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Fulltext Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review

Ibrahim B, Suppiah S, Ibrahim N, Mohamad M, Hassan HA, Nasser NS, et al.

Hum Brain Mapp, 2021 06 15;42(9):2941-2968.
PMID: 33942449 DOI: 10.1002/hbm.25369

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.
A survey on smartphone dependence and psychological effects among undergraduate students in a Malaysian University

Nasser NS, Loh JL, Rashid AA, Sharifat H, Ahmad U, Ibrahim B, et al.

Med J Malaysia, 2020 07;75(4):356-362.
PMID: 32723994

OBJECTIVE: Problematic smartphone use (PSU) is the development of pathological dependence at the expense of performing activities of daily living, thus having negative health and psychological impact on the users. Previous PSU studies focused on medical students and little is known regarding its effect on students undergoing other courses. The objective of this study is to identify the pattern of smartphone usage and determine the psychological factors affecting PSU among undergraduate students in Malaysia and compare the pattern among students from different fields of study.
METHOD: A prospective cross-sectional study was conducted using the validated Smartphone Addiction Scale-Malay version (SAS-M) questionnaire. One-way ANOVA was used to determine the correlation between the PSU among the students categorised by their ethnicity, hand dominance and by their field of study. MLR analysis was applied to predict PSU based on socio-demographic data, usage patterns, psychological factors and fields of study.
RESULTS: A total of 1060 students completed the questionnaire. Most students had some degree of problematic usage of the smartphone. Students used smartphones predominantly to access SNAs, namely Instagram. Longer duration on the smartphone per day, younger age at first using a smartphone and underlying depression carried higher risk of developing PSU, whereas the field of study (science vs. humanities based) did not contribute to an increased risk of developing PSU.
CONCLUSION: Findings from this study can help better inform university administrators about at- risk groups of undergraduate students who may benefit from targeted intervention designed to reduce their addictive behaviour patterns.
Fulltext Cue-Reactivity Among Young Adults With Problematic Instagram Use in Response to Instagram-Themed Risky Behavior Cues: A Pilot fMRI Study

Nasser NS, Sharifat H, Rashid AA, Hamid SA, Rahim EA, Loh JL, et al.

Front Psychol, 2020;11:556060.
PMID: 33224051 DOI: 10.3389/fpsyg.2020.556060

Background: Problematic Instagram use (PIGU), a specific type of internet addiction, is prevalent among adolescents and young adults. In certain instances, Instagram acts as a platform for exhibiting photos of risk-taking behavior that the subjects with PIGU upload to gain likes as a surrogate for gaining peer acceptance and popularity.
Aims: The primary objective was to evaluate whether addiction-specific cues compared with neutral cues, i.e., negative emotional valence cues vs. positive emotional valence cues, would elicit activation of the dopaminergic reward network (i.e., precuneus, nucleus accumbens, and amygdala) and consecutive deactivation of the executive control network [i.e., medial prefrontal cortex (mPFC) and dorsolateral prefrontal cortex (dlPFC)], in the PIGU subjects.
Method: An fMRI cue-induced reactivity study was performed using negative emotional valence, positive emotional valence, and truly neutral cues, using Instagram themes. Thirty subjects were divided into PIGU and healthy control (HC) groups, based on a set of diagnostic criteria using behavioral tests, including the Modified Instagram Addiction Test (IGAT), to assess the severity of PIGU. In-scanner recordings of the subjects' responses to the images and regional activity of the neural addiction pathways were recorded.
Results: Negative emotional valence > positive emotional valence cues elicited increased activations in the precuneus in the PIGU group. A negative and moderate correlation was observed between PSC at the right mPFC with the IGAT scores of the PIGU subjects when corrected for multiple comparisons [r = -0.777, (p < 0.004, two-tailed)].
Conclusion: Addiction-specific Instagram-themed cues identify the neurobiological underpinnings of Instagram addiction. Activations of the dopaminergic reward system and deactivation of the executive control network indicate converging neuropathological pathways between Instagram addiction and other types of addictions.

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