In fact, although both a-MCI and AD patients showed a similar pattern of impairment on the free recall test, a-MCI patients were able to normalise their performance on the recognition test, thus overcoming their deficits at the time of recall.
Cox regression analyses were used to determine whether 3-year slopes on the preclinical Alzheimer's cognitive composite predicted MCI diagnosis and global Clinical Dementia Rating>0 in 267 Aβ+ CN individuals participating in the Harvard Aging Brain Study, Australian Imaging, Biomarker and Lifestyle Study, and Alzheimer's Disease Neuroimaging Initiative.
This self-rating tool appears useful for identifying significant memory complaints in a normal population and may also be helpful in discriminating between functional/na-MCI and a-MCI/AD patients.
Twenty-nine healthy older adults and twenty-three with MCI due to Alzheimer's disease were included as controls, i.e. individuals with no evidence of Lewy body disease.
<b>Methods:</b> We enrolled 107 participants (45 amyloid-β-negative cognitively unimpaired [CU-], 7 amyloid-β-positive cognitively unimpaired [CU+], 31 with prodromal AD [mild cognitive impairment; MCI+], and 24 with AD dementia [DEM+]) who completed 2 baseline PET scans (<sup>18</sup>F-flortaucipir and <sup>18</sup>F-florbetaben), MRI, and neuropsychologic tests.
We compared Neuropsychiatric Inventory (NPI) total score and NPI subdomain score between mild cognitive impairment-converters (MCI-C) and mild cognitive impairment-nonconverters (MCI-NC) longitudinally for 6 years using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
Published reports show that eight drugs, that is, dantrolene, erythropoietin, lithium, memantine, minocycline, piracetam, riluzole, and silymarin, address many of the pathways underlying MCI and AD.
The first cohort comprised 842 participants (513 cognitively unimpaired [CU], 265 with mild cognitive impairment [MCI], and 64 with AD dementia) from the Swedish BioFINDER study.
Our comparisons of the classifiers trained on structural MRI, diffusion MRI, FLAIR, and CSF biomarkers showed the promising utility of the white matter structural connectomes in classifying AD and MCI in addition to the widely used structural MRI-based morphometry, when combined with machine learning.
Secondary efficacy assessments included change from baseline in Clinical Dementia Rating scale-Sum of Boxes (CDR-SB), Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog11) and Alzheimer's Disease Cooperative Study-Activities of Daily Living scale (ADCS-ADL; mild cognitive impairment [MCI] version for prodromal patients) after 12 weeks' treatment.
Another mixed effects model was used to compare T/S ratio changes pre- to post-conversion to MCI or AD to telomere change in participants with stable diagnoses.
Al is a well-established neurotoxicant and is suspected to be linked with various neurodegenerative diseases including Alzheimer's disease (AD) and MCI.
Inflammatory-based proteins were more reliably found to be elevated leading to preliminary work focused on an algorithmic approach with predominantly inflammatory-based proteins to detect AD and MCI as well as predict risk of incidence among DS.
The conjunction analysis showed a reduction of rostral-caudal hippocampal convergence strength from early MCI to AD, independent of hippocampal atrophy.
We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional brain structural MRI scan.
In this paper, we propose a network analysis-based approach to help experts in their analyses of subjects with mild cognitive impairment (hereafter, MCI) and Alzheimer's disease (hereafter, AD) and to investigate the evolution of these subjects over time.
Here, we propose a new approach to pathway analysis of blood gene expression profiles to classify healthy (control [CTL]), mildly cognitively impaired (mild cognitive impairment [MCI]; preclinical stage of AD), and AD subjects.
However, the application of APOE genotyping in the diagnosis of MCI progressing to AD is limited by its low sensitivity and specificity, which often leads to high false-positive rate.
Most of the early studies were performed with PET and [ <sup>18</sup> F]fluorodeoxyglucose, and occasionally with [ <sup>15</sup> O]water, reporting a significant association between higher occupation/education and lower glucose metabolism (blood flow) in associative temporo-parietal cortex in patients with AD and also in patients with MCI, after correcting for the degree in the cognitive impairment.
These results suggest that low levels of depressive symptoms may be predictive of clinical symptom onset within approximately 7 years among cognitively normal individuals and may be useful in identifying persons at risk for MCI due to Alzheimer's disease.
The MINT successfully detects naming deficits at different levels of cognitive impairment in patients with MCI or AD dementia, but comparison to age, sex, race, and education-corrected norms to determine impairment is essential.
The present meta-analysis showed that neurogranin CSF levels are significantly higher in AD patients compared to NC [SMD: 268.26, 95% CI (143.47, 393.04), Z = 4.21, P = 0.0001], MCI [SMD: 23.45 (15.97, 30.92), Z = 6.15, P < 0.00001], FTD [SMD: 1.91 (0.92, 2.89), Z = 3.80, P < 0.0001], but no significant difference was found between AD and LBD [SMD: 138.51 (- 14.92, 291.95), Z = 1.77, P = 0.08].