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Data-driven tensor independent component analysis for model-based connectivity neurofeedback

Related publications (45)

The topology of structural brain connectivity in diseases and spatio-temporal connectomics

The brain is a complex system, composed of multiple neural units interconnected at different spatial and temporal scales. Diffusion MRI allows probing in vivo the anatomical connectivity between different cortical areas through white matter tracts. In para ...

EPFL2015

Social reward improves the voluntary control over localized brain activity in fMRI-based neurofeedback training

Yury Koush

Neurofeedback (NF) based on real-time functional magnetic resonance imaging (rt-fMRI) allows voluntary regulation of the activity in a selected brain region. For the training of this regulation, a well-designed feedback system is required. Social reward ma ...

Frontiers Research Foundation2015

EEG source imaging of brain states using spatiotemporal regression

Dimitri Nestor Alice Van De Ville, Christoph Michel

Relating measures of electroencephalography (EEG) back to the underlying sources is a long-standing inverse problem. Here we propose a new method to estimate the EEG sources of identified electrophysiological states that represent spontaneous activity, or ...

Elsevier2014

Self-regulation of inter-hemispheric visual cortex balance through real-time fMRI neurofeedback training

Dimitri Nestor Alice Van De Ville, Frank Scharnowski, Yury Koush

Recent advances in neurofeedback based on real-time functional magnetic resonance imaging (fMRI) allow for learning to control spatially localized brain activity in the range of millimeters across the entire brain. Realtime fMRI neurofeedback studies have ...

Elsevier2014

Statistical models of effective connectivity in neural microcircuits

Joao Emanuel Felipe Gerhard

To appreciate how neural circuits in the brain control behaviors, we must identify how the neurons comprising the circuit are connected. Neuronal connectivity is difficult to determine experimentally, whereas neuronal activity can often be readily measured ...

EPFL2014

RELICA: A method for estimating the reliability of independent components

Silvestro Micera, Fiorenzo Artoni

Independent Component Analysis (ICA) is a widely applied data-driven method for parsing brain and non-brain EEG source signals, mixed by volume conduction to the scalp electrodes, into a set of maximally temporally and often functionally independent compon ...

Elsevier2014

Dynamic brain networks explored by structure-revealing methods

Nora Leonardi

The human brain is a complex system able to continuously adapt. How and where brain activity is modulated by behavior can be studied with functional magnetic resonance imaging (fMRI), a non-invasive neuroimaging technique with excellent spatial resolution ...

EPFL2014

Statistics for brain connectomics revisited by two-step procedures

Dimitri Nestor Alice Van De Ville, Djalel Eddine Meskaldji

State-of-the-art neuroimaging data provides several ways today to construct "brain connectomics" based on structural and functional measures. Connection-wise statistical assessment suffers from the large amount of multiple comparisons and is suboptimal as ...

2014

Sparsity-Promoting Spatiotemporal Regularization for Data Mining in Functional Magnetic Resonance Imaging

Fikret Isik Karahanoglu

Magnetic resonance imaging (MRI) has opened unprecedented avenues to observe the human brain non-invasively. In particular, for about two decades, functional MRI (fMRI) has enabled to monitor brain function using the blood-oxygen-level-dependent (BOLD) con ...

EPFL2013

Dynamic reconfiguration of human brain functional networks through neurofeedback

Dimitri Nestor Alice Van De Ville, Frank Scharnowski, Rotem Roza Kopel

Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brai ...

Elsevier2013

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