Biology subjects

Debnath, R.

Publications and source records attributed to Debnath, R..

4 recordsLinked to original sources

bioRxiv2023

Bursting with potential: How sensorimotor beta bursts develop from infancy to adulthood

Beta activity is thought to play a critical role in sensorimotor processes. However, little is known about how activity in this frequency band develops. Here, we investigated the developmental trajectory of sensorimotor beta activity from infancy to adulthood. We recorded electroencephalography (EEG) from adults, 12-month-olds, and 9-month-olds while they observed and executed grasping movements. We analysed beta burst activity using a novel method that combines time-frequency decomposition and principal component analysis (PCA). We then examined the changes in burst rate and waveform motifs along the selected principal components. Our results reveal systematic changes in beta activity during action execution across development. We found a decrease in beta burst rate during movement execution in all age groups, with the greatest decrease observed in adults. Additionally, we identified four principal components that defined waveform motifs that systematically changed throughout the trial. We found that bursts with waveform shapes closer to the median waveform were not rate-modulated, whereas those with waveform shapes further from the median were differentially rate-modulated. Interestingly, the decrease in the rate of certain burst motifs occurred earlier during movement and was more lateralized in adults than in infants, suggesting that the rate modulation of specific types of beta bursts becomes increasingly refined with age.

neuroscience↗

bioRxiv2022

Processing of task-irrelevant sounds during typical everyday activities in children

Our ability to focus on a task and ignore task-irrelevant stimuli is critical for efficient cognitive functioning. Attention control is especially required in the auditory modality as sound has privileged access to perception and consciousness. Despite this important function, little is known about auditory attention during typical everyday activities in childhood. We investigated the impact of task-irrelevant sounds on attention during three everyday activities - playing a game, reading a book, watching a movie. During these activities, environmental novel sounds were presented within a sequence of standard sounds to 7-8-year-old children and adults. We measured ERPs reflecting early sound processing and attentional orienting and theta power evoked by standard and novel sounds during these activities. Playing a game vs. reading or watching reduced early encoding of sounds in children and affected ongoing information processing and attention allocation in both groups. In adults, theta power was reduced during playing at mid-central brain areas. Results show a pattern of immature neuronal mechanisms underlying perception and attention of task-irrelevant sounds in 7-8-year-old children. While the type of activity affected the processing of irrelevant sounds in both groups, early stimulus encoding processes were more sensitive to the type of activities in children.

developmental biology↗

bioRxiv2020

The Maryland Analysis of Developmental EEG (MADE) Pipeline

Compared to adult EEG, EEG signals recorded from pediatric populations have shorter recording periods and contain more artifact contamination. Therefore, pediatric EEG data necessitate specific preprocessing approaches in order to remove environmental noise and physiological artifacts without losing large amounts of data. However, there is presently a scarcity of standard automated preprocessing pipelines suitable for pediatric EEG. In an effort to achieve greater standardization of EEG preprocessing, and in particular for the analysis of pediatric data, we developed the Maryland Analysis of Developmental EEG (MADE) pipeline as an automated preprocessing pipeline compatible with EEG data recorded with different hardware systems, different populations, levels of artifact contamination, and length of recordings. MADE uses EEGLAB and functions from some EEGLAB plugins, and includes additional customizable features particularly useful for EEG data collected from pediatric populations. MADE processes event-related and resting state EEG from raw data files through a series of preprocessing steps and outputs processed clean data ready to be analyzed in time, frequency, or time-frequency domain. MADE provides a report file at the end of the preprocessing that describes a variety of features of the processed data to facilitate the assessment of the quality of processed data. In this paper we discuss some practical issues, which are specifically relevant to pediatric EEG preprocessing. We also provide custom-written scripts to address these practical issues. MADE is freely available under the terms of the GNU General Public License at https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline.

neuroscience↗

bioRxiv2019

Adjusting ADJUST: Optimizing the ADJUST Algorithm for Pediatric Data Using Geodesic Nets

A major challenge for electroencephalograph (EEG) studies on pediatric populations is that large amounts of data are lost due to artifacts (e.g., movement and blinks). Independent component analysis (ICA) can separate artifactual and neural activity, allowing researchers to remove such artifactual activity and retain a greater percentage of EEG data for analyses. However, manual identification of artifactual components is time consuming and requires subjective judgment. Automated algorithms, like ADJUST and ICLabel, have been validated on adults using the international 10-20 system, but to our knowledge no such algorithms have been optimized for the geodesic sensor net, which is often used with infants and children. Therefore, in an attempt to automate artifact selection for pediatric data collected with geodesic nets, we modified ADJUSTs algorithm. Our \"adjusted-ADJUST\" algorithm was compared to the \"original-ADJUST\" algorithm and ICLabel in adults, children, and infants on three different performance measures: respective classification agreement with expert coders, the number of trials retained following artifact removal, and the reliability of the EEG signal after pre-processing with each algorithm. Overall, the adjusted-ADJUST algorithm performed better than the original-ADJUST algorithm and no ICA correction with adult and pediatric data. Moreover, it performed better than ICLabel for pediatric data. These results indicate that optimizing existing algorithms for data collected with a geodesic net improves artifact classification and retains more trials, potentially facilitating EEG studies with pediatric populations.

neuroscience↗