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The genetic causes of primary antibody deficiencies and autism spectrum disorder (ASD) are largely unknown. Here, we report a patient with hypogammaglobulinemia and ASD who carries biallelic mutations in the transcription factor PAX5. A patient-specific Pax5 mutant mouse revealed an early B cell developmental block and impaired immune responses as the cause of hypogammaglobulinemia. Pax5 mutant mice displayed behavioral deficits in all ASD domains. The patient and the mouse model showed aberrant cerebellar foliation and severely impaired sensorimotor learning. PAX5 deficiency also caused profound hypoplasia of the substantia nigra and ventral tegmental area due to loss of GABAergic neurons, thus affecting two midbrain hubs, controlling motor function and reward processing, respectively. Heterozygous Pax5 mutant mice exhibited similar anatomic and behavioral abnormalities. Lineage tracing identified Pax5 as a crucial regulator of cerebellar morphogenesis and midbrain GABAergic neurogenesis. These findings reveal new roles of Pax5 in brain development and unravel the underlying mechanism of a novel immunological and neurodevelopmental syndrome.

The genetic causes of primary antibody deficiencies and autism spectrum disorder (ASD) are largely unknown. Here, we report a patient with hypogammaglobulinemia and ASD who carries biallelic mutations in the transcription factor PAX5. A patient-specific Pax5 mutant mouse revealed an early B cell developmental block and impaired immune responses as the cause of hypogammaglobulinemia. Pax5 mutant mice displayed behavioral deficits in all ASD domains. The patient and the mouse model showed aberrant cerebellar foliation and severely impaired sensorimotor learning. PAX5 deficiency also caused profound hypoplasia of the substantia nigra and ventral tegmental area due to loss of GABAergic neurons, thus affecting two midbrain hubs, controlling motor function and reward processing, respectively. Heterozygous Pax5 mutant mice exhibited similar anatomic and behavioral abnormalities. Lineage tracing identified Pax5 as a crucial regulator of cerebellar morphogenesis and midbrain GABAergic neurogenesis. These findings reveal new roles of Pax5 in brain development and unravel the underlying mechanism of a novel immunological and neurodevelopmental syndrome.

Introduction Predicting upper limb capacity recovery is important to set treatment goals, select therapies and plan discharge. We introduce a prediction model of the patient-specific profile of upper limb capacity recovery up to 6 months poststroke by incorporating all serially assessed clinical information from patients. Methods Model input was recovery profile of 450 patients with a first-ever ischaemic hemispheric stroke measured using the Action Research Arm Test (ARAT). Subjects received at least three assessment sessions, starting within the first week until 6 months poststroke. We developed mixed-effects models that are able to deal with one or multiple measurements per subject, measured at non-fixed time points. The prediction accuracy of the different models was established by a fivefold cross-validation procedure. Results A model with only ARAT time course, finger extension and shoulder abduction performed as good as models with more covariates. For the final model, cross-validation prediction errors at 6 months poststroke decreased as the number of measurements per subject increased, from a median error of 8.4 points on the ARAT (Q1-Q3:1.7-28.1) when one measurement early poststroke was used, to 2.3 (Q1-Q3:1-7.2) for seven measurements. An online version of the recovery model was developed that can be linked to data acquisition environments. Conclusion Our innovative dynamic model can predict real-time, patient-specific upper limb capacity recovery profiles up to 6 months poststroke. The model can use all available serially assessed data in a flexible way, creating a prediction at any desired moment poststroke, stand-alone or linked with an electronic health record system.

Objective Spontaneous recovery is an important determinant of upper extremity recovery after stroke and has been described by the 70% proportional recovery rule for the Fugl-Meyer motor upper extremity (FM-UE) scale. However, this rule is criticized for overestimating the predictability of FM-UE recovery. Our objectives were to develop a longitudinal mixture model of FM-UE recovery, identify FM-UE recovery subgroups, and internally validate the model predictions.Methods We developed an exponential recovery function with the following parameters: subgroup assignment probability, proportional recovery coefficient r( k), time constant in weeks tau( k), and distribution of the initial FM-UE scores. We fitted the model to FM-UE measurements of 412 first-ever ischemic stroke patients and cross-validated endpoint predictions and FM-UE recovery cluster assignment.Results The model distinguished 5 subgroups with different recovery parameters ( r(1) = 0.09, tau(1) = 5.3, r(2) = 0.46, tau(2) = 10.1, r(3) = 0.86, tau(3) = 9.8, r(4) = 0.89, tau(4) = 2.7, r(5) = 0.93, tau(5) = 1.2). Endpoint FM-UE was predicted with a median absolute error of 4.8 (interquartile range [IQR] = 1.3-12.8) at 1 week poststroke and 4.2 (IQR = 1.3-9.8) at 2 weeks. Overall accuracy of assignment to the poor (subgroup 1), moderate (subgroups 2 and 3), and good (subgroups 4 and 5) FM-UE recovery clusters was 0.79 (95% equal-tailed interval [ETI] = 0.78-0.80) at 1 week poststroke and 0.81 (95% ETI = 0.80-0.82) at 2 weeks.Interpretation FM-UE recovery reflects different subgroups, each with its own recovery profile. Cross-validation indicates that FM-UE endpoints and FM-UE recovery clusters can be well predicted. Results will contribute to the understanding of upper limb recovery patterns in the first 6 months after stroke. ANN NEUROL 2020;87:383-393 Ann Neurol 2020;87:383-393

Background: Changes in transcranial magnetic stimulation motor map parameters can be used to quantify plasticity in the human motor cortex. The golden standard uses a counting analysis of motor evoked potentials (MEPs) acquired with a predefined grid. Recently, digital reconstruction methods have been proposed, allowing MEPs to be acquired with a faster pseudorandom procedure. However, the reliability of these reconstruction methods has never been compared to the golden standard.Objective: To compare the absolute reliability of the reconstruction methods with the golden standard.Methods: In 21 healthy subjects, both grid and pseudorandom acquisition were performed twice on the first day and once on the second day. The standard error of measurement was calculated for the counting analysis and the digital reconstructions.Results: The standard error of measurement was at least equal using digital reconstructions.Conclusion: Pseudorandom acquisition and digital reconstruction can be used in intervention studies without sacrificing reliability. (C) 2018 The Authors. Published by Elsevier Inc.

Human movement relies on noisy processes in neurons, muscle cells and sensory cells. Therefore, movements are variable and can never be exactly reproduced. The nervous system seems to exploit this movement noise for motor learning and specifically motor adaptation. However, a positive relation between movement noise and motor adaptation has not been consistently found in motor adaptation literature. Possibly, noise is comprised of distinct processes which contribute to motor adaptation in different ways. In Kalman filter theory, motor adaptation rate is calculated optimally from state noise and output noise, with state noise and adaptation rate positively correlated and output noise and adaptation rate negatively correlated. Therefore, if people learn (close) optimally from error, we would expect a similar relation. To investigate the relation between state noise, output noise and adaptation rate, we performed a visuomotor reaching adaptation experiment with a baseline and a perturbation block in 69 subjects. State noise, output noise and adaptation rate in the baseline and perturbation block were extracting using Bayesian fitting of a trial-to-trial state-space model. We found that adaptation rate in the perturbation block correlates positively with baseline state noise (r=0.27; 95%HDI=[0.05 0.50]) and negatively with baseline output noise (r= 0.41; 95%HDI=[ 0.63 0.16]). In addition, the steady-state Kalman gain calculated from baseline state and output noise correlated positively with adaptation rate in the perturbation block (r = 0.31; 95%HDI = [0.09 0.54]). Therefore, noise can be viewed both as a supporting factor for motor adaptation (state noise) and as a noise factor hampering optimal performance (output noise), and in order to understand the relationship of noise to learning, one must decompose noise into its constituent components.