Biology subjects

Perera, M. P. N.

Publications and source records attributed to Perera, M. P. N..

4 recordsLinked to original sources

bioRxiv2024

Experienced meditators show greater forward travelling cortical alpha wave strengths

Mindfulness meditation involves training attention, commonly towards the current sensory experience, with an attitude of non-judgemental awareness. Theoretical perspectives suggest meditation alters the brains predictive processing mechanisms, increasing the synaptic gain and precision with which sensory information is processed, and reducing the generation or elaboration of higher-order beliefs. Recent research suggests that forwards and backwards travelling cortical alpha waves provide an indication of these predictive processing functions. Here, we used electroencephalography (EEG) to test whether the strength of forwards and backwards travelling cortical alpha waves differed between experienced meditators and a matched sample of non-meditators, both during an eyes-closed resting state (N = 97) and during a visual cognitive (Go/No-go) task (N = 126). Our results showed that meditators produced stronger forwards travelling cortical alpha waves compared to non-meditators, both while resting with their eyes closed and during task performance. Meditators also exhibited weaker backwards travelling cortical alpha wave strength while resting with their eyes closed. These results may be indicative of a neural mechanism underpinning enhanced attention associated with meditation practice, as well as a potential neural marker of the reductions in resting mind-wandering that are suggested to be associated with meditation practice. The results also support models of brain function that suggest attention modification can be achieved by mental training aimed at increased processing of sensory information, which might be indexed by greater strength of forwards travelling cortical alpha waves.

neuroscience↗

bioRxiv2024

EEG is better when cleaning effectively targets artifacts

Electroencephalography (EEG) is a useful tool to measure neural activity. However, EEG data are usually contaminated with non-neural artifacts, including voltage shifts generated by eye movements and muscle activity, and other artifacts that are less easily characterizable. The confounding influence of artifacts is often addressed by decomposing data into components, subtracting probable artifactual components, then reconstructing data back into the electrode space. This approach is commonly applied using independent component analysis (ICA). Here, we demonstrate the counterintuitive finding that due to imperfect component separation, component subtraction can artificially inflate effect sizes for event-related potentials (ERPs) and connectivity measures, bias source localisation estimates, and remove neural signals. To address this issue, we developed a method that targets cleaning to the artifact periods of eye movement components and artifact frequencies of muscle components. When tested across different EEG systems and cognitive tasks, our results showed that the targeted artifact reduction method is effective in cleaning artifacts while also reducing the artificial inflation of ERP and connectivity effect sizes and minimizing source localisation biases. Our results suggest EEG pre-processing is better when targeted cleaning is applied, as this improves preservation of neural signals and mitigates effect size inflation and source localisation biases that result from approaches which subtract artifact components across the entire time-series. These improvements enhance the reliability and validity of EEG data analysis. Our method is provided in the freely available RELAX pipeline, which includes a graphical user interface for ease of use and is available as an EEGLAB plugin (https://github.com/NeilwBailey/RELAX).

neuroscience↗

bioRxiv2022

Obsessive-Compulsive Disorder (OCD) is Associated with Increased Engagement of Frontal Brain Regions Across Multiple Event Related Potentials

BackgroundObsessive-Compulsive Disorder (OCD) is a psychiatric condition leading to significant distress and poor quality of life. Successful treatment of OCD is restricted by the limited knowledge about its pathophysiology. This study aimed to investigate the pathophysiology of OCD using electroencephalographic (EEG) event related potentials (ERP), elicited from multiple tasks to characterise disorder-related differences in underlying brain activity across multiple neural processes. MethodsERP data were obtained from 25 OCD patients and 27 age- and sex-matched healthy controls (HC) by recording EEG during Flanker and Go/Nogo tasks. Error-related negativity (ERN) was elicited by the Flanker task, while N200 and P300 were generated using the Go/Nogo task. Primary comparisons of the neural response amplitudes and the topographical distribution of neural activity were conducted using scalp field differences across all time points and electrodes. ResultsCompared to HC, the OCD group showed altered ERP distributions. Contrasting with the previous literature on ERN and N200 topographies in OCD where fronto-central negative voltages were reported, we detected positive voltages. Additionally, the P300 was found to be less negative in the frontal regions. None of these ERP findings were associated with OCD symptom severity. ConclusionsThese results indicate that individuals with OCD show altered frontal neural activity across multiple executive function related processes, supporting the frontal dysfunction theory of OCD. Furthermore, due to the lack of association between altered ERPs and OCD symptom severity, they may be considered potential candidate endophenotypes for OCD.

neuroscience↗

bioRxiv2022

Obsessive-Compulsive Disorder (OCD) is Associated with Increased Electroencephalographic (EEG) Delta and Theta Oscillatory Power but Reduced Delta Connectivity

BackgroundObsessive-Compulsive Disorder (OCD) is a mental health condition causing significant decline in the quality of life of sufferers and the limited knowledge on the pathophysiology hinders successful treatment. The aim of the current study was to examine electroencephalographic (EEG) findings of OCD to broaden our understanding of the disease. MethodsResting-state eyes-closed EEG data was recorded from 25 individuals with OCD and 27 healthy controls (HC). The 1/f arrhythmic activity was removed prior to computing oscillatory powers of all frequency bands (delta, theta, alpha, beta, gamma). Cluster-based permutation was used for between-group statistical analyses, and comparisons were performed for the 1/f slope and intercept parameters. Functional connectivity (FC) was measured using coherence and debiased weighted phase lag index (d-wPLI), and statistically analysed using the Network Based Statistic method. ResultsCompared to HC, the OCD group showed increased oscillatory power in the delta and theta bands in the fronto-temporal and parietal brain regions. However, there were no significant between-group findings in other bands or 1/f parameters. The coherence measure showed significantly reduced FC in the delta band in OCD compared to HC but the d-wPLI analysis showed no significant differences. ConclusionsOCD is associated with raised oscillatory power in slow frequency bands in the fronto-temporal brain regions, which agrees with the previous literature and therefore is a potential biomarker. Although delta coherence was found to be lower in OCD, due to inconsistencies found between measures and the previous literature, further research is required to ascertain definitive conclusions.

neuroscience↗