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T2w appearance of periventricular white matter cystic-like lesions in different TSC patients. Sometimes assuming simple (a, b) or loculated (c) cystic configuration (black arrowheads), they are frequently consistent with dilated perivascular spaces (d, f) closely related to a central deep venule (white arrows)
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IntroductionTuberous sclerosis complex (TSC) is a rare autosomal dominant disorder affecting multiple systems, due to inactivating mutations of TSC1 or TSC2 mTOR pathway genes. Neurological manifestations are observed in about 95% cases, representing the most frequent cause of morbidity and one of the most common causes of mortality.Background
Neur...
Citations
... 12 Cerebral white matter migration abnormalities, called MR "solar zone" signs, appear on T2weighted and FLAIR images as linear or curved high-intensity radiation extending from the periventricular area to the subcortical area. 13 They are believed to represent heterotopic neuronal and glial elements arrested during cortical migration. ...
Objectives
Tuberous sclerosis complex (TSC) is a genetic disorder caused by a TSC1 or TSC2 gene variation characterized by widespread hamartomas in organs such as the skin, brain, heart, lungs, liver, and kidneys.
Methods
We report a case of a patient with TSC who presented with broad clinical manifestations, including epilepsy.
Results
An 18-year-old man was diagnosed with recurrent drug-resistant epilepsy. Neuroimaging revealed bilateral cortical and subcortical tubers with multiple calcified subependymal nodules. His skin involvement and psychomotor retardation raised the suspicion of TSC. Genetic testing confirmed the diagnosis, and a combined treatment including mTOR inhibitors was initiated.
Discussion
TSC, although considered rare, needs to be considered when evaluating patients with broad clinical manifestations. Our report has significant implications for understanding the impact of genotype on the prognosis of TSC and the selection of treatment strategies for TSC-related refractory epilepsy.
... Currently, MR imaging provides excellent tissue contrast, which is a technique used routinely to diagnose TSC disease. 13 Radiologists have difficulty distinguishing patients with pediatric refractory (resistant seizure) disease from those with seizure-controlled disease because these patients may have similar appearances in MR imaging. Recent artificial intelligence tools have been used to help radiologists assess the cortical tubers in rare pediatric TSC disease, including from MR images. ...
Background and purpose:
Tuberous sclerosis complex disease is a rare, multisystem genetic disease, but appropriate drug treatment allows many pediatric patients to have positive outcomes. The purpose of this study was to predict the effectiveness of antiseizure medication treatment in children with tuberous sclerosis complex-related epilepsy.
Materials and methods:
We conducted a retrospective study involving 300 children with tuberous sclerosis complex-related epilepsy. The study included the analysis of clinical data and T2WI and FLAIR images. The clinical data consisted of sex, age of onset, age at imaging, infantile spasms, and antiseizure medication numbers. To forecast antiseizure medication treatment, we developed a multitechnique deep learning method called WAE-Net. This method used multicontrast MR imaging and clinical data. The T2WI and FLAIR images were combined as FLAIR3 to enhance the contrast between tuberous sclerosis complex lesions and normal brain tissues. We trained a clinical data-based model using a fully connected network with the above-mentioned variables. After that, a weighted-average ensemble network built from the ResNet3D architecture was created as the final model.
Results:
The experiments had shown that age of onset, age at imaging, infantile spasms, and antiseizure medication numbers were significantly different between the 2 drug-treatment outcomes (P < .05). The hybrid technique of FLAIR3 could accurately localize tuberous sclerosis complex lesions, and the proposed method achieved the best performance (area under the curve = 0.908 and accuracy of 0.847) in the testing cohort among the compared methods.
Conclusions:
The proposed method could predict antiseizure medication treatment of children with rare tuberous sclerosis complex-related epilepsy and could be a strong baseline for future studies.
... Crucially, one study has suggested a link between postmortem dendritic synaptic surplus in ASD and hyperactivity of the mTOR pathway [120]. mTOR is a key regulator of synaptic protein synthesis [121], and aberrations in mTOR signaling have been linked to synaptic and neuroanatomical abnormalities that are associated with ASD [122]. The mTOR inhibitor may be helpful for the pharmacological treatment of ASD. ...
Special diets or nutritional supplements are regularly given to treat children with autism spectrum disorder (ASD). The increased consumption of particular foods has been demonstrated in numerous trials to lessen autism-related symptoms and comorbidities. A case study on a boy with moderate autism who significantly improved after three years of following a healthy diet consisting of pumpkin and walnuts was examined in this review in connection to a few different neurophenotypes of ASD. We are able to suggest that a diet high in pumpkin and walnuts was useful in improving the clinical presentation of the ASD case evaluated by reducing oxidative stress, neuroinflammation, glutamate excitotoxicity, mitochondrial dysfunction, and altered gut microbiota, all of which are etiological variables. Using illustrated figures, a full description of the ways by which a diet high in pumpkin and nuts could assist the included case is offered.
... Neurological symptoms are prevalent in nearly all children with TSC, and multicontrast magnetic resonance imaging (MRI) is frequently employed for a clinical diagnosis [11]. To date, T2-weighted imaging (T2W) and fluid-attenuated inversion recovery (FLAIR) have been commonly utilized in a pediatric TSC diagnosis, allowing for the identification of lesions and facilitating high lesion-to-brain contrast visualization. ...
Multi-contrast magnetic resonance imaging (MRI) is wildly applied to identify tuberous sclerosis complex (TSC) children in a clinic. In this work, a deep convolutional neural network with multi-contrast MRI is proposed to diagnose pediatric TSC. Firstly, by combining T2W and FLAIR images, a new synthesis modality named FLAIR3 was created to enhance the contrast between TSC lesions and normal brain tissues. After that, a deep weighted fusion network (DWF-net) using a late fusion strategy is proposed to diagnose TSC children. In experiments, a total of 680 children were enrolled, including 331 healthy children and 349 TSC children. The experimental results indicate that FLAIR3 successfully enhances the visibility of TSC lesions and improves the classification performance. Additionally, the proposed DWF-net delivers a superior classification performance compared to previous methods, achieving an AUC of 0.998 and an accuracy of 0.985. The proposed method has the potential to be a reliable computer-aided diagnostic tool for assisting radiologists in diagnosing TSC children.
... Nervous system manifestations can be observed in almost all cases of TSC, and MR imaging is a technique used routinely to diagnose TSC. 11 Cortical tubers are major TSC-related brain manifestations, which show abnormal high or low signals in FLAIR sequences. 12 In addition, FLAIR imaging is widely used to study the epileptogenic zone 13 and epilepsy mechanism of TSC. 14 Jesmanas et al 9 reported that MR imaging-defined tuber types were found to be associated with early seizure onset in TSC. ...
Background and purpose:
Highly predictive markers of drug treatment outcomes of tuberous sclerosis complex-related epilepsy are a key unmet clinical need. The objective of this study was to identify meaningful clinical and radiomic predictors of outcomes of epilepsy drug treatment in patients with tuberous sclerosis complex.
Materials and methods:
A total of 105 children with tuberous sclerosis complex-related epilepsy were enrolled in this retrospective study. The pretreatment baseline predictors that were used to predict drug treatment outcomes included patient demographic and clinical information, gene data, electroencephalogram data, and radiomic features that were extracted from pretreatment MR imaging scans. The Spearman correlation coefficient and least absolute shrinkage and selection operator were calculated to select the most relevant features for the drug treatment outcome to build a comprehensive model with radiomic and clinical features for clinical application.
Results:
Four MR imaging-based radiomic features and 5 key clinical features were selected to predict the drug treatment outcome. Good discriminative performances were achieved in testing cohorts (area under the curve = 0.85, accuracy = 80.0%, sensitivity = 0.75, and specificity = 0.83) for the epilepsy drug treatment outcome. The model of radiomic and clinical features resulted in favorable calibration curves in all cohorts.
Conclusions:
Our results suggested that the radiomic and clinical features model may predict the epilepsy drug treatment outcome. Age of onset, infantile spasms, antiseizure medication numbers, epileptiform discharge in left parieto-occipital area of electroencephalography, and gene mutation type are the key clinical factors to predict the epilepsy drug treatment outcome. The texture and first-order statistic features are the most valuable radiomic features for predicting drug treatment outcomes.
... Caracterizada pelo crescimento de neoplasmas benignos em múltiplos órgãos, que pode afetar qualquer sistema (OLIVEIRA et al., 2023;PEREIRA;DANTAS;MANREZA, 2022;RUSSO et al., 2020). No mundo, aproximadamente 2 milhões de pessoas são acometidas pela doença, não existindo preferência por sexo ou etnia (UYSAL; ŞAHİN, 2020). ...
... É fornecido ainda informações de que o avanço de técnicas servem como papel para proporcionar novos insights sobre a doença, bem como oportunidade de oferecer uma visão geral sobre novas perspectivas do uso da neuroimagem como forma de melhorar a compreensão da fisiopatologia da doença (RUSSO et al., 2020). ...
... Embora sejam evidenciados avançados no diagnóstico, tratamento e terapêutica da doença, melhorando consequentemente a morbidade da doença, o prognóstico permanece ruim, onde cerca de 40% dos pacientes morrem antes dos 35 anos de idade (BUSTOS; VANEGAS, 2018; GOERGEN; FAHEY, 2022). Em decorrência da complexidade da doença, variados exames são necessários para garantir o seu diagnóstico e monitoramento, dentre eles os exames de imagem, como a ultrassonografia (USG), ressonância magnética (RM), eletroencefalograma (EEG), eletrocardiograma (ECG), tomografia computadorizada (TC), as quais serão escolhidos/definidos conforme a necessidade de cada paciente, à medida que vários exames possuem papel primordial na investigação diagnóstica precoce da TSC, bem como seu acompanhamento e monitoramento(HULSHOF et al., 2021; NORTHRUP et al., 2021).No processo diagnóstico aponta-se que a tomografia computadorizada e ressonância magnética estão instituídas como técnicas de investigação de primeira linha, sendo amplamente reconhecido e definido como método para definir características da doença(RUSSO et al., 2020). Contudo, a combinação de exames seriados, associados a práticas terapêuticas, possibilitará um diagnóstico precoce, melhor monitoramento e tratamento do paciente(HULSHOF et al., 2021).O modelo padrão de diagnóstico enfatiza que, apesar da existência de critérios para a detecção da doença, o diagnóstico genético da esclerose tuberosa é primordial e benéfico como forma de garantir que os indivíduos possam ser submetidos a vigilância necessária e acompanhados quanto as manifestações clínicas da TSC o mais cedo possível, permitindo resultados clínicos mais ideais (NORTHRUP et al., 2021). ...
A esclerose tuberosa é uma doença genética rara, multissistêmica, de caráter autossômico dominante, com incidência de 1 em 6.000-10.000 nascidos vivos anualmente. Na esclerose tuberosa, os órgãos mais acometidos são: o cérebro, coração, pele, olhos, rins e pulmões. Em decorrência da abrangência da patologia, as suas manifestações clínicas são variadas em termos de gravidade e diversidade de órgãos e tecidos acometidos. Não existindo assim um sintoma único e específico para TSC. No processo diagnóstico aponta-se que a tomografia computadorizada e ressonância magnética estão instituídas como técnicas de investigação de primeira linha, sendo a presença de rabdomiomas a razão mais comum para suspeita da doença. A antecipação do diagnóstico é primordial, à medida que se torna um diferencial para uma melhor triagem, monitoramento e sequenciamento do paciente. O manejo clínico é principalmente realizado de forma sintomática, com alternativas que variam entre intervenções farmacológicas, cirúrgica ou comportamental. Nesse contexto, o objetivo desse estudo foi descrever os principais achados radiológicos da esclerose tuberosa, descritos na literatura. Conclui-se que a evolução da esclerose tuberosa estar associada ao período em que a mesma é diagnosticada, sendo o diagnóstico precoce crucial para uma melhor triagem, monitoramento e sequenciamento do paciente.
... In infants, tubers appear as localized cortical thickening with moderate hyperintensity on T1-weighted images and hypointensity on T2-weighted images compared with normal unmyelinated tissue. After myelin maturation, tubers typically appear as areas of increased signal from the cerebral cortex and decreased signal from the subcortical white matter on T1-weighted sequences, along with increased signals from the cortex and subcortical white matter on T2-weighted sequences and fluid-attenuated inversion recovery (FLAIR) images [15,25]. ...
Patients with tuberous sclerosis complex present with cognitive, behavioral, and psychiatric impairments, such as intellectual disabilities, autism spectrum disorders, and drug-resistant epilepsy. It has been shown that these disorders are associated with the presence of cortical tubers. Tuberous sclerosis complex results from inactivating mutations in the TSC1 or TSC2 genes, resulting in hyperactivation of the mTOR signaling pathway, which regulates cell growth, proliferation, survival, and autophagy. TSC1 and TSC2 are classified as tumor suppressor genes and function according to Knudson’s two-hit hypothesis, which requires both alleles to be damaged for tumor formation. However, a second-hit mutation is a rare event in cortical tubers. This suggests that the molecular mechanism of cortical tuber formation may be more complicated and requires further research. This review highlights the issues of molecular genetics and genotype–phenotype correlations, considers histopathological characteristics and the mechanism of morphogenesis of cortical tubers, and also presents data on the relationship between these formations and the development of neurological manifestations, as well as treatment options.
... A growing body of evidence indicates that the neurobiological basis of both ASD and TSC-related neurological symptoms may be due to abnormal synaptic transmission, plasticity, and alterations in neuronal connectivity as a result of pathology within synapses [21,31,32]. On the other hand, alterations in neural network connectivity in patients with TSC may result from defects in RHOA-mediated regulation of cytoskeletal dynamics during neuronal development [33]. ...
Tuberous sclerosis complex (TSC) is a rare genetic multisystem disorder caused by loss-of-function mutations in the tumour suppressors TSC1/TSC2, both of which are negative regulators of the mammalian target of rapamycin (mTOR) kinase. Importantly, mTOR hyperactivity seems to be linked with the pathobiology of autism spectrum disorders (ASD). Recent studies suggest the potential involvement of microtubule (MT) network dysfunction in the neuropathology of “mTORopathies”, including ASD. Cytoskeletal reorganization could be responsible for neuroplasticity disturbances in ASD individuals. Thus, the aim of this work was to study the effect of Tsc2 haploinsufficiency on the cytoskeletal pathology and disturbances in the proteostasis of the key cytoskeletal proteins in the brain of a TSC mouse model of ASD. Western-blot analysis indicated significant brain-structure-dependent abnormalities in the microtubule-associated protein Tau (MAP-Tau), and reduced MAP1B and neurofilament light (NF-L) protein level in 2-month-old male B6;129S4-Tsc2tm1Djk/J mice. Alongside, pathological irregularities in the ultrastructure of both MT and neurofilament (NFL) networks as well as swelling of the nerve endings were demonstrated. These changes in the level of key cytoskeletal proteins in the brain of the autistic-like TSC mice suggest the possible molecular mechanisms responsible for neuroplasticity alterations in the ASD brain.
... However, understanding the neurobiology of autism is an evolving area and given the heterogenous nature of this group it may well be the case that underlying neurobiology differs significantly between subgroups. Autistic people with single gene disorders are imaged separately for this reason (212)(213)(214)(215)(216)(217). Neuroimaging differences have been shown in those with ASD and lower performing IQ as compared to those with higher IQ scores (218)(219)(220)(221). ...
Autism Spectrum Disorder (ASD) is a common neurodevelopmental condition typically
diagnosed at 2-4 years of age when deficits in social interaction and communication are noted
by carers. Our knowledge of ASD is advancing with greater awareness of the needs of autistic
children and adults and a move towards improving services for these patients. The underlying
neurobiology of ASD is a unifying aetiological agent, likely altered through both genetic and
environmental influences. There is compelling evidence to suggest that abnormalities in
Excitatory (E) glutamate and inhibitory (I) Gamma-Aminobutyric Acid (GABA) signalling in the brain may underpin ‘atypical’ development. Therefore I chose to examine relationships within the glutamatergic system in the striatum.
First I looked at metabotropic glutamate receptor 5 (mGluR5) in adults with and without ASD and found higher levels of mGluR5 among autistic participants. This is consistent with other recent studies. Despite the close functional ties between mGluR5 and E/I signalling, no-one had directly examined the relationship between mGluR5 and glutamate or GABA in vivo in the human brain of autistic individuals. I found a strong negative relationship between GABA+ and mGluR5. I then looked at mGluR5 in three animal models associated with ASD to see whether
any of these models might explain the greater availability of mGluR5 in autism. CNTNAP2 KO mice had significantly higher mGlu5 receptor binding in the striatum (caudate-putamen) as compared to wild-type (WT) mice. Given that CNTNAP2 is associated with a specific striatal deficit of parvalbumin positive GABA interneurons and ‘autistic’ features, this finding suggests that an increase in mGluR5 in ASD may relate to developmental GABAergic interneuron abnormalities.
Neurodevelopment requires careful coordination of neuronal and glial processes spanning proliferation, differentiation, myelination and pruning. Disruption to this process can result in neurodevelopmental difficulties and disorders such as ASD. Therefore I conducted early life studies examining the relationship between subcortical Glx (Glutamate and Glutamine), N-acetylaspartate (a marker of neuronal health) and myo-Inositol (a marker of glial activity) at three early life time points: in utero, within 4 weeks of birth (neonatal time point) and at 4-6-months of age (‘infant’ time point). I compared these to later neurodevelopmental outcomes
finding that higher neonatal NAA concentrations corresponded to better general
neurodevelopmental scores and lower ADOS-2 scores. As NAA is a marker of neuronal health this implies that we can mark neuronal health at birth and demonstrate that this correlates with neurodevelopmental outcomes. I then went on to examine these same relationships at the 4-6-month timepoint. Higher levels of myo-Inositol (and therefore greater glial activity) corresponded to poorer general and social developmental outcomes. Higher levels of Glx and therefore excess excitation predicted greater social deficits. This is in keeping with the theory of
E/I imbalance.
... Nervous system manifestations are observed in almost all TSC cases. Magnetic resonance imaging (MRI) provides excellent tissue contrast in clinical settings, and is a modality used routinely to diagnose TSC [10]. Yang et al. [4] predicted drug-resistant epilepsy patients based on MRI lesion location and type of information features, and achieved an area under curve (AUC) of 0.812 with multilayer perceptron. ...
Background: Ability to predict the outcomes of pharmacological treatment of epilepsy in pediatric patients with tuberous sclerosis complex (TSC) can confer a distinct leverage and guide therapeutic decision-making. Multi-contrast magnetic resonance imaging (MRI) is routinely used for diagnosis of TSC by pediatricians. We propose a parameter-efficient convolutional neural network with multi-contrast images to predict the drug treatment outcomes of pediatric epilepsy in TSC.
Methods: Image-based models were generated using the EfficientNet3D-B0 network architecture. A weighted average ensemble network with multi-contrast images was created as the final model. The proposed neural network is named as Efficient Tuberous sclerosis complex-Net (eTSC-Net).We compared our methods with a Residual Network 3D（ResNet3D) model. We trained a 3D-ResNet on our T2FLAIR data. Binary classification models were trained to distinguish non-controlled group patients from controlled group patients on T2W and T2FLAIR images. We trained all the models using an Nvidia RTX A6000 Graphical Processing Unit (GPU) card. Area under curve(AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to assess the classification performance for each model in each cohort. The differences between subgroups were assessed using independent samples t test and pvalues < 0.05 were considered indicative of statistical significance.
Results: The proposed neural network (eTSC-Net) achieved the best performance with an AUC value of 0.833 and 90.0% accuracy in the testing cohort, which was better than other models.
Conclusions: The results demonstrated the ability of the proposed method for predicting drug treatment outcomes in pediatric TSC-related epilepsy. eTSC-Net can serve as a useful computer-aided diagnostic tool to help clinical radiologists formulate more targeted treatment.
