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Multimodality Neuroimaging in Mild Cognitive Impairment: A Cross-sectional Comparison Study.

ABSTRACT: Background and Purpose:Mild cognitive impairment (MCI) is a focus of considerable research. The present study aimed to test the utility of a logistic regression-derived classifier, combining specific quantitative multimodal magnetic resonance imaging (MRI) data for the early objective phenotyping of MCI in the clinic, over structural MRI data. Methods:Thirty-three participants with cognitively stable amnestic MCI; 15 MCI converters to early Alzheimer's disease (AD; diseased controls) and 20 healthy controls underwent high-resolution T1-weighted volumetric MRI, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H MR spectroscopy). The regional volumes were obtained from T1-weighted MRI. The fractional anisotropy and mean diffusivity maps were derived from DTI over multiple white matter regions. The 1H MRS voxels were placed over posterior cingulate gyri, and N-acetyl aspartate (NAA)/creatine (Cr), choline (Cho)/Cr, myoinositol (mI/Cr), and NAA/mI ratios were obtained. A multimodal classifier comprising MR volumetry, DTI, and MRS was prepared. A cutoff point was arrived based on receiver operator characteristics analysis. Results were considered significant, if P < 0.05. Results:The most sensitive individual marker to discriminate MCI from controls was DTI (90.9%), with a specificity of 50%. For classifying MCI from AD, the best individual modality was DTI (72.7%), with a high specificity of 87.9%. The multimodal classifier approach for MCI control classification achieved an area under curve (AUC) (AUC = 0.89; P < 0.001), with 93.9% sensitivity and 70% specificity. The combined classifier for MCI-AD achieved a highest AUC (AUC = 0.93; P < 0.001), with 93% sensitivity and 85.6% specificity. Conclusions:The combined method of gray matter atrophy, white matter tract changes, and metabolite variation achieved a better performance at classifying MCI compared to the application of individual MRI biomarkers.

SUBMITTER: Sheelakumari R

PROVIDER: S-EPMC6073958 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveReceptor for advanced glycation end products (AGEs; RAGE) binds to both AGEs and amyloid-beta peptides. RAGE is involved in chronic complications of type 2 diabetes and Alzheimer's disease. We aimed to investigate the roles of RAGE, AGEs and the Gly82Ser polymorphism of RAGE in mild cognitive impairment (MCI) among type 2 diabetes patients.MethodsOf the 167 hospitalized type 2 diabetes patients recruited, 82 satisfied the diagnostic criteria for MCI, and 85 matched control individuals were classified as non-MCI. Demographic data were collected, and the soluble RAGE (sRAGE) concentrations, serum AGE-peptide (AGE-P) levels, RAGE Gly82Ser genotype and neuropsychological test results were examined.ResultsThe MCI group exhibited a decreased sRAGE level (0.87±0.35 vs. 1.05±0.52 ng/ml, p<0.01) and an increased serum AGE-P level (3.54±1.27 vs. 2.71±1.18 U/ml, p<0.01) compared with the control group. Logistic regression analysis indicated that each unit reduction in the sRAGE concentration increased the MCI risk by 54% (OR 0.46[95% CI 0.22-0.96], p = 0.04) and that each unit increase in the AGE-P level increased the MCI risk by 72% in the type 2 diabetes patients (OR 1.72[95% CI 1.31-2.28], p<0.01). The serum sRAGE level was negatively correlated with the score on the trail making test-B (TMT-B) (r = -0.344, p = 0.002), which indicates early cognitive deficits related to diabetes. Moreover, the AGE-P level was positively correlated with multiple cognitive domains (all p<0.05). No significant differences in the neuropsychological test results or serum RAGE concentrations between the different RAGE genotypes or in the RAGE genotype frequencies between the MCI and control groups were identified (all p>0.05).ConclusionsThe RAGE pathway partially mediates AGE-induced MCI in diabetic patients. The serum AGE-P level may serve as a serum biomarker of MCI in these individuals, and sRAGE represents a predictor and even a potential intervention target of early cognitive decline in type 2 diabetes patients.Trial registrationAdvanced Glycation End Products Induced Cognitive Impairment in Diabetes: BDNF Signal Meditated Hippocampal Neurogenesis ChiCTR-OCC-15006060.

Project description:BACKGROUND:Discrimination between early-stage dementia and other cognitive impairment diagnoses is central to enable appropriate interventions. Previous studies indicate that dual-task testing may be useful in such differentiation. The objective of this study was to investigate whether dual-task test outcomes discriminate between groups of individuals with dementia disorder, mild cognitive impairment, subjective cognitive impairment, and healthy controls. METHODS:A total of 464 individuals (mean age 71?years, 47% women) were included in the study, of which 298 were patients undergoing memory assessment and 166 were cognitively healthy controls. Patients were grouped according to the diagnosis received: dementia disorder, mild cognitive impairment, or subjective cognitive impairment. Data collection included participants' demographic characteristics. The patients' cognitive test results and diagnoses were collected from their medical records. Healthy controls underwent the same cognitive tests as the patients. The mobility test Timed Up-and-Go (TUG single-task) and two dual-task tests including TUG (TUGdt) were carried out: TUGdt naming animals and TUGdt months backwards. The outcomes registered were: time scores for TUG single-task and both TUGdt tests, TUGdt costs (relative time difference between TUG single-task and TUGdt), number of different animals named, number of months recited in correct order, number of animals per 10?s, and number of months per 10?s. Logistic regression models examined associations between TUG outcomes pairwise between groups. RESULTS:The TUGdt outcomes "animals/10?s" and "months/10?s" discriminated significantly (p <?0.001) between individuals with an early-stage dementia diagnosis, mild cognitive impairment, subjective cognitive impairment, and healthy controls. The TUGdt outcome "animals/10?s" showed an odds ratio of 3.3 (95% confidence interval 2.0-5.4) for the groups dementia disorders vs. mild cognitive impairment. TUGdt cost outcomes, however, did not discriminate between any of the groups. CONCLUSIONS:The novel TUGdt outcomes "words per time unit", i.e. "animals/10?s" and "months/10?s", demonstrate high levels of discrimination between all investigated groups. Thus, the TUGdt tests in the current study could be useful as complementary tools in diagnostic assessments. Future studies will be focused on the predictive value of TUGdt outcomes concerning dementia risk for individuals with mild cognitive impairment or subjective cognitive impairment.

Project description:OBJECTIVE:To identify a neuroimaging signature predictive of brain amyloidosis as a screening tool to identify individuals with mild cognitive impairment (MCI) that are most likely to have high levels of brain amyloidosis or to be amyloid-free. METHODS:The prediction model cohort included 62 MCI subjects screened with structural magnetic resonance imaging (MRI) and (11) C-labeled Pittsburgh compound B positron emission tomography (PET). We identified an anatomical shape variation-based neuroimaging predictor of brain amyloidosis and defined a structural MRI-based brain amyloidosis score (sMRI-BAS). Amyloid beta positivity (A?(+) ) predictive power of sMRI-BAS was validated on an independent cohort of 153 MCI patients with cerebrospinal fluid A?1-42 biomarker data but no amyloid PET scans. We compared the A?(+) predictive power of sMRI-BAS to those of apolipoprotein E (ApoE) genotype and hippocampal volume, the 2 most relevant candidate biomarkers for the prediction of brain amyloidosis. RESULTS:Anatomical shape variations predictive of brain amyloidosis in MCI embraced a characteristic spatial pattern known for high vulnerability to Alzheimer disease pathology, including the medial temporal lobe, temporal-parietal association cortices, posterior cingulate, precuneus, hippocampus, amygdala, caudate, and fornix/stria terminals. A?(+) prediction performance of sMRI-BAS and ApoE genotype jointly was significantly better than the performance of each predictor separately (area under the curve [AUC] = 0.88 vs AUC = 0.70 and AUC = 0.81, respectively) with >90% sensitivity and specificity at 20% false-positive rate and false-negative rate thresholds. Performance of hippocampal volume as an independent predictor of brain amyloidosis in MCI was only marginally better than random chance (AUC = 0.56). INTERPRETATION:As one of the first attempts to use an imaging technique that does not require amyloid-specific radioligands for identification of individuals with brain amyloidosis, our findings could lead to development of multidisciplinary/multimodality brain amyloidosis biomarkers that are reliable, minimally invasive, and widely available.

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Multimodality Neuroimaging in Mild Cognitive Impairment: A Cross-sectional Comparison Study.

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