Abnormal characterization of dynamic functional connectivity in Alzheimer’s disease

Numerous studies have shown abnormal brain functional connectivity in individuals with Alzheimer’s disease(AD)or amnestic mild cognitive impairment(aMCI).However,most studies examined traditional resting state functional connections,ignoring the instantaneous connection mode of the whole brain.In this case-control study,we used a new method called dynamic functional connectivity(DFC)to look for abnormalities in patients with AD and aMCI.We calculated dynamic functional connectivity strength from functional magnetic resonance imaging data for each participant,and then used a support vector machine to classify AD patients and normal controls.Finally,we highlighted brain regions and brain networks that made the largest contributions to the classification.We found differences in dynamic function connectivity strength in the left precuneus,default mode network,and dorsal attention network among normal controls,aMCI patients,and AD patients.These abnormalities are potential imaging markers for the early diagnosis of AD.

Personality–brain connection:Based on resting-state functional magnetic resonance imaging data-driven exploration

Background:The personality-brain association mechanism has been a topic of interest in the field of neuroscience.Usually,the previous research strategy was to first group the population based on different personality traits,and then explore the brain mechanisms corresponding to different personality groups.At present,a“brain-first”research strategy,which uses data-driven approaches instead of personality traits to first group the population,has been adopted to further enhance study objectivity.Methods:Here,we used a data-driven approach following the“brain-first”research strategy to deeply mine the resting-state brain functional magnetic resonance imaging data of 119 healthy participants,classified subjects into different groups based on brain image characteristics,and used the Sixteen Personality Factor Questionnaire to explain the variabilities of resting-state brain characteristics between different groups.Results:We have identified 3 personality–brain connections,including the privateness–left frontoparietal network,liveliness–sensory–motor network,and vigilance–sensory–motor network.Conclusion:We conclude that the above-mentioned three personality factors are based on brain neural activity,independent of the subjective experience of the personality scale creator,and have stronger explanatory power of brain imaging features.

Altered cerebral activities and functional connectivity in depression:a systematic review of fMRI studies

Background:As one of the leading causes of global disability,major depressive disorder(MDD)places a noticeable burden on individuals and society.Despite the great expectation on finding accurate biomarkers and effective treatment targets of MDD,studies in applying functional magnetic resonance imaging(fMRI)are still faced with challenges,including the representational ambiguity,small sample size,low statistical power,relatively high false positive rates,etc.Thus,reviewing studies with solid methodology may help achieve a consensus on the pathology of MDD.Methods:In this systematic review,we screened fMRI studies on MDD through strict criteria to focus on reliable studies with sufficient sample size,adequate control of head motion,and a proper multiple comparison control strategy.Results:We found consistent evidence regarding the dysfunction within and among the default mode network(DMN),the frontoparietal network(FPN),and other brain regions.However,controversy remains,probably due to the heterogeneity of participants and data processing strategies.Conclusion:Future studies are recommended to apply a comprehensive set of neuro-behavioral measurements,consider the heterogeneity of MDD patients and other potentially confounding factors,apply surface-based neuroscientific network fMRI approaches,and advance research transparency and open science by applying state-ofthe-art pipelines along with open data sharing.