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Language dysfunction is common in Alzheimer’s disease. There is increasing interest in the preclinical or asymptomatic phase of Alzheimer’s disease. Here we examined in 35 cognitively intact older adults (age range 52–78 years at baseline, 17 male) in a longitudinal study design the association between accumulation of amyloid over a 5–6-year period, measured using PET, and functional changes in the language network measured over the same time period using task-related functional MRI. In the same participants, we also determined the association between the longitudinal functional MRI changes and a cross-sectional measure of tau load as measured with 18F-AV1451 PET. As predicted, the principal change occurred in posterior temporal cortex. In the cortex surrounding the right superior temporal sulcus, the response amplitude during the associative-semantic versus visuo-perceptual task increased over time as amyloid load accumulated (Pcorrected = 0.008). In a whole-brain voxel-wise analysis, amyloid accumulation was also associated with a decrease in response amplitude in the left inferior frontal sulcus (Pcorrected = 0.009) and the right dorsomedial prefrontal cortex (Pcorrected = 0.005). In cognitively intact older adults, cross-sectional tau load was not associated with longitudinal changes in functional MRI response amplitude. Our findings confirm the central role of the neocortex surrounding the posterior superior temporal sulcus as the area of predilection within the language network in the earliest stages of Alzheimer’s disease. Amyloid accumulation has an impact on cognitive brain circuitry in the asymptomatic phase of Alzheimer’s disease., Using fMRI and amyloid PET in a longitudinal multimodal study, Reinartz et al. reveal functional changes in the language network occurring with increasing amyloid accumulation in an early asymptomatic phase of Alzheimer's disease.

Purpose End-of-life studies have validated the binary visual reads of 18F-labeled amyloid PET tracers as an accurate tool for the presence or absence of increased neuritic amyloid plaque density. In this study, the performance of a support vector machine (SVM)-based classifier will be tested against pathological ground truths and its performance determined in cognitively healthy older adults. Methods We applied SVM with a linear kernel to an 18F-Flutemetamol end-of-life dataset to determine the regions with the highest feature weights in a data-driven manner and to compare between two different pathological ground truths: based on neuritic amyloid plaque density or on amyloid phases, respectively. We also trained and tested classifiers based on the 10% voxels with the highest amplitudes of feature weights for each of the two neuropathological ground truths. Next, we tested the classifiers’ diagnostic performance in the asymptomatic Alzheimer’s disease (AD) phase, a phase of interest for future drug development, in an independent dataset of cognitively intact older adults, the Flemish Prevent AD Cohort-KU Leuven (F-PACK). A regression analysis was conducted between the Centiloid (CL) value in a composite volume of interest (VOI), as index for amyloid load, and the distance to the hyperplane for each of the two classifiers, based on the two pathological ground truths. A receiver operating characteristic analysis was also performed to determine the CL threshold that optimally discriminates between neuritic amyloid plaque positivity versus negativity, or amyloid phase positivity versus negativity, within F-PACK. Results The classifiers yielded adequate specificity and sensitivity within the end-of-life dataset (neuritic amyloid plaque density classifier: specificity of 90.2% and sensitivity of 83.7%; amyloid phase classifier: specificity of 98.4% and sensitivity of 84.0%). The regions with the highest feature weights corresponded to precuneus, caudate, anteromedial prefrontal, and also posterior inferior temporal and inferior parietal cortex. In the cognitively normal cohort, the correlation coefficient between CL and distance to the hyperplane was −0.66 for the classifier trained with neuritic amyloid plaque density, and −0.88 for the classifier trained with amyloid phases. This difference was significant. The optimal CL cut-off for discriminating positive versus negative scans was CL = 48–51 for the different classifiers (area under the curve (AUC) = 99.9%), except for the classifier trained with amyloid phases and based on the 10% voxels with highest feature weights. There the cut-off was CL = 26 (AUC = 99.5%), which closely matched the CL threshold for discriminating phases 0–2 from 3–5 based on the end-of-life dataset and the neuropathological ground truth. Discussion Among a set of neuropathologically validated classifiers trained with end-of-life cases, transfer to a cognitively normal population works best for a classifier trained with amyloid phases and using only voxels with the highest amplitudes of feature weights.

PURPOSE: End-of-life studies have validated the binary visual reads of 18F-labeled amyloid PET tracers as an accurate tool for the presence or absence of increased neuritic amyloid plaque density. In this study, the performance of a support vector machine (SVM) based classifier will be tested against pathological groundtruths and its performance determined in cognitively healthy older adults.METHODS: We applied SVM with a linear kernel to an 18F-Flutemetamol end-of-life dataset to determine the regions with the highest feature weights in a data-driven manner and to compare between two different pathological groundtruths: based on neuritic amyloid plaque density or on amyloid phases, respectively. We also trained and tested classifiers based on the 10% voxels with the highest feature weights for each of the two neuropathological groundtruths. Next, we tested the classifiers’ diagnostic performance in the asymptomatic Alzheimer’s disease (AD) phase, a phase of interest for future drug development, in an independent dataset of cognitively intact older adults, the Flemish Prevent AD Cohort-KU Leuven (F-PACK). A regression analysis was conducted between the Centiloid (CL) value in a composite volume of interest (VOI), as index for amyloid load, and the distance to the hyperplane for each of the two classifiers, based on the two pathological groundtruths. A Receiver-Operating-Characteristic analysis was also performed to determine the CL threshold that optimally discriminates between neuritic amyloid plaque positivity versus negativity, or amyloid phase positivity versus negativity, within F-PACK.RESULTS: The classifiers yielded adequate sensitivity and specificity within the end-of-life dataset (neuritic amyloid plaque density classifier: specificity of 90.2% and sensitivity of 83.7%; amyloid phase classifier: specificity of 98.4% and sensitivity of 84.0%). The regions with the highest feature weights corresponded to precuneus, caudate, anteromedial prefrontal, and also posterior inferior temporal and inferior parietal cortex. In the cognitively normal cohort, the correlation coefficient between CL and distance to the hyperplane was -0.66 for the classifier trained with neuritic amyloid plaque density, and -0.88 for the classifier trained with amyloid phases. This difference was significant. The optimal CL cut-off for discriminating positive versus negative scans was CL = 48-51 for the different classifiers (area under the curve (AUC) = 99.9%), except for the classifier trained with amyloid phases and based on the 10% voxels with highest feature weights. There the cut-off was CL = 26 (AUC = 99.5%).DISCUSSION: A neuropathologically validated classifier applied in cognitively normal older adults reveals that amyloid PET values (Centiloids) correlate best with amyloid phases. A CL cut-off of 26 reliably discriminated between amyloid phase 0-2 and 3-5 while only a CL around 50 discriminated between no or sparse and moderate to severe neuritic amyloid plaque density.

End-of-life studies have validated the binary visual reads ofWe applied SVM with a linear kernel to anThe classifiers yielded adequate specificity and sensitivity within the end-of-life dataset (neuritic amyloid plaque density classifier: specificity of 90.2% and sensitivity of 83.7%; amyloid phase classifier: specificity of 98.4% and sensitivity of 84.0%). The regions with the highest feature weights corresponded to precuneus, caudate, anteromedial prefrontal, and also posterior inferior temporal and inferior parietal cortex. In the cognitively normal cohort, the correlation coefficient between CL and distance to the hyperplane was -0.66 for the classifier trained with neuritic amyloid plaque density, and -0.88 for the classifier trained with amyloid phases. This difference was significant. The optimal CL cut-off for discriminating positive versus negative scans was CL = 48-51 for the different classifiers (area under the curve (AUC) = 99.9%), except for the classifier trained with amyloid phases and based on the 10% voxels with highest feature weights. There the cut-off was CL = 26 (AUC = 99.5%), which closely matched the CL threshold for discriminating phases 0-2 from 3-5 based on the end-of-life dataset and the neuropathological ground truth.Among a set of neuropathologically validated classifiers trained with end-of-life cases, transfer to a cognitively normal population works best for a classifier trained with amyloid phases and using only voxels with the highest amplitudes of feature weights.

The bridging integrator 1(TheForty-four percent of F-PACK participants wereWe could not confirm the association between

Increasing evidence suggests that apoE influences the innate immune response in neurodegeneration. Here, Ulrich et al. report that apoE influences amyloid plaque morphology and the microglial response to amyloid plaques, along with plaque-associated neuronal toxicity., One of the hallmarks of Alzheimer’s disease is the presence of extracellular diffuse and fibrillar plaques predominantly consisting of the amyloid-β (Aβ) peptide. Apolipoprotein E (ApoE) influences the deposition of amyloid pathology through affecting the clearance and aggregation of monomeric Aβ in the brain. In addition to influencing Aβ metabolism, increasing evidence suggests that apoE influences microglial function in neurodegenerative diseases. Here, we characterize the impact that apoE has on amyloid pathology and the innate immune response in APPPS1ΔE9 and APPPS1-21 transgenic mice. We report that Apoe deficiency reduced fibrillar plaque deposition, consistent with previous studies. However, fibrillar plaques in Apoe-deficient mice exhibited a striking reduction in plaque compaction. Hyperspectral fluorescent imaging using luminescent conjugated oligothiophenes identified distinct Aβ morphotypes in Apoe-deficient mice. We also observed a significant reduction in fibrillar plaque–associated microgliosis and activated microglial gene expression in Apoe-deficient mice, along with significant increases in dystrophic neurites around fibrillar plaques. Our results suggest that apoE is critical in stimulating the innate immune response to amyloid pathology.