Alzheimer's Disease Neuroimaging Initiative (ADNI)

Petersen, Ronald C.; Aisen, Paul S.; Beckett, Laurel A.; Donohue, Michael; Gamst, Anthony; Harvey, Danielle; Jack, Clifford R.; Jagust, William J.; Shaw, Leslie M.; Toga, Arthur W.; Trojanowski, John Q.; Weiner, Michael W.

doi:10.1212/wnl.0b013e3181cb3e25

Cited by 1,611 publications

(1,422 citation statements)

References 27 publications

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“…34 Use of amyloid biomarkers could have helped reclassify the disease, while clinical history was not suggestive of a classical PrP-associated syndrome. In clinical practice, the diagnosis of AD is usually based on recent McKahnn's criteria, 35 also used in research cohorts 36 and international clinical trials. 37 Although the sensitivity of these clinical criteria is rather high (80%), their specificity is o70% for probable AD.…”

Section: Discussionmentioning

confidence: 99%

Screening of dementia genes by whole-exome sequencing in early-onset Alzheimer disease: input and lessons

Nicolas¹,

Wallon²,

Charbonnier³

et al. 2015

Eur J Hum Genet

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Causative variants in APP, PSEN1 or PSEN2 account for a majority of cases of autosomal dominant early-onset Alzheimer disease (ADEOAD, onset before 65 years). Variant detection rates in other EOAD patients, that is, with family history of lateonset AD (LOAD) (and no incidence of EOAD) and sporadic cases might be much lower. We analyzed the genomes from 264 patients using whole-exome sequencing (WES) with high depth of coverage: 90 EOAD patients with family history of LOAD and no incidence of EOAD in the family and 174 patients with sporadic AD starting between 51 and 65 years. We found three PSEN1 and one PSEN2 causative, probably or possibly causative variants in four patients (1.5%). Given the absence of PSEN1, PSEN2 and APP causative variants, we investigated whether these 260 patients might be burdened with protein-modifying variants in 20 genes that were previously shown to cause other types of dementia when mutated. For this analysis, we included an additional set of 160 patients who were previously shown to be free of causative variants in PSEN1, PSEN2 and APP: 107 ADEOAD patients and 53 sporadic EOAD patients with an age of onset before 51 years. In these 420 patients, we detected no variant that might modify the function of the 20 dementia-causing genes. We conclude that EOAD patients with family history of LOAD and no incidence of EOAD in the family or patients with sporadic AD starting between 51 and 65 years have a low variant-detection rate in AD genes.

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Section: Discussionmentioning

confidence: 99%

Screening of dementia genes by whole-exome sequencing in early-onset Alzheimer disease: input and lessons

Nicolas¹,

Wallon²,

Charbonnier³

et al. 2015

Eur J Hum Genet

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“…Mild cognitive impairment (MCI) in older adults is another risk factor for AD, as a relatively high proportion of older adults with MCI, particularly those with amnestic symptoms, will progress to clinical dementia 172 . There is an extensive literature on functional and structural brain changes in MCI, much of which has shown that individuals with MCI have greater activation in the MTL during memory tasks relative to healthy older controls 173,174 .…”

Section: Risk Factors For Alzheimer's Disease: Mild Cognitive Impairmentmentioning

confidence: 99%

The cognitive neuroscience of ageing

2012

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PrefaceThe availability of neuroimaging technology has spurred a marked increase in the human cognitive neuroscience literature, including the study of cognitive aging. Although there is a growing consensus that the aging brain retains considerable plasticity of function, currently measured primarily by means of functional magnetic resonance imaging, it is less clear how age differences in brain activity relate to cognitive performance. The field also is hampered by the complexity of the aging process itself and the large number of factors that are influenced by age. In this review, current trends and unresolved issues in the cognitive neuroscience of aging are discussed.

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“…Table 3 lists clinical scores' acronyms. Details on Alzheimer's Disease and schizophrenia related scores can be found respectively in Petersen et al (2010) and Calhoun et al (2012).…”

Section: Experiments: Learning Multiple Neuropsychiatric Profiles Fromentioning

confidence: 99%

Joint prediction of multiple scores captures better individual traits from brain images

et al. 2017

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To probe individual variations in brain organization, population imaging relates features of brain images to rich descriptions of the subjects such as genetic information or behavioral and clinical assessments. Capturing common trends across these measurements is important: they jointly characterize the disease status of patient groups. In particular, mapping imaging features to behavioral scores with predictive models opens the way toward more precise diagnosis. Here we propose to jointly predict all the dimensions (behavioral scores) that make up the individual profiles, using so-called multi-output models. This approach often boosts prediction accuracy by capturing latent shared information across scores. We demonstrate the efficiency of multi-output models on two independent resting-state fMRI datasets targeting different brain disorders (Alzheimer's Disease and schizophrenia). Furthermore, the model with joint prediction generalizes much better to a new cohort: a model learned on one study is more accurately transferred to an independent one. Finally, we show how multi-output models can easily be extended to multi-modal settings, combining heterogeneous data sources for a better overall accuracy.

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Alzheimer's Disease Neuroimaging Initiative (ADNI)

Abstract: Background: Neuroimaging measures and chemical biomarkers may be important indices of clini-

Cited by 1,611 publications

References 27 publications

Screening of dementia genes by whole-exome sequencing in early-onset Alzheimer disease: input and lessons

Screening of dementia genes by whole-exome sequencing in early-onset Alzheimer disease: input and lessons

The cognitive neuroscience of ageing

Joint prediction of multiple scores captures better individual traits from brain images

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