Prefrontal Cortical Activation While Doing Mindfulness Task: a Pilot Functional Near-Infrared Spectroscopy Study

Neural correlates of fine motor grasping skills: Longitudinal insights into motor cortex activation using fNIRS

BACKGROUND

Motor learning is essential for performing specific tasks and progresses through distinct stages, including the rapid learning phase (initial skill acquisition), the consolidation phase (skill refinement), and the stable performance phase (skill mastery and maintenance). Understanding the cortical activation dynamics during these stages can guide targeted rehabilitation interventions.

METHODS

In this longitudinal randomized controlled trial, functional near-infrared spectroscopy was used to explore the temporal dynamics of cortical activation in hand-related motor learning. Thirty-one healthy right-handed individuals were randomly assigned to perform either easy or intricate motor tasks with their non-dominant hand over 10 days. We conducted 10 monitoring sessions to track cortical activation in the right hemisphere (according to lateralization principles, the primary hemisphere for motor control) and evaluated motor proficiency concurrently.

RESULTS

The study delineated three stages of nondominant hand motor learning: rapid learning (days 1 and 2), consolidation (days 3-7), and stable performance (days 8-10). There was a power-law enhancement of motor skills correlated with learning progression. Sustained activation was observed in the supplementary motor area (SMA) and parietal lobe (PL), whereas activation in the right primary motor cortex (M1R) and dorsolateral prefrontal cortex (PFCR) decreased. These cortical activation patterns exhibited a high correlation with the augmentation of motor proficiency.

CONCLUSIONS

The findings suggest that early rehabilitation interventions, such as transcranial magnetic stimulation and transcranial direct current stimulation (tDCS), could be optimally directed at M1 and PFC in the initial stages. In contrast, SMA and PL can be targeted throughout the motor learning process. This research illuminates the path for developing tailored motor rehabilitation interventions based on specific stages of motor learning.

NEW AND NOTEWORTHY

In an innovative approach, our study uniquely combines a longitudinal design with the robustness of generalized estimating equations (GEEs). With the synergy of functional near-infrared spectroscopy (fNIRS) and the Minnesota Manual Dexterity Test (MMDT) paradigm, we precisely trace the evolution of neural resources during complex, real-world fine-motor task learning. Centering on right-handed participants using their nondominant hand magnifies the intricacies of right hemisphere spatial motor processing. We unravel the brain's dynamic response throughout motor learning stages and its potent link to motor skill enhancement. Significantly, our data point toward the early-phase rehabilitation potential of TMS and transcranial direct current stimulation on the M1 and PFC regions. Concurrently, SMA and PL appear poised to benefit from ongoing interventions during the entire learning curve. Our findings carve a path for refined motor rehabilitation strategies, underscoring the importance of timely noninvasive brain stimulation treatments.

An Energy-Efficient Wearable Functional Near-infrared Spectroscopy System Employing Dual-level Adaptive Sampling Technique

Functional near-infrared spectroscopy (fNIRS) is a powerful medical imaging tool in brain science and psychology, it can also be employed in brain-computer interface (BCI) due to its noninvasive and artifact-less-sensitive characteristics. Conventional ways to detect large-area brain activity using near-infrared (NIR) technology are based on Time-division or Frequency-division modulation technique, which traverses all physical sensory channels in a specific period. To achieve higher imaging resolution or brain-tasks classification accuracy, the NIRS system require higher density and more channels, which conflict with the limited battery capacity. Inspired by the functional atlas of the human brain, this paper proposes a spatial adaptive sampling (SAS) method. It can change the active channel pattern of the fNIRS system to match with the real-time brain activity, to increase the energy efficiency without significant reduction on the brain imaging quality or the accuracy of brain activity classification. Therefore, the number of the averaging enabled channels will be dramatically reduced in practice. To verify the proposed SAS technique, a wearable and flexible NIRS system has been implemented, in which each channel of light-emitting diode (LED) drive circuits and photodiode (PD) detection circuits can be power gated independently. Brain task experiments have been conducted to validate the proposed method, the power consumption of the LED drive module is reduced by 46.58% compared to that without SAS technology while maintaining an average brain imaging PSNR (Peak Signal to Noise Ratio) of 35 dB. The brain-task classification accuracy is 80.47%, which has a 2.67% reduction compared to that without the SAS technique.

Integrative psycho-biophysiological markers in predicting psychological resilience

The hypothalamic-pituitary-adrenal (HPA) and parasympathetic nervous systems have been reported to play important roles in emotion regulation and stress coping. Yet, their direct relationship with psychological resilience remains unclear. These biophysiological features should be considered together with the traditional psychometric properties in studying resilience more comprehensively. The current study aimed to examine the role of these systems during a laboratory stress task and to determine the prediction power of resilience by combining psychological and biophysiological features. One hundred and seven (52 females) university students without psychiatric disorders underwent the Trier Social Stress Task (TSST). Psychometric properties of resilience were measured at rest; vagal heart rate variability (HRV), salivary cortisol, and dehydroepiandrosterone (DHEA) levels were captured at baseline, during, and after TSST. Multivariate linear regression as well as support vector regression machine-learning analyses were performed to investigate significant predictors and the prediction power of resilience. Results showed that positive and negative affects, HRV during the anticipatory phase of stress, and the ratio of cortisol/DHEA at the first recovery time point were significant predictors of resilience. The addition of biophysiological features increased the prediction power of resilience by 1.2-fold compared to psychological features alone. Results from machine learning analyses further demonstrated that the increased prediction power of resilience by adding the ratio of cortisol/DHEA was significant in "cortisol responders"; whereas a trend level was observed in "cortisol non-responders". Our findings extend the knowledge from the literature that high vagal activity during the anticipating phase of stress and the ability to restore the balance between cortisol and DHEA after a stress event could be an important feature in predicting resilience. Our findings also further support the need of combining psychological and biophysiological features in studying/predicting resilience.