Background: Diverse factors including seizure onset age, seizure frequency, epilepsy duration, total number of antiseizure medications trialed are considered as seizures-related neurocognitive loads in children with drug-resistant focal epilepsy (DRE). However, their associations with the structural integrity of neurocognitive networks remain largely unknown.Purpose: This study investigates a novel diffusion-weighted imaging (DWI) connectome methodology that can extract seizure-associated structural abnormality biomarkers from clinical DWI tractography, use them to classify neurocognitive impairments prior to surgery, and unveil the relationship between epilepsy-related factors and neurocognitive impairments.Methods: Thirty-three DRE children (age: 11.8±3.3 years, 17 boys) and 29 age-matched healthy controls were enrolled to create seizure-affected networks whose edges connect epileptogenic regions to key brain regions of 6 neurocognitive networks. The deviations of local efficiency values were averaged across the seizure-affected brain regions and used as new imaging-based biomarkers quantifying the degrees of seizure-associated structural abnormalities accumulated on individual neurocognitive networks and classifying the neurocognitive impairments along with the epilepsy-related factors.Results: Effect sizes of the proposed biomarkers for differentiating DRE from healthy controls were consistently very large across various subgroups defined by lesion types, lobar locations of epileptogenic foci, seizure frequency categories, and seizure types (i.e., Cohen d value >1.8). Compared with the epilepsy-related factors, the proposed biomarkers demonstrated superior classification accuracy for identifying neurocognitive impairments in general, verbal, and nonverbal domains. When combined with the epilepsy-related factors, the classification performance further improved, achieving an accuracy range of 90%–98% in the independent test patients. The subsequent association analysis using the proposed biomarkers as seizure-associated structural abnormality indicators demonstrated that the inclusion of such imaging indicators significantly enhances the strength of associations between epilepsy factors and neurocognitive impairments.Conclusion: These findings offer strong potential for objectively identifying neurocognitive impairments in DRE children, supporting early, data-driven decisions for personalized interventions to mitigate long-term effects.

Background: Most patients with Sturge-Weber syndrome (SWS) have unilateral brain involvement associated with a wide range of neurocognitive outcomes. Aims: To evaluate cognitive deficit patterns in young patients with unilateral SWS and identify clinical and imaging variables associated with the various cognitive deficit patterns. Methods: Forty-six young patients with SWS were stratified by the side of brain involvement and assigned to four cognitive groups, based on formal neuropsychology evaluation: 1-no deficit, 2-traditional deficit (verbal deficit in left, non-verbal in right SWS), 3-reorganized with crowding (e.g., only non-verbal deficit in left-hemispheric cases), and 4-global (verbal and non-verbal) deficit. Age, seizure variables, motor functions, and the extent of brain magnetic resonance imaging abnormalities were compared among the cognitive groups. Results: The reorganized/crowding pattern was seen only in patients with left SWS (6/20, 30%), most of whom were young with extensive abnormalities. Traditional cognitive deficit was seen mostly in right SWS (7/26, 27%). Global deficit was equally common (35%) in left- and right SWS and was associated with extensive hemispheric abnormalities, early seizure onset, and severe motor deficits. In multivariate analysis, extensive calcifications and severe motor deficits were independently associated with global cognitive deficit. Conclusions: Early, extensive left hemispheric abnormalities can be associated with preserved verbal but impaired non-verbal functions consistent with right-hemispheric reorganization and crowding. However, more than onethird of both left and right-hemispheric patients may develop a global cognitive impairment. This severe cognitive outcome is associated with extensive hemispheric calcification and severe motor deficit.

Accurate preoperative identification of true positive white matter pathways involved in critical eloquent functions such as motor, language, and vision plays a vital role in minimizing the risk of postoperative functional deficits and improving postoperative functional outcomes in pediatric epilepsy surgery. This study proposes a novel deep learning model: “ESM-AnatTractNet” that can accurately classify true positive eloquent white matter pathways across preoperative diffusion weighted imaging tractography data of 85 drug-resistant epilepsy patients (age: 10.70 ± 4.41 years). To enhance geometric and anatomical consistency of true positive tract classification, the ESM-AnatTractNet integrated two features in a point-cloud-based framework, 1) electro-physiologically confirmed spatial coordinates using electrical stimulation mapping (ESM) and 2) anatomically-contexted labels of the end-to-end neural connection using a standard brain atlas. Its overall performance was validated by accurately classifying 14 eloquent functional areas in whole brain, objectively optimizing resection margins to preserve eloquent functions using Kalman filter, and precisely predicting postoperative language outcomes using canonical correlation. Our ESM-AnatTractNet outperformed other baseline models, achieving an accuracy of 97% in correctly classifying eloquent areas within 10mm spatial resolution of clinical subdural grid electroencephalography. The Kalman filter analysis achieved 94% accuracy in predicting no deficits when the ESM-AnatTractNet-defined preservation zones were not resected. Postoperative decrease in language-related white matter connection efficacy defined by the ESM-AnatTractNet analysis was significantly associated with worse postoperative language outcome (R=0.73, p < 0.001). Our findings demonstrate that the ESM-AnatTractNet improves non-invasive localization of true positive eloquent white matter pathways, supporting its potential to enhance current preoperative evaluation of pediatric epilepsy surgery.

Abstract Purpose: We conducted a phase I trial to evaluate radiotherapy (RT) and nivolumab with the further addition of an indoleamine 2,3-dioxygenase 1 (IDO1) enzyme inhibitor (BMS-986205) in newly diagnosed patients with glioblastoma (GBM) IDH wild-type. Patients and Methods: In the current study, there were two primary cohorts of individuals. Cohort A included patients with O6-methylguanine-DNA methyltransferase (MGMT)–unmethylated GBM who received RT with concurrent and adjuvant nivolumab with escalating BMS-986205 doses. Cohort B included patients with MGMT-methylated GBM who received BMS-986205 at 25 mg daily with RT, nivolumab, and temozolomide (TMZ) followed by adjuvant TMZ. Patient outcomes were correlated with flow cytometric, transcriptome, general metabolite, and microbial metabolite analyses. Results: The treatments for both cohorts were moderately safe and tolerable. The treatment-emergent adverse events (TEAE) were mostly related to RT, TMZ, or the underlying disease and tumor progression. In cohort A, serious adverse events and TEAEs were predominantly lower grade, with no differences between the IDO1 enzyme inhibitor dosing cohorts. Dose-limiting toxicities reflected by increased transaminases (grade 3) were observed in two and three patients at the 50 and 100 mg levels of BMS-986205, respectively, with malaise observed in the 50 mg arm only. The 50 mg daily schedule was established as the recommended phase II dose (RP2D) in combination with RT and nivolumab. A number of exploratory correlative studies were also conducted. Conclusions: This single-arm, small phase I trial establishes a safety profile and RP2D for RT in combination with nivolumab and BMS-986205 for newly diagnosed patients with MGMT-unmethylated GBM (ClinicalTrials.gov: NCT04047706).

Phenylketonuria (PKU) is associated with neurocognitive and neuropsychological symptoms despite early treatment and well-controlled phenylalanine (Phe) levels. PKU impairs the conversion of Phe to tyrosine and causes excess phenylalanine to accumulate. This accumulation can competitively block some other amino acids from entering the brain. This may reduce production of key neurotransmitters like dopamine and serotonin, which can affect brain function and development. However, direct correlations between blood Phe levels and central nervous system (CNS) neurotransmitter concentrations have not been established, and reliable CNS biomarkers are lacking. Positron emission tomography (PET) enables in vivo assessment of brain metabolism and neurotransmitter-related processes; therefore, we aim to investigate its potential value as a biomarker. A systematic literature search was conducted in accordance with PRISMA guidelines using MEDLINE and Embase databases up to April 25, 2026. Studies evaluating brain PET imaging in individuals with PKU or hyperphenylalaninemia were included. Data on study design, patient characteristics, PET tracers, and key findings were extracted and synthesized qualitatively. A total of 380 records were screened, and 13 studies were included, with a mean sample size of 8.8 (median 6). PET tracers included 18fluorodeoxyglucose (FDG), amino acids, including tyrosine-, methionine-, leucine-, aminocyclohexanecarboxylate-based tracers, and fluorodopa-related tracers. FDG PET studies demonstrated regionally heterogeneous abnormalities in brain glucose metabolism, independent of plasma Phe levels. Amino acid PET studies demonstrated reduced cerebral protein synthesis associated with impaired large neutral amino acid transport and increased brain Phe levels. Fluorodopa-related PET studies indicated reduced dopaminergic activity, with no significant correlation between plasma Phe levels and striatal fluorodopa utilization. PET is a promising tool to bridge the gap between peripheral biochemical markers and CNS dysfunction in PKU. Further studies are needed to overcome limitations which include small sample sizes, heterogeneous metabolic control, restricted availability of tracer types, predominantly cross-sectional study designs, and lack of correlation with clinical signs and symptoms.

RATIONALE AND OBJECTIVES:Sturge-Weber syndrome (SWS) is a sporadic neurocutaneous disorder marked by cerebral venous abnormalities, progressive parenchymal damage, and early-onset neuro-cognitive complications. Existing imaging assessments lack standardized, quantitative approaches to capture the full disease burden. Here we tested an magnetic resonance imaging (MRI)-based scoring system that comprehensively captures both vascular and parenchymal brain abnormalities in SWS. MATERIALS AND METHODS:Twenty-five young patients (mean age, 9.5 years; range, 1-24 years) with unilateral SWS brain involvement underwent 3 T MRI using a standardized protocol (with pre- and post-contrast sequences) and formal neuro-cognitive evaluation. Six imaging features, four vascular and two parenchymal, were scored by two investigators across lobes using a 3-point scale. Interrater reliability was assessed using intra-class correlation coefficients (ICC), and associations with neuro-cognitive variables were tested using Spearman's rank correlations. RESULTS:Both the total MRI score and each MRI subscore demonstrated excellent interrater reliability (ICC range: 0.91-0.99). Motor functions showed strong inverse correlations with the total MRI scores (ρ = -0.82, p < 0.0001). Low verbal IQ correlated with extensive calcifications (ρ = -0.55, p < 0.01). High seizure frequency correlated with greater pial enhancement (p < 0.05) and choroid plexus scores (p < 0.01). The new multiparametric score outperformed a previously established asymmetry-based MRI score in its associations with cognitive outcomes and seizure frequency. CONCLUSION:This reliable and user-friendly MRI scoring system, that integrates multiple vascular and parenchymal features relevant to SWS pathophysiology, can be highly suitable for longitudinal monitoring, prognostication, and standardized outcome assessment in multicenter research and therapeutic trials.

Increased amino acid transport in gliomas allows imaging of metabolically active tumor volume by PET. Tryptophan analog PET radiotracers can provide additional information by tracking tumoral metabolism via the upregulated immunosuppressive tryptophan-kynurenine pathway. We tested the recently developed tryptophan analog PET tracer [18F]-fluoro-ethyl-L-tryptophan ([18F]FETrp) for detecting post-treatment glioblastoma while using a non-invasive approach to generate parametric tryptophan metabolic maps and comparing them with static tracer uptake maps and contrast-enhanced MRI. Five patients (age: 22–67 years) with previously treated glioblastoma underwent [18F]FETrp PET/CT imaging. A dynamic acquisition protocol sampled the brain and blood pool non-invasively using the FlowMotion Multiparametric PET software (Siemens Healthineers). Parametric brain images of the unidirectional uptake rate constant (Ki), characterizing irreversible tryptophan trapping and the volume of distribution (VD) were fused with static uptake (SUV) maps and contrast-enhanced brain MRI. Voxels with elevated Ki, VD, and SUV were defined, and their spatial associations with contrast-enhanced volumes were characterized by their

OBJECTIVE:To develop a novel deep-learning model of clinical DWI tractography that can accurately predict the general assessment of epilepsy severity (GASE) in pediatric drug-resistant epilepsy (DRE) and test if it can screen diverse neurocognitive impairments identified through neuropsychological assessments. METHODS:DRE children and age-sex-matched healthy controls were enrolled to construct an epilepsy severity network (ESN), whose edges were significantly correlated with GASE scores of DRE children. An ESN-based biomarker called the predicted GASE score was obtained using dilated deep convolutional neural network with a relational network (dilated DCNN+RN) and used to quantify the risk of neurocognitive impairments using global/verbal/non-verbal neuropsychological assessments of 36/37/32 children performed on average 3.2 ± 2.7 months prior to the MRI scan. To warrant the generalizability, the proposed biomarker was trained and evaluated using separate development and independent test sets, with the random score learning experiment included to assess potential overfitting. RESULTS:The dilated DCNN+RN outperformed other state-of-the art methods to create the predicted GASE scores with significant correlation (r = 0.92 and 0.83 for development and test sets with clinical GASE scores) and minimal overfitting (r = -0.25 and 0.00 for development and test sets with random GASE scores). Both univariate and multivariate models demonstrated that compared with the clinical GASE scores, the predicted GASE scores provide better model fit and discriminatory ability, suggesting more adjusted and accurate estimate of epilepsy severity contributing to the overall risk. INTERPRETATION:The proposed biomarker shows strong potential for early identification of DRE children at risk of neurocognitive impairments, enabling timely, personalized interventions to prevent long-term effects.

Increased amino acid transport in gliomas allows imaging of metabolically active tumor volume by PET. Tryptophan analog PET radiotracers can provide additional information by tracking tumoral metabolism via the upregulated immunosuppressive tryptophan-kynurenine pathway. We tested the clinically feasible tryptophan analog PET tracer [18]F-fluoro-ethyl-L-tryptophan ([18]FETrp) for detecting post-treatment glioblastoma infiltration while using a non-invasive approach to generate parametric tryptophan metabolic maps and comparing them with static tracer uptake maps and contrast-enhanced MRI. Five patients (age: 22-67 years) with previously treated glioblastoma showing MRI signs of tumor progression underwent [18]FETrp PET imaging. A dynamic acquisition protocol was applied to sample the brain and blood pool non-invasively using the FlowMotion Multiparametric PET software (Siemens Healthineers). Parametric brain images of the unidirectional uptake rate constant (Ki), characterizing irreversible tryptophan trapping, were fused with static uptake maps and contrast-enhanced brain MRI. Voxels with elevated Ki and uptake values were defined (>2SD above mean values in contralateral normal brain), and their spatial association with contrast-enhanced volumes was characterized by their % volume overlap and the distance between their centroids. A substantial spatial volume overlap of 66±12% (average centroid distance: 5mm) was observed between MRI contrast-enhancing regions and elevated static [18]FETrp uptake. The overlap between contrast-enhancing regions and elevated Ki metabolic volumes was lower (6±5%, p<0.001), with increased Ki areas extending deeper into non-enhancing brain (12mm average spatial separation). These non-enhancing areas with high Ki values showed new contrast-enhancement on subsequent MRI, consistent with tumor progression. Areas of high tryptophan metabolism detected by [18]FETrp PET-derived parametric maps extend outside the contrast-enhancing glioblastoma mass in adjacent non-enhancing brain regions that can be missed or underestimated by static uptake images. [18]FETrp PET metabolic maps have the potential for enhanced detection of non-enhancing glioma infiltration for improved treatment targeting.

This phase I trial evaluated the IDO1 enzyme inhibitor, BMS-986205, with radiation (RT) and nivolumab treatment in newly diagnosed patients with GBM IDHwt. Cohort A received RT + nivolumab with escalating BMS-986205 doses in MGMT unmethylated GBM patients. Cohort B received the highest dose of BMS-986205 with nivolumab and standard RT/temozolomide (TMZ) TMZ in MGMT methylated GBM patients. The treatments were found to be safe and tolerable. The median overall survival was 11.5 (95% CI: 3.71, 33.8) and 26.9 months (95% CI: 8.94-NR) while the 2-year survival rates were 33% (95% CI: 10.3%, 58.8%) and 60% (95% CI: 12.6%, 88.2%) for MGMT unmethylated and methylated GBM, respectively. Longer patient survival was associated with higher CD8+ T cell levels, higher microbial aryl-lactate levels, higher abundance of Massilioclostridium coli, Dysosmobacter welbionis, and Phocaeicola plebeius in the stool, a younger age, and a lack of gross total resection. (ClinicalTrials.gov: NCT04047706).

BACKGROUND:Common intracranial vascular abnormalities in Sturge-Weber syndrome (SWS) include leptomeningeal venous malformations (LVMs) and enlarged deep veins. A few small studies have reported absent deep veins in some patients. We used susceptibility-weighted imaging (SWI), a magnetic resonance imaging (MRI) sequence sensitive to detecting small veins, to evaluate deep cerebral veins and the basal vein of Rosenthal (BVR) and assess the radiological correlates and clinical impact of their absence. METHODS:Fifty young subjects, including 30 patients with unilateral SWS and 20 healthy controls, underwent 3T brain MRI prospectively. The presence or absence of the internal cerebral vein (ICV), its two main tributaries, and the BVR were evaluated on SWI in all 50 subjects and correlated with other brain abnormalities and clinical symptoms in the SWS group. RESULTS:Although deep veins and the BVR were identified bilaterally in all control subjects, absent veins were observed in 70% of patients with SWS: in the SWS-affected hemisphere, absent ICV in 15 (50%), thalamostriate vein in 11 (37%), septal vein in seven (23%), and BVR in nine (30%) patients. Absent contralateral veins were also observed. Absent veins were associated with enlarged and collateral veins. Absent BVR and ICV were associated with extensive LVM, brain atrophy, and worse motor functions (P < 0.05); absent BVR was also associated with stroke-like episodes. CONCLUSIONS:Absence of deep and/or basal cerebral veins is common in SWS and is associated with venous vascular anomalies, parenchymal damage, and motor impairment. Absent BVR may also increase the risk for stroke-like episodes.

Background Sturge-Weber syndrome (SWS) is a rare neurocutaneous disorder associated with venous capillary malformations, atrophy, and calcifications. Longitudinal imaging is limited by risks of sedation and gadolinium exposure in children. Purpose To evaluate whether strategically acquired gradient echo (STAGE), a rapid multi-contrast quantitative MRI method, can reliably detect vascular and parenchymal abnormalities in SWS compared with conventional pre-/post-contrast MRI. Study Type Observational cross-sectional. Population Twenty-two patients with unilateral SWS diagnosed by previous MRI (13 female; ages 2-24 years). Field Strength/Sequence 3T/T1-weighted (T1W) and T2-weighted (T2W) turbo-spin-echo, fluid attenuated inversion recovery, and a 3D gradient echo-based STAGE sequence providing T1, proton density (PD), T2*, and R2* maps, susceptibility-weighted imaging (SWI), quantitative susceptibility mapping (QSM), T1W with enhanced gray matter to white matter contrast (T1WE), and synthetic images of T2W, FLAIR, and gradient echo images. Assessment Conventional MRI and STAGE images were reviewed in 10 patients (training group), side-by-side, to determine the STAGE-derived images that identify SWS abnormalities, including leptomeningeal venous capillary malformations (LVCM), enlarged deep medullary veins, choroid plexus enlargement, cerebral atrophy, and calcifications. In the remaining test group of 12 patients, three reviewers scored these abnormalities on STAGE images and compared them with scores from conventional MRI. Statistical Tests Interrater reliability with intraclass correlation coefficient (ICC), Spearman's rank correlation, Wilcoxon signed-ranked test, Mann-Whitney U-test, Fisher's exact test. Statistical significance level was set as p < 0.05. Results LVCMs were visualized on STAGE with SWI and R2*. Calcifications were differentiated from venous abnormalities using PD, T1WE, synthetic gradient echo, and QSM. STAGE-derived scores had excellent interrater reliability (ICCs > 0.90) and were similar to the conventional MRI scores despite some minor differences in some individual cases (total scores from conventional MRI vs. STAGE 8.9 vs. 8.7, p = 0.29). Data Conclusion STAGE provided rapid, non-contrast, multi-parametric imaging that reliably detected vascular and parenchymal SWS abnormalities seen on conventional MRI. Evidence Level 2. Technical Efficacy Stage 3.

Objective: To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modular networks (LMNs) within the preoperative whole-brain diffusion-weighted imaging connectome (wDWIC). Methods: We employed a three-step approach. First, our previous DCNN-based tract classification to detect true-positive eloquent tracts was extended using an open-source database of high-quality wDWIC to facilitate the accurate classification of truepositive tracts within the preoperative backbone wDWIC of individual patients. Next, we applied psychometry-driven DWIC analysis to the resulting DCNN-based backbone wDWIC in order to create core, expressive, and receptive LMNs. Finally, graph and circuit theory-based connectivity markers were assessed within the three LMNs and compared using a series of machine learning algorithms to predict the presence of postoperative language improvement from a given LMN. Results: The results showed that the extended DCNN tract classification significantly improved the reproducibility of connectivity markers by up to 35.5% of F-statistics across different LMNs. The prediction accuracy increased by up to 40% across different machine learning algorithms. Notably, the best algorithm achieved the accuracy of 96%/94%/96% to predict the presence of language improvement about two months after surgery in core/expressive/receptive domain of an independent validation cohort. Conclusion: These domains hold great potential to assist physicians in identifying candidates whose language skills stand to benefit from early surgery. Significance: DCNN tract classification may be an effective tool to improve predicting short-term postoperative language improvement in pediatric epilepsy surgery

The “crowding” effect (CE), wherein verbal functions are preserved presumably at the expense of nonverbal functions, which diminish following inter-hemispheric transfer of language functions, is recognized as a specific aspect of functional reorganization, offering an insight about neural plasticity in children with neural insult to the dominant hemisphere. CE is hypothesized as a marker for language preservation or improvement after left-hemispheric injury, yet it remains challenging to fully discern it in preoperative evaluation. We present a novel DWI connectome (DWIC) approach to predict the presence of CE in 24 drug-resistant epilepsy (DRE) patients with a left-hemispheric focus and 29 young healthy controls. Psychometry-driven DWIC analysis was applied to create verbal and non-verbal modular networks. Local efficiency (LE) was assessed at individual regions of the two networks and its Z-score was compared to predict the presence of CE. Compared with a traditional organization (TO) group, wherein verbal functions are adversely affected, while non-verbal functions are preserved, the CE group showed significantly higher Z-scores in verbal network and significantly lower Z-scores in non-verbal network, corresponding to network reorganization in CE. A larger number of antiseizure drugs was significantly associated with more decreased Z-score in the right non-verbal network of the CE group and left verbal network of the TO group. These findings hold great potential to identify DRE patients whose verbal/language skills may over time be preserved due to effective inter-hemispheric reorganization and identify those whose verbal/language impairments may persist due to lack of inter-hemispheric reorganization.

Background:Enlarged deep medullary veins (EDMVs) in patients with Sturge-Weber syndrome (SWS) may channel venous blood from the surface to the deep vein system in brain regions affected by the leptomeningeal venous malformation. Thus, the quantification of EDMV volume may provide an objective imaging marker for this vascular compensatory process. The present study proposes a novel analytical method to quantify enlarged EDMV volumes in the affected hemisphere of patients with unilateral SWS. Methods:Twenty young subjects, including 10 patients with unilateral SWS and 10 healthy siblings (age 14.5±6.7 and 16.0±7.0 years, respectively) underwent 3T brain MRI scanning using susceptibility-weighted imaging (SWI) and volumetric T1-weighted sequences. The proposed image analytic steps segmented EDMVs in white matter regions, defined on the volumetric T1-weighted images, by statistically associating the likelihood of intensity, location, and tubular shape on SWI. The volumes of the segmented EDMVs, calculated in each hemisphere, were compared between affected and unaffected hemispheres. EDMV volumes were also correlated with visually assessed EDMV scores, hemispheric white matter volumes, and cortical surface areas. Parametric tests including Pearson's correlation, unpaired and paired t-tests, were used. A P value <0.05 was considered statistically significant. Results:It was found that EDMVs were identified well in SWS-affected hemispheres while calcified regions were excluded. Mean EDMV volumes in the SWS-affected hemispheres were 10-12-fold greater than in the unaffected or healthy control hemispheres; while white matter volumes and cortical surface areas were lower. EDMV volumes in the SWS-affected hemispheres showed a strong positive correlation with the visual EDMV scores (r=0.88, P=0.001) and an inverse correlation with cortical surface area ratios (r=-0.65, P=0.04) but no correlation with white matter volume ratios. Conclusions:EDMVs were detected in the SWS-affected atrophic hemispheres reliably while avoiding calcified regions. The approach can be used to quantify enlarged deep cerebral veins in the human brain, which may provide a potential marker of cerebral venous remodeling.

BACKGROUND:Postcontrast magnetic resonance imaging (MRI), obtained under anesthesia, is often used to evaluate brain parenchymal and vascular abnormalities in young children, including those with Sturge-Weber syndrome. However, anesthesia and contrast administration may carry risks. We explored the feasibility and potential diagnostic value of a noncontrast, nonsedate MRI acquisition in Sturge-Weber syndrome children and their siblings with a wide range of cognitive and behavioral functioning. METHODS:Twenty children (10 with Sturge-Weber syndrome and 10 healthy siblings; age: 0.7-13.5 years) underwent nonsedate 3-tesla (T) brain MRI acquisition with noncontrast sequences (including susceptibility-weighted imaging) prospectively along with neuropsychology assessment. All images were evaluated for quality, and MRI abnormalities identified in the Sturge-Weber syndrome group were compared to those identified on previous clinical pre- and postcontrast MRI. RESULTS:Nineteen participants (95%) completed the MRI with good (n = 18) or adequate (n = 1) quality, including all children with Sturge-Weber syndrome and all 5 children ≤5 years of age. The Sturge-Weber syndrome group had lower cognitive functions than the controls, and both groups had several children with behavioral issues, without an apparent effect on the success and quality of the MR images. Susceptibility-weighted imaging detected key venous vascular abnormalities and calcifications and, along with the other noncontrast sequences, provided diagnostic information comparable to previous clinical MRI performed with contrast administration under anesthesia. CONCLUSION:This study demonstrates the feasibility and the potential diagnostic value of a nonsedate, noncontrast MRI acquisition protocol in young children including those with cognitive impairment and/or behavioral concerns. This approach can facilitate clinical trials in children where safe serial MRI is warranted.