Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns.
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ABSTRACT:
SUBMITTER: Kwak K
PROVIDER: S-EPMC8714856 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundHistopathological studies and animal models suggest that hippocampal subfields may be differently affected by aging, Alzheimer's disease (AD), and other diseases. High-resolution images at 4 Tesla depict details of the internal structure of the hippocampus allowing for in vivo volumetry of different subfields. The aims of this study were as follows: (1) to determine patterns of volume loss in hippocampal subfields in normal aging, AD, and amnestic mild cognitive impairment (MCI). (2) To determine if measurements of hippocampal subfields provide advantages over total hippocampal volume for differentiation between groups.MethodsNinety-one subjects (53 controls (mean age: 69.3 ± 7.3), 20 MCI (mean age: 73.6 ± 7.1), and 18 AD (mean age: 69.1 ± 9.5) were studied with a high-resolution T2 weighted imaging sequence aimed at the hippocampus. Entorhinal cortex (ERC), subiculum, CA1, CA1-CA2 transition zone (CA1-2), CA3 & dentate gyrus (CA3&DG) were manually marked in the anterior third of the hippocampal body. Hippocampal volume was obtained from the Freesurfer and manually edited.ResultsCompared to controls, AD had smaller volumes of ERC, subiculum, CA1, CA1-2, and total hippocampal volumes. MCI had smaller CA1-2 volumes. Discriminant analysis and power analysis showed that CA1-2 was superior to total hippocampal volume for distinction between controls and MCI.ConclusionThe patterns of subfield atrophy in AD and MCI were consistent with patterns of neuronal cell loss/reduced synaptic density described by histopathology. These preliminary findings suggest that hippocampal subfield volumetry might be a better measure for diagnosis of early AD and for detection of other disease effects than measurement of total hippocampus.
| S-EPMC2943433 | biostudies-literature
Project description:The objective of this study was to evaluate the A/T/N biomarker scheme in relation with brain atrophy patterns in individuals with mild cognitive impairment (MCI). Of the 154 participants with MCI, 74 progressed to AD within 36-months, and 80 remained stable. In addition, 101 cognitively healthy participants and 102 participants with AD were included. The A/T/N classification was assessed with cerebrospinal fluid markers. Each individual was rated as either positive (abnormal) or negative (normal) on each biomarker. Brain atrophy was assessed with visual ratings from magnetic resonance imaging. None of the individuals with MCI progressed to AD if they had a negative "A" biomarker in conjunction with minimal atrophy. In contrary, several individuals with MCI progressed to AD if they had a positive "A" biomarker in conjunction with minimal atrophy. Numerous individuals with MCI showed inconsistency in the neurodegeneration domain ("N") regarding t-tau and atrophy. The assessment of the A/T/N classification scheme in addition with brain atrophy patterns in MCI, increases the knowledge of the clinical trajectories and the variability within the neurodegeneration domain. This emphasises that individuals with MCI display heterogeneous longitudinal patterns closely connected to their biomarker profiles, which could have important clinical implications.
| S-EPMC5976713 | biostudies-literature
Project description:Peripheral (plasma) and central (cerebrospinal fluid, CSF) measures of tau are higher in Alzheimer's disease (AD) relative to prodromal stages and controls. While elevated CSF tau concentrations have been shown to be associated with lower grey matter density (GMD) in AD-specific regions, this correlation has yet to be examined for plasma in a large study.Determine the neuroanatomical correlates of plasma tau using voxel-based analysis.Cross-sectional data for 508 ADNI participants were collected for clinical, plasma total-tau (t-tau), CSF amyloid (A?42) and tau, and MRI variables. The relationship between plasma tau and GMD and between CSF t-tau and GMD were assessed on a voxel-by-voxel basis using regression models. Age, sex, APOE?4 status, diagnosis, and total intracranial volume were used as covariates where appropriate. Participants were defined as amyloid positive (A?+) if CSF A?42 was <192 pg/mL.Plasma tau was negatively correlated with GMD in the medial temporal lobe (MTL), precuneus, thalamus, and striatum. The associations with thalamus and striatum were independent of diagnosis. A negative correlation also existed between plasma tau and GMD in A?+ participants in the MTL, precuneus, and frontal lobe. When compared to CSF t-tau, plasma tau showed a notably different associated brain atrophy pattern, with only small overlapping regions in the fusiform gyrus.Plasma tau may serve as a non-specific marker for neurodegeneration but is still relevant to AD considering low GMD was associated with plasma tau in A?+ participants and not A?-participants.
| S-EPMC5523909 | biostudies-other
Project description:BackgroundPredicting future Alzheimer's disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions.MethodsA cohort of 332 individuals with SCD/MCI were included from the Swedish BioFINDER-1 study. The goal was to predict longitudinal SCD/MCI-to-AD dementia progression and change in Mini-Mental State Examination (MMSE) over four years. Four models were evaluated using different predictors: (1) clinical data only, including demographics, cognitive tests and APOE ε4 status, (2) clinical data plus hippocampal volume, (3) clinical data plus all regional MRI gray matter volumes (N = 68) extracted using FreeSurfer software, (4) a DL model trained using multi-task learning with MRI images, Jacobian determinant images and baseline cognition as input. A double cross-validation scheme, with five test folds and for each of those ten validation folds, was used. External evaluation was performed on part of the ADNI dataset, including 108 patients. Mann-Whitney U-test was used to determine statistically significant differences in performance, with p-values less than 0.05 considered significant.ResultsIn the BioFINDER cohort, 109 patients (33%) progressed to AD dementia. The performance of the clinical data model for prediction of progression to AD dementia was area under the curve (AUC) = 0.85 and four-year cognitive decline was R2 = 0.14. The performance was improved for both outcomes when adding hippocampal volume (AUC = 0.86, R2 = 0.16). Adding FreeSurfer brain regions improved prediction of four-year cognitive decline but not progression to AD (AUC = 0.83, R2 = 0.17), while the DL model worsened the performance for both outcomes (AUC = 0.84, R2 = 0.08). A sensitivity analysis showed that the Jacobian determinant image was more informative than the MRI image, but that performance was maximized when both were included. In the external evaluation cohort from ADNI, 23 patients (21%) progressed to AD dementia. The results for predicted progression to AD dementia were similar to the results for the BioFINDER test data, while the performance for the cognitive decline was deteriorated.ConclusionsThe DL model did not significantly improve the prediction of clinical disease progression in AD, compared to regression models with a single pre-defined brain region.
| S-EPMC10949809 | biostudies-literature
Project description:Recent genetic and proteomic studies demonstrate that clusterin/apolipoprotein-J is associated with risk, pathology, and progression of Alzheimer's disease (AD). Our main aim was to examine associations between plasma clusterin concentration and longitudinal changes in brain volume in normal aging and mild cognitive impairment (MCI). A secondary objective was to examine associations between peripheral concentration of clusterin and its concentration in the brain within regions that undergo neuropathological changes in AD. Non-demented individuals (N=139; mean baseline age 70.5 years) received annual volumetric MRI (912 MRI scans in total) over a mean six-year interval. Sixteen participants (92 MRI scans in total) were diagnosed during the course of the study with amnestic MCI. Clusterin concentration was assayed by ELISA in plasma samples collected within a year of the baseline MRI. Mixed effects regression models investigated whether plasma clusterin concentration was associated with rates of brain atrophy for control and MCI groups and whether these associations differed between groups. In a separate autopsy sample of individuals with AD (N=17) and healthy controls (N=4), we examined the association between antemortem clusterin concentration in plasma and postmortem levels in the superior temporal gyrus, hippocampus and cerebellum. The associations of plasma clusterin concentration with rates of change in brain volume were significantly different between MCI and control groups in several volumes including whole brain, ventricular CSF, temporal gray matter as well as parahippocampal, superior temporal and cingulate gyri. Within the MCI but not control group, higher baseline concentration of plasma clusterin was associated with slower rates of brain atrophy in these regions. In the combined autopsy sample of AD and control cases, representing a range of severity in AD pathology, we observed a significant association between clusterin concentration in the plasma and that in the superior temporal gyrus. Our findings suggest that clusterin, a plasma protein with roles in amyloid clearance, complement inhibition and apoptosis, is associated with rate of brain atrophy in MCI. Furthermore, peripheral concentration of clusterin also appears to reflect its concentration within brain regions vulnerable to AD pathology. These findings in combination suggest an influence of this multi-functional protein on early stages of progression in AD pathology.
| S-EPMC3425349 | biostudies-literature
Project description:AimsCognitive impairment is a common symptom in the trajectory of Parkinson's disease (PD). However, the pathological underpinning is not fully known. We aimed to explore the critical structural alterations in the process of cognitive decline and its relationships with the dopaminergic deficit and the level of related cerebrospinal fluid (CSF) proteins.MethodsNinety-four patients with PD and 32 controls were included in this study. Neuropsychological tests were performed at baseline and after 28 months to identify which patients had normal cognition and which ones developed PD-MCI after follow-up ("converters"). Gray matter atrophy was assessed in cross-sectional and longitudinal analyses, respectively. The associations between altered GMV with dopamine transporter (DAT) results and the level of CSF proteins were assessed.ResultsAmong the 94 patients with normal cognition at baseline, 24 (mean age, 63.1 years) developed PD-MCI after 28 months of follow-up, and 70 (mean age, 62.3 years) remained nonconverters. The converters showed significant right temporal atrophy at baseline and extensive atrophy in temporal lobe at follow-up. Progressive bilateral frontal lobe atrophy was found in the converters. Baseline right temporal atrophy was correlated with the striatal dopaminergic degeneration in the converters. No correlation was found between the right temporal atrophy and the alterations of CSF proteins.ConclusionEarly atrophy in temporal lobes and progressive atrophy in frontal lobes might be a biomarker for developing multidomain impairment of cognition and converting to PD-MCI. Furthermore, cognition-related temporal atrophy might be associated with dopaminergic deficit reflected by DAT scan but independent of CSF proteins in patients with PD who convert to PD-MCI.
| S-EPMC6930819 | biostudies-literature
Project description:BackgroundAmyloid pathology in subjects with mild cognitive impairment (MCI) is an important risk factor for progression to dementia due to Alzheimer's disease. Predicting the onset of dementia is challenging even in the presence of amyloid, as time to progression varies considerably among patients and depends on the onset of neurodegeneration. Survival analysis can account for variability in time to event, but has not often been applied to MRI measurements beyond singular predefined brain regions such as the hippocampus. Here we used a voxel-wise survival analysis to identify in an unbiased fashion brain regions where decreased gray matter volume is associated with time to dementia, and assessed the effects of amyloid on these associations.MethodsWe included 276 subjects with MCI (mean age 67?±?8, 41% female, mean Mini-Mental State Examination 26.6?±?2.4), baseline 3D T1-weighted structural MRI, baseline cerebrospinal fluid (CSF) biomarkers, and prospective clinical follow-up. We fitted for each voxel a proportional Cox hazards regression model to study whether decreased gray matter volume predicted progression to dementia in the total sample, and stratified for baseline amyloid status.ResultsDementia at follow-up occurred in 122 (44%) subjects over an average follow-up period of 2.5?±?1.5 years. Baseline amyloid positivity was associated with progression to dementia (hazard ratio 2.4, p?<?0.001). Within amyloid-positive subjects, decreased gray matter volume in the hippocampal, temporal, parietal, and frontal regions was associated with more rapid progression to dementia (median (interquartile range) hazard ratio across significant voxels 1.35 (1.32-1.40)). Repeating the analysis in amyloid-negative subjects revealed similar patterns (median (interquartile range) hazard ratio 1.76 (1.66-1.91)).ConclusionsIn subjects with MCI, both abnormal amyloid CSF and decreased gray matter volume were associated with future progression to dementia. The spatial pattern of decreased gray matter volume associated with progression to dementia was consistent for amyloid-positive and amyloid-negative subjects.
| S-EPMC5596472 | biostudies-literature
Project description:Mild cognitive impairment (MCI) is a clinical state with a high risk of conversion to Alzheimer's Disease (AD). Since there is no effective treatment for AD, it is extremely important to diagnose MCI as early as possible, as this makes it possible to delay its progression toward AD. However, it's challenging to identify early MCI (EMCI) because there are only mild changes in the brain structures of patients compared with a normal control (NC). To extract remarkable features for these mild changes, in this paper, a multi-modality diagnosis approach based on deep learning is presented. Firstly, we propose to use structure MRI and diffusion tensor imaging (DTI) images as the multi-modality data to identify EMCI. Then, a convolutional neural network based on transfer learning technique is developed to extract features of the multi-modality data, where an L1-norm is introduced to reduce the feature dimensionality and retrieve essential features for the identification. At last, the classifier produces 94.2% accuracy for EMCI vs. NC on an ADNI dataset. Experimental results show that multi-modality data can provide more useful information to distinguish EMCI from NC compared with single modality data, and the proposed method can improve classification performance, which is beneficial to early intervention of AD. In addition, it is found that DTI image can act as an important biomarker for EMCI from the point of view of a clinical diagnosis.
| S-EPMC7498722 | biostudies-literature
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:BackgroundA disease severity index (SI) for Alzheimer's disease (AD) has been proposed that summarizes MRI-derived structural measures into a single score using multivariate data analysis.ObjectivesTo longitudinally evaluate the use of the SI to monitor disease progression and predict future progression to AD in mild cognitive impairment (MCI). Further, to investigate the association between longitudinal change in the SI and cognitive impairment, Apolipoprotein E (APOE) genotype as well as the levels of cerebrospinal fluid amyloid-beta 1-42 (A?) peptide.MethodsThe dataset included 195 AD, 145 MCI and 228 control subjects with annual follow-up for three years, where 70 MCI subjects progressed to AD (MCI-p). For each subject the SI was generated at baseline and follow-ups using 55 regional cortical thickness and subcortical volumes measures that extracted by the FreeSurfer longitudinal stream.ResultsMCI-p subjects had a faster increase of the SI over time (p < 0.001). A higher SI at baseline in MCI-p was related to progression to AD at earlier follow-ups (p < 0.001) and worse cognitive impairment (p < 0.001). AD-like MCI patients with the APOE ?4 allele and abnormal A? levels had a faster increase of the SI, independently (p = 0.003 and p = 0.004).ConclusionsLongitudinal changes in the SI reflect structural brain changes and can identify MCI patients at risk of progression to AD. Disease-related brain structural changes are influenced independently by APOE genotype and amyloid pathology. The SI has the potential to be used as a sensitive tool to predict future dementia, monitor disease progression as well as an outcome measure for clinical trials.
| S-EPMC5573795 | biostudies-literature