Respiration

The brain processes information from the external environment alongside signals generated by the body. Among bodily rhythms, respiration emerges as a key modulator of sensory processing. Multisensory integration, the non-linear combination of information from multiple senses to reduce environmental uncertainty, may be influenced by respiratory dynamics. This study investigated how respiration modulates reaction times and multisensory integration in a simple detection task. Forty healthy participants were presented with unimodal (Auditory, Visual, Tactile) and bimodal (Audio-Tactile, Audio-Visual, Visuo-Tactile) stimuli while their respiratory activity was recorded. Results revealed that reaction times systematically varied with respiration, with faster responses during peak inspiration and early expiration but slower responses during the expiration-to-inspiration transition. Applying the race model inequality approach to quantify multisensory integration, we found that Audio-Tactile and Audio-Visual stimuli exhibited the highest integration during the expiration-to-inspiration phase. These findings conceivably reflect respiration phase-locked changes in cortical excitability which in turn orchestrates multisensory integration. Interestingly, participants also tended to adapt their respiratory cycles, aligning response onsets preferentially with early expiration. This suggests that, rather than a mere bottom-up mechanism, respiration is actively adjusted to maximize the signal-to-noise balance between interoceptive and exteroceptive signals.

Cerebrospinal fluid dynamics play a crucial role in maintaining brain homeostasis by delivering nutrients, transmitting immune signals, and clearing waste products. While cardiac activity primarily drives the pulsatile movement of cerebrospinal fluid, respiration has been shown to facilitate low-frequency oscillations and contribute to bulk flow. Recent studies suggest that enhancing respiratory function may be an effective intervention to modulate cerebrospinal fluid dynamics. This study included 20 individuals with long-term formal training in Seokmun Hoheup, a lower belly-centered breathing practice (mean ± SD age, 58.1 ± 17.3 years; 8 females), and 25 controls with no formal long-term breathing practice (mean ± SD age, 49.2 ± 20.2 years; 12 females). All underwent real-time velocity-encoding magnetic resonance imaging to assess cerebrospinal fluid movement at the foramen magnum and lateral ventricle during both regular breathing and deep breathing. Deep breathing enhances cerebrospinal fluid dynamics in both groups, increasing displacement and net flow, particularly at the foramen magnum. Seokmun Hoheup trained participants show greater cerebrospinal fluid movement than controls at both the foramen magnum and lateral ventricle. Even during regular breathing, trained participants show higher cerebrospinal fluid mean speed, displacement, and net flow. Among respiratory factors, inhale length and diaphragm displacement show the strongest correlations with cerebrospinal fluid movement. Respiration modulated cerebrospinal fluid dynamics through both mechanical enhancement of venous outflow and autonomic modulation of the heart, with mechanical effects predominating in the lateral ventricle and both pathways contributing to the foramen magnum. Our findings identify respiration in the awake state as a modifiable, noninvasive mechanism that influences involuntary functions such as cerebrospinal fluid dynamics and may have implications for cerebrospinal fluid-mediated brain homeostasis.

Neuronal oscillations are tightly linked to physiological rhythms, yet little is known about how sensory stimuli influence their coupling. Here, we investigated whether pleasant odors and pleasant music modulate the coupling between respiration, autonomic activity, and brain oscillations. Using a within-subject design, we exposed participants to a personally pleasant odor and a personally pleasant music while recording respiratory and cardiac activity, feeling of relaxation, and respiration-locked EEG responses. Our results reveal that only odors induced a significant coupling between physiological and neural rhythms. Pleasant odors decreased respiratory rate, increased inspiratory volume, reduced heart rate, and enhanced heart rate variability (HRV). EEG analyses showed that this odor-induced respiratory slowing enhanced respiration-related brain activity in a temporo-parieto-central network, which is not observed with music. In contrast, music, regardless of tempo, increased subjective arousal without affecting autonomic or neural coupling. These findings suggest that pleasant odors engage an olfactomotor response, where an emotionally driven change in breathing rhythm enhances respiration-related neural oscillations. This mechanism may facilitate brain states associated with an increased feeling of relaxation and interoception, distinguishing odors as a unique sensory modality capable of promoting brain-body coupling. Our study highlights the potential of olfactory stimulation as a non-invasive tool for modulating brain rhythms and autonomic function.

In medical emergency scenarios, conventional single-parameter monitoring cannot fully assess patient conditions, and current technologies lack intelligent emergency-state recognition, which delays timely treatment. To overcome these challenges, this study develops a high-performance flexible thermoelectric textile-based wearable system that generates a 139.7 mV open-circuit voltage at ΔT = 30 K. To prevent skin-thermoelectric contact from distorting sensor readings, a Kirigami spacer is integrated to ensure proper air gaps while maintaining flexibility and breathability. By integrating optical cardiac sensing, the system establishes a thermoelectric-optical system for wireless real-time cooperative monitoring of both respiration and cardiac activity. The research systematically investigates the effects of airflow velocity on thermoelectric output, leading to the development of an innovative dynamic airflow-thermoelectric response theory. This theory enables speech recognition through airflow variations with 98% accuracy. Additionally, the study creates a multi-source fusion recognition model that combines respiratory patterns, speech-airflow, and cardiac signals to identify emergency states with 98% accuracy. These advances supplement the theoretical understanding of thermoelectric responses to physiological activities while providing decision support for emergency conditions, demonstrating considerable potential for clinical application.