Centromedian region thalamic responsive neurostimulation mitigates idiopathic generalized and multifocal epilepsy with focal to bilateral tonic-clonic seizures.

OBJECTIVE: Although >30% of epilepsy patients have drug-resistant epilepsy (DRE), typically those with generalized or multifocal disease have not traditionally been considered surgical candidates. Responsive neurostimulation (RNS) of the centromedian (CM) region of the thalamus now appears to be a promising therapeutic option for this patient population. We present outcomes following CM RNS for 13 patients with idiopathic generalized epilepsy (IGE) and eight with multifocal onsets that rapidly generalize to bilateral tonic-clonic (focal to bilateral tonic-clonic [FBTC]) seizures. METHODS: A retrospective review of all patients undergoing bilateral CM RNS by the senior author through July 2022 were reviewed. Electrodes were localized and volumes of tissue activation were modeled in Lead-DBS. Changes in patient seizure frequency were extracted from electronic medical records. RESULTS: Twenty-one patients with DRE underwent bilateral CM RNS implantation. For 17 patients with at least 1 year of postimplantation follow-up, average seizure reduction from preoperative baseline was 82.6% (SD = 19.0%, median = 91.7%), with 18% of patients Engel class 1, 29% Engel class 2, 53% Engel class 3, and 0% Engel class 4. There was a trend for average seizure reduction to be greater for patients with nonlesional FBTC seizures than for other patients. For patients achieving at least Engel class 3 outcome, median time to worthwhile seizure reduction was 203.5 days (interquartile range = 110.5-343.75 days). Patients with IGE with myoclonic seizures had a significantly shorter time to worthwhile seizure reduction than other patients. The surgical targeting strategy evolved after the first four subjects to achieve greater anatomic accuracy. SIGNIFICANCE: Patients with both primary and rapidly generalized epilepsy who underwent CM RNS experienced substantial seizure relief. Subsets of these patient populations may particularly benefit from CM RNS. The refinement of lead targeting, tuning of RNS system parameters, and patient selection are ongoing areas of investigation.

Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants.

Brain computer interfaces (BCI) provide unprecedented spatiotemporal precision that will enable significant expansion in how numerous brain disorders are treated. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for identifying and advancing novel clinical BCI approaches does not exist. Here, we developed a platform that integrates brain signal decoding with connectomics and demonstrate its utility across 123 hours of invasively recorded brain data from 73 neurosurgical patients treated for movement disorders, depression and epilepsy. First, we introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the US, Europe and China. Next, we reveal network targets for emotion decoding in left prefrontal and cingulate circuits in DBS patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our platform provides rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neuromodulation therapies in response to the individual needs of patients.

Deep net detection and onset prediction of electrographic seizure patterns in responsive neurostimulation.

OBJECTIVE: Managing the progress of drug-resistant epilepsy patients implanted with the Responsive Neurostimulation (RNS) System requires the manual evaluation of hundreds of hours of intracranial recordings. The generation of these large amounts of data and the scarcity of experts' time for evaluation necessitate the development of automatic tools to detect intracranial electroencephalographic (iEEG) seizure patterns (iESPs) with expert-level accuracy. We developed an intelligent system for identifying the presence and onset time of iESPs in iEEG recordings from the RNS device. METHODS: An iEEG dataset from 24 patients (36 293 recordings) recorded by the RNS System was used for training and evaluating a neural network model (iESPnet). The model was trained to identify the probability of seizure onset at each sample point of the iEEG. The reliability of the net was assessed and compared to baseline methods, including detections made by the device. iESPnet performance was measured using balanced accuracy and the F1 score for iESP detection. The prediction time was assessed via both the error and the mean absolute error. The model was evaluated following a hold-one-out strategy, and then validated in a separate cohort of 26 patients from a different medical center. RESULTS: iESPnet detected the presence of an iESP with a mean accuracy value of 90% and an onset time prediction error of approximately 3.4 s. There was no relationship between electrode location and prediction outcome. Model outputs were well calibrated and unbiased by the RNS detections. Validation on a separate cohort further supported iESPnet applicability in real clinical scenarios. Importantly, RNS device detections were found to be less accurate and delayed in nonresponders; therefore, tools to improve the accuracy of seizure detection are critical for increasing therapeutic efficacy. SIGNIFICANCE: iESPnet is a reliable and accurate tool with the potential to alleviate the time-consuming manual inspection of iESPs and facilitate the evaluation of therapeutic response in RNS-implanted patients.

Multiorganizational consensus to define guiding principles for perioperative pain management in patients with chronic pain, preoperative opioid tolerance, or substance use disorder.

Significant knowledge gaps exist in the perioperative pain management of patients with a history of chronic pain, substance use disorder, and/or opioid tolerance as highlighted in the US Health and Human Services Pain Management Best Practices Inter-Agency Task Force 2019 report. The report emphasized the challenges of caring for these populations and the need for multidisciplinary care and a comprehensive approach. Such care requires stakeholder alignment across multiple specialties and care settings. With the intention of codifying this alignment into a reliable and efficient processes, a consortium of 15 professional healthcare societies was convened in a year-long modified Delphi consensus process and summit. This process produced seven guiding principles for the perioperative care of patients with chronic pain, substance use disorder, and/or preoperative opioid tolerance. These principles provide a framework and direction for future improvement in the optimization and care of 'complex' patients as they undergo surgical procedures.

The microrandomized trial for developing digital interventions: Experimental design and data analysis considerations.

Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted-weekly, daily, or even many times a day. The microrandomized trial (MRT) has emerged for use in informing the construction of JITAIs. MRTs can be used to address research questions about whether and under what circumstances JITAI components are effective, with the ultimate objective of developing effective and efficient JITAI. The purpose of this article is to clarify why, when, and how to use MRTs; to highlight elements that must be considered when designing and implementing an MRT; and to review primary and secondary analyses methods for MRTs. We briefly review key elements of JITAIs and discuss a variety of considerations that go into planning and designing an MRT. We provide a definition of causal excursion effects suitable for use in primary and secondary analyses of MRT data to inform JITAI development. We review the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as R (R Core Team, 2019). Throughout we illustrate the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to increase physical activity among sedentary individuals. We supplement the HeartSteps MRT with two other MRTs, SARA and BariFit, each of which highlights different research questions that can be addressed using the MRT and experimental design considerations that might arise. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A multisociety organizational consensus process to define guiding principles for acute perioperative pain management.

The US Health and Human Services Pain Management Best Practices Inter-Agency Task Force initiated a public-private partnership which led to the publication of its report in 2019. The report emphasized the need for individualized, multimodal, and multidisciplinary approaches to pain management that decrease the over-reliance on opioids, increase access to care, and promote widespread education on pain and substance use disorders. The Task Force specifically called on specialty organizations to work together to develop evidence-based guidelines. In response to this report's recommendations, a consortium of 14 professional healthcare societies committed to a 2-year project to advance pain management for the surgical patient and improve opioid safety. The modified Delphi process included two rounds of electronic voting and culminated in a live virtual event in February 2021, during which seven common guiding principles were established for acute perioperative pain management. These principles should help to inform local action and future development of clinical practice recommendations.

Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing.

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

Mobile health use predicts self-efficacy and self-management in adolescents with sickle cell disease.

Sickle cell disease (SCD) is associated with significant health challenges that often worsen during adolescence. Living with SCD requires a substantial amount of self-management and mobile health (mHealth) holds considerable promise for assessing and changing behaviors to improve health outcomes. We integrated a mobile app as an adjunct to a group intervention (SCThrive) and hypothesized that more engagement with the mHealth app would increase self-management and self-efficacy for adolescents and young adults (AYA) with SCD. Twenty-six AYA ages 13-21 years (54% female; 46% HbSS genotype; all African-American/Black) received six weekly group sessions (three in-person, three online). Participants were provided with the mobile app (iManage for SCD) to record progress on their self-management goals and log pain and mood symptoms. The Transition Readiness Assessment Questionnaire (TRAQ-5) assessed self-management skills and the Patient Activation Measure (PAM-13) assessed self-efficacy at baseline and post-treatment. Logging on to the app more frequently was associated higher mood ratings (r = .54, CI[.18, .77], p = .006) and lower pain ratings (r = -.48, CI[-.77, -.02], p = .04). Regression analyses demonstrated that after controlling for scores at baseline, the number of logins to the app predicted self-management skills (p = .05, η2 = .17) and possibly self-efficacy (p = .08, η2 = .13). Our study findings indicate that it can be challenging to maintain engagement in mHealth for AYA with SCD, but for those who do engage, there are significant benefits related to self-management, self-efficacy, and managing pain and mood.

Neuroethics in the Shadow of a Pandemic.

Neuroethics under the BRAIN Initiative has been focused upon both the neuroethical implications of basic advances in neuroscience, as well as the ethics attending the development of ever more powerful tools to both understand the brain and treat dysfunction. It has focused on health and disease in the context of the pre-pandemic status quo, essentially divorced from issues like infectious disease and large-scale disruption of social and economic structures. The questions animating the neuroethics of the BRAIN Initiative, on first glance, seemingly fail to intersect with the primary concerns of a post-Covid world, but careful consideration shows that they of course do. After all, the brain's job is to model and respond to the pressures of our environment, and the environment of virtually all of humanity has changed in a dramatic way, unprecedented since the rise of modern neuroscience. Here we consider ways in which neuroethics work aligned with the BRAIN Initiative can inform our response to the Covid crisis, as well as ways in which the pandemic may shape future work in neuroethics. In particular we focus on neuroethics work on agency.

Ethical dilemmas posed by mobile health and machine learning in psychiatry research.

The application of digital technology to psychiatry research is rapidly leading to new discoveries and capabilities in the field of mobile health. However, the increase in opportunities to passively collect vast amounts of detailed information on study participants coupled with advances in statistical techniques that enable machine learning models to process such information has raised novel ethical dilemmas regarding researchers' duties to: (i) monitor adverse events and intervene accordingly; (ii) obtain fully informed, voluntary consent; (iii) protect the privacy of participants; and (iv) increase the transparency of powerful, machine learning models to ensure they can be applied ethically and fairly in psychiatric care. This review highlights emerging ethical challenges and unresolved ethical questions in mobile health research and provides recommendations on how mobile health researchers can address these issues in practice. Ultimately, the hope is that this review will facilitate continued discussion on how to achieve best practice in mobile health research within psychiatry.

L'application des technologies numériques à la recherche psychiatrique entraîne rapidement de nouvelles découvertes et capacités en matière de santé mobile. Cependant, la multiplication des opportunités de recueillir passivement d'immenses quantités d'informations détaillées sur les participants aux études combinée aux progrès des techniques statistiques permettant aux modèles d'apprentissage automatique de traiter de telles informations a soulevé de nouveaux dilemmes éthiques concernant l'obligation des chercheurs: (i) de surveiller les effets indésirables et d'intervenir en conséquence; (ii) d'obtenir un consentement pleinement éclairé et volontaire; (iii) de protéger la vie privée des participants; et enfin, (iv) d'améliorer la transparence des puissants modèles d'apprentissage automatique afin de garantir une application éthique et impartiale dans le domaine des soins psychiatriques. Ce rapport identifie les défis qui en découlent ainsi que les questions éthiques non résolues en matière de santé mobile. Il formule également des recommandations sur la façon dont les chercheurs en santé mobile peuvent résoudre ces problèmes dans la pratique. À terme, nous espérons que ce rapport favorisera la poursuite des discussions portant sur les moyens de définir des méthodes de recherche adéquates pour la santé mobile en psychiatrie.

La aplicación de la tecnología digital a la investigación en psiquiatría está conduciendo rápidamente a descubrimientos y capacidades nuevas en el ámbito de la salud móvil. No obstante, el incremento de las oportunidades para recopilar pasivamente grandes volúmenes de información detallada sobre los participantes en los estudios, junto con los avances en las técnicas de estadística que permiten a los modelos de aprendizaje automático procesar tal información, ha planteado nuevos dilemas éticos relativos a los deberes de los investigadores: (i) hacer un seguimiento de los eventos adversos e intervenir en consecuencia; (ii) obtener un consentimiento voluntario plenamente informado; (iii) proteger la privacidad de los participantes; y (iv) aumentar la transparencia de los modelos potentes de aprendizaje automático para asegurar que puedan aplicarse de manera ética y justa en la atención psiquiátrica. En este análisis se destacan tanto los desafíos éticos nuevos como las cuestiones éticas aún sin resolver en la investigación sobre la salud móvil y se formulan recomendaciones sobre cómo los investigadores de la salud móvil pueden abordar dichas cuestiones en la práctica. En última instancia, se espera que este análisis facilite un debate continuo sobre cómo lograr las mejores prácticas en la investigación de la salud móvil dentro de la psiquiatría.

Histone hyperacetylation disrupts core gene regulatory architecture in rhabdomyosarcoma.

Core regulatory transcription factors (CR TFs) orchestrate the placement of super-enhancers (SEs) to activate transcription of cell-identity specifying gene networks, and are critical in promoting cancer. Here, we define the core regulatory circuitry of rhabdomyosarcoma and identify critical CR TF dependencies. These CR TFs build SEs that have the highest levels of histone acetylation, yet paradoxically the same SEs also harbor the greatest amounts of histone deacetylases. We find that hyperacetylation selectively halts CR TF transcription. To investigate the architectural determinants of this phenotype, we used absolute quantification of architecture (AQuA) HiChIP, which revealed erosion of native SE contacts, and aberrant spreading of contacts that involved histone acetylation. Hyperacetylation removes RNA polymerase II (RNA Pol II) from core regulatory genetic elements, and eliminates RNA Pol II but not BRD4 phase condensates. This study identifies an SE-specific requirement for balancing histone modification states to maintain SE architecture and CR TF transcription.

Robustness of RNA sequencing on older formalin-fixed paraffin-embedded tissue from high-grade ovarian serous adenocarcinomas.

Formalin-fixed paraffin-embedded (FFPE) tissues are among the most widely available clinical specimens. Their potential utility as a source of RNA for transcriptome studies would greatly enhance population-based cancer studies. Although preliminary studies suggest FFPE tissue may be used for RNA sequencing, the effect of storage time on these specimens needs to be determined. We conducted this study to determine whether RNA in archived FFPE high-grade ovarian serous adenocarcinomas from Surveillance, Epidemiology and End Results (SEER) registries was present in sufficient quantity and quality for RNA-Seq analysis. FFPE tissues, stored from 7 to 32 years, were obtained from three SEER sites. RNA was extracted, quantified, quality assessed, and subjected to RNA-Seq (a whole transcriptome sequencing technology). FFPE specimens stored for longer periods of time had poorer RNA sample quality as indicated by negative correlations between specimen storage time and fragment distribution values (DV). In addition, sample contamination was a common issue among the RNA, with 41 of 67 samples having 5% to 48% bacterial contamination. However, regardless of specimen storage time and bacterial contamination, 60% of the samples yielded data that enabled gene expression quantification, identifying more than 10,000 genes, with the correlations among most biological replicates above 0.7. This study demonstrates that FFPE high-grade ovarian serous adenocarcinomas specimens stored in repositories for up to 32 years and under varying storage conditions are a promising source of RNA for RNA-Seq. We also describe certain caveats to be considered when designing RNA-Seq studies using archived FFPE tissues.

Author Correction: Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration.

In the version of this article initially published, in Supplementary Data 5, the logFC, FC, P value and adjusted P value for advanced AMD versus control (DE 4/1) without age correction did not correspond to the correct gene IDs. The errors have been corrected in the HTML version of the article.

Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration.

Genome-wide association studies (GWAS) have identified genetic variants at 34 loci contributing to age-related macular degeneration (AMD)1-3. We generated transcriptional profiles of postmortem retinas from 453 controls and cases at distinct stages of AMD and integrated retinal transcriptomes, covering 13,662 protein-coding and 1,462 noncoding genes, with genotypes at more than 9 million common SNPs for expression quantitative trait loci (eQTL) analysis of a tissue not included in Genotype-Tissue Expression (GTEx) and other large datasets4,5. Cis-eQTL analysis identified 10,474 genes under genetic regulation, including 4,541 eQTLs detected only in the retina. Integrated analysis of AMD-GWAS with eQTLs ascertained likely target genes at six reported loci. Using transcriptome-wide association analysis (TWAS), we identified three additional genes, RLBP1, HIC1 and PARP12, after Bonferroni correction. Our studies expand the genetic landscape of AMD and establish the Eye Genotype Expression (EyeGEx) database as a resource for post-GWAS interpretation of multifactorial ocular traits.

Multi-person and multisensory synchronization during group dancing.

Synchronized group dancing is one of the hallmarks of both coordination and cooperation in the humans species. While a large amount of research has focused on joint action in dyads, the mechanisms of coordination in larger groups are not well understood. In the present study, we explored the coordination dynamics of a group of folk dancers by examining the influence of three sensory-coupling channels on the stability of group coordination. Using 3D motion capture, we recorded a group of 13 expert folk dancers performing to the beat of music (auditory coupling) while holding hands in a circle (haptic coupling) and seeing their fellow dancers (visual coupling). Analyses of group synchrony using cluster phase analysis demonstrated that selective elimination of any one of the three types of sensory coupling significantly reduced group synchrony, where haptic coupling had the strongest effect on movements in the horizontal plane, but also impacted the vertical axis. This study provides some of the first evidence of how sensory couplings support multi-person coordination in a large group, and in particular the effect of body contact on this coordination.

Development of a Hydroxyurea Decision Aid for Parents of Children With Sickle Cell Anemia.

National evidence-based guidelines recommend offering hydroxyurea to patients with sickle cell anemia 9 months of age and older using shared decision making, but offer no strategies to aid implementation. We developed a hydroxyurea multicomponent decision aid via a needs assessment, clinic observations, and iterative feedback to address parent decision needs and promote a discussion between clinicians and parents. A total of 75 parents and 28 clinicians participated across all phases. The decision aid was rated as useful. Hydroxyurea knowledge improved and decisional conflict decreased supporting the potential for use to facilitate shared decision making in pediatric sickle cell anemia.

MEK inhibition induces MYOG and remodels super-enhancers in RAS-driven rhabdomyosarcoma.

The RAS isoforms are frequently mutated in many types of human cancers, including PAX3/PAX7 fusion-negative rhabdomyosarcoma. Pediatric RMS arises from skeletal muscle progenitor cells that have failed to differentiate normally. The role of mutant RAS in this differentiation blockade is incompletely understood. We demonstrate that oncogenic RAS, acting through the RAF-MEK [mitogen-activated protein kinase (MAPK) kinase]-ERK (extracellular signal-regulated kinase) MAPK effector pathway, inhibits myogenic differentiation in rhabdomyosarcoma by repressing the expression of the prodifferentiation myogenic transcription factor, MYOG. This repression is mediated by ERK2-dependent promoter-proximal stalling of RNA polymerase II at the MYOG locus. Small-molecule screening with a library of mechanistically defined inhibitors showed that RAS-driven RMS is vulnerable to MEK inhibition. MEK inhibition with trametinib leads to the loss of ERK2 at the MYOG promoter and releases the transcriptional stalling of MYOG expression. MYOG subsequently opens chromatin and establishes super-enhancers at genes required for late myogenic differentiation. Furthermore, trametinib, in combination with an inhibitor of IGF1R, potently decreases rhabdomyosarcoma cell viability and slows tumor growth in xenograft models. Therefore, this combination represents a potential therapeutic for RAS-mutated rhabdomyosarcoma.

Clinically Relevant Cytotoxic Immune Cell Signatures and Clonal Expansion of T-Cell Receptors in High-Risk MYCN-Not-Amplified Human Neuroblastoma.

Purpose: High-risk neuroblastoma is an aggressive disease. DNA sequencing studies have revealed a paucity of actionable genomic alterations and a low mutation burden, posing challenges to develop effective novel therapies. We used RNA sequencing (RNA-seq) to investigate the biology of this disease, including a focus on tumor-infiltrating lymphocytes (TIL).Experimental Design: We performed deep RNA-seq on pretreatment diagnostic tumors from 129 high-risk and 21 low- or intermediate-risk patients with neuroblastomas. We used single-sample gene set enrichment analysis to detect gene expression signatures of TILs in tumors and examined their association with clinical and molecular parameters, including patient outcome. The expression profiles of 190 additional pretreatment diagnostic neuroblastomas, a neuroblastoma tissue microarray, and T-cell receptor (TCR) sequencing were used to validate our findings.Results: We found that MYCN-not-amplified (MYCN-NA) tumors had significantly higher cytotoxic TIL signatures compared with MYCN-amplified (MYCN-A) tumors. A reported MYCN activation signature was significantly associated with poor outcome for high-risk patients with MYCN-NA tumors; however, a subgroup of these patients who had elevated activated natural killer (NK) cells, CD8+ T cells, and cytolytic signatures showed improved outcome and expansion of infiltrating TCR clones. Furthermore, we observed upregulation of immune exhaustion marker genes, indicating an immune-suppressive microenvironment in these neuroblastomas.Conclusions: This study provides evidence that RNA signatures of cytotoxic TIL are associated with the presence of activated NK/T cells and improved outcomes in high-risk neuroblastoma patients harboring MYCN-NA tumors. Our findings suggest that these high-risk patients with MYCN-NA neuroblastoma may benefit from additional immunotherapies incorporated into the current therapeutic strategies. Clin Cancer Res; 24(22); 5673-84. ©2018 AACR.

Optimizing Digital Integrated Care via Micro-Randomized Trials.

Mobile health (mHealth) interventions are a promising tool in providing digitally mediated integrative care. They can extend care outside of the clinic by providing reminders to take medications, assisting in managing symptoms, and supporting healthy behaviors including physical activity, healthy eating, and stress management. mHealth interventions can adapt the delivery of care across time in order to optimize treatment effectiveness. Yet there exists limited empirical evidence useful to the development of adaptive mHealth interventions. This article describes a new randomized trial design, the Micro-Randomized Trial (MRT), for informing the development of mHealth interventions. We provide examples of scientific questions important to the development of an mHealth intervention, and describe how these questions can be answered using an MRT.