Resting‐state fMRI (rs‐fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). FC of the default mode network (DMN) is commonly impaired in AD and MCI. We conducted a systematic review aimed at determining the diagnostic power of rs‐fMRI to identify FC abnormalities in the DMN of patients with AD or MCI compared with healthy controls (HCs) using machine learning (ML) methods. Multimodal support vector machine (SVM) algorithm was the commonest form of ML method utilized. Multiple kernel approach can be utilized to aid in the classification by incorporating various discriminating features, such as FC graphs based on “nodes” and “edges” together with structural MRI‐based regional cortical thickness and gray matter volume. Other multimodal features include neuropsychiatric testing scores, DTI features, and regional cerebral blood flow. Among AD patients, the posterior cingulate cortex (PCC)/Precuneus was noted to be a highly affected hub of the DMN that demonstrated overall reduced FC. Whereas reduced DMN FC between the PCC and anterior cingulate cortex (ACC) was observed in MCI patients. Evidence indicates that the nodes of the DMN can offer moderate to high diagnostic power to distinguish AD and MCI patients. Nevertheless, various concerns over the homogeneity of data based on patient selection, scanner effects, and the variable usage of classifiers and algorithms pose a challenge for ML‐based image interpretation of rs‐fMRI datasets to become a mainstream option for diagnosing AD and predicting the conversion of HC/MCI to AD.

Objective: Problematic smartphone use (PSU) is the development of pathological dependence at the expense of performing activities of daily living, thus having a negative health and psychosocial impact on the users. Previous PSU studies focused on medical students and little is known regarding its effect on students undergoing other fields of study. The objective of this study is to identify the pattern of smartphone usage and determine the psychosocial factors affecting PSU among undergraduate students in Malaysia and compare the pattern among different fields of study. Method: A prospective cross-sectional study was conducted using validated Smartphone Addiction Scale −Malay version (SAS −M) questionnaire. One−way ANOVA was used to determine the correlation between the patterns of smartphone usage among the students categorised by their ethnic groups, hand dominance and by their field of study. MLR analysis was applied to predict PSU based on socio−demographic data, smartphone usage patterns, psychosocial factors and field of study. Results: A total of 1060 students completed the questionnaire. The majority of students had PSU (60.7%). Students used smartphones predominantly to access SNAs, namely Instagram. Longer duration on the smartphone per day (>9 hours), age at first using a smartphone and depression carried higher risk of developing PSU, whereas the field of study (science vs. arts based) did not contribute to an increased risk of developing PSU.Conclusion: Findings from this study can help better inform university administrators about atrisk groups of undergraduate students who may benefit from targeted intervention designed to re−duce their addictive behavior patterns. Keywords: Smartphone Addiction Scale, education, social networking, Malaysia

Resting state fMRI (rs-fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI). FC of the default mode network (DMN), which is involved in memory consolidation, is commonly impaired in AD and MCI. We aimed to determine the diagnostic power of rs-fMRI to identify FC abnormalities in the DMN, which help to distinguish patients with AD or MCI from healthy controls (HCs). We searched articles in PubMed and Scopus databases using the search terms such as AD, MCI, resting-state fMRI, sensitivity and specificity through to 27th March 2020 and removed duplicate papers. We screened 390 published articles, and shortlisted 12 articles for the final analysis. The range of sensitivity of DMN FC at the posterior cingulate cortex (PCC) for diagnosing AD was between 65.7% - 100% and specificity ranged from 66 - 95%. Reduced DMN FC between the PCC and anterior cingulate cortex (ACC) in the frontal lobes was observed in MCI patients. AD patients had impaired FC in most regions of the DMN; particularly the PCC in early AD. This indicates that DMN's rs-fMRI FC can offer moderate to high diagnostic power to distinguish AD and MCI patients. fMRI detected abnormal DMN FC, particularly in the PCC that helps to differentiate AD and MCI patients from healthy controls (HCs). Combining multivariate method of analysis with other MRI parameters such as structural changes improve the diagnostic power of rs-fMRI in distinguishing patients with AD or MCI from HCs.

Background Alzheimer’s disease (AD) is a major neurocognitive disorder identified by memory loss and a significant cognitive decline based on previous level of performance in one or more cognitive domains that interferes in the independence of everyday activities. The accuracy of imaging helps to identify the neuropathological features that differentiate AD from its common precursor, mild cognitive impairment (MCI). Identification of early signs will aid in risk stratification of disease and ensures proper management is instituted to reduce the morbidity and mortality associated with AD. Magnetic resonance imaging (MRI) using structural MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (1H-MRS) performed alone is inadequate. Thus, the combination of multiparametric MRI is proposed to increase the accuracy of diagnosing MCI and AD when compared to elderly healthy controls. Methods This protocol describes a non-interventional case control study. The AD and MCI patients and the healthy elderly controls will undergo multi-parametric MRI. The protocol consists of sMRI, fMRI, DTI, and single-voxel proton MRS sequences. An eco-planar imaging (EPI) will be used to perform resting-state fMRI sequence. The structural images will be analysed using Computational Anatomy Toolbox-12, functional images will be analysed using Statistical Parametric Mapping-12, DPABI (Data Processing & Analysis for Brain Imaging), and Conn software, while DTI and 1H-MRS will be analysed using the FSL (FMRIB’s Software Library) and Tarquin respectively. Correlation of the MRI results and the data acquired from the APOE genotyping, neuropsychological evaluations (i.e. Montreal Cognitive Assessment [MoCA], and Mini–Mental State Examination [MMSE] scores) will be performed. The imaging results will also be correlated with the sociodemographic factors. The diagnosis of AD and MCI will be standardized and based on the DSM-5 criteria and the neuropsychological scores. Discussion The combination of sMRI, fMRI, DTI, and MRS sequences can provide information on the anatomical and functional changes in the brain such as regional grey matter volume atrophy, impaired functional connectivity among brain regions, and decreased metabolite levels specifically at the posterior cingulate cortex/precuneus. The combination of multiparametric MRI sequences can be used to stratify the management of MCI and AD patients. Accurate imaging can decide on the frequency of follow-up at memory clinics and select classifiers for machine learning that may aid in the disease identification and prognostication. Reliable and consistent quantification, using standardised protocols, are crucial to establish an optimal diagnostic capability in the early detection of Alzheimer’s disease.

No abstract

The characteristics of smartphone addiction (SPA) can be evaluated by neuroimaging studies. Information on the brain structural alterations, and effects on psychosocial wellbeing, however, have not been concurrently evaluated. The aim of this study was to identify abnormalities in gray matter volume using voxel-based morphometry (VBM) and neuronal functional alterations using resting-state functional MRI (rs-fMRI) in emerging adults with SPA.  We correlated the neuroimaging parameters with indices for psychosocial wellbeing such as depression, anxiety, stress, and impulsivity. Forty participants (20 SPA and 20 age-matched healthy controls) were assessed using VBM and rs-fMRI. The smartphone addiction scale – Malay version (SAS-M) questionnaire scores were used to categorize the SPA and healthy control groups. DASS-21 and BIS-11 questionnaires were used to assess for psychosocial wellbeing and impulsivity, respectively.  VBM identified the SPA group to have reduced gray matter volume in the insula and precentral gyrus; and increased grey matter volume in the precuneus relative to controls. Moderate correlation was observed between the precuneus volume and the SAS-M scores. Individuals with SPA showed significant rs-fMRI activations in the precuneus, and posterior cingulate cortex (FWE uncorrected, p<0.001). The severity of SPA was correlated with depression. Anxiety score was moderately correlated with reduced GMV at the precentral gyrus. Collectively, these results can be used to postulate that the structural and neuronal functional changes in the insula are linked to the neurobiology of SPA that shares similarities with other behavioural addictions.

Four hundred and forty-one school children studying in local primary schools were referred during 1980-1981 to the Psychological Assessment Clinic in Nairobi (Kenya) for poor academic performance. The most frequent cause for the learning difficulties of these children was mental retardation. In one third, emotional disorders were responsible for their learning difficulties. Currently, even mentally retarded children are admitted into the existing educational system and are unable to cope. Recommendations for the future are made.

Urothelial cell carcinoma (UCC) is the ninth most common cancer that accounts for 4.7% of all the new cancer cases globally. UCC development and progression are due to complex and stochastic genetic programmes. To study the cascades molecular events underlying the poor prognosis that are due to limited treatment options for advance disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using the modern high-throughput sequencing platforms has been employed to address these limitations. Here we review the principles of RNA-Seq technology and summarize the recent studies on human bladder cancer that employed this technique to unravel the pathogenesis of the disease, identify biomarkers, discover pathways and classify the disease state. We list the commonly used computational platforms and software that are publicly available for RNA-Seq analysis. Moreover, we discussed the future perspective for RNA-Seq studies on bladder cancer and recommend the application of new technology called single cell sequencing (scRNA-Seq) to further understand bladder cancer.