Biology subjects

Delgado Saa, J. F.

Publications and source records attributed to Delgado Saa, J. F..

3 recordsLinked to original sources

bioRxiv2021

Neural oscillation coupling selectively predicts speech reception in young children with Autism Spectrum Disorder

Communication difficulties in autism spectrum disorder (ASD) involve a speech reception deficit, whose biological causes are not yet identified. This deficit could denote atypical neuronal ensemble activity, as reflected by neural oscillations. Atypical cross-frequency oscillation coupling in particular could disrupt the possibility to jointly track and predict dynamic acoustic stimuli, a dual process that is essential for speech comprehension. Whether such oscillation anomalies can already be found in very young children with ASD, and with what specificity they relate to individual language reception capacity is unknown. In this study, neural activity was collected using EEG in 64 very young children with and without ASD (mean age 3) while they were exposed to naturalistic-continuous speech via an age-appropriate cartoon. EEG power typically associated with phrase-level chunking (delta, 1-3Hz), phonemic encoding (low-gamma, 25-35Hz) and top-down control (beta, 12-20Hz) was markedly reduced in ASD relative to typically developing (TD) children. Speech neural-tracking by delta and theta oscillations was also weaker in ASD than TD children. Critically, children with ASD exhibited slightly atypical theta/gamma coupling (PAC) involving a higher-than-normal gamma frequency, and markedly atypical beta/gamma PAC. Even though many oscillation features were atypical in our sample of 31 very young children with ASD, the beta/gamma coupling anomaly was the single best predictor of individual speech reception difficulties. These findings suggest that early interventions targeting the normalization of low-gamma and low-beta activity, might help young children with ASD to engage more in oral interactions. HighlightsVery young children diagnosed with autism spectrum disorder already present marked alterations of neural oscillatory activity in response to natural speech. The hierarchical processing of phonemic- and syllabic-range information (theta/gamma coupling) is atypical in young ASD children. Speech reception deficit in children with ASD is indexed by abnormal bottom-up (low-gamma) and top-down (low-beta) coordination.

neuroscience↗

bioRxiv2019

Audio-visual combination of syllables involves time-sensitive dynamics following from fusion failure

In face-to-face communication, audio-visual (AV) stimuli can be fused, combined or perceived as mismatching. While the left superior temporal sulcus (STS) is admittedly the locus of AV integration, the process leading to combination is unknown. Analysing behaviour and time-/source-resolved human MEG data, we show that while fusion and combination both involve early detection of AV physical features discrepancy in the STS, combination is associated in with activity of AV asynchrony-sensitive regions (auditory and inferior frontal cortices). Based on dynamic causal modelling, and neural signal decoding, we further show that AV speech integration outcome primarily depends on whether the STS can quickly converge onto an existing multimodal syllable representation, and that combination results from subsequent temporal processing, presumably re-ordering, of discrepant AV stimuli.

neuroscience↗

bioRxiv2018

Coherence-based spectro-spatial fillters for stimulus features prediction from electrocorticographic recordings.

The traditional approach in neuroscience relies on encoding models where brain responses to different stimuli are related to the latter to establish reproducible dependencies. To reduce neuronal and experimental noise, brain signals are usually averaged across trials to detect reliable and coherent brain activity. However, neural representations of stimulus features can be spread over time, frequency, and space, motivating the use of alternative methods that relate stimulus features to brain responses. We propose a Coherence-based spectro-spatial filter method that reconstructs stimulus features from intracortical brain signals. The proposed method models trials of an experiment as realizations of a random process and extracts patterns that are common across brain signals and the presented stimuli. These patterns, originating from different recording sites, are then combined (spatial filtering) to form a final prediction. Our results from three different cognitive tasks (motor movements, speech perception and speech production), concur to show that the proposed method significantly improves the ability to predict stimulus features over traditional methods such as multilinear regression with distributed lags and artificial neural networks. Furthermore, analyses of the model parameters show anatomical discriminability for execution of different motor movements. This anatomical discriminability is also observed in the perception and production of different words. These features could be exploited in the design of neuroprosthesis, as well as for exploring normal brain functioning.

neuroscience↗