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In the brains of Alzheimer's disease patients, the entorhinal cortex is known to show signs of early pathology. In this study, Khan et al . performed cerebral blood volume imaging of patients with preclinical Alzheimer's disease and mouse models of disease. Their results pinpoint the subregion in the entorhinal cortex most sensitive to the disease, and show how amyloid and tau interact in driving dysfunction and how dysfunction spreads to distal cortical regions. The entorhinal cortex has been implicated in the early stages of Alzheimer's disease, which is characterized by changes in the tau protein and in the cleaved fragments of the amyloid precursor protein (APP). We used a high-resolution functional magnetic resonance imaging (fMRI) variant that can map metabolic defects in patients and mouse models to address basic questions about entorhinal cortex pathophysiology. The entorhinal cortex is divided into functionally distinct regions, the medial entorhinal cortex (MEC) and the lateral entorhinal cortex (LEC), and we exploited the high-resolution capabilities of the fMRI variant to ask whether either of them was affected in patients with preclinical Alzheimer's disease. Next, we imaged three mouse models of disease to clarify how tau and APP relate to entorhinal cortex dysfunction and to determine whether the entorhinal cortex can act as a source of dysfunction observed in other cortical areas. We found that the LEC was affected in preclinical disease, that LEC dysfunction could spread to the parietal cortex during preclinical disease and that APP expression potentiated tau toxicity in driving LEC dysfunction, thereby helping to explain regional vulnerability in the disease.

'Normal aging' in the brain refers to age-related changes that occur independent of disease, in particular Alzheimer's disease. A major barrier to mapping normal brain aging has been the difficulty in excluding the earliest preclinical stages of Alzheimer's disease. Here, before addressing this issue we first imaged a mouse model and learn that the best MRI measure of dendritic spine loss, a known pathophysiological driver of normal aging, is one that relies on the combined use of functional and structural MRI. In the primary study, we then deployed the combined functional-structural MRI measure to investigate over 100 cognitively-normal people from 20-72 years of age. Next, to cover the tail end of aging, in secondary analyses we investigated structural MRI acquired from cognitively-normal people, 60-84 years of age, who were Alzheimer's-free via biomarkers. Collectively, the results from the primary functional-structural study, and the secondary structural studies revealed that the dentate gyrus is a hippocampal region differentially affected by aging, and that the entorhinal cortex is a region most resistant to aging. Across the cortex, the primary functional-structural study revealed and that the inferior frontal gyrus is differentially affected by aging, however, the secondary structural studies implicated other frontal cortex regions. Together, the results clarify how normal aging may affect the brain and has possible mechanistic and therapeutic implications.

Background The three core pathologies of Alzheimer’s disease (AD) are amyloid pathology, tau pathology, and neurodegeneration. Biomarkers exist for each. Neurodegeneration is often detected by neuroimaging, and we hypothesized that a voxel-based deep learning approach using structural MRI might outperform other neuroimaging methods. Methods First, we implement an MRI-based deep learning model, trained with a data augmentation strategy, which classifies Alzheimer’s dementia and generates class activation maps. Next, we tested the model in prodromal AD and compared its performance to other biomarkers of amyloid pathology, tau pathology, and neuroimaging biomarkers of neurodegeneration. Results The model distinguished between controls and AD with high accuracy (AUROC = 0.973) with class activation maps that localized to the hippocampal formation. As hypothesized, the model also outperformed other neuroimaging biomarkers of neurodegeneration in prodromal AD (AUROC = 0.788) but also outperformed biomarkers of amyloid (CSF Aβ = 0.702) or tau pathology (CSF tau = 0.682), and the findings are interpreted in the context of AD’s known anatomical biology. Conclusions The advantages of using deep learning to extract biomarker information from conventional MRIs extend practically, potentially reducing patient burden, risk, and cost.

White matter lesions (WML) increase the risk of dementia. The relevance of WML location is less clear. We sought to determine whether a particular WML profile, based on the density and location of lesions, could be associated with an increased risk of mild cognitive impairment (MCI) or dementia over the following 7 years. In 426 healthy subjects from a cohort of community-dwelling people aged 65 years and over (ESPRIT Project), standardized cognitive and neurological evaluations were repeated after 2, 4 and 7 years. Patterns of WML were computed with a supervised data mining approach (decision trees) using the regional WML volumes (frontal, parietal, temporal, and occipital regions) and the total WML volume estimated at baseline. Cox proportional hazard models were then constructed to study the association between WML patterns and risk of MCI/dementia. Total WML volume and percentage of WML in the temporal region proved to be the best predictors of progression to MCI and dementia. Specifically, severe total WML load with a high proportion of lesions in the temporal region was significantly associated with the risk of developing MCI or dementia. Above a certain threshold of damage, a pattern of WML clustering in the temporal region identifies individuals at increased risk of MCI or dementia. As this WML pattern is observed before the onset of clinical symptoms, it may facilitate the detection of patients at risk of MCI/dementia.

Introduction Positron emission tomography (PET) imaging targeting neurofibrillary tau tangles is increasingly used in the study of Alzheimer's disease (AD), but its utility may be limited by conventional quantitative or qualitative evaluation techniques in earlier disease states. Convolutional neural networks (CNNs) are effective in learning spatial patterns for image classification. Methods 18F‐MK6240 (n = 320) and AV‐1451 (n = 446) PET images were pooled from multiple studies. We performed iterations with differing permutations of radioligands, heuristics, and architectures. Performance was compared to a standard region of interest (ROI)‐based approach on prediction of memory impairment. We visualized attention of the network to illustrate decision making. Results Overall, models had high accuracy (> 80%) with good average sensitivity and specificity (75% and 82%, respectively), and had comparable or higher accuracy to the ROI standard. Visualizations of model attention highlight known characteristics of tau radioligand binding. Discussion CNNs could improve tau PET's role in early disease and extend the utility of tau PET across generations of radioligands.

This study provides causal evidence demonstrating that consuming a high flavanol diet improves dentate gyrus function and dentate gyrus–dependent cognitive functions in aged humans. The dentate gyrus (DG) is a region in the hippocampal formation whose function declines in association with human aging and is therefore considered to be a possible source of age-related memory decline. Causal evidence is needed, however, to show that DG-associated memory decline in otherwise healthy elders can be improved by interventions that enhance DG function. We addressed this issue by first using a high-resolution variant of functional magnetic resonance imaging (fMRI) to map the precise site of age-related DG dysfunction and to develop a cognitive task whose function localized to this anatomical site. Then, in a controlled randomized trial, we applied these tools to study healthy 50–69-year-old subjects who consumed either a high or low cocoa flavanol–containing diet for 3 months. A high-flavanol intervention was found to enhance DG function, as measured by fMRI and by cognitive testing. Our findings establish that DG dysfunction is a driver of age-related cognitive decline and suggest non-pharmacological means for its amelioration.

Gait speed is associated with multiple adverse outcomes of aging. White matter hyperintensities (WMH) on magnetic resonance imaging (MRI) have been associated with gait speed, though few studies have examined changes in gait speed over time in population-based studies comprising participants from diverse cultural backgrounds. The purpose of this study was to examine the association between a decline in gait speed and total and regional WMH volumes in a community-based study of aging. Participants ( n  = 701) underwent gait-speed measurement via a 4-m walk test at the time of initial enrollment and MRI at a second time interval (mean 4.7 [SD = 0.5] years apart). Logistic regression was used to examine the association between large WMH volume and regional WMH volume with gait speed <0.5 m/s (abnormal speed), and a transition to abnormal gait speed. Analyses were adjusted for demographic and clinical factors. Large WMH volume was associated with abnormal gait speed and a transition to abnormal gait speed between the two visits, but not after adjustment for modifiable vascular disease risk factors. Increased frontal lobe WMH volume was associated with abnormal gait speed and transition to abnormal gait speed, but not in adjusted models. WMH are associated with slowing of gait over time. Prevention of WMH presents a potential strategy for the prevention of gait speed decline.

Abstract Brain imaging has revealed that the CA1 subregion of the hippocampus is hyperactive in prodromal and diagnosed patients with schizophrenia (SCZ), and that glutamate is a driver of this hyperactivity. Strikingly, mice deficient in the glutamate synthetic enzyme glutaminase have CA1 hypoactivity and a SCZ-resilience profile, implicating glutamate-metabolizing enzymes. To address this further, we examined mice with a brain-wide deficit in the glutamate-metabolizing enzyme glutamate dehydrogenase (GDH), encoded by Glud1, which should lead to glutamate excess due to reduced glutamate metabolism in astrocytes. We found that Glud1-deficient mice have behavioral abnormalities in the 3 SCZ symptom domains, with increased baseline and amphetamine-induced hyperlocomotion as a positive symptom proxy, nest building and social preference as a negative symptom proxy, and reversal/extradimensional set shifting in the water T-maze and contextual fear conditioning as a cognitive symptom proxy. Neuroimaging of cerebral blood volume revealed hippocampal hyperactivity in CA1, which was associated with volume reduction. Parameters of hippocampal synaptic function revealed excess glutamate release and an elevated excitatory/inhibitory balance in CA1. Finally, in a direct clinical correlation using imaging-guided microarray, we found a significant SCZ-associated postmortem reduction in GLUD1 expression in CA1. These findings advance GLUD1 deficiency as a driver of excess hippocampal excitatory transmission and SCZ symptoms, and identify GDH as a target for glutamate modulation pharmacotherapy for SCZ. More broadly, these findings point to the likely involvement of alterations in glutamate metabolism in the pathophysiology of SCZ.

Few studies have applied multiple imaging modalities to examine cognitive correlates of white matter. We examined the utility of T2-weighted magnetic resonance imaging (MRI) -derived white matter hyperintensities (WMH) and diffusion tensor imaging-derived fractional anisotropy (FA) to predict cognitive functioning among older adults. Quantitative MRI and neuropsychological evaluations were performed in 112 older participants from an ongoing study of the genetics of Alzheimer's disease (AD) in African Americans. Regional WMH volumes and FA were measured in multiple regions of interest. We examined the association of regional WMH and an FA summary score with cognitive test performance. Differences in WMH and FA were compared across diagnostic groups (i.e., normal controls, mild cognitive impairment, and probable AD). Increased WMH volume in frontal lobes was associated with poorer delayed memory performance. FA did not emerge as a significant predictor of cognition. White matter hyperintensity volume in the frontal and parietal lobes was increased in MCI participants and more so in AD patients relative to controls. These results highlight the importance of regionally distributed small vessel cerebrovascular disease in memory performance and AD among African American older adults. White matter microstructural changes, quantified with diffusion tensor imaging, appear to play a lesser role in our sample. (JINS, 2012, 18, 414–427)

Alzheimer's disease (AD) is a growing public health crisis. The disease is defined neuropathologically by accumulation of amyloid-β plaques and neurofibrillary tangles (NFTs) composed of abnormal tau protein in the brain. Early neurofibrillary degeneration in the entorhinal cortex (EC) is a hallmark of AD and a critical initiating event in the hierarchical pathoanatomical progression. However, the factors triggering initial tau deposition in the EC remain unclear. We propose a novel biomechanical cascade hypothesis, positing that the unique anatomical inferomedial positioning of the EC, including proximity to the tentorial incisura (TI) and other skull base structures, renders it susceptible to very mild yet persistent age-related mechanical stress, analogous to the effects of repetitive mild traumatic brain injury, triggering tau pathology. To test this hypothesis, we developed a method to quantify Entorhinal-Tentorial (EC-TI) proximity and applied it to multimodal imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI; =47). Based on this neuroanatomical contact coefficient (NCC), participants were heuristically stratified into high ( =24) and low ( =23) adjacency groups. When controlling for other risk factors, tau PET signal in the EC predicted conversion from mild cognitive impairment to AD only in the high-adjacency group (LLR =0.009, tau PET in EC =0.036). These findings identify EC-TI proximity as a novel and anatomically grounded biomarker of AD progression risk. More broadly, they suggest a previously unrecognized biomechanical contribution to the initiation of tau pathology in aging and sporadic AD, opening new avenues for early detection, risk stratification, and mechanistically targeted prevention strategies.