Cortical signatures of cognition and their relationship to Alzheimer’s disease

Gross, Alden L.; Manly, Jennifer J.; Pa, Judy; Johnson, Julene K.; Park, Lovingly Quitania; Mitchell, Meghan B.; Inouye, Sharon K.; McLaren, Donald G.

doi:10.1007/s11682-012-9180-5

Cited by 23 publications

(22 citation statements)

References 68 publications

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“…However, biomarkers from multiple modalities examined together tend to predict cognitive decline better than any single biomarker (71; 82; 83; 85; 87; 89; 92; 95-97). Choosing appropriate biomarkers for the disease stage studied and consistently controlling for confounding covariates will empower future studies to more efficiently detect the effects of treatment and prevention efforts.…”

Section: Resultsmentioning

confidence: 97%

“…In most studies, the model that combined information from multiple modalities, including MRI, predicted decline better than any one biomarker alone (71; 82; 83; 85; 87; 89; 92; 95-97), underscoring the importance of tools flexible enough to accommodate information from multiple sources. Even so, several studies that examined multiple types of biomarkers found that baseline cognitive measures were the best single predictors of future conversion to AD (88; 90; 92-95).…”

Section: Predicting Cognitive Declinementioning

confidence: 99%

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A Focus on Structural Brain Imaging in the Alzheimer’s Disease Neuroimaging Initiative

Braskie¹,

Thompson²

2014

Biological Psychiatry

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In recent years, there has been a wealth of laboratories and consortia that use neuroimaging to evaluate the risk for and progression of Alzheimer's disease (AD). One such consortium is the Alzheimer's Disease Neuroimaging Initiative (ADNI) - a longitudinal, multi-center study that evaluates a range of biomarkers for use in AD diagnoses, predicting patient outcomes, and for clinical trials. These biomarkers include brain metrics derived from magnetic resonance imaging (MRI) and positron emission tomography (PET) scans, and metrics derived from blood and cerebrospinal fluid (CSF). Here we focus on ADNI studies, published between 2011 and March 2013, for which structural MRI was a major outcome measure. Our main goal here was to review key papers offering insights into AD progression, and the relationships of structural MRI measures to cognition and to other biomarkers in AD. In the Supplemental Materials, we also discuss genetic and environmental risk factors for AD, and exciting new analysis tools for the efficient evaluation of large scale structural MRI data sets such as ADNI.

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Section: Resultsmentioning

confidence: 97%

Section: Predicting Cognitive Declinementioning

confidence: 99%

A Focus on Structural Brain Imaging in the Alzheimer’s Disease Neuroimaging Initiative

Braskie¹,

Thompson²

2014

Biological Psychiatry

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“…The first set of articles focused on the measurement of cognition and on improving reliability, sensitivity, and validity of measurements of different cognitive domains [449, 454, 464, 465]. A second set examined neuroimaging-cognition relationships [448, 453, 466], and a third set examined sequencing of biomarker and cognitive changes in relation to the model by Jack et al [14, 467, 468]. A final area examined genetic contributions to cognition using high-dimensional genetic data [469–471].…”

Section: Studies Of the Adni Cohortmentioning

confidence: 99%

“…Prediction accuracy of the MCI to AD conversion was consistently lower by a similar degree in the ADNI setting, suggesting that these models are portable and robust in clinical settings. Gross et al [466] combined cortical thickness and cognitive data to predict MCI to AD conversion. They used regression analysis to develop cortical signatures of cognition specific to memory, executive function, language, and visuospatial processing domains, empirically defined by their correlation with domain-specific cognitive factor scores derived from new ADNI neuropsychological battery scores [112].…”

Section: Studies Of the Adni Cohortmentioning

confidence: 99%

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Weiner

Veitch

Aisen

et al. 2015

Alzheimer's & Dementia

280

224

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The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer’s disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer’s Dis 2006;9(Suppl 3):151–3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [18F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding ...

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“…Patients with AD exhibit memory impairment and cognitive decline due to neuronal loss mainly in the neocortex and the hippocampus [46]. AD currently affects over 36 million people worldwide with an estimated global economic impact of approximately $605 billion dollars in 2010 in addition to incalculable social and emotional burdens [47, 48].…”

Section: Td and Neurodegenerationmentioning

confidence: 99%

Thiamine Deficiency and Neurodegeneration: the Interplay Among Oxidative Stress, Endoplasmic Reticulum Stress, and Autophagy

2016

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Thiamine (Vitamin B1) is an essential nutrient and indispensable for normal growth and development of the organism due to its multilateral participation in key biochemical and physiological processes. Humans must obtain thiamine from their diet since it is synthesized only in bacteria, fungi and plants. Thiamine deficiency (TD) can result from inadequate intake, increased requirement, excessive deletion and chronic alcohol consumption. TD affects multiple organ systems, including the cardiovascular, muscular, gastrointestinal, and central and peripheral nervous systems. In the brain, TD causes a cascade of events including mild impairment of oxidative metabolism, neuroinflammation and neurodegeneration, which are commonly observed in neurodegenerative diseases, such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and Huntington’s disease (HD). Thiamine metabolites may serve as promising biomarkers for neurodegenerative diseases and thiamine supplementations exhibit therapeutic potential for patients of some neurodegenerative diseases. Experimental TD has been used to model aging-related neurodegenerative diseases. However, to date, the cellular and molecular mechanisms underlying TD-induced neurodegeneration are not clear. Recent research evidence indicates that TD causes oxidative stress, endoplasmic reticulum (ER) stress and autophagy in the brain, which are known to contribute to the pathogenesis of various neurodegenerative diseases. In this review, we discuss the role of oxidative stress, ER stress and autophagy in TD-mediated neurodegeneration. We propose that it is the interplay of oxidative stress, ER stress and autophagy that contributes to TD-mediated neurodegeneration.

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Cortical signatures of cognition and their relationship to Alzheimer’s disease

Cited by 23 publications

References 68 publications

A Focus on Structural Brain Imaging in the Alzheimer’s Disease Neuroimaging Initiative

A Focus on Structural Brain Imaging in the Alzheimer’s Disease Neuroimaging Initiative

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Thiamine Deficiency and Neurodegeneration: the Interplay Among Oxidative Stress, Endoplasmic Reticulum Stress, and Autophagy

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