Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS

This research uses fNIRS to explore whether brain activity can be measured during four common yoga asanas, aiming to understand yoga's neurological impact on the prefrontal cortex. This research addresses the gap in understanding how yoga asana affects brain activity by utilizing mobile neural imaging to measure brain function during movement, a previously limited area of study. fNIRS offers a key advantage by enabling brain activity measurement during movement and in natural settings unlike fMRI, EEG, which are limited to static, controlled environments.

Our lab will develop fNIRS methodologies to measure cognitive workload and treatment outcomes across diverse real-world contexts, including dyslexia therapy, yoga-based depression interventions, medical training, and manufacturing assembly tasks.

[Narrator] To begin, use the portable fNIRS device to collect data. Position the light sources and detectors into the cap holders according to the bilateral prefrontal cortex layout provided by the manufacturer. Maintain a source detector separation of 3.0 centimeters for optimal cortical signal detection. Include two short separation channels spaced 1.0 centimeter apart to isolate cortical hemodynamic responses by regressing out superficial signals like scalp blood flow. Next, select four foundational Hatha yoga postures to be used in the study, which form the core of Surya Namaskar, as they are widely practiced and allow assessment of inversions and back extensions. Design the experimental presentation in PowerPoint format with a visual and auditory guide for participants. Ensure each slide displays a photo and the name of one posture in Spanish with its Sanskrit name in parentheses. Cycle through postures B, C and D every 30 seconds in random order during the presentation, while simultaneously calling out the posture names aloud. Randomly intersperse the baseline posture between active poses to balance the sequence and reduce practice or habitation effects. For data management and consistency, standardize all visual materials by adjusting image resolution and text style and label each with both Spanish and Sanskrit posture names. Ensure each participant can clearly view the presentation screen before starting. Then introduce the active and baseline postures, describing their visual characteristics and alignment. Prepare the experimental space by placing a yoga mat, wall-mounted rope system and a visible presentation screen in front of the participant. Confirm the screen is readable without blocking movement. Now calibrate the fNIRS system by checking battery, functionality, and software compatibility. Position the optodes on the participant's scalp according to the international 10-20 system and adjust for head shape and hair density. Connect the fNIRS cap to the acquisition software using Bluetooth to monitor real-time signal acquisition during both pre and post resting states and throughout the yoga asana practice. Use the system interface to verify signal quality parameters, such as signal-to-noise ratio and scalp coupling index. With the participant seated, confirm their feet are flat, hands on their lap and back supported. Ask if they are comfortable before beginning. Next, secure the optode cap with a chin strap, and use a bamboo stick to part hair for direct contact with scalp. With the cap secured and the participant seated, instruct them to relax with their eyes closed to begin the pre-asana resting state measurement for six minutes. Collect fNIRS data at 25 hertz during this time. Use the acquisition software to assess signal quality through DAQ module. To ensure minimal movement, instruct participants not to move their eyebrows or touch their face and to avoid falling asleep during the resting state. Afterward, ask participants if they accidentally fell asleep to verify data accuracy. Once the first resting state is complete, remove the chair. Have participants stand while confirming the signal remains stable. Instruct them to follow the pre-programmed presentation, which progresses automatically with posture cues. Provide verbal instructions as the presentation runs. Manually record posture markers labeled A, B, C, and D using the event annotation feature in the acquisition software to timestamp each transition for data analysis. After the active session, review that all posture markers were correctly matched with the corresponding slide transitions to ensure accurate data labeling. With the cap still secured and signal quality confirmed, instruct participants to sit again. Then begin the post-asana resting state measurement with eyes closed for six minutes. Wait two to five minutes post-asana to ensure system calibration and clear signals before beginning the post-resting measurement. Remind participants to avoid movement and remain awake as in the earlier rest period, then begin the post resting measurement. The preliminary analysis of active channels revealed significant differences between control posture A and the postures B and C. Posture B elicited significantly greater oxygenated hemoglobin responses relative to baseline posture A across all measured channels. Posture C also resulted in significantly elevated HBO responses relative to baseline posture A. Significant increases in prefrontal cortex activity were observed during all three active yoga postures compared to the baseline posture, with activity particularly pronounced in the right hemisphere. Posture B induced the largest neural activation in the right inferior and medial prefrontal cortex. Posture C resulted in bilateral prefrontal cortex activation. Posture D exhibited only one significant channel with a moderate increase in oxygenated hemoglobin concentration, suggesting the least impact on prefrontal cortex activity among all three postures. Resting-State Functional Connectivity analysis showed a decrease in connectivity mainly in the left medial prefrontal cortex after yoga asana practice, which is a region associated with the default mode network.