Introduction Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a “Holy Grail” of AD research and intensively sought; however, there are no well-established plasma markers. Methods A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed. Results Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71). Discussion Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation.

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Alzheimer's disease (AD) brain magnetic resonance imaging (MRI) biomarkers based on larger-scale tissue neurodegeneration changes, such as atrophy, are currently widely used. Texture analysis evaluates the statistical properties of the tissue image quantitatively; therefore, it could detect smaller-scale changes of neurodegeneration. Entorhinal cortex is the first region affected, and no study has investigated texture analysis on this region before. This study aims to differentiate AD patients from Normal Control (NC) and Mild Cognitive Impairment (MCI) subjects using entorhinal cortex texture features. Furthermore, it was evaluated whether texture has association to MCI beyond that of volume, to evaluate if atrophy development may precede. Texture features were extracted from 194 NC, 200 MCI, 84 MCI who converted to AD (MCIc), and 130 AD subjects. Receiving operating characteristic curves determined the performance of the various features in discriminating the groups, and a predictive model was used to predict conversion of MCIc subjects to AD. An area under the curve (AUC) of 0.872, 0.710, 0.730, and 0.764 was seen between NC vs. AD, NC vs. MCI, MCI vs. MCIc, and MCI vs. AD subjects, respectively. Including entorhinal cortex volume improved the AUCs to 0.914, 0.740, 0.756, and 0.780, respectively. For the disease prediction, binary logistic regression was applied on five randomly selected test groups and achieved on average AUC's of 0.760 and 0.764 on the training and validation cohorts, respectively. Entorhinal cortex texture features were significantly different between the four groups and in many cases provided better results compared to other methods such as volumetry.

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Objective: There is increasing evidence for a subgroup of major depressive disorder (MDD) associated with heightened peripheral blood inflammatory markers. In this study, the authors sought to understand the mechanistic brain-immune axis in inflammation-linked depression by investigating associations between functional connectivity (FC) of brain networks and peripheral inflammation in depression. Methods: Resting-state functional magnetic resonance imaging (fMRI) and peripheral blood immune marker data (C-reactive protein; CRP, interleukin-6; IL-6 and immune cells) were collected on N=46 healthy controls (HC; CRP ≤ 3mg/L) and N=83 cases of MDD, stratified further into low CRP (loCRP MDD; ≤ 3 mg/L; N=50) and high CRP (hiCRP MDD; > 3 mg/L; N=33). In a two-part analysis, network-based statistics (NBS) was firstly performed to ascertain FC differences via HC vs hiCRP MDD comparison. Association between this network of interconnected brain regions and peripheral CRP (N=83), IL-6 (N=72), neutrophils and CD4+ T-cells (N=36) were then examined in MDD cases only. Results: Case-control NBS testing revealed a single network of abnormally attenuated FC in hiCRP MDD, chiefly comprising default mode network (DMN) and ventral attentional network (VA) coupled regions, anatomically connecting the insula/frontal-operculum and posterior cingulate cortex (PCC). Across all MDD cases, FC within the identified network scaled negatively with CRP, IL-6 and neutrophils. Conclusions: The findings suggest that inflammation is associated with attenuation of functional connectivity within a brain network deemed critical for interoceptive signalling, e.g. accurate communication of peripheral bodily signals such as immune states to the brain, with implications for the etiology of inflammation-linked depression. keywords: functional connectivity, network-based statistics, peripheral inflammation, immune cells, depression.

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