Search

Objective: Tuberous sclerosis complex (TSC) is highly associated with autism spectrum disorder (ASD). Objectives of the study were to characterize autistic features in young children with TSC. Methods: Participants included 138 children followed from ages 3 to 36 months with TSC from the Tuberous Sclerosis Complex Autism Center of Excellence Research Network (TACERN), a multicenter, prospective observational study aimed at understanding the underlying mechanisms of ASD in TSC. Developmental and autism‐specific assessments were administered, and a clinical diagnosis of ASD was determined for all participants at 36 months. Further analyses were performed on 117 participants with valid autism assessments based on nonverbal mental age greater than 15 months. Results: Prevalence of clinical diagnosis of ASD at 36 months was 25%. Nearly all autistic behaviors on the Autism Diagnostic Observation Schedule‐2 (ADOS‐2) and Autism Diagnostic Interview‐Revised (ADI‐R) were more prevalent in children diagnosed with ASD; however, autism‐specific behaviors were also observed in children without ASD. Overall quality of social overtures, facial expressions, and abnormal repetitive interests and behaviors were characteristics most likely to distinguish children with ASD from those without an ASD diagnosis. Participants meeting ADOS‐2 criteria but not a clinical ASD diagnosis exhibited intermediate developmental and ADOS‐2 scores compared to individuals with and without ASD. Interpretation: ASD is highly prevalent in TSC, and many additional individuals with TSC exhibit a broad range of subthreshold autistic behaviors. Our findings reveal a broader autism phenotype that can be identified in young children with TSC, which provides opportunity for early targeted treatments. ANN NEUROL 2021;90:874–886 [ABSTRACT FROM AUTHOR]

Objective: Approximately 50% of patients with tuberous sclerosis complex develop infantile spasms, a sudden onset epilepsy syndrome associated with poor neurological outcomes. An increased burden of tubers confers an elevated risk of infantile spasms, but it remains unknown whether some tuber locations confer higher risk than others. Here, we test whether tuber location and connectivity are associated with infantile spasms. Methods: We segmented tubers from 123 children with (n = 74) and without (n = 49) infantile spasms from a prospective observational cohort. We used voxelwise lesion symptom mapping to test for an association between spasms and tuber location. We then used lesion network mapping to test for an association between spasms and connectivity with tuber locations. Finally, we tested the discriminability of identified associations with logistic regression and cross‐validation as well as statistical mediation. Results: Tuber locations associated with infantile spasms were heterogenous, and no single location was significantly associated with spasms. However, >95% of tuber locations associated with spasms were functionally connected to the globi pallidi and cerebellar vermis. These connections were specific compared to tubers in patients without spasms. Logistic regression found that globus pallidus connectivity was a stronger predictor of spasms (odds ratio [OR] = 1.96, 95% confidence interval [CI] = 1.10–3.50, p = 0.02) than tuber burden (OR = 1.65, 95% CI = 0.90–3.04, p = 0.11), with a mean receiver operating characteristic area under the curve of 0.73 (±0.1) during repeated cross‐validation. Interpretation: Connectivity between tuber locations and the bilateral globi pallidi is associated with infantile spasms. Our findings lend insight into spasm pathophysiology and may identify patients at risk. ANN NEUROL 2021;89:726–739 [ABSTRACT FROM AUTHOR]

Objective: To develop and test a deep learning algorithm to automatically detect cortical tubers in magnetic resonance imaging (MRI), to explore the utility of deep learning in rare disorders with limited data, and to generate an open-access deep learning standalone application. Methods: T2 and FLAIR axial images with and without tubers were extracted from MRIs of patients with tuberous sclerosis complex (TSC) and controls, respectively. We trained three different convolutional neural network (CNN) architectures on a training dataset and selected the one with the lowest binary cross-entropy loss in the validation dataset, which was evaluated on the testing dataset. We visualized image regions most relevant for classification with gradient-weighted class activation maps (Grad-CAM) and saliency maps. Results: 114 patients with TSC and 114 controls were divided into a training set, a validation set, and a testing set. The InceptionV3 CNN architecture performed best in the validation set and was evaluated in the testing set with the following results: sensitivity: 0.95, specificity: 0.95, positive predictive value: 0.94, negative predictive value: 0.95, F1-score: 0.95, accuracy: 0.95, and area under the curve: 0.99. Grad-CAM and saliency maps showed that tubers resided in regions most relevant for image classification within each image. A stand-alone trained deep learning App was able to classify images using local computers with various operating systems. Conclusion: This study shows that deep learning algorithms are able to detect tubers in selected MRI images, and deep learning can be prudently applied clinically to manually selected data in a rare neurological disorder. [ABSTRACT FROM AUTHOR]

Background: Autism spectrum disorder (ASD) is prevalent in tuberous sclerosis complex (TSC), occurring in approximately 50% of patients, and is hypothesized to be caused by disruption of neural circuits early in life. Tubers, or benign hamartomas distributed stochastically throughout the brain, are the most conspicuous of TSC neuropathology, but have not been consistently associated with ASD. Widespread neuropathology of the white matter, including deficits in myelination, neuronal migration, and axon formation, exist and may underlie ASD in TSC. We sought to identify the neural circuits associated with ASD in TSC by identifying white matter microstructural deficits in a prospectively recruited, longitudinally studied cohort of TSC infants. Methods: TSC infants were recruited within their first year of life and longitudinally imaged at time of recruitment, 12 months of age, and at 24 months of age. Autism was diagnosed at 24 months of age with the ADOS-2. There were 108 subjects (62 TSC-ASD, 55% male; 46 TSC+ASD, 52% male) with at least one MRI and a 24-month ADOS, for a total of 187 MRI scans analyzed (109 TSC-ASD; 78 TSC+ASD). Diffusion tensor imaging properties of multiple white matter fiber bundles were sampled using a region of interest approach. Linear mixed effects modeling was performed to test the hypothesis that infants who develop ASD exhibit poor white matter microstructural integrity over the first 2 years of life compared to those who do not develop ASD. Results: Subjects with TSC and ASD exhibited reduced fractional anisotropy in 9 of 17 white matter regions, sampled from the arcuate fasciculus, cingulum, corpus callosum, anterior limbs of the internal capsule, and the sagittal stratum, over the first 2 years of life compared to TSC subjects without ASD. Mean diffusivity trajectories did not differ between groups. Conclusions: Underconnectivity across multiple white matter fiber bundles develops over the first 2 years of life in subjects with TSC and ASD. Future studies examining brain-behavior relationships are needed to determine how variation in the brain structure is associated with ASD symptoms. [ABSTRACT FROM AUTHOR]

Background and Purpose: There are no published studies examining resting state networks (RSNs) and their relationship with neurodevelopmental metrics in tuberous sclerosis complex (TSC). We aimed to identify major resting-state functional magnetic resonance imaging (rs-fMRI) networks in infants with TSC and correlate network analyses with neurodevelopmental assessments, autism diagnosis, and seizure history.Methods: Rs-fMRI data from 34 infants with TSC, sedated with propofol during the scan, were analyzed to identify auditory, motor, and visual RSNs. We examined the correlations between auditory, motor, and visual RSNs at approximately 11.5 months, neurodevelopmental outcome at approximately 18.5 months, and diagnosis of autism spectrum disorders at approximately 36 months of age.Results: RSNs were obtained in 76.5% (26/34) of infants. We observed significant negative correlations between auditory RSN and auditory comprehension test scores (p = .038; r = -.435), as well as significant positive correlations between motor RSN and gross motor skills test scores (p = .023; r = .564). Significant positive correlations between motor RSNs and gross motor skills (p = .012; r = .754) were observed in TSC infants without autism, but not in TSC infants with autism, which could suggest altered motor processing. There were no significant differences in RSNs according to seizure history.Conclusions: Negative correlation between auditory RSN, as well as positive correlation between motor RSN and developmental outcome measures might reflect different brain mechanisms and, when identified, may be helpful in predicting later function. A larger study of TSC patients with a healthy control group is needed before auditory and motor RSNs could be considered as neurodevelopmental outcome biomarkers. [ABSTRACT FROM AUTHOR]

Byline: Jun Yang, Jie Zhang, Mehmet E. Aydin, Joyce Y. Wu Accuracy and the computational load are two important issues of Wideband Code-Division Multiple Access (WCDMA) network planning and optimisation. Link-level performance factors such as the impact of Soft Handover (SHO) and fast power control need to be taken into account in system-level simulations to obtain a good trade-off between accuracy and the computational load. Although such link-level factors have been investigated separately in previous works, there remains a strong need to consider all factors together. In this paper, we consider these factors on both Uplink (UL) and Downlink (DL) in modelling the network planning with mixed integer programming model. We also, present optimisation strategies based on two meta-heuristics, namely Tabu Search (TS) and Simulated Annealing (SA). Experimental results are provided to compare the performance of those heuristic algorithms with respect to the solution quality and the level of robustness.

Background NR5 A1 loss-of-function mutations are increasingly found to be the cause of 46, XY disorders of sex development ( DSD). Objective To determine the presence of NR5A1 mutations in an Australasian cohort of 17 46, XY DSD patients with presumed androgen insensitivity syndrome ( AIS) who were negative for androgen receptor gene ( AR) mutation. Design Exons 2-7 of NR5A1 were PCR amplified and sequenced. Gene expression and cellular localization studies were performed on a novel NR5A1 variant c.74 A> G (p. Y25 C) identified in this study. Results We identified one novel mutation, c.74 A> G (p. Y25 C) in a patient characterized by penoscrotal hypospadias with bifid scrotum. He had elevated testosterone and gonadotropins in early infancy. Functional analysis of p. Y25 C in vitro demonstrated reduced transcriptional activation by SF-1 and partially impaired nuclear localization in a proportion of transfected human adrenal NCI- H295 R cells. Conclusion This is the first reported case of a DSD patient with a NR5 A1 mutation and elevated testosterone levels. Our finding supports evaluation of NR5 A1 mutations in 46, XY DSD patients with a range of testosterone levels. [ABSTRACT FROM AUTHOR]

21-Hydroxylase deficiency (21-OHD) is the most common cause of congenital adrenal hyperplasia, with an incidence of 1:14000 live births and equal prevalence among males and females. Newborns with the most severe "salt-wasting" form of 21-OHD are susceptible to salt-wasting crises in the first few weeks of life. This is associated with morbidity and mortality. 21-OHD newborn screening (NBS) is currently performed in many countries. Despite several prominent medical societies recommending 21-OHD NBS, no state in Australia currently screens for this condition. We report a case that illustrates the need to reconsider including 21-OHD in NBS. 21-OHD NBS can be reliable, sensitive and effective in reducing morbidity and mortality. [ABSTRACT FROM AUTHOR]

The Tuberous Sclerosis Complex Autism Center of Excellence Network (TACERN) is a 6-site collaborative conducting longitudinal research on infants with tuberous sclerosis complex (TSC), focused on identifying early biomarkers for autism spectrum disorder (ASD). A multidisciplinary research team that includes the specialties of psychology, neurology, pediatrics, medical genetics, and speech-language pathology, its members work together to conduct studies on neurological status, brain structure and function, neurodevelopmental phenotype, and behavioral challenges in this population. This article provides insights into the roles of the multidisciplinary multisite team and lessons learned from the collaboration, in terms of research as well as training of future researchers and clinicians. In addition, the authors detail the major findings to date, including those related to the identification and measurement of early symptoms of ASD, relationship between seizures and early development, and early biomarkers for epilepsy and developmental delay in infants and young children with TSC. [ABSTRACT FROM AUTHOR]