Biology subjects

Wei, P.-H.

Publications and source records attributed to Wei, P.-H..

3 recordsLinked to original sources

bioRxiv2024

Du-IN-v2: Unleashing the Power of Vector Quantization for Decoding Cognitive States from Intracranial Neural Signals

While invasive brain-computer interfaces have shown promise for high-performance speech decoding under medical use, the potential of intracranial stereoElectroEn-cephaloGraphy (sEEG), which causes less damage to patients, remains underex-plored. With the rapid progress in representation learning, leveraging abundant pure recordings to further enhance speech decoding becomes increasingly attractive. However, some popular methods pre-train temporal models based on brain-level tokens, overlooking the brains desynchronization nature; others pre-train spatial-temporal models based on channel-level tokens, yet fail to evaluate them on more challenging tasks, e.g., speech decoding, which demands intricate processing in specific brain regions. To tackle these issues, we introduce a general pre-training framework for speech decoding - Du-IN-v2, which can extract contextual embeddings based on region-level tokens through discrete codex-guided mask modeling. To further push its limits, we propose Decoupling Product Quantization (DPQ), where different codexes are designed to extract different parts of brain dynamics. Our model achieves SOTA performance on both the 61-word classification task and the 49-syllable sequence classification task, surpassing all baselines. Model comparison and ablation studies reveal that our design choices, including (i) temporal modeling based on region-level tokens by utilizing 1D depthwise convolution to fuse channels in vSMC and STG regions and (ii) self-supervision by discrete decoupling codex-guided mask modeling, significantly contribute to these performances. Collectively, our approach, inspired by neuroscience findings, capitalizing on region-level representations from specific brain regions, is suitable for invasive brain modeling. It marks a promising neuro-inspired AI approach in BCI.

bioengineering↗

bioRxiv2023

Integration of recognition, episodic, and associative memories during complex human behavior

The ability to transiently remember what happened where and when is a cornerstone of cognitive function. Forming and recalling working memories depends on detecting novelty, building associations to prior knowledge, and dynamically retrieving context-relevant information. Previous studies have scrutinized the neural machinery for individual components of recognition or associative memory under laboratory conditions, such as recalling elements from arbitrary lists of words or pictures. In this study, we implemented a well-known card- matching game that integrates multiple components of memory formation together in a naturalistic setting to investigate the dynamic neural processes underlying complex natural human memory. We recorded intracranial field potentials from 1,750 depth or subdural electrodes implanted in 20 patients with pharmacologically-intractable epilepsy while they were performing the task. We leveraged generalized linear models to simultaneously assess the relative contribution of neural responses to distinct task components. Neural activity in the gamma frequency band signaled novelty and graded degrees of familiarity, represented the strength and outcome of associative recall, and finally reflected visual feedback on a trial-by-trial basis. We introduce an attractor-based neural network model that provides a plausible first-order approximation to capture the behavioral and neurophysiological observations. The large-scale data and models enable dissociating and at the same time dynamically tracing the different cognitive components during fast, complex, and natural human memory behaviors.

neuroscience↗

bioRxiv2022

Nuclear receptor interaction protein (NRIP) as a novel actin-binding protein involved in invadosome formation for myoblast fusion

To investigate the role of nuclear receptor interaction protein (NRIP) in myoblast fusion, both the primary myoblasts from muscle-specific NRIP-knockout mice and NRIP-null C2C12 cells (KO19 cells) exhibited a significant deficit in the fusion index during myogenesis; on the other hand, overexpressed NRIP in KO19 cells could rescue myotube formation. Furthermore, NRIP was found to interact with actin directly and reciprocally that is an invadosome component for myoblast fusion. Endogenous NRIP colocalized with components of invadosome such as F-actin, Tks5, and cortactin at the tips of cells during C2C12 differentiation, and exogenous NRIP was enriched with actin at the tip of attacking cells during myogenic fusion, implying that NRIP is a novel invadosome component. Using time-lapse microscopy and cell-cell fusion assays further confirmed NRIP directly participates in cell fusion through actin. Moreover, to map the domain of NRIP-actin binding, NRIP interacted with actin either through WD40 domains directly for binding or indirectly through the IQ domain for -actinin 2 binding with actin. NRIP with actin binding was strongly correlated with invadosome formation and myotube fusion. Collectively, NRIP acts as a novel actin-binding protein through its WD40 or the IQ to form invadosomes that trigger myoblast fusion.

cell biology↗