13 research outputs found
A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks
BACKGROUND: The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment.
METHODS: A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group.
RESULTS:The study results show the existence of a statistically significant difference (p \u3c 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis.
CONCLUSIONS:The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed
Treatment effects in epilepsy:a mathematical framework for understanding response over time
Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, including both mono- and poly-therapy. Should these fail, more invasive therapies such as surgery, electrical stimulation and focal drug delivery are often considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time. The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis a significant challenge when treating epilepsy. Here we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. For small networks we show vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom
Treatment effects in epilepsy: a mathematical framework for understanding response over time
Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, including both mono- and poly-therapy. Should these fail, more invasive therapies such as surgery, electrical stimulation and focal drug delivery are often considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time. The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis a significant challenge when treating epilepsy. Here we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. For small networks we show vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom
Intracranial Volume Estimation and Graph Theoretical Analysis of Brain Functional Connectivity Networks
Understanding pathways of neurological disorders requires extensive research on both functional and structural characteristics of the brain. This dissertation introduced two interrelated research endeavors, describing (1) a novel integrated approach for constructing functional connectivity networks (FCNs) of brain using non-invasive scalp EEG recordings; and (2) a decision aid for estimating intracranial volume (ICV). The approach in (1) was developed to study the alterations of networks in patients with pediatric epilepsy. Results demonstrated the existence of statistically significant (
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence
or early adulthood. It reduces the life expectancy of patients by 15 years.
Abnormal behavior, perception of emotions, social relationships, and reality
perception are among its most significant symptoms. Past studies have revealed
that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased
volume of white and gray matter can be observed due to this disease. Magnetic
resonance imaging (MRI) is the popular neuroimaging technique used to
explore structural/functional brain abnormalities in SZ disorder, owing to its
high spatial resolution. Various artificial intelligence (AI) techniques have been
employed with advanced image/signal processing methods to accurately diagnose
SZ. This paper presents a comprehensive overview of studies conducted on
the automated diagnosis of SZ using MRI modalities. First, an AI-based computer
aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections
are presented. Then, this section introduces the most important conventional
machine learning (ML) and deep learning (DL) techniques in the diagnosis of
diagnosing SZ. A comprehensive comparison is also made between ML and DL
studies in the discussion section. In the following, the most important challenges
in diagnosing SZ are addressed. Future works in diagnosing SZ using AI
techniques and MRI modalities are recommended in another section. Results,
conclusion, and research findings are also presented at the end.Ministerio de Ciencia e Innovación
(España)/ FEDER under the RTI2018-098913-B100 projectConsejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and
FEDER under CV20-45250 and A-TIC-080-UGR18 project
An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works
Schizophrenia (SZ) is a mental disorder that typically emerges in late
adolescence or early adulthood. It reduces the life expectancy of patients by
15 years. Abnormal behavior, perception of emotions, social relationships, and
reality perception are among its most significant symptoms. Past studies have
revealed the temporal and anterior lobes of hippocampus regions of brain get
affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and
decreased volume of white and gray matter can be observed due to this disease.
The magnetic resonance imaging (MRI) is the popular neuroimaging technique used
to explore structural/functional brain abnormalities in SZ disorder owing to
its high spatial resolution. Various artificial intelligence (AI) techniques
have been employed with advanced image/signal processing methods to obtain
accurate diagnosis of SZ. This paper presents a comprehensive overview of
studies conducted on automated diagnosis of SZ using MRI modalities. Main
findings, various challenges, and future works in developing the automated SZ
detection are described in this paper
Effects of EEG-neurofeedback training on brain functional connectivity
Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica, 2022, Universidade de Lisboa, Faculdade de CiênciasO neurofeedback (NF) consiste em medir a atividade cerebral, usando técnicas como a
eletroencefalografia (EEG) ou a imagem por ressonância magnética funcional (fMRI), e apresentar ao
participante, em tempo real, uma representação de um padrão de atividade de interesse, enquanto lhe é
pedido para manipular essa mesma representação através da autorregulação da atividade cerebral
(Sitaram et al., 2017). As bases neurofisiológicas desta técnica ainda não são conhecidas na sua
totalidade, apesar de vários estudos terem demonstrado que o treino através de NF tende a reorganizar
as redes cerebrais. Posto isto, existem poucos estudos que tentam comparar a influência da utilização de
diferentes modalidades sensoriais de apresentação do “feedback” nos resultados do treino por NF em
EEG, e os poucos estudos existentes não investigam possíveis efeitos nas métricas de conectividade
funcional do cérebro.
Neste projeto, pretendemos avaliar o efeito da utilização de diferentes modalidades de
“feedback” no treino de NF através EEG (EEG-NF) para o incremento da amplitude relativa da banda
alfa superior no canal Cz, e investigar se existe um efeito significativo nos padrões de conectividade
funcional do cérebro. Para esse fim, será efetuada a análise de dados previamente recolhidos em 20
participantes saudáveis que realizaram quatro sessões de treino por EEG-NF, que visava incrementar a
densidade espectral na banda alfa superior, e que utilizaram diferentes modalidades de feedback (visual,
realidade virtual (VR), e auditiva). Os dados de EEG foram pré-processados, com remoção de artefactos
através de análise de componentes independentes. Adicionalmente, duas técnicas de re-referenciação do
sinal EEG foram utilizadas para comparação posterior, sendo estas a re-referenciação para a média de
todos os canais EEG, e a re-referenciação através da aplicação de um Laplaciano de Superfície com
parâmetro de rigidez de valores 4 e 3, respetivamente. A avaliação dos resultados foi efetuada a diversos
níveis, com a análise: i) das variações intra-sessão da amplitude relativa da banda alfa superior no canal
Cz, ii) da distribuição topológica da banda alfa superior no decorrer do treino, iii) das variações intrasessão dos padrões de conectividade funcional da banda alfa superior, utilizando a parte imaginária da
coerência como métrica de conectividade, e iv) por fim, em termos de uma análise de redes, que visava
avaliar a importância de nodos de rede, verificada através das métricas como betweeness centrality e
força, da atividade segregada, verificada através da métrica de transitividade, e da atividade integrada,
verificada através de métricas como caminho característico e eficiência global da rede cerebral.
Relativamente aos resultados para a análise espectral e topológica, encontram-se correlações
estatisticamente significativas entre o valor da amplitude relativa da banda alfa superior e o número de
set, em todos os grupos, principalmente nas duas primeiras sessões, sendo cada set composto por 6 trials
com duração de 30 segundos Posto isto, não são registadas diferenças estatisticamente significativas
intra-sessão, isto é, do set 1 para o set 5 de cada sessão. Para a análise topológica, não se realizaram
testes de significância, mas é possível visualizar uma acentuação da amplitude relativa da banda alfa
superior em zonas parietais/occipitais, e é também possível verificar que o treino realizado, não afetou
somente a banda de interesse mas também a banda theta, cuja atividade não focal diminui, a banda alfa
inferior, cuja amplitude relativa parece incrementar. Relativamente aos resultados da análise de
conetividade, os mesmos sugerem que o treino de EEG-NF para o incremento da banda alfa superior
resulta num incremento mais pronunciado nas fases iniciais do treino, isto é, nas duas primeiras sessões
de treino. Este incremento é representado pelo do número de canais que apresentam conectividade
funcional com a zona parietal central, com canais como o Pz, e com a zona parietal direita, CP6, P4,
entre outros, independentemente da modalidade de feedback, ou seja, para a generalidade dos
"Learners”. De facto, os próprios canais parietais direitos, P4, P8, CP6, TP10 aumentam de forma
estatisticamente significativa a conectividade entre eles. Isto parece indiciar a criação de um complexo focado na zona parietal direita. Em todas as modalidades, à exceção da VR, verifica-se ainda um
aumento significativo intra-sessão da transitividade e eficiência global enquanto uma diminuição
estatisticamente significativa intra-sessão é observada para a métrica caminho característico. Posto isto,
a metodologia de neurofeedback no contexto experimental que foi implementado, parece promover a
atividade cerebral segregada, isto é, a atividade que resulta de uma atividade cerebral mais localizada, e
também integrada, isto é, que resulta da integração da atividade de áreas cerebrais dispersas. A não
existência de variações significativas na modalidade VR não parece estar relacionada com a modalidade
em si, mas sim devido a uma menor amostra do respetivo grupo. Assim, futuramente será necessário
aumentar a amostra, pelo menos para este grupo, por forma a poderem ser extraídos resultados
significativos da análise do mesmo. Interessantemente, e independentemente do método de rereferenciação utilizado, enquanto para o grupo do treino NF para a modalidade visual se observa a partir
da terceira sessão de treino a estabilização do número de conexões funcionais entre os diferentes
elétrodos, ou seja deixa de haver um crescimento acentuado da transitividade e da eficiência global com
diminuição simultânea do caminho característico, para o grupo do treino NF com a modalidade auditiva
a generalidade dos incrementos verificados, estão presentes em todas as sessões, incluindo a última
sessão.
No referente ao estudo sobre o método de re-referenciação dos dados EEG, com interesse específico na
utilização de um Laplaciano de superfície comparativamente à simples utilização da média dos sinais
EEG, a análise topológica das diferentes bandas cerebrais confirma que a utilização do Laplaciano de
superfície contribuiu para aumento da resolução espacial dos dados de EEG, uma vez que atenuou para
as diferentes bandas a amplitude relativa da atividade periférica, ou seja não focal, que estará relacionada
com frequências espaciais mais baixas. Relativamente à análise da conectividade funcional intra-sessão,
verifica-se que a aplicação do Laplaciano se reflete na mudança das configurações de variações de
conexões funcionais no cérebro, nomeadamente eliminando determinados aumentos estatisticamente
significativos, por exemplo para a sessão 1 dos “Learners”, após a aplicação do Laplaciano de superfície,
o incremento da conectividade funcional entre Pz e O2 deixa de ser estatisticamente significativo.
Possivelmente, isto poderá estar relacionado com uma eliminação de conexões espúrias. Também na
análise de redes, a aplicação do Laplaciano afeta a configuração dos dados e outputs embora não se
consiga precisar uma relação causa efeito. Posto isto, a variação da própria configuração do Laplaciano,
no que se refere à rigidez do mesmo, de parâmetro m=4 para m=3, não se traduz em resultados tão
diferentes, pese embora algumas alterações notadas na análise de redes. De facto, para análise de
conectividade funcional, os heatmaps resultantes da aplicação de Laplaciano de superfície com m=4,
são exatamente iguais aos heatmaps resultantes da aplicação de Laplaciano de superfície com m=3.
Quanto à análise de redes, nomeadamente nas métricas de transitividade, caminho característico e
eficiência global, se verificarmos os gráficos e tabelas apresentadas, apesar de serem notados ligeiros
desvios quer nas curvas quer em valores de correlação ou variação intra-sessão, o nível de significância
é quase sempre atingido, independentemente da rigidez do Laplaciano aplicado, para a mesma sessão.
Posto isto, não é possível reportar claramente uma relação causa-efeito vantajosa decorrente da aplicação
do Laplaciano de superfície nos dados aqui tratados. De facto, reitera-se que, pela análise topológica se
confirma que este possa estar associado a um filtro espacial, mas nas restantes análises não se consegue
confirmar se este “melhorou ou não” os nossos dados.Neurofeedback (NF) consists in measuring brain activity and presenting a real-time
representation of a brain activity pattern of interest to an individual, while instructing him to manipulate
the feedback representation through self-regulation. The neurophysiological basis for NF remains to be
fully elucidated, whereas several studies support that NF training tends to reorganize the brain networks.
Only a handful of studies compare how different feedback sensory modalities affect the outcomes of
EEG-based NF training, and none of them analyzes such effect on the functional connectivity or network
metrics.
In this project, we evaluate how using different feedback modalities on the EEG-based NFtraining will affect the brain’s functional connectivity, by analyzing previously collected data from a
total of 20 healthy subjects, who underwent four sessions of upper-alpha (UA) band EEG-based NF
training, with different feedback modalities (visual, auditory, or virtual reality (VR)). The EEG data was
preprocessed and re-referenced with three different methods for posterior comparison, the common
average reference (avgREF), and spline lines Surface Laplacian with stiffness parameters equals 4 and
3. The data were evaluated in terms of: i) the within-sessions’ variations of the relative amplitude of the
UA at the Cz channel, ii) relative band amplitude topological distribution across sets and sessions, iii)
the within-sessions’ variations of the UA functional connectivity patterns, computed with the imaginary
part of coherency, and iv) an UA band network analysis of the metrics betweenness centrality, strength,
transitivity, charpath and global efficiency.
Our results suggest that the UA EEG-based NF-training is associated with an early increment
of functional connections with channels over parietal areas (e.g. Pz), independently of the feedback
sensory modality. All the modalities, except the VR, which had a reduced sample, verify statistically
significant intra-session increases in the transitivity and global efficiency, while showing statistically
significant intra-session decreases of the charpath, suggesting that this protocol promotes both clustered
and integrated brain activity. While for the visual NF-training group the third session seems to be a
breakthrough point, where the number of functional connections stabilize, for the auditory NF-training
group longer lasting “variations” are reported. Through the topological analysis we confirm that the
application of Laplacian leads to higher spatial resolutions on the EEG data. Regarding the connectivity
analysis and network analysis, we note that the application of the Surface Laplacian creates different
values when compared to the avgREF data, yet no advantageous outcome can be reported
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EEG-based functional connectivity analysis of brain abnormalities: A systematic review study
Several imaging modalities and many signal recording techniques have been used to study the brain activities. Significant advancements in medical device technologies like electroencephalographs have provided conditions for recording neural information with high temporal resolution. These recordings can be used to calculate the connections between different brain areas. It has been proved that brain abnormalities affect the brain activity in different brain regions and the connectivity patterns between them are changed as a result. This paper studies the electroencephalogram (EEG) functional connectivity methods and investigates the impacts of brain abnormalities on brain functional connectivities. The effects of different brain abnormalities including stroke, depression, emotional disorders, epilepsy, attention deficit hyperactivity disorder (ADHD), autism, and Alzheimer's disease on functional connectivity of the EEG recordings have been explored in this study. The EEG-based metrics and network properties of different brain abnormalities have been discussed to present a comparison of the connectivities affected by each abnormality. Also, the effects of therapy and medical intake on the EEG functional connectivity network of each abnormality have been reviewed.This research received no external funding
Scalp EEG brain functional connectivity networks in pediatric epilepsy
This study establishes a new data-driven approach to brain functional connectivity networks using scalp EEG recordings for classifying pediatric subjects with epilepsy from pediatric controls. Graph theory is explored on the functional connectivity networks of individuals where three different sets of topological features were defined and extracted for a thorough assessment of the two groups. The rater’s opinion on the diagnosis could also be taken into consideration when deploying the general linear model (GLM) for feature selection in order to optimize classification. Results demonstrate the existence of statistically significant (p<0.05) changes in the functional connectivity of patients with epilepsy compared to those of control subjects. Furthermore, clustering results demonstrate the ability to discriminate pediatric epilepsy patients from control subjects with an initial accuracy of 87.5%, prior to initiating the feature selection process and without taking into consideration the clinical rater’s opinion. Otherwise, leave-one-out cross validation (LOOCV) showed a significant increase in the classification accuracy to 96.87% in epilepsy diagnosis.
•Introducing a new data driven graph theory-based methodology for constructing brain functional connectivity networks.•Proposing a decision support system for pediatric epilepsy diagnosis.•Developing a framework to assess the functional connectivity networks alterations using scalp EEG time series.•Evaluation of graph theory measures of brain functional connectivity in pediatric epilepsy diagnosis