Croarkin, Paul E.

AbstractIntroductionInvasive 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.MethodsWe 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.ResultsData 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.ConclusionYoung 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.

AbstractBackground and ObjectivesTranscranial 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.MethodsA 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.ResultsEight 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 ConclusionsOur 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 SignificanceAdds 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.

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.

High-frequency repetitive transcranial magnetic stimulation (rTMS) to the left dorsolateral prefrontal cortex (L-DLPFC) shows promise as a treatment for treatment-resistant depression in adolescents. Conventional rTMS coil placement strategies include the 5 cm, the Beam F3, and the magnetic resonance imaging (MRI) neuronavigation methods. The purpose of this study was to use electric field (E-field) models to compare the three targeting approaches to a computational E-field optimization coil placement method in depressed adolescents. Ten depressed adolescents (4 females, age: 15.9±1.1) participated in an open-label rTMS treatment study and were offered MRI-guided rTMS five times per week over 6–8 weeks. Head models were generated based on individual MRI images, and E-fields were simulated for the four targeting approaches. Results showed a significant difference in the induced E-fields at the L-DLPFC between the four targeting methods (χ2=24.7, p<0.001). Post hoc pairwise comparisons showed that there was a significant difference between any two of the targeting methods (Holm adjusted p<0.05), with the 5 cm rule producing the weakest E-field (46.0±17.4V/m), followed by the F3 method (87.4±35.4V/m), followed by MRI-guided (112.1±14.6V/m), and followed by the computational approach (130.1±18.1V/m). Variance analysis showed that there was a significant difference in sample variance between the groups (K2=8.0, p<0.05), with F3 having the largest variance. Participants who completed the full course of treatment had median E-fields correlated with depression symptom improvement (r=−0.77, p<0.05). E-field models revealed limitations of scalp-based methods compared to MRI guidance, suggesting computational optimization could enhance dose delivery to the target.