UNASSIGNED: This study characterizes glucose metabolism and tau protein deposition distribution in patients with Alzheimer\'s disease (AD) and to evaluate the relationships between neuropsychological performance and tau protein deposition or glucose metabolism using 18F-FDG and 18F-AV1451 positron emission tomography/computed tomography (PET/CT).
UNASSIGNED: Sixty-four patients with β-amyloid-positive (Aβ+) AD and twenty-five healthy participants were enrolled in this study. All participants underwent 18F-FDG and 18F-AV1451 PET/CT. Clinical data and neuropsychological scores were collected. Patients with AD were divided into mild, moderate, and severe groups according to mini-mental state examination (MMSE) scores. The standardized uptake value ratios (SUVRs) for both FDG and AV1451 PET images were calculated using the cerebellar vermis as reference. The SUVRs of the whole cerebral cortex and each brain region were calculated. The volume of interest (VOI) was obtained using automated anatomical atlas (AAL) and Brodmann regions. Student\'s t-test was used to perform intergroup comparisons of SUVR. The partial correlation coefficient between SUVR and neuropsychological scores was computed in an inter-subject manner using age and education as covariates.
UNASSIGNED: The mild subgroup showed a reduction in glucose metabolism and aggregation of tau protein in the temporoparietal cortex. With a decline in neuropsychiatric performance, the SUVR on FDG PET decreased and SUVR on tau PET increased gradually. The areas of glucose metabolism reduction and tau protein deposition appeared first in the parietal cortex, followed by the temporal and frontal cortex, successively. Both FDG and tau SUVRs significantly correlated with MMSE, Montreal cognitive assessment (MOCA), auditory verbal learning test (AVLT), Boston naming test (BNT), clock drawing task (CDT), and verbal fluency test (VFT) (p < 0.05). The SUVR on FDG PET significantly correlated with activities of daily living (ADL) and the Hamilton depression scale (HAMD). There was no significant correlation between the tau SUVRs and ADL or HAMD.
UNASSIGNED: The extension of tau protein deposition was similar but not exactly consistent with the area of glucose metabolism reduction. Both tau and FDG SUVRs correlated with cognitive function in domain-specific patterns, and the results of FDG PET more closely correlated with neuropsychological function than tau PET results did.

Background: In recent years, predicting and modeling the progression of Alzheimer\'s disease (AD) based on neuropsychological tests has become increasingly appealing in AD research. Objective: In this study, we aimed to predict the neuropsychological scores and investigate the non-linear progression trend of the cognitive declines based on multimodal neuroimaging data. Methods: We utilized unimodal/bimodal neuroimaging measures and a non-linear regression method (based on artificial neural networks) to predict the neuropsychological scores in a large number of subjects (n = 1143), including healthy controls (HC) and patients with mild cognitive impairment non-converter (MCI-NC), mild cognitive impairment converter (MCI-C), and AD. We predicted two neuropsychological scores, i.e., the clinical dementia rating sum of boxes (CDRSB) and Alzheimer\'s disease assessment scale cognitive 13 (ADAS13), based on structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) biomarkers. Results: Our results revealed that volumes of the entorhinal cortex and hippocampus and the average fluorodeoxyglucose (FDG)-PET of the angular gyrus, temporal gyrus, and posterior cingulate outperform other neuroimaging features in predicting ADAS13 and CDRSB scores. Compared to a unimodal approach, our results showed that a bimodal approach of integrating the top two neuroimaging features (i.e., the entorhinal volume and the average FDG of the angular gyrus, temporal gyrus, and posterior cingulate) increased the prediction performance of ADAS13 and CDRSB scores in the converting and stable stages of MCI and AD. Finally, a non-linear AD progression trend was modeled to describe the cognitive decline based on neuroimaging biomarkers in different stages of AD. Conclusion: Findings in this study show an association between neuropsychological scores and sMRI and FDG-PET biomarkers from normal aging to severe AD.

The neuropsychological scores and Functional Activities Questionnaire (FAQ) are significant to measure the cognitive and functional domain of the patients affected by the Alzheimer\'s Disease. Further, there are standardized dataset available today that are curated from several centers across the globe that aid in development of Computer Aided Diagnosis tools. However, there are numerous clinical tests to measure these scores that lead to a challenging task for their assessment in diagnosis. Also, the datasets suffer from common missing and imbalanced data issues. In this paper, we propose a machine learning based framework to overcome these issues. Empirical results demonstrate that improved performance of Genetic Algorithm is obtained for the neuropsychological scores after Miss Forest Imputation and for FAQ scores is obtained after subjecting it to the Synthetic Minority Oversampling Technique.

BACKGROUND: Electroencephalogram Neurofeedback therapy (EEG-NFT) has several potential beneficial effects in terms of improving cognition and electrophysiological regulation among patients with brain injury. However, in vivo structural and functional changes remain less explored.
OBJECTIVE: The aim of the present study is to explore EEG-NFT induced in vivo changes in traumatic brain injury (TBI) patients.
METHODS: Two patients with mean age of 15 years with moderate head injury who had more than seven post concussion symptoms and poor cognitive performances (<5 percentile) were subjected to 20 sessions of EEG-NFT. The neuropsychological test scores, post concussion symptoms and MRI scan of the brain were recorded pre-post to EEG-NFT.
RESULTS: During EEG-NFT the cognitive scores and concussion symptoms improved significantly (p < 0.05). The EEG-NFT has shown significant increase in cortical grey matter (GM) volumes (p < 0.0001) and fractional anisotropy (FA) of cortical white matter (WM) tracts (p < 0.0001, voxel max 60 and above). There was a significant decrease in global, local efficiency, cost and clustering coefficient of functional connectivity (Wilcoxon Sign Rank Test p < 0.05). Interestingly there was a significant increase in thalamo-cortical connection (increase FA value) after EEG-NFT.
CONCLUSIONS: The EEG-NFT therapy has shown significant changes in structural and functional connectivity among young moderately injured TBI patients.