Learning and comparing functional connectomes across subjects

Functional connectomes capture brain interactions via synchronized fluctuations in the functional magnetic resonance imaging signal. If measured during rest, they map the intrinsic functional architecture of the brain. With task-driven experiments they represent integration mechanisms between specialized brain areas. Analyzing their variability across subjects and conditions can reveal markers of brain pathologies and mechanisms underlying cognition. Methods of estimating functional connectomes from the imaging signal have undergone rapid developments and the literature is full of diverse strategies for comparing them. This review aims to clarify links across functional-connectivity methods as well as to expose different steps to perform a group study of functional connectomes

Exploring the combined use of electrical and hemodynamic brain activity to investigate brain function

This thesis explored the relationship between electrical and metabolic aspects of brain functioning in health and disease, measured with QEEG and NIRS, in order to evaluate its clinical potential. First the limitations of NIRS were investigated, depicting its susceptibility to different types of motion artefacts and the inability of the CBSI-method to remove them from resting state data. Furthermore, the quality of the NIRS signals was poor in a significant portion of the investigated sample, reducing clinical potential. Different analysis methods were used to explore both EEG and NIRS, and their coupling in an eyes open eyes closed paradigm in healthy participants. It could be reproduced that during eyes closed blocks less HbO2 (p = 0.000), more Hbb (p = 0.008), and more alpha activity (p = 0.000) was present compared to eyes open blocks. Furthermore, dynamic cross correlation analysis reproduced a positive correlation between alpha and Hbb (r: 0.457 and 0.337) and a negative correlation between alpha and HbO2 (r: -0.380 and -0.366) with a delayed hemodynamic response (7 to 8s). This was only possible when removing all questionable and physiological illogical data, suggesting that an 8s hemodynamic delay might not be the golden standard. Also the inability of the cross correlation to take non-linear relationships into account may distort outcomes. Therefore, In chapter 5 non-linear aspects of the relationship were evaluated by introducing the measure of relative cross mutual information. A newly suggested approach and the most valuable contribution of the thesis since it broadens knowledge in the fields of EEG, NIRS and general time series analysis. Data of two stroke patients then showed differences from the healthy group between the coupling of EEG and NIRS. The differences in long range temporal correlations (p= 0.000 for both cases), entropy (p< 0.040 and p =0.000), and relative cross mutual information (p < 0.003 and p < 0.013) provide the proof of principle that these measures may have clinical utility. Even though more research is necessary before widespread clinical use becomes possible

Novel non-linear approaches to understanding the dynamic brain: knowledge from rsfMRI and EEG studies

Advances in neuroimaging techniques have been critical to identifying new biomarkers for brain diseases. Resting State Functional Magnetic Resonance Imaging (rsfMRI) non-invasively quantifies the Blood Oxygen Level Dependent (BOLD) signal across brain regions with high spatial resolution; whilst temporal resolution of Electroencephalography (EEG) in measuring the brain's electrical response is unsurpassed. Most of the statistical and machine learning methods used to analyze rsfMRI and EEG data, are static and linear, fail to capture the dynamics and complexity of the brain, and are prone to residual noise. The general goals of this thesis dissertation are i) to provide methodological insight by proposing a statistical method namely point process analysis (PPA) and a machine learning (ML) multiband non-linear EEG method. These methods are especially useful to investigate the brain configuration of older participants and individuals with neurodegenerative diseases, and to predict age and sleep quality; and ii) to share biological insights about synchronization between brain regions (i.e., functional connectivity and dynamic functional connectivity) in different stages of mild cognitive impairment and in Alzheimer's disease. The findings, reported and discussed in this thesis, open a path for new research ideas such as applying PPA to EEG data, adjusting the non-linear ML algorithm to apply it to rsfMRI and use these methods to better understand other neurological diseases. Los avances en las técnicas de neuroimagen han sido fundamentales para identificar nuevos biomarcadores de enfermedades cerebrales. La resonancia magnética funcional en estado de reposo (rsfMRI) cuantifica de forma no invasiva la señal dependiente del nivel de oxígeno en sangre (BOLD) en todas las regiones del cerebro con una alta resolución espacial, mientras que la resolución temporal de la electroencefalografía (EEG) para medir la respuesta eléctrica del cerebro es insuperable. La mayoría de los métodos estadísticos y de aprendizaje automático utilizados para analizar datos de rsfMRI y EEG son estáticos y lineales, no captan el dinamismo y la complejidad del cerebro y son propensos al ruido residual. Los objetivos generales de esta tesis doctoral son i) proporcionar una visión metodológica proponiendo un método estadístico, llamado análisis por proceso de puntos (PPA), y un método de aprendizaje automático (ML) multibanda no lineal de EEG. Estos métodos son especialmente útiles para investigar la configuración cerebral de participantes de edad avanzada y de individuos con enfermedades neurodegenerativas, y para predecir la edad y la calidad del sueño; y ii) compartir conocimientos biológicos sobre la sincronización entre regiones cerebrales (es decir, la conectividad funcional y la conectividad funcional dinámica) en diferentes etapas del deterioro cognitivo leve y en la enfermedad de Alzheimer. Los hallazgos, comunicados y discutidos en esta tesis, abren un camino para nuevas ideas de investigación, como la aplicación de PPA a datos de EEG, el ajuste del algoritmo ML no lineal para aplicarlo a rsfMRI y el uso de estos métodos para comprender mejor otras enfermedades neurológicas

Physiological and pathological modulations of intrinsic brain activity assessed via resting-state fMRI

Since its inception in 1992, functional magnetic resonance imaging (fMRI) has considerably boosted our knowledge of the human brain function, primarily due to its non-invasive nature, and its relative high spatial and temporal resolution. Among the available fMRI contrasts, blood-oxygenation level-dependent (BOLD) signal plays a leading role in this field. The contrast is based on the different magnetic properties of the haemoglobin which - combined with the specific relation existing between neuronal, vascular and metabolic activity - allows to ascribe variations in the measured signal to variations in the underlying neuronal activity. During BOLD acquisitions, the comparison of different cognitive states in task-based experiment (alternating rest states to sensory or cognitive stimulations) has revealed the modular organization of the human brain function, an operation that is commonly referred to as functional brain mapping. Surprisingly, task-induced activity requires an increase in brain’s energy consumption by less than 5 percent of the underlying baseline activity. Most of the brain’s energy demand, from 60 to 80 percent, is used to sustain intrinsic, task-unrelated, neural activity (Raichle, 2006). In this light, functional brain mapping, utilizing task-based fMRI, focuses only on the tip of the iceberg, whereas most of the brain’s activity remains largely uncharted. The notion that the brain has an intrinsic or spontaneous activity is known from early electro-encephalography (EEG) measures due to Hans Berger. However, only in recent years, after the seminal work of Biswal and colleagues (Biswal et al., 1995), the study of spontaneous brain activity has overwhelmingly emerged as a primary field of research in neuroscience. In the so called resting-state condition (i.e., when the brain is not focused on the external world), Biswal reported BOLD low-frequency (< 0.1 Hz) fluctuations (LFFs) synchronized across functionally related and anatomically connected regions. Thereafter, several studies have consistently shown that specific patterns of synchronized spontaneous LFFs identify different resting-state networks, including, but not limited to, visual, motor, auditory, and attentive network. The overall picture emerging from thousands of resting-state fMRI studies depicts a never-resting brain, continuously engaged in maintaining communications within several wide-distributed networks. Such intrinsic brain activity, reflected in spontaneous BOLD LFFs, is the focus of the present thesis. The study of LFFs in spontaneous BOLD signal can reveal much about brain’s functional organization, especially considering that signal variability has been related to variability in behaviour (Fox et al., 2007). In addition, the simplicity of data acquisition – subjects just lie in the scanner refraining from falling asleep - makes the technique particularly suited for studying pathological conditions, in which subject’s cooperation might not fulfil the demands of task-based studies. Indeed, several psychiatric and neurological disorders, including degenerative dementia, have shown altered patterns of LFFs, even in the absence of observable anatomical abnormalities (Barkhof et al., 2014). Thus, how the intrinsic brain’s activity is modulated in response to different behavioural states and in response to pathological conditions can give insights into the brain functionality and into the mechanisms behind illnesses, respectively. Importantly, correct result interpretation is highly influenced by the type of metrics adopted and how they are implemented. The resting-state approach to the study of the brain’s function has required the development of more sophisticated processing and analysis techniques compared to those commonly applied in task-based fMRI. While seeking for task-responding regions in the brain is guided by information embedded in the experimental paradigm, in steady-state fMRI no a priori cue is provided. In such experiment the extraction of relevant information is based on (i) the temporal synchronization between spatially segregated elements of the brain, feature known as functional connectivity, and on (ii) the amplitude of the oscillation per se, a measure of the strength of the intrinsic brain activity. Despite such simple classification, the field of resting-state fMRI is scattered with a disparate amount of metrics, each of which highlight different facets of spontaneous LFFs. Before turning to the study of spontaneous LFF modulations, we will provide a comprehensive and optimized mathematical framework for the extraction of relevant information from resting-state data (Chapter 2). The results of this effort is an easy-to-use matlab toolbox specifically designed for the processing and analysis of steady-state fMRI data. In principle, the information coded in functional connectivity and in oscillation amplitude are unrelated. While the former assesses the degree of cooperation between segregated elements of the brain, the latter quantifies the neural workload of each single brain’s element, independently from the activity of other regions. Nonetheless, modulations in both measurements have been reported in several pathological conditions - yet in separate studies - suggesting a possible relation between them. In this context, we sought to investigate the potential coupling between the functional connectivity and the oscillation amplitude in cohort of healthy elderly and the probable modulations induced by dementia of the Alzheimer’s type (Chapter 3). Regardless of how the brain relates the two types of measures extractable from resting-state data, their disease-induced modulations are relevant per se in uncovering the illness. Indeed, Alzheimer’s disease is known to produce alterations in spontaneous brain activity, both at the synchronization and the amplitude level (Wang et al., 2007). Since the hallmark of the pathology is a profound deficit in episodic memory, much effort has been done in characterizing the alterations in spontaneous brain activity underlying such deficit. Contrarily, little is known about another commonly reported deficit, the language related impairment (Taler and Phillips, 2008). In the second part of Chapter 3 we sought to disclose the brain regions underpinning language deficits by looking at the alterations in functional connectivity of the relevant network. While the study of LFFs in pathological conditions can contribute to reveal the mechanisms behind the pathology and how it spreads into the brain, the study of spontaneous brain activity in physiological conditions can disclose the intrinsic brain functionality. In healthy subjects the resting brain has been extensively characterized and its network topology has shown to be a consistent and reliable physiological feature (Damoiseaux et al., 2006). An intriguing issue is how the brain reorganizes its patterns of spontaneous BOLD LFF while it is focusing on the external world. Indeed, the intrinsic brain activity is not an exclusive feature of the resting condition, instead it is present also on the top of the task-evoked response. In chapter 4, with peculiar experimental paradigms we separated the task-evoked response from the intrinsic brain activity during sustained cognitive stimulations. In a first experiment we sought to characterize the spatio-temporal proprieties and the dynamic of the transition from a resting to a stimulated condition. In the second part we specifically investigated how the brain reorganizes its internal functional architecture during visuospatial attention. Indeed, besides strongly affecting the processing of visual incoming stimuli, visual spatial attention also affects brain networks. Recent studies suggest that visual attention affects functional connectivity within and between the visual network and the attention network (Spadone et al., 2015), yet modulations of attention on brain networks are still poorly understood

DEEP MULTI-MODAL SCHIZOPHRENIA DISORDER DIAGNOSIS VIA A GRU-CNN ARCHITECTURE

The file on this institutional repository is embargoed indefinitely due to licensing and copyright restrictions. Individuals may download a copy of this article from the publisher's website for personal, non-commercial use at: https://doi.org/10.14311/NNW.2022.32.009 .Schizophrenia is a complex mental disorder associated with a change in the functional and structural of the brain. Accurate automatic diagnosis of schizophrenia is crucial and still a challenge. In this paper, we propose an automatic diagnosis of schizophrenia disorder method based on the fusion of different neuroimaging features and a deep learning architecture. We propose a deep-multimodal fusion (DMMF) architecture based on gated recurrent unit (GRU) network and 2D-3D convolutional neural networks (CNN). The DMMF model combines functional connectivity (FC) measures extracted from functional magnetic resonance imaging (fMRI) data and low-level features obtained from fMRI, magnetic resonance imaging (MRI), or diffusion tensor imaging (DTI) data and creates latent and discriminative feature maps for classification. The fusion of ROI-based FC with fractional anisotropy (FA) derived from DTI images achieved state-of-theart diagnosis-accuracy of 99.50% and an area under the curve (AUC) of 99.7% on COBRE dataset. The results are promising for the combination of features. The high accuracy and AUC in our experiments show that the proposed deep learning architecture can extract latent patterns from neuroimaging data and can help to achieve accurate classification of schizophrenia and healthy groups

Typical and aberrant functional brain flexibility: lifespan development and aberrant organization in traumatic brain injury and dyslexia

Intrinsic functional connectivity networks derived from different neuroimaging methods and connectivity estimators have revealed robust developmental trends linked to behavioural and cognitive maturation. The present study employed a dynamic functional connectivity approach to determine dominant intrinsic coupling modes in resting-state neuromagnetic data from 178 healthy participants aged 8–60 years. Results revealed significant developmental trends in three types of dominant intra- and inter-hemispheric neuronal population interactions (amplitude envelope, phase coupling, and phase-amplitude synchronization) involving frontal, temporal, and parieto-occipital regions. Multi-class support vector machines achieved 89% correct classification of participants according to their chronological age using dynamic functional connectivity indices. Moreover, systematic temporal variability in functional connectivity profiles, which was used to empirically derive a composite flexibility index, displayed an inverse U-shaped curve among healthy participants. Lower flexibility values were found among age-matched children with reading disability and adults who had suffered mild traumatic brain injury. The importance of these results for normal and abnormal brain development are discussed in light of the recently proposed role of cross-frequency interactions in the fine-grained coordination of neuronal population activity

Data-analysis perspectives on naturalistic stimulation in functional magnetic resonance imaging

Modern brain imaging allows to study human brain function during naturalistic stimulus conditions, which entail specific challenges for the analysis of the brain signals. The conventional analysis of data obtained by functional magnetic resonance imaging (fMRI) is based on user-specified models of the temporal behavior of the signals (general linear model, GLM). Alongside these approaches, data-based methods can be applied to model the signal behavior either on the basis of the measured data, as in seed-point correlations or inter-subject correlations (ISC), or alternatively the temporal behavior is not modeled, but spatial signal sources and related time courses are estimated directly from the measured data (independent component analysis, ICA). In this Thesis, fMRI data-analysis methods were studied and compared in experiments that gradually proceeded towards more naturalistic and complex stimuli. ICA showed superior performance compared with GLM-based method in the analysis of naturalistic situations. The particular strengths of the ICA were its capability to reveal activations when signal behavior deviated from an expected model, and to show similarities between signals of different brain areas and of different individuals. The practical difficulty of ICA in naturalistic conditions is that the user may not be able to determine, purely on the basis of the components' spatial distribution or temporal behavior, the brain networks that are related to the given stimuli. In this Thesis, a new solution to sort the components was proposed that ordered the components according to the ISC map, and thereby facilitated the selection of stimulus-related components. The method prioritized brain areas closely related to sensory processing, but it also revealed circuitries of intrinsic processing if they were affected similarly across individuals by external stimulation. Analysis issues related to the impact of physiological noise in fMRI signals were also considered. Cardiac-triggered fMRI improved detection of touch-related activation both in the thalamus and in the secondary somatosensory cortex. The most common way to eliminate noise is to filter the data. In this Thesis, however, aberrations in temporal behavior, as well as in functional connectivities in chronic pain patients were observed, which likely could not have been revealed with conventional temporal filtering

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

Fluctuations of attention network activation during the resting state reflect fluctuations in subjective attentional state.

peer reviewe

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