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1. Trial‐to‐Trial Variability in Electrodermal Activity to Odor in Autism

   https://doi.org/10.1002/aur.2377  

   Haigh, Sarah M. ; Endevelt‐Shapira, Yaara ; Behrmann, Marlene ( August 2020 , Autism Research)

   Abnormal trial‐to‐trial variability (TTV) has been identified as a key feature of neural processing that is related to increased symptom severity in autism. The majority of studies evaluating TTV have focused on cortical processing. However, identifying whether similar atypicalities are evident in the peripheral nervous system will help isolate perturbed mechanisms in autism. The current study focuses on TTV in responses from the peripheral nervous system, specifically from electrodermal activity (EDA). We analyzed previously collected EDA data from 17 adults with autism and 19 neurotypical controls who viewed faces while being simultaneously exposed to fear (fear‐induced sweat) and neutral odors. Average EDA peaks were significantly smaller and TTV was reduced in the autism group compared to controls, particularly during the fear odor condition. Amplitude and TTV were positively correlated in both groups, but the relationship was stronger in the control group. In addition, TTV was reduced in those with higher Autism Quotient scores but only for the individuals with autism. These findings confirm the existing results that atypical TTV is a key feature of autism and that it reflects symptom severity, although the smaller TTV in EDA contrasts with the previous findings of greater TTV in cortical responses. Identifying the relationship between cortical and peripheral TTV in autism is key for furthering our understanding of autism physiology.

   We compared the changes in electrodermal activity (EDA) to emotional faces over the course of repeated faces in adults with autism and their matched controls. The faces were accompanied by smelling fear‐inducing odors. We found smaller and less variable responses to the faces in autism when smelling fear odors, suggesting that the peripheral nervous system may be more rigid. These findings were exaggerated in those who had more severe autism‐related symptoms.

    

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2. Neural Correlates of Audiovisual Temporal Binding Window in Individuals With Schizotypal and Autistic Traits: Evidence From Resting‐State Functional Connectivity

   https://doi.org/10.1002/aur.2456  

   Zhou, Han‐yu ; Wang, Yong‐ming ; Zhang, Rui‐ting ; Cheung, Eric F. C. ; Pantelis, Christos ; Chan, Raymond C. K. ( December 2020 , Autism Research)

   Temporal proximity is an important clue for multisensory integration. Previous evidence indicates that individuals with autism and schizophrenia are more likely to integrate multisensory inputs over a longer temporal binding window (TBW). However, whether such deficits in audiovisual temporal integration extend to subclinical populations with high schizotypal and autistic traits are unclear. Using audiovisual simultaneity judgment (SJ) tasks for nonspeech and speech stimuli, our results suggested that the width of the audiovisual TBW was not significantly correlated with self‐reported schizotypal and autistic traits in a group of young adults. Functional magnetic resonance imaging (fMRI) resting‐state activity was also acquired to explore the neural correlates underlying inter‐individual variability of TBW width. Across the entire sample, stronger resting‐state functional connectivity (rsFC) between the left superior temporal cortex and the left precuneus, and weaker rsFC between the left cerebellum and the right dorsal lateral prefrontal cortex were correlated with a narrower TBW for speech stimuli. Meanwhile, stronger rsFC between the left anterior superior temporal gyrus and the right inferior temporal gyrus was correlated with a wider audiovisual TBW for non‐speech stimuli. The TBW‐related rsFC was not affected by levels of subclinical traits. In conclusion, this study indicates that audiovisual temporal processing may not be affected by autistic and schizotypal traits and rsFC between brain regions responding to multisensory information and timing may account for the inter‐individual difference in TBW width.

   Individuals with ASD and schizophrenia are more likely to perceive asynchronous auditory and visual events as occurring simultaneously even if they are well separated in time. We investigated whether similar difficulties in audiovisual temporal processing were present in subclinical populations with high autistic and schizotypal traits. We found that the ability to detect audiovisual asynchrony was not affected by different levels of autistic and schizotypal traits. We also found that connectivity of some brain regions engaging in multisensory and timing tasks might explain an individual's tendency to bind multisensory information within a wide or narrow time window.Autism Res2021, 14: 668–680. © 2020 International Society for Autism Research and Wiley Periodicals LLC

    

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3. Coupling analysis of heart rate variability and cortical arousal using a deep learning algorithm

   https://doi.org/10.1371/journal.pone.0284167  

   Huo, Jiayan ; Quan, Stuart F. ; Roveda, Janet ; Li, Ao ( April 2023 , PLOS ONE)

   Veauthier, Christian (Ed.)

   Frequent cortical arousal is associated with cardiovascular dysfunction among people with sleep-disordered breathing. Changes in heart rate variability (HRV) can represent pathological conditions associated with autonomic nervous system dysfunction. Previous studies showed changes in cardiac activity due to cortical arousals. However, few studies have examined the instantaneous association between cortical arousal and HRV in an ethnically diverse population. In this study, we included 1,069 subjects’ full night ECG signals from unattended polysomnography in the Multi-Ethnic Study of Atherosclerosis dataset. An automated deep learning tool was employed to annotate arousal events from ECG signals. The etiology (e.g., respiratory, or spontaneous) of each arousal event was classified through a temporal analysis. Time domain HRVs and mean heart rate were calculated on pre-, intra-, and post-arousal segments of a 25-s period for each arousal event. We observed that heart rate and HRVs increased during the arousal onsets in the intra-arousal segments, regardless of arousal etiology. Furthermore, HRVs response to cortical arousal occurrence differed according to gender and the sleep stages in which arousal occurred. The more intense HRVs variation due to arousal in females can contribute to a potentially stronger association between arousal burden and long-term mortality. The excessive abrupt sympathetic tone elevation in REM caused by arousal may provide insights on the association between sleep and sudden cardiac death. 

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4. Maternal Social Loneliness Detection Using Passive Sensing Through Continuous Monitoring in Everyday Settings: Longitudinal Study

   https://doi.org/10.2196/47950  

   Sarhaddi, Fatemeh ; Azimi, Iman ; Niela-Vilen, Hannakaisa ; Axelin, Anna ; Liljeberg, Pasi ; Rahmani, Amir M ( January 2023 , JMIR Formative Research)

   Maternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness noninvasively through wearable devices and passive sensing provides opportunities to prevent or reduce the impact of loneliness on the health and well-being of the mother and her child.

   The aim of this study is to use objective health data collected passively by a wearable device to predict maternal (social) loneliness during pregnancy and the postpartum period and identify the important objective physiological parameters in loneliness detection.

   We conducted a longitudinal study using smartwatches to continuously collect physiological data from 31 women during pregnancy and the postpartum period. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire in gestational week 36 and again at 12 weeks post partum. Responses to this questionnaire and background information of the participants were collected through our customized cross-platform mobile app. We leveraged participants’ smartwatch data from the 7 days before and the day of their completion of the UCLA questionnaire for loneliness prediction. We categorized the loneliness scores from the UCLA questionnaire as loneliness (scores≥12) and nonloneliness (scores<12). We developed decision tree and gradient-boosting models to predict loneliness. We evaluated the models by using leave-one-participant-out cross-validation. Moreover, we discussed the importance of extracted health parameters in our models for loneliness prediction.

   The gradient boosting and decision tree models predicted maternal social loneliness with weighted F1-scores of 0.897 and 0.872, respectively. Our results also show that loneliness is highly associated with activity intensity and activity distribution during the day. In addition, resting heart rate (HR) and resting HR variability (HRV) were correlated with loneliness.

   Our results show the potential benefit and feasibility of using passive sensing with a smartwatch to predict maternal loneliness. Our developed machine learning models achieved a high F1-score for loneliness prediction. We also show that intensity of activity, activity pattern, and resting HR and HRV are good predictors of loneliness. These results indicate the intervention opportunities made available by wearable devices and predictive models to improve maternal well-being through early detection of loneliness.

    

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5. Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study

   https://doi.org/10.3389/fneur.2022.858333  

   Chen, Zhe Sage ; Hsieh, Aaron ; Sun, Guanghao ; Bergey, Gregory K. ; Berkovic, Samuel F. ; Perucca, Piero ; D'Souza, Wendyl ; Elder, Christopher J. ; Farooque, Pue ; Johnson, Emily L. ; et al ( March 2022 , Frontiers in Neurology)

   Objective Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls. Methods This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve. Results The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73–0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction. Conclusions Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies. 

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