arXiv subjects

Mingzhe Jiang

Publications and source records attributed to Mingzhe Jiang.

3 recordsLinked to original sources

arXiv2026

A pilot study examining transcranial photobiomodulation therapy intervention in college students with insomnia

College students commonly report insufficient sleep and poor sleep quality, with ~30% meeting insomnia criteria, posing significant threats to their physical growth, cognitive development, and overall well-being, as well as imposing a substantial economic burden on society [1]. The hyperarousal model of insomnia [2] emphasizes that hyperarousal across cognitive, emotional, and physiological domains mutually reinforces one another. Neuroimaging studies have further identified prefrontal hypoactivity as a key neural substrate underlying these dysfunctional cognitions and elevated arousal, reflecting a failure of top-down modulatory control over both limbic reactivity [3] and brainstem arousal nuclei [4]. Moreover, transcranial photobiomodulation (tPBM) therapy targeting the prefrontal cortex has demonstrated therapeutic efficacy across neuropsychiatric disorders with insomnia comorbidities [5,6], providing preliminary support for its application in insomnia. However, the neuro mechanisms underlying tPBM's therapeutic effects on insomnia remain to be elucidated.

q-bio.NC↗

arXiv2025

EEG Dynamic Microstate Patterns Induced by Pulsed Wave Transcranial Photobiomodulation Therapy

Transcranial photobiomodulation (tPBM) therapy is an emerging, non-invasive neuromodulation technique that has demonstrated considerable potential in the field of neuropsychiatric disorders. Several studies have found that pulsed wave (PW) tPBM therapy yields superior biomodulatory effects. However, its neural mechanisms are still unknown which poses a significant barrier to the development of an optimized protocol. A randomized, single-blind study including 29 participants was conducted using a crossover design, with sham and continuous wave (CW) groups as controls. The EEG microstate analysis was utilized to explore the relative variations in temporal parameters and brain functional connectivity. To further elucidate the dynamic activity patterns of microstates, a 10-repeat 10-fold cross-validation with nine machine learning algorithms and kernel Shapley additive explanations analysis was employed. Results indicated that the pulsed wave mode enhanced the global efficiency, local efficiency, and betweenness centrality of microstate C in brain functional networks as well as the mean durations parameter achieving a middle to large effect size, with superior effects compared to the sham and continuous wave groups. Furthermore, the support vector machine based on the radial basis function method with kernel Shapley additive explanations analysis demonstrated the best performance with an area under the curve (AUC) reaching 0.956, and found that the 8 of top-10 microstate features related to microstate C contributed most significantly to the PW mode. In conclusion, the EEG microstate analysis found that PW tPBM therapy modulates the microstate C-specific patterns in the human brain, suggesting that microstate dynamics may serve as a state-dependent biomarker for the optimization of tPBM protocol.

q-bio.NC↗

arXiv2018

An Analog Neural Network Computing Engine using CMOS-Compatible Charge-Trap-Transistor (CTT)

An analog neural network computing engine based on CMOS-compatible charge-trap transistor (CTT) is proposed in this paper. CTT devices are used as analog multipliers. Compared to digital multipliers, CTT-based analog multiplier shows significant area and power reduction. The proposed computing engine is composed of a scalable CTT multiplier array and energy efficient analog-digital interfaces. Through implementing the sequential analog fabric (SAF), the engine mixed-signal interfaces are simplified and hardware overhead remains constant regardless of the size of the array. A proof-of-concept 784 by 784 CTT computing engine is implemented using TSMC 28nm CMOS technology and occupied 0.68mm2. The simulated performance achieves 76.8 TOPS (8-bit) with 500 MHz clock frequency and consumes 14.8 mW. As an example, we utilize this computing engine to address a classic pattern recognition problem -- classifying handwritten digits on MNIST database and obtained a performance comparable to state-of-the-art fully connected neural networks using 8-bit fixed-point resolution.

cs.ET↗