Mohammed Salman Shazeeb

Chronic migraine (CM) is associated with neurobiological alterations beyond episodic attacks, yet cerebral microvascular perfusion changes during the interictal phase of CM remain underexplored. Intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) provides diffusion‐derived pseudoperfusion parameters that reflect regional microvascular alterations in the brain parenchyma. Accordingly, this study aimed to investigate right‐to‐left hemispheric asymmetry of IVIM‐derived perfusion parameters in migraine‐implicated cortical and subcortical regions during the interictal phase of CM. Thirty patients with CM imaged during the interictal phase, along with 30 age‐ and sex‐matched healthy controls, underwent IVIM imaging. IVIM parameters, including the pseudodiffusion coefficient ( D* ), perfusion fraction ( f ), and their composite metric ( fD* ), were estimated using a Bayesian probability‐based fitting algorithm. Region‐of‐interest (ROI) analysis was performed bilaterally in migraine‐implicated cortical and subcortical regions. Hemispheric asymmetry indices (HAIs) of IVIM parameters were calculated and compared between groups using Benjamini–Hochberg (FDR) false discovery rate correction. No absolute IVIM parameter showed significant interhemispheric differences in either group after FDR correction. In addition, no significant group differences in HAI of IVIM parameters were identified across the six cortical regions or pontine tegmentum. In contrast, posterior thalamic fD* asymmetry was significantly greater in patients with CM than in controls (median HAI: 27.91 vs. 10.43; p = 0.002, q = 0.017). Exploratory analysis confirmed greater separation between bilateral posterior thalamic fD* measurements in CM versus control subjects (0.429 vs. 0.151 × 10 ⁻³ mm ² /s; p = 0.002), whereas leave‐one‐out analysis showed that this outcome was not driven by any single participant. Interobserver agreement for HAI measurements was good for D* (ICC = 0.81) and moderate for f and fD* (ICC = 0.70 and 0.69, respectively). These findings indicate that CM is associated with increased hemispheric asymmetry of IVIM‐derived microvascular perfusion in the posterior thalamus during interictal phase, suggesting lateralized thalamic microvascular alterations in CM.

Objective: The objective of this study was to investigate the relationship between intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and dynamic contrast-enhanced MRI (DCE-MRI) parameters in soft tissue tumors (STTs). Methods: This retrospective study included patients with histopathologically confirmed STTs who underwent both DCE-MRI and IVIM-DWI between March 2022 and February 2024. Patients with prior therapy and lipomatous tumors were excluded. DCE-MRI parameters (K_trans, K_ep, V_e, iAUC) were obtained from pharmacokinetic maps using manually placed regions of interest (ROIs) in the most perfused tumor areas, avoiding necrotic and cystic regions. Corresponding ROIs were applied to IVIM-DWI maps. IVIM parameters (D, D*, f) were calculated using 11 b-values. Results: Twenty-nine patients (mean age, 56 ± 18 years; 14 malignant, 15 benign) were included. Interobserver agreement was excellent for DCE-MRI parameters, whereas IVIM-DWI parameters showed moderate-to-good agreement, with D showing the lowest reproducibility. In malignant tumors, f demonstrated strong positive correlations with K_trans (r = 0.81, p < 0.001) and iAUC (r = 0.79, p < 0.001), both of which remained significant after correction for multiple comparisons. fD* was higher in malignant than in benign lesions in the unadjusted group comparison; however, diagnostic performance was not evaluated in the present study. No significant differences were observed for DCE-MRI parameters between benign and malignant tumors. Conclusions: IVIM-DWI parameters demonstrated associations with DCE-MRI metrics in malignant STTs and may provide complementary information regarding tumor perfusion. However, the findings should be interpreted cautiously because ROI analysis was limited to a single representative slice. Further validation using larger cohorts and volumetric tumor assessment is required.

Intravoxel incoherent motion (IVIM) MRI allows for simultaneous assessment of tissue microcirculation (perfusion) and diffusion of water. In single‐center studies, IVIM has shown great potential for diagnosis, treatment outcome prediction, and treatment monitoring for many different diseases and organs. However, heterogeneity in data acquisition protocols, pre‐processing pipelines, and post‐processing routines yields differences in reported IVIM parameters, which has constrained large‐scale deployment of IVIM. Moreover, deploying IVIM protocols and analysis typically requires technical expertise, further challenging wider use, especially for clinicians. In this consensus paper, to accelerate the deployment of IVIM, we provide recommendations and harmonize protocols for brain, breast, kidney, liver, muscle, and pancreas IVIM studies. For this goal we organized multiple questionnaires and held a dedicated workshop. To ensure a level of standardized, reproducible results, without restricting innovation, we suggest a small subset of b ‐values to always be measured and analyzed separately, and to which more extensive b ‐value sampling can be added for advanced investigations. We further introduce detailed recommendations on acquisition protocols and analysis pipelines. To increase consistency, repeatability, and reproducibility, we highly recommend that these protocols and pipelines be deployed by scientists and clinicians for IVIM studies. For advanced users who desire different protocols or analysis approaches, we suggest adding results from our suggested protocols and analysis pipeline in the supplemental part of their paper to enable retrospective studies.

The dual rAAVrh8-HEXA and rAAVrh8-HEXB vector can restore central nervous system hexosaminidase (Hex) enzyme activity, decrease GM2 levels in cerebrospinal fluid and rescue phenotypic consequences of GM2 gangliosidosis, Tay-Sachs and Sandhoff diseases in animal models following simultaneous bi-thalamic (BiT) injections. Following up on an n = 2 expanded access trial, we initiated a phase 1/2, single-dose, dose-escalation of combined BiT, intra-cisterna magna and intrathecal infusion in children with Tay-Sachs and Sandhoff diseases (six infantile, three juvenile). The BiT injection volume and vector dose were doubled between four cohorts, with the lowest dose matching the earlier expanded access trial. Cerebrospinal fluid HexA enzyme activity, serum total Hex activity and GM2 levels showed a dose-dependent biochemical correction of the disease. Serum Hex activity surpassed 40 nmol h^-1 ml^-1, two times the lower limit of normal, and neuroimaging demonstrated increased fiber tracts. Correction was greatest at 12 weeks, but in decline by 24 weeks postdosing. Infantile patients experienced global clinical stabilization and prolonged oral feeding without aspiration until 3-3.5 years. Seizures had a later onset, were less frequent, less severe and more responsive to anti-convulsant medication. Adverse events were rare in infantile patients, but worsening dystonia was observed in juvenile patients, who were excluded from ongoing enrollment. ClinicalTrials.gov registration: NCT04669535 and NCT06614569 .

Background: Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared five fully automated segmentation pipelines, including FSL, Freesurfer, volBrain, SPM12, and SimNIBS, with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls. Methods: We analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans from 8 late-infantile GM1 gangliosidosis patients, and 19 MRI scans from 11 neurotypical controls. We compared the results for seven brain structures, including volumes of the total brain, bilateral thalamus, ventricles, bilateral caudate nucleus, bilateral lentiform nucleus, corpus callosum, and cerebellum. Results: We found volBrain’s vol2Brain pipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer’s recon-all pipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain’s vol2Brain and SimNIBS’ headreco to have the strongest correlations, depending on the cohort. For the lentiform nucleus, we found a combination of recon-all and FSL’s FIRST to give the strongest correlations, depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable. Conclusions: Previous studies have considered automated segmentation techniques to be unreliable, particularly in neurodegenerative diseases. However, in our study, we produced results comparable to those obtained with a manual segmentation process. While manual segmentation processes conducted by neuroradiologists remain the gold standard, we present evidence to the capabilities and advantages of using an automated process that includes the ability to segment white matter throughout the brain or analyze large datasets, which pose feasibility issues to fully manual processes. Future investigations should consider the use of artificial intelligence-based segmentation pipelines to determine their accuracy in GM1 gangliosidosis, lysosomal storage disorders, and other neurodegenerative diseases.

Background: Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared 5 fully automated segmentation pipelines including FSL, Freesurfer, volBrain, SPM12, and SimNIBS with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls. Methods: We analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans from 8 late-infantile GM1 gangliosidosis patients, and 19 MRI scans from 11 neurotypical controls. We compared results for 7 brain structures including volumes of the total brain, bilateral thalamus, ventricles, bilateral caudate nucleus, bilateral lentiform nucleus, corpus callosum, and cerebellum. Results: We found volBrain’s vol2Brain pipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer’s recon-all pipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain’s vol2Brain and SimNIBS’ headreco to have the strongest correlations depending on the cohort. For the lentiform nucleus, we found a combination of recon-all and FSL’s FIRST to give the strongest correlations depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable. Conclusion: Previous studies have considered automated segmentation techniques to be unreliable, particularly in neurodegenerative diseases. However, in our study we produced results comparable to those obtained with a manual segmentation process. While manual segmentation processes conducted by neuroradiologists remain the gold standard, we present evidence to the capabilities and advantages of using an automated process including the ability to segment white matter throughout the brain or analyze large datasets, which pose feasibility issues to fully manual processes. Future investigations should consider the use of artificial intelligence-based segmentation pipelines to determine their accuracy in GM1 gangliosidosis, lysosomal storage disorders, and other neurodegenerative diseases.

Rare diseases are individually rare, affecting less than 5 in 10,000 people worldwide, yet collectively common, affecting 6-8% of the human population (1). An estimated 6,000 to 8,000 disease entities have been described with the numbers rising steadily with new diagnostic techniques such as exome, genome, and RNA sequencing. The diagnostic odyssey can be lengthy, affecting quality of life for patients and their caregivers and can lead to reduced life-expectancy. Definitive treatment for most rare diseases is lacking although progress is being made. Many rare disorders affect the central nervous system (CNS) and advanced neuroimaging modalities like magnetic resonance imaging (MRI) have been shown to aid both in diagnosis and in monitoring patient outcomes in clinical trials. In this Research Topic, we highlight advances made in the diagnosis and/or treatment of rare diseases using MRI neuroimaging biomarkers. The diseases include type II GM1 gangliosidosis, idiopathic dystonia, pediatric brain tumors including pediatric posterior fossa brain tumor and diffuse midline glioma of the pons, and birth nonprogressive neuromuscular diseases.Type II GM1 gangliosidosis (GM1) is an ultra-rare autosomal recessive disorder of ganglioside degradation with an incidence of 1:100,000 to 200,000 live births (2,3).Deficiency of lysosomal hydrolase b-galactosidase impairs the degradation of GM1 ganglioside, a normal component of the neuronal membrane, leading to demyelination, neuronal loss and progressive brain atrophy. The disorder is uniformly fatal with no approved therapies. Since the neurodegenerative changes and atrophy in GM1 patients are severe, automated methods are yet to be developed to appropriately register GM1 brains to conventional brain atlases to perform automated segmentations. The study by Zoppo and Kolstad et al. proposed a standardized method to perform brain MRI volumetric measurements in GM1 patients (4). The volumetric analysis (using manual and semi-automated segmentations of small and large brain structures, respectively) proposed in this study established good inter-and intra-rater reliability in measuring the brain structures to characterize the late-infantile and juvenile subtypes of GM1. With the emergence of gene therapy to treat GM1 patients, brain volumetrics can serve as a useful neuroimaging biomarker to track the state of the disease and response to treatment.Dystonia is a rare neurological disease characterized by involuntary muscle contractions leading to abnormal posture and movements. Classification of the disease based on distinct symptom distribution is clinically important and widely used for different implications of therapy (5). Cervical dystonia (CD) and generalized dystonia (GD) are two common subtypes. While the premier subtype is a typical focal form involving exclusive neck twisting/abnormal posture, the latter mainly affects the trunk and even wider body regions. The global prevalence of idiopathic dystonia is estimated to be around 16-30 per 100,000 (6). Since idiopathic dystonia begins without certain causes or positive findings in conventional imaging scans, it is challenging to trace the abnormal brain structures or circuits involved in the mechanism of the disease. Wu, Wang, and Ding et al. investigated volumetric and morphometric changes in the cortical and subcortical regions of the brains of patients with CD and GD (7). They demonstrated that cortical thickness abnormalities in different brain regions and reduced subcortical volumes with morphometric changes indicated disease progression. Moreover, clinical associations with volumetric/morphometric changes correlated with disease severity of CD and GD and can serve as neuroimaging biomarkers in tracking disease progression and response to potential treatments.Brain tumors are the leading cause of pediatric cancer deaths in the United States. In children between the ages of 0-19 years the incidence of brain and other CNS tumors between 2014-2018 was 6.23 per 100,000 (8). From these tumor occurrences, over half appear in the posterior fossa and can be treated with surgical resection, chemotherapy, and irradiation. However, diffuse intrinsic pontine glioma (DIPG) are highly aggressive brain tumors that carry a poor prognosis due to treatment difficulty. Tanedo et al.implemented diffusion tensor imaging (DTI) using fractional anisotropy maps to visualize the impact of surgery and chemotherapy on microstructural changes in white matter tracts in pediatric posterior fossa brain tumor survivors (9). The white matter changes can potentially help understand neurocognitive functioning in these patients as they grow into adulthood. Another study by Szychot et al. used imaging to assess the distribution of infusate in children with DIPG treated with convection-enhanced delivery (CED) of therapeutic drugs (10). They proposed that volumetric T2-weighted MRI in combination with the apparent diffusion coefficient from diffusion-weighted imaging can provide a reliable method to evaluate CED infusate distribution. These neuroimaging biomarkers could prove valuable in future clinical trials of CED infusions within brain tissue, where accurate anatomical localization of infusate distribution will be essential for demonstrating treatment efficacy.Birth nonprogressive neuromuscular disease refers to motor deficits observed in newborns that can be caused by congenital disease or obstetrical trauma. Examples of this disease include arthrogryposis multiplex congenita (AMC) and obstetrical brachial plexus palsy (OBPP). The incidence of AMC and OBPP is reported to be around 0.03-0.1 and 0.4-4 per 1,000 live births, respectively (11). The mini-review article presented by Tolmacheva et al. highlights the importance of brain mapping in this patient population by presenting several studies that use functional MRI (fMRI) to investigate cortical activation to assess motor performance (11). Quantification of cortical...