Neurite orientation dispersion and density imaging parameters may help for the evaluation of epileptogenic tubers in tuberous sclerosis complex patients

Neurite density of the hippocampus is associated with trace eyeblink conditioning latency in 4- to 6-year-olds

Limited options exist to evaluate the development of hippocampal function in young children. Research has established that trace eyeblink conditioning (EBC) relies on a functional hippocampus. Hence, we set out to investigate whether trace EBC is linked to hippocampal structure, potentially serving as a valuable indicator of hippocampal development. Our study explored potential associations between individual differences in hippocampal volume and neurite density with trace EBC performance in young children. We used onset latency of conditioned responses (CR) and percentage of conditioned responses (% CR) as measures of hippocampal-dependent associative learning. Using a sample of typically developing children aged 4 to 6 years (N = 30; 14 girls; M = 5.70 years), participants underwent T1- and diffusion-weighted MRI scans and completed a 15-min trace eyeblink conditioning task conducted outside the MRI. % CR and CR onset latency were calculated based on all trials involving tone-puff presentations and tone-alone trials. Findings revealed a connection between greater left hippocampal neurite density and delayed CR onset latency. Children with higher neurite density in the left hippocampus tended to blink closer to the onset of the unconditioned stimulus, indicating that structural variations in the hippocampus were associated with more precise timing of conditioned responses. No other relationships were observed between hippocampal volume, cerebellum volume or neurite density, hippocampal white matter connectivity and any EBC measures. Preliminary results suggest that trace EBC may serve as a straightforward yet innovative approach for studying hippocampal development in young children and populations with atypical development.

Deep Learning Approaches for Imaging-Based Automated Segmentation of Tuberous Sclerosis Complex

The present study presents a novel approach for identifying epileptogenic tubers in patients with tuberous sclerosis complex (TSC) and automating tuber segmentation using a three-dimensional convolutional neural network (3D CNN). The study retrospectively included 31 TSC patients whose lesions were manually annotated from multiparametric neuroimaging data. Epileptogenic tubers were determined via presurgical evaluation and stereoelectroencephalography recording. Neuroimaging metrics were extracted and compared between epileptogenic and non-epileptogenic tubers. Additionally, five datasets with different preprocessing strategies were used to construct and train 3D CNNs for automated tuber segmentation. The normalized positron emission tomography (PET) metabolic value was significantly lower in epileptogenic tubers defined via presurgical evaluation (p = 0.001). The CNNs showed high performance for localizing tubers, with an accuracy between 0.992 and 0.994 across the five datasets. The automated segmentations were highly correlated with clinician-based features. The neuroimaging characteristics for epileptogenic tubers were demonstrated, increasing surgical confidence in clinical practice. The validated deep learning detection algorithm yielded a high performance in determining tubers with an excellent agreement with reference clinician-based segmentation. Collectively, when coupled with our investigation of minimal input requirements, the approach outlined in this study represents a clinically invaluable tool for the management of TSC.

Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre study

BACKGROUND

More than half of patients with tuberous sclerosis complex (TSC) suffer from drug-resistant epilepsy (DRE), and resection surgery is the most effective way to control intractable epilepsy. Precise preoperative localization of epileptogenic tubers among all cortical tubers determines the surgical outcomes and patient prognosis. Models for preoperatively predicting epileptogenic tubers using 18F-FDG PET images are still lacking, however. We developed noninvasive predictive models for clinicians to predict the epileptogenic tubers and the outcome (seizure freedom or no seizure freedom) of cortical tubers based on 18F-FDG PET images.

METHODS

Forty-three consecutive TSC patients with DRE were enrolled, and 235 cortical tubers were selected as the training set. Quantitative indices of cortical tubers on 18F-FDG PET were extracted, and logistic regression analysis was performed to select those with the most important predictive capacity. Machine learning models, including logistic regression (LR), linear discriminant analysis (LDA), and artificial neural network (ANN) models, were established based on the selected predictive indices to identify epileptogenic tubers from multiple cortical tubers. A discriminating nomogram was constructed and found to be clinically practical according to decision curve analysis (DCA) and clinical impact curve (CIC). Furthermore, testing sets were created based on new PET images of 32 tubers from 7 patients, and follow-up outcome data from the cortical tubers were collected 1, 3, and 5 years after the operation to verify the reliability of the predictive model. The predictive performance was determined by using receiver operating characteristic (ROC) analysis.

RESULTS

PET quantitative indices including SUVmean, SUVmax, volume, total lesion glycolysis (TLG), third quartile, upper adjacent and standard added metabolism activity (SAM) were associated with the epileptogenic tubers. The SUVmean, SUVmax, volume and TLG values were different between epileptogenic and non-epileptogenic tubers and were associated with the clinical characteristics of epileptogenic tubers. The LR model achieved the better performance in predicting epileptogenic tubers (AUC = 0.7706; 95% CI 0.70-0.83) than the LDA (AUC = 0.7506; 95% CI 0.68-0.82) and ANN models (AUC = 0.7425; 95% CI 0.67-0.82) and also demonstrated good calibration (Hosmer‒Lemeshow goodness-of-fit p value = 0.7). In addition, DCA and CIC confirmed the clinical utility of the nomogram constructed to predict epileptogenic tubers based on quantitative indices. Intriguingly, the LR model exhibited good performance in predicting epileptogenic tubers in the testing set (AUC = 0.8502; 95% CI 0.71-0.99) and the long-term outcomes of cortical tubers (1-year outcomes: AUC = 0.7805, 95% CI 0.71-0.85; 3-year outcomes: AUC = 0.8066, 95% CI 0.74-0.87; 5-year outcomes: AUC = 0.8172, 95% CI 0.75-0.87).

CONCLUSIONS

The 18F-FDG PET image-based LR model can be used to noninvasively identify epileptogenic tubers and predict the long-term outcomes of cortical tubers in TSC patients.

Applications of advanced diffusion MRI in early brain development: a comprehensive review

Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.

The role of neurosurgery in the management of tuberous sclerosis complex–associated epilepsy: a systematic review

OBJECTIVE

Tuberous sclerosis complex (TSC) is an autosomal dominant, multisystem neurocutaneous disorder associated with cortical tubers, brain lesions seen in nearly all patients with TSC, which are frequently epileptogenic. Seizures are often the earliest clinical manifestation of TSC, leading to epilepsy in over 70% of patients. Medical management with antiepileptic drugs constitutes early therapy, but over 50% develop medically refractory epilepsy, necessitating surgical evaluation and treatment. The objective of this study was to summarize the literature and report seizure outcomes following surgical treatment for TSC-associated epilepsy.

METHODS

A systematic literature review was performed in accordance with the PRISMA guidelines. The PubMed and Embase databases were searched for journal articles reporting seizure outcomes following epilepsy surgery in TSC patients. Included studies were placed into one of two groups based on the surgical technique used. Excellent and worthwhile seizure reductions were defined for each group as outcomes and extracted from each study.

RESULTS

A total of 46 studies were included. Forty of these studies reported seizure outcomes following any combination of resection, disconnection, and ablation on a collective 1157 patients. Excellent and worthwhile seizure reductions were achieved in 59% (683/1157) and 85% (450/528) of patients, respectively. Six of these studies reported seizure outcomes following treatment with neuromodulation. Excellent and worthwhile seizure reductions were achieved in 34% (24/70) and 76% (53/70) of patients, respectively.

CONCLUSIONS

Surgery effectively controls seizures in select patients with TSC-associated epilepsy, but outcomes vary. Further understanding of TSC-associated epilepsy, improving localization strategies, and emerging surgical techniques represent promising avenues for improving surgical outcomes.

Adverse childhood experiences predict neurite density differences in young children with and without attention deficit hyperactivity disorder

Adverse childhood experiences (ACEs) put millions of children at risk for later health problems. As childhood represents a critical developmental period, it is important to understand how ACEs impact brain development in young children. In addition, children with attention-deficit/hyperactivity disorder (ADHD) are more likely than typically developing (TD) peers to experience ACEs. Therefore, the current study examined the impact of ACEs on early brain development, using a cumulative risk approach, in a large sample of children with and without ADHD. We examined 198 young children (M_age  = 5.45, 82.3% Hispanic/Latino; 52.5% ADHD) across measures of brain volume, cortical thickness, neurite density index (NDI), and orientation dispersion index (ODI). For the NDI measure, there was a significant interaction between group and cumulative risk (ß = .18, p = .048), such that for children with ADHD, but not TD children, greater cumulate risk was associated with increased NDI in corpus callosum. No other interactions were detected. Additionally, when examining across groups, greater cumulative risk was associated with reduced ODI and volume in the cerebellum, although these findings did not survive a correction for multiple comparisons. Our results highlight the role early cumulative ACEs play in brain development across TD and children with ADHD.

White matter structural connectivity as a biomarker for detecting juvenile myoclonic epilepsy by transferred deep convolutional neural networks with varying transfer rates

Objective. By detecting abnormal white matter changes, diffusion magnetic resonance imaging (MRI) contributes to the detection of juvenile myoclonic epilepsy (JME). In addition, deep learning has greatly improved the detection performance of various brain disorders. However, there is almost no previous study effectively detecting JME by a deep learning approach with diffusion MRI.Approach. In this study, the white matter structural connectivity was generated by tracking the white matter fibers in detail based on Q-ball imaging and neurite orientation dispersion and density imaging. Four advanced deep convolutional neural networks (CNNs) were deployed by using the transfer learning approach, in which the transfer rate searching strategy was proposed to achieve the best detection performance.Main results. Our results showed: (a) Compared to normal control, the white matter' neurite density of JME was significantly decreased. The most significantly abnormal fiber tracts between the two groups were found to be cortico-cortical connection tracts. (b) The proposed transfer rate searching approach contributed to find each CNN's best performance, in which the best JME detection accuracy of 92.2% was achieved by using the Inception_resnet_v2 network with a 16% transfer rate.Significance. The results revealed: (a) Through detection of the abnormal white matter changes, the white matter structural connectivity can be used as a useful biomarker for detecting JME, which helps to characterize the pathophysiology of epilepsy. (b) The proposed transfer rate, as a new hyperparameter, promotes the CNNs transfer learning performance in detecting JME.

Choroid Plexus Carcinomas With TP53 Germline Mutations: Management and Outcome

BACKGROUND

Choroid plexus carcinomas (CPCs) are rare pediatric tumors commonly associated with Li-Fraumeni syndrome (LFS), which involves a germline mutation of the tumor suppressor gene TP53.

MATERIALS AND METHODS

We retrospectively analyzed the corresponding information of 12 cases, including the effects of surgery and radiotherapy and TP53 germline mutations, to analyse the management strategies. Kaplan-Meier curves and the log-rank test were used to evaluate the progression-free survival (PFS).

RESULTS

Twelve CPC patients were included, of which TP53 germline mutations were found in eight cases. All patients underwent surgical resection, and six patients received radiotherapy following with operation after initial diagnosis, one patient received radiotherapy following relapse. It was significantly different (P=0.012 and 0.028) that patients with TP53 germline mutation receiving the gross total resection (GTR) without radiotherapy showed survival advantages. Without TP53 germline mutations also showed survival advantages, but there is no statistical significance (P=0.063).

CONCLUSIONS

These findings provide evidence for the therapeutic strategy that radiotherapy should not be considered for patients with TP53 germline mutations.

Making the Invisible Visible: Advanced Neuroimaging Techniques in Focal Epilepsy

It has been a clinically important, long-standing challenge to accurately localize epileptogenic focus in drug-resistant focal epilepsy because more intensive intervention to the detected focus, including resection neurosurgery, can provide significant seizure reduction. In addition to neurophysiological examinations, neuroimaging plays a crucial role in the detection of focus by providing morphological and neuroanatomical information. On the other hand, epileptogenic lesions in the brain may sometimes show only subtle or even invisible abnormalities on conventional MRI sequences, and thus, efforts have been made for better visualization and improved detection of the focus lesions. Recent advance in neuroimaging has been attracting attention because of the potentials to better visualize the epileptogenic lesions as well as provide novel information about the pathophysiology of epilepsy. While the progress of newer neuroimaging techniques, including the non-Gaussian diffusion model and arterial spin labeling, could non-invasively detect decreased neurite parameters or hypoperfusion within the focus lesions, advances in analytic technology may also provide usefulness for both focus detection and understanding of epilepsy. There has been an increasing number of clinical and experimental applications of machine learning and network analysis in the field of epilepsy. This review article will shed light on recent advances in neuroimaging for focal epilepsy, including both technical progress of images and newer analytical methodologies and discuss about the potential usefulness in clinical practice.