Information flow between resting state networks

The resting brain dynamics self-organizes into a finite number of correlated patterns known as resting state networks (RSNs). It is well known that techniques like independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting state magnetic resonance imaging. After haemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of Transfer Entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k = 1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k greater than one our method calculates the k-multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension-dependent, increasing from k =1 (i.e., the average voxels activity) up to a maximum occurring at k =5 to finally decay to zero for k greater than 10. This suggests that a small number of components (close to 5) is sufficient to describe the IF pattern between RSNs. Our method - addressing differences in IF between RSNs for any generic data - can be used for group comparison in health or disease. To illustrate this, we have calculated the interRSNs IF in a dataset of Alzheimer's Disease (AD) to find that the most significant differences between AD and controls occurred for k =2, in addition to AD showing increased IF w.r.t. controls.Comment: 47 pages, 5 figures, 4 tables, 3 supplementary figures. Accepted for publication in Brain Connectivity in its current for

Lagged and instantaneous dynamical influences related to brain structural connectivity

Contemporary neuroimaging methods can shed light on the basis of human neural and cognitive specializations, with important implications for neuroscience and medicine. Different MRI acquisitions provide different brain networks at the macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural connectivity (SC) coincident with the bundles of parallel fibers between brain areas, functional MRI (fMRI) accounts for the variations in the blood-oxygenation-level-dependent T2* signal, providing functional connectivity (FC).Understanding the precise relation between FC and SC, that is, between brain dynamics and structure, is still a challenge for neuroscience. To investigate this problem, we acquired data at rest and built the corresponding SC (with matrix elements corresponding to the fiber number between brain areas) to be compared with FC connectivity matrices obtained by 3 different methods: directed dependencies by an exploratory version of structural equation modeling (eSEM), linear correlations (C) and partial correlations (PC). We also considered the possibility of using lagged correlations in time series; so, we compared a lagged version of eSEM and Granger causality (GC). Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C nor PC) provides information about directionality of the functional interactions. Second, interactions on a time scale much smaller than the sampling time, captured by instantaneous connectivity methods, are much more related to SC than slow directed influences captured by the lagged analysis. Indeed the performance in correlating with SC was much worse for GC and for the lagged version of eSEM. We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.Comment: Accepted and published in Frontiers in Psychology in its current form. 27 pages, 1 table, 5 figures, 2 suppl. figure

Enhanced pre-frontal functional-structural networks to support postural control deficits after traumatic brain injury in a pediatric population

Traumatic brain injury (TBI) affects the structural connectivity, triggering the re-organization of structural-functional circuits in a manner that remains poorly understood. We focus here on brain networks re-organization in relation to postural control deficits after TBI. We enrolled young participants who had suffered moderate to severeTBI, comparing them to young typically developing control participants. In comparison to control participants, TBI patients (but not controls) recruited prefrontal regions to interact with two separated networks: 1) a subcortical network including part of the motor network, basal ganglia, cerebellum, hippocampus, amygdala, posterior cingulum and precuneus; and 2) a task-positive network, involving regions of the dorsal attention system together with the dorsolateral and ventrolateral prefrontal regions

Temporal instability of salience network activity in migraine with aura.

This study aims to investigate whether intra-network dynamic functional connectivity and causal interactions of the salience network is altered in the interictal term of migraine. 32 healthy controls, 37 migraineurs without aura and 20 migraineurs with aura were recruited. Participants underwent a T1-weighted scan and resting-state fMRI protocol inside a 1.5T MR scanner. We obtained average spatial maps of resting-state networks using group independent component analysis, which yielded subject-specific time series via a dual regression approach. Salience network ROIs (bilateral insulae and prefrontal cortices, dorsal anterior cingulate cortex) were obtained from the group average map via cluster-based thresholding. To describe intra-network connectivity, average and dynamic conditional correlation was calculated. Causal interactions between the default-mode, dorsal attention and salience network were characterised by spectral Granger's causality. Time-averaged correlation was lower between the right insula and prefrontal cortex in migraine without aura vs. with aura and healthy controls (p<0.038, p<0.037). Variance of dynamic conditional correlation was higher in migraine with aura vs. healthy controls and migraine with aura vs. without aura between the right insula and dorsal anterior cingulate cortex (p<0.011, p<0.026), and in migraine with aura vs. healthy controls between the dorsal anterior cingulate and left prefrontal cortex (p<0.021). Causality was weaker in the <0.05 Hz frequency range between the salience and dorsal attention networks in migraine with aura (p<0.032). Overall, migraineurs with aura exhibit more fluctuating connections in the salience network, which also affect network interactions, and could be connected to altered cortical excitability and increased sensory gain

A Controlled Thermoalgesic Stimulation Device for Exploring Novel Pain Perception Biomarkers

Objective: To develop a new device for identifying physiological markers of pain perception by reading the brain's electrical activity and hemodynamic interactions while applying thermoalgesic stimulation. Methods: We designed a compact prototype that generates well-controlled thermal stimuli using a computer-driven Peltier cell while simultaneously capturing electroencephalography (EEG) and photoplethysmography (PPG) signals. The study was performed on 35 healthy subjects (mean age 30.46 years, SD 4.93 years; 20 males, 15 females). We first determined the heat pain threshold (HPT) for each subject, defined as the maximum temperature that the subject can withstand when the Peltier cell gradually increased the temperature. Next, we defined the painful condition as the one occurring at temperature equal to 90% of the HPT, comparing this to the no-pain state (control) in the absence of thermoalgesic stimulation. Results: Both the one-dimensional and the two-dimensional spectral entropy (SE) obtained from both the EEG and PPG signals differentiated the condition of pain. In particular, the SE for PPG was significantly reduced in association with pain, while the SE for EEG increased slightly. Moreover, significant discrimination occurred within a specific range of frequencies, 26-30 Hz for EEG and about 5-10 Hz for PPG. Conclusion: Hemodynamics, brain dynamics and their interactions can discriminate thermal pain perception. Significance: The possibility of monitoring on-line variations in thermal pain perception using a similar device and algorithms may be of interest to study different pathologies that affect the peripheral nervous system, such as small fiber neuropathies, fibromyalgia or painful diabetic neuropathy

Brain connectivity and cognitive functioning in individuals six months after multiorgan failure

Abstract Multiorgan failure (MOF) is a life-threating condition that affects two or more systems of organs not involved in the disorder that motivates admission to an Intensive Care Unit (ICU). Patients who survive MOF frequently present long-term functional, neurological, cognitive, and psychiatric sequelae. However, the changes to the brain that explain such symptoms remain unclear. OBJECTIVE: To determine brain connectivity and cognitive functioning differences between a group of MOF patients six months after ICU discharge and healthy controls (HC). METHODS: 22 MOF patients and 22 HC matched by age, sex, and years of education were recruited. Both groups were administered a 3T magnetic resonance imaging (MRI), including structural T1 and functional BOLD, as well as a comprehensive neuropsychological evaluation that included tests of learning and memory, speed of information processing and attention, executive function, visual constructional abilities, and language. Voxel-based morphometry was used to analyses T1 images. For the functional data at rest, functional connectivity (FC) analyses were performed. RESULTS: There were no significant differences in structural imaging and neuropsychological performance between groups, even though patients with MOF performed worse in all the cognitive tests. Functional neuroimaging in the default mode network (DMN) showed hyper-connectivity towards sensory-motor, cerebellum, and visual networks. DMN connectivity had a significant association with the severity of MOF during ICU stay and with the neuropsychological scores in tests of attention and visual constructional abilities. CONCLUSIONS: In MOF patients without structural brain injury, DMN connectivity six months after ICU discharge is associated with MOF severity and neuropsychological impairment, which supports the use of resting-state functional MRI as a potential tool to predict the onset of long-term cognitive deficits in these patients.Similar to what occurs at the onset of other pathologies, the observed hyper-connectivity might suggest network re-adaptation following MOF.This research was founded by Ministerio Economia, Industria y Competitividad, Spain and FEDER (grant no. DPI2016-79874-R) to JC and JCAL. ID's time was founded by the Department of Education of the Basque Country, postdoctoral program. JR's time was founded by the Ministry of Education, Language Policy and Culture (Basque Government). JMC's time was founded by Ikerbasque and the Department of Economic Development and Infrastructure of the Basque Country, Elkartek Program (grant no. KK-2018/00032). JCAL's time was founded by Ikerbasque and Fundacion Mutua Madrilena (grant no. AP169812018). IG's time was founded by the Instituto de Salud Carlos III for a Juan Rodes (grant no. JR15/00008) co-funded by the European Regional Development Fund/European Social Fund 'Investing in Your Future'. AJM's time was partly founded by Euskampus Fundazioa

Brain connectivity and cognitive functioning in individuals six months after multiorgan failure

Multiorgan failure (MOF) is a life-threating condition that affects two or more systems of organs not involved in the disorder that motivates admission to an Intensive Care Unit (ICU). Patients who survive MOF frequently present long-term functional, neurological, cognitive, and psychiatric sequelae. However, the changes to the brain that explain such symptoms remain unclear. Objective: To determine brain connectivity and cognitive functioning differences between a group of MOF patients six months after ICU discharge and healthy controls (HC). Methods: 22 MOF patients and 22 HC matched by age, sex, and years of education were recruited. Both groups were administered a 3T magnetic resonance imaging (MRI), including structural T1 and functional BOLD, as well as a comprehensive neuropsychological evaluation that included tests of learning and memory, speed of information processing and attention, executive function, visual constructional abilities, and language. Voxel-based morphometry was used to analyses T1 images. For the functional data at rest, functional connectivity (FC) analyses were performed. Results: There were no significant differences in structural imaging and neuropsychological performance between groups, even though patients with MOF performed worse in all the cognitive tests. Functional neuroimaging in the default mode network (DMN) showed hyper-connectivity towards sensory-motor, cerebellum, and visual networks. DMN connectivity had a significant association with the severity of MOF during ICU stay and with the neuropsychological scores in tests of attention and visual constructional abilities. Conclusions: In MOF patients without structural brain injury, DMN connectivity six months after ICU discharge is associated with MOF severity and neuropsychological impairment, which supports the use of resting-state functional MRI as a potential tool to predict the onset of long-term cognitive deficits in these patients. Similar to what occurs at the onset of other pathologies, the observed hyper-connectivity might suggest network re-adaptation following MOF.This research was founded by Ministerio Economia, Industria y Competitividad, Spain and FEDER (grant no. DPI2016-79874-R) to JC and JCAL. ID's time was founded by the Department of Education of the Basque Country, postdoctoral program. JR's time was founded by the Ministry of Education, Language Policy and Culture (Basque Government). JMC's time was founded by Ikerbasque and the Department of Economic Development and Infrastructure of the Basque Country, Elkartek Program (grant no. KK-2018/00032). JCAL's time was founded by Ikerbasque and Fundacion Mutua Madrileña (grant no. AP169812018). IG's time was founded by the Instituto de Salud Carlos III for a Juan Rodes (grant no. JR15/00008 ) co-funded by the European Regional Development Fund/European Social Fund ‘Investing in Your Future’. AJM's time was partly founded by Euskampus Fundazioa

The effect of using multiple connectivity metrics in brain Functional Connectivity studies

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa, Faculdade de Ciências, 2022Resting-state functional magnetic resonance imaging (rs-fMRI) has the potential to assist as a diagnostic or prognostic tool for a diverse set of neurological and neuropsychiatric disorders, which are often difficult to differentiate. fMRI focuses on the study of the brain functional Connectome, which is characterized by the functional connections and neuronal activity among different brain regions, also interpreted as communications between pairs of regions. This Functional Connectivity (FC) is quantified through the statistical dependences between brain regions’ blood-oxygen-level-dependent (BOLD) signals time-series, being traditionally evaluated by correlation coefficient metrics and represented as FC matrices. However, several studies underlined limitations regarding the use of correlation metrics to fully capture information from these signals, leading investigators towards different statistical metrics that would fill those shortcomings. Recently, investigators have turned their attention to Deep Learning (DL) models, outperforming traditional Machine Learning (ML) techniques due to their ability to automatically extract relevant information from high-dimensional data, like FC data, using these models with rs-fMRI data to improve diagnostic predictions, as well as to understand pathological patterns in functional Connectome, that can lead to the discovery of new biomarkers. In spite of very encouraging performances, the black-box nature of DL algorithms makes difficult to know which input information led the model to a certain prediction, restricting its use in clinical settings. The objective of this dissertation is to exploit the power of DL models, understanding how FC matrices created from different statistical metrics can provide information about the brain FC, beyond the conventionally used correlation family. Two publicly available datasets where studied, the ABIDE I dataset, composed by healthy and autism spectrum disease (ASD) individuals, and the ADHD-200 dataset, with typically developed controls and individuals with attention-deficit/hyperactive disorder (ADHD). The computation of the FC matrices of both datasets, using different statistical metrics, was performed in MATLAB using MULAN’s toolbox functions, encompassing the correlation coefficient, non-linear correlation coefficient, mutual information, coherence and transfer entropy. The classification of FC data was performed using two DL models, the improved ConnectomeCNN model and the innovative ConnectomeCNN-Autoencoder model. Moreover, another goal is to study the effect of a multi-metric approach in classification performances, combining multiple FC matrices computed from the different statistical metrics used, as well as to study the use of Explainable Artificial Intelligence (XAI) techniques, namely Layer-wise Relevance Propagation method (LRP), to surpass the black-box problem of DL models used, in order to reveal the most important brain regions in ADHD. The results show that the use of other statistical metrics to compute FC matrices can be a useful complement to the traditional correlation metric methods for the classification between healthy subjects and subjects diagnosed with ADHD and ASD. Namely, non-linear metrics like h2 and mutual information, achieved similar and, in some cases, even slightly better performances than correlation methods. The use of FC multi-metric, despite not showing improvements in classification performance compared to the best individual method, presented promising results, namely the ability of this approach to select the best features from all the FC matrices combined, achieving a similar performance in relation to the best individual metric in each of the evaluation measures of the model, leading to a more complete classification. The LRP analysis applied to ADHD-200 dataset proved to be promising, identifying brain regions related to the pathophysiology of ADHD, which are in broad accordance with FC and structural study’s findings.A ressonância magnética funcional em estado de repouso (rs-fMRI) tem o potencial de ser uma ferramenta auxiliar de diagnóstico ou prognóstico para um conjunto diversificado de distúrbios neurológicos e neuropsiquiátricos, que muitas vezes são difíceis de diferenciar. A análise de dados de rs-fMRI recorre muitas vezes ao conceito de conectoma funcional do cérebro, que se caracteriza pelas conexões funcionais entre as diferentes regiões do cérebro, sendo estas conexões interpretadas como comunicações entre diferentes pares de regiões cerebrais. Esta conectividade funcional é quantificada através de dependências estatísticas entre os sinais fMRI das regiões cerebrais, sendo estas tradicionalmente calculadas através da métrica coeficiente de correlação, e representadas através de matrizes de conectividade funcional. No entanto, vários estudos demonstraram limitações em relação ao uso de métricas de correlação, em que estas não conseguem capturar por completo todas as informações presentes nesses sinais, levando os investigadores à procura de diferentes métricas estatísticas que pudessem preencher essas lacunas na obtenção de informações mais completas desses sinais. O estudo destes distúrbios neurológicos e neuropsiquiátricos começou por se basear em técnicas como mapeamento paramétrico estatístico, no contexto de estudos de fMRI baseados em tarefas. Porém, essas técnicas apresentam certas limitações, nomeadamente a suposição de que cada região cerebral atua de forma independente, o que não corresponde ao conhecimento atual sobre o funcionamento do cérebro. O surgimento da rs-fMRI permitiu obter uma perspetiva mais global e deu origem a uma vasta literatura sobre o efeito de patologias nos padrões de conetividade em repouso, incluindo tentativas de diagnóstico automatizado com base em biomarcadores extraídos dos conectomas. Nos últimos anos, os investigadores voltaram a sua atenção para técnicas de diferentes ramos de Inteligência Artificial, mais propriamente para os algoritmos de Deep Learning (DL), uma vez que são capazes de superar os algoritmos tradicionais de Machine Learning (ML), que foram aplicados a estes estudos numa fase inicial, devido à sua capacidade de extrair automaticamente informações relevantes de dados de alta dimensão, como é o caso dos dados de conectividade funcional. Esses modelos utilizam os dados obtidos da rs-fMRI para melhorar as previsões de diagnóstico em relação às técnicas usadas atualmente em termos de precisão e rapidez, bem como para compreender melhor os padrões patológicos nas conexões funcionais destes distúrbios, podendo levar à descoberta de novos biomarcadores. Apesar do notável desempenho destes modelos, a arquitetura natural em caixa-preta dos algoritmos de DL, torna difícil saber quais as informações dos dados de entrada que levaram o modelo a executar uma determinada previsão, podendo este utilizar informações erradas dos dados para alcançar uma dada inferência, restringindo o seu uso em ambientes clínicos. O objetivo desta dissertação, desenvolvida no Instituto de Biofísica e Engenharia Biomédica, é explorar o poder dos modelos DL, de forma a avaliar até que ponto matrizes de conectividade funcional criadas a partir de diferentes métricas estatísticas podem fornecer mais informações sobre a conectividade funcional do cérebro, para além das métricas de correlação convencionalmente usadas neste tipo de estudos. Foram estudados dois conjuntos de dados bastante utilizados em estudos de Neurociência e que estão disponíveis publicamente: o conjunto de dados ABIDE-I, composto por indivíduos saudáveis e indivíduos com doenças do espectro do autismo (ASD), e o conjunto de dados ADHD-200, com controlos tipicamente desenvolvidos e indivíduos com transtorno do défice de atenção e hiperatividade (ADHD). Numa primeira fase foi realizada a computação das matrizes de conetividade funcional de ambos os conjuntos de dados, usando as diferentes métricas estatísticas. Para isso, foi desenvolvido código de MATLAB, onde se utilizam as séries temporais dos sinais BOLD obtidas dos dois conjuntos de dados para criar essas mesmas matrizes de conectividade funcional, incorporando funções de diferentes métricas estatísticas da caixa de ferramentas MULAN, compreendendo o coeficiente de correlação, o coeficiente de correlação não linear, a informação mútua, a coerência e a entropia de transferência. De seguida, a classificação dos dados de conectividade funcional, de forma a avaliar o efeito do uso de diferentes métricas estatísticas para a criação de matrizes de conectividade funcional na discriminação de sujeitos saudáveis e patológicos, foi realizada usando dois modelos de DL. O modelo ConnectomeCNN melhorado e o modelo inovador ConnectomeCNN-Autoencoder foram desenvolvidos com recurso à biblioteca de Redes Neuronais Keras, juntamente com o seu backend Tensorflow, ambos em Python. Estes modelos, desenvolvidos previamente no Instituto de Biofísica e Engenharia Biomédica, tiveram de ser otimizados de forma a obter a melhor performance, onde vários parâmetros dos modelos e do respetivo treino dos mesmos foram testados para os dados a estudar. Pretendeu-se também estudar o efeito de uma abordagem multi-métrica nas tarefas de classificação dos sujeitos de ambos os conjuntos de dados, sendo que, para estudar essa abordagem as diferentes matrizes calculadas a partir das diferentes métricas estatísticas utilizadas, foram combinadas, sendo usados os mesmos modelos que foram aplicados às matrizes de conectividade funcional de cada métrica estatística individualmente. É importante realçar que na abordagem multi-métrica também foi realizada a otimização dos parâmetros dos modelos utilizados e do respetivo treino, de modo a conseguir a melhor performance dos mesmos para estes dados. Para além destes dois objetivos, estudou-se o uso de técnicas de Inteligência Artificial Explicável (XAI), mais especificamente o método Layer-wise Relevance Propagation (LRP), com vista a superar o problema da caixa-preta dos modelos de DL, com a finalidade de explicar como é que os modelos estão a utilizar os dados de entrada para realizar uma dada previsão. O método LRP foi aplicado aos dois modelos utilizados anteriormente, usando como dados de entrada o conjunto de dados ADHD-200, permitindo assim revelar quais as regiões cerebrais mais importantes no que toca a um diagnóstico relacionado com o ADHD. Os resultados obtidos mostram que o uso de outras métricas estatísticas para criar as matrizes de Conectividade Funcional podem ser um complemento bastante útil às métricas estatísticas tradicionalmente utilizadas para a classificação entre indivíduos saudáveis e indivíduos como ASD e ADHD. Nomeadamente métricas estatísticas não lineares como o h2 e a informação mútua, obtiveram desempenhos semelhantes e, em alguns casos, desempenhos ligeiramente melhores em relação aos desempenhos obtidos por métodos de correlação, convencionalmente usados nestes estudos de conectividade funcional. A utilização da multi-métrica de conectividade funcional, apesar de não apresentar melhorias no desempenho geral da classificação em relação ao melhor método das matrizes de conectividade funcional individuais do conjunto de métricas estatísticas abordadas, apresenta resultados que justificam a exploração mais aprofundada deste tipo de abordagem, de forma a compreender melhor a complementaridade das métricas e a melhor maneira de as utilizar. O uso do método LRP aplicado ao conjunto de dados do ADHD-200 mostrou a sua aplicabilidade a este tipo de estudos e a modelos de DL, identificando as regiões cerebrais mais relacionadas à fisiopatologia do diagnóstico do ADHD que são compatíveis com o que é reportado por diversos estudos de conectividade funcional e estudos de alterações estruturais associados a esta doença. O facto destas técnicas de XAI demonstrarem como é que os modelos de DL estão a usar os dados de entrada para efetuar as previsões, pode significar uma mais rápida e aceite adoção destes algoritmos em ambientes clínicos. Estas técnicas podem auxiliar o diagnóstico e prognóstico destes distúrbios neurológicos e neuropsiquiátricos, que são na maioria das vezes difíceis de diferenciar, permitindo aos médicos adquirirem um conhecimento em relação à previsão realizada e poder explicar a mesma aos seus pacientes

New neuroimaging methods for clinical neuroscience and neurological disorders

236 p.Clinical neuroscience today makes use of state-of-the-art neuroimaging to study structural and functional brain data to improve diagnosis and prognosis in different neurological disorders.In this thesis dissertation, I focused on Magnetic Resonance Imaging (MRI), a non-invasive neuroimaging modality to study brain functional and structural data. Different new methods for brain connectivity analysis are described and applied to three pathologies: Disorder of Consciousness, Alzheimer's Disease and Traumatic Axonal Injury. This work is at the frontiers between two fields, the Biomedical Engineering of Image Processing and the Clinical Neuroscience

Development of new methods in biomedical engineering for brain connectivity biomarkers in epilepsy and other pathological conditions

243 p.The aim of this thesis is to humbly explore the application of diverse methodologies and theories comingfrom Computer Sciences, Mathematics and Physics in the field of neurosciences, with an special focus onneurodegenerative diseases.In this thesis brain network analysis was used to unveil functional and structural patterns in bothpathological and healthy brains. We explore in a different manner various aspects related with theepilepsy, AD and healthy aging