BACKGROUND: Cognitive changes in Huntington\'s disease (HD) precede motor manifestations. ENROLL-HD platform includes four cognitive measures of information processing speed (IPS). Our group is eager to seek clinical markers in the life stage that is as close as possible to the age of onset (ie, the so called prodromal HD phase) because this is the best time for therapeutic interventions.
OBJECTIVE: Our study aimed to test whether cognitive scores in prodromal ENROLL-HD mutation carriers show the potential to predict the severity of motor and behavioral changes once HD became fully manifested.
METHODS: From the global ENROLL-HD cohort of 21,343 participants, we first selected a premanifest Cohort#1 (ie, subjects with Total Motor Score (TMS) <10 and Diagnostic Confidence Level (DCL) <4, N = 1.222). From this cohort, we then focused on a prodromal Cohort#2 of subjects who were ascertained to phenoconvert into manifest HD at follow-up visits (ie, subjects from 6 ≤ TMS≤9 and DCL <4 to TMS≥10 and DCL = 4, n = 206).
RESULTS: The main results of our study showed that low IPS before phenoconversion in Cohort#2 predicted the severity of motor and behavioral manifestations. By combining the four IPS cognitive measures (eg, the Categorical Verbal Fluency Test; Stroop Color Naming Test; Stroop Word Reading; Symbol Digit Modalities Test), we generated a Composite Cognition Score (CCS). The lower the CCS score the higher the TMS and the apathy scores in the same longitudinally followed-up patients after phenoconversion.
CONCLUSIONS: CCS might represent a clinical instrument to predict the prognosis of mutation carriers who are close to manifesting HD.

UNASSIGNED: The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques.
UNASSIGNED: To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer\'s dementia.
UNASSIGNED: We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia.
UNASSIGNED: In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases.
UNASSIGNED: The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a \"stamp\" of the disease reflected by the model.

BACKGROUND: Endoscopic Third Ventriculostomy (ETV) is increasingly being accepted as the treatment of choice in place of Ventriculo-Peritoneal (VP) Shunt for hydrocephalus. However, their differences in cognitive and Quality of Life (QOL) scores have not been studied much in children.
OBJECTIVE: To compare the outcome, cognitive function, and QOL between ETV and VP shunt.
METHODS: Patients of non-tumor hydrocephalus treated with ETV or/and VP shunt underwent cognitive assessment (using modified child MMSE standardized as per the age group) and QOL (using PedsQL as per the age group in Physical, Emotional, Social, and School Functioning domains) in addition to the outcome of not requiring additional intervention.
RESULTS: Out of 139 patients, there were 29 infants and 40 children upto 14 years. Among these children, ETV was the primary intervention in 45, VP shunt in 24, and could be studied for a mean follow-up of 1.7 years. Though ETV required lesser additional intervention than VP shunt (19.2% vs. 28.6%) in toddlers and older children, there was no overall significant difference. Subnormal cognitive scores were noted in 25%, 40%, and 50% after ETV, single shunt procedure, and multiple shunt procedures, respectively, with no statistically significant difference. Among the different domains of QOL, the child reported scores in the social domain were significantly better after ETV than VP shunt (475[+13] vs. 387[+43], P value 0.03), whereas most other scores were non-significantly better following ETV.
CONCLUSIONS: Patients who underwent ETV show a trend for better clinical outcome, cognitive function, and QOL with significantly better child-reported QOL scores in the social domain.

Alzheimer\'s disease is a common neurodegenerative brain disease that affects the elderly population worldwide. Its early automatic detection is vital for early intervention and treatment. A common solution is to perform future cognitive score prediction based on the baseline brain structural magnetic resonance image (MRI), which can directly infer the potential severity of disease. Recently, several studies have modelled disease progression by predicting the future brain MRI that can provide visual information of brain changes over time. Nevertheless, no studies explore the intra correlation of these two solutions, and it is unknown whether the predicted MRI can assist the prediction of cognitive score. Here, instead of independent prediction, we aim to predict disease progression in multi-view, i.e., predicting subject-specific changes of cognitive score and MRI volume concurrently. To achieve this, we propose an end-to-end integrated framework, where a regression model and a generative adversarial network are integrated together and then jointly optimized. Three integration strategies are exploited to unify these two models. Moreover, considering that some brain regions, such as hippocampus and middle temporal gyrus, could change significantly during the disease progression, a region-of-interest (ROI) mask and a ROI loss are introduced into the integrated framework to leverage this anatomical prior knowledge. Experimental results on the longitudinal Alzheimer\'s Disease Neuroimaging Initiative dataset demonstrated that the integrated framework outperformed the independent regression model for cognitive score prediction. And its performance can be further improved with the ROI loss for both cognitive score and MRI prediction.

Binaural beat (BB) is a promising technique for memory improvement in elderly or people with neurological conditions. However, the related modulation of cortical networks followed by behavioral changes has not been investigated. The objective of this study is to establish a relationship between BB oscillatory brain activity evoked by stimulation and a behavioral response in a short term memory task. Three Groups A, B, and C of 20 participants each received alpha (10 Hz), beta (14 Hz), and gamma (30 Hz) BB, respectively, for 15 min. Their EEG was recorded in pre, during, and post BB states. Participants performed a digit span test before and after a BB session. A significant increase in the cognitive score was found only for Group A while a significant decrease in reaction time was noted for Groups A and C. Group A had a significant decrease of theta and increase of alpha power, and a significant increase of theta and decrease of gamma imaginary coherence (ICH) post BB. Group C had a significant increase in theta and gamma power accompanied by the increase of theta and gamma ICH post BB. The effectiveness of BB depends on the frequency of stimulation. A putative neural mechanism involves an increase in theta ICH in parieto-frontal and interhemispheric frontal networks.

It is well-known that cognitive function declines with age. In order to detect changes in cognitive function, cognitive tests should be performed repeatedly. Currently existing cognitive tests come in only a single version, so the subject is likely to remember the contents with repeated testing. And, under the outbreak of coronavirus disease 2019 (COVID-19), in-person assessment should be avoided. This study was performed to develop a new cognitive test (brain assessment, BA) that has 5 versions and can be performed on a personal computer (PC) through the Internet.
Five thousand subjects performed the online BA, which consisted of 5 subtests: number memory, word memory, mental rotation test, N-back test, and judgment test. We standardized the raw scores (cognitive scores, CSs) using mean and standard deviation, which were 50 and 10, respectively. Then, we calculated the mean CS for each sex and age, plotted the relationships between ages and mean CSs on figures, and calculated the formula of cognitive changes during normal aging.
The CSs of all subtests decreased with aging. The regression coefficient was from -0.31 to -0.45. It is noteworthy that in most subtests, the CSs started to increase at 85 years of age.
Our BA has 5 versions and can be done on a PC using the Internet. We tested the BA in a large number of subjects, and the standard values of CSs were measured in individuals up to 89 years of age. By performing this test repeatedly, subjects can evaluate the degree of their cognitive decline. If the rate of cognitive decline is greater than that predicted using the normalized formula, the subjects can undertake strategies to improve their control of lifestyle-related diseases or other habits of daily living.
The BA can be easily taken online using a PC, and its scores linearly declined with normal aging. The BA will be useful for detecting longitudinal cognitive changes and comparing them to the pattern seen in normal aging.

South Asia contributes substantially to global low birth weight population (i.e. those with birth weight < 2500 g). Synthesized evidence is lacking on magnitude of cognitive and motor deficits in low birth weight (LBW) children compared to those with normal birth weight (NBW) (i.e. birth weight ≥ 2500 g). The meta-analysis aimed to generate this essential evidence.
Literature search was performed using PubMed and Google Scholar. Original research articles from south Asia that compared cognitive and/or motor scores among LBW and NBW individuals were included. Weighted mean differences (WMD) and pooled relative risks (RR) were calculated. All analyses were done using STATA 14 software.
Nineteen articles (n = 5999) were included in the analysis. Children < 10 years of age born LBW had lower cognitive (WMD -4.56; 95% CI: -6.38, - 2.74) and motor scores (WMD -4.16; 95% CI: -5.42, - 2.89) compared to children with NBW. Within LBW children, those with birth weight < 2000 g had much lower cognitive (WMD -7.23, 95% CI; - 9.20, - 5.26) and motor scores (WMD -6.45, 95% CI; - 9.64, - 3.27).
In south Asia, children born LBW, especially with < 2000 g birth weight, have substantial cognitive and motor impairment compared to children with NBW. Early child development interventions should lay emphasis to children born LBW.

OBJECTIVE: To determine whether high intake of intralipid (IL) in extremely low birthweight (ELBW) neonates is associated with higher rates of neuroimpairment and Bayley III scores at two years of corrected age.
METHODS: Quartiles of IL received by 389 ELBW infants were linked to neurodevelopmental outcomes. Logistic regression analyses, adjusted for confounders, were performed to determine the association between IL dose and neuroimpairment. Linear regression analyses were performed to predict Bayley III scores.
RESULTS: No association was found between IL dose and neuroimpairment A significant association was found between higher IL intake and lower Bayley Cognitive, motor and language scores. Adding breast milk intake to the linear regression eliminated the associations.
CONCLUSIONS: Higher IL intake was associated with lower cognitive, motor and language scores. Breast milk intake eliminated the latter associations, which underscores the important role of breast milk in developmental outcome.

The assessment of spatial cognitive learning in rodents is a central approach in neuroscience, as it enables one to assess and quantify the effects of treatments and genetic manipulations from a broad perspective. Although the Morris water maze (MWM) is a well-validated paradigm for testing spatial learning abilities, manual categorization of performance in the MWM into behavioral strategies is subject to individual interpretation, and thus to biases. Here we offer a support vector machine (SVM) - based, automated, MWM unbiased strategy classification (MUST-C) algorithm, as well as a cognitive score scale. This model was examined and validated by analyzing data obtained from five MWM experiments with changing platform sizes, revealing a limitation in the spatial capacity of the hippocampus. We have further employed this algorithm to extract novel mechanistic insights on the impact of members of the Toll-like receptor pathway on cognitive spatial learning and memory. The MUST-C algorithm can greatly benefit MWM users as it provides a standardized method of strategy classification as well as a cognitive scoring scale, which cannot be derived from typical analysis of MWM data.

Alzheimer\'s Disease (AD) is the most common neurodegenerative disorder associated with aging. Understanding how the disease progresses and identifying related pathological biomarkers for the progression is of primary importance in the clinical diagnosis and prognosis of Alzheimer\'s disease. In this paper, we develop novel multi-task learning techniques to predict the disease progression measured by cognitive scores and select biomarkers predictive of the progression. In multi-task learning, the prediction of cognitive scores at each time point is considered as a task, and multiple prediction tasks at different time points are performed simultaneously to capture the temporal smoothness of the prediction models across different time points. Specifically, we propose a novel convex fused sparse group Lasso (cFSGL) formulation that allows the simultaneous selection of a common set of biomarkers for multiple time points and specific sets of biomarkers for different time points using the sparse group Lasso penalty and in the meantime incorporates the temporal smoothness using the fused Lasso penalty. The proposed formulation is challenging to solve due to the use of several non-smooth penalties. One of the main technical contributions of this paper is to show that the proximal operator associated with the proposed formulation exhibits a certain decomposition property and can be computed efficiently; thus cFSGL can be solved efficiently using the accelerated gradient method. To further improve the model, we propose two non-convex formulations to reduce the shrinkage bias inherent in the convex formulation. We employ the difference of convex (DC) programming technique to solve the non-convex formulations. We have performed extensive experiments using data from the Alzheimer\'s Disease Neuroimaging Initiative (ADNI). Results demonstrate the effectiveness of the proposed progression models in comparison with existing methods for disease progression. We also perform longitudinal stability selection to identify and analyze the temporal patterns of biomarkers in disease progression.