412 research outputs found

    Pro: Can biomarkers be gold standards in Alzheimer's disease?

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    Recent advances in biomarkers for Alzheimer's disease (AD) now allow the visualization of one of the hallmark pathologies of AD in vivo, and combination biomarker profiles can now approximate the diagnostic accuracy of autopsy in patients with dementia. Biomarkers are already employed in clinical trials in prodromal AD for both subject selection and in monitoring therapeutic response. Ultimately the greatest utility of biomarkers may be in the preclinical stages of AD, to identify and track progression of the disease prior to significant cognitive impairment, at the point when disease modifying therapies are likely to be most efficacious

    Preclinical Alzheimer Disease - The Challenges Ahead

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    There is growing recognition that the pathophysiological process of Alzheimer disease (AD) begins many years prior to clinically obvious symptoms, and the concept of a presymptomatic or preclinical stage of AD is becoming more widely accepted. Advances in biomarker studies have enabled detection of AD pathology in vivo in clinically normal older individuals. The predictive value of these biomarkers at the individual patient level, however, remains to be elucidated. The ultimate goal of identifying individuals in the preclinical stages of AD is to facilitate early intervention to delay and perhaps even prevent emergence of the clinical syndrome. A number of challenges remain to be overcome before this concept can be validated and translated into clinical practice

    Large-Scale Functional Brain Network Abnormalities in Alzheimerā€™s Disease: Insights from Functional Neuroimaging

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    Functional MRI (fMRI) studies of mild cognitive impairment (MCI) and Alzheimerā€™s disease (AD) have begun to reveal abnormalities in large-scale memory and cognitive brain networks. Since the medial temporal lobe (MTL) memory system is a site of very early pathology in AD, a number of studies have focused on this region of the brain. Yet it is clear that other regions of the large-scale episodic memory network are affected early in the disease as well, and fMRI has begun to illuminate functional abnormalities in frontal, temporal, and parietal cortices as well in MCI and AD. Besides predictable hypoactivation of brain regions as they accrue pathology and undergo atrophy, there are also areas of hyperactivation in brain memory and cognitive circuits, possibly representing attempted compensatory activity. Recent fMRI data in MCI and AD are beginning to reveal relationships between abnormalities of functional activity in the MTL memory system and in functionally connected brain regions, such as the precuneus. Additional work with ā€œresting stateā€ fMRI data is illuminating functional-anatomic brain circuits and their disruption by disease. As this work continues to mature, it will likely contribute to our understanding of fundamental memory processes in the human brain and how these are perturbed in memory disorders. We hope these insights will translate into the incorporation of measures of task-related brain function into diagnostic assessment or therapeutic monitoring, which will hopefully one day be useful for demonstrating beneficial effects of treatments being tested in clinical trials

    Promising developments in neuropsychological approaches for the detection of preclinical Alzheimerā€™s disease: a selective review

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    Recently published guidelines suggest that the most opportune time to treat individuals with Alzheimerā€™s disease is during the preclinical phase of the disease. This is a phase when individuals are defined as clinically normal but exhibit evidence of amyloidosis, neurodegeneration and subtle cognitive/behavioral decline. While our standard cognitive tests are useful for detecting cognitive decline at the stage of mild cognitive impairment, they were not designed for detecting the subtle cognitive variations associated with this biomarker stage of preclinical Alzheimerā€™s disease. However, neuropsychologists are attempting to meet this challenge by designing newer cognitive measures and questionnaires derived from translational efforts in neuroimaging, cognitive neuroscience and clinical/experimental neuropsychology. This review is a selective summary of several novel, potentially promising, approaches that are being explored for detecting early cognitive evidence of preclinical Alzheimerā€™s disease in presymptomatic individuals

    Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimerā€™s disease

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    We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimerā€™s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloidā€“positive (AĪ²+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in AĪ²+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.United States. National Institutes of Health (1K25EB013649-01)United States. National Institutes of Health (1R21AG050122-01A1)United States. National Institutes of Health (P01AG036694)United States. National Institutes of Health (F32AG044054
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