Correction to: The Toronto cognitive assessment (TorCA): normative data and validation to detect amnestic mild cognitive impairment.
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ABSTRACT:
SUBMITTER: Freedman M
PROVIDER: S-EPMC6286597 | biostudies-literature | 2018 Dec
REPOSITORIES: biostudies-literature
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Project description:BackgroundAlzheimer's disease (AD) is generally considered to be characterized by pathology in gray matter of the brain, but convergent evidence suggests that white matter degradation also plays a vital role in its pathogenesis. The evolution of white matter deterioration and its relationship with gray matter atrophy remains elusive in amnestic mild cognitive impairment (aMCI), a prodromal stage of AD.MethodsWe studied 155 cognitively normal (CN) and 27 'late' aMCI individuals with stable diagnosis over 2 years, and 39 'early' aMCI individuals who had converted from CN to aMCI at 2-year follow up. Diffusion tensor imaging (DTI) tractography was used to reconstruct six white matter tracts three limbic tracts critical for episodic memory function - the fornix, the parahippocampal cingulum, and the uncinate fasciculus; two cortico-cortical association fiber tracts - superior longitudinal fasciculus and inferior longitudinal fasciculus; and one projection fiber tract - corticospinal tract. Microstructural integrity as measured by fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AxD) was assessed for these tracts.ResultsCompared with CN, late aMCI had lower white matter integrity in the fornix, the parahippocampal cingulum, and the uncinate fasciculus, while early aMCI showed white matter damage in the fornix. In addition, fornical measures were correlated with hippocampal atrophy in late aMCI, whereas abnormality of the fornix in early aMCI occurred in the absence of hippocampal atrophy and did not correlate with hippocampal volumes.ConclusionsLimbic white matter tracts are preferentially affected in the early stages of cognitive dysfunction. Microstructural degradation of the fornix preceding hippocampal atrophy may serve as a novel imaging marker for aMCI at an early stage.
| S-EPMC3597581 | biostudies-literature
Project description:BackgroundThe Montreal Cognitive Assessment (MoCA) is one of the most widely-used cognitive screening instruments and has been translated into several different languages and dialects. Although the original validation study suggested to use a cutoff of ≤26, subsequent studies have shown that lower cutoff values may yield fewer false-positive indications of cognitive impairment. The aim of this study was to summarize the diagnostic accuracy and mean difference of the MoCA when comparing cognitively unimpaired (CU) older adults to those with amnestic mild cognitive impairment (aMCI).MethodsPubMed and EMBASE databases were searched from inception to 22 February 2022. Meta-analyses for area under the curve (AUC) and standardized mean difference (SMD) values were performed.ResultsFifty-five observational studies that included 17,343 CU and 8,413 aMCI subjects were selected for inclusion. Thirty-nine studies were used in the AUC analysis while 44 were used in the SMD analysis. The overall AUC value was 0.84 (95% CI: 0.81, 0.87) indicating good diagnostic accuracy and a large effect size was noted for the SMD analysis (Hedge's g = 1.49, 95% CI: 1.33, 1.64). Both analyses had high levels of between-study heterogeneity. The median cutoff score for identifying aMCI was <24.Discussion and conclusionThe MoCA has good diagnostic accuracy for detecting aMCI across several different languages. The findings of this meta-analysis also support the use of 24 as the optimal cutoff when the MoCA is used to screen for suspected cognitive impairment.
| S-EPMC10896827 | biostudies-literature
Project description:Amnestic MCI is often considered an early clinical stage of AD, but its subtle presentation leads to infrequent neurological evaluations. Early plasma biomarker screening during routine check-ups in community hospitals could enhance aMCI detection rates. Advances in proteomics, particularly mass spectrometry, have enabled the detection of low-abundance plasma proteins, opening new possibilities for aMCI biomarker identification. This study included 84 participants from the STAR cohort. We utilized deep plasma proteomics to detect low-abundance proteins, enriched with biofunctional magnetic beads. An early diagnostic model for aMCI was developed through bioinformatics and machine learning, with performance compared to the Simoa assay.
2024-09-26 | PXD056265 |
Project description:BACKGROUND:We tested if rates of brain atrophy accelerate in individuals with amnestic mild cognitive impairment (aMCI) as they progress to typical late onset Alzheimer disease (AD). We included comparisons to subjects with aMCI who did not progress (labeled aMCI-S) and also to cognitively normal elderly subjects (CN). METHODS:We studied 46 subjects with aMCI who progressed to AD (labeled aMCI-P), 46 CN, and 23 aMCI-S. All subjects must have had three or more serial MRI scans. Rates of brain shrinkage and ventricular expansion were measured across all available serial MRI scans in each subject. Change in volumes relative to the point at which subjects progressed to a clinical diagnosis of AD (the index date) was modeled in aMCI-P. Change in volumes relative to age was modeled in all three clinical groups. RESULTS:In aMCI-P the change in pre to post index rate (i.e., acceleration) of ventricular expansion was 1.7 cm(3)/year, and acceleration in brain shrinkage was 5.3 cm(3)/year. Brain volume declined and ventricular volume increased in all three groups with age. Volume changes decelerated with increasing age in aMCI-P, and to a lesser extent in aMCI-S, but were linear in the matched CN. Among all subjects with aMCI, rates of atrophy were greater in apolipoprotein E epsilon 4 carriers than noncarriers. CONCLUSIONS:Rates of atrophy accelerate as individuals progress from amnestic mild cognitive impairment (aMCI) to typical late onset Alzheimer disease (AD). Rates of atrophy are greater in younger than older subjects with aMCI who progressed to AD and subjects with aMCI who did not progress. We did not find that atrophy rates varied with age in 70- to 90-year-old cognitively normal subjects.
| S-EPMC2734477 | biostudies-literature
Project description:BackgroundPrevious work examining normal controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) identified substantial biological heterogeneity. We hypothesized that ADNI mild cognitive impairment (MCI) subjects would also exhibit heterogeneity with possible clinical implications.MethodsADNI subjects diagnosed with amnestic MCI (n=138) were clustered based on baseline magnetic resonance imaging, cerebrospinal fluid, and serum biomarkers. The clusters were compared with respect to longitudinal atrophy, cognitive trajectory, and time to conversion.ResultsFour clusters emerged with distinct biomarker patterns: The first cluster was biologically similar to normal controls and rarely converted to Alzheimer's disease (AD) during follow-up. The second cluster had characteristics of early Alzheimer's pathology. The third cluster showed the most severe atrophy but barely abnormal tau levels and a substantial proportion converted to clinical AD. The fourth cluster appeared to be pre-AD and nearly all converted to AD.ConclusionsSubjects with MCI who were clinically similar showed substantial heterogeneity in biomarkers.
| S-EPMC4092059 | biostudies-literature
Project description:Mild cognitive impairment (MCI) is widely regarded as an intermediate stage between typical aging and dementia, with nearly 50% of patients with amnestic MCI (aMCI) converting to Alzheimer's dementia (AD) within 30?months of follow-up (Fischer et al., 2007). The growing literature using resting-state functional magnetic resonance imaging reveals both increased and decreased connectivity in individuals with MCI and connectivity loss between the anterior and posterior components of the default mode network (DMN) throughout the course of the disease progression (Hillary et al., 2015; Sheline & Raichle, 2013; Tijms et al., 2013). In this paper, we use dynamic connectivity modeling and graph theory to identify unique brain "states," or temporal patterns of connectivity across distributed networks, to distinguish individuals with aMCI from healthy older adults (HOAs). We enrolled 44 individuals diagnosed with aMCI and 33 HOAs of comparable age and education. Our results indicated that individuals with aMCI spent significantly more time in one state in particular, whereas neural network analysis in the HOA sample revealed approximately equivalent representation across four distinct states. Among individuals with aMCI, spending a higher proportion of time in the dominant state relative to a state where participants exhibited high cost (a measure combining connectivity and distance), predicted better language performance and less perseveration. This is the first report to examine neural network dynamics in individuals with aMCI.
| S-EPMC6827720 | biostudies-literature
Project description:BackgroundThe Parkinson's Disease-Cognitive Rating Scale (PD-CRS) assesses posterior-cortical and frontal-subcortical cognitive functioning and distinguishes mild cognitive impairment in Parkinson's disease (PD-MCI); however, it was not evaluated in Brazil.ObjectivesTo investigate PD-CRS's reliability, validity, normative data, and accuracy for PD-MCI screening in Brazil.MethodsThe effects of age, education, and sex on PD-CRS scores were explored. The instrument was tested in 714 individuals (53% female, 21-94 years), with a broad range of education and no neurodegenerative disorder. Trail Making, Consonant Trigrams, Five-Point, and semantic fluency tests were administered for comparison. A second study enrolled patients with PD and intact cognition (n = 44, 59.75 ± 10.79 years) and with PD-MCI (n = 25, 65.76 ± 10.33 years) to investigate criterion validity. PD-CRS subtests were compared with the Cambridge Automated Neuropsychological Battery memory and executive tasks.ResultsPD-CRS was unidimensional and reliable (McDonald's ω = 0.83). Using robust multiple regressions, age, and education predicted the total and derived scores in the normative sample. At the 85-point cutoff, PD-MCI was detected with 68% sensitivity and 86% specificity (area under the curve = 0.870). PD-CRS scores strongly correlated with executive and verbal/visual memory tests in both normative and clinical samples.ConclusionsThis study investigated the applicability of PD-CRS in the Brazilian context. The scale seems helpful in screening for PD-MCI, with adequate internal consistency and construct validity. The PD-CRS variance is influenced by age and educational level, a critical issue for cognitive testing in countries with educational and cultural heterogeneity.
| S-EPMC10026291 | biostudies-literature
Project description:The objective of this study was to develop and validate a practical computerized prognostic model that uses baseline psychometric and imaging data, including results of PET imaging of amyloid deposition, to predict the progression to dementia in patients at risk for Alzheimer's disease (AD).Data from patients in a phase II trial of [F]flutemetamol for PET imaging of brain amyloid and from the Alzheimer's Disease Neuroimaging Initiative were used to train the prognostic model to yield a disease state index (DSI), a measure of the similarity of an individual patient's data to data from patients in specific diagnostic groups. Inputs to the model included amyloid PET results, MRI measurements of hippocampal volume, and the results of psychometric tests. The model was subsequently validated by using data from a prospective study of an independent cohort of patients with mild cognitive impairment.In total, data from 223 patients of the 233 enroled were suitable for analysis. The DSI predicted by the model and the risk of progression to AD dementia within 3 years were higher for patients with amyloid deposition and neurodegeneration than for patients with amyloid deposition without neurodegeneration. Rates of non-AD dementia among patients with neurodegeneration at baseline were consistent with the results of other studies. The results were consistent with the Jack model of AD progression.The DSI from the model that included psychometric, MRI, and PET amyloid data provides useful prognostic information in cases of mild cognitive impairment.
| S-EPMC5882247 | biostudies-other
Project description:Multiple regression voxel-based morphometry analyses were used to examine the relationship between regional gray matter volumes and neurocognitive performance in 10 patients with amnestic mild cognitive impairment and 20 healthy age-matched controls. Cognitive functioning was assessed with seven standardized neuropsychological tests. Patients with amnestic mild cognitive impairment exhibited impaired cognitive performance (on the Mini Mental State Examination, tests of verbal fluency, verbal and spatial learning and memory, and visual-motor abilities) and reduced gray matter volume in the right temporal pole. Across all participants, better performance on several neuropsychological tests was associated with higher regional gray matter volumes. Voxel-based morphometry provides an operator-unbiased means to investigate volumetric differences, which may be related to impaired neuropsychological functioning.
| S-EPMC3041512 | biostudies-literature
Project description:BACKGROUND:Mild cognitive impairment (MCI) is a heterogeneous syndrome resulting from Alzheimer's disease (AD) as well as to non-AD and non-neurodegenerative conditions. A subset of patients with amnestic MCI (aMCI) present with an unusually long-lasting course, a slow rate of clinical neuropsychological progression, and evidence of focal involvement of medial temporal lobe structures. In the present study, we explored positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers in a sample of subjects with aMCI with such clinical features in order to provide in vivo evidence to improve disease characterisation in this subgroup. METHODS:Thirty consecutive subjects with aMCI who had long-lasting memory impairment (more than 4 years from symptom onset) and a very slow rate of cognitive progression were included. All subjects underwent fluorodeoxyglucose-positron emission tomography (FDG-PET) metabolic imaging. A measure of cerebral amyloid load, by PET and/or CSF, was obtained in 26 of 30 subjects. The mean clinical follow-up was 58.3?±?10.1 months. RESULTS:No patient progressed to dementia during the follow-up. The typical AD FDG-PET pattern of temporoparietal hypometabolism was not present in any of the subjects. In contrast, a selective medial temporal lobe hypometabolism was present in all subjects, with an extension to frontolimbic regions in some subjects. PET imaging showed absent or low amyloid load in the majority of samples. The values were well below those reported in prodromal AD, and they were slightly elevated in only two subjects, consistent with the CSF ?-amyloid (1-42) protein values. Notably, no amyloid load was present in the hippocampal structures. CONCLUSIONS:FDG-PET and amyloid-PET together with CSF findings questioned AD pathology as a unique neuropathological substrate in this aMCI subgroup with long-lasting disease course. The possibility of alternative pathological conditions, such as argyrophilic grain disease, primary age-related tauopathy or age-related TDP-43 proteinopathy, known to spread throughout the medial temporal lobe and limbic system structures should be considered in these patients with MCI.
| S-EPMC5918759 | biostudies-literature