Autistic youth exhibit wide variability in emotional and behavioral challenges, yet few studies have identified meaningful subgroups based on these profiles. This study applied a random forests ensemble clustering algorithm to item-level parent-report data from the Emotion Dysregulation Inventory (EDI) and the Child Behavior Checklist (CBCL) in a combined sample of 1311 autistic youth (ages 6-17), drawn from the Autism Inpatient Collection (n = 446) and the Interactive Autism Network (n = 865). Four distinct subgroups emerged: Global High (GH; 22%), characterized by elevated scores across all four subscales (EDI-Reactivity, EDI-Dysphoria, CBCL Internalizing, and CBCL Externalizing); High-Reactivity Dominant (H-RD; 41%), marked primarily by high emotional reactivity; Moderate-Internalizing Dominant (M-ID; 18%), with elevated internalizing scores; and Global Low (GL; 19%), showing uniformly low scores. Feature importance analyses identified EDI-Reactivity and CBCL Aggression items as the strongest drivers of subgroup membership. No significant differences were found across subgroups in age, sex, race, or ethnicity. However, subgroups with greater emotional and behavioral challenges were associated with lower household income, single-parent status, and higher rates of family psychiatric history. These findings suggest that emotional reactivity and aggression severity are key differentiating features among autistic youth, and that sociodemographic and family mental health factors meaningfully shape emotional and behavioral outcomes.

Nonsuicidal self-injury (NSSI) is the intentional destruction of one's own body tissue without suicidal intent and for purposes that are not socially or culturally accepted or practiced (e.g., intentional self-cutting, self-biting). Research on NSSI in autistic people is limited but increasing. NSSI is strongly associated with suicide, and it is an important behavior to better understand given the high rates of NSSI and suicide in autistic people. To date, research that focused on autistic people has mostly used self-report questionnaires to assess NSSI, with a more limited application of clinical interviews of NSSI. However, researchers and clinicians may find it challenging to determine whether a behavior is categorized as NSSI in autistic people, especially since autistic people may present with other behaviors that cause self-injury. We build upon the International Society for the Study of Self-Injury's key elements in defining NSSI to support better reliability of NSSI assessment across studies. We emphasize that when assessing for NSSI the behavior must meet these key elements: (1) not intended to cause death, (2) the physical self-injury/harm is intentional, (3) there is immediate physical injury following the behavior, (4) the physical injury is to the external body, not internal body, (5) the physical injury is self-imposed and not done by another being, and (6) it is not a part of social or cultural practices. It will be important for future work to develop measures that can accurately assess NSSI in autistic people and advance mechanistic and intervention research related to NSSI.

BACKGROUND:Self-injurious behaviors (SIB) are common in autistic people. SIB is mainly studied as a broad category, rather than by specific SIB types. We aimed to determine associations of distinct SIB types with common psychiatric, emotional, medical, and socio-demographic factors. METHODS:Participants included 323 autistic youth (~50% non-/minimally-speaking) with high-confidence autism diagnoses ages 4-21 years. Data were collected by the Autism Inpatient Collection during admission to a specialized psychiatric inpatient unit (www.sfari.org/resource/autism-inpatient-collection/). Caregivers completed questionnaires about their child, including SIB type and severity. The youth completed assessments with clinicians. Elastic net regressions identified associations between SIB types and factors. RESULTS:No single factor relates to all SIB types. SIB types have unique sets of associations. Consistent with previous work, more repetitive motor movements and lower adaptive skills are associated with most types of SIB; female sex is associated with hair/skin pulling and self-rubbing/scratching. More attention-deficit/hyperactivity disorder symptoms are associated with self-rubbing/scratching, skin picking, hair/skin pulling, and inserts finger/object. Inserts finger/object has the most medical condition associations. Self-hitting against surface/object has the most emotion dysregulation associations. CONCLUSIONS:Specific SIB types have unique sets of associations. Future work can develop clinical likelihood scores for specific SIB types in inpatient settings, which can be tested with large community samples. Current approaches for SIB focus on the behavior functions, but there is an opportunity to further develop interventions by considering the specific SIB type in assessment and treatment. Identifying factors associated with specific SIB types may aid with screening, prevention, and treatment of these often-impairing behaviors.

Emotion dysregulation (ED) is common and severe in older autistic youth, but is rarely the focus of early autism screening or intervention. Moreover, research characterizing ED in the preschool years (when autism is typically diagnosed) is limited. This study aimed to characterize ED in autistic children by examining (1) prevalence and severity of ED as compared to children without an autism diagnosis; and (2) correlates of ED in autistic children. A sample of 1864 parents (Mean child age = 4.21 years, SD = 1.16 years; 37% female) of 2-5 year-old children with (1) autism; (2) developmental concerns, but no autism; and (3) no developmental concerns or autism completed measures via an online questionnaire. ED was measured using the Emotion Dysregulation Inventory-Young Child, a parent report measure characterizing ED across two dimensions: Reactivity (fast, intense emotional reactions) and dysphoria (low positive affect, sadness, unease). Autistic preschoolers, compared to peers without developmental concerns, had more severe ED (+1.12 SD for reactivity; +0.60 SD for dysphoria) and were nearly four and three times more likely to have clinically significant reactivity and dysphoria, respectively. Autistic traits, sleep problems, speaking ability, and parent depression were the strongest correlates of ED in the autism sample. While more work is needed to establish the prevalence, severity, and correlates of ED in young autistic children, this study represents an important first step. Results highlight a critical need for more high-quality research in this area as well as the potential value of screening and intervention for ED in young autistic children.

Objective: Despite heightened rates of aggressive behaviors among older autistic youth relative to non-autistic peers, less is known about these behaviors during early childhood. This study included 3 objectives to address this gap: (1) to establish the prevalence and topography (frequency, severity, type, context) of aggressive behaviors in a large sample of preschool-aged children using a developmentally sensitive parent-report measure; (2) to identify clinical correlates of aggression; and (3) to investigate whether different subgroups of autistic children can be identified based on their profiles of aggression, emotional reactivity, and autism traits. Method: Data were analyzed from parents of 1,199 children 2 to 5 years of age (n = 622 autistic children) who completed the Multidimensional Assessment Profiles Scales (MAPS) aggression subscale and the Emotion Dysregulation Inventory-Young Child (EDI-YC) reactivity subscale. Results: Autistic preschoolers had 2 to 6 times higher odds of experiencing frequent aggression (more days than not) compared with non-autistic preschoolers. Hierarchical multiple regression analyses revealed that autism diagnosis, traits, and suspected and diagnosed attention-deficit/ hyperactivity disorder (ADHD) were positively associated with aggression; however, heightened emotional reactivity explained the greatest degree of added variance in aggression total scores. Machine learning clustering techniques revealed 3 distinct subgroups of autistic preschoolers, with cluster membership driven primarily by aggression and reactivity scores, and less so by autism traits. Conclusion: Autistic preschoolers display more frequent parent-reported aggressive behaviors, and emotional reactivity may play an important role in aggressive behavior presentation. Future developmental screening and early intervention tailoring for aggression may benefit from assessing reactivity early in development. Plain language summary: In this study of 1,199 children aged 2 to 5 years of age (n = 622 autistic children), parents completed validated questionnaires quantifying disruptive behaviors and emotion dysregulation to evaluate prevalence and topography of aggressive behaviors, identifying clinical correlates of aggression, and determining distinct subgroups of children with autism based on patterns of aggression, reactivity, and autism traits. The authors found that preschoolers with autism had higher rates of frequent aggressive behaviors compared to non-autistic children. Although more aggression was associated with having an autism diagnosis, greater levels of autism traits, and attention-deficit/hyperactivity disorder, high levels of aggression were most strongly associated with heightened emotional reactivity. Subgroups of preschoolers with autism emerged principally on their aggression and emotional reactivity scores, rather than on their levels of autistic traits. Diversity & Inclusion Statement: One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper self-identifies as living with a disability. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

Objective.Our aim is to enhance sensory perception and spatial presence in artificial interfaces guided by EEG. This is done by developing a closed-loop electro-tactile system guided by EEG that adaptively update the electrical stimulation parameters to achieve EEG responses similar to the EEG responses generated from touching textured surface.Approach.In this work, we introduce a model that defines the relationship between the contact force profiles and the electrical stimulation parameters. This is done by using the EEG and force data collected from two experiments. The first was conducted by moving a set of textured surfaces against the subjects' fingertip, while collecting both EEG and force data. Whereas the second was carried out by applying a set of different pulse and amplitude modulated electrical stimuli to the subjects' index finger while recording EEG.Main results.We were able to develop a model which could generate electrical stimulation parameters corresponding to different textured surfaces. We showed by offline testing and validation analysis that the average error between the EEG generated from the estimated electrical stimulation parameters and the actual EEG generated from touching textured surfaces is around 7%.Significance.Haptic feedback plays a vital role in our daily life, as it allows us to become aware of our environment. Even though a number of methods have been developed to measure perception of spatial presence and provide sensory feedback in virtual reality environments, there is currently no closed-loop control of sensory stimulation. The proposed model provides an initial step towards developing a closed loop electro-tactile haptic feedback model that delivers more realistic touch sensation through electrical stimulation.

Mindfulness has growing empirical support for improving emotion regulation in individuals with Autism Spectrum Disorder (ASD). Mindfulness is cultivated through meditation practices. Assessing the role of mindfulness in improving emotion regulation is challenging given the reliance on self-report tools. Electroencephalography (EEG) has successfully quantified neural responses to emotional arousal and meditation in other populations, making it ideal to objectively measure neural responses before and after mindfulness (MF) practice among individuals with ASD. We performed an EEG-based analysis during a resting state paradigm in 35 youth with ASD. Specifically, we developed a machine learning classifier and a feature and channel selection approach that separates resting states preceding (Pre-MF) and following (Post-MF) a mindfulness meditation exercise within participants. Across individuals, frontal and temporal channels were most informative. Total power in the beta band (16-30 Hz), Total power (4-30 Hz), relative power in alpha band (8-12 Hz) were the most informative EEG features. A classifier using a non-linear combination of selected EEG features from selected channel locations separated Pre-MF and Post-MF resting states with an average accuracy, sensitivity, and specificity of 80.76%, 78.24%, and 82.14% respectively. Finally, we validated that separation between Pre-MF and Post-MF is due to the MF prime rather than linear-temporal drift. This work underscores machine learning as a critical tool for separating distinct resting states within youth with ASD and will enable better classification of underlying neural responses following brief MF meditation.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions. Also, certain EEG components are associated with ER. Our overarching goal is to develop a technology that will use EEG to monitor real-time changes in ER and perform intervention based on these changes. As a first step, an EEG-based brain computer interface that is based on an Affective Posner task was developed to identify patterns associated with ER on a single trial basis, and EEG data collected from 21 individuals with ASD. Accordingly, our aim in this study is to investigate EEG features that could differentiate between distress and non-distress conditions. Specifically, we investigate if the EEG time-locked to the visual feedback presentation could be used to classify between WIN (non-distress) and LOSE (distress) conditions in a game with deception. Results showed that the extracted EEG features could differentiate between WIN and LOSE conditions (average accuracy of 81%), LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (average accuracy 94.9%).

During daily activities, humans use their hands to grasp surrounding objects and perceive sensory information which are also employed for perceptual and motor goals. Multiple cortical brain regions are known to be responsible for sensory recognition, perception and motor execution during sensorimotor processing. While various research studies particularly focus on the domain of human sensorimotor control, the relation and processing between motor execution and sensory processing is not yet fully understood. Main goal of our work is to discriminate textured surfaces varying in their roughness levels during active tactile exploration using simultaneously recorded electroencephalogram (EEG) data, while minimizing the variance of distinct motor exploration movement patterns. We perform an experimental study with eight healthy participants who were instructed to use the tip of their dominant hand index finger while rubbing or tapping three different textured surfaces with varying levels of roughness. We use an adversarial invariant representation learning neural network architecture that performs EEG-based classification of different textured surfaces, while simultaneously minimizing the discriminability of motor movement conditions (i.e., rub or tap). Results show that the proposed approach can discriminate between three different textured surfaces with accuracies up to 70%, while suppressing movement related variability from learned representations.

Epilepsy is a chronic brain disorder, and for at least one- third of epilepsy patients, medications do not adequately control seizures and surgery is the only potential cure. An automated seizure detector that requires a short period of normal EEG would shorten the seizure monitoring durations, decrease the need for manual assessment of large amount of recorded EEG data, and accordingly assist the neurologists focus on improving quality of care. In this work, we propose a novel approach based on the extended cumulative sum test to detect seizures using intracranial EEG. Different than the existing machine learning approaches, this method requires only a short period of normal EEG for training. In addition to a new proposed feature based on partial directed coherence and random graph theory, in this work, previously developed features used to characterize seizures are used as features in cumulative sum test for seizure detection. A total of 33 intracranial EEG recordings collected from a total of 9 patients corresponding to three different datasets have been used for the analysis purposes: (i) the first dataset includes 11 recorded EEG files (-12.5 min) from 4 patients; (ii) the second dataset includes 20 recorded EEG files (-30 min) from 4 patients; and (iii) two 24-hour long EEG files from a single patient. The proposed detector achieved a mean sensitivity and mean specificity for the first (0.77, 0.86), second (0.88, 0.9) and third datasets (0.92, 0.94) respectively. Statistical detection of seizures has shown success in detecting seizures without the need for highly customizable parameters or previously labelled EEG data. The proposed method could be used in real-time at hospital settings with a minimum requirement of training data.

Trial-by-trial texture classification analysis and identifying salient texture related EEG features during active touch that are minimally influenced by movement type and frequency conditions are the main contributions of this work. A total of twelve healthy subjects were recruited. Each subject was instructed to use the fingertip of their dominant hand’s index finger to rub or tap three textured surfaces (smooth flat, medium rough, and rough) with three levels of movement frequency (approximately 2, 1 and 0.5 Hz). EEG and force data were collected synchronously during each touch condition. A systematic feature selection process was performed to select temporal and spectral EEG features that contribute to texture classification but have low contribution towards movement type and frequency classification. A tenfold cross validation was used to train two 3-class (each for texture and movement frequency classification) and a 2-class (movement type) Support Vector Machine classifiers. Our results showed that the total power in the mu (8–15 Hz) and beta (16–30 Hz) frequency bands showed high accuracy in discriminating among textures with different levels of roughness (average accuracy > 84%) but lower contribution towards movement type (average accuracy < 65%) and frequency (average accuracy < 58%) classification.

In this paper, we introduce electroencephalography (EEG)- PDC based network connectivity average mean degrees (E-PDC) measure to analyze the interhemispheric interaction between the left and right motor cortices after stroke. E-PDC uses a graph and partial directed coherence (PDC) approach to quantify the directional functional connectivity between the motor cortices, which is not only altered after stroke but also is one of the important mechanisms linked with poor recovery of hand function. The brain activity between the two motor cortices is calculated via PDC and is used to form a graph. The PDC based network connectivity average mean degree of connectivity defined over this graph is defined as the E-PDC, which quantifies the directional connectivity between the two motor cortices. We preliminarily validated the novel E-PDC measure with three individuals with stroke, where one individual received a non-invasive brain stimulation (NIBS) intervention and the other two received sham-NIBS intervention. Unlike the two individuals who received sham-NIBS, the individual who received the NIBS intervention showed improvement in E-PDC after intervention, which strongly correlated with improvement in hand function after intervention (Fugl Meyer Upper Extremity Subscale and grip strength). This implies that the introduced E-PDC measure quantifies the interactions between the motor cortices and could be used to elucidate the underlying mechanism in restoring hand function after stroke.

Modern approaches to providing haptic feedback focus mainly on robotic manipulators, vibrators, and tactors. This type of feedback tends to be cumbersome and limited to a small number of contact points. On the contrary, electrotactile displays are compact and wearable, and recent discoveries demonstrate that naturalistic sensations of touch can be provided by electrical stimulation of peripheral nerves. Haptic feedback is essential for daily activities, as it allows us to become aware of our surroundings. The aim of this study is to develop techniques to extract EEG features that are markers for real world haptic interactions. The extracted EEG features will be used to model the EEG evidence that will later be employed in the closed-loop guidance system to adaptively control the electrical stimulation. In this work, twelve healthy subjects were recruited to perform a tactile stimulation experiment. Each subject was instructed to use their index finger to rub or tap 3 textured surfaces having varying levels of roughness (smooth flat, medium rough, and rough). EEG and force data were collected synchronously during each movement condition. Analysis of the EEG data showed that the amplitude of the EEG segment and the total power in the Mu (815 Hz) and Beta (1630 Hz) bands could be used to identify the roughness of textures using EEG data. We used a 10-fold cross validation to train a 3-class Support Vector machine classifier with chance level of 33%. The results show that it is possible to discriminate EEG activity with very high accuracy among surfaces with different textures.

We develop a dynamic system identification model to identify relationships among simultaneously recorded electroencephalography (EEG), electromyography (EMG) and force signals measured from 12 participants performing haptic interactions with 3D printed surfaces having different textures. In the first stage, we solve for the maximum likelihood (ML) parameter vector of a parsimonious integrated vector autoregression model (VAR) to estimate the latency between endogenous time variables, utilizing a grid search over the log likelihood scores. In the second stage, we explore the modality dependencies between synchronized EEG, EMG and haptic interactions by training parsimonious VAR models of the same structure. We use our knowledge of signal latency, lag orders and modality dependencies to predict EEG and haptic forces from any provided different combination of EEG, EMG and force measurements. In our future work, this model will guide external stimulation parameters for haptic interaction simulation in scenarios, including teleoperations and virtual environments.

Although automated seizure detection methods using intracranial EEG (iEEG) have achieved high accuracy in previous studies, they acquire many labeled datasets. Also, due to the non-stationarity nature of seizures and the inter and intra-individual variability in signal characteristics, these methods are difficult to implement prospectively in clinical practice. We propose an automated seizure detection method using a cumulative sum (CUSUM) detector that can be used online with fewer training parameters and minimal overall training without the need for labeled datasets. The proposed seizure detector is composed of two main steps, feature extraction followed by detection.The features extracted are a line length (LL), relative energy (RE), coefficient of variation of amplitude (CVA), and the relative amplitude (RA).The main assumption for the extended CUSUM analysis is that the distributions corresponding to normal and seizure EEG are different. Feature vectors are calculated using windows of length N, subdivided into M segments of length n. At each point, the average of each of the M segments is calculated.Assuming that n is large enough, the central limit theorem applies and the sample mean vector of each segment follows a Gaussian distribution, which can be characterized by its mean and variance.A null hypothesis is formed that incoming data will be governed by the same distribution.During training, normal EEG data from the same subject is used to calculate the mean and variance bound distributions, representing the null hypothesis.During detection, for each incoming data segment, the log likelihood cumulative sum needs to be determined for each of these bound distributions.If the null hypothesis is rejected in any case, then a change is assumed to have occurred.Two 24-h long iEEG recordings containing 3 and 9 seizures respectively, were collected (sampling frequency of 2 kHz) from one patient, undergoing right parietal stereo-electroencephalography, (University of Pittsburgh IRB No. PRO15100311). Recordings were labeled by an expert closely familiar with the patients.For each iEEG file, the learning period was chosen to be the first seizure-free hour.The window length used for learning is 2.5 min. The CUSUM detector managed to detect the three labeled seizures of the first iEEG recording with a Good Detection Rate of 100%.While the Good Detection Rate results of the second iEEG recording are (LL = 78%, RE = 78%, RA = 88% and CVA = 78%). The number of false detections per hour results for the first EEG recording as follows (LL = 1.6, RE = 1.3, RA = 1.4 and CVA = 1.5). While for the second recording (LL = 1.3, RE = 1, RA = 1.2 and CVA = 1.25). Seizure detection using the extended CUSUM test appears to be a promising technique for clinical monitoring purposes.This novel method for automated seizure detection using iEEG is capable of differentiating seizures from normal activity, without the need for highly customizable parameters or previously labeled data.The method also could be applied toward scalp EEG data.

Background: Spatial neglect (SN) is a neuropsychological syndrome that impairs automatic attention orienting to stimuli in the contralesional visual space of stroke patients. SN is commonly assessed using paper and pencil tests. Recently, computerized tests have been proposed to provide a dynamic assessment of SN. However, both paper- and computer-based methods have limitations. New method: Electroencephalography (EEG) shows promise for overcoming the limitations of current assessment methods. The aim of this work is to introduce an objective passive BO system that records EEG signals in response to visual stimuli appearing in random locations on a screen with a dynamically changing background. Our preliminary experimental studies focused on validating the system using healthy participants with intact brains rather than employing it initially in more complex environments with patients having cortical lesions. Therefore, we designed a version of the test in which we simulated SN by hiding target stimuli appearing on the left side of the screen so that the subject's attention is shifted to the right side. Results: Results showed that there are statistically significant differences between EEG responses due to right and left side stimuli reflecting different processing and attention levels towards both sides of the screen. The system achieved average accuracy, sensitivity and specificity of 74.24%, 75.17% and 71.36% respectively. Comparison with existing methods: The proposed test can examine both presence and severity of SN, unlike traditional paper and pencil tests and computer-based methods. Conclusions: The proposed test is a promising objective SN evaluation method. Published by Elsevier B.V.

Measures of electrodermal activity (EDA) have advanced research in a wide variety of areas including psychophysiology; however, the majority of this research is typically undertaken in laboratory settings. To extend the ecological validity of laboratory assessments, researchers are taking advantage of advances in wireless biosensors to gather EDA data in ambulatory settings, such as in school classrooms. While measuring EDA in naturalistic contexts may enhance ecological validity, it also introduces analytical challenges that current techniques cannot address. One limitation is the limited efficiency and automation of analysis techniques. Many groups either analyze their data by hand, reviewing each individual record, or use computationally inefficient software that limits timely analysis of large data sets. To address this limitation, we developed a method to accurately and automatically identify SCRs using curve fitting methods. Curve fitting has been shown to improve the accuracy of SCR amplitude and location estimations, but have not yet been used to reduce computational complexity. In this paper, sparse recovery and dictionary learning methods are combined to improve computational efficiency of analysis and decrease run time, while maintaining a high degree of accuracy in detecting SCRs. Here, a dictionary is first created using curve fitting methods for a standard SCR shape. Then, orthogonal matching pursuit (OMP) is used to detect SCRs within a dataset using the dictionary to complete sparse recovery. Evaluation of our method, including a comparison to for speed and accuracy with existing software, showed an accuracy of 80% and a reduced run time.