Biology subjects

Carpenter, L.

Publications and source records attributed to Carpenter, L..

3 recordsLinked to original sources

bioRxiv2024

Dysfunctional oscillatory bursting patterns underlie working memory deficits in adolescents with ADHD

Identifying neural markers of clinical symptom fluctuations is prerequisite to developing more precise brain-targeted treatments in psychiatry. We have recently shown that working memory (WM) in healthy adults is dependent on the rise and fall interplay between alpha/beta and gamma bursts within frontoparietal regions, and that deviations in these patterns lead to WM performance errors. However, it is not known whether such bursting deviations underlie clinically relevant WM-related symptoms or clinical status in individuals with WM deficits. In adolescents (n=27) with attention deficit hyperactivity disorder (ADHD), we investigated WM-related dynamics between alpha/beta and gamma bursts in relation to clinical status fluctuations. Participants repeatedly completed a visual Sternberg spatial working memory task during EEG recording as part of their participation in two research studies (n=224 person-sessions). Source localizing EEG data to each participants structural MRI, the rate and volume of alpha, beta, and gamma bursts were examined within the dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex (PPC). Alpha/beta and gamma bursts at the DLPFC and PPC displayed complimentary roles in WM processes. Alpha/beta bursting decreased during stimuli encoding and increased during the delay, while gamma bursting was elevated during encoding and decreased during the delay. Deviations in bursting patterns were associated with WM errors and clinical symptoms. We conclude that dysfunctional alpha/beta and gamma burst dynamics within the frontoparietal region underlie both intra-individual WM performance and WM symptom fluctuations in adolescents with ADHD. Such burst dynamics reflect a novel target and biomarker for WM-related treatment development.

neuroscience↗

bioRxiv2024

The association between oscillatory burst features and deviations during human working memory

Oscillatory power across multiple frequency bands has been associated with distinct working memory (WM) processes. Recent research has shown that previous observations based on averaged power are driven by the presence of transient, oscillatory burst-like events, particularly within the alpha, beta, and gamma bands. However, the interplay between different burst events in human WM is not well understood. The current EEG study aimed to investigate the dynamics between alpha (8-12 Hz)/beta (15-29 Hz) and high frequency activity (HFA; 55-80 Hz) bursts in human WM, particularly burst features and error-related deviations during the encoding and maintenance of WM in healthy adults. Oscillatory burst features within the alpha, beta and HFA bands were examined at frontal and parietal electrodes in healthy young adults during a Sternberg working memory task. Averaged power dynamics were driven by oscillatory burst features, most consistently the burst rate and burst power. Alpha/beta and HFA bursts displayed complementary roles in WM processes, in that alpha and beta bursting decreased during encoding and increased during delay, while HFA bursting had the opposite pattern, i.e., increased during encoding and decreased during the delay. Critically, weaker variation in burst dynamics across stages was associated with incorrect responses and impaired overall task performance. Together, these results indicate that successful human WM is dependent on the rise and fall interplay between alpha/beta and HFA bursts, with such burst dynamics reflecting a novel target for the development of treatment in clinical populations with WM deficits.

neuroscience↗

bioRxiv2022

Synapses, predictions, and prediction errors: a neocortical computational study of MDD using the temporal memory algorithm of HTM.

BackgroundSynapses and spines are central in major depressive disorder (MDD) pathophysiology, recently highlighted by ketamines and psilocybins rapid antidepressant effects. The Bayesian brain and interoception perspectives formalize MDD as being "stuck" in affective states constantly predicting negative energy balance. We examined how synaptic atrophy relates to the predictive function of the neocortex and thus to symptoms, using temporal memory (TM), an unsupervised machine-learning algorithm. TM represents a single neocortical layer, learns in real-time using local Hebbian-learning rules, and extracts and predicts temporal sequences. MethodsWe trained a TM model on random sequences of upper-case alphabetical letters, representing sequences of affective states. To model depression, we progressively destroyed synapses in the TM model and examined how that affected the predictive capacity of the network. ResultsDestroying 50% of the synapses slightly reduced the number of predictions, followed by a marked drop with further destruction. However, reducing the synapses by 25% dropped the confidence in the predictions distinctly. So even though the network was making accurate predictions, the network was no longer confident about these predictions. ConclusionsThese findings explain how interoceptive cortices could be stuck in limited affective states with high prediction error. Growth of new synapses, e.g., with ketamine and psilocybin, would allow representing more futuristic predictions with higher confidence. To our knowledge, this is the first study to use the TM model to connect changes happening at synaptic levels to the Bayesian formulation of psychiatric symptomatology, making it possible to understand treatment mechanisms and possibly, develop new treatments. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

neuroscience↗