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.