Brain Imaging in COVID-19.

Considering the neurological and neuropsychiatric manifestations of coronavirus disease 2019 (COVID-19), its early diagnosis is crucial. This Viewpoint aims to highlight these manifestations through multimodal neuroimaging studies reflecting neurochemical and structural impairment.

Comparative contribution of magnetoencephalography (MEG) and single-photon emission computed tomography (SPECT) in pre-operative localization for epilepsy surgery: A prospective blinded study.

PURPOSE: The aim of this study was to compare the diagnostic value and accuracy of ictal SPECT and inter-ictal magnetoencephalography (MEG) in localizing the site for surgery in persons with drug resistant epilepsy. METHOD: This was a prospective observational study. Patients expected to undergo epilepsy surgery were enrolled consecutively and the localization results from different imaging modalities were discussed in an epilepsy surgery meet. Odds ratio of good outcome (Engel I) were calculated in patients who underwent surgery in concordance with MEG and SPECT findings. Post-surgical seizure freedom lasting at least 36 months or more was considered the gold standard for determining the diagnostic output of SPECT and MEG. RESULTS: MEG and SPECT were performed in 101 and 57 patients respectively. In 45 patients SPECT could not be done due to delay in injection or technical factors. The accuracy of MEG and SPECT in localizing the epileptogenic zone was found to be 74.26 % and 78.57 % respectively. The diagnostic odds ratio for Engel I surgical outcome was reported as 2.43 and 5.0 for MEG and SPECT respectively. The diagnostic odds ratio for MEG in whom SPECT was non-informative was found to be 6.57 [95 % CI 1.1, 39.24], although it was not significantly associated with good surgical outcome. MEG was useful in indicating sites for SEEG implantation. CONCLUSION: SPECT was found to be non-informative for most patients, but reported better diagnostic output than MEG. MEG may be a useful alternative for patients in whom SPECT cannot be done or was non-localizing.

Quantitation of in vivo brain glutathione conformers in cingulate cortex among age-matched control, MCI, and AD patients using MEGA-PRESS.

Oxidative stress (OS) plays an important role in Alzheimer's disease (AD) and glutathione (GSH) mitigates this effect by maintaining redox-imbalance and free-radical neutralization. Quantified brain GSH concentration provides distinct information about OS among age-matched normal control (NC), mild cognitive impairment (MCI) and AD patients. We report alterations of in vivo GSH conformers, along with the choline, creatine, and N-acetylaspartate levels in the cingulate cortex (CC) containing anterior (ACC) and posterior (PCC) regions of 64 (27 NC, 19 MCI, and 18 AD) participants using MEscher-GArwood-Point-RESolved spectroscopy sequence. Result indicated, tissue corrected GSH depletion in PCC among MCI (p = .001) and AD (p = .028) and in ACC among MCI (p = .194) and AD (p = .025) as compared to NC. Effects of the group, region, and group × region on GSH with age and gender as covariates were analyzed using a generalized linear model with Bonferroni correction for multiple comparisons. A significant effect of group with GSH depletion in AD and MCI was observed as compared to NC. Receiver operator characteristic (ROC) analysis of GSH level in CC differentiated between MCI and NC groups with an accuracy of 82.8% and 73.5% between AD and NC groups. Multivariate ROC analysis for the combined effect of the GSH alteration in both ACC and PCC regions provided improved diagnostic accuracy of 86.6% for NC to MCI conversion and 76.4% for NC to AD conversion. We conclude that only closed GSH conformer depletion in the ACC and PCC regions is critical and constitute a potential biomarker for AD.

BHARAT: An Integrated Big Data Analytic Model for Early Diagnostic Biomarker of Alzheimer's Disease.

Alzheimer's disease (AD) is a devastating neurodegenerative disorder affecting millions of people worldwide. Progressive and relentless efforts are being made for therapeutic development by way of advancing understanding of non-invasive imaging modalities for the causal molecular process of AD. We present a Hadoop-based big data framework integrating non-invasive magnetic resonance imaging (MRI), MR spectroscopy (MRS) as well as neuropsychological test outcomes to identify early diagnostic biomarkers of AD. This big data framework for AD incorporates the three "V"s (volume, variety, velocity) with advanced data mining, machine learning, and statistical modeling algorithms. A large volume of longitudinal information from non-invasive imaging modalities with colligated parametric variety and speed for both data acquisition and processing as velocity complete the fundamental requirements of this big data framework for early AD diagnosis. Brain structural, neurochemical, and behavioral features are extracted from MRI, MRS, and neuropsychological scores, respectively. Subsequently, feature selection and ensemble-based classification are proposed and their outputs are fused based on the combination rule for final accurate classification and validation from clinicians. A multi-modality-based decision framework (BHARAT) for classification of early AD will be immensely helpful.