Determination of the sleep-wake pattern and feasibility of NREM/REM discrimination using the non-invasive piezoelectric system in rats.

The piezoelectric cage-floor sensors have been used to successfully dissect sleep patterns in mice based on signal features related to respiration and body movements. We studied performance of the piezoelectric system to quantify the sleep-wake pattern in the rat over 7 days of recording compared with a visual electroencephalogram/electromyogram scoring, and under two light/dark (LD12:12 and LD16:8) photoperiods leading to change in the 24-hr sleep characteristics (N = 7 per group). The total sleep time (%/24 hr) over the 7 days recording and hourly sleep time over the last 24-hr recording were not statistically different between methods under the two photoperiods. Both methods detected higher total sleep time with the LD16:8 photoperiod compared with LD12:12 (p < .05), and correlated significantly (p < .001) at light and dark periods during each photoperiod. The accuracies for discrimination of sleep-wake patterns between methods were 81.9% and 84.9% for LD12:12 and LD16:8, respectively. In addition, spectral analysis of the respiratory signal given by piezo during all 10-s periods of the corresponding non-rapid eye movement and rapid eye movement sleep periods recorded by electroencephalogram/electromyogram resulted in selection of 36 features that could be inserted in an automated non-rapid eye movement sleep and rapid eye movement sleep classification, with 90% accuracy with the electroencephalogram/electromyogram visual scoring. The piezo system proved to be a reliable non-invasive alternative to electroencephalogram recording to study total sleep time in rat, with feasibility to discriminate between non-rapid eye movement and rapid eye movement sleep stages. This will be interesting in pharmacological or bio-behavioural studies evaluating sleep patterns or the restorative functions of sleep in the body and the brain.

Low-intensity focused ultrasound to the human insular cortex differentially modulates the heartbeat-evoked potential: a proof-of-concept study.

Background: The heartbeat evoked potential (HEP) is a brain response time-locked to the heartbeat and a potential marker of interoceptive processing. The insula and dorsal anterior cingulate cortex (dACC) are brain regions that may be involved in generating the HEP. Low-intensity focused ultrasound (LIFU) is a non-invasive neuromodulation technique that can selectively target sub-regions of the insula and dACC to better understand their contributions to the HEP. Objective: Proof-of-concept study to determine whether LIFU modulation of the anterior insula (AI), posterior insula (PI), and dACC influences the HEP. Methods: In a within-subject, repeated-measures design, healthy human participants (n=16) received 10 minutes of stereotaxically targeted LIFU to the AI, PI, dACC or Sham at rest during continuous electroencephalography (EEG) and electrocardiography (ECG) recording on separate days. Primary outcome was change in HEP amplitudes. Relationships between LIFU pressure and HEP changes were examined using linear mixed modelling. Peripheral indices of visceromotor output including heart rate and heart rate variability (HRV) were explored between conditions. Results: Relative to sham, LIFU to the PI, but not AI or dACC, decreased HEP amplitudes; this was partially explained by increased LIFU pressure. LIFU did not affect time or frequency dependent measures of HRV. Conclusions: These results demonstrate the ability to modulate HEP amplitudes via non-invasive targeting of key interoceptive brain regions. Our findings have implications for the causal role of these areas in bottom-up heart-brain communication that could guide future work investigating the HEP as a marker of interoceptive processing in healthy and clinical populations.

Heartbeat-evoked neural response abnormalities in generalized anxiety disorder during peripheral adrenergic stimulation.

Hyperarousal symptoms in generalized anxiety disorder (GAD) are often incongruent with the observed physiological state, suggesting that abnormal processing of interoceptive signals is a characteristic feature of the disorder. To examine the neural mechanisms underlying interoceptive dysfunction in GAD, we evaluated whether adrenergic modulation of cardiovascular signaling differentially affects the heartbeat-evoked potential (HEP), an electrophysiological marker of cardiac interoception, during concurrent electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) scanning. Intravenous infusions of the peripheral adrenergic agonist isoproterenol (0.5 and 2.0 micrograms, µg) were administered in a randomized, double-blinded and placebo-controlled fashion to dynamically perturb the cardiovascular system while recording the associated EEG-fMRI responses. During the 0.5 µg isoproterenol infusion, the GAD group (n = 24) exhibited significantly larger changes in HEP amplitude in an opposite direction than the healthy comparison (HC) group (n = 24). In addition, the GAD group showed significantly larger absolute HEP amplitudes than the HC group during saline infusions, when cardiovascular tone did not increase. No significant group differences in HEP amplitude were identified during the 2.0 µg isoproterenol infusion. Using analyzable blood oxygenation level-dependent fMRI data from participants with concurrent EEG-fMRI data (21 GAD and 21 HC), we found that the aforementioned HEP effects were uncorrelated with fMRI signals in the insula, ventromedial prefrontal cortex, dorsal anterior cingulate cortex, amygdala, and somatosensory cortex, brain regions implicated in cardiac signal processing in prior fMRI studies. These findings provide additional evidence of dysfunctional cardiac interoception in GAD and identify neural processes at the electrophysiological level that may be independent from blood oxygen level-dependent responses during peripheral adrenergic stimulation.

Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications.

Interoception-the perception of internal bodily signals-has emerged as an area of interest due to its implications in emotion and the prevalence of dysfunctional interoceptive processes across psychopathological conditions. Despite the importance of interoception in cognitive neuroscience and psychiatry, its experimental manipulation remains technically challenging. This is due to the invasive nature of existing methods, the limitation of self-report and unimodal measures of interoception, and the absence of standardized approaches across disparate fields. This article integrates diverse research efforts from psychology, physiology, psychiatry, and engineering to address this oversight. Following a general introduction to the neurophysiology of interoception as hierarchical predictive processing, we review the existing paradigms for manipulating interoception (e.g., interoceptive modulation), their underlying mechanisms (e.g., interoceptive conditioning), and clinical applications (e.g., interoceptive exposure). We suggest a classification for interoceptive technologies and discuss their potential for diagnosing and treating mental health disorders. Despite promising results, considerable work is still needed to develop standardized, validated measures of interoceptive function across domains and before these technologies can translate safely and effectively to clinical settings.

Posture analysis in predicting fall-related injuries during French Navy Special Forces selection course using machine learning: a proof-of-concept study.

INTRODUCTION: Injuries induced by falls represent the main cause of failure in the French Navy Special Forces selection course. In the present study, we made the assumption that probing the posture might contribute to predicting the risk of fall-related injury at the individual level. METHODS: Before the start of the selection course, the postural signals of 99 male soldiers were recorded using static posturography while they were instructed to maintain balance with their eyes closed. The event to be predicted was a fall-related injury during the selection course that resulted in the definitive termination of participation. Following a machine learning methodology, we designed an artificial neural network model to predict the risk of fall-related injury from the descriptors of postural signal. RESULTS: The neural network model successfully predicted with 69.9% accuracy (95% CI 69.3-70.5) the occurrence of a fall-related injury event during the selection course from the selected descriptors of the posture. The area under the curve value was 0.731 (95% CI 0.725-0.738), the sensitivity was 56.8% (95% CI 55.2-58.4) and the specificity was 77.7% (95% CI 76.8-0.78.6). CONCLUSION: If confirmed with a larger sample, these findings suggest that probing the posture using static posturography and machine learning-based analysis might contribute to inform risk assessment of fall-related injury during military training, and could ultimately lead to the development of novel programmes for personalised injury prevention in military population.

Heartbeat-evoked neural response abnormalities in generalized anxiety disorder during peripheral adrenergic stimulation.

Hyperarousal symptoms in generalized anxiety disorder (GAD) are often incongruent with the observed physiological state, suggesting that abnormal processing of interoceptive signals is a characteristic feature of the disorder. To examine the neural mechanisms underlying interoceptive dysfunction in GAD, we evaluated whether adrenergic modulation of cardiovascular signaling differentially affects the heartbeat evoked potential (HEP), an electrophysiological marker of cardiac interoception, during concurrent electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) scanning. Intravenous infusions of the peripheral adrenergic agonist isoproterenol (0.5 and 2.0 micrograms, µg) were administered in a randomized, double-blinded and placebo-controlled fashion to dynamically perturb the cardiovascular system while recording the associated EEG-fMRI responses. During the 0.5 µg isoproterenol infusion, the GAD group (n=24) exhibited significantly larger changes in HEP amplitude in an opposite direction than the HC group (n=24). In addition, the GAD group showed significantly larger absolute HEP amplitudes than HC during saline infusions, when cardiovascular tone did not increase. No significant group differences in HEP amplitude were identified during the 2.0 µg isoproterenol infusion. Using analyzable blood oxygenation level dependent fMRI data from participants with concurrent EEG-fMRI data (21 GAD and 21 HC), we found that the aforementioned HEP effects were uncorrelated with fMRI signals in the insula, ventromedial prefrontal cortex, dorsal anterior cingulate cortex, amygdala, and somatosensory cortex, brain regions implicated in cardiac signal processing according to prior fMRI studies. These findings provide additional evidence of dysfunctional cardiac interoception in GAD and identify neural processes at the electrophysiological level that may be independent from blood oxygen level-dependent responses during peripheral adrenergic stimulation.

Position: A study protocol for the prevention of fall injuries in french special forces selection courses using a body-centered intervention.

INTRODUCTION: The Military Physical and Sports Training program was developed by the French Army in order to train, optimize, and maintain individual readiness. Although the health benefits of sport practice do not need to be demonstrated, such activities can cause acute musculoskeletal injuries that need to be addressed. The prevalence of lower limb injury is rather high in the French military population and, in particular, ranges from 15 to 45% during Special Forces selection courses. Thus, this project aims to investigate the efficiency of a body-centered program designed to enhance body awareness. The program seeks to train the mind to actively pay attention to body information, while the latter is viewed as a protective factor against fall injuries. We assume: (i) that postural control can be improved by enhancing the level of body awareness; and (ii) that greater postural awareness could be beneficial in reducing the risk of fall injuries. The body-centered prevention program is based on the Optimization of the Resources of the Armed Forces (ORAF) intervention, which focuses on mental preparation and recovery, and has been deployed in the French Army for many years. METHOD AND ANALYSES: The study focuses on five French Special Forces selection courses (400 soldiers/ participants). It is divided into two stages (year 1, year 2). The first year is dedicated to data collection from the control group (200 participants), while in the second year the ORAF intervention will be deployed. In both year, participants will be subjected to the same enrollment schedule (Fig 3). The main objective is to evaluate the effectiveness of the ORAF intervention in reducing the rate of fall injuries during military selection, based on a multidisciplinary method that captures demographic, biological, biometric, clinical, and para-clinical measures. TRIAL REGISTRATION: Registration number: IDRCB number 2021-A02108-33, Clinical Trial: NCT05451394.

Environment and body-brain interplay affect inhibition and decision-making.

The fine-tuned interplay of brain and body underlies human ability to cope with changes in the internal and external milieus. Previous research showed that cardiac interoceptive changes (e.g., cardiac phase) affect cognitive functions, notably inhibition that is a key element for adaptive behaviour. Here we investigated the influence on cognition of vestibular signal, which provides the brain with sensory information about body position and movement. We used a centrifuge-based design to disrupt vestibular signal in healthy human volunteers while their inhibition and decision-making functions were assessed with the stop-signal paradigm. Participants performed the standard and a novel, sensorial version of the stop-signal task to determine whether disrupted vestibular signal influences cognition as a function of its relevance to the context. First, we showed that disrupted vestibular signal was associated with a larger variability of longest inhibition latencies, meaning that participants were even slower to inhibit in the trials where they had the most difficulty inhibiting. Second, we revealed that processing of bodily information, as required in the sensorial stop-signal task, also led to a larger variability of longest inhibition latencies, which was all the more important when vestibular signal was disrupted. Lastly, we found that such a degraded response inhibition performance was due in part to the acceleration of decision-making process, meaning that participants made a decision more quickly even when strength of sensory evidence was reduced. Taken together, these novel findings provide direct evidence that vestibular signal affects the cognitive functions of inhibition and decision-making.

Probing the posture with machine learning provides physiological evidence supporting the enhanced body awareness hypothesis in trait mindfulness.

Enhanced body awareness has been suggested as one of the cognitive mechanisms that characterize mindfulness. Yet neuroscience literature still lacks strong empirical evidence to support this claim. Body awareness contributes to postural control during quiet standing; in particular, it may be argued that body awareness is more strongly engaged when standing quietly with eyes closed, because only body cues are available, than with eyes open. Under these theoretical assumptions, we recorded the postural signals of 156 healthy participants during quiet standing in Eyes closed (EC) and Eyes open (EO) conditions. In addition, each participant completed the Freiburg Mindfulness Inventory, and his/her mindfulness score was computed. Following a well-established machine learning methodology, we designed two numerical models per condition: one regression model intended to estimate the mindfulness score of each participant from his/her postural signals, and one classifier intended to assign each participant to one of the classes "Mindful" or "Non-mindful." We show that the two models designed from EC data are much more successful in their regression and classification tasks than the two models designed from EO data. We argue that these findings provide the first physiological evidence that contributes to support the enhanced body awareness hypothesis in mindfulness.