Journal: Journal of Alzheimer's Disease

Article Title: Shortening of Saccades as a Possible Easy-to-Use Biomarker to Detect Risk of Alzheimer’s Disease

doi: 10.3233/JAD-215551

Figure Lengend Snippet: Division of participants into analysis groups. A) Non-demented participants underwent testing and were divided into control and mild cognitive impairment (MCI) groups. The Alzheimer’s disease (AD) group was confirmed similarly. B) Quality control of analysis groups. All eye-tracking (ET) data went through quality control and recordings with < 60% success rate were disqualified. All audio recordings went through quality control, with garbled or silent recordings being disqualified before the automated King-Devick (KD) results analysis. CERAD-NB, Consortium to Establish a Registry for Alzheimer’s Disease –neuropsychological test battery; CDR, clinical dementia rating; QC, quality control. * One participant from the KD MCI group was removed from the KD analysis due to an excessive number of errors (> 30) made in the last KD card, as they read the test in vertical columns instead of horizontal rows.

Article Snippet: To examine whether computer-based eye-tracking (ET) analysis of King-Devick (KD) test results differentiates cognitively healthy persons from persons with minor problems in cognitive testing or diagnosed mild AD.

Techniques: Control, Battery

Journal: Journal of Alzheimer's Disease

Article Title: Shortening of Saccades as a Possible Easy-to-Use Biomarker to Detect Risk of Alzheimer’s Disease

doi: 10.3233/JAD-215551

Figure Lengend Snippet: King-Devick (KD) reading test scores and related eye-tracking (ET) recordings

Article Snippet: To examine whether computer-based eye-tracking (ET) analysis of King-Devick (KD) test results differentiates cognitively healthy persons from persons with minor problems in cognitive testing or diagnosed mild AD.

Techniques:

Journal: Molecular Psychiatry

Article Title: A systematic review exploring the association between the human gut microbiota and brain connectivity in health and disease

doi: 10.1038/s41380-023-02146-4

Figure Lengend Snippet: Overview of methodological characteristics.

Article Snippet: , [ – ] , Cai (2021)(a)/Zhu (2022)(b)/Zheng (2022) (c) (three publications based on the same study) , Collection: fresh Sequencing type: 16s rRNA sequencing Sequencing technology: Illumina Hiseq Region: V4 Pipeline: UPARSE/USEARCH (v9.1.13) Database: RDP classifier (v2.2) with GreenGenes (v201305) as training database Clustering: OTU Rarified: no Filtering: no Transformation: NA , Richness (a,b): Sobs, Ace, Chao Diversity: Shannon, Simpson , , Bacteroides -, Prevotella - and Ruminococcus -high clusters, based on Jensen-Shannon distance and PAM , rs-fMRI Eyes closed DTI (b,c) , (a): Anterior/ posterior DMN, ECN, left/right FPN, SN, DAN, VAN, dorsal/ventral SMN, AN, lateral/posterior VN, based on ICA (b) Data-driven selection based on outcomes multi-set canonical association analysis plus joint ICA (c) No a priori ROI selection , (a) Internetwork: network-to-network Fisher’s z-transformed correlations Intranetwork: contribution of the time course to each voxel comprising an independent component (b) Mixing coefficients from multiset canonical association analysis plus joint ICA, with as input: > FC: FCD computed as number of voxel-to-voxel correlations of a particular voxel to all other voxels with r > 0.25, normalized by the global mean FCD > SC: voxelwise FA > Other: Regional homogeneity, cerebral blood flow, gray matter volume (c): SC computed as the number of fibers between each set of ROIs based on fiber tracking algorithm. FC computed as Fisher z-transformed correction between all ROIs, resulting in two 90×90 matrices > For SC and FC: computation of network topological metrics from ROI matrices as measure of connectivity ((normalized) clustering coefficient, (normalized) characteristic path length, small-worldness property, nodal degree centrality, nodal efficiency, nodal betweenness > SC-FC coupling: Pearson correlation between SC and FC ROI matrices , > Partial correlations (alpha diversity) and ANOVA (clusters) to test the association with network connectivity/FCD/FA > For significant associations: mediation analysis between alpha diversity/clusters and cognition, with connectivity as mediator Covariates: frame-wise displacement (a,c), age and sex Multiple testing correction: FDR, cluster-level FWE (b,c) or Bonferroni (c) , > Letter 3-back task to assess working memory > Digit span task to assess attention > Go/No-Go task to assess response inhibition.

Techniques: Selection, Sequencing, Transformation Assay, Inhibition, Preserving, Functional Assay, Control, Activity Assay, Diffusion-based Assay