Biology subjects

Huang, Y. T.

Publications and source records attributed to Huang, Y. T..

4 recordsLinked to original sources

bioRxiv2024

Temporal Prediction through Integration of Probability Distributions of Event Timings at Multiple Levels

Our brain uses prior experience to anticipate the timing of upcoming events. This dynamical process can be modeled using a hazard function derived from the probability distribution of event timings. However, the contexts of an event can lead to various probability distributions for the same event, and it remains unclear how the brain integrates these distributions into a coherent temporal prediction. In this study, we create a local-global foreperiod paradigm consisting of a sequence of paired trials, where in each trial, participants respond to a target signal after a specified time interval (i.e. foreperiod) following a warning cue. The prediction of the target onset in the second trial can be based on the probability distribution of the second foreperiod (local level) and its conditional probability given the foreperiod in the first trial (global level). These probability distributions are then transformed into hazard functions to represent the local and global temporal predictions. Reaction times to the target signal are best explained by incorporating both local and global predictions, indicating that both levels of temporal information contribute to making predictions. We further show that electroencephalographic source signals are best reconstructed when integrating both predictions. Specifically, the local and global predictions are separately encoded in the posterior and anterior brain regions, and to achieve synergy between both predictions, a third region--particularly the right posterior cingulate area--is needed. Our study reveals brain networks that integrate multilevel temporal information, providing a comprehensive view of hierarchical predictive coding of time.

neuroscience↗

bioRxiv2024

A mediating framework in resting-state connectivity between the medial prefrontal cortex and anterior cingulate in mild cognitive impairment

Mild cognitive impairment (MCI) is recognized as the prodromal phase of dementia, a condition that can be either maintained or reversed through timely medical interventions to prevent cognitive decline. Considerable studies using functional magnetic resonance imaging (fMRI) have indicated that altered activity in the medial prefrontal cortex (mPFC) serves as an indicator of various cognitive stages of aging. However, the impacts of intrinsic functional connectivity in the mPFC as a mediator on cognitive performance in individuals with and without MCI have not been fully understood. In this study, we recruited 42 MCI patients and 57 healthy controls, assessing their cognitive abilities and functional brain connectivity patterns through neuropsychological evaluations and resting-state fMRI, respectively. The MCI patients exhibited poorer performance on multiple neuropsychological tests compared to the healthy controls. At the neural level, functional connectivity between the mPFC and the anterior cingulate cortex (ACC) was significantly weaker in the MCI group and correlated with multiple neuropsychological test scores. The result of the mediation analysis further demonstrated that functional connectivity between the mPFC and ACC notably mediated the relationship between the MCI and semantic working memory. These findings suggest that altered mPFC-ACC connectivity may have a plausible causal influence on cognitive decline and provide implications for early identifications of neurodegenerative diseases and precise monitoring of disease progression.

neuroscience↗

bioRxiv2023

Dissecting Mismatch Negativity: Early and Late Subcomponents for Detecting Deviants in Local and Global Sequence Regularities

Mismatch negativity (MMN) is commonly recognized as a neural signal of prediction error evoked by deviants in the expected pattern of sensory input. Studies show that MMN diminishes when a sequence pattern becomes more predictable over a longer timescale. This implies that MMN is comprised of multiple subcomponents, each responding to different levels of temporal regularities. To probe the hypothesized subcomponents in MMN, we record human electroencephalography during an auditory local-global oddball paradigm where the tone-to-tone transition probability (local regularity) and the overall sequence probability (global regularity) are manipulated to control temporal predictabilities at two hierarchical levels. We find that the size of MMN is correlated with both probabilities and the spatiotemporal structure of MMN can be decomposed into two distinct subcomponents. Both subcomponents appear as negative waveforms which peak early in the central-frontal area and late in a more frontal area, respectively. With a quantitative predictive coding model, we map the early and late subcomponents to the prediction errors that are tied to local and global regularities, respectively. Our study highlights the hierarchical complexity of MMN and offers an experimental and analytical platform for developing a multi-tiered neural marker, applicable in clinical settings.

neuroscience↗

bioRxiv2023

Crossmodal Hierarchical Predictive Coding for Audiovisual Sequences in Human Brain

Predictive-coding theory proposes that the brain actively predicts sensory inputs based on prior knowledge. While this theory has been extensively researched within individual sensory modalities, there is a crucial need for empirical evidence supporting hierarchical predictive processing across different modalities to further generalize the theory. Here, we examine how crossmodal knowledge is represented and learned in the brain by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) in humans during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at two hierarchical levels: stimulus-to-stimulus transition (local level) and multi-stimulus sequence structure (global level). With a model-fitting approach, we decompose the EEG data using three distinct predictive-coding models: one with no audiovisual integration, one with audiovisual integration at the global level, and one with audiovisual integration at both the local and global levels. The best-fitting model demonstrates that audiovisual integration occurs at both levels. This highlights a convergence of auditory and visual information to construct crossmodal predictions, even in the more basic interactions that occur between individual stimuli. Furthermore, we reveal the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and show that auditory and visual prediction-error signals are progressively redirected to the central-parietal area of the brain as learning progresses. Our findings unveil a crossmodal predictive coding mechanism, where the unimodal framework is implemented through more distributed brain networks to process hierarchical crossmodal knowledge.

neuroscience↗