Biology subjects

Liljestrom, M.

Publications and source records attributed to Liljestrom, M..

3 recordsLinked to original sources

bioRxiv2025

Capturing individual variation in children's oscillatory brain activity during nREM sleep

Human brain dynamics are highly unique between individuals: functional neuroimaging studies have recently described functional features that can be used as neural fingerprints. As such studies have generally been performed on adults, it is unclear if the results are applicable during brain development. Here we applied Bayesian reduced-rank regression (BRRR), to extract low-dimensional representations of electroencephalography (EEG) power spectra measured from normally developing children at different ages. The clinical EEG recordings represented different sleep stages in a pediatric population (N=782) of ages varying from 6 weeks to 19 years. The representations learned within specific sleep stages successfully separated between the subjects. Furthermore, a model trained with data across sleep stages generalized to unseen data, suggesting that informative neural fingerprints could be extracted. Fingerprint stability increased with the age of the subjects. In comparison to correlation-based measures in differentiating individuals from each other, the BRRR model performed better, highlighting the usefulness of the dimensionality reduction for data obtained in demanding clinical settings. While further studies are needed to address the possible non-linear maturation effects over developmental periods, our results demonstrate, for the first time, the existence of stable within-session neurofunctional fingerprints in pediatric populations.

neuroscience↗

bioRxiv2024

Automated speech artefact removal from MEG data utilizing facial gestures and mutual information

The ability to speak is one of the most crucial human skills, motivating neuroscientific studies of speech production and speech-related neural dynamics. Increased knowledge in this area, allows e.g., for development of rehabilitation protocols for language-related disorders. While our understanding of speech-related neural processes has greatly enhanced owing to non-invasive neuroimaging techniques, the interpretations have been limited by speech artefacts caused by the activation of facial muscles that mask important languagerelated information. Despite earlier approaches applying independent component analysis (ICA), the artefact removal process continues to be time-consuming, poorly replicable and affected by inconsistencies between different observers, typically requiring manual selection of artefactual components. The artefact component selection criteria have been variable, leading to non-standardized speech artefact removal processes. To address these issues, we propose here a pipeline for automated speech artefact removal from MEG data. We developed an ICA-based speech artefact removal routine by utilizing EMG data measured from facial muscles during a facial gesture task for isolating the speech-induced artefacts. Additionally, we used mutual information (MI) as a similarity measure between the EMG signals and the ICA-decomposed MEG to provide a feasible way to identify the artefactual components. Our approach efficiently and in an automated manner removed speech artefacts from MEG data. The method can be feasibly applied to improve the understanding of speech-related cortical dynamics, while transparently evaluating the removed and preserved MEG activation.

neuroscience↗

bioRxiv2023

Human sensorimotor resting state beta events and 1/f response show good test-retest reliability

Neurological conditions affecting the sensorimotor system have a profound impact on individuals physical independence and are associated with a considerable socioeconomic burden. Reliable functional biomarkers allowing early diagnosis of these conditions or targeting treatment and rehabilitation can reduce this burden. Magnetoencephalography (MEG) can non-invasively measure the brains salient rhythmic patterns such as the somatomotor ( rolandic) rhythm. This rhythm shows intermittent high amplitude events in the beta (14-30 Hz) frequency range which predict behavior across tasks and species and are altered by neurological diseases affecting the sensorimotor system. Thus, the sensorimotor resting beta phenotype is a promising candidate biomarker of sensorimotor function. A prerequisite for use as a biomarker is that it can be quantified reliably across different measurement sessions. Here, using MEG, we assessed the test-retest stability of spontaneously occurring sensorimotor power spectral characteristics, including both aperiodic (1/f) as well as beta band fluctuations ( beta events) in a cohort of 50 healthy human controls. Test-retest reliability across two separate measurement sessions was assessed using the intraclass correlation coefficient (ICC). Beta events were determined using a thresholding-based approach on a narrow-band filtered amplitude envelope obtained using Morlet wavelet decomposition across a range of parameters (recording length, amplitude threshold and filtering bandwidth). We find that both aperiodic power spectral features as well as several beta event characteristics show good to excellent testretest stability. Especially aperiodic component power spectral features (ICC 0.77-0.88), but also measures of beta event amplitude (ICC 0.74-0.82) were found to be very stable, while measures of individual beta event duration were less reliable, especially for the left hemisphere (ICC right [~]0.7, left [~]0.55). Recordings of 2-3 minutes were sufficient to obtain stable results for most parameters. Important for potential clinical applications, automatization of beta event extraction was successful in 86 % of cases. Beta event rate and duration measures were more sensitive to analysis parameters than the measures of event amplitude. The results suggest the sensorimotor beta phenotype is a stable feature of an individuals resting brain activity even for short, 2-3 minute recordings which can be easily measured in patient populations, facilitating its use as a potential clinical biomarker.

neuroscience↗