Search

Background and Objectives: Magnetoencephalography (MEG) could be a valuable tool in the presurgical evaluation of drug-resistant epilepsy (DRE), especially when the initial evaluation is inconclusive. In this retrospective study, we describe the profile of MEG in patients with DRE and normal magnetic resonance imaging (MRI). Methods: We included patients with focal epilepsy and normal MRI who underwent presurgical evaluation for DRE. MEG profiles of these patients, including the frequency of spikes, density of clusters, number of clusters, and concordance with video electroencephalography (VEEG), were analyzed. Results: Of the 73 patients included, magnetic source imaging (MSI) provided localizing information in 51 (69.9%) patients. Among patients with localizing MEG findings, localizing information on VEEG too was noted in 42 (57.5% of the whole cohort). Thirty-one (42.5%) patients had concordant findings with region-specific localization, six (8.2%) patients had partial concordance, and five (6.8%) subjects showed discordant findings. There was a moderate agreement for the presumed epileptogenic zone in comparing findings derived from MEG and VEEG (kappa value of 0.451, P < 0.001). The agreement was lower when MEG localized to the frontal lobe (kappa value of 0.379, P = 0.001) than the temporal lobe (kappa value 0.442, P = 0.002). Conclusions: MEG can provide localizing information in most patients with a normal MRI. A moderate degree of agreement between localization by MEG and VEEG was noted. These findings highlight the usefulness of MSI in the presurgical evaluation of MRI-negative DRE.

Abstract Psychotic symptoms occur in a majority of schizophrenia patients and in ~50% of all Parkinson’s disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little is known, however, about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as in schizophrenia and PD. The present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals in at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms. SCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD, PDN and Con-PD, as well as PDN and PDP, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC ~0.80) of FEP and Con-Psy, and fair performance (AUC ~0.72) when differentiating PDP from Con-PD. Importantly, the best performance was found in partly the same networks, including the thalamus. Alterations within selected SCNs may be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.

Abstract Background Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impaired social and communication skills, narrow interests, and repetitive behavior. It is known that the cerebellum plays a vital role in controlling movement and gait posture. However, recently, researchers have reported that the cerebellum may also be responsible for other functions, such as social cognition, reward, anxiety, language, and executive functions. Methods In this study, we ascertained volumetric differences from cerebellar lobular analysis from children with ASD, ASD siblings, and typically developing healthy controls. In this cross-sectional study, a total of 30 children were recruited, including children with ASD (N = 15; mean age = 27.67 ± 5.1 months), ASD siblings (N = 6; mean age = 17.5 ± 3.79 months), and typically developing children (N = 9; mean age = 17.67 ± 3.21 months). All the MRI data was acquired under natural sleep without using any sedative medication. We performed a correlation analysis with volumetric data and developmental and behavioral measures obtained from these children. Two-way ANOVA and Pearson correlation was performed for statistical data analysis. Results We observed intriguing findings from this study, including significantly increased gray matter lobular volumes in multiple cerebellar regions including; vermis, left and right lobule I–V, right CrusII, and right VIIb and VIIIb, respectively, in children with ASD, compared to typically developing healthy controls and ASD siblings. Multiple cerebellar lobular volumes were also significantly correlated with social quotient, cognition, language, and motor scores with children with ASD, ASD siblings, and healthy controls, respectively. Conclusions This research finding helps us understand the neurobiology of ASD and ASD-siblings, and critically advances current knowledge about the cerebellar role in ASD. However, results need to be replicated for a larger cohort from longitudinal research study in future.

The purpose of this reply is to address the comments given by Kelly et al. on our original paper “Unique Brain Network Identification Number for Parkinson’s and Healthy Individuals using Structural MRI”. We agree to the inadvertent rounding pitfall in our original paper due to the non-inclusion of symbolic math toolbox (MATLAB). We now provide the actual ranges (with decimal values) of the UBNIN values of healthy individuals and those with Parkinson’s disease and further observations. Upon further introspection, we propose another variant, called Modified-UBNIN (UBNIN-MT,MN) which is highly weighted on the node with the highest network degree (i.e., connections). The italicized sentences within inverted commas are statements from Kelly et al.’s comment paper.

OBJECTIVE: To assess the resting-state functional connectivity within the default mode network (DMN) and the whole brain in minimally disabled Parkinson’s disease (MiPD) patients and compare it with age- and gender-matched healthy controls (HCs). METHODS: Twenty-seven MiPD patients (Hoehn and Yahr score, H–Y = 1–3) and 21 HCs underwent resting-state functional magnetic resonance imaging. The data collected included demographic profiles of both groups along with duration of illness, age of PD onset, and the H–Y score in PD patients. The Fazekas scores were calculated in all PD patients and HCs. The region of interest (ROI) functional connectivity method was used to assess resting-state functional connectivity within DMN and whole brain using the CONN toolbox. A total of 132 ROIs were selected in the whole brain using the Harvard Atlas. RESULTS: The mean age, gender, and median Fazekas scores in both groups were comparable. Mean (±SD) age of PD onset in patients was 52.92 ± 8.32 years. The median (range) H–Y score was 2 (1–3). Regions of increased functional connectivity were observed in the whole-brain analysis in the MiPD group. Four ROI pairs showed increased functional connectivity in the MiPD group compared to HCs (p < 0.001), prominently involving the frontoparietal, cerebellar, and basal ganglia region. Functional connectivity within DMN was comparable between the two groups. CONCLUSION: Compared to age- and gender-matched healthy controls, MiPD patients showed increased functional connectivity in selected regions, in particular, those involving the precuneus, thalamus, pallidum, and vermis, which may be related to initial compensation in PD.

Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here, the authors present the largest FL study to-date to generate an automatic tumor boundary detector for glioblastoma.

Coronavirus disease 2019 (COVID-19) pandemic has led to significant reduction in specialty care causing delayed presentations and decreased resources for elective procedures. We present here a case of a 29-year-old female, 34 weeks primigravida who presented with a twice ruptured right choroidal artery arteriovenous malformation (AVM). She underwent an immediate external ventricular drain placement followed by an emergency cesarean. She underwent a diagnostic angiogram showing a right choroidal AVM with a feeder artery aneurysm along with small supply to its cortical component by the M4 segment of right middle cerebral artery, draining into the basal vein of Rosenthal and vein of Labbe. The patient underwent embolization of the aneurysm and the choroidal feeders. She improved symptomatically following the procedure with external cerebrospinal fluid diversion for 5 days. At follow-up magnetic resonance imaging, the residual AVM remained stable and is planned for Gamma knife radiosurgery. The newborn was taken care at a neonatal intensive care unit and was started on breastfeed on 16th day of birth once the mother had a negative COVID-19 reverse transcription polymerase chain reaction. In this study, we highlight our experience with the above-mentioned patient profile, the multidisciplinary effort during the pandemic, and the measures taken (isolation ward, COVID-19 dedicated Cath-lab and personal precautions) and advised for tackling COVID-19 patients for endovascular procedures.

We propose a novel algorithm called Unique Brain Network Identification Number (UBNIN) for encoding the brain networks of individual subjects. To realize this objective, we employed structural MRI on 180 Parkinson’s disease (PD) patients and 70 healthy controls (HC) from the National Institute of Mental Health and Neurosciences, India. We parcellated each subject’s brain volume and constructed an individual adjacency matrix using the correlation between the gray matter volumes of every pair of regions. The unique code is derived from values representing connections for every node (i), weighted by a factor of 2−(i−1). The numerical representation (UBNIN) was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities. UBNIN ranges observed for PD were 15,360 to 17,768,936,615,460,608, and HC ranges were 12,288 to 17,733,751,438,064,640. This model may be implemented as a neural signature of a person’s unique brain connectivity, thereby making it useful for brainprinting applications. Additionally, we segregated the above datasets into five age cohorts: A: ≤32 years (n1 = 4, n2 = 5), B: 33–42 years (n1 = 18, n2 = 14), C: 43–52 years (n1 = 42, n2 = 23), D: 53–62 years (n1 = 69, n2 = 22), and E: ≥63 years (n1 = 46, n2 = 6), where n1 and n2 are the number of individuals in PD and HC, respectively, to study the variation in network topology over age. Sparsity was adopted as the threshold estimate to binarize each age-based correlation matrix. Connectivity metrics were obtained using Brain Connectivity toolbox (Version 2019-03-03)-based MATLAB (R2020a) functions. For each age cohort, a decreasing trend was observed in the mean clustering coefficient with increasing sparsity. Significantly different clustering coefficients were noted in PD between age-cohort B and C (sparsity: 0.63, 0.66), C and E (sparsity: 0.66, 0.69), and in HC between E and B (sparsity: 0.75 and above 0.81), E and C (sparsity above 0.78), E and D (sparsity above 0.84), and C and D (sparsity: 0.9). Our findings suggest network connectivity patterns change with age, indicating network disruption may be due to the underlying neuropathology. Varying clustering coefficients for different cohorts indicate that information transfer between neighboring nodes changes with age. This provides evidence of age-related brain shrinkage and network degeneration. We also discuss limitations and provide an open-access link to software codes and a help file for the entire study.

Abstract Complex febrile seizures (CFS), a subset of paediatric febrile seizures (FS), have been studied for their prognosis, epileptogenic potential and neurocognitive outcome. We evaluated their functional connectivity differences with simple febrile seizures (SFS) in children with recent-onset FS. Resting-state fMRI (rs-fMRI) datasets of 24 children with recently diagnosed FS (SFS-n = 11; CFS-n = 13) were analysed. Functional connectivity (FC) was estimated using time series correlation of seed region–to-whole-brain-voxels and network topology was assessed using graph theory measures. Regional connectivity differences were correlated with clinical characteristics (FDR corrected p

Background: Fluorodeoxyglucose-positron emission tomography (FDG-PET) in autoimmune encephalitis (AE) as an adjunctive investigation helps in characterizing the type of AE based on characteristic metabolic patterns. Objectives: We aimed to study the following: (i) the sensitivity of FDG-PET in the diagnosis of AE, (ii) describe abnormal patterns of metabolism of various subtypes of AE, and (iii) correlate serum serology with FDG-PET abnormalities. Materials and Methods: This study was conducted at a tertiary university hospital in South India. The demographic profile, clinical features, and investigations (FDG-PET, magnetic resonance imaging (MRI) brain, electroencephalography (EEG), cerebrospinal fluid (CSF)) were reviewed. The nuclear medicine physician performed blinded qualitative visual and semi-quantitative analysis of the 18-FDG-PET (fluorine 18-FDG-PET) findings of these patients. Results: Twenty-nine (M:F: 11:18) patients were recruited; among them, 22 (75.8%) patients had autoimmune antibodies; the rest seven (24.1%) patients were seronegative. Among the 22 seropositive patients, 9 (31%) patients were positive for anti-N-methyl-D-aspartate receptor (NMDAR), 8 (28%) for anti-leucine-rich glioma inactivated 1 (LGI-1), 4 (14%) for anti-contactin-associated protein 2 (CASPR2), 1 (3%) for anti-glutamic acid decarboxylase (GAD)-65, and rest 7 (24%) patients were seronegative. The patterns most commonly observed were isolated hypermetabolism (41%), isolated hypometabolism (41%), and combined hypermetabolism with hypometabolism (18%). The fraction of abnormalities was lower for MRI (17/22; 73.9%) than for FDG-PET (27/29; 93.1%). FDG-PET correlated with serology in 10 (34%) cases [NMDAR: 6 (60%) and LGI-1: 4 (40%)]. The sensitivity of FDG-PET was 94.1% when compared with MRI. Discussion and Conclusion: FDG-PET correlated with serology in only one-third of patients. The most consistent pattern in both seropositive and seronegative AE is characterized by parieto-occipital hypometabolism and fronto-temporal with basal ganglia hypermetabolism.

Abstract Prediction of mutational status of different graded glioma is extremely crucial for its diagnosis and treatment planning. Currently FISH and the surgical biopsy techniques are the ‘gold standard’ in the field of diagnostics; the analyses of which helps to decide appropriate treatment regime. In this study we proposed a novel approach to analyze structural MRI image signature pattern for predicting 1p/19q co-deletion status non-invasively. A total of 159 patients with grade-II and grade-III glioma were included in the analysis. These patients earlier underwent biopsy; the report of which confirmed 57 cases with no 1p/19q co-deletion and 102 cases with 1p/19q co-deletion. Tumor tissue heterogeneity was investigated by variance of cross correlation (VoCC). Significant differences in the pattern of VoCC between two classes was quantified using Lomb-Scargle (LS) periodogram. Energy and the cut-off frequency of LS power spectral density were derived and utilized as the features for classification. RUSBoost classifier was used that yield highest classification accuracy of 84% for G-II and 87% for G-III glioma respectively in classifying 1p/19q co-deleted and 1p/19q non-deleted glioma. In clinical practice the proposed technique can be utilized as a non-invasive pre-confirmatory test of glioma mutation, before wet-lab validation.

When data is pooled across multiple sites, the extracted features are confounded by site effects. Harmonization methods attempt to correct these site effects while preserving the biological variability within the features. However, little is known about the sample size requirement for effectively learning the harmonization parameters and their relationship with the increasing number of sites. In this study, we performed experiments to find the minimum sample size required to achieve multisite harmonization (using neuroHarmonize) using volumetric and surface features by leveraging the concept of learning curves. Our first two experiments show that site-effects are effectively removed in a univariate and multivariate manner; however, it is essential to regress the effect of covariates from the harmonized data additionally. Our following two experiments with actual and simulated data showed that the minimum sample size required for achieving harmonization grows with the increasing average Mahalanobis distances between the sites and their reference distribution. We conclude by positing a general framework to understand the site effects using the Mahalanobis distance. Further, we provide insights on the various factors in a cross-validation design to achieve optimal inter-site harmonization.

Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder associated with several motor and non-motor dysfunctions. The wide variety of clinical features often leads to divergent symptom progressions. Most PD studies have attempted subgrouping based on clinical features to help understand the disease etiology and thereby contribute toward specific treatment. However, clinical symptoms have proven to be overlapping, arbitrary, and non-reliable in several cases, often biasing the deciphered subgroups. Moreover, the prodromal phase complicates diagnosis and subgrouping as it is characterized by limited clinical symptom expression. Hence, recent studies have used data-driven machine learning and deep learning methods to data-mine the heterogeneity and obtain subgroups. Structural Magnetic Resonance Imaging (sMRI) is a non-invasive approach for visualization and analysis of anatomical tissue properties of brain. It has enabled the detection of brain abnormalities and is a potential modality for subgrouping.This review article starts with a comprehensive discussion of clinical symptoms-based and data-driven structural neuroimaging-based subgrouping approaches in PD. Secondly, we summarize the work done in brain connectivity studies using structural MRI for PD. We give an overview of mathematical definitions, connectivity metrics, brain connectivity software, and widespread network atlases. Finally, we discuss the inherent challenges and give practical suggestions on selecting methods that could be attempted for subgrouping and connectivity analysis using structural MRI data for future Parkinson's research.

Abstract Purpose The aim of this study is to compare the diagnostic accuracy of amino acid PET, MR perfusion and diffusion as stand-alone modalities and in combination in identifying recurrence in post-treatment gliomas and to qualitatively assess spatial concordance between the three modalities using simultaneous PET-MR acquisition. Methods A retrospective review of 48 cases of post-treatment gliomas who underwent simultaneous PET-MRI using C11 methionine as radiotracer was performed. MR perfusion and diffusion sequences were acquired during the PET study. The following parameters were obtained: TBRmax, TBRmean, SUVmax, and SUVmean from the PET images; rCBV from perfusion; and ADCmean and ADCratio from the diffusion images. The final diagnosis was based on clinical/imaging follow-up and histopathology when available. ROC curve analysis in combination with logistic regression analysis was used to compare the diagnostic performance. Spatial concordance between modalities was graded as 0, 1, and 2 representing discordance, 50% concordance respectively. Results There were 35 cases of recurrence and 13 cases of post-treatment changes without recurrence. The highest area under curve (AUC) was obtained for TBRmax followed by rCBV and ADCratio. The AUC increased significantly with a combination of rCBV and TBRmax. Amino acid PET showed the highest diagnostic accuracy and maximum agreement with the final diagnosis. There was discordance between ADC and PET in 22.9%, between rCBV and PET in 16.7% and between PET and contrast enhancement in 14.6% cases. Conclusion Amino acid PET had the highest diagnostic accuracy in identifying recurrence in post-treatment gliomas. Combination of PET with MRI further increased the AUC thus improving the diagnostic performance.

Abstract The possibility of early treatment and a better outcome is the direct product of early identification and characterization of any pathological condition. Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in social communication, restricted, and repetitive patterns of behavior. In recent times, various tools and methods have been developed for the early identification and characterization of ASD features as early as 6 months of age. Thorough and exhaustive research has been done to identify biomarkers in ASD using noninvasive neuroimaging and various molecular methods. By employing advanced assessment tools such as MRI and behavioral assessment methods for accurate characterization of the ASD features and may facilitate pre-emptive interventional and targeted therapy programs. However, the application of advanced quantitative MRI methods is still confined to investigational/laboratory settings, and the clinical implication of these imaging methods in personalized medicine is still in infancy. Longitudinal research studies in neurodevelopmental disorders are the need of the hour for accurate characterization of brain–behavioral changes that could be monitored over a period of time. These findings would be more reliable and consistent with translating into the clinics. This review article aims to focus on the recent advancement of early biomarkers for the characterization of ASD features at a younger age using behavioral and quantitative MRI methods.

INTRODUCTION: Numerous studies in Huntington’s disease (HD) have shown striatum as the major site of neuronal loss, but recently the presence of neurodegeneration in other regions of the brain is gaining attention. In our study, we used voxel-based morphometry and diffusion tensor imaging to identify other areas in the brain that are involved in the disease. METHODS: The present study is a prospective study conducted in the Departments of Neurology, Psychiatry, and Neuroimaging and Interventional Radiology (NIIR), NIMHANS, Bengaluru. The study included 20 genetically confirmed HD patients and 20 healthy controls. Magnetic resonance imaging was performed on a 3-Tesla Philips Achieva scanner with a 32-channel head coil with the acquisition of whole-brain T1-weighted and DTI. RESULTS: The patients (41.25 ± 10.04 years) and controls (38.27 ± 11.29 years) were age-matched (P = 0.38), and the mean age at the onset of the symptoms of the disease was 37.53 ± 10.11 years, and the expanded CAG repeat allele was 45.95 ± 7.27 (range 40–73) repeats. All patients had psychiatric symptoms at presentation such as anger outbursts, irritability, abusive behavior, apathy, low mood, crying spells, delusions, lack of initiation, and obsessive–compulsive disorder. Compared with controls, HD patients had significant atrophy of bilateral caudate nuclei, right globus pallidus, left culmen, right precuneus, hypothalamus, and right superior temporal gyrus. Fractional anisotropy was increased in bilateral cerebral white matter and thalamus with the reduction in mean diffusivity. CONCLUSIONS: In addition to atrophy of caudate, atrophy was also observed in globus pallidus, thalamus, hypothalamus and right superior temporal gyrus. This may explain the neuropsychiatric and cognitive symptoms observed in these patients.

BACKGROUND AND PURPOSE: The corpus callosum (CC) consists of topographically arranged white matter (WM) fibers. Previous studies have indicated the CC to be discretely involved in WD. In this study, we strived to characterize the macrostructural properties of the CC using midsagittal cross-sectional area and thickness profile measurements. MATERIALS AND METHODS: This study was performed using archived magnetic resonance imaging (MRI) scans of 14 patients with WD and 14 age- and gender-matched healthy controls. Using an automated software pipeline for morphometric profiling, the midsagittal CC was segmented into five sub-regions (CC1–5) according to the Hofer–Frahm scheme. The mean thickness and area of different CC segments and their clinical and cognitive correlates were identified. RESULTS: The mean area was significantly different only in CC2 segment (94.2 ± 25.5 vs. 118.6 ± 19.7 mm2, corrected P < 0.05). The mean thickness was significantly different in CC1 (5.06 ± 1.15 vs. 6.93 ± 0.89 mm, corrected P < 0.05), CC2 (3.73 ± 0.96 vs. 4.87 ± 1.01 mm, corrected P < 0.05), and CC3 segments (3.42 ± 0.84 vs. 3.94 ± 0.72 mm, corrected P < 0.05). The age at onset of neurological symptoms and MMSE score was significantly correlated with the morphometric changes of CC1 and CC2 segments. CONCLUSION: Morphological changes of the CC are discrete in WD. Morphometric loss of CC was associated with an earlier onset of neurological symptoms and cognitive dysfunction in WD.

High grade glioma (HGG) are the most common, highly infiltrative brain tumors usually with a grim outcome with low survival. Recent comprehensive genomic profiling has greatly elucidated the molecular markers in gliomas that include the mutations in isocitrate dehydrogenase (IDH), 1p/19q co-deletion and alterations in O6 methyl-guanine methyltransferase (MGMT). Prognosis of these markers can support early clinical decision making that can consequently improve the survival outcomes. Multi-modal MRI based phenotypic tools such as radiomics based multi-variate models and state-of-art convolutional neural networks (CNNs) have shown promise in identifying these genotypes. However, current techniques do not facilitate comprehensive genomic profiling of the HGG as these are focused only on a single mutation. Moreover, the models are trained on small datasets and cannot employ unlabeled data, which is abundant as pathological labelling is invasive, expensive and inaccessible in many places. In this work, we build a semi-supervised hierarchical multi-task model that can incorporate unlabeled glioma data and learn to predict multiple molecular markers simultaneously. Our framework employs the latent space from an encoder to incorporate the unlabeled data while the hierarchical multi-task model accounts for the similarity between tasks and utilizes the shared information, resulting in inductive learning that facilitates precise delineation of IDH, MGMT, 1p/19q and grade. We applied our framework to 120 labeled, 149 semi-labeled and 48 unlabeled data using T1-contrast enhanced, T2 and FLAIR images and illustrate that our model performs with an average test accuracy of 82.35% and verified the results using task wise and modality wise ablation analysis. Moreover, the class activation maps computed from each local task branch provide clinical interpretability.

Purpose: Pial arteriovenous fistulae (PAVF) are rare intracranial vascular malformations, predominantly seen in children and distinct from arteriovenous malformations and dural arteriovenous fistulae. PAVF often leads to high morbidity and mortality. The aim of our study was to describe the clinical features and endovascular management of PAVF at various intracranial locations; to analyze the use of liquid embolic agents and coils alone or in combination in the treatment of PAVF and to analyze the outcome of embolization. Materials and Methods: Retrospective review of diagnostic angiography and neurointerventional database of our institution identified a cohort of 15 patients with non-galenic PAVF from 2008 to 2014 out of 6750 patients. Fourteen patients were treated endovascularly with coils and liquid embolic materials in combination or alone. Patients were followed up for evaluation of prognosis. Results: Age of the patients ranged from 3 to 37 years. Most patients were male and most common presentation was headache followed by seizure. Most common location of fistula was frontal lobe. The most common type was single artery single hole fistula with venous varix. Satisfactory obliteration was seen in all cases. One patient developed intraparenchymal hematoma on the first post procedural day and outcome was poor. Conclusions: PAVF are rare intracranial vascular malformations which can effectively be managed endovascularly with liquid embolic, coils alone, or in combination. Complete occlusion of the fistula can be achieved in most cases in a single sitting with a reasonable morbidity related to the procedure, compared with the natural history of this disease.

BACKGROUND AND PURPOSE: Re-emergent tremor (ReT) is the tremor that reappears after a variable period of latency while maintaining posture. Little is known about the mechanisms that contribute to the origin of this silent period (SP). Our aim was to compare the imaging characteristics of patients with and without ReT and find the structural correlate of SP if any. MATERIALS AND METHODS: Fifteen patients with ReT (Group 1) and eighteen patients without ReT (patients with rest tremor and postural tremor, but no latency) were evaluated clinically, electrophysiologically, with diffusion tensor imaging (DTI) and voxel-based morphometry (VBM). DTI parameters of different regions of interest were analyzed and compared with that of 37 healthy age- and gender-matched controls. RESULTS: No statistically significant difference was observed between the two groups in terms of age, duration of disease, levodopa equivalent dose, or severity of the disease. However, in the left supplementary motor area (SMA), there was a significant reduction of fractional anisotropy and an increase of radial diffusivity and mean diffusivity in patients with ReT (Group 1) as compared to patients without ReT (Group 2) and healthy controls. The results of the VBM analysis were not significant. CONCLUSION: The presence of abnormality of SMA suggests that patients with ReT have a different pathophysiological mechanism as compared to patients without ReT. This is a novel finding implicating a possible contribution of the frontal lobe to the genesis of SP in ReT. ReT could be a distinct clinical entity within the tremor dominant subtype.

BackgroundA multimodal connectomic analysis using diffusion and functional MRI can provide complementary information on the structure–function network dynamics involved in complex neurodegenerative network disorders such as Parkinson’s disease (PD). Deep learning-based graph neural network models generate higher-level embeddings that could capture intricate structural and functional regional interactions related to PD.ObjectiveThis study aimed at investigating the role of structure–function connections in predicting PD, by employing an end-to-end graph attention network (GAT) on multimodal brain connectomes along with an interpretability framework.MethodsThe proposed GAT model was implemented to generate node embeddings from the structural connectivity matrix and multimodal feature set containing morphological features and structural and functional network features of PD patients and healthy controls. Graph classification was performed by extracting topmost node embeddings, and the interpretability framework was implemented using saliency analysis and attention maps. Moreover, we also compared our model with unimodal models as well as other state-of-the-art models.ResultsOur proposed GAT model with a multimodal feature set demonstrated superior classification performance over a unimodal feature set. Our model demonstrated superior classification performance over other comparative models, with 10-fold CV accuracy and an F1 score of 86% and a moderate test accuracy of 73%. The interpretability framework highlighted the structural and functional topological influence of motor network and cortico-subcortical brain regions, among which structural features were correlated with onset of PD. The attention maps showed dependency between large-scale brain regions based on their structural and functional characteristics.ConclusionMultimodal brain connectomic markers and GAT architecture can facilitate robust prediction of PD pathology and provide an attention mechanism-based interpretability framework that can highlight the pathology-specific relation between brain regions.

Abstract Objective The Accelerator program for Discovery in Brain disorders using Stem cells (ADBS) is a longitudinal study on five cohorts of patients with major psychiatric disorders from genetically high‐risk families, their unaffected first‐degree relatives, and healthy subjects. We describe the ADBS protocols for acquisition, quality assurance (QA), and quality check (QC) for multimodal magnetic resonance brain imaging studies. Methods We describe the acquisition and QC protocols for structural, functional, and diffusion images. For QA, we acquire proton density and functional images on phantoms, along with repeated scans on human volunteer. We describe the analysis of phantom data and test–retest reliability of volumetric and diffusion measures. Results Analysis of acquired phantom data shows linearity of proton density signal with increasing proton fraction, and an overall stability of various spatial and temporal QA measures. Examination of dice coefficient and statistical analyses of coefficient of variation in test–retest data on the human volunteer showed consistency of volumetric and diffusivity measures at whole‐brain, regional, and voxel‐level. Conclusion The described acquisition and QA–QC procedures can yield consistent and reliable quantitative measures. It is expected that this longitudinal neuroimaging dataset will, upon its release, serve the scientific community well and pave the way for interesting discoveries.

Abstract Persistent embryological connections between the anterior and posterior circulations are rare entities. Persistent hypoglossal artery is the second most common persistent carotid-basilar anastomosis. As it is often associated with hypoplasia of vertebral arteries, it poses a challenge during endovascular interventions. We present a case of a 32-year-old woman who presented with occipital headache of four weeks’ duration. Magnetic Resonance Angiography showed hypoplastic vertebral arteries with a persistent hypoglossal artery arising from the cervical segment of the left internal carotid artery and supplying the entire posterior circulation, associated with a dissecting aneurysm of the right posterior cerebral artery. Endovascular parent vessel occlusion was performed for the dissecting posterior cerebral artery aneurysm by navigating the guide catheter, microwire, and microcatheter through the persistent hypoglossal artery because the vertebral arteries were hypoplastic. Post-intervention, the patient did not develop any neurological deficit and was discharged in a stable condition.

Purpose Existing class activation mapping (CAM) techniques extract the feature maps only from a single layer of the convolutional neural net (CNN), generally from the final layer and then interpolate to upsample to the original image resolution to locate the discriminative regions. Consequently these provide a coarse localization that may not be able to capture subtle abnormalities in medical images. To alleviate this, our work proposes a technique called high resolution class activation mapping (HR-CAMs) that can provide enhanced visual explainability to the CNN models. Methods HR-CAMs fuse feature maps by training a network using the input from multiple layers of a trained CNN, thus gaining information from every layer that can localize abnormalities with greater details in original image resolution. The technique is validated qualitatively and quantitatively on a simulated dataset of 8,000 images followed by applications on multiple image analysis tasks that include (1) skin lesion classification (ISIC open dataset—25,331 cases) and (2) predicting bone fractures (MURA open dataset—40,561 images) (3) predicting Parkinson’s disease (PD) from neuromelanin sensitive MRI (small cohort-80 subjects). Results We demonstrate that our model creates clinically interpretable subject specific high resolution discriminative localizations when compared to widely used CAMs and Gradient-CAMs. Conclusion HR-CAMs provide finer delineation of abnormalities thus facilitating superior explainability to CNNs as has been demonstrated from its rigorous validation.

Rationale and Objectives: To build a machine learning-based diagnostic model that can accurately distinguish adult supratentorial extraventricular ependymoma (STEE) from similarly appearing high-grade gliomas (HGG) using quantitative radiomic signatures from a multi-parametric MRI framework.Materials and Methods: We computed radiomic features on the preprocessed and segmented tumor masks from a pre-operative multimodal MRI dataset [contrast-enhanced T1 (T1ce), T2, fluid-attenuated inversion recovery (FLAIR), apparent diffusion coefficient (ADC)] from STEE (n = 15), HGG-Grade IV (HGG-G4) (n = 24), and HGG-Grade III (HGG-G3) (n = 36) patients, followed by an optimum two-stage feature selection and multiclass classification. Performance of multiple classifiers were evaluated on both unimodal and multimodal feature sets and most discriminative radiomic features involved in classification of STEE from HGG subtypes were obtained.Results: Multimodal features demonstrated higher classification performance over unimodal feature set in discriminating STEE and HGG subtypes with an accuracy of 68% on test data and above 80% on cross validation, along with an overall above 90% specificity. Among unimodal feature sets, those extracted from FLAIR demonstrated high classification performance in delineating all three tumor groups. Texture-based radiomic features particularly from FLAIR were most important in discriminating STEE from HGG-G4, whereas first-order features from T2 and ADC consistently ranked higher in differentiating multiple tumor groups.Conclusions: This study illustrates the utility of radiomics-based multimodal MRI framework in accurately discriminating similarly appearing adult STEE from HGG subtypes. Radiomic features from multiple MRI modalities could capture intricate and complementary information for a robust and highly accurate multiclass tumor classification.

Abstract Background Dysfunction in inwardly rectifying potassium channel Kir4.1 has been implicated in SeSAME syndrome, an autosomal-recessive (AR), rare, multi-systemic disorder. However, not all neurological, intellectual disability, and comorbid phenotypes in SeSAME syndrome can be mechanistically linked solely to Kir4.1 dysfunction. Methods We therefore performed whole-exome sequencing and identified additional genetic risk-elements that might exert causative effects either alone or in concert with Kir4.1 in a family diagnosed with SeSAME syndrome. Results Two variant prioritization pipelines based on AR inheritance and runs of homozygosity (ROH), identified two novel homozygous variants in KCNJ10 and PI4KB and five rare homozygous variants in PVRL4, RORC, FLG2, FCRL1, NIT1 and one common homozygous variant in HSPA6 segregating in all four patients. The novel mutation in KCNJ10 resides in the cytoplasmic domain of Kir4.1, a seat of phosphatidylinositol bisphosphate (PIP2) binding. The mutation altered the subcellular localization and stability of Kir4.1 in patient-specific lymphoblastoid cells (LCLs) compared to parental controls. Barium-sensitive endogenous K+ currents in patient-specific LCLs using whole-cell patch-clamp electrophysiology revealed membrane depolarization and defects in inward K+ ion conductance across the membrane, thereby suggesting a loss-of-function effect of KCNJ10 variant. Conclusion Altogether, our findings implicate the role of new genes in SeSAME syndrome without electrolyte imbalance and thereby speculate the regulation of Kir4.1 channel activity by PIP2 and integrin-mediated adhesion signaling mechanisms.

Background: The occurrence of impulse control disorders (ICDs) in Parkinson's disease (PD) is frequently attributed to dopamine replacement therapy. However, not all patients who receive medication develop ICDs. Recent imaging studies have suggested specific neuroanatomical abnormalities in patients with PD and ICD. Objectives: This study aims to identify changes in volumes of subcortical structures and cortical thickness specific to patients with PD and ICDs. Methodology: A total of 11 patients with PD and ICD (PDICD(+)), 15 patients with PD without ICD (PDICD(−)), and 15 healthy controls were analyzed in this study. ICDs were diagnosed and quantified using the Questionnaire for Impulsive-Compulsive Disorders in PD-Rating Scale (QUIP-RS). Structural imaging was performed on a 3T scanner; volumes of subcortical structures and cortical thickness were obtained using first in FSL and FreeSurfer. Results: Significant volume loss of the nucleus accumbens was observed in the PDICD(+) group. Several areas of significant cortical thinning were observed in the PDICD(+) group in comparison PDICD(−) group. Thinning of the left middle temporal gyrus, transverse temporal gyrus, and bilateral temporal poles was observed in the PDICD(+) group. No correlations were observed between QUIP-RS scores and areas of cortical thinning. Conclusions: The PDICD(+) group has specific neuroanatomical variations in the nucleus accumbens and temporal lobes, which may contribute to the development of ICD and perhaps predispose a patient to ICDs on exposure to dopamine replacement therapy.

PurposeThe study is conducted to identify the best corpus callosum (CC) sub-region that corresponds to highest callosal tissue alteration occurred due to Parkinsonism. In this regard the efficacy of local binary pattern (LBP) based texture analysis (TA) of CC is performed to quantify the changes in topographical distribution of callosal fiber connected to different regions of cortex. The extent of highest texture alteration in CC is used for differential diagnosis.Materials and MethodsStudy included subjects with Parkinson’s disease (PD) (n = 20), and atypical Parkinsonian disorders – multiple system atrophy (MSA) (n = 20), Progressive supranuclear palsy (PSP) (n = 20), and healthy controls (n = 20). For each subject, we have automated the ROI extraction within mid-sagittal CC, followed by LBP TA. Two-class support vector machine (SVM) classification for each disorder as against HC is performed using extracted LBP features like energy and entropy. Correct classification ratio (CCR) is computed as the fraction of correctly classified ROIs at each of the CC sub-regions based on well-known Witelson and Hofer schemes. Based on CCR values, the “Scatter Index (SI)” is proposed to capture how localized (closer to 0) or scattered (closer to 1) the textural changes are among the CC sub-regions, across all subjects per class. The CCR values are further utilized to classify the disease groups.ResultsHighest alteration of texture is observed in mid-body of CC. The consistency of this finding is quantified using SI for all subjects in a specific class that results more localized textural changes in PSP (15%) and MSA (25%), in comparison to PD (47%). Classification among disease groups results maximum classification accuracy of 90% in classifying PSP from PD-NC.ConclusionOur result demonstrates the efficacy of proposed methodology in analyzing tissue alteration in MRI of Parkinsonian disorders and thus has potential to become valuable tool in computer aided differential diagnosis.

Acute high-altitude cerebral edema can occur in an unacclimatised individual on exposure to high altitudes and sometimes it can be fatal. Here we have described the neuroimaging features of a patient who suffered from fatal high altitude cerebral edema. Available literature is reviewed. Probable pathogenesis is discussed. The risk of acute mountain sickness is reported up to 25% in individuals who ascend to an altitude of 3500 meter and in more than 50% subjects at an altitude of 6000 meter. The lack of availability of advanced imaging facilities at such a higher altitude makes imaging of such condition a less described entity.

Abstract Background There is emerging evidence that there are shared genetic, environmental and developmental risk factors in psychiatry, that cut across traditional diagnostic boundaries. With this background, the Discovery biology of neuropsychiatric syndromes (DBNS) proposes to recruit patients from five different syndromes (schizophrenia, bipolar disorder, obsessive compulsive disorder, Alzheimer’s dementia and substance use disorders), identify those with multiple affected relatives, and invite these families to participate in this study. The families will be assessed: 1) To compare neuro-endophenotype measures between patients, first degree relatives (FDR) and healthy controls., 2) To identify cellular phenotypes which differentiate the groups., 3) To examine the longitudinal course of neuro-endophenotype measures., 4) To identify measures which correlate with outcome, and 5) To create a unified digital database and biorepository. Methods The identification of the index participants will occur at well-established specialty clinics. The selected individuals will have a strong family history (with at least another affected FDR) of mental illness. We will also recruit healthy controls without family history of such illness. All recruited individuals (N = 4500) will undergo brief clinical assessments and a blood sample will be drawn for isolation of DNA and peripheral blood mononuclear cells (PBMCs). From among this set, a subset of 1500 individuals (300 families and 300 controls) will be assessed on several additional assessments [detailed clinical assessments, endophenotype measures (neuroimaging- structural and functional, neuropsychology, psychophysics-electroencephalography, functional near infrared spectroscopy, eye movement tracking)], with the intention of conducting repeated measurements every alternate year. PBMCs from this set will be used to generate lymphoblastoid cell lines, and a subset of these would be converted to induced pluripotent stem cell lines and also undergo whole exome sequencing. Discussion We hope to identify unique and overlapping brain endophenotypes for major psychiatric syndromes. In a proportion of subjects, we expect these neuro-endophenotypes to progress over time and to predict treatment outcome. Similarly, cellular assays could differentiate cell lines derived from such groups. The repository of biomaterials as well as digital datasets of clinical parameters, will serve as a valuable resource for the broader scientific community who wish to address research questions in the area.

There are reports of co-occurrence of obsessive–compulsive disorder (OCD) in patients with temporal lobe epilepsy (TLE). We present a report of a patient with refractory TLE due to hippocampal sclerosis with concomitant OCD on pharmacotherapy for both. She underwent surgery for standard anterior temporal lobectomy with amygdalohippocampectomy and reported improvement in obsessive–compulsive symptoms subsequently. We seek to further evidence of interaction between the two conditions and argue to undertake future research exploration on the same.

Abstract Human consciousness is considered a result of the synchronous “humming” of multiple dynamic networks. We performed a dynamic functional connectivity analysis using resting state functional magnetic resonance imaging (rsfMRI) in 14 patients before and during a propofol infusion to characterize the sedation-induced alterations in consciousness. A sliding 36-second window was used to derive 59 time points of whole brain integrated local connectivity measurements. Significant changes in the connectivity strength (Z Corr) at various time points were used to measure the connectivity fluctuations during awake and sedated states. Compared with the awake state, sedation was associated with reduced cortical connectivity fluctuations in several areas connected to the default mode network and around the perirolandic cortex with a significantly decreased correlation of connectivity between their anatomical homologues. In addition, sedation was associated with increased connectivity fluctuations in the frequency range of 0.027 to 0.063 Hz in several deep nuclear regions, including the cerebellum, thalamus, basal ganglia and insula. These findings advance our understanding of sedation-induced altered consciousness by visualizing the altered dynamics in several cortical and subcortical regions and support the concept of defining consciousness as a dynamic and integrated network.

ObjectiveTo assess the inter-technique agreement of relative cerebral blood volume (rCBV) measurements obtained using T1- and T2*-perfusion MRI on 3T scanner in glioma patients.MethodsA total of 49 adult patients with gliomas underwent both on T1- and T2*-perfusion in the same scanning session, and rCBV maps were estimated using both methods. For the quantitative analysis; Two independent observers recorded the rCBV values from the tumor as well as contralateral brain tissue from both T1- and T2*-perfusion. Inter-observer and inter-technique rCBV measurement agreement were determined by using 95% Bland-Altman limits of agreement and intra-class correlation coefficient (ICC) statistics.ResultsQualitative analysis of the conventional and perfusion images showed that 16/49 (32.65%) tumors showed high susceptibility, and in these patients T2*-perfusion maps were suboptimal. Bland-Altman plots revealed an agreement between two independent observers recorded rCBV values for both T1- and T2*-perfusion. The ICC demonstrated strong agreement between rCBV values recorded by two observers for both T2* (ICC = 0.96, p = 0.040) and T1 (ICC = 0.97, p = 0.026) perfusion and similarly, good agreement was noted between rCBV estimated using two methods (ICC = 0.74, PConclusionsIn the current study, T1- and T2*-perfusion showed similar diagnostic performance for discrimination of grade III and grade IV gliomas; however, T1-perfusion was found to be better for the evaluation of tumors with intratumoral hemorrhage, postoperative recurrent tumors, and lesions near skull base. We conclude that T1-perfusion MRI with a single dose of contrast could be used as an alternative to T2*-perfusion to overcome the issues associated with this technique in brain tumors for reliable perfusion quantification.

Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinson's disease (PD) as PD is characterized by loss of dopaminergic neurons in the SNc. Current techniques employ estimation of contrast ratios of the SNc, visualized on NMS-MRI, to discern PD patients from the healthy controls. However, the extraction of these features is time-consuming and laborious and moreover provides lower prediction accuracies. Furthermore, these do not account for patterns of subtle changes in PD in the SNc. To mitigate this, our work establishes a computer-based analysis technique that uses convolutional neural networks (CNNs) to create prognostic and diagnostic biomarkers of PD from NMS-MRI. Our technique not only performs with a superior testing accuracy (80%) as compared to contrast ratio-based classification (56.5% testing accuracy) and radiomics classifier (60.3% testing accuracy), but also supports discriminating PD from atypical parkinsonian syndromes (85.7% test accuracy). Moreover, it has the capability to locate the most discriminative regions on the neuromelanin contrast images. These discriminative activations demonstrate that the left SNc plays a key role in the classification in comparison to the right SNc, and are in agreement with the concept of asymmetry in PD. Overall, the proposed technique has the potential to support radiological diagnosis of PD while facilitating deeper understanding into the abnormalities in SNc. Keywords: Parkinson's disease, Machine learning, Neuromelanin, Convolutional neural networks

Type III GM1 gangliosidosis is the adult or chronic variant of a lysosomal storage disorder, which occurs secondary to deficiency of β-galactosidase. The wishbone pattern of iron deposition which involves the medial and latter parts of the globus pallidus is pathognomonic of this disease. The diagnosis of type III GM1 gangliosidosis should be considered in patient with young onset, progressive generalized dystonia with prominent facial dystonia, and a wishbone pattern of iron deposition.

Context: Primary intracranial tumors in children are commonly located in the posterior fossa. Conventional MRI offers limited information regarding the histopathological type of tumor which is essential for better patient management. Aims: The purpose of the study was to evaluate the usefulness of advanced MR imaging techniques like diffusion tensor imaging (DTI) in distinguishing the various histopathological types of posterior fossa tumors in children. Settings and Design: DTI was performed on a 3T MRI scanner in 34 untreated children found to have posterior fossa lesions. Materials and Methods: Using third party software, various DTI parameters [apparent diffusion coefficient (ADC), fractional anisotropy (FA), radial diffusivity, planar index, spherical index, and linear index] were calculated for the lesion. Statistical Analysis Used: Data were subjected to statistical analysis [analysis of variance (ANOVA)] using SPSS 15.0 software. Results: We observed significant correlation (P < 0.01) between ADC mean and maximum, followed by radial diffusivity (RD) with the histopathological types of the lesions. Rest of the DTI parameters did not show any significant correlation in our study. Conclusions: The results of our study support the hypothesis that most cellular tumors and those with greater nuclear area like medulloblastoma would have the lowest ADC values, as compared to less cellular tumors like pilocytic astrocytoma.

Background: Writer’s cramp (WC) is one of the commonly observed focal dystonias. The pathophysiology of WC has not been fully understood. The role of the cerebellum has been increasingly recognized in the pathogenesis of dystonia. As the cerebellum is crucial for maintaining accurate eye–hand coordination (EHC), its role in the pathogenesis of WC can be investigated by studying the EHC in patients with WC.Methods: Fifteen patients with WC (women:men, 3:12) and 15 age- and gender-matched controls performed oculomotor and EHC tasks. A visually guided stimulus (VGS) task was first performed with eye-only condition (EOC) and then with EHC.Results: A significant interaction between the groups (controls and patients) and tasks (EOC and EHC) with age as a covariate confirmed that the two groups reacted differently to the tasks in saccadic latency (F(1,27) = 4.8; p = 0.039) and average saccade acceleration (F(1,27) = 10.6; p = 0.003). The curvature index of acceleration of the hand was significantly more in patients compared to controls (patients vs. controls, 2.4±0.4 vs. 1.8±0.2, p = 0.01). While performing the EHC task, there was a significant correlation of the Writer’s Cramp Rating Score with the average saccadic speed (–0.61, p = 0.016), peak saccadic deceleration (0.59, p = 0.019) and average saccadic acceleration (–0.63, p = 0.012).Discussion: Saccadic acceleration and latency are abnormal while performing EHC tasks in patients with WC. Our study gives further insights into the possible role of the cerebellum in the pathogenesis of WC.

Abetalipoproteinemia is an uncommon cause of ataxia and retinitis pigmentosa (RP). Most of the neurological and ocular manifestations occur secondary to deficiency syndromes that is consequent to fat malabsorption from the small intestine. In this report, we have described the phenotype of a young adult female who manifested with recurrent diarrheal illness in her first decade, followed by anemia, RP, and neurological involvement with progressive deafness, cerebellar and sensory ataxia, and subclinical neuropathy in her second decade of life. While RP and sensory ataxia due to vitamin E deficiency are well-recognized features of abetalipoproteinemia, deafness is rarely described. In addition, we have highlighted the abnormal posterior column signal changes in the cervical cord in this patient. Early recognition avoids unnecessary investigations and has a potential to retard the disease progression by replacing some of the deficient vitamins.

Aims: In progressive supranuclear palsy (PSP) tissue damage occurs in specific cortical and subcortical regions. Voxel based analysis using T1-weighted images depict quantitative gray matter (GM) atrophy changes. Magnetization transfer (MT) imaging depicts qualitative changes in the brain parenchyma. The purpose of our study was to investigate whether MT imaging could indicate abnormalities in PSP. Settings and Design: A total of 10 patients with PSP (9 men and 1 woman) and 8 controls (5 men and 3 women) were studied with T1-weighted magnetic resonance imaging (MRI) and 3DMT imaging. Voxel based analysis of T1-weighted MRI was performed to investigate brain atrophy while MT was used to study qualitative abnormalities in the brain tissue. We used SPM8 to investigate group differences (with two sample t-test) using the GM and white matter (WM) segmented data. Results: T1-weighted imaging and MT are equally sensitive to detect changes in GM and WM in PSP. Magnetization transfer ratio images and magnetization-prepared rapid acquisition of gradient echo revealed extensive bilateral volume and qualitative changes in the orbitofrontal, prefrontal cortex and limbic lobe and sub cortical GM. The prefrontal structures involved were the rectal gyrus, medial, inferior frontal gyrus (IFG) and middle frontal gyrus (MFG). The anterior cingulate, cingulate gyrus and lingual gyrus of limbic lobe and subcortical structures such as caudate, thalamus, insula and claustrum were also involved. Cerebellar involvement mainly of anterior lobe was also noted. Conclusions: The findings suggest that voxel based MT imaging permits a whole brain unbiased investigation of central nervous system structural integrity in PSP.