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Abdalmalak, A.

Publications and source records attributed to Abdalmalak, A..

2 recordsLinked to original sources

fNIRS Characterization of Temporal Potentiation During Optimized Nerve Stimulation

Restoring stable somatosensory feedback through transcutaneous nerve stimulation (TNS) represents a significant challenge in neural engineering; however, the cortical dynamics underlying sensory habituation are not yet well characterized. This study utilizes a multimethod functional near-infrared spectroscopy framework to quantify the temporal evolution of cortical engagement during optimized TNS. It was hypothesized that stimulation parameters fundamentally constrain the spatiotemporal profile of the hemodynamic response function. A General Linear Model with temporal and dispersion derivatives was used to analyze hemodynamic responses in 10 participants across three phases: validation, duration optimization (2, 5, or 10 seconds), and sustained engagement (45 minutes). During the optimization phase, 5-second stimulation produced the highest canonical amplitude with optimal dispersion characteristics, while 10-second trains resulted in significant temporal delays. During the sustained phase, parametric modeling indicated no linear decay in cortical response. Instead, the cortex demonstrated temporal potentiation, characterized by a transition from sparse early-phase activation to widespread late-phase recruitment across the sensorimotor network. The left posterior parietal cortex (BA5) served as a continuous integration hub, whereas primary somatosensory regions exhibited dynamic late-onset engagement. These findings quantify a nonlinear adaptation mechanism whereby engineered temporal spacing prevents neural saturation. The observed intersubject heterogeneity highlights the necessity for adaptive, closed-loop TNS controllers guided by real-time hemodynamic biomarkers.

neuroscience↗

Improved short-channel regression for mapping resting-state functional connectivity networks using functional near-infrared spectroscopy

Resting-state functional connectivity (rsFC) is an attractive biomarker of brain function that can vary with brain injury. The simplicity of resting-state protocols coupled with the main features of functional near-infrared spectroscopy (fNIRS), such as portability and versatility, can facilitate the monitoring of unresponsive patients in acute settings at the bedside. However, accurately mapping rsFC networks is challenging due to signal contamination from non-neural components, such as scalp hemodynamics and systemic physiology. Physiological noise may be mitigated through the use of short channels which may be able to provide sufficient information to eliminate the need for additional measurement devices, decreasing the complexity of the experimental setup. To this end, we examined the extent to which systemic physiology is embedded in the short-channel data and improved short-channel regression to account for temporal heterogeneity in the scalp hemodynamics. Our findings indicate that using temporal shifts in the short-channel data increases the agreement, by 70% on average, between short-channel regression and regression that includes short channels and physiological recordings. Overall, this method decreases the need for additional physiological recordings when mapping rsFC networks, providing a viable alternative when such measurements are not available or feasible.

neuroscience↗