Biology subjects

Caldwell, B.

Publications and source records attributed to Caldwell, B..

4 recordsLinked to original sources

bioRxiv2025

The effects of mindfulness on working memory: a systematic review and meta-analysis

ObjectivesMindfulness is a promising health intervention, showing potential effects on cognitive functions like memory. Despite evidence suggesting mindfulness improves working memory, inconsistencies in results and methodologies prevent definitive conclusions. This meta-analysis examines the effects of mindfulness interventions on working memory across clinical and healthy populations, and various age groups. MethodsA systematic search for relevant English and Persian articles was conducted in WOS, Scopus, PsycINFO, and PubMed databases, along with nine meta-analyses up to February 2023. Included studies consisted of randomized controlled trials (RCTs), controlled trials (CTs), and single-group studies. Overall, 29 studies with 2076 participants aged 5-85 years were analyzed. ResultsMindfulness interventions demonstrated a medium effect size on working memory: RCT two-group studies (Hedges g = 0.438, p < 0.001), CT two-group studies (Hedges g = 0.385, p < 0.005), and single-group studies (Hedges g = 0.583, p < 0.001). These findings confirm the effectiveness of mindfulness interventions in improving working memory. ConclusionsMindfulness interventions exhibit promising effects on working memory. However, further primary research, particularly rigorous RCTs, is needed to better understand their impacts on clinical versus healthy populations and across diverse age groups.

neuroscience↗

bioRxiv2023

TMS-EEG shows mindfulness meditation is associated with an altered excitation/inhibition balance in the dorsolateral prefrontal cortex

ObjectiveMindfulness meditation is associated with functional brain changes in regions subserving higher order cognitive processes such as attention. However, no research to date has causally probed these areas in meditators using combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG). This study aimed to investigate whether cortical reactivity to TMS differs in a community sample of experienced mindfulness meditators when compared to matched controls MethodsTMS was applied to the left and right dorsolateral prefrontal cortices (DLPFC) of 19 controls and 15 meditators while brain responses were measured using EEG. TMS-evoked potentials (P60 and N100) were analysed, and exploratory analyses using the whole EEG scalp field were performed to test whether TMS-evoked global neural response strength or the distribution of neural activity differed between groups. ResultsMeditators were found to have statistically larger P60/N100 ratios in response to left and right hemisphere DLPFC stimulation compared to controls (pFDR = 0.004, BF10 > 39). No differences were observed in P60 or N100 amplitudes when examined in isolation. We also found preliminary evidence for differences in the distribution of neural activity 269-332ms post stimulation. ConclusionThese findings demonstrate differences in cortical reactivity to TMS in meditators. Differences in the distribution of neural activity approximately 300ms following stimulation suggest differences in cortico-subcortical reverberation in meditators that may be indicative of greater inhibitory activity in frontal regions. This research contributes to our current understanding of the neurophysiology of mindfulness and highlights opportunities for further exploration into the mechanisms underpinning the benefits of mindfulness meditation.

neuroscience↗

bioRxiv2023

Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction

Previous research has examined resting electroencephalographic (EEG) data to explore brain activity related to meditation. However, previous research has mostly examined power in different frequency bands. Here we compared >7000 time-series features of the EEG signal to comprehensively characterize brain activity differences in meditators, using many measures that are novel in meditation research. Eyes-closed resting-state EEG data from 49 meditators and 46 non-meditators was decomposed into the top eight principal components (PCs). We extracted 7381 time-series features from each PC and each participant and used them to train classification algorithms to identify meditators. Highly differentiating individual features from successful classifiers were analysed in detail. Only the third PC (which had a central-parietal maximum) showed above-chance classification accuracy (67%, pFDR = 0.007), for which 405 features significantly distinguished meditators (all pFDR < 0.05). Top-performing features indicated that meditators exhibited more consistent statistical properties across shorter subsegments of their EEG time-series (higher stationarity) and displayed an altered distributional shape of values about the mean. By contrast, classifiers trained with traditional band-power measures did not distinguish the groups (pFDR > 0.05). Our novel analysis approach suggests the key signatures of meditators brain activity are higher temporal stability and a distribution of time-series values suggestive of longer, larger, or more frequent non-outlying voltage deviations from the mean within the third PC of their EEG data. The higher temporal stability observed in this EEG component might underpin the higher attentional stability associated with meditation. The novel time-series properties identified here have considerable potential for future exploration in meditation research and the analysis of neural dynamics more broadly.

neuroscience↗

bioRxiv2019

Human Neocortical Neurosolver (HNN): A new software tool for interpreting the cellular and network origin of human MEG/EEG data

Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNNs core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signals origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNNs ability to associate signals across scales makes it a unique tool for translational neuroscience research.

neuroscience↗