Assessment of the excitation-inhibition ratio in the Fmr1 KO2 mouse using neuronal oscillation dynamics.

In vitro and ex vivo studies have shown consistent indications of hyperexcitability in the Fragile X Messenger Ribonucleoprotein 1 (Fmr1) knockout mouse model of autism spectrum disorder. We recently introduced a method to quantify network-level functional excitation-inhibition ratio from the neuronal oscillations. Here, we used this measure to study whether the implicated synaptic excitation-inhibition disturbances translate to disturbances in network physiology in the Fragile X Messenger Ribonucleoprotein 1 (Fmr1) gene knockout model. Vigilance-state scoring was used to extract segments of inactive wakefulness as an equivalent behavioral condition to the human resting-state and, subsequently, we performed high-frequency resolution analysis of the functional excitation-inhibition biomarker, long-range temporal correlations, and spectral power. We corroborated earlier studies showing increased high-frequency power in Fragile X Messenger Ribonucleoprotein 1 (Fmr1) knockout mice. Long-range temporal correlations were higher in the gamma frequency ranges. Contrary to expectations, functional excitation-inhibition was lower in the knockout mice in high frequency ranges, suggesting more inhibition-dominated networks. Exposure to the Gamma-aminobutyric acid (GABA)-agonist clonazepam decreased the functional excitation-inhibition in both genotypes, confirming that increasing inhibitory tone results in a reduction of functional excitation-inhibition. In addition, clonazepam decreased electroencephalogram power and increased long-range temporal correlations in both genotypes. These findings show applicability of these new resting-state electroencephalogram biomarkers to animal for translational studies and allow investigation of the effects of lower-level disturbances in excitation-inhibition balance.

Corrigendum to "Effectiveness of L-serine supplementation in children with a GRIN2B loss-of-function mutation: Rationale and protocol for single patient (n-of-1) multiple cross-over trials" [Contemp. Clin. Trials Commun. 36 (2023)/ DOI: 10.1016/j.conctc.2023.101233].

[This corrects the article DOI: 10.1016/j.conctc.2023.101233.].

Effects of Bumetanide on Neurocognitive Functioning in Children with Autism Spectrum Disorder: Secondary Analysis of a Randomized Placebo-Controlled Trial.

We present the secondary-analysis of neurocognitive tests in the 'Bumetanide in Autism Medication and Biomarker' (BAMBI;EUDRA-CT-2014-001560-35) study, a randomized double-blind placebo-controlled (1:1) trial testing 3-months bumetanide treatment (≤ 1 mg twice-daily) in unmedicated children 7-15 years with ASD. Children with IQ ≥ 70 were analyzed for baseline deficits and treatment-effects on the intention-to-treat-population with generalized-linear-models, principal component analysis and network analysis. Ninety-two children were allocated to treatment and 83 eligible for analyses. Heterogeneous neurocognitive impairments were found that were unaffected by bumetanide treatment. Network analysis showed higher modularity after treatment (mean difference:-0.165, 95%CI:-0.317 to - 0.013,p = .034) and changes in the relative importance of response inhibition in the neurocognitive network (mean difference:-0.037, 95%CI:-0.073 to - 0.001,p = .042). This study offers perspectives to include neurocognitive tests in ASD trials.

Effectiveness of L-serine supplementation in children with a GRIN2B loss-of-function mutation: Rationale and protocol for single patient (n-of-1) multiple cross-over trials.

Rationale: Loss-of-function (LoF) mutations in GRIN2B result in neurologic abnormalities due to N-methyl-D-aspartate receptor (NMDAR) dysfunction. Affected persons present with various symptoms, including intellectual developmental disability (IDD), hypotonia, communication deficits, motor impairment, complex behavior, seizures, sleep disorders and gastrointestinal disturbance. Recently, in vitro experiments showed that D-serine mitigates function to GluN2B (mutation)-containing NMDARs. 11 previous case reports are published on (experimental) L-serine treatment of patients between 1.5 and 12 years old with GRIN2B missense or null mutations, some of whom showed notable improvement in motor and cognitive performance, communication, behavior and abnormalities on electro encephalography (EEG). Our objective is to further evaluate the effectiveness of L-serine for GRIN2B-related neurodevelopmental disorder (GRIN2B-NDD), using an n-of-1 trial design, increasing the level of evidence. Methods/design: These n-of-1 trials, consisting of 2 cycles of 6 months, will be performed to evaluate the effect of L-serine compared to placebo in 4 patients with a GRIN2B LoF mutation. The aggregation of multiple n-of-1 trials will provide an estimate of the average treatment effects.The primary outcome is the Perceive-Recall-Plan-Perform of Task Analysis, assessing developmental skills. Secondary outcomes include Goal Attainment Scaling, seizure log books, EEGs, sleep log books, the irritability subscale of the Aberrant Behavior Checklist, the Bristol Stool Scale and the Pediatric Quality of Life Inventory. Conclusion: This study employs an innovative methodological approach to evaluate the effectiveness of L-serine for patients with a GRIN2B LoF mutation. The results will establish a foundation for implementing L-serine as a disease-modifying treatment in GRIN2B-NDD.

The effects of COVID-19 on child mental health: Biannual assessments up to April 2022 in a clinical and two general population samples.

Background: The COVID-19 pandemic has had an acute impact on child mental and social health, but long-term effects are still unclear. We examined how child mental health has developed since the start of the COVID-19 pandemic up to 2 years into the pandemic (April 2022). Methods: We included children (age 8-18) from two general population samples (N = 222-1333 per measurement and N = 2401-13,362 for pre-covid data) and one clinical sample receiving psychiatric care (N = 334-748). Behavioral questionnaire data were assessed five times from April 2020 till April 2022 and pre-pandemic data were available for both general population samples. We collected parent-reported data on internalizing and externalizing problems with the Brief Problem Monitor and self-reported data on Anxiety, Depressive symptoms, Sleep-related impairments, Anger, Global health, and Peer relations with the Patient-Reported Outcomes Measurement Information System (PROMIS®). Results: In all samples, parents reported overall increased internalizing problems, but no increases in externalizing problems, in their children. Children from the general population self-reported increased mental health problems from before to during the pandemic on all six PROMIS domains, with generally worst scores in April 2021, and scores improving toward April 2022 but not to pre-pandemic norms. Children from the clinical sample reported increased mental health problems throughout the pandemic, with generally worst scores in April 2021 or April 2022 and no improvement. We found evidence of minor age effects and no sex effects. Conclusions: Child mental health in the general population has deteriorated during the first phase of the COVID-19 pandemic, has improved since April 2021, but has not yet returned to pre-pandemic levels. Children in psychiatric care show worsening of mental health problems during the pandemic, which has not improved since. Changes in child mental health should be monitored comprehensively to inform health care and policy.

Profiles of autism characteristics in thirteen genetic syndromes: a machine learning approach.

BACKGROUND: Phenotypic studies have identified distinct patterns of autistic characteristics in genetic syndromes associated with intellectual disability (ID), leading to diagnostic uncertainty and compromised access to autism-related support. Previous research has tended to include small samples and diverse measures, which limits the generalisability of findings. In this study, we generated detailed profiles of autistic characteristics in a large sample of > 1500 individuals with rare genetic syndromes. METHODS: Profiles of autistic characteristics based on the Social Communication Questionnaire (SCQ) scores were generated for thirteen genetic syndrome groups (Angelman n = 154, Cri du Chat n = 75, Cornelia de Lange n = 199, fragile X n = 297, Prader-Willi n = 278, Lowe n = 89, Smith-Magenis n = 54, Down n = 135, Sotos n = 40, Rubinstein-Taybi n = 102, 1p36 deletion n = 41, tuberous sclerosis complex n = 83 and Phelan-McDermid n = 35 syndromes). It was hypothesised that each syndrome group would evidence a degree of specificity in autistic characteristics. To test this hypothesis, a classification algorithm via support vector machine (SVM) learning was applied to scores from over 1500 individuals diagnosed with one of the thirteen genetic syndromes and autistic individuals who did not have a known genetic syndrome (ASD; n = 254). Self-help skills were included as an additional predictor. RESULTS: Genetic syndromes were associated with different but overlapping autism-related profiles, indicated by the substantial accuracy of the entire, multiclass SVM model (55% correctly classified individuals). Syndrome groups such as Angelman, fragile X, Prader-Willi, Rubinstein-Taybi and Cornelia de Lange showed greater phenotypic specificity than groups such as Cri du Chat, Lowe, Smith-Magenis, tuberous sclerosis complex, Sotos and Phelan-McDermid. The inclusion of the ASD reference group and self-help skills did not change the model accuracy. LIMITATIONS: The key limitations of our study include a cross-sectional design, reliance on a screening tool which focuses primarily on social communication skills and imbalanced sample size across syndrome groups. CONCLUSIONS: These findings replicate and extend previous work, demonstrating syndrome-specific profiles of autistic characteristics in people with genetic syndromes compared to autistic individuals without a genetic syndrome. This work calls for greater precision of assessment of autistic characteristics in individuals with genetic syndromes associated with ID.

Prediction of Behavioral Improvement Through Resting-State Electroencephalography and Clinical Severity in a Randomized Controlled Trial Testing Bumetanide in Autism Spectrum Disorder.

BACKGROUND: Mechanism-based treatments such as bumetanide are being repurposed for autism spectrum disorder. We recently reported beneficial effects on repetitive behavioral symptoms that might be related to regulating excitation-inhibition (E/I) balance in the brain. Here, we tested the neurophysiological effects of bumetanide and the relationship to clinical outcome variability and investigated the potential for machine learning-based predictions of meaningful clinical improvement. METHODS: Using modified linear mixed models applied to intention-to-treat population, we analyzed E/I-sensitive electroencephalography (EEG) measures before and after 91 days of treatment in the double-blind, randomized, placebo-controlled Bumetanide in Autism Medication and Biomarker study. Resting-state EEG of 82 subjects out of 92 participants (7-15 years) were available. Alpha frequency band absolute and relative power, central frequency, long-range temporal correlations, and functional E/I ratio treatment effects were related to the Repetitive Behavior Scale-Revised (RBS-R) and the Social Responsiveness Scale 2 as clinical outcomes. RESULTS: We observed superior bumetanide effects on EEG, reflected in increased absolute and relative alpha power and functional E/I ratio and in decreased central frequency. Associations between EEG and clinical outcome change were restricted to subgroups with medium to high RBS-R improvement. Using machine learning, medium and high RBS-R improvement could be predicted by baseline RBS-R score and EEG measures with 80% and 92% accuracy, respectively. CONCLUSIONS: Bumetanide exerts neurophysiological effects related to clinical changes in more responsive subsets, in whom prediction of improvement was feasible through EEG and clinical measures.

Internalizing problems before and during the COVID-19 pandemic in independent samples of Dutch children and adolescents with and without pre-existing mental health problems.

The aim of the study was to assess internalizing problems before and during the pandemic with data from Dutch consortium Child and adolescent mental health and wellbeing in times of the COVID-19 pandemic, consisting of two Dutch general population samples (GS) and two clinical samples (CS) referred to youth/psychiatric care. Measures of internalizing problems were obtained from ongoing data collections pre-pandemic (NGS = 35,357; NCS = 4487) and twice during the pandemic, in Apr-May 2020 (NGS = 3938; clinical: NCS = 1008) and in Nov-Dec 2020 (NGS = 1489; NCS = 1536), in children and adolescents (8-18 years) with parent (Brief Problem Monitor) and/or child reports (Patient-Reported Outcomes Measurement Information System®). Results show that, in the general population, internalizing problems were higher during the first peak of the pandemic compared to pre-pandemic based on both child and parent reports. Yet, over the course of the pandemic, on both child and parent reports, similar or lower levels of internalizing problems were observed. Children in the clinical population reported more internalizing symptoms over the course of the pandemic while parents did not report differences in internalizing symptoms from pre-pandemic to the first peak of the pandemic nor over the course of the pandemic. Overall, the findings indicate that children and adolescents of both the general and clinical population were affected negatively by the pandemic in terms of their internalizing problems. Attention is therefore warranted to investigate long-term effects and to monitor if internalizing problems return to pre-pandemic levels or if they remain elevated post-pandemic.

Improved Manual Annotation of EEG Signals through Convolutional Neural Network Guidance.

The development of validated algorithms for automated handling of artifacts is essential for reliable and fast processing of EEG signals. Recently, there have been methodological advances in designing machine-learning algorithms to improve artifact detection of trained professionals who usually meticulously inspect and manually annotate EEG signals. However, validation of these methods is hindered by the lack of a gold standard as data are mostly private and data annotation is time consuming and error prone. In the effort to circumvent these issues, we propose an iterative learning model to speed up and reduce errors of manual annotation of EEG. We use a convolutional neural network (CNN) to train on expert-annotated eyes-open and eyes-closed resting-state EEG data from typically developing children (n = 30) and children with neurodevelopmental disorders (n = 141). To overcome the circular reasoning of aiming to develop a new algorithm and benchmarking to a manually-annotated gold standard, we instead aim to improve the gold standard by revising the portion of the data that was incorrectly learned by the network. When blindly presented with the selected signals for re-assessment (23% of the data), the two independent expert-annotators changed the annotation in 25% of the cases. Subsequently, the network was trained on the expert-revised gold standard, which resulted in improved separation between artifacts and nonartifacts as well as an increase in balanced accuracy from 74% to 80% and precision from 59% to 76%. These results show that CNNs are promising to enhance manual annotation of EEG artifacts and can be improved further with better gold-standard data.

Single-case experimental designs for bumetanide across neurodevelopmental disorders: BUDDI protocol.

BACKGROUND: Bumetanide is a selective NKCC1 chloride importer antagonist which is being repurposed as a mechanism-based treatment for neurodevelopmental disorders (NDDs). Due to their specific actions, these kinds of interventions will only be effective in particular subsets of patients. To anticipate stratified application, we recently completed three bumetanide trials each focusing on different stratification strategies with the additional objective of deriving the most optimal endpoints. Here we publish the protocol of the post-trial access combined cohort study to confirm previous effects and stratification strategies in the trial cohorts and in new participants. METHOD/DESIGN: Participants of the three previous cohorts and a new cohort will be subjected to 6 months bumetanide treatment using multiple baseline Single Case Experimental Designs. The primary outcome is the change, relative to baseline, in a set of patient reported outcome measures focused on direct and indirect effects of sensory processing difficulties. Secondary outcome measures include the conventional questionnaires 'social responsiveness scale', 'repetitive behavior scale', 'sensory profile' and 'aberrant behavior scale'. Resting-state EEG measurements will be performed at several time-points including at Tmax after the first administration. Assessment of cognitive endpoints will be conducted using the novel Emma Tool box, an in-house designed battery of computerized tests to measure neurocognitive functions in children. DISCUSSION: This study aims to replicate previously shown effects of bumetanide in NDD subpopulations, validate a recently proposed treatment prediction effect methodology and refine endpoint measurements. TRIAL REGISTRATION: EudraCT: 2020-002196-35, registered 16 November 2020, https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-002196-35/NL.

Towards PErsonalised PRognosis for children with traumatic brain injury: the PEPR study protocol.

INTRODUCTION: Traumatic brain injury (TBI) in children can be associated with poor outcome in crucial functional domains, including motor, neurocognitive and behavioural functioning. However, outcome varies between patients and is mediated by complex interplay between demographic factors, premorbid functioning and (sub)acute clinical characteristics. At present, methods to understand let alone predict outcome on the basis of these variables are lacking, which contributes to unnecessary follow-up as well as undetected impairments in children. Therefore, this study aims to develop prognostic models for the individual outcome of children with TBI in a range of important developmental domains. In addition, the potential added value of advanced neuroimaging data and the use of machine learning algorithms in the development of prognostic models will be assessed. METHODS AND ANALYSIS: 210 children aged 4-18 years diagnosed with mild-to-severe TBI will be prospectively recruited from a research network of Dutch hospitals. They will be matched 2:1 to a control group of neurologically healthy children (n=105). Predictors in the model will include demographic, premorbid and clinical measures prospectively registered from the TBI hospital admission onwards as well as MRI metrics assessed at 1 month post-injury. Outcome measures of the prognostic models are (1) motor functioning, (2) intelligence, (3) behavioural functioning and (4) school performance, all assessed at 6 months post-injury. ETHICS AND DISSEMINATION: Ethics has been obtained from the Medical Ethical Board of the Amsterdam UMC (location AMC). Findings of our multicentre prospective study will enable clinicians to identify TBI children at risk and aim towards a personalised prognosis. Lastly, findings will be submitted for publication in open access, international and peer-reviewed journals. TRIAL REGISTRATION NUMBER: NL71283.018.19 and NL9051.

Resting-state network organisation in children with traumatic brain injury.

Children with traumatic brain injury are at risk of neurocognitive and behavioural impairment. Although there is evidence for abnormal brain activity in resting-state networks after TBI, the role of resting-state network organisation in paediatric TBI outcome remains poorly understood. This study is the first to investigate the impact of paediatric TBI on resting-state network organisation using graph theory, and its relevance for functional outcome. Participants were 8-14 years and included children with (i) mild TBI and risk factors for complicated TBI (mildRF+, n = 20), (ii) moderate/severe TBI (n = 15), and (iii) trauma control injuries (n = 27). Children underwent resting-state functional magnetic resonance imaging (fMRI), neurocognitive testing, and behavioural assessment at 2.8 years post-injury. Graph theory was applied to fMRI timeseries to evaluate the impact of TBI on global and local organisation of the resting-state network, and relevance for neurocognitive and behavioural functioning. Children with TBI showed atypical global network organisation as compared to the trauma control group, reflected by lower modularity (mildRF + TBI and moderate/severe TBI), higher smallworldness (mildRF + TBI) and lower assortativity (moderate/severe TBI ps < .04, Cohen's ds: > .6). Regarding local network organisation, the relative importance of hub regions in the network did not differ between groups. Regression analyses showed relationships between global as well as local network parameters with neurocognitive functioning (i.e., working memory, memory encoding; R2 = 23.3 - 38.5%) and behavioural functioning (i.e., externalising problems, R2 = 36.1%). Findings indicate the impact of TBI on global functional network organisation, and the relevance of both global and local network organisation for long-term neurocognitive and behavioural outcome after paediatric TBI. The results suggest potential prognostic value of resting-state network organisation for outcome after paediatric TBI.

Bumetanide Effects on Resting-State EEG in Tuberous Sclerosis Complex in Relation to Clinical Outcome: An Open-Label Study.

Neuronal excitation-inhibition (E/I) imbalances are considered an important pathophysiological mechanism in neurodevelopmental disorders. Preclinical studies on tuberous sclerosis complex (TSC), suggest that altered chloride homeostasis may impair GABAergic inhibition and thereby E/I-balance regulation. Correction of chloride homeostasis may thus constitute a treatment target to alleviate behavioral symptoms. Recently, we showed that bumetanide-a chloride-regulating agent-improved behavioral symptoms in the open-label study Bumetanide to Ameliorate Tuberous Sclerosis Complex Hyperexcitable Behaviors trial (BATSCH trial; Eudra-CT: 2016-002408-13). Here, we present resting-state EEG as secondary analysis of BATSCH to investigate associations between EEG measures sensitive to network-level changes in E/I balance and clinical response to bumetanide. EEGs of 10 participants with TSC (aged 8-21 years) were available. Spectral power, long-range temporal correlations (LRTC), and functional E/I ratio (fE/I) in the alpha-frequency band were compared before and after 91 days of treatment. Pre-treatment measures were compared against 29 typically developing children (TDC). EEG measures were correlated with the Aberrant Behavioral Checklist-Irritability subscale (ABC-I), the Social Responsiveness Scale-2 (SRS-2), and the Repetitive Behavior Scale-Revised (RBS-R). At baseline, TSC showed lower alpha-band absolute power and fE/I than TDC. Absolute power increased through bumetanide treatment, which showed a moderate, albeit non-significant, correlation with improvement in RBS-R. Interestingly, correlations between baseline EEG measures and clinical outcomes suggest that most responsiveness might be expected in children with network characteristics around the E/I balance point. In sum, E/I imbalances pointing toward an inhibition-dominated network are present in TSC. We established neurophysiological effects of bumetanide although with an inconclusive relationship with clinical improvement. Nonetheless, our results further indicate that baseline network characteristics might influence treatment response. These findings highlight the possible utility of E/I-sensitive EEG measures to accompany new treatment interventions for TSC. Clinical Trial Registration: EU Clinical Trial Register, EudraCT 2016-002408-13 (www.clinicaltrialsregister.eu/ctr-search/trial/2016-002408-13/NL). Registered 25 July 2016.

Personalized medicine for rare neurogenetic disorders: can we make it happen?

Rare neurogenetic disorders are collectively common, affecting 3% of the population, and often manifest with complex multiorgan comorbidity. With advances in genetic, -omics, and computational analysis, more children can be diagnosed and at an earlier age. Innovations in translational research facilitate the identification of treatment targets and development of disease-modifying drugs such as gene therapy, nutraceuticals, and drug repurposing. This increasingly allows targeted therapy to prevent the often devastating manifestations of rare neurogenetic disorders. In this perspective, successes in diagnosis, prevention, and treatment are discussed with a focus on inherited disorders of metabolism. Barriers for the identification, development, and implementation of rare disease-specific therapies are discussed. New methodologies, care networks, and collaborative frameworks are proposed to optimize the potential of personalized genomic medicine to decrease morbidity and improve lives of these vulnerable patients.

Bumetanide for Irritability in Children With Sensory Processing Problems Across Neurodevelopmental Disorders: A Pilot Randomized Controlled Trial.

BACKGROUND: Treatment development for neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) is impeded by heterogeneity in clinical manifestation and underlying etiologies. Symptom traits such as aberrant sensory reactivity are present across NDDs and might reflect common mechanistic pathways. Here, we test the effectiveness of repurposing a drug candidate, bumetanide, on irritable behavior in a cross-disorder neurodevelopmental cohort defined by the presence of sensory reactivity problems. METHODS: Participants, aged 5-15 years and IQ ≥ 55, with ASD, ADHD, and/or epilepsy and proven aberrant sensory reactivity according to deviant Sensory Profile scores were included. Participants were randomly allocated (1:1) to bumetanide (max 1 mg twice daily) or placebo tablets for 91 days followed by a 28-day wash-out period using permuted block design and minimization. Participants, parents, healthcare providers, and outcome assessors were blinded for treatment allocation. Primary outcome was the differences in ABC-irritability at day 91. Secondary outcomes were differences in SRS-2, RBS-R, SP-NL, BRIEF parent, BRIEF teacher at D91. Differences were analyzed in a modified intention-to-treat sample with linear mixed models and side effects in the intention-to-treat population. RESULTS: A total of 38 participants (10.1 [SD 3.1] years) were enrolled between June 2017 and June 2019 in the Netherlands. Nineteen children were allocated to bumetanide and nineteen to placebo. Five patients discontinued (n = 3 bumetanide). Bumetanide was superior to placebo on the ABC-irritability [mean difference (MD) -4.78, 95%CI: -8.43 to -1.13, p = 0.0125]. No effects were found on secondary endpoints. No wash-out effects were found. Side effects were as expected: hypokalemia (p = 0.046) and increased diuresis (p = 0.020). CONCLUSION: Despite the results being underpowered, this study raises important recommendations for future cross-diagnostic trial designs.

Assessing the landscape of STXBP1-related disorders in 534 individuals.

Disease-causing variants in STXBP1 are among the most common genetic causes of neurodevelopmental disorders. However, the phenotypic spectrum in STXBP1-related disorders is wide and clear correlations between variant type and clinical features have not been observed so far. Here, we harmonized clinical data across 534 individuals with STXBP1-related disorders and analysed 19 973 derived phenotypic terms, including phenotypes of 253 individuals previously unreported in the scientific literature. The overall phenotypic landscape in STXBP1-related disorders is characterized by neurodevelopmental abnormalities in 95% and seizures in 89% of individuals, including focal-onset seizures as the most common seizure type (47%). More than 88% of individuals with STXBP1-related disorders have seizure onset in the first year of life, including neonatal seizure onset in 47%. Individuals with protein-truncating variants and deletions in STXBP1 (n = 261) were almost twice as likely to present with West syndrome and were more phenotypically similar than expected by chance. Five genetic hotspots with recurrent variants were identified in more than 10 individuals, including p.Arg406Cys/His (n = 40), p.Arg292Cys/His/Leu/Pro (n = 30), p.Arg551Cys/Gly/His/Leu (n = 24), p.Pro139Leu (n = 12), and p.Arg190Trp (n = 11). None of the recurrent variants were significantly associated with distinct electroclinical syndromes, single phenotypic features, or showed overall clinical similarity, indicating that the baseline variability in STXBP1-related disorders is too high for discrete phenotypic subgroups to emerge. We then reconstructed the seizure history in 62 individuals with STXBP1-related disorders in detail, retrospectively assigning seizure type and seizure frequency monthly across 4433 time intervals, and retrieved 251 anti-seizure medication prescriptions from the electronic medical records. We demonstrate a dynamic pattern of seizure control and complex interplay with response to specific medications particularly in the first year of life when seizures in STXBP1-related disorders are the most prominent. Adrenocorticotropic hormone and phenobarbital were more likely to initially reduce seizure frequency in infantile spasms and focal seizures compared to other treatment options, while the ketogenic diet was most effective in maintaining seizure freedom. In summary, we demonstrate how the multidimensional spectrum of phenotypic features in STXBP1-related disorders can be assessed using a computational phenotype framework to facilitate the development of future precision-medicine approaches.

Following Excitation/Inhibition Ratio Homeostasis from Synapse to EEG in Monogenetic Neurodevelopmental Disorders.

Pharmacological options for neurodevelopmental disorders are limited to symptom suppressing agents that do not target underlying pathophysiological mechanisms. Studies on specific genetic disorders causing neurodevelopmental disorders have elucidated pathophysiological mechanisms to develop more rational treatments. Here, we present our concerted multi-level strategy 'BRAINMODEL', focusing on excitation/inhibition ratio homeostasis across different levels of neuroscientific interrogation. The aim is to develop personalized treatment strategies by linking iPSC-based models and novel EEG measurements to patient report outcome measures in individual patients. We focus our strategy on chromatin- and SNAREopathies as examples of severe genetic neurodevelopmental disorders with an unmet need for rational interventions.

EEG-based visual deviance detection in freely behaving mice.

The mouse is widely used as an experimental model to study visual processing. To probe how the visual system detects changes in the environment, functional paradigms in freely behaving mice are strongly needed. We developed and validated the first EEG-based method to investigate visual deviance detection in freely behaving mice. Mice with EEG implants were exposed to a visual deviant detection paradigm that involved changes in light intensity as standard and deviant stimuli. By subtracting the standard from the deviant evoked waveform, deviant detection was evident as bi-phasic negativity (starting around 70 ms) in the difference waveform. Additionally, deviance-associated evoked (beta/gamma) and induced (gamma) oscillatory responses were found. We showed that the results were stimulus-independent by applying a "flip-flop" design and the results showed good repeatability in an independent measurement. Together, we put forward a validated, easy-to-use paradigm to measure visual deviance processing in freely behaving mice.

The sensory-reactivity PROM set: identification of a parent reported outcome measure set for autism spectrum disorder.

BACKGROUND: Most children with autism spectrum disorder (ASD) suffer from aberrant responses to sensory stimuli that significantly impact the quality of life. To develop sensory interventions, individually tailored outcome measures are crucially needed for the domain of sensory reactivity problems. Here, we describe the identification of relevant sensory themes according to caregivers of children with ASD according to the guidelines for developing a (parent proxy) patient-reported outcome measure set. Subsequently, we identify parallels between these themes and a well-validated and supported PROMIS® portal to facilitate implementation. Interviews with clinicians and focus groups and interviews with parents of children with ASD were used in the initial phase for concept elicitation. Codes and themes were generated by qualitative thematic data analysis on the transcripts and cognitive interviews with different parents were used for revisions. The resulting themes were compared to existing generic PROMIS-item banks and other existing questionnaires. RESULTS: A total of 11 parent-reported outcomes were identified that could be either classified as directly or indirectly related to sensory reactivity. Directly related themes comprised of: (1) sensory stimulation tolerance and (2) sensitivity to sensory stimuli. Indirectly related themes were: (3) irritable behavior (4) anxiety problems (5) mood problems (6) sleep problems (7) fatigue (8) physical complaints (9) daily functioning and participation (10) routines, structure and dealing with change and (11) problems in social interaction and communication. Seven out of 11 themes could be measured with generic PROMIS item banks. The four remaining outcomes (sensory stimulation tolerance; irritable behaviour; routines, structure and dealing with change; and sensitivity to sensory stimuli) were found suitable to be inventoried by existing PROMs. CONCLUSION: The majority of parent-reported problems seemed related to indirect consequences of sensory reactivity, which are suitable to be measured with generic item banks. In sum, we identified a sensory-reactivity PROM (parent-proxy) set consisting of PROMIS® item banks and additional domains that together form a comprehensive and readily available outcome set for sensory reactivity problems in children with ASD.