Voort, Jennifer L. Vande

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.

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.

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Intravenous (IV) ketamine and FDA-approved intranasal (IN) esketamine are increasingly used for treatment-resistant depression (TRD). Preliminary studies have suggested a synergistic effect of ketamine and lamotrigine, although the data are inconclusive. Herein, we report the response to serial ketamine/esketamine treatment among patients with TRD with or without lamotrigine therapy. In this historical cohort study, we included adult patients with TRD who received serial IV racemic ketamine (0.5 mg/kg over 40−100 min) or IN esketamine (56/84 mg) treatments. A change in depressive symptoms was assessed using the 16-item Quick Inventory of Depressive Symptomatology self-report (QIDS-SR) scale. There were no significant differences in response or remission rates among the patients on or not on lamotrigine during the ketamine/esketamine treatments. For a percent change in the QIDS-SR from baseline, no interaction was found between the lamotrigine groups and treatment number (p = 0.70), nor the overall effect of the group (p = 0.38). There was a trend towards lower dissociation (based on the CADSS score) among current lamotrigine users, especially in patients who received IV ketamine. A major limitation is the limited number of patients taking lamotrigine (n = 13). This preliminary study provides insufficient evidence that continuing lamotrigine therapy attenuates the antidepressant effect of repeated ketamine/esketamine; however, there seems to be a signal toward attenuating dissociation with lamotrigine in patients receiving serial ketamine treatments. Further observational studies or randomized controlled trials are needed to replicate these findings.

Abstract Background Implicit cognitive markers may assist with the prediction of suicidality beyond clinical risk factors. The aim of this study was to investigate neural correlates associated with the Death/Suicide Implicit Association Test (DS-IAT) via event-related potentials (ERP) in suicidal adolescents. Methods Thirty inpatient adolescents with suicidal ideations and behaviors (SIBS) and 30 healthy controls from the community were recruited. All participants underwent 64-channel electroencephalography, DS-IAT, and clinical assessments. Hierarchical generalized linear models with spatiotemporal clustering were used to identify significant ERPs associated with the behavioral outcome of DS-IAT (D scores) and group differences. Results Behavioral results (D scores) showed that the adolescents with SIBS had stronger implicit associations between “death” and “self” than the healthy group (P = .02). Within adolescents with SIBS, participants with stronger implicit associations between “death” and “self” reported more difficulty in controllability of suicidal ideation in the past 2 weeks based on the Columbia-Suicide Severity Rating Scale (P = .03). For the ERP data, the D scores and N100 component over the left parieto-occipital cortex had significant correlations. Significant group differences without behavioral correlation were observed for a second N100 cluster (P = .01), P200 (P = .02), and late positive potential (5 clusters, all P ≤ .02). Exploratory predictive models combining both neurophysiological and clinical measures distinguished adolescents with SIBS from healthy adolescents. Conclusions Our results suggest that N100 may be a marker of attentional resources involved in the distinction of stimuli that are congruent or incongruent to associations between death and self. Combined clinical and ERP measures may have utility in future refinements of assessment and treatment approaches for adolescents with suicidality.

Background: The cortical silent period (CSP) and long-interval intracortical inhibition (LICI) are putative markers of γ-aminobutyric acid receptor type B (GABAB)-mediated inhibitory neurotransmission. We aimed to assess the association between LICI and CSP in youths. Methods: We analyzed data from three previous studies of youth who underwent CSP and LICI measurements with transcranial magnetic stimulation and electromyography. We assessed CSP and LICI association using Spearman rank correlation tests and multiple linear regression analyses adjusted for demographic and clinical covariates. Results: The sample included 16 healthy participants and 45 participants with depression. The general mean (SD) age was 15.5 (1.7), 14.3 (1.7) for healthy participants, and 15.9 (1.6) years for participants with depression. Measures were nonnormally distributed (Shapiro–Wilk, p < 0.001). CSP and LICI were not correlated at 100-millisecond (ρ = −0.2421, p = 0.06), 150-millisecond (ρ = −0.1612, p = 0.21), or 200-millisecond (ρ = −0.0507, p = 0.70) interstimulus intervals using Spearman rank correlation test. No correlations were found in the multiple regression analysis (p = 0.35). Conclusions: Although previous studies suggest that cortical silent period and long-interval intracortical inhibition measure GABAB receptor-mediated activity, these biomarkers were not associated in our sample of youths. Future studies should focus on the specific physiologic and pharmacodynamic properties assessed by CSP and LICI in younger populations.

Abstract Aim To appraise the current evidence on the efficacy and safety of lamotrigine (LAM) in the treatment of pediatric mood disorders (PMD) (i.e., Major Depressive disorder [MDD], bipolar disorder [BD]). Methods Major databases were searched for randomized controlled trials (RCTs), open‐label trials, and observational studies reporting on pediatric (age < 18 years) patients treated with LAM for mood disorders. Results A total of 3061 abstracts were screened and seven articles were selected for inclusion. Seven studies (319 BD and 43 MDD patients), including one RCT ( n = 173), three prospective ( n = 105), and three retrospective ( n = 84) studies, met the study criteria with a study duration range from 8 to 60.9 weeks. The mean age of this pooled data is 14.6 ± 2.0 years. LAM daily dosage varied from 12.5 to 391.3 mg/day among the studies. In an important finding, the RCT reported favorable outcomes with LAM (HR = 0.46; p = 0.02) in 13‐ to 17‐year‐old age group as compared with 10‐ to 12‐year‐old age group (HR = 0.93; p = 0.88). In addition, time to occurrence of a bipolar event trended toward favoring LAM over placebo. All the studies identified LAM as an effective and safe drug in PMDs especially, BDs. Overall, LAM was well tolerated with no major significant side effects and no cases of Stevens–Johnson syndrome. Conclusions Most studies suggested that LAM was safe and effective in pediatric patients with mood disorders. However, the data regarding the therapeutic range for LAM are lacking. Based on the data, there is inconsistent evidence to make conclusive recommendations on therapeutic LAM dosage for mood improvement in the pediatric population. Further studies including larger sample sizes are required to address this relevant clinical question.

Introduction: The Clinical Global Impressions-Improvement (CGI-I) scale is widely used in clinical research to assess symptoms and functioning in the context of treatment. The correlates of the CGI-I with efficacy scales for adolescent major depressive disorder are poorly understood. This study focused on benchmarking CGI-I scores with changes in the Children's Depression Rating Scale-Revised (CDRS-R) and the Quick Inventory of Depressive Symptomatology-Adolescent (17-item) Self-Report (QIDS-A17-SR). Methods: We examined three datasets with the clinician-rated CDRS-R to ascertain equivalent percent changes in total scores and CGI-I ratings. Exploratory analyses examined corresponding percentage changes in the QIDS-A17-SR and the CGI-I ratings. The CGI-I was the reference scale for nonparametric equipercentile linking with the Equate package in R. Results: CGI-I scores of 1 mapped to ≥78%–95% change in CDRS-R scores at 4–6 weeks across three datasets. CGI-I scores of 2 mapped to 56%–94% change in CDRS-R scores at 4–6 weeks across three studies. CGI-I scores of 3 mapped to 30%–68% changes in CDRS-R scores at 4–6 weeks across three studies. CGI-I scores of 4 mapped to a range of 29%–44% at 4–6 weeks across three studies. There was no significant difference ( p ≥ 0.6) between treatment groups in both the Treatment of Adolescents with Depression and Treatment of Resistant Depression in Adolescents studies, for each CGI-I score ( = 1, or = 2 or = 3, or ≥4), associated mapping of total depression severity score, or associated percent change from baseline for corresponding follow-up visits. There was no significant sex difference ( p > 0.2) in CGI-I linkages to CDRS-R total or percentage changes. Conclusions: These findings establish clear relationships among CGI-I scores and the CDRS-R and the QIDS-A17-SR. These benchmarks have utility for clinical trial study design, inter-rater reliability training, and clinical implementation.

Background The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. Methods The study samples included training datasets ( N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine ( N = 255) or placebo ( N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. Results Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self‐esteem, and depressed feelings) assessed with the Children’s Depression Rating Scale‐Revised at 4–6 weeks predicted treatment outcomes with fluoxetine at 10–12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10–12 week outcomes at 4–6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo‐treated patients with accuracies of 67%. In placebo‐treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. Conclusions PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.