351 research outputs found
Progressive modulation of resting‑state brain activity during neurofeedback of positive‑social emotion regulation networks
Neurofeedback allows for the self-regulation of brain circuits implicated in specific maladaptive behaviors, leading to persistent changes in brain activity and connectivity. Positive-social emotion regulation neurofeedback enhances emotion regulation capabilities, which is critical for reducing the severity of various psychiatric disorders. Training dorsomedial prefrontal cortex (dmPFC) to exert a top-down influence on bilateral amygdala during positive-social emotion regulation progressively (linearly) modulates connectivity within the trained network and induces positive mood. However, the processes during rest that interleave the neurofeedback training remain poorly understood. We hypothesized that short resting periods at the end of training sessions of positive-social emotion regulation neurofeedback would show alterations within emotion regulation and neurofeedback learning networks. We used complementary model-based and data-driven approaches to assess how resting-state connectivity relates to neurofeedback changes at the end of training sessions. In the experimental group, we found lower progressive dmPFC self-inhibition and an increase of connectivity in networks engaged in emotion regulation, neurofeedback learning, visuospatial processing, and memory. Our findings highlight a large-scale synergy between neurofeedback and resting-state brain activity and connectivity changes within the target network and beyond. This work contributes to our understanding of concomitant learning mechanisms post training and facilitates development of efficient neurofeedback training.publishedVersio
Progressive modulation of resting-state brain activity during neurofeedback of positive-social emotion regulation networks
Neurofeedback allows for the self-regulation of brain circuits implicated in specific maladaptive behaviors, leading to persistent changes in brain activity and connectivity. Positive-social emotion regulation neurofeedback enhances emotion regulation capabilities, which is critical for reducing the severity of various psychiatric disorders. Training dorsomedial prefrontal cortex (dmPFC) to exert a top-down influence on bilateral amygdala during positive-social emotion regulation progressively (linearly) modulates connectivity within the trained network and induces positive mood. However, the processes during rest that interleave the neurofeedback training remain poorly understood. We hypothesized that short resting periods at the end of training sessions of positive-social emotion regulation neurofeedback would show alterations within emotion regulation and neurofeedback learning networks. We used complementary model-based and data-driven approaches to assess how resting-state connectivity relates to neurofeedback changes at the end of training sessions. In the experimental group, we found lower progressive dmPFC self-inhibition and an increase of connectivity in networks engaged in emotion regulation, neurofeedback learning, visuospatial processing, and memory. Our findings highlight a large-scale synergy between neurofeedback and resting-state brain activity and connectivity changes within the target network and beyond. This work contributes to our understanding of concomitant learning mechanisms post training and facilitates development of efficient neurofeedback training
Brain enhancement through cognitive training: A new insight from brain connectome
Owing to the recent advances in neurotechnology and the progress in understanding of brain cognitive functions, improvements of cognitive performance or acceleration of learning process with brain enhancement systems is not out of our reach anymore, on the contrary, it is a tangible target of contemporary research. Although a variety of approaches have been proposed, we will mainly focus on cognitive training interventions, in which learners repeatedly perform cognitive tasks to improve their cognitive abilities. In this review article, we propose that the learning process during the cognitive training can be facilitated by an assistive system monitoring cognitive workloads using electroencephalography (EEG) biomarkers, and the brain connectome approach can provide additional valuable biomarkers for facilitating leaners' learning processes. For the purpose, we will introduce studies on the cognitive training interventions, EEG biomarkers for cognitive workload, and human brain connectome. As cognitive overload and mental fatigue would reduce or even eliminate gains of cognitive training interventions, a real-time monitoring of cognitive workload can facilitate the learning process by flexibly adjusting difficulty levels of the training task. Moreover, cognitive training interventions should have effects on brain sub-networks, not on a single brain region, and graph theoretical network metrics quantifying topological architecture of the brain network can differentiate with respect to individual cognitive states as well as to different individuals' cognitive abilities, suggesting that the connectome is a valuable approach for tracking the learning progress. Although only a few studies have exploited the connectome approach for studying alterations of the brain network induced by cognitive training interventions so far, we believe that it would be a useful technique for capturing improvements of cognitive function
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The role of HG in the analysis of temporal iteration and interaural correlation
Applications of realtime fMRI for non-invasive brain computer interface-decoding and neurofeedback
Non-invasive brain-computer interfaces (BCIs) seek to enable or restore brain function by using neuroimaging e.g. functional magnetic resonance imaging (fMRI), to engage brain activations without the need for explicit behavioural output or surgical implants. Brain activations are converted into output signals, for use in communication interfaces, motor prosthetics, or to directly shape brain function via a feedback loop. The aim of this thesis was to develop cognitive BCIs using realtime fMRI (rt-fMRI), with the potential for use as a communication interface, or for initiating neural plasticity to facilitate neurorehabilitation. Rt-fMRI enables brain activation to be manipulated directly to produce changes in function, such as perception. Univariate and multivariate classification approaches were used to decode brain activations produced by the deployment of covert spatial attention to simple visual stimuli. Primary and higher order visual areas were examined, as well as potential control regions. The classification platform was then developed to include the use of real-world visual stimuli, exploiting the use of category-specific visual areas, and demonstrating real-world applicability as a communications interface. Online univariate classification of spatial attention was successfully achieved, with individual classification accuracies for 4-quadrant spatial attention reaching 70%. Further, a novel implementation of m-sequences enabled the use of the timing of stimuli presentation to enhance signal characterisation. An established rt-fMRI analysis loop was then used for neurofeedback-led manipulation of category-specific visual brain regions, modulating their functioning, and, as a result, biasing visual perception during binocular rivalry. These changes were linked with functional and effective connectivity changes in trained regions, as well as in a putative top-down control region. The work presented provides proof-of-principle for non-invasive BCIs using rt-fMRI, with the potential for translation into the clinical environment. Decoding and 4 neurofeedback applied to non-invasive and implantable BCIs form an evolving continuum of options for enabling and restoring brain function
Applications of multi-way analysis for characterizing paediatric electroencephalogram (EEG) recordings
This doctoral thesis outlines advances in multi-way analysis for characterizing
electroencephalogram (EEG) recordings from a paediatric population, with the aim to
describe new links between EEG data and changes in the brain. This entails establishing the
validity of multi-way analysis as a framework for identifying developmental information at the
individual and collective level. Multi-way analysis broadens matrix analysis to a multi-linear
algebraic architecture to identify latent structural relationships in naturally occurring higher
order (n-way) data, like EEG. We use the canonical polyadic decomposition (CPD) as a
multi-way model to efficiently express the complex structures present in paediatric EEG
recordings as unique combinations of low-rank matrices, offering new insights into child
development. This multi-way CPD framework is explored for both typically developing (TD)
children and children with potential developmental delays (DD), e.g. children who suffer from
epilepsy or paediatric stroke.
Resting-state EEG (rEEG) data serves as an intuitive starting point in analyzing paediatric
EEG via multi-way analysis. Here, the CPD model probes the underlying relationships
between the spatial, spectral and subject modes of several rEEG datasets. We demonstrate the
CPD can reveal distinct population-level features in rEEG that reflect unique developmental
traits in varying child populations. These development-affiliated profiles are evaluated with
respect to capturing structures well-established in childhood EEG. The identified features are
also interrogated for their predictive abilities in anticipating new subjects’ ages. Assessing
simulations and real rEEG datasets of TD and DD children establishes the multi-way analysis
framework as well suited for identifying developmental profiles from paediatric rEEG.
We extend the multi-way analysis scheme to more complex EEG scenarios common in
EEG rehabilitation technology, like brain-computer interfaces. We explore the feasibility of
multi-way modelling for interventions where developmental changes often pose as barriers.
The multi-way CPD model is expanded to include four modes- task, spatial, spectral and
subject data, with non-negativity and orthogonality constraints imposed. We analyze a visual
attention task that elucidates a steady-state visual evoked potential and present the advantages
gained from the extended CPD model. Through direct multi-linear projection, we demonstrate
that linear profiles of the CPD can be capitalized upon for rapid task classification sans
individual subject classifier calibration.
Incorporating concepts from the multi-way analysis scheme with child development measured
by psychometric tests, we propose the Joint EEG Development Inference (JEDI) model for
inferring development from paediatric EEG. We utilize a common EEG task (button-press) to
establish a 4-way CPD model of paediatric EEG data. Structured data fusion of the CPD model
and cognitive scores from psychometric evaluations then permits joint decomposition of the
two datasets to identify common features associated with each representation of development.
Use of grid search optimization and a fully cross-validated design supports the JEDI model as
another technique for rapidly discerning the developmental status of a child via EEG.
We then briefly turn our attention to associating child development as measured by
psychometric tests to markers in the EEG using graph network properties. Using graph
networks, we show how the functional connectivity can inform on potential developmental
delays in very young epileptic children using routine, clinical rEEG measures. This establishes
a potential tool complementary to the JEDI model for identifying and inferring links between
the established psychometric evaluation of developing children and functional analysis of the
EEG.
Multi-way analysis of paediatric EEG data offers a new approach for handling the
developmental status and profiles of children. The CPD model offers flexibility in terms of
identifying development-related features, and can be integrated into EEG tasks common in
rehabilitation paradigms. We aim for the multi-way framework and associated techniques
pursued in this thesis to be integrated and adopted as a useful tool clinicians can use for
characterizing paediatric development
EEG To FMRI Synthesis: Is Deep Learning a Candidate?
Advances on signal, image and video generation underly major breakthroughs on generative medical imaging tasks, including Brain Image Synthesis. Still, the extent to which functional Magnetic Ressonance Imaging (fMRI) can be mapped from the brain electrophysiology remains largely unexplored. This work provides the first comprehensive view on how to use state-of-the-art principles from Neural Processing to synthesize fMRI data from electroencephalographic (EEG) data. Given the distinct spatiotemporal nature of haemodynamic and electrophysiological signals, this problem is formulated as the task of learning a mapping function between multivariate time series with highly dissimilar structures. A comparison of state-of-the-art synthesis approaches, including Autoencoders, Generative Adversarial Networks and Pairwise Learning, is undertaken. Results highlight the feasibility of EEG to fMRI brain image mappings, pinpointing the role of current advances in Machine Learning and showing the relevance of upcoming contributions to further improve performance. EEG to fMRI synthesis offers a way to enhance and augment brain image data, and guarantee access to more affordable, portable and long-lasting protocols of brain activity monitoring. The code used in this manuscript is available in Github and the datasets are open source
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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