contribute to an autism diagnosis. Achieving high global state-of-the-art cross-validated accuracy confirms the benefits of feature representation and feature engineering in extracting useful information, as well as the potential benefits of utilizing neuroimaging in the diagnosis of autism. This should enable an early, automated, and more objective personalized diagnosis

The Effects of Methylphenidate on Resource Allocation in the Mental Processing of ADD Children

The effect of Methylphenidate (MPH) on the short-term memory scanning of boys with Attention Deficit Disorder (ADD) was examined from the perspectives of a linear stages model and a capacity model of information processing. A total of 36, six- to ten-year-old boys participated in three age- and IQ-matched groups. The boys with ADD were selected on the basis of a clinical diagnosis, meeting DSM-III criteria on the SNAP and a score of 15 or more on the Abbreviated Conners Rating Scale (ACRS). They received treatment with placebo and methylphenidate (MPH) for three weeks each in a double-blind, single crossover procedure that was counterbalanced with respect the order of treatment. Two comparison groups were comprised of boys who were normal achievers (NA) and boys who were reading disabled (RD) and who did not receive medication. It was hypothesized that the administration of MPH to the boys with ADD would result in reductions in Sternberg reaction time and that this effect of medication would be restricted to the second half of the sessions. In partial support of the hypothesis, a significant effect of MPH administration on Sternberg reaction time was found in comparing reaction times obtained while the boys with ADD were receiving the active drug (MPH) with their reaction times at baseline. A similar drop in reaction from baseline was observed for boys with ADD who were administered placebo and for boys in the comparison groups after the first three week time period. This drop in reaction time was attributed to practice. The results of exploratory investigations using the additive factor method suggested that this practice effect involved an increased rate of serial comparison. Other effects of MPH were found to occur as a significantly reduced reaction time in the MPH condition relative to the placebo condition and as an order of treatment or carry-over effect. The carry-over effect was seen as improved Sternberg reaction time in the placebo condition when it followed the MPH condition as opposed to when it preceded it

Scalable Machine Learning Methods for Massive Biomedical Data Analysis.

Modern data acquisition techniques have enabled biomedical researchers to collect and analyze datasets of substantial size and complexity. The massive size of these datasets allows us to comprehensively study the biological system of interest at an unprecedented level of detail, which may lead to the discovery of clinically relevant biomarkers. Nonetheless, the dimensionality of these datasets presents critical computational and statistical challenges, as traditional statistical methods break down when the number of predictors dominates the number of observations, a setting frequently encountered in biomedical data analysis. This difficulty is compounded by the fact that biological data tend to be noisy and often possess complex correlation patterns among the predictors. The central goal of this dissertation is to develop a computationally tractable machine learning framework that allows us to extract scientifically meaningful information from these massive and highly complex biomedical datasets. We motivate the scope of our study by considering two important problems with clinical relevance: (1) uncertainty analysis for biomedical image registration, and (2) psychiatric disease prediction based on functional connectomes, which are high dimensional correlation maps generated from resting state functional MRI.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111354/1/takanori_1.pd

Dynamic deep learning for automatic facial expression recognition and its application in diagnosis of ADHD & ASD

Neurodevelopmental conditions like Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) impact a significant number of children and adults worldwide. Currently, the means of diagnosing of such conditions is carried out by experts, who employ standard questionnaires and look for certain behavioural markers through manual observation. Such methods are not only subjective, difficult to repeat, and costly but also extremely time consuming. However, with the recent surge of research into automatic facial behaviour analysis and it's varied applications, it could prove to be a potential way of tackling these diagnostic difficulties. Automatic facial expression recognition is one of the core components of this field but it has always been challenging to do it accurately in an unconstrained environment. This thesis presents a dynamic deep learning framework for robust automatic facial expression recognition. It also proposes an approach to apply this method for facial behaviour analysis which can help in the diagnosis of conditions like ADHD and ASD. The proposed facial expression algorithm uses a deep Convolutional Neural Networks (CNN) to learn models of facial Action Units (AU). It attempts to model three main distinguishing features of AUs: shape, appearance and short term dynamics, jointly in a CNN. The appearance is modelled through local image regions relevant to each AU, shape is encoded using binary masks computed from automatically detected facial landmarks and dynamics is encoded by using a short sequence of image as input to CNN. In addition, the method also employs Bi-directional Long Short Memory (BLSTM) recurrent neural networks for modelling long term dynamics. The proposed approach is evaluated on a number of databases showing state-of-the-art performance for both AU detection and intensity estimation tasks. The AU intensities estimated using this approach along with other 3D face tracking data, are used for encoding facial behaviour. The encoded facial behaviour is applied for learning models which can help in detection of ADHD and ASD. This approach was evaluated on the KOMAA database which was specially collected for this purpose. Experimental results show that facial behaviour encoded in this way provide a high discriminative power for classification of people with these conditions. It is shown that the proposed system is a potentially useful, objective and time saving contribution to the clinical diagnosis of ADHD and ASD

An information theoretic learning framework based on Renyi’s α entropy for brain effective connectivity estimation

The interactions among neural populations distributed across different brain regions are at the core of cognitive and perceptual processing. Therefore, the ability of studying the flow of information within networks of connected neural assemblies is of fundamental importance to understand such processes. In that regard, brain connectivity measures constitute a valuable tool in neuroscience. They allow assessing functional interactions among brain regions through directed or non-directed statistical dependencies estimated from neural time series. Transfer entropy (TE) is one such measure. It is an effective connectivity estimation approach based on information theory concepts and statistical causality premises. It has gained increasing attention in the literature because it can capture purely nonlinear directed interactions, and is model free. That is to say, it does not require an initial hypothesis about the interactions present in the data. These properties make it an especially convenient tool in exploratory analyses. However, like any information-theoretic quantity, TE is defined in terms of probability distributions that in practice need to be estimated from data. A challenging task, whose outcome can significantly affect the results of TE. Also, it lacks a standard spectral representation, so it cannot reveal the local frequency band characteristics of the interactions it detects.Las interacciones entre poblaciones neuronales distribuidas en diferentes regiones del cerebro son el núcleo del procesamiento cognitivo y perceptivo. Por lo tanto, la capacidad de estudiar el flujo de información dentro de redes de conjuntos neuronales conectados es de fundamental importancia para comprender dichos procesos. En ese sentido, las medidas de conectividad cerebral constituyen una valiosa herramienta en neurociencia. Permiten evaluar interacciones funcionales entre regiones cerebrales a través de dependencias estadísticas dirigidas o no dirigidas estimadas a partir de series de tiempo. La transferencia de entropía (TE) es una de esas medidas. Es un enfoque de estimación de conectividad efectiva basada en conceptos de teoría de la información y premisas de causalidad estadística. Ha ganado una atención cada vez mayor en la literatura porque puede capturar interacciones dirigidas puramente no lineales y no depende de un modelo. Es decir, no requiere de una hipótesis inicial sobre las interacciones presentes en los datos. Estas propiedades la convierten en una herramienta especialmente conveniente en análisis exploratorios. Sin embargo, como cualquier concepto basado en teoría de la información, la TE se define en términos de distribuciones de probabilidad que en la práctica deben estimarse a partir de datos. Una tarea desafiante, cuyo resultado puede afectar significativamente los resultados de la TE. Además, carece de una representación espectral estándar, por lo que no puede revelar las características de banda de frecuencia local de las interacciones que detecta.DoctoradoDoctor(a) en IngenieríaContents List of Figures xi List of Tables xv Notation xvi 1 Preliminaries 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 Probability distribution estimation as an intermediate step in TE computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.2 The lack of a spectral representation for TE . . . . . . . . . . . . 7 1.3 Theoretical background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.1 Transfer entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.2 Granger causality . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.3 Information theoretic learning from kernel matrices . . . . . . . . 12 1.4 Literature review on transfer entropy estimation . . . . . . . . . . . . . . 14 1.4.1 Transfer entropy in the frequency domain . . . . . . . . . . . . . . 17 1.5 Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5.1 General aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5.2 Specific aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.6 Outline and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.6.1 Kernel-based Renyi’s transfer entropy . . . . . . . . . . . . . . . . 24 1.6.2 Kernel-based Renyi’s phase transfer entropy . . . . . . . . . . . . 24 1.6.3 Kernel-based Renyi’s phase transfer entropy for the estimation of directed phase-amplitude interactions . . . . . . . . . . . . . . . . 25 1.7 EEG databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Contents ix 1.7.1 Motor imagery . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.7.2 Working memory . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 1.8 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2 Kernel-based Renyi’s transfer entropy 34 2.1 Kernel-based Renyi’s transfer entropy . . . . . . . . . . . . . . . . . . . . 35 2.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2.1 VAR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2.2 Modified linear Kus model . . . . . . . . . . . . . . . . . . . . . . 38 2.2.3 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.2.4 Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.3.1 VAR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.3.2 Modified linear Kus model . . . . . . . . . . . . . . . . . . . . . . 46 2.3.3 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 2.3.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3 Kernel-based Renyi’s phase transfer entropy 60 3.1 Kernel-based Renyi’s phase transfer entropy . . . . . . . . . . . . . . . . 61 3.1.1 Phase-based effective connectivity estimation approaches considered in this chapter . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.2.1 Neural mass models . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.2.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 3.2.3 Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.3.1 Neural mass models . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.3.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.3.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4 Kernel-based Renyi’s phase transfer entropy for the estimation of directed phase-amplitude interactions 84 4.1 Kernel-based Renyi’s phase transfer entropy for the estimation of directed phase-amplitude interactions . . . . . . . . . . . . . . . . . . . . . . . . . 85 x Contents 4.1.1 Transfer entropy for directed phase-amplitude interactions . . . . 85 4.1.2 Cross-frequency directionality . . . . . . . . . . . . . . . . . . . . 85 4.1.3 Phase transfer entropy and directed phase-amplitude interactions 86 4.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 4.2.1 Simulated phase-amplitude interactions . . . . . . . . . . . . . . . 88 4.2.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.2.3 Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . 91 4.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4.3.1 Simulated phase-amplitude interactions . . . . . . . . . . . . . . . 92 4.3.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.3.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5 Final Remarks 100 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.3 Academic products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.1 Journal papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.2 Conference papers . . . . . . . . . . . . . . . . . . . . . . . . . . 105 5.3.3 Conference presentations . . . . . . . . . . . . . . . . . . . . . . . 105 Appendix A Kernel methods and Renyi’s entropy estimation 106 A.1 Reproducing kernel Hilbert spaces . . . . . . . . . . . . . . . . . . . . . . 106 A.1.1 Reproducing kernels . . . . . . . . . . . . . . . . . . . . . . . . . 106 A.1.2 Kernel-based learning . . . . . . . . . . . . . . . . . . . . . . . . . 107 A.2 Kernel-based estimation of Renyi’s entropy . . . . . . . . . . . . . . . . . 109 Appendix B Surface Laplacian 113 Appendix C Permutation testing 115 Appendix D Kernel-based relevance analysis 117 Appendix E Cao’s criterion 120 Appendix F Neural mass model equations 122 References 12

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

Do informal caregivers of people with dementia mirror the cognitive deficits of their demented patients?:A pilot study

Recent research suggests that informal caregivers of people with dementia (ICs) experience more cognitive deficits than noncaregivers. The reason for this is not yet clear. Objective: to test the hypothesis that ICs ‘mirror' the cognitive deficits of the demented people they care for. Participants and methods: 105 adult ICs were asked to complete three neuropsychological tests: letter fluency, category fluency, and the logical memory test from the WMS-III. The ICs were grouped according to the diagnosis of their demented patients. One-sample ttests were conducted to investigate if the standardized mean scores (t-scores) of the ICs were different from normative data. A Bonferroni correction was used to correct for multiple comparisons. Results: 82 ICs cared for people with Alzheimer's dementia and 23 ICs cared for people with vascular dementia. Mean letter fluency score of the ICs of people with Alzheimer's dementia was significantly lower than the normative mean letter fluency score, p = .002. The other tests yielded no significant results. Conclusion: our data shows that ICs of Alzheimer patients have cognitive deficits on the letter fluency test. This test primarily measures executive functioning and it has been found to be sensitive to mild cognitive impairment in recent research. Our data tentatively suggests that ICs who care for Alzheimer patients also show signs of cognitive impairment but that it is too early to tell if this is cause for concern or not

Interrogating autism from a multidimensional perspective: an integrative framework.

Autism Spectrum Disorder (ASD) is a condition characterized by social and behavioral impairments, affecting approximately 1 in every 44 children in the United States. Common symptoms include difficulties in communication, interpersonal interactions, and behavior. While symptoms can manifest as early as infancy, obtaining an accurate diagnosis may require multiple visits to a pediatric specialist due to the subjective nature of the assessment, which may yield varying scores from different specialists. Despite growing evidence of the role of differences in brain development and/or environmental and/or genetic factors in autism development, the exact pathology of this disorder has yet to be fully elucidated by scientists. At present, the diagnosis of ASD typically involves a set of gold-standard diagnostic evaluations, such as the Autism Diagnostic Observation Schedule (ADOS), the Autism Diagnostic Interview-Revised (ADI-R), and the more cost-effective Social Responsive Scale (SRS). Administering these diagnostic tests, which involve assessing communication and behavioral patterns, along with obtaining a clinical history, requires the expertise of a team of qualified clinicians. This process is time-consuming, effortful, and involves a degree of subjectivity due to the reliance on clinical judgment. Aside from conventional observational assessments, recent developments in neuroimaging and machine learning offer a fast and objective alternative for diagnosing ASD using brain imaging. This comprehensive work explores the use of different imaging modalvities, namely structural MRI (sMRI) and resting-state functional MRI (rs-fMRI), to investigate their potential for autism diagnosis. The proposed study aims to offer a new approach and perspective in comprehending ASD as a multidimensional problem, within a behavioral space that is defined by one of the available ASD diagnostic tools. This dissertation introduces a thorough investigation of the utilization of feature engineering tools to extract distinctive insights from various brain imaging modalities, including the application of novel feature representations. Additionally, the use of a machine learning framework to aid in the precise classification of individuals with autism is also explored in detail. This extensive research, which draws upon large publicly available datasets, sheds light on the influence of various decisions made throughout the pipeline on diagnostic accuracy. Furthermore, it identifies brain regions that may be impacted and contribute to an autism diagnosis. The attainment of high global state-of-the-art cross-validated, and hold-out set accuracy validates the advantages of feature representation and engineering in extracting valuable information, as well as the potential benefits of employing neuroimaging for autism diagnosis. Furthermore, a suggested diagnostic report has been put forth to assist physicians in mapping diagnoses to underlying neuroimaging markers. This approach could enable an earlier, automated, and more objective personalized diagnosis

Assessing brain functional connectivity in Parkinson’s disease using explainable Artificial Intelligence methods

Tese de Mestrado, Engenharia Biomédica e Biofísica, 2023, Universidade de Lisboa, Faculdade de CiênciasA doença de Parkinson (DP) é uma patologia neurogenerativa caracterizada pela perda de neurónios dopaminérgicos, em particular nos gânglios da base, e acumulação da proteína α-sinucleína. A DP é caracterizada por quatro sinais cardinais motores: tremores, bradicinesia, rigidez muscular e instabilidade postural. A doença é também manifestada por sintomas não motores como perda do olfato, doenças neuropsiquiátricas como depressão e ansiedade, e distúrbios do sono. Esta doença progressiva não tem cura, sendo que os tratamentos procuram a melhoria da qualidade de vida dos pacientes atenuando os sintomas. Relativamente ao diagnóstico, este é ainda principalmente baseado na análise da apresentação clínica dos sintomas. Entidades como a Sociedade de Doenças do Movimento apresentam uma série de critérios clínicos para aferir o diagnóstico da DP. Não existindo qualquer exame de imagiologia ou teste analítico que confirme um diagnóstico, as técnicas de neuroimagem surgem como ferramentas complementares com o fim de detetar alterações neuroquímicas relacionadas com a DP. O exame imagiológico mais comum é o DatScan, um tipo de aquisição de tomografia computorizada de emissão de fotão único que visa a deteção do transportador de dopamina, um biomarcador da degeneração dos neurónios dopaminérgicos. Dada a precisão e confiança insuficiente nos critérios clínicos de diagnóstico, bem como a falta de consistência do DaTScan, métodos de neuroimagem alternativos têm sido considerados para averiguar alterações cerebrais funcionais relacionadas com a DP, como por exemplo, a ressonância magnética (RM). Em particular, o fluxo sanguíneo cerebral e a conectividade do cérebro são analisadas através de RM funcional (RMf), uma técnica de RM que determina a atividade cerebral, em repouso ou perante uma tarefa, através da deteção de alterações no fluxo sanguíneo. Deste modo, vários estudos têm apontado como uma potencial e inovadora abordagem a utilização de aprendizagem profunda (AP) para auxiliar e automatizar o diagnóstico de doenças neurológicas como a doença de Parkinson, baseando em dados de neuroimagem como a RMf. Não obstante, estas investigações ao nível da DP, AP e RMf não incluem, até ao momento e à luz do nosso conhecimento, estudos em larga escala: os números de sujeitos são ainda consideravelmente reduzidos, na ordem das dezenas. Ademais, os modelos de AP apresentam uma natureza de "caixa negra", ou seja, não é possível aferir de que forma o algoritmo chegou às decisões que levaram à classificação efetuada. Assim, a inteligência artificial explicável (IAE), um conjunto de métodos que pretende explicar e interpretar as decisões tomadas por modelos de inteligência artificial, surge como uma ferramenta apropriada para ultrapassar a falta de transparência dos modelos de AP. Posto isto, o trabalho que surge no âmbito desta dissertação tem como objetivo o desenvolvimento de métodos para estudar e detetar alterações ao nível da conectividade funcional (CF) do cérebro relacionadas com a DP, recorrendo a um modelo de classificação baseado na arquitetura de redes neuronais convolucionais (RNC), e a métodos de IAE. Adicionalmente, pretende-se identificar potenciais biomarcadores funcionais da DP. Para este fim, utilizaram-se aquisições de RMf do conjunto de dados do PPMI, que inclui 120 scans de doentes com DP, e 22 de controlos saudáveis. Como este conjunto apresentava um desequilíbrio devido ao reduzido número de dados de controlos, recorreu-se ao conjunto de dados ADNI para recolher mais 131 scans de controlos. Este ajustamento foi efetuado considerando que a diferença entre os parâmetros de aquisição de RMf entre os dois consórcios, em particular o tempo de repetição, não leva a alterações significativas na avaliação da CF. Os dados de RMf foram pré-processados de acordo com uma sequência de métodos que incluíram: realinhamento funcional e distorção, correção temporal, identificação de outliers, segmentação e normalização, e atenuação funcional. Foi ainda removido ruído dos dados, através da regressão de potenciais efeitos perturbadores e da aplicação de um filtro passa-banda entre os 0,008 Hz e os 0,09 Hz. Os dados foram segmentados de acordo com um atlas que inclui 14 redes neuronais de repouso. A conetividade funcional de cada sujeito foi aferida através do cálculo das matrizes de CF, que correspondem a matrizes de correlação entre as 14 redes funcionais de repouso. Para tal, foi aplicado o cálculo do coeficiente de correlação de Pearson e a transformada de Fisher. As matrizes de conetividade foram inseridas numa RNC denominada de ExtendedConnectomeCNN, uma rede inspirada na ConnectomeCNN. Esta é composta por três camadas convolucionais e uma camada totalmente conectada. O tamanho da janela dos filtros é de 3 por 3 e o passo igual a 2. O número de filtros diminui ao longo das camadas convolucionais, de 256 para 128, e para 64. Em termos de parâmetros de treino, foram selecionados um número de épocas igual a 200 e um tamanho de grupo igual a 16. Como hiperparâmetros a otimizar, foram selecionados: a taxa de dropout, a taxa de aprendizagem, e a presença de uma camada de normalização em lote em cada camada convolucional. O processo de otimização dos hiperparâmetros foi efetuado através de validação cruzada com 10 folds (ou subconjuntos). Neste processo foi utilizado o conjunto de desenvolvimento dos dados, que corresponde a 90% do conjunto total das matrizes de CF. Da otimização de hiperparâmetros, foi selecionado o conjunto de hiperparâmetros que apresentou a melhor performance, isto é, com valores de médias das métricas de avaliação satisfatórios e balanceados. O conjunto com melhor performance apresentava uma taxa de dropout de 0,1 nas camadas convolucionais e de 0,4 na camada totalmente conectada, uma taxa de aprendizagem de 0,00001, e não tinha inseridas camadas de normalização em lote. Destacamse os valores de exatidão de treino, 0,8814, de exatidão de validação, 0,7760, e de área sob a curva de característica de operação do receptor (AUC ROC) de 0,7496. Estes valores refletem modelos generalizáveis que detetam tanto as classes positiva (DP) como negativa (controlo). Foi, de seguida, desenvolvido um modelo final com os melhores hiperparâmetros, treinado no conjunto de desenvolvimento e testado no conjunto de teste reservado à parte. Foram obtidas: uma extaidão de treino de 0,8776, exatidão de teste de 0,8214, e uma AUC ROC de 0,8230. Logo, o modelo construído apresenta valores de performance satisfatórios e balanceados, e potencial de interpretabilidade, o que permite a aplicação de métodos de IAE. Ao modelo final foram aplicados três métodos de IAE: propagação de relevância camada a camada (do inglês LRP, layer-wise relevance propagation), rede de deconvolução, e retropropagação direcionada. Para cada método foi calculada a área de curva de perturbação do mais relevante primeiro, ou AOPC do inglês area over the MoRF perturbation curve, que avalia o quão relevantes são as explicações fornecidas pelos métodos de IAE. Considerando que o método LRP produziu mapas de explicação mais específicos e não dispersos, e que apresentou ainda valores de AOPC maiores e melhor distribuídos, considerou-se esse método como o que melhor explica a classificação de DP. A partir das explicações fornecidas pelo método LRP foram extraídas as redes funcionais de repouso que mais relevância têm na classificação de DP. Não foram identificadas quaisquer alterações referentes à rede dos gânglios da base, apesar de tal ser esperado. No entanto, identificaram-se como potenciais biomarcadores funcionais da DP as redes de modo padrão dorsal, de modo padrão ventral, e de saliência posterior, essencialmente envolvidas em manifestações não-motoras da doença. Considerando que (1) o pré-processamento dos dados de RMf seguiu métodos adequados e produziu resultados satisfatórios, (2) o modelo de RNC para classificação de DP demonstrou ser suficientemente generalizável, com métricas de avaliação satisfatórias e equilibradas, e (3) a análise de IAE aparenta ser fidedigna e concordante com a literatura referente às alterações de redes funcionais de repouso perante a DP, conclui-se que a abordagem tomada para o estudo da CF relacionada com a DP utilizando métodos de IAE foi bem sucedida. Assim, os objetivos da dissertação foram cumpridos, com a expetativa de que este estudo resultará num progresso no desenvolvimento de técnicas inovadoras de diagnóstico de DP assistido por métodos de inteligência artificial.Parkinson’s disease (PD) is a neurodegenerative disease characterised by dopaminergic neuron loss and α-synuclein accumulation. It exhibits both motor symptoms (such as tremors, bradykinesia, and rigidity) and non-motor symptoms. Diagnosis relies on clinical presentation and DaTScan, though their reliability varies. Functional magnetic resonance imaging (fMRI) and brain connectivity analysis have aided PD assessment. Studies have shown promise in diagnosing PD using deep learning (DL) but lack large-scale studies and transparency due to their black-box nature. Explainable AI (XAI) aims to provide understandable explanations for AI model decisions. This dissertation proposes methods to assess functional connectivity in PD using a convolutional neural network (CNN) classifier and XAI. Resting-state fMRI scans from the PPMI and ADNI data sets were pre-processed following an atlas composed of 14 resting-state networks. The FC matrices were computed through Pearson correlation coefficient and Fisher transform. The FC matrices were fed to the ExtendedConnectomeCNN, optimised through 10-fold crossvalidation, and tested, yielding a final model with 0.8214 accuracy, satisfactory performance metrics, balanced metrics, and interpretability potential. Three XAI methods were applied: layer-wise relevance propagation (LRP), deconvolutional network (DeconvNet) and guided backpropagation. The LRP method provided more specific explanations, achieving higher AOPC value. Therefore, it is the method that better explains the classification of PD. No basal ganglia network alterations were found, but changes in dorsal and ventral default mode, and posterior salience networks – involved in PD pathophysiology – were identified as potential biomarkers. An attempt to perform transfer learning by training a model on the larger ABIDE set was executed. The model presented a poor performance and was not generalising, hence, we disregarded this possibility. The approach to assessing functional connectivity changes in PD using XAI methods was fairly successful. The objectives of the dissertation were fulfilled, with hopes for contribution to novel PD diagnosis techniques