Biology subjects

Lesenfants, D.

Publications and source records attributed to Lesenfants, D..

5 recordsLinked to original sources

bioRxiv2019

Tracking attention in a visual active paradigm for the diagnosis of disorders of consciousness

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSClinical assessment of patients with disorders of consciousness (DOC) relies on the clinicians ability to detect a behavioral response to an instruction (e.g., "squeeze my hand"). However, recent studies have shown that some of these patients can produce volitional brain responses to command while no behavioral response is present. This highlights the importance of developing motor-independent diagnostic tool for this population, complementing standardized behavioral evaluation. We here evaluate the ability of a novel gaze-independent attention-based EEG paradigm to detect volitional attentional processes in patients with disorders of consciousness. MethodsThirty patients with DOC were included in the study: 12 with an unresponsive wakefulness syndrome, 16 in a minimally conscious state (MCS), two who emerged from a MCS. Patients were randomly instructed to either concentrate on a task or rest while brain activity was recorded using EEG during a gaze-independent paradigm. ResultsOne of two EMCS, one of 16 MCS and one of 12 UWS patients showed a response to command using the attention task. Interestingly, this method could detect a brain-based response to command in one MCS patient who did not present a behavioral response to command at the bedside the day of the assessment. ConclusionThis study show that task-related variation of attention during an active task could help to objectively detect response to command in patients with DOC.

neuroscience↗

bioRxiv2019

Top-down modulation of neural envelope tracking: the interplay with behavioral, self-reported and neural measures of listening effort

When listening to natural speech, our neural activity tracks the speech envelope. Moreover, recent research has demonstrated that this neural envelope tracking can be affected by top-down processes. The present study was designed to examine if neural envelope tracking is modulated by the effort that a person expends during listening. Five measures were included to quantify listening effort: two behavioral measures based on a novel dual-task paradigm, a self-report effort measure and two neural measures related to neural phase synchronization and alpha power. Electroencephalography responses to sentences, presented at a wide range of subject-specific signal-to-noise ratios, were recorded in thirteen young, normal-hearing adults. A comparison of the five measures revealed different effects of listening effort as a function of speech understanding. Reaction times on the primary task and self-reported effort decreased with increasing speech understanding. In contrast, reaction times on the secondary task and alpha power showed a peak-shaped behavior with highest effort at intermediate speech understanding levels. We found a positive association between envelope tracking and speech understanding. While a significant effect of listening effort was found on theta-band envelope tracking, the effect size was negligible. Therefore, our results suggest that listening effort is not a confound when using envelope tracking to objectively measure speech understanding in young, normal-hearing adults.

neuroscience↗

bioRxiv2019

The interplay of top-down focal attention and the cortical tracking of speech

AO_SCPLOWBSTRACTC_SCPLOWMany active neuroimaging paradigms rely on the assumption that the participant sustains attention to a task. However, in practice, there will be momentary distractions, potentially influencing the results. We investigated the effect of focal attention, objectively quantified using a measure of brain signal entropy, on cortical tracking of the speech envelope. The latter is a measure of neural processing of naturalistic speech. We let participants listen to 44 minutes of natural speech, while their electroencephalogram was recorded, and quantified both entropy and cortical envelope tracking. Focal attention affected the later brain responses to speech, between 100 and 300 ms latency. By only taking into account periods with higher attention, the measured cortical speech tracking improved by 47%. This illustrates the impact of the participants active engagement in the modeling of the brain-speech response and the importance of accounting for it. Our results suggests a cortico-cortical loop that initiates during the early-stages of the auditory processing, then propagates through the parieto-occipital and frontal areas, and finally impacts the later-latency auditory processes in a top-down fashion. The proposed framework could be transposed to other active electrophysiological paradigms (visual, somatosensory, etc) and help to control the impact of participants engagement on the results.

neuroscience↗

bioRxiv2019

Data-driven spatial filtering for improved measurement of cortical tracking of multiple representations of speech

ObjectiveMeasurement of the cortical tracking of continuous natural speech from electroencephalography (EEG) recordings using a forward model is becoming an important tool in auditory neuroscience. However, it requires a manual channel selection based on visual inspection or prior knowledge to obtain a summary measure of cortical tracking. In this study, we present a method to on the one hand remove non-stimulus-related activity from the EEG signals to be predicted, and on the other hand automatically select the channels of interest. We also aim to show that the EEG prediction from phonology-related speech features is possible in Dutch. ApproachEighteen participants listened to a Flemish story, while their EEG was recorded. Subject-specific and grand-average temporal responses functions were determined between the EEG activity in different frequency bands and several stimulus features: the envelope, spectrogram, phonemes, phonetic features or a combination. The temporal response functions were then used to predict EEG from the stimulus, and the predicted was compared with the recorded EEG, yielding a measure of cortical tracking of stimulus features. A spatial filter was calculated based on the generalized eigenvalue decomposition (GEVD), and the effect on EEG prediction accuracy was determined. Main resultsA model including both low- and high-level speech representations was able to better predict the brain responses to the speech than a model only including low-level features. The inclusion of a GEVD-based spatial filter in the model increased the prediction accuracy of cortical responses to each speech feature at both single-subject (270% improvement) and group-level (310 %). SignificanceWe showed that the inclusion of acoustical and phonetic speech information and the addition of a data-driven spatial filter allow improved modelling of the relationship between the speech and its brain response and offer an automatic channel selection. HighlightsO_LIAutomatic channel selection for evaluating the cortical tracking of continuous natural speech C_LIO_LIData-driven spatial filtering for removing non-stimulus-related activity from the EEG signals C_LIO_LIImproved prediction of brain responses to speech by combining acoustical and phonetic speech information in Dutch C_LI DisclosureThe authors report no disclosures relevant to the manuscript.

neuroscience↗

bioRxiv2018

Predicting individual speech intelligibility from the neural tracking of acoustic- and phonetic-level speech representations

ObjectiveTo objectively measure speech intelligibility of individual subjects from the EEG, based on cortical tracking of different representations of speech: low-level acoustical, higher-level discrete, or a combination. To compare each models prediction of the speech reception threshold (SRT) for each individual with the behaviorally measured SRT.\n\nMethodsNineteen participants listened to Flemish Matrix sentences presented at different signal-to-noise ratios (SNRs), corresponding to different levels of speech understanding. For different EEG frequency bands (delta, theta, alpha, beta or low-gamma), a model was built to predict the EEG signal from various speech representations: envelope, spectrogram, phonemes, phonetic features or a combination of phonetic Features and Spectrogram (FS). The same model was used for all subjects. The model predictions were then compared to the actual EEG of each subject for the different SNRs, and the prediction accuracy in function of SNR was used to predict the SRT.\n\nResultsThe model based on the FS speech representation and the theta EEG band yielded the best SRT predictions, with a difference between the behavioral and objective SRT below 1 decibel for 53% and below 2 decibels for 89% of the subjects.\n\nConclusionA model including low- and higher-level speech features allows to predict the speech reception threshold from the EEG of people listening to natural speech. It has potential applications in diagnostics of the auditory system.\n\nSearch Termscortical speech tracking, objective measure, speech intelligibility, auditory processing, speech representations.\n\nHighlightsO_LIObjective EEG-based measure of speech intelligibility\nC_LIO_LIImproved prediction of speech intelligibility by combining speech representations\nC_LIO_LICortical tracking of speech in the delta EEG band monotonically increased with SNRs\nC_LIO_LICortical responses in the theta EEG band best predicted the speech reception threshold\nC_LI\n\nDisclosureThe authors report no disclosures relevant to the manuscript.

neuroscience↗