Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy-to-use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity. and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (>=85% for sensitivity and 100% for specificity).

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Plasma biomarkers have shown promising performance in research cohorts in discriminating between different stages of Alzheimer’s disease (AD). Studies in clinical populations are necessary to provide insights on the clinical utility of plasma biomarkers before their implementation in real-world settings. Here we investigated plasma biomarkers (glial fibrillary acidic protein (GFAP), tau phosphorylated at 181 and 231 (pTau181, pTau231), amyloid β (Aβ) 42/40 ratio, neurofilament light) in 126 patients (age = 65 ± 8) who were admitted to the Clinic for Cognitive Disorders, at Karolinska University Hospital. After extensive clinical assessment (including CSF analysis), patients were classified as: mild cognitive impairment (MCI) (n = 75), AD (n = 25), non-AD dementia (n = 16), no dementia (n = 9). To refine the diagnosis, patients were examined with [18F]flutemetamol PET (Aβ-PET). Aβ-PET images were visually rated for positivity/negativity and quantified in Centiloid. Accordingly, 68 Aβ+ and 54 Aβ– patients were identified. Plasma biomarkers were measured using single molecule arrays (SIMOA). Receiver-operated curve (ROC) analyses were performed to detect Aβ-PET+ using the different biomarkers. In the whole cohort, the Aβ-PET centiloid values correlated positively with plasma GFAP, pTau231, pTau181, and negatively with Aβ42/40 ratio. While in the whole MCI group, only GFAP was associated with Aβ PET centiloid. In ROC analyses, among the standalone biomarkers, GFAP showed the highest area under the curve discriminating Aβ+ and Aβ– compared to other plasma biomarkers. The combination of plasma biomarkers via regression was the most predictive of Aβ-PET, especially in the MCI group (prior to PET, n = 75) (sensitivity = 100%, specificity = 82%, negative predictive value = 100%). In our cohort of memory clinic patients (mainly MCI), the combination of plasma biomarkers was sensitive in ruling out Aβ-PET negative individuals, thus suggesting a potential role as rule-out tool in clinical practice.

Reactive astrogliosis is an early event in the continuum of Alzheimer’s disease (AD). Current advances in positron emission tomography (PET) imaging provide ways of assessing reactive astrogliosis in the living brain. In this review, we revisit clinical PET imaging and in vitro findings using the multi-tracer approach, and point out that reactive astrogliosis precedes the deposition of Aβ plaques, tau pathology, and neurodegeneration in AD. Furthermore, considering the current view of reactive astrogliosis heterogeneity—more than one subtype of astrocyte involved—in AD, we discuss how astrocytic body fluid biomarkers might fit into trajectories different from that of astrocytic PET imaging. Future research focusing on the development of innovative astrocytic PET radiotracers and fluid biomarkers may provide further insights into the heterogeneity of reactive astrogliosis and improve the detection of AD in its early stages.

Background The TAUPS2APP Alzheimer’s disease (AD) mouse model expresses mutant APPSWE, PSEN2N141I and TAUP301L mutations and displays age‐dependent accumulation of Aβ plaques and neurofibrillary tangles. In humans, abnormal protein accumulation and spreading are related to neurodegenerative processes, most stereotypically in the hippocampus and medial temporal lobe. Prior reports in different AD models have demonstrated various brain structural phenotypes (Grandjean et al.,2016). Here we assess neuroanatomical trajectories in the TAUPS2APP model of AD using longitudinal MRIs. Method Thirteen C67BL/6 (11f/2m) and seventeen TAUPS2APP (10f/7m) mice underwent T2‐weighted MRI (voxel size 117x147x147 μm, matrix 256x102x120, FoV 3x1.5x1.5 cm, TR=2500 ms TE=50 ms) at 4,9,13,18, and 24 months of age. Data were analyzed following two‐level deformation‐based morphometry pipeline that uses ANTs SyN nonlinear registration. At the first level within subjects’ minimum deformation averages are created and estimates of volume change are computed (log Jacobian determinants). Next, a second level average is created out of unbiased within‐subject averages and the Jacobians are resampled into the common space Result A linear mixed‐effect model with subject as random effect (second order spline fit, sex as a covariate) revealed significant differences in interaction between age and genotype in neuroanatomical trajectory (absolute Jacobian) of bilateral hippocampus, lateral septal area (LSA) and isocortical areas (at False Discovery Rate of 5%, fig.1). TAUPS2APP showed an accelerated overgrowth of hippocampus at young age but larger volumetric decline with age in comparison to non‐TG animals (fig.2a). A similar pattern was observed in LSA (fig.2b). On the contrary in the isocortex, where non‐TG declined with age, TAUPS2APP mice showed volume enlargement (fig. 3c). Conclusion These data suggest that TAUPS2APP model is characterized by abnormal neuroanatomical trajectory throughout its lifespan relative to controls. While hippocampal atrophy recapitulates clinical markers of late‐onset AD, enlarged cortex that had been reported earlier in some AD mouse models (TgCRND8, APP/J20 and PSAPP) does not. It was suggested that elevated APP levels may affect the rate of local region growth in adult mice ‐ due to space occupancy by amyloidosis and astrogliosis. In line with this Maheswaran et al. had reported a link between transgene expression, age, and volume increase over time (Maheswaran et al., 2009).