Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity.

Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals. Trial Registration: NCT03946618.

A retrospective, naturalistic study of deep brain stimulation and vagal nerve stimulation in young patients.

INTRODUCTION: Invasive neuromodulation interventions such as deep brain stimulation (DBS) and vagal nerve stimulation (VNS) are important treatments for movement disorders and epilepsy, but literature focused on young patients treated with DBS and VNS is limited. This retrospective study aimed to examine naturalistic outcomes of VNS and DBS treatment of epilepsy and dystonia in children, adolescents, and young adults. METHODS: We retrospectively assessed patient demographic and outcome data that were obtained from electronic health records. Two researchers used the Clinical Global Impression scale to retrospectively rate the severity of neurologic and psychiatric symptoms before and after patients underwent surgery to implant DBS electrodes or a VNS device. Descriptive and inferential statistics were used to examine clinical effects. RESULTS: Data from 73 patients were evaluated. Neurologic symptoms improved for patients treated with DBS and VNS (p < .001). Patients treated with DBS did not have a change in psychiatric symptoms, whereas psychiatric symptoms worsened for patients treated with VNS (p = .008). The frequency of postoperative complications did not differ between VNS and DBS groups. CONCLUSION: Young patients may have distinct vulnerabilities for increased psychiatric symptoms during treatment with invasive neuromodulation. Child and adolescent psychiatrists should consider a more proactive approach and greater engagement with DBS and VNS teams that treat younger patients.

Deep transcranial magnetic stimulation for adolescents with treatment-resistant depression: A preliminary dose-finding study exploring safety and clinical effectiveness.

BACKGROUND: Transcranial magnetic stimulation (TMS) is an intervention for treatment-resistant depression (TRD) that modulates neural activity. Deep TMS (dTMS) can target not only cortical but also deeper limbic structures implicated in depression. Although TMS has demonstrated safety in adolescents, dTMS has yet to be applied to adolescent TRD. OBJECTIVE/HYPOTHESIS: This pilot study evaluated the safety, tolerability, and clinical effects of dTMS in adolescents with TRD. We hypothesized dTMS would be safe, tolerable, and efficacious for adolescent TRD. METHODS: 15 adolescents with TRD (Age, years: M = 16.4, SD = 1.42) completed a six-week daily dTMS protocol targeting the left dorsolateral prefrontal cortex (BrainsWay H1 coil, 30 sessions, 10 Hz, 3.6 s train duration, 20s inter-train interval, 55 trains; 1980 total pulses per session, 80 % to 120 % of motor threshold). Participants completed clinical, safety, and neurocognitive assessments before and after treatment. The primary outcome was depression symptom severity measured by the Children's Depression Rating Scale-Revised (CDRS-R). RESULTS: 14 out of 15 participants completed the dTMS treatments. One participant experienced a convulsive syncope; the other participants only experienced mild side effects (e.g., headaches). There were no serious adverse events and minimal to no change in cognitive performance. Depression symptom severity significantly improved pre- to post-treatment and decreased to a clinically significant degree after 10 treatment sessions. Six participants met criteria for treatment response. LIMITATIONS: Main limitations include a small sample size and open-label design. CONCLUSIONS: These findings provide preliminary evidence that dTMS may be tolerable and associated with clinical improvement in adolescent TRD.

The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses.

BACKGROUND: When job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for the prediction of occupational burnout before it occurs. Early warning signs of burnout would facilitate preemptive institutional responses for preventing individual, organizational, and public health consequences of occupational burnout. This protocol describes the design and methodology for the decentralized Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) Study. This study aims to develop predictive models of occupational burnout and estimate burnout-associated costs using consumer-grade wearable smartwatches and systems-level data. METHODS: A total of 360 registered nurses (RNs) will be recruited in 3 cohorts. These cohorts will serve as training, testing, and validation datasets for developing predictive models. Subjects will consent to one year of participation, including the daily use of a commodity smartwatch that collects heart rate, step count, and sleep data. Subjects will also complete online baseline and quarterly surveys assessing psychological, workplace, and sociodemographic factors. Routine administrative systems-level data on nursing care outcomes will be abstracted weekly. DISCUSSION: The BROWNIE study was designed to be decentralized and asynchronous to minimize any additional burden on RNs and to ensure that night shift RNs would have equal accessibility to study resources and procedures. The protocol employs novel engagement strategies with participants to maintain compliance and reduce attrition to address the historical challenges of research using wearable devices. TRIAL REGISTRATION: NCT05481138.

High Probability of Gene-Drug Interactions Associated with Medication Side Effects in Adolescent Depression: Results from a Randomized Controlled Trial of Pharmacogenetic Testing.

Introduction: Combinatorial pharmacogenetic testing panels are widely available in clinical practice and often separate medications into columns/bins associated with low, medium, or high probability of gene-drug interactions. The objective of the Adolescent Management of Depression (AMOD) study was to determine the clinical utility of combinatorial pharmacogenetic testing in a double-blind, randomized, controlled effectiveness study by comparing patients who had genetic testing results at time of medication initiation to those that did not have results until week 8. The objective of this post hoc analysis was to assess and report additional outcomes with respect to significant gene-drug interactions (i.e., a medication in the high probability gene-drug interaction bin as defined by a proprietary algorithm) compared with patients taking a medication with minimal to moderate gene-drug interactions (i.e., a medication from the low or medium probability gene-drug interaction bin, respectively). Methods: Adolescents 13-18 years (N = 170) with moderate to severe major depressive disorder received pharmacogenetic testing. Symptom improvement and side effects were assessed at baseline, week 4, week 8, and 6 months. Patients were grouped into three categories based on whether the medication they were prescribed was associated with low, medium, or high risk for gene-drug interactions. Patients taking a medication from the low/medium gene-drug interaction bin were compared with patients taking a medication from the high gene-drug interaction bin. Results: Patients taking a medication from the high gene-drug interaction bin were more likely to endorse side effects compared with patients taking a medication in the low/medium gene-drug interaction bin at week 8 (p = 0.001) and 6 months (p < 0.0001). Depressive symptom severity scores did not differ significantly across the medication bins. Conclusions: This study demonstrates the utility of gene-drug interaction testing to guide medication decisions to minimize side effect burden rather than solely prioritizing the search for the most efficacious medication. (Clinical Trials Registration Identifier: NCT02286440).

Efficacy and safety of transcranial magnetic stimulation on cognition in mild cognitive impairment, Alzheimer's disease, Alzheimer's disease-related dementias, and other cognitive disorders: a systematic review and meta-analysis.

OBJECTIVE: We aim to analyze the efficacy and safety of TMS on cognition in mild cognitive impairment (MCI), Alzheimer's disease (AD), AD-related dementias, and nondementia conditions with comorbid cognitive impairment. DESIGN: Systematic review, Meta-Analysis. SETTING: We searched MEDLINE, Embase, Cochrane database, APA PsycINFO, Web of Science, and Scopus from January 1, 2000, to February 9, 2023. PARTICIPANTS AND INTERVENTIONS: RCTs, open-label, and case series studies reporting cognitive outcomes following TMS intervention were included. MEASUREMENT: Cognitive and safety outcomes were measured. Cochrane Risk of Bias for RCTs and MINORS (Methodological Index for Non-Randomized Studies) criteria were used to evaluate study quality. This study was registered with PROSPERO (CRD42022326423). RESULTS: The systematic review included 143 studies (n = 5,800 participants) worldwide, encompassing 94 RCTs, 43 open-label prospective, 3 open-label retrospective, and 3 case series. The meta-analysis included 25 RCTs in MCI and AD. Collectively, these studies provide evidence of improved global and specific cognitive measures with TMS across diagnostic groups. Only 2 studies (among 143) reported 4 adverse events of seizures: 3 were deemed TMS unrelated and another resolved with coil repositioning. Meta-analysis showed large effect sizes on global cognition (Mini-Mental State Examination (SMD = 0.80 [0.26, 1.33], p = 0.003), Montreal Cognitive Assessment (SMD = 0.85 [0.26, 1.44], p = 0.005), Alzheimer's Disease Assessment Scale-Cognitive Subscale (SMD = -0.96 [-1.32, -0.60], p < 0.001)) in MCI and AD, although with significant heterogeneity. CONCLUSION: The reviewed studies provide favorable evidence of improved cognition with TMS across all groups with cognitive impairment. TMS was safe and well tolerated with infrequent serious adverse events.

Systematic review and meta-analysis: Combining transcranial magnetic stimulation or direct current stimulation with pharmacotherapy for treatment of substance use disorders.

BACKGROUND AND OBJECTIVES: Transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) have evidence for their potential in the treatment of substance use disorders (SUD). Medication for addiction treatment (MAT) is underutilized and not always effective. We identified randomized controlled trials (RCTs) and case studies that evaluated the effectiveness of TMS or tDCS used concurrently with MAT in SUD treatment. METHODS: A systematic review of published literature following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted on 6/1/2023 by a medical librarian. Craving-related scales were extracted for an effect size calculation. The Physiotherapy Evidence Database (PEDro) scale assessed study quality. RESULTS: Eight studies (7 RCT, 1 case) including 253 individuals were published from 2015 to 2022, 5 of which had available data for meta-analysis. TMS or tDCS combined with MAT significantly reduced craving-related measures relative to sham stimulation (Hedges' g = -0.42, confidence interval: -0.73 to -0.11, p < .01). Opioid use disorder, methadone, and the dorsolateral prefrontal cortex were the most commonly studied SUD, MAT, and target region. DISCUSSION AND CONCLUSIONS: Our results show a significant effect; however, is limited by a small number of studies with heterogeneous methodology across intervention methods and SUDs. Additional trials are needed to fully assess the clinical impact and mechanisms of combined brain stimulation and pharmacotherapy. We discuss a possible mechanism for synergism from these treatment combinations. SCIENTIFIC SIGNIFICANCE: Adds the first systematic review of combination treatment with TMS or tDCS and MAT in SUD patients to the literature and estimates its overall effect size.

Optimal Frequency in Repetitive Transcranial Magnetic Stimulation for the Management of Chronic Pain: A Network Meta-Analysis of Randomized Controlled Trials.

OBJECTIVE: Repetitive Transcranial Magnetic Stimulation (rTMS) has been shown to be effective for pain modulation in a variety of pathological conditions causing neuropathic pain. The purpose of this study is to conduct a network meta-analysis (NMA) of randomized control trials to identify the most optimal frequency required to achieve chronic pain modulation using rTMS. METHODS: A comprehensive search was conducted in electronic databases to identify randomized controlled trials investigating the efficacy of rTMS for chronic pain management. A total of 24 studies met the inclusion criteria, and a NMA was conducted to identify the most effective rTMS frequency for chronic pain management. RESULTS: Our analysis revealed that high frequency rTMS (20 Hz) was the most effective frequency for chronic pain modulation. Patients treated with 20 Hz had lower pain levels than those treated at 5 Hz (mean difference [MD] = -3.11 [95% confidence interval {CI}: -5.61 - -0.61], P = 0.032) and control (MD = -1.99 [95% CI: -3.11 - -0.88], P = 0.023). Similarly, treatment with 10 Hz had lower pain levels compared to 5 Hz (MD = -2.56 [95% CI: -5.05 - -0.07], P = 0.045) and control (MD = -1.44 [95% CI: -2.52 - -0.36], P = 0.031). 20 Hz and 10 Hz were not statistically different. CONCLUSIONS: This NMA suggests that high frequency rTMS (20 Hz) is the most optimal frequency for chronic pain modulation. These findings have important clinical implications and can guide healthcare professionals in selecting the most effective frequency for rTMS treatment in patients with chronic pain.

Baseline markers of cortical excitation and inhibition predict response to theta burst stimulation treatment for youth depression.

Theta burst stimulation (TBS), a specific form of repetitive transcranial magnetic stimulation (TMS), is a promising treatment for youth with Major Depressive Disorder (MDD) who do not respond to conventional therapies. However, given the variable response to TBS, a greater understanding of how baseline features relate to clinical response is needed to identify which patients are most likely to benefit from this treatment. In the current study, we sought to determine if baseline neurophysiology, specifically cortical excitation and/or inhibition, is associated with antidepressant response to TBS. In two independent open-label clinical trials, youth (aged 16-24 years old) with MDD underwent bilateral dorsolateral prefrontal cortex (DLPFC) TBS treatment. Clinical trial one and two consisted of 10 and 20 daily sessions of bilateral DLPFC TBS, respectively. At baseline, single-pulse TMS combined with electroencephalography was used to assess the neurophysiology of 4 cortical sites: bilateral DLPFC and inferior parietal lobule. Measures of cortical excitation and inhibition were indexed by TMS-evoked potentials (i.e., P30, N45, P60, N100, and P200). Depression severity was measured before, during and after treatment completion using the Hamilton Rating Scale for Depression-17. In both clinical trials, the baseline left DLPFC N45 and P60, which are believed to reflect inhibitory and excitatory mechanisms respectively, were predictors of clinical response. Specifically, greater (i.e., more negative) N45 and smaller P60 baseline values were associated with greater treatment response to TBS. Accordingly, cortical excitation and inhibition circuitry of the left DLPFC may have value as a TBS treatment response biomarker for youth with MDD.Clinical trial 1 registration number: NCT02472470 (June 15, 2015).Clinical trial 2 registration number: NCT03708172 (October 17, 2018).

Machine Learning Identifies Smartwatch-Based Physiological Biomarker for Predicting Disruptive Behavior in Children: A Feasibility Study.

Objective: Parents frequently purchase and inquire about smartwatch devices to monitor child behaviors and functioning. This pilot study examined the feasibility and accuracy of using smartwatch monitoring for the prediction of disruptive behaviors. Methods: The study enrolled children (N = 10) aged 7-10 years hospitalized for the treatment of disruptive behaviors. The study team completed continuous behavioral phenotyping during study participation. The machine learning protocol examined severe behavioral outbursts (operationalized as episodes that preceded physical restraint) for preparing the training data. Supervised machine learning methods were trained with cross-validation to predict three behavior states-calm, playful, and disruptive. Results: The participants had a 90% adherence rate for per protocol smartwatch use. Decision trees derived conditional dependencies of heart rate, sleep, and motor activity to predict behavior. A cross-validation demonstrated 80.89% accuracy of predicting the child's behavior state using these conditional dependencies. Conclusion: This study demonstrated the feasibility of 7-day continuous smartwatch monitoring for children with severe disruptive behaviors. A machine learning approach characterized predictive biomarkers of impending disruptive behaviors. Future validation studies will examine smartwatch physiological biomarkers to enhance behavioral interventions, increase parental engagement in treatment, and demonstrate target engagement in clinical trials of pharmacological agents for young children.