2,309 research outputs found
Combined distributed source and single-trial EEG-fMRI modeling: Application to effortful decision making processes
No full textThe Added Value of EEG-fMRI in Imaging Neuroscience
No full textThe main objective of functional neuroimaging is to detect and characterize in space and time neurophysiologically relevant changes of brain states. Functional MRI (fMRI) and electro-encephalography (EEG) assume that a given brain state can be decoded from the precise anatomical localization and the detailed temporal evolution of neuro-electrical brain signals, respectively. Mapping brain states with fMRI at a spatial resolution in the millimeter range allows imaging neuroscientists to test diverse neurophysiological and neuropathological hypotheses in the normal and clinical populations. Simultaneously recorded EEG offers the possibility to greatly enrich topological results by tracking subjects’ state-representative patterns over time at the millisecond temporal scale. The main purpose of this chapter is to illustrate how the imaging neuroscientist can integrate detailed temporal information provided by simultaneously recorded EEG signals into fMRI spatio-temporal modeling. We discuss the problem of optimizing a common source space for fMRI and EEG signal projection through the use of anatomical and functional MRI models and EEG distributed inverse models, thereby gathering a fully integrated framework for the comparative analysis of simultaneously acquired EEG-fMRI data sets
Extracting functional networks with spatial independent component analysis: the role of dimensionality, reliability and aggregation scheme
No full textPurpose of review
Clinical studies have differentiated functional brain networks in neurological patient and
control populations using independent component analysis (ICA) applied to functional
MRI (fMRI). Principal component analysis (PCA) is used to reduce the data
dimensionality to make this feasible. The role of this choice is reviewed in connection
with the accuracy and the reliability of the ICA results and the schemes of data
aggregation in population studies.
Recent findings
It has been pointed out recently that it is important to critically explore the ICA model
orders without relying on strictly predetermined PCA cutoffs for the number of
components.Wefurther illustrate this aspect empirically by showing that a large enough
range of dimensions may exist where ICA components remain accurate but also that the
minimum PCA dimension required to reliably extract the best ICA maps may vary
substantially across subjects. Moreover, with the aid of a simple simulation, we show
that reliable independent components can still be recovered beyond a theoretical PCA
cutoff.
Summary
The role of the PCA cutoff and its impact on the accuracy and reliability of the ICA results
should be carefully considered in future clinical fMRI studies
Automated selection of brain regions for real-time fMRI brain-computer interfaces
Full text linkObjective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications
Semantic fMRI neurofeedback of emotions: from basic principles to clinical applications
No full textDuring fMRI neurofeedback participants learn to self-regulate activity in relevant brain areas and networks based on ongoing feedback extracted from measured responses in those regions. This closed-loop approach has been successfully applied to reduce symptoms in mood disorders such as depression by showing participants a thermometer-like display indicating the strength of activity in emotion-related brain areas. The hitherto employed conventional neurofeedback is, however, 'blind' with respect to emotional content, i.e. patients instructed to engage in a specific positive emotion could drive the neurofeedback signal by engaging in a different (positive or negative) emotion. In this future perspective, we present a new form of neurofeedback that displays semantic information of emotions to the participant. Semantic information is extracted online using real-time representational similarity analysis of emotion-specific activity patterns. The extracted semantic information can be provided to participants in a two-dimensional semantic map depicting the current mental state as a point reflecting its distance to pre-measured emotional mental states (e.g. 'happy', 'content', 'sad', 'angry'). This new approach provides transparent feedback during self-regulation training, and it has the potential to enable more specific training effects for future therapeutic applications such as clinical interventions in mood disorders.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'
Towards semantic fMRI neurofeedback:navigating among mental states using real-time representational similarity analysis
Full text linkOBJECTIVE: Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a non-invasive MRI procedure allowing examined participants to learn to self-regulate brain activity by performing mental tasks. A novel two-step rt-fMRI-NF procedure is proposed whereby the feedback display is updated in real-time based on high-level representations of experimental stimuli (e.g. objects to imagine) via real-time representational similarity analysis of multi-voxel patterns of brain activity.APPROACH: In a localizer session, the stimuli become associated with anchored points on a two-dimensional representational space where distances approximate between-pattern (dis)similarities. In the NF session, participants modulate their brain response, displayed as a movable point, to engage in a specific neural representation. The developed method pipeline is verified in a proof-of-concept rt-fMRI-NF study at 7 Tesla involving a single healthy participant imagining concrete objects. Based on this data and artificial data sets with similar (simulated) spatio-temporal structure and variable (injected) signal and noise, the dependence on noise is systematically assessed.MAIN RESULTS: The participant in the proof-of-concept study exhibited robust activation patterns in the localizer session and managed to control the neural representation of a stimulus towards the selected target in the NF session. The offline analyses validated the rt-fMRI-NF results, showing that the rapid convergence to the target representation is noise-dependent.SIGNIFICANCE: Our proof-of-concept study introduces a new NF method allowing the participant to navigate among different mental states. Compared to traditional NF designs (e.g. using a thermometer display to set the level of the neural signal), the proposed approach provides content-specific feedback to the participant and extra degrees of freedom to the experimenter enabling real-time control of the neural activity towards a target brain state without suggesting a specific mental strategy to the subject.</p
Coat Cooke & Joe Poole | Coat Cooke & Rainer Wiens: Reviews
No full textCoat Cooke album reviews by Randy Raine-Reusch. Coat Cooke (sax); Joe Poole (drums); Rainer Wiens (guitar)
The Added Value of EEG-fMRI in Imaging Neuroscience
No full textThe main objective of functional neuroimaging is to detect and characterise (in space and time) relevant changes in brain states and their relation to neuronal activity. Functional mri (fmri), electroencephalography (eeg) and magnetoencephalography (meg) are the most widespread noninvasive techniques that are available to experimental and clinical neuroscientists to achieve this objective starting from in vivo measures of brain electrical activity. Both fmri and eeg assume that a given brain state can be decoded from the precise anatomical localisation and the detailed temporal evolution of neuroelectrical brain activation signals, respectively. Starting from these common assumptions, fmri neuroscientists have developed many different approaches for mapping brain states at a spatial resolution of a few millimetres and testing many different neurophysiological and neuropathological hypotheses in normal and clinical populations, despite the limited temporal resolution of the available signals (see previous chapters). On the other hand, eeg neuroscientists have posed analogous questions and addressed similar problems by developing different approaches for the detailed temporal analysis of eeg recordings, despite the limited spatial detail in their findings. The previous chapter illustrated how fmri can be used by eeg neuroscientists to improve the quality of eeg results and to help with the problem of source localisation. The purpose of this chapter is to illustrate how the fmri neuroscientist can integrate detailed temporal information by incorporating simultaneously recorded eeg signals into standard as well as sophisticated fmri spatiotemporal modelling. We discuss how this can be achieved in such a way that new effects become detectable in the fmri domain even when the original event or state change causing possible fmri effects can only be characterised at very rapid temporal scales (e.g. Milliseconds) or frequency bands (above 1 hz). Our discussion occurs at a conceptual level, and we refer the reader to other chapters in part 2 for more details regarding problems such as eeg preprocessing.keywordsindependent component analysisfmri datafmri signalfmri experimentequivalent current dipolethese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves
Best-fit connectivity superimposed on an MR-image of the macaque brain.
No full text<p>MR-image courtesy of R. Goebel and N. K. Logothetis, rendered with BrainVoyager software. Added connections are red.</p
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