Psychological wellbeing among parents of a child living with a serious chronic illness: A cross-sectional survey study.

Parents of a child with a chronic illness can experience greater distress than the average population, yet little is understood about differences between illness groups. This cross-sectional survey study aimed to compare parents' psychological distress and perceived wellbeing across five chronic illnesses. Parents from one Australian pediatric hospital completed the Kessler Psychological Distress Scale and seven purpose-designed items about their wellbeing. Data from 106 parents (cancer = 48, cystic fibrosis [CF] = 27, kidney disease = 12, gastrointestinal condition/disorder = 9, developmental and epileptic encephalopathy [DEE] = 10) was analysed using bivariate Pearson's Correlation and linear mixed-effects models. Parents' distress scores differed between groups (F(4,80) = 2.50, p = .049), with the DEE group reporting higher distress than the CF group (mean difference = 6.76, 95% CI [0.11, 13.42]). Distress scores were moderately correlated to parents' perceptions of their child's health and their own wellbeing. Parents' self-reported coping with their child's condition/treatments differed (F(4,81) = 3.24, p = .016), with the DEE group rating their coping as poorer than the CF group (mean difference = -25.32, 95% CI [-46.52, 4.11]). Across all groups, parents reported unmet needs, particularly for psychosocial support and practical/financial assistance. Support interventions may be most effective if tailored to the child's illness, with greater support potentially needed for parents who have a child with DEE and/or severe comorbidities.

FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech.

BACKGROUND: Timely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures. MATERIALS AND METHODS: In this research, we have introduced an advanced feature engineering model incorporating a novel feature extraction function, the Factor Lattice Pattern (FLP), which is a quantum-inspired method and uses a superposition-like mechanism, making it dynamic in nature. The FLP encompasses eight distinct patterns, from which the most appropriate pattern was discerned based on the data structure. Through the implementation of the FLP, we automatically extracted signal-specific textural features. Additionally, we developed a new feature engineering model to assess the efficacy of the FLP. This model is self-organizing, producing nine potential results and subsequently choosing the optimal one. Our speech classification framework consists of (1) feature extraction using the proposed FLP and a statistical feature extractor; (2) feature selection employing iterative neighborhood component analysis and an intersection-based feature selector; (3) classification via support vector machine and k-nearest neighbors; and (4) outcome determination using combinational majority voting to select the most favorable results. RESULTS: To validate the classification capabilities of our proposed feature engineering model, designed to automatically detect PD and SLI, we employed three speech datasets of PD and SLI patients. Our presented FLP-centric model achieved classification accuracy of more than 95% and 99.79% for all PD and SLI datasets, respectively. CONCLUSIONS: Our results indicate that the proposed model is an accurate alternative to deep learning models in classifying neurological conditions using speech signals.

"We are not a typical family anymore": Exploring the experiences and support needs of fathers of children with Fragile X syndrome in Australia.

A diagnosis of the X-linked condition Fragile X syndrome (FXS) in a child commonly reveals the mother's carrier status. Previous research focused on the genetic counseling process for the child and maternal family, despite calls for more research on the support needs of fathers. This study explored experiences and support needs of fathers at least 1 year after their child's FXS diagnosis to understand barriers and enablers and optimize health outcomes for the family. In-depth interviews were conducted with 11 fathers recruited through the Australian Genetics of Learning Disability Service and the Fragile X Association. Deidentified transcripts were analyzed using thematic analysis guided by an inductive approach. Four themes emerged: (1) making life easier through understanding-yesterday and today, (2) the path to a new normal-today and tomorrow, (3) seeking information and support, and (4) what men want. Fathers reported diagnostic odysseys, postdiagnostic grief, and challenges adjusting. They highlighted difficulties in understanding their child's unique behaviors and needs, responding to their partner's psychological support needs, planning for their child's future, and navigating complex health and disability systems. Participants suggested health professionals facilitate father-to-father support and psychological counseling. These findings highlight the unmet needs of fathers and suggest that a strengths-based approach is critically important given the recognized mental health impact.

Equitable and accessible informed healthcare consent process for people with intellectual disability: a systematic literature review.

OBJECTIVE: To identify factors acting as barriers or enablers to the process of healthcare consent for people with intellectual disability and to understand how to make this process equitable and accessible. DATA SOURCES: Databases: Embase, MEDLINE, PsychINFO, PubMed, SCOPUS, Web of Science and CINAHL. Additional articles were obtained from an ancestral search and hand-searching three journals. ELIGIBILITY CRITERIA: Peer-reviewed original research about the consent process for healthcare interventions, published after 1990, involving adult participants with intellectual disability. SYNTHESIS OF RESULTS: Inductive thematic analysis was used to identify factors affecting informed consent. The findings were reviewed by co-researchers with intellectual disability to ensure they reflected lived experiences, and an easy read summary was created. RESULTS: Twenty-three studies were included (1999 to 2020), with a mix of qualitative (n=14), quantitative (n=6) and mixed-methods (n=3) studies. Participant numbers ranged from 9 to 604 people (median 21) and included people with intellectual disability, health professionals, carers and support people, and others working with people with intellectual disability. Six themes were identified: (1) health professionals' attitudes and lack of education, (2) inadequate accessible health information, (3) involvement of support people, (4) systemic constraints, (5) person-centred informed consent and (6) effective communication between health professionals and patients. Themes were barriers (themes 1, 2 and 4), enablers (themes 5 and 6) or both (theme 3). CONCLUSIONS: Multiple reasons contribute to poor consent practices for people with intellectual disability in current health systems. Recommendations include addressing health professionals' attitudes and lack of education in informed consent with clinician training, the co-production of accessible information resources and further inclusive research into informed consent for people with intellectual disability. PROSPERO REGISTRATION: CRD42021290548.

International Undiagnosed Diseases Programs (UDPs): components and outcomes.

Over the last 15 years, Undiagnosed Diseases Programs have emerged to address the significant number of individuals with suspected but undiagnosed rare genetic diseases, integrating research and clinical care to optimize diagnostic outcomes. This narrative review summarizes the published literature surrounding Undiagnosed Diseases Programs worldwide, including thirteen studies that evaluate outcomes and two commentary papers. Commonalities in the diagnostic and research process of Undiagnosed Diseases Programs are explored through an appraisal of available literature. This exploration allowed for an assessment of the strengths and limitations of each of the six common steps, namely enrollment, comprehensive clinical phenotyping, research diagnostics, data sharing and matchmaking, results, and follow-up. Current literature highlights the potential utility of Undiagnosed Diseases Programs in research diagnostics. Since participants have often had extensive previous genetic studies, research pipelines allow for diagnostic approaches beyond exome or whole genome sequencing, through reanalysis using research-grade bioinformatics tools and multi-omics technologies. The overall diagnostic yield is presented by study, since different selection criteria at enrollment and reporting processes make comparisons challenging and not particularly informative. Nonetheless, diagnostic yield in an undiagnosed cohort reflects the potential of an Undiagnosed Diseases Program. Further comparisons and exploration of the outcomes of Undiagnosed Diseases Programs worldwide will allow for the development and improvement of the diagnostic and research process and in turn improve the value and utility of an Undiagnosed Diseases Program.

EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population.

BACKGROUND: Epilepsy is one of the most common neurological conditions globally, and the fourth most common in the United States. Recurrent non-provoked seizures characterize it and have huge impacts on the quality of life and financial impacts for affected individuals. A rapid and accurate diagnosis is essential in order to instigate and monitor optimal treatments. There is also a compelling need for the accurate interpretation of epilepsy due to the current scarcity in neurologist diagnosticians and a global inequity in access and outcomes. Furthermore, the existing clinical and traditional machine learning diagnostic methods exhibit limitations, warranting the need to create an automated system using deep learning model for epilepsy detection and monitoring using a huge database. METHOD: The EEG signals from 35 channels were used to train the deep learning-based transformer model named (EpilepsyNet). For each training iteration, 1-min-long data were randomly sampled from each participant. Thereafter, each 5-s epoch was mapped to a matrix using the Pearson Correlation Coefficient (PCC), such that the bottom part of the triangle was discarded and only the upper triangle of the matrix was vectorized as input data. PCC is a reliable method used to measure the statistical relationship between two variables. Based on the 5 s of data, single embedding was performed thereafter to generate a 1-dimensional array of signals. In the final stage, a positional encoding with learnable parameters was added to each correlation coefficient's embedding before being fed to the developed EpilepsyNet as input data to epilepsy EEG signals. The ten-fold cross-validation technique was used to generate the model. RESULTS: Our transformer-based model (EpilepsyNet) yielded high classification accuracy, sensitivity, specificity and positive predictive values of 85%, 82%, 87%, and 82%, respectively. CONCLUSION: The proposed method is both accurate and robust since ten-fold cross-validation was employed to evaluate the performance of the model. Compared to the deep models used in existing studies for epilepsy diagnosis, our proposed method is simple and less computationally intensive. This is the earliest study to have uniquely employed the positional encoding with learnable parameters to each correlation coefficient's embedding together with the deep transformer model, using a huge database of 121 participants for epilepsy detection. With the training and validation of the model using a larger dataset, the same study approach can be extended for the detection of other neurological conditions, with a transformative impact on neurological diagnostics worldwide.

ExHiF: Alzheimer's disease detection using exemplar histogram-based features with CT and MR images.

PURPOSE: The classification of medical images is an important priority for clinical research and helps to improve the diagnosis of various disorders. This work aims to classify the neuroradiological features of patients with Alzheimer's disease (AD) using an automatic hand-modeled method with high accuracy. MATERIALS AND METHOD: This work uses two (private and public) datasets. The private dataset consists of 3807 magnetic resonance imaging (MRI) and computer tomography (CT) images belonging to two (normal and AD) classes. The second public (Kaggle AD) dataset contains 6400 MR images. The presented classification model comprises three fundamental phases: feature extraction using an exemplar hybrid feature extractor, neighborhood component analysis-based feature selection, and classification utilizing eight different classifiers. The novelty of this model is feature extraction. Vision transformers inspire this phase, and hence 16 exemplars are generated. Histogram-oriented gradients (HOG), local binary pattern (LBP) and local phase quantization (LPQ) feature extraction functions have been applied to each exemplar/patch and raw brain image. Finally, the created features are merged, and the best features are selected using neighborhood component analysis (NCA). These features are fed to eight classifiers to obtain highest classification performance using our proposed method. The presented image classification model uses exemplar histogram-based features; hence, it is called ExHiF. RESULTS: We have developed the ExHiF model with a ten-fold cross-validation strategy using two (private and public) datasets with shallow classifiers. We have obtained 100% classification accuracy using cubic support vector machine (CSVM) and fine k nearest neighbor (FkNN) classifiers for both datasets. CONCLUSIONS: Our developed model is ready to be validated with more datasets and has the potential to be employed in mental hospitals to assist neurologists in confirming their manual screening of AD using MRI/CT images.

PatchResNet: Multiple Patch Division-Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images.

Modern computer vision algorithms are based on convolutional neural networks (CNNs), and both end-to-end learning and transfer learning modes have been used with CNN for image classification. Thus, automated brain tumor classification models have been proposed by deploying CNNs to help medical professionals. Our primary objective is to increase the classification performance using CNN. Therefore, a patch-based deep feature engineering model has been proposed in this work. Nowadays, patch division techniques have been used to attain high classification performance, and variable-sized patches have achieved good results. In this work, we have used three types of patches of different sizes (32 × 32, 56 × 56, 112 × 112). Six feature vectors have been obtained using these patches and two layers of the pretrained ResNet50 (global average pooling and fully connected layers). In the feature selection phase, three selectors-neighborhood component analysis (NCA), Chi2, and ReliefF-have been used, and 18 final feature vectors have been obtained. By deploying k nearest neighbors (kNN), 18 results have been calculated. Iterative hard majority voting (IHMV) has been applied to compute the general classification accuracy of this framework. This model uses different patches, feature extractors (two layers of the ResNet50 have been utilized as feature extractors), and selectors, making this a framework that we have named PatchResNet. A public brain image dataset containing four classes (glioblastoma multiforme (GBM), meningioma, pituitary tumor, healthy) has been used to develop the proposed PatchResNet model. Our proposed PatchResNet attained 98.10% classification accuracy using the public brain tumor image dataset. The developed PatchResNet model obtained high classification accuracy and has the advantage of being a self-organized framework. Therefore, the proposed method can choose the best result validation prediction vectors and achieve high image classification performance.

"I am not a number!" Opinions and preferences of people with intellectual disability about genetic healthcare.

There is limited research exploring the knowledge and experiences of genetic healthcare from the perspective of people with intellectual disability. This study, conducted in New South Wales (Australia), addresses this gap. Eighteen adults with intellectual disability and eight support people were interviewed in this inclusive research study. The transcribed interviews were analysed using inductive content analysis. The findings were discussed in a focus group with ten adults with intellectual disability and in three multi-stakeholder advisory workshops, contributing to the validity and trustworthiness of the findings. Five main themes emerged: (i) access to genetic healthcare services is inequitable, with several barriers to the informed consent process; (ii) the experiences and opinions of people with intellectual disability are variable, including frustration, exclusion and fear; (iii) genetic counselling and diagnoses can be profoundly impactful, but translating a genetic diagnosis into tailored healthcare, appropriate support, peer connections and reproductive planning faces barriers; (iv) people with intellectual disability have a high incidence of exposure to trauma and some reported that their genetic healthcare experiences were associated with further trauma; (v) recommendations for a more respectful and inclusive model of genetic healthcare. Co-designed point-of-care educational and consent resources, accompanied by tailored professional education for healthcare providers, are required to improve the equity and appropriateness of genetic healthcare for people with intellectual disability.

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review.

Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren't any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.

TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition worldwide. In this research, we used an ADHD electroencephalography (EEG) dataset containing more than 4000 EEG signals. Moreover, these EEGs are noisy signals. A new hand-modeled EEG classification model has been proposed to separate healthy versus ADHD individuals using the EEG signals. In this model, a new ternary motif pattern (TMP) has been incorporated. We have mimicked deep learning networks to create this hand-modeled classification method. The Tunable Q Wavelet Transform (TQWT) has been utilized to generate wavelet subbands. We applied the proposed TMP and statistics to construct informative features from both raw EEG signals and wavelet bands by generating TQWT. Herein, features have been generated by 18 subbands and the original EEG signal. Thus, this model is named TMP19. The most informative features have been chosen by deploying neighborhood component analysis (NCA), and the selected features have been classified using the k-nearest neighbor (kNN) classifier. The used ADHD EEG dataset has 14 channels. Thus, these three phases-(i) feature extraction with TQWT, TMP, and statistics; (ii) feature selection by deploying NCA; and (iii) classification with kNN-have been applied to each channel. Iterative hard majority voting (IHMV) has been applied to obtain a higher and more general classification response. Our model attained 95.57% and 77.93% classification accuracies by deploying 10-fold and leave one subject out (LOSO) cross-validations, respectively.

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.

Diagnostic Yield of Whole Genome Sequencing After Nondiagnostic Exome Sequencing or Gene Panel in Developmental and Epileptic Encephalopathies.

OBJECTIVE: To assess the benefits and limitations of whole genome sequencing (WGS) compared to exome sequencing (ES) or multigene panel (MGP) in the molecular diagnosis of developmental and epileptic encephalopathies (DEE). METHODS: We performed WGS of 30 comprehensively phenotyped DEE patient trios that were undiagnosed after first-tier testing, including chromosomal microarray and either research ES (n = 15) or diagnostic MGP (n = 15). RESULTS: Eight diagnoses were made in the 15 individuals who received prior ES (53%): 3 individuals had complex structural variants; 5 had ES-detectable variants, which now had additional evidence for pathogenicity. Eleven diagnoses were made in the 15 MGP-negative individuals (68%); the majority (n = 10) involved genes not included in the panel, particularly in individuals with postneonatal onset of seizures and those with more complex presentations including movement disorders, dysmorphic features, or multiorgan involvement. A total of 42% of diagnoses were autosomal recessive or X-chromosome linked. CONCLUSION: WGS was able to improve diagnostic yield over ES primarily through the detection of complex structural variants (n = 3). The higher diagnostic yield was otherwise better attributed to the power of re-analysis rather than inherent advantages of the WGS platform. Additional research is required to assist in the assessment of pathogenicity of novel noncoding and complex structural variants and further improve diagnostic yield for patients with DEE and other neurogenetic disorders.

A De Novo Mutation in the Sodium-Activated Potassium Channel KCNT2 Alters Ion Selectivity and Causes Epileptic Encephalopathy.

Early infantile epileptic encephalopathies (EOEE) are a debilitating spectrum of disorders associated with cognitive impairments. We present a clinical report of a KCNT2 mutation in an EOEE patient. The de novo heterozygous variant Phe240Leu SLICK was identified by exome sequencing and confirmed by Sanger sequencing. Phe240Leu rSlick and hSLICK channels were electrophysiologically, heterologously characterized to reveal three significant alterations to channel function. First, [Cl-]i sensitivity was reversed in Phe240Leu channels. Second, predominantly K+-selective WT channels were made to favor Na+ over K+ by Phe240Leu. Third, and consequent to altered ion selectivity, Phe240Leu channels had larger inward conductance. Further, rSlick channels induced membrane hyperexcitability when expressed in primary neurons, resembling the cellular seizure phenotype. Taken together, our results confirm that Phe240Leu is a "change-of-function" KCNT2 mutation, demonstrating unusual altered selectivity in KNa channels. These findings establish pathogenicity of the Phe240Leu KCNT2 mutation in the reported EOEE patient.

Spinocerebellar ataxia type 29 due to mutations in ITPR1: a case series and review of this emerging congenital ataxia.

BACKGROUND: Spinocerebellar ataxia type 29 (SCA29) is an autosomal dominant, non-progressive cerebellar ataxia characterized by infantile-onset hypotonia, gross motor delay and cognitive impairment. Affected individuals exhibit cerebellar dysfunction and often have cerebellar atrophy on neuroimaging. Recently, missense mutations in ITPR1 were determined to be responsible. RESULTS: Clinical information on 21 individuals from 15 unrelated families with ITPR1 mutations was retrospectively collected using standardized questionnaires, including 11 previously unreported singletons and 2 new patients from a previously reported family. We describe the genetic, clinical and neuroimaging features of these patients to further characterize the clinical features of this rare condition and assess for any genotype-phenotype correlation for this disorder. Our cohort consisted of 9 males and 12 females, with ages ranging from 28 months to 49 years. Disease course was non-progressive with infantile-onset hypotonia and delays in motor and speech development. Gait ataxia was present in all individuals and 10 (48%) were not ambulating independently between the ages of 3-12 years of age. Mild-to-moderate cognitive impairment was present in 17 individuals (85%). Cerebellar atrophy developed after initial symptom presentation in 13 individuals (72%) and was not associated with disease progression or worsening functional impairment. We identified 12 different mutations including 6 novel mutations; 10 mutations were missense (with 4 present in >1 individual), 1 a splice site mutation leading to an in-frame insertion and 1 an in-frame deletion. No specific genotype-phenotype correlations were observed within our cohort. CONCLUSIONS: Our findings document significant clinical heterogeneity between individuals with SCA29 in a large cohort of molecularly confirmed cases. Based on the retrospective observed clinical features and disease course, we provide recommendations for management. Further research into the natural history of SCA29 through prospective studies is an important next step in better understanding the condition.

The molecular and phenotypic spectrum of IQSEC2-related epilepsy.

OBJECTIVE: IQSEC2 is an X-linked gene associated with intellectual disability (ID) and epilepsy. Herein we characterize the epilepsy/epileptic encephalopathy of patients with IQSEC2 pathogenic variants. METHODS: Forty-eight patients with IQSEC2 variants were identified worldwide through Medline search. Two patients were recruited from our early onset epileptic encephalopathy cohort and one patient from personal communication. The 18 patients who have epilepsy in addition to ID are the subject of this study. Information regarding the 18 patients was ascertained by questionnaire provided to the treating clinicians. RESULTS: Six affected individuals had an inherited IQSEC2 variant and 12 had a de novo one (male-to-female ratio, 12:6). The pathogenic variant types were as follows: missense (8), nonsense (5), frameshift (1), intragenic duplications (2), translocation (1), and insertion (1). An epileptic encephalopathy was diagnosed in 9 (50%) of 18 patients. Seizure onset ranged from 8 months to 4 years; seizure types included spasms, atonic, myoclonic, tonic, absence, focal seizures, and generalized tonic-clonic (GTC) seizures. The electroclinical syndromes could be defined in five patients: late-onset epileptic spasms (three) and Lennox-Gastaut or Lennox-Gastaut-like syndrome (two). Seizures were pharmacoresistant in all affected individuals with epileptic encephalopathy. The epilepsy in the other nine patients had a variable age at onset from infancy to 18 years; seizure types included GTC and absence seizures in the hereditary cases and GTC and focal seizures in de novo cases. Seizures were responsive to medical treatment in most cases. All 18 patients had moderate to profound intellectual disability. Developmental regression, autistic features, hypotonia, strabismus, and white matter changes on brain magnetic resonance imaging (MRI) were prominent features. SIGNIFICANCE: The phenotypic spectrum of IQSEC2 disorders includes epilepsy and epileptic encephalopathy. Epileptic encephalopathy is a main clinical feature in sporadic cases. IQSEC2 should be evaluated in both male and female patients with an epileptic encephalopathy.

Asparagine Synthetase Deficiency causes reduced proliferation of cells under conditions of limited asparagine.

Asparagine Synthetase Deficiency is a recently described cause of profound intellectual disability, marked progressive cerebral atrophy and variable seizure disorder. To date there has been limited functional data explaining the underlying pathophysiology. We report a new case with compound heterozygous mutations in the ASNS gene (NM_183356.3:c. [866G>C]; [1010C>T]). Both variants alter evolutionarily conserved amino acids and were predicted to be pathogenic based on in silico protein modelling that suggests disruption of the critical ATP binding site of the ASNS enzyme. In patient fibroblasts, ASNS expression as well as protein and mRNA stability are not affected by these variants. However, there is markedly reduced proliferation of patient fibroblasts when cultured in asparagine-limited growth medium, compared to parental and wild type fibroblasts. Restricting asparagine replicates the physiology within the blood-brain-barrier, with limited transfer of dietary derived asparagine, resulting in reliance of neuronal cells on intracellular asparagine synthesis by the ASNS enzyme. These functional studies offer insight into the underlying pathophysiology of the dramatic progressive cerebral atrophy associated with Asparagine Synthetase Deficiency.