The need for serial in-office nonstress tests (NSTs) adds substantial burden to high-risk pregnancies and exacerbates care disparities.1American College of Obstetricians and Gynecologists’ Committee on Obstetric Practice, Society for Maternal-Fetal MedicineIndications for Outpatient Antenatal Fetal Surveillance: ACOG Committee Opinion, Number 828.Obstet Gynecol. 2021; 137: e177-e197Crossref PubMed Scopus (43) Google Scholar,2Tucker Edmonds B. Mogul M. Shea J.A. Understanding low-income African American women’s expectations, preferences, and priorities in prenatal care.Fam Community Health. 2015; 38: 149-157Crossref PubMed Scopus (43) Google Scholar INVU by Nuvo Group, Ltd, is a novel Food and Drug Administration-cleared, remote, self-administered maternal-fetal monitoring solution, previously validated for fetal and maternal heart rates and uterine activity measurements.3Mhajna M. Schwartz N. Levit-Rosen L. et al.Wireless, remote solution for home fetal and maternal heart rate monitoring.Am J Obstet Gynecol MFM. 2020; 2100101Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar, 4Schwartz N. Mhajna M. Moody H.L. et al.Novel uterine contraction monitoring to enable remote, self-administered nonstress testing.Am J Obstet Gynecol. 2022; 226: 554.e1-554.e12Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar, 5Mhajna M. Sadeh B. Yagel S. et al.A novel, cardiac-derived algorithm for uterine activity monitoring in a wearable remote device.Front Bioeng Biotechnol. 2022; 10933612Crossref PubMed Scopus (5) Google Scholar Here, we sought to determine the clinical interpretability and usability of INVU to enable patients to perform NSTs from home. This was a prospective, open-label, single-site study of a wireless, remote pregnancy monitoring system (INVU by Nuvo Group, Ltd) in high-risk pregnancies to remotely perform clinically indicated NSTs instead of in-clinic NSTs. This study was approved by the University of Pennsylvania Institutional Review Board, and participants provided informed consent. The INVU belt contains 8 biopotential and 4 acoustic sensors, which passively record abdominal signals (Figure, A). The monitoring system includes a Health Insurance Portability and Accountability Act–compliant mobile application that allows clinicians to view and interpret the NST and communicate with the patient in real time (Figure, B). We enrolled singleton pregnancies at ≥32 weeks of gestation with clinical indications for antenatal fetal surveillance, pregravid body mass indices (BMIs) of ≤50 and ≥15 kg/m2, and Wi-Fi access. Exclusions were uncontrolled hypertension, major fetal anomaly, abdominal skin issues, or an implanted electronic device. The primary outcome was whether the remote NST was deemed acceptable for clinical utility (interpretability). For the secondary outcomes, we evaluated reactivity rates among NSTs deemed clinically acceptable (reactivity) and the frequency and reason for recommending an in-clinic evaluation. Moreover, participants completed the validated System Usability Scale (SUS). Descriptive analyses were performed using SAS (version 9.4; SAS Institute, Cary, NC). We enrolled 34 high-risk patients at a mean gestational age of 34.5±1.1 weeks and a BMI of 35.8±6.3 kg/m2 from December 10, 2020, to March 6, 2022. Fetal testing indications included advanced maternal age (n=14), BMI of ≥40 kg/m2 (n=11), gestational diabetes mellitus (n=10), chronic hypertension (n=3), antiphospholipid syndrome (n=2), and other (n=9). Of note, 5 consented patients failed to complete device training. The remaining 29 patients had 131 qualifying remote NST sessions. INVU successfully obtained an interpretable NST in 93.9% of appointments (n=123), of which 98.3% (n=121) were deemed reactive. Patients avoided an in-office visit in 88.5% of visits. Only 2 appointments (1.5%) resulted in a recommendation for nonurgent delivery, both for elevated blood pressure and neither related to the NST tracing. Only 1 patient (2.9%) experienced mild, transient soreness at the sensor site without redness or bruising. Of note, 23 patients (79.3%) who attempted at least 1 remote NST completed the SUS, with a mean score of 76.5 (±15.9) of 100.0, indicating “good” usability, with 22 patients (95.7%) agreeing they would prefer remote NSTs vs in-office testing in a future pregnancy. Further details and discussion are available in the Supplemental Materials and Methods. In this prospective cohort study, >90% of NSTs performed remotely using INVU were acceptable for clinical utility, and >88% of NST appointments were completed without in-clinic evaluation. In addition, INVU demonstrated an excellent safety profile and good patient usability. Future research is warranted to determine how to best leverage this novel capability to address inequities in patient access and improve perinatal outcomes.

BACKGROUND: The serial fetal monitoring recommended for women with high-risk pregnancies places a substantial burden on the patient, often disproportionately affecting underprivileged and rural populations. A telehealth solution that can empower pregnant women to obtain recommended fetal surveillance from the comfort of their own home has the potential to promote health equity and improve outcomes. We have previously validated a novel, wireless pregnancy monitor that can remotely capture fetal and maternal heart rates. However, such a device must also detect uterine contractions if it is to be used to robustly conduct remote nonstress tests. OBJECTIVE: This study aimed to describe and validate a novel algorithm that uses biopotential and acoustic signals to noninvasively detect uterine contractions via a wireless pregnancy monitor. STUDY DESIGN: A prospective, open-label, 2-center study evaluated simultaneous detection of uterine contractions by the wireless pregnancy monitor and an intrauterine pressure catheter in women carrying singleton pregnancies at >= 32 0/7 weeks' gestation who were in the first stage of labor (ClinicalTrials.gov Identifier: NCT03889405). The study consisted of a training phase and a validation phase. Simultaneous recordings from each device were passively acquired for 30 to 60 minutes. In a subset of the monitoring sessions in the validation phase, tocodynamometry was also deployed. Three maternal-fetal medicine specialists, blinded to the data source, identified and marked contractions in all modalities. The positive agreement and false-positive rates of both the wireless monitor and tocodynamometry were calculated and compared with that of the intrauterine pressure catheter. RESULTS: A total of 118 participants were included, 40 in the training phase and 78 in the validation phase (of which 39 of 78 participants were monitored simultaneously by all 3 devices) at a mean gestational age of 38.6 weeks. In the training phase, the positive agreement for the wireless monitor was 88.4% (1440 of 1692 contractions), with a false-positive rate of 15.3% (260/1700). In the validation phase, using the refined and finalized algorithm, the positive agreement for the wireless pregnancy monitor was 84.8% (2722/3210), with a false-positive rate of 24.8% (897/3619). For the subgroup who were monitored only with the wireless monitor and intrauterine pressure catheter, the positive agreement was 89.0% (1191/1338), with a similar false-positive rate of 25.4% (406/1597). For the subgroup monitored by all 3 devices, the positive agreement for the wireless monitor was significantly better than for tocodynamometry (P<.0001), whereas the false-positive rate was significantly higher (P<.0001). Unlike tocodynamometry, whose positive agreement was significantly reduced in the group with obesity compared with the group with normal weight (P=.024), the positive agreement of the wireless monitor did not vary across the body mass index groups. CONCLUSION: This novel method to noninvasively monitor uterine activity, via a wireless pregnancy monitoring device designed for self-administration at home, was more accurate than the commonly used tocodynamometry and unaffected by body mass index. Together with the previously reported remote fetal heart rate monitoring capabilities, this added ability to detect uterine contractions has created a complete telehealth solution for remote administration of nonstress tests.

Background: Uterine activity (UA) monitoring is an essential element of pregnancy management. The gold-standard intrauterine pressure catheter (IUPC) is invasive and requires ruptured membranes, while the standard-of-care, external tocodynamometry (TOCO)'s accuracy is hampered by obesity, maternal movements, and belt positioning. There is an urgent need to develop telehealth tools enabling patients to remotely access care. Here, we describe and demonstrate a novel algorithm enabling remote, non-invasive detection and monitoring of UA by analyzing the modulation of the maternal electrocardiographic and phonocardiographic signals. The algorithm was designed and implemented as part of a wireless, FDA-cleared device designed for remote pregnancy monitoring. Two separate prospective, comparative, open-label, multi-center studies were conducted to test this algorithm. Methods: In the intrapartum study, 41 laboring women were simultaneously monitored with IUPC and the remote pregnancy monitoring device. Ten patients were also monitored with TOCO. In the antepartum study, 147 pregnant women were simultaneously monitored with TOCO and the remote pregnancy monitoring device. Results: In the intrapartum study, the remote pregnancy monitoring device and TOCO had sensitivities of 89.8 and 38.5%, respectively, and false discovery rates (FDRs) of 8.6 and 1.9%, respectively. In the antepartum study, a direct comparison of the remote pregnancy monitoring device to TOCO yielded a sensitivity of 94% and FDR of 31.1%. This high FDR is likely related to the low sensitivity of TOCO. Conclusion: UA monitoring via the new algorithm embedded in the remote pregnancy monitoring device is accurate and reliable and more precise than TOCO standard of care. Together with the previously reported remote fetal heart rate monitoring capabilities, this novel method for UA detection expands the remote pregnancy monitoring device's capabilities to include surveillance, such as non-stress tests, greatly benefiting women and providers seeking telehealth solutions for pregnancy care.

The fetal non-stress test (NST) for antepartum surveillance involves frequent, burdensome in-office visits. INVUTM is a wireless, remote, FDA-cleared monitoring device, designed to perform self-administered NST from home. Here, we present (1) our initial experience with INVUTM home NST monitoring, and (2) a novel machine learning decision support system (DSS) to detect fetal heart rate (FHR) accelerations, with the potential to alert clinicians on NST reactivity. A single-center, prospective, open label study was conducted, in which women with ≥32 week high-risk singleton gestations performed self-administered NST using the INVUTM device at home, replacing in-clinic NSTs. NSTs were reviewed by clinicians via the INVUTM web application who determined clinically interpretability and reactivity. Interpretable sessions were retrospectively analyzed by the DSS for reactivity and compared to clinician assessment (Figure). The time to reactivity as analyzed by the DSS was compared to the session length until reactivity was determined by clinicians. Of the first 30 INVUTM sessions (across 9 subjects), clinicians deemed 27 (90%) interpretable, and 27/27 as reactive (100%). The DSS correctly classified 92.6% (25/27) of the interpretable NSTs compared with clinicians. In the 2 cases where DSS did not match the clinician decision, DSS did not rule the NST reactive, whereas the clinician did. On review, these cases may have been due to clinician averaging the FHR baseline from which accelerations are measured, compared to the conservative nature of the DSS. In the cases deemed reactive by DSS, time to reactivity was reduced by 14.72±13.79 min as compared to clinician determination. Early clinical experience conducting self-administered, remote NSTs at home using the INVUTM device supports the feasibility of bringing this telehealth solution to high risk pregnancy care. In addition, a novel DSS tool accurately identifies reactivity, which could reduce the length of NST sessions and improve clinician workflow.

Uterine activity (UA) monitoring is an essential element of pregnancy management. The gold standard intrauterine pressure catheter (IUPC), is invasive and requires ruptured membranes. Conventional, external tocodynamometry (TOCO) is hampered by obesity, maternal movements, and belt positioning, with positive agreement rates (PA) as low as 54-73% and false detection rates (FDR) as high as 15-32%. We prospectively tested a novel algorithm that enables non-invasive and reliable detection of UA. A prospective, comparative, open label, multicenter study was conducted, in which 40 laboring women (≥32 weeks; BMI<50) were simultaneously monitored for 30-60 minutes with IUPC and INVU (Nuvo Group), a wireless device FDA-cleared for remote fetal and maternal heart rate monitoring. Ten subjects were monitored with TOCO as well. UA detection by INVU was based on the modulation of the maternal electrocardiographic and phonocardiographic signals. 3 blinded assessors marked the contractions for each modality. PA and FDR of INVU and TOCO compared to IUPC were calculated. Overall, INVU correctly identified 87.7% of the IUPC contractions. Sub-analysis based on BMI categories (normal: <25, overweight: 25-30, obese: ≥30) showed that UA detection was not affected by BMI (PA: 88.5%, 85.9% and 90.0%, respectively; p=0.42). Overall, INVU FDR was 15.2%. In the subset of sessions recorded with TOCO, TOCO PA and FDR were 46% and 7.1% respectively. UA monitoring via INVU is accurate and reliable, even in obese women, and more precise than TOCO. There is a pressing need for telehealth solutions in pregnancy monitoring, underscored by the current COVID-19 pandemic. INVU (Nuvo Group) is a self-administered device, FDA-cleared for wireless, remote monitoring of fetal and maternal heart rate. This novel method for UA detection via INVU expands its remote pregnancy monitoring capabilities to include surveillance such as non-stress tests, which would be of great benefit to women and providers seeking telehealth solutions for high risk pregnancy care.

Study Design Prospective longitudinal cohort study Background Adolescent athletes may be more susceptible to the long-term effects of mild traumatic brain injury (mTBI). A diagnostic and prognostic neuromarker may optimize management and return-to-activity decision-making in athletes who experience mTBI. Objective Measure an event-related potential (ERP) component captured with electroencephalography (EEG), called processing negativity (PN), at baseline and post-injury in adolescents who suffered mTBI and determine their longitudinal response relative to healthy controls. Methods Thirty adolescents had EEG recorded during an auditory oddball task at a pre-mTBI baseline session and subsequent post-mTBI sessions. Longitudinal EEG data from patients and healthy controls (n= 77) were obtained from up to four sessions in total and processed using Brain Network Analysis algorithms. Results The average PN amplitude in healthy controls significantly decreased over sessions 2 and 3; however, it remained steady in the mTBI group's 2nd (post-mTBI) session and decreased only in sessions 3 and 4. Pre- to post-mTBI amplitude changes correlated with the time interval between sessions. Conclusion These results demonstrate that PN amplitude changes may be associated with mTBI exposure and subsequent recovery in adolescent athletes. Further study of PN may lead to it becoming a neuromarker for mTBI prognosis and return-to-activity decision-making in adolescents.

Existing methods for uterine activity (UA) monitoring have major drawbacks. The accuracy of tocodynamometry (TOCO) is impacted by maternal movement, habitus and sensor placement. IUPC, the gold standard, is invasive and limited to intrapartum use. Both methods require restriction to the bedside. Previously, we have demonstrated the ability to remotely obtain accurate fetal and maternal heart rate (FHR, MHR) via a wireless, self-administered INVU belt (Nuvo Group). Here we present an innovative approach for UA monitoring based on abdominal maternal EKG (mEKG) collected from the same device. We sought to validate this novel tool as compared to TOCO We recruited singleton pregnancies at ≥32 weeks and BMI≤45 to undergo 30 minutes of simultaneous monitoring with CTG and INVU. We have previously validated INVU's FHR and MHR in this cohort. In this study, UA was extracted from the modulation of the R-wave component of the mEKG recorded over the abdomen. Tracings with contractions on TOCO were identified as the reference dataset. A blinded expert assessor then manually identified and marked contractions on TOCO and INVU tracings. Sensitivity and false detection rate (FDR) were analyzed. A second comparative trial to both TOCO and IUPC is currently underway. INVU correctly identified 90 of the 96 contractions of the reference dataset for a sensitivity of 94%, with an FDR of 18%. Preliminary results from the second trial are promising. An example of a simultaneous 30 minute recording of IUPC, INVU, and TOCO (age 19, gestational week 38, BMI 41.4) is presented (Figure) The results demonstrate and validate a novel method to monitor UA, based on the analysis of the abdominal mEKG signal. INVU UA has shown excellent sensitivity compared to TOCO. The FDR is anticipated given the known limitations of TOCO. Preliminary results from the direct comparison to IUPC and TOCO strengthen this possibility. Taken together with the previous validation of INVU's MHR and FHR monitoring, the added UA capability yields a complete, wireless, non-invasive remote monitoring system for clinical use.

Objective: To investigate the association between dual-task gait performance and brain network activation (BNA) using an electroencephalography (EEG)-based Go/No-Go paradigm among children and adolescents with concussion. Methods: Participants with a concussion completed a visual Go/No-Go task with collection of electroencephalogram brain activity. Data were treated with BNA analysis, which involves an algorithmic approach to EEG-ERP activation quantification. Participants also completed a dual-task gait assessment. The relationship between dual-task gait speed and BNA was assessed using multiple linear regression models. Results: Participants (n=20, 13.9 +/- 2.3years of age, 50% female) were tested at a mean of 7.0 +/- 2.5days post-concussion and were symptomatic at the time of testing (post-concussion symptom scale=40.4 +/- 21.9). Slower dual-task average gait speed (mean=82.2 +/- 21.0cm/s) was significantly associated with lower relative time BNA scores (mean=39.6 +/- 25.8) during the No-Go task (=0.599, 95% CI=0.214, 0.985, p=0.005, R-2=0.405), while controlling for the effect of age and gender. Conclusion: Among children and adolescents with a concussion, slower dual-task gait speed was independently associated with lower BNA relative time scores during a visual Go/No-Go task. The relationship between abnormal gait behaviour and brain activation deficits may be reflective of disruption to multiple functional abilities after concussion.

Exposure to explosive blasts places one at risk for traumatic brain injury, especially for special weapons and tactics (SWAT) and military personnel, who may be repeatedly exposed to blasts. In the current study, the effectiveness of a jugular vein compression collar to prevent alterations in resting-state electrocortical activity following a single-SWAT breacher training session was investigated. SWAT team personnel were randomly assigned to wear a compression collar during breacher training and resting state electroencephalography (EEG) was measured within 2 days prior to and two after breacher training. It was hypothesized that significant changes in brain dynamics—indicative of possible underlying neurodegenerative processes—would follow blast exposure for those who did not wear the collar, with ameliorated changes for the collar-wearing group. Using recurrence quantification analysis (RQA) it was found that participants who did not wear the collar displayed longer periods of laminar electrocortical behavior (as indexed by RQA’s vertical max line measure) after breacher training. It is proposed that the blast wave exposure for the no-collar group may have reduced the number of pathways, via axonal disruption—for electrical transmission—resulting in the EEG signals becoming trapped in laminar states for longer periods of time. Longer laminar states have been associated with other electrocortical pathologies, such as seizure, and may be important for understanding head trauma and recovery.

Mild traumatic brain injury (mTBI) in adolecents has gained increased attention in recent years amongst parents, clinicians, and researchers due to their growing rate and hazardous outcomes. Electroencephalography (EEG) and Event-Related Potential (ERP) have been encouraged as a diagnostic tool for mTBI due to its objectivity and cost-effectiveness. However, extracting clinically meaningful neuro-cognitive information from human EEG/ERP is challenging, particularly in highly variable groups like adolescents. Therefore, it is not surprising that despite them being especially susceptible to the effects of mTBI, no sensitive and specific application of EEG has yet been determined for this age group. To overcome these challenges, we have developed and applied a framework, termed Brain Network Activity (BNA) analysis, which utilizes a hybrid approach of very large datasets, novel signal processing technology, and academic knowledge (see Stern, Y., Reches, A., and Geva A. “Using Spatiotemporal Features in the Brain Network Activation Analysis for Improved Data Classification”. Frontiers in computational neuroscience, 2016). For the BNA core database, spatiotemporal data from 15,100 EEG recording files of healthy individuals performing various computerized cognitive tasks were extracted. Advanced machine learning and prior knowledge from the ERP literature was used to assemble an optimal set of brain network models for various cognitive functions, such as attention, memory, motor control, and sensory processing. This resulted in reference brain network models, to which the BNA of an individual or of a whole patient group can be compared to in terms of quantitative scores and qualitative brain map illustrations. In cases of specific sub-groups such as adolescents with mTBI, these BNA scores and maps may be applied to identify biomarkers specific to that population. In order to generate an mTBI biomarker for adolescents, BNA analysis was applied to EEG recordings of 107 healthy participants and 36 mTBI adolescent patients while they performed an auditory oddball task. The BNA features of all participants were extracted and included in repeated-measures analyses of variance (ANOVA) with EEG session gain (2nd vs 1st, 3rd vs 2nd), interval between sessions (<2 months, >2 months) and group (healthy, mTBI) as factors. Results showed a large negative evoked potential component in frontal-central regions between ∼200 and 500 ms following the standard stimulus for both groups (mTBI and healthy) during the baseline EEG recording sessions. The average negative amplitude decreased in the healthy control group’s 2nd EEG recording session. It remained steady in the concussed group’s 2nd (post-mTBI) session, and was significantly different from healthy controls. The pre- to post-mTBI gain in amplitude was correlated with the time interval between recording sessions (r = −0.47, p-value < 0.05). These results indicate that the negative component’s amplitude gains are associated with mTBI in adolescent, making it a potential biomarker for mTBI diagnosis and monitoring. BNA analysis may therefore not only assist clinicians as a neural assessment tool in individual patients, but may also be utilized for generating potential markers specific to certain patient groups.

BACKGROUND:A previous small study suggested that Brain Network Activation (BNA), a novel ERP-based brain network analysis, may have diagnostic utility in attention deficit hyperactivity disorder (ADHD). In this study we examined the diagnostic capability of a new advanced version of the BNA methodology on a larger population of adults with and without ADHD.METHOD:Subjects were unmedicated right-handed 18- to 55-year-old adults of both sexes with and without a DSM-IV diagnosis of ADHD. We collected EEG while the subjects were performing a response inhibition task (Go/NoGo) and then applied a spatio-temporal Brain Network Activation (BNA) analysis of the EEG data. This analysis produced a display of qualitative measures of brain states (BNA scores) providing information on cortical connectivity. This complex set of scores was then fed into a machine learning algorithm.RESULTS:The BNA analysis of the EEG data recorded during the Go/NoGo task demonstrated a high discriminative capacity between ADHD patients and controls (AUC = 0.92, specificity = 0.95, sensitivity = 0.86 for the Go condition; AUC = 0.84, specificity = 0.91, sensitivity = 0.76 for the NoGo condition).CONCLUSIONS:BNA methodology can help differentiate between ADHD and healthy controls based on functional brain connectivity. The data support the utility of the tool to augment clinical examinations by objective evaluation of electrophysiological changes associated with ADHD. Results also support a network-based approach to the study of ADHD.

In this study we employed a novel EEG/ERP analysis tool known as Brain-Network-Activation (BNA) to assess the reorganization of brain dynamics following focal tDCS stimulation in fibromyalgia patients. BNA is an integral treatment progression monitoring system based on the high-temporal resolution of ERPs, and by using a formal graph representation depicts the evolving network dynamics in time, location and frequency in high temporal resolution. We examined whether the BNA score, a measure reflecting the network-level synchronization, could serve as a predictor of responsiveness and the length of treatment.

Objective There is a need for objective biomarkers to help identify concussion, monitor recovery, and assist clinicians in managing patients. Brain network activation (BNA) analysis is a high-density, multi-channel mapping and analysis technology that uses a subject’s event-related potentials to describe cortical activity and functional connectivity. The current study describes BNA analysis in three patients who sustained a concussion. Design Case series. Setting Three primary concussion clinics in the USA. Participants Three subjects who sustained a concussion, ages 16 to 31 years, one male and two females. Outcome measures BNA score, a measure of the how similar the subject’s brain activity was to a normative population (0%=no similarity, 100%=complete similarity); Patient-reported symptoms. Main results In the acute time period following their concussion, each patient’s BNA score decreased to 10–20%, indicating little similarity to brain activity and connectivity of normative populations. The immediate reduction in BNA score during this time period was accompanied by concussion-related symptoms reported by each patient. In a longitudinal BNA analysis, the patients’ BNA scores returned to within a normal range between 23 and 30 days following concussion. In contrast, the recovery trajectory of patients’ symptoms varied, with one patient no longer reporting symptoms of concussion though the BNA score was still outside the normal range, while another patient continued to report symptoms of concussion after the BNA score returned to the normal range. Conclusions BNA analysis could provide a biomarker to augment current approaches to assessing and managing patients with concussion. Competing interests AR and HO are employed by ElMindA, the company that manufacturers the BNA technology. APK acts as a consultant for ElMindA, the National Basketball Association, National Hockey League Players’ Association, National Football League Players’ Association, and US Ski and Snowboard Association. JK, RJE, JG, and DJM None.

BACKGROUND:The clinical diagnosis and management of patients with sport-related concussion is largely dependent on subjectively reported symptoms, clinical examinations, cognitive, balance, vestibular and oculomotor testing. Consequently, there is an unmet need for objective assessment tools that can identify the injury from a physiological perspective and add an important layer of information to the clinician's decision-making process.OBJECTIVE:The goal of the study was to evaluate the clinical utility of the EEG-based tool named Brain Network Activation (BNA) as a longitudinal assessment method of brain function in the management of young athletes with concussion.METHODS:Athletes with concussion (n = 86) and age-matched controls (n = 81) were evaluated at four time points with symptom questionnaires and BNA. BNA scores were calculated by comparing functional networks to a previously defined normative reference brain network model to the same cognitive task.RESULTS:Subjects above 16 years of age exhibited a significant decrease in BNA scores immediately following injury, as well as notable changes in functional network activity, relative to the controls. Three representative case studies of the tested population are discussed in detail, to demonstrate the clinical utility of BNA.CONCLUSION:The data support the utility of BNA to augment clinical examinations, symptoms and additional tests by providing an effective method for evaluating objective electrophysiological changes associated with sport-related concussions.

PURPOSE: Helmets have been redesigned to reduce the incidence of concussion in sports, but research has shown that even newer helmets are ineffective at preventing concussions. We propose a novel device, worn around the neck, as a solution to reduce concussions in sport. The collar causes gentle compression of the internal jugular veins, thus restricting venous outflow and increasing venous sinus engorgement. This reduces brain movement within the cranial cavity upon impact (slosh). The purpose of this study was to measure brain neurophysiological changes after head impacts. A brain network activation analysis (BNA) evaluated the network dynamics associated with event related potentials in subjects performing a neuro-cognitive task. We hypothesized that the group wearing the collar would demonstrate fewer neurophysiological changes than the control group and that the changes in the control group would correlate with relative G force exposure during the hockey season. METHODS: Fourteen male high school ice hockey players (mean age 16.74±1.13 y) participated in a prospective, randomized clinical trial. Subjects underwent pre-season and mid-season EEG assessment; Helmet sensors were used to collect head impact and acceleration data. BNA analysis assessed the similarity of subjects’ EEG signals to a reference group and relative to accumulated head impact data. RESULTS: Subjects wearing the collar (n=7) exhibited fewer changes in their BNA scores (4.05±4.02) from pre- to mid-season, compared to those who did not wear the collar (n=7, 20.21±13.35, p=0.007). Subjects who did not wear the collar exhibited a correlation between the accumulated G forces (linear acceleration >20g) and the change in BNA score from pre- to mid-season (Spearman’s rho=0.82, p=.023). The accumulated G forces for subjects who did or did not wear the collar (6364±1902 and 4583±1304, respectively) were not statistically different. CONCLUSION: Subjects who sustained multiple head impacts while playing ice hockey exhibited changes in their EEG data, as measured by BNA analysis. Subjects who wore a jugular vein-compression collar exhibited a smaller change in BNA score than subjects who did not wear the collar. These data support the contention that mild jugular vein compression may be a protective mechanism against sport-related mTBI.

Post-traumatic migraine (PTM) (i.e., headache, nausea, light and/or noise sensitivity) is an emerging risk factor for prolonged recovery following concussion. Concussions and migraine share similar pathophysiology characterized by specific ionic imbalances in the brain. Given these similarities, patients with PTM following concussion may exhibit distinct electrophysiological patterns, although researchers have yet to examine the electrophysiological brain activation in patients with PTM following concussion. A novel approach that may help differentiate brain activation in patients with and without PTM is brain network activation (BNA) analysis. BNA involves an algorithmic analysis applied to multichannel EEG-ERP data that provides a network map of cortical activity and quantitative data during specific tasks. A prospective, repeated measures design was used to evaluate BNA (during Go/NoGo task), EEG-ERP, cognitive performance, and concussion related symptoms at 1, 2, 3, and 4 weeks post-injury intervals among athletes with a medically diagnosed concussion with PTM (n = 15) and without (NO-PTM) (n = 22); and age, sex, and concussion history matched controls without concussion (CONTROL) (n = 20). Participants with PTM had significantly reduced BNA compared to NO-PTM and CONTROLS for Go and NoGo components at 3 weeks and for NoGo component at 4 weeks post-injury. The PTM group also demonstrated a more prominent deviation of network activity compared to the other two groups over a longer period of time. The composite BNA algorithm may be a more sensitive measure of electrophysiological change in the brain that can augment established cognitive assessment tools for detecting impairment in individuals with PTM.

A known difficulty in pain management is the reliance on a patient’s self-reported measure of pain to guide treatment. We conducted a randomized, double-blinded, placebo-controlled crossover study to examine the utility of brain network activation (BNA) technology to quantify responses in healthy volunteers to pain induced by noxious thermal stimuli following the administration of an opioid analgesic. We examined the relationship between BNA scores and numeric pain scale (NPS) scores and compared the sensitivity, specificity and accuracy of BNA and NPS. Forty-two subjects underwent baseline electroencephalographic (EEG) measurements followed by two testing sessions after receiving placebo and two sessions after receiving 20 mg of oxycodone in a double-blinded fashion. Subjects received brief heat stimuli at 42°C, 50°C, and 52°C to the upper forearm in randomized sequences, and 128-lead EEGs were recorded. For each subject, similarity scores to a pain reference brain network model were calculated for the contact heat-evoked potentials. BNA provided an objective, sensitive, and quantitative method to measure pain perception associated with the acute noxious thermal stimulus. BNA scores revealed a statistically significant drug effect (attenuation of pain) when compared to baseline (p<0.0001) as well as to placebo (p=0.006), with similar results for NPS scores. A statistically significant association was observed between overall NPS and BNA scores, although this relationship did not hold for individual subjects. Additionally, BNA scores showed moderate to substantial repeatability (intraclass correlation coefficient 0.64 – 0.70), and differentiated between warmth and pain in a receiver operating characteristic analysis. These findings suggest that BNA may be useful not only to provide clinicians with an electrophysiological “imaging” tool to optimize treatment but also to measure treatment effect and to screen subjects for drug development studies. Supported by a grant from Purdue Pharma.