Learning Metabolic Brain Networks in MCI and AD by Robustness and Leave-One-Out Analysis: An FDG-PET Study.

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI.Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores.Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively).Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the two brain networks assessed using magnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) in patients with MCI. Methods: This study included 137 patients with MCI and 80 healthy controls (HCs). Sequential interictal scans were performed using FDG-PET and MRI. The MCI metabolic and structural brain networks were constructed according to the standardized uptake value ratio (SUVR) obtained using FDG-PET and gray matter volume obtained using MRI. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer's Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores. Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions by scanning the hubs and found that the betweenness centrality of the right calcarine fissure and its surrounding cortex (CAL.R), left lingual gyrus (LING.L), and left globus pallidus (PAL.L) differed significantly between HCs and patients with MCI in both structural and metabolic networks (all p<0.05). The volume of gray matter atrophy in the PAL.L was significantly positively correlated with comprehension of spoken language (p=0.024) and word-finding difficulty in spontaneous speech item scores (p=0.007) in the ADAS-cog. Glucose intake in the three key brain regions (CAL.R, LING.L, and PAL.L) was significantly negatively correlated with remembering test instructions items in ADAS-cog (p=0.020, p=0.014, and p=0.008, respectively). Conclusion: MRI brain networks showed more changes than FDG-PET brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

To the Editor: Alzheimer disease (AD) is the most common form of dementia, and the central pathogenic event is the abnormal accumulation of amyloid β–protein (Aβ) in extracellular amyloid deposits and cerebral blood vessels.1 AD is a complex and genetically heterogeneous disease. Mild cognitive impairment (MCI), a cognitive disorder in the transition between normal cognition and AD, is a known risk factor for AD, with a conversion rate of ≈10% per year.2 Apolipoprotein E (apoE), a lipid transporter, has been found to be contained in amyloid plaques. The apoE4 isoform or APOE e4 allele is associated with the development of AD1 and an increased risk of MCI.3 Cholesterol has also been identified as a risk factor for AD1,4 and MCI.5 A direct role of cholesterol in the pathogenesis of AD has been suggested by studies in transgenic animal models of AD: cholesterol feeding increases Aβ accumulation and accelerates AD-related pathology,6 whereas cholesterol lowering with statin reduces Aβ pathology.7 Although CAD is a prevalent finding in AD,8 whether or not plasma lipoprotein subfractions are associated with MCI and AD has not yet been investigated. The separation and determination of lipoprotein subfractions are generally labor-intensive and time-consuming. Recently, however, the research group of Schmitz and coworkers developed a new automated technique to separate and quantify lipoprotein subfractions in minutes using capillary isotachophoresis (cITP).9,10 Therefore, in the present study, we investigated the associations among lipoprotein subfractions as determined by cITP, apoE phenotype, MCI, and AD. Twenty-eight patients with MCI, 47 patients with AD, and 26 nondemented control subjects were evaluated at the Neurology Department of Fukuoka …

Mild cognitive impairment and subjective cognitive impairment

High Activities of BACE1 in Brains with Mild Cognitive Impairment

Fluorodeoxyglucose (FDG) positron emission tomography (PET) is useful to predict Alzheimer's disease (AD) conversion in patients with mild cognitive impairment (MCI). However, few studies have examined the extent to which FDG PET alone can predict AD conversion and compared the efficacy between visual and computer-assisted analysis directly. The current study aimed to evaluate the value of FDG PET in predicting the conversion to AD in patients with MCI and to compare the predictive values of visual reading and computer-assisted analysis. A total of 54 patients with MCI were evaluated with FDG PET and followed-up for 2years with final diagnostic evaluation. FDG PET images were evaluated by (1) traditional visual rating, (2) composite score of visual rating of the brain cortices, and (3) composite score of computer-assisted analysis. Receiver operating characteristics (ROC) curves were compared to analyze predictive values. Nineteen patients (35.2%) converted to AD from MCI. The area under the curve (AUC) of the ROC curve of the traditional visual rating, composite score of visual rating, and computer-assisted analysis were 0.67, 0.76, and 0.79, respectively. ROC curves of the composite scores of the visual rating and computer-assisted analysis were comparable (Z = 0.463, p = 0.643). Visual rating and computer-assisted analysis of FDG PET scans were analogously accurate in predicting AD conversion in patients with MCI. Therefore, FDG PET may be a useful tool for screening AD conversion in patients with MCI, when using composite score, regardless of the method of interpretation.

Fil: Surace, Ezequiel Ignacio. Fundacion para la Lucha Contra las Enfermedades Neurologicas de la Infancia. Instituto de Investigaciones Neurologicas "Raul Carrea"; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas; Argentina

Anosognosia, or impaired illness awareness, is a common feature of Alzheimer's disease (AD) and less so of mild cognitive impairment (MCI). Importantly, anosognosia negatively influences clinical outcomes for patients and their caregivers and may predict the conversion from MCI to AD. This study aimed to examine (1) the relationship between brain glucose metabolism as measured by fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET) and anosognosia in patients with MCI and AD and (2) the predictive utility of anosognosia in patients with MCI for later conversion to AD, even when controlling for other factors, including gender, education, apolipoprotein E ε4 carrier status, dementia severity, and cognitive dysfunction. Data for 1,062 participants from the Alzheimer's Disease Neuroimaging Initiative database (2003 to August 2015) classified as having AD (n = 191) or MCI (n = 499) or as healthy comparison (HC) subjects (n = 372) were analyzed. HC participants had Mini-Mental State Examination (MMSE) scores from 24 to 30 and a Clinical Dementia Rating (CDR) of 0. MCI participants had MMSE scores from 24 to 30, a memory complaint, objective memory loss, a CDR of 0.5, absence of significant levels of impairment in other cognitive domains, and essentially preserved activities of daily living. AD participants had MMSE scores ≤ 26 and a CDR of ≥ 0.5, and met National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association criteria for probable AD. Anosognosia was measured with the composite discrepancy score of the study partner and participants' scores on the Everyday Cognition scale (ECog). Bivariate correlations and multiple regression analyses were performed to assess the relationship between anosognosia and FDG-PET findings in each group. Lastly, logistic regression and receiver operating characteristic curve analyses were performed in the MCI sample to determine if anosognosia was predictive of conversion from MCI to AD. Hypometabolism was independently associated with anosognosia in AD, particularly in the posterior cingulate cortex and right angular gyrus. Anosognosia was associated with conversion from MCI to AD within 5 years (OR = 2.74 [95% CI, 1.95 to 3.85], χ²₁ = 33.65, P < .001), even after including covariates (OR = 1.64 [95% CI, 1.12 to 2.40], χ²₁ = 6.43, P = .011). ECog-composite scores ≤ -0.75 were 93% sensitive and 15% specific for conversion from MCI to AD. Anosognosia in AD is related to brain glucose hypometabolism. Further, anosognosia independently predicts conversion from MCI to AD. The absence of anosognosia may be clinically useful to identify those patients that are unlikely to convert from MCI to AD.

18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) and 18F-florbetapir PET are approved neuroimaging biomarkers for the Alzheimer's disease (AD) and mild cognitive impairment (MCI). This study aims to compare the efficacy of 18F-FDG and 18F-florbetapir PET at evaluating the cognitive performance of patients with AD, MCI, and normal controls (NC). 63 subjects (36 male/27 female, mean age = 68.3) including 19 AD, 23 MCI, and 21 NC underwent 18F-FDG and 18F-florbetapir PET imaging. A global quantification approach was applied on supra-tentorial, frontal, parieto-occipital, temporal, and cerebellar brain regions by calculating the global SUVmean ratios (GSUVr) as the weighted average of all regional SUVmean. 18F-FDG and 18F-florbetapir GSUVr of each region were subsequently correlated with the Mini-Mental State Examination (MMSE). Subjects were studied in five categories as NC, MCI patients, AD patients, MCI and AD patients grouped together (MCI/AD), and a group including all the subjects (NC/MCI/AD). Both 18F-FDG and 18F-florbetapir could successfully detect subjects with dementia (p < 0.001). Studied in all regions and groups, the correlation analysis of 18F-FDG GSUVr with MMSE scores was significant in more regions and groups compared to that of 18F-florbetapir. We also demonstrated that the correlation of 18F-FDG GSUVr with MMSE is stronger than that of 18F-florbetapir in the supra-tentorial and temporal regions. This study reveals how 18F-FDG-PET global quantification is a superior indicator of cognitive performance in AD and MCI patients compared to 18F-florbetapir PET. Accordingly, we still recommend 18F-FDG-PET over amyloid imaging in the evaluation for AD and MCI.

BackgroundPlasma biomarker has the potential to be the reliable and propagable approach in the early stage diagnosis of Alzheimer’s disease (AD). However, conventional methods appear powerless in the detection of these biomarkers at low concentrations in plasma. Here, we determined plasma biomarker concentrations of patients across the AD spectrum by an improved digital enzyme-linked immunosorbent assay (ELISA) technique. Confirms the predictive and diagnostic value of this method for AD patients and study the relationships between these biomarkers and cognitive status.MethodsPlasma concentrations of amyloid-beta 40 (Aβ40), amyloid-beta 42 (Aβ42) and plasma phosphorylated tau at threonine 181 (p-tau181) were determined in 43 AD patients, 33 mild cognitive impairment (MCI) patients and 40 normal cognition (NC) subjects as healthy controls using the improved digital ELISA technique. In addition, all subjects were required to receive neuropsychological assessments.ResultsPlasma p-tau181 level showed certain discrepancies between NC and MCI (p < 0.05), AD (p < 0.01) groups. The level of plasma Aβ42 (p < 0.05) and Aβ40 (p < 0.01) was significantly different between AD and NC group. The p-tau181 level was able to distinguish AD (AUC = 0.8768) and MCI (AUC = 0.7932) from NC with higher accuracy than Aβ42/Aβ40 ratio (AUC = 0.8343, AUC = 0.6569). Both p-tau181 (CDR: r = 0.388 p < 0.001; MMSE: r = −0.394 p < 0.001) and Aβ42/Aβ40 ratio (CDR: r = −0.413 p < 0.001; MMSE: r = 0.358 p < 0.001) showed stronger positive correlation with clinical dementia rating (CDR) and mini mental state examination (MMSE) scores than Aβ42 (CDR: r = −0.280 p = 0.003; MMSE: r = 0.266 p = 0.005) or Aβ40 (CDR: r = 0.373 p < 0.001; MMSE: r = −0.288 p = 0.002) alone.ConclusionPlasma p-tau181 level and Aβ42/Aβ40 ratio showed promising values in diagnosis of AD and MCI. Our results indicate that this improved digital ELISA diagnosis approach can facilitate early recognition and management of AD and pre-AD patients.

Objective To evaluate reliability and concurrent validity of the Alzheimer’s Disease Assessment Scale - Cognitive Subscale, Chinese Version (ADAS-Cog-C) among Chinese community older adults. Method Three groups, comprising of 1,276 community-dwelling older adults, were included in this study: a normal control (NC), a mild cognitive impairment (MCI), and an Alzheimer’s disease (AD) group. All participants were assessed through ADAS-Cog-C, clinical interviews, physical examinations, Mini Mental State Examination (MMSE), and the Clinical Dementia Rating Scale (CDR). Internal consistency was assessed to evaluate the reliability of ADAS-Cog-C. Pearson and Spearman correlation coefficients were calculated to evaluate the concurrent validity between ADAS-Cog-C, MMSE, and CDR. Results Overall, the Cronbach’s alpha coefficients of ADAS-Cog-C for the AD and MCI groups were 0.843 and 0.554, respectively. The split-half reliability coefficients for the AD and MCI groups were 0.860 and 0.539, respectively. ADAS-Cog-C scores were negatively correlated with MMSE scores (r = −0.706, p < 0.001) and positively associated with CDR scores (r = 0.546, p < 0.001). After excluding the MCI group from the analysis, the internal consistency of ADAS-Cog-C for the total population improved (α = 0.813, r hh = 0.852, all p < 0.001), as did the correlation between ADAS-Cog-C and MMSE (r = −0.828, p < 0.001) and CDR (r = 0.429, all p < 0.001) scores. Conclusions ADAS-Cog-C has good internal consistency and concurrent validity for assessing Chinese community older adults with AD, but poor consistency, good concurrent validity with the MMSE while moderate concurrent validity with the CDR for MCI.

To assess the extent to which multiple Alzheimer disease (AD) biomarkers improve the ability to predict future decline in subjects with mild cognitive impairment (MCI) compared with predictions based on clinical parameters alone. All protocols were approved by the institutional review board at each site, and written informed consent was obtained from all subjects. The study was HIPAA compliant. Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline magnetic resonance (MR) imaging and fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) studies for 97 subjects with MCI were used. MR imaging-derived gray matter probability maps and FDG PET images were analyzed by using independent component analysis, an unbiased data-driven method to extract independent sources of information from whole-brain data. The loading parameters for all MR imaging and FDG components, along with cerebrospinal fluid (CSF) proteins, were entered into logistic regression models (dependent variable: conversion to AD within 4 years). Eight models were considered, including all combinations of MR imaging, PET, and CSF markers with the covariates (age, education, apolipoprotein E genotype, Alzheimer's Disease Assessment Scale-Cognitive subscale score). Combining MR imaging, FDG PET, and CSF data with routine clinical tests significantly increased the accuracy of predicting conversion to AD compared with clinical testing alone. The misclassification rate decreased from 41.3% to 28.4% (P < .00001). FDG PET contributed more information to routine tests (P < .00001) than CSF (P = .32) or MR imaging (P = .08). Imaging and CSF biomarkers can improve prediction of conversion from MCI to AD compared with baseline clinical testing. FDG PET appears to add the greatest prognostic information.

P2‐443: ASSOCIATION BETWEEN SUBFIELD VOLUMES OF THE MEDIAL TEMPORAL LOBE AND NEUROPSYCHOLOGICAL ASSESSMENTS

Cognitive assessments and neuroimaging are routinely combined in clinical practice to diagnose dementia represented by Alzheimer's disease (AD). The Montreal Cognitive Assessment (MoCA) is reported to be more suitable than the Mini-Mental State Examination (MMSE) for screening mild cognitive impairment (MCI) and mild AD. On the other hand, attention to the subfield volumes of the medial temporal lobe has recently been considered important for the differential diagnosis and early detection of AD. The aim of this study was to uncover which specific hippocampal subfields and adjacent extrahippocampal structures contribute to deficits in cognitive assessment scores in patients with MCI and AD. We recruited from our institute 31 Japanese patients—14 with amnestic MCI and 17 with probable AD, with a clinical dementia rating (CDR) of 0.5 and 1, respectively—and 50 healthy elderly individuals with a CDR of 0. All participants underwent magnetic resonance imaging and cognitive assessments with the MMSE, Wechsler Memory Scale-Revised Logical Memory I and II, and Japanese version of the MoCA (MoCA-J). With adjustment for age and sex, we performed partial correlation analysis of the cognitive assessment scores with the subfield volumes of the medial temporal lobe measured by software-mediated automatic segmentation of hippocampal subfields using high-resolution T1-and T2-weighted images. Compared with normal controls, patients with MCI and AD showed subfield volume reductions in cornu ammonis (CA) 1, CA2, Brodmann area (BA) 35, BA36, the dentate gyrus (DG), the subiculum, and the entorhinal cortex (ERC). All participants showed high correlation coefficients (above 0.6) between cognitive assessment scores and subfield volumes in CA1, the DG, the subiculum, the ERC, and BA36. In patients with MCI and AD, the MoCA-J showed higher correlations than the MMSE with subfield volumes in CA1, the DG, the subiculum, and the ERC. These results suggest that the combination of the in vivo analysis of subfield morphometry of the medial temporal lobe with the MoCA-J paradigm provides important insights into whether changes within specific subfields are related to the cognitive profile in MCI and AD.