Investigating Gamma Frequency Band PSD in Alzheimer's Disease Using qEEG from Eyes-Open and Eyes-Closed Resting States.

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

A neuroinflammatory reaction in Alzheimer disease (AD) brains involves reactive astrocytes that overexpress monoamine oxidase-B (MAO-B). 18F-(S)-(2-methylpyrid-5-yl)-6-[(3-fluoro-2-hydroxy)propoxy]quinoline (18F-SMBT-1) is a novel 18F PET tracer highly selective for MAO-B. We characterized the clinical performance of 18F-SMBT-1 PET across the AD continuum as a potential surrogate marker of reactive astrogliosis. Methods: We assessed 18F-SMBT-1 PET regional binding in 77 volunteers (76 ± 5.5 y old; 41 women, 36 men) across the AD continuum: 57 who were cognitively normal (CN) (44 amyloid-β [Aβ]-negative [Aβ-] and 13 Aβ-positive [Aβ+]), 12 who had mild cognitive impairment (9 Aβ- and 3 Aβ+), and 8 who had AD dementia (6 Aβ+ and 2 Aβ-). All participants also underwent Aβ and tau PET imaging, 3-T MRI, and neuropsychologic evaluation. Tau imaging results were expressed in SUV ratios using the cerebellar cortex as a reference region, whereas Aβ burden was expressed in centiloids. 18F-SMBT-1 outcomes were expressed as SUV ratio using the subcortical white matter as a reference region. Results: 18F-SMBT-1 yielded high-contrast images at steady state (60-80 min after injection). When compared with the Aβ- CN group, there were no significant differences in 18F-SMBT-1 binding in the group with Aβ- mild cognitive impairment. Conversely, 18F-SMBT-1 binding was significantly higher in several cortical regions in the Aβ+ AD group but also was significantly lower in the mesial temporal lobe and basal ganglia. Most importantly, 18F-SMBT-1 binding was significantly higher in the same regions in the Aβ+ CN group as in the Aβ- CN group. When all clinical groups were considered together, 18F-SMBT-1 correlated strongly with Aβ burden and much less with tau burden. Although in most cortical regions 18F-SMBT-1 did not correlate with brain volumetrics, regions known for high MAO-B concentrations presented a direct association with hippocampal and gray matter volumes, whereas the occipital lobe was directly associated with white matter hyperintensity. 18F-SMBT-1 binding was inversely correlated with Mini Mental State Examination and the Australian Imaging Biomarkers and Lifestyle's Preclinical Alzheimer Cognitive Composite in some neocortical regions such as the frontal cortex, lateral temporal lobe, and supramarginal gyrus. Conclusion: Cross-sectional human PET studies with 18F-SMBT-1 showed that Aβ+ AD patients, but most importantly, Aβ+ CN individuals, had significantly higher regional 18F-SMBT-1 binding than Aβ- CN individuals. Moreover, in several regions in the brain, 18F-SMBT-1 retention was highly associated with Aβ load. These findings suggest that increased 18F-SMBT-1 binding is detectable at the preclinical stages of Aβ accumulation, providing strong support for its use as a surrogate marker of astrogliosis in the AD continuum.

A challenge in developing informative neuroimaging biomarkers for early diagnosis of Alzheimer's disease is the need to identify biomarkers that are evident before the onset of clinical symptoms, and which have sufficient sensitivity and specificity on an individual patient basis. Recent literature suggests that spatial patterns of brain atrophy discriminate amongst Alzheimer's disease, mild cognitive impairment (MCI) and cognitively normal (CN) older adults with high accuracy on an individual basis, thereby offering promise that subtle brain changes can be detected during prodromal Alzheimer's disease stages. Here, we investigate whether these spatial patterns of brain atrophy can be detected in CN and MCI individuals and whether they are associated with cognitive decline. Images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to construct a pattern classifier that recognizes spatial patterns of brain atrophy which best distinguish Alzheimer's disease patients from CN on an individual person basis. This classifier was subsequently applied to longitudinal magnetic resonance imaging scans of CN and MCI participants in the Baltimore Longitudinal Study of Aging (BLSA) neuroimaging study. The degree to which Alzheimer's disease-like patterns were present in CN and MCI subjects was evaluated longitudinally in relation to cognitive performance. The oldest BLSA CN individuals showed progressively increasing Alzheimer's disease-like patterns of atrophy, and individuals with these patterns had reduced cognitive performance. MCI was associated with steeper longitudinal increases of Alzheimer's disease-like patterns of atrophy, which separated them from CN (receiver operating characteristic area under the curve equal to 0.89). Our results suggest that imaging-based spatial patterns of brain atrophy of Alzheimer's disease, evaluated with sophisticated pattern analysis and recognition methods, may be useful in discriminating among CN individuals who are likely to be stable versus those who will show cognitive decline. Future prospective studies will elucidate the temporal dynamics of spatial atrophy patterns and the emergence of clinical symptoms.

BackgroundBiomarkers of Alzheimer’s disease (AD) and mild cognitive impairment (MCI, or prodromal AD) are highly significant for early diagnosis, clinical trials and treatment outcome evaluations. Electroencephalography (EEG), being noninvasive and easily accessible, has recently been the center of focus. However, a comprehensive understanding of EEG in dementia is still needed. A primary objective of this study is to investigate which of the many EEG characteristics could effectively differentiate between individuals with AD or prodromal AD and healthy individuals.MethodsWe collected resting state EEG data from individuals with AD, prodromal AD, and normal cognition. Two distinct preprocessing pipelines were employed to study the reliability of the extracted measures across different datasets. We extracted 41 different EEG features. We have also developed a stand-alone software application package, Feature Analyzer, as a comprehensive toolbox for EEG analysis. This tool allows users to extract 41 EEG features spanning various domains, including complexity measures, wavelet features, spectral power ratios, and entropy measures. We performed statistical tests to investigate the differences in AD or prodromal AD from age-matched cognitively normal individuals based on the extracted EEG features, power spectral density (PSD), and EEG functional connectivity.ResultsSpectral power ratio measures such as theta/alpha and theta/beta power ratios showed significant differences between cognitively normal and AD individuals. Theta power was higher in AD, suggesting a slowing of oscillations in AD; however, the functional connectivity of the theta band was decreased in AD individuals. In contrast, we observed increased gamma/alpha power ratio, gamma power, and gamma functional connectivity in prodromal AD. Entropy and complexity measures after correcting for multiple electrode comparisons did not show differences in AD or prodromal AD groups. We thus catalogued AD and prodromal AD-specific EEG features.ConclusionsOur findings reveal that the changes in power and connectivity in certain frequency bands of EEG differ in prodromal AD and AD. The spectral power, power ratios, and the functional connectivity of theta and gamma could be biomarkers for diagnosis of AD and prodromal AD, measure the treatment outcome, and possibly a target for brain stimulation.

BackgroundNeurofibrillary tangle pathology is thought to drive neurodegeneration in beta‐amyloid positive (A+) cognitively normal (CN) individuals, i.e., preclinical Alzheimer’s disease (AD). However, in beta‐amyloid negative (A‐) CN, the contribution of tau pathology [primary age‐related tauopathy (PART)] to neurodegeneration remains uncertain. We investigate the correlation between tau burden measured by PET in the medial temporal lobe (MTL) and MRI‐derived cross‐sectional and longitudinal structural atrophy in these cohorts.Methods420 CN (A‐/A+: 294/101, Table 1) individuals from ADNI with AV1451 PET and T1‐weighted MRI acquired within one year were included. Bilateral anterior/posterior hippocampal volume and thickness of entorhinal cortex (ERC), Brodmann areas 35/36 (BA35/BA36) and parahipocampal cortex (PHC) were obtained from baseline MRI scans. Bilateral MTL tau burden was computed as AV1451 uptake across ERC and BA35. Beta‐amyloid status was determined with PET by standard cut‐offs (Florbetapir: 1.11; Florbetaben: 1.08). In a subset of participants with prospective longitudinal MRI scans (up to 4.5 years), annualized volume change rate of each MTL subregion was estimated. Partial Pearson’s correlation, controlling for age, sex, and MRI‐PET date difference, was performed between MTL tau burden and structural atrophy measurements. Intracranial volume and MRI follow‐up time were additional covariates for cross‐sectional and longitudinal analysis respectively. We performed the analysis separately for each hemisphere in the whole CN cohort and its A+ and A‐ subgroups.ResultsTau burden was significantly associated with cross‐sectional left BA35/36 thickness in the whole cohort and bilateral posterior hippocampus volume in both A+ CN and the whole cohort (Table 2, Figure 1), but not in in A‐ CN. Stronger correlations between MTL tau burden and longitudinal atrophy, despite smaller sample size, was observed in almost all the MTL subregions regardless of amyloid status (Table 3, Figure 1). In general, effects from the left hemisphere were stronger than those from the right hemisphere. All significant correlations were maintained when corrected for beta‐amyloid PET SUVR.ConclusionsThe results demonstrated that elevated tau predicts subsequent neurodegeneration in early Braak regions in CN subjects regardless of amyloid status. This indicates that PART may be an important driver of neurodegeneration already during normal ageing in cognitively normal individuals.

BackgroundNeurofibrillary tangle pathology is thought to drive neurodegeneration in beta‐amyloid positive (A+) cognitively normal (CN) individuals, i.e., preclinical Alzheimer’s disease (AD).However, in beta‐amyloid negative (A‐) CN, the contribution of tau pathology [primary age‐related tauopathy (PART)] to neurodegeneration remains uncertain. We investigate the correlation between tau burden measured by PET in the medial temporal lobe (MTL) and MRI‐derived cross‐sectional and longitudinal structural atrophy in these cohorts.Methods420 CN (A‐/A+: 294/101, Table 1) individuals from ADNI with AV1451 PET and T1‐weighted MRI acquired within one year were included. Bilateral anterior/posterior hippocampal volume and thickness of entorhinal cortex (ERC), Brodmann areas 35/36 (BA35/BA36) and parahipocampal cortex (PHC) were obtained from baseline MRI scans. Bilateral MTL tau burden was computed as AV1451 uptake across ERC and BA35. Beta‐amyloid status was determined with PET by standard cut‐offs (Florbetapir: 1.11; Florbetaben: 1.08). In a subset of participants with prospective longitudinal MRI scans (up to 4.5 years), annualized volume change rate of each MTL subregion was estimated. Intracranial volume and MRI follow‐up time were additional covariates for cross‐sectional and longitudinal analysis respectively. We performed the analysis separately for each hemisphere in the whole CN cohort and its A+ and A‐ subgroups.ResultsTau burden was significantly associated with cross‐sectional left BA35/36 thickness in the whole cohort and bilateral volume in both A+ CN and the whole cohort (Table 2, Figure 1), but not in in A‐ CN. Stronger correlations between MTL tau burden and longitudinal atrophy, despite smaller sample size, was observed in almost all the MTL subregions regardless of amyloid status (Table 3, Figure 1). In general, effects from the left hemisphere were stronger than those from the right hemisphere. All significant correlations were maintained when corrected for beta‐amyloid PET SUVR.ConclusionsThe results demonstrated that elevated tau predicts subsequent neurodegeneration in early Braak regions in CN subjects regardless of amyloid status. This indicates that PART may be an important driver of neurodegeneration already during normal ageing in cognitively normal individuals.

IntroductionLongitudinal assessment of cognitive decline in amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) often involves the use of both informant-based and objective cognitive assessments. As efforts have focused on identifying individuals in pre-clinical stages, instruments that are sensitive to subtle cognitive changes are needed. The Alzheimer’s Questionnaire (AQ) has demonstrated high sensitivity and specificity in identifying aMCI and AD; however its ability to measure longitudinal change has not been assessed. The aims of this study are to assess the sensitivity to change of the AQ and to determine whether the AQ predicts change in global cognition and function in cognitively normal (CN), aMCI, and AD subjects.MethodsData from 202 individuals participating in a brain and body donation program were utilized for this study (101 CN, 62 aMCI, 39 AD). AD and aMCI individuals were matched on age, education, and gender to CN individuals. Sensitivity to change of the AQ was assessed in addition to the AQ’s ability to predict change in global cognition and function. The Mini Mental State Exam (MMSE) and Functional Activities Questionnaire (FAQ) were used as gold standard comparisons of cognition and function. Sample size calculations for a 25% treatment effect were also carried out for all three groups.ResultsThe AQ demonstrated small sensitivity to change in the aMCI and CN groups (d = 0.33, d = 0.23, respectively) and moderate sensitivity to change in the AD group (d = 0.43). The AQ was associated with increases in the Clinical Dementia Rating Global Score (OR = 1.20 (1.09, 1.32), P <0.001). Sample size calculations found that the AQ would require substantially fewer subjects than the MMSE given a 25% treatment effect.ConclusionsAlthough the AQ demonstrated small sensitivity to change in aMCI and CN individuals in terms of effect size, the AQ may be superior to objective cognitive tests in terms of required sample size for a clinical trial. As clinicians and researchers continue to identify and treat individuals in earlier stages of AD, there is a need for instruments that are sensitive to cognitive changes in these earlier stages.

BackgroundNeuroinflammation is a key process in initiating and propagating Alzheimer’s disease (AD). Even though it is widely known that the deposit of amyloid plaques and CSF levels of amyloid distinguishes patients with AD or mild cognitive impairment (MCI) from cognitively normal (CN) individuals, little is known about the role of amyloid‐specific immune response in cognitive decline.MethodUsing a polyfunctionality assay typically used for detecting virus‐specific T cell responses, we tested participants from the Epidemiology of Mild Cognitive Impairment in Taiwan study (EMCIT) and the Taiwan Precision Medicine Initiative of Cognitive impairment and dementia (TPMIC) study to compare the amyloid‐specific T cell responses between CN and MCI individuals. The abilities of T cell response parameters and plasma p‐Tau181 to distinguish MCI from CN were tested.ResultResults from both cohorts showed an enhanced amyloid‐specific T‐cell response in individuals with MCI. In the EMCIT cohort, the individual’s amyloid‐specific CD4+ response frequency of total CD4+ cells was significantly larger in MCI (n = 69, 0.93%) than in CN (n = 69, 0.51%, p &lt; 0.001). CD4+ T cell response discriminated MCI versus CN (area under curve [AUC], 0.72 [0.64‐0.81]) with significantly higher accuracy than p‐Tau181 (AUC: 0.59 [0.5‐0.69], p &lt; 0.01). In the TPMIC cohort, both CD4+ and CD8+ response frequencies were higher in MCI individuals (n = 21, CD4: 1.2%, CD8: 2.02%) than in CN (n = 30, CD4: 0.14%, CD8:0.27%; both p &lt; 0.001). CD4+ T cell response frequency and CD8+ response frequency also outperform p‐Tau181 in their discriminative accuracy of MCI versus NC (CD4+ AUC, 0.97, [0.94‐1.01]; CD8+ AUC, 0.96, [0.92‐1.01]; p‐Tau181 AUC, 0.83, [0.69‐0.96]; both p &lt; 0.05).ConclusionOur study validates the amyloid hypothesis by showing that amyloid‐associated neuroinflammation is involved in the process of neurodegeneration and demonstrated the accuracy of using amyloid‐specific T cell response to discriminate MCI from CN individuals. The TPMIC cohort is an ongoing longitudinal study that includes amyloid PET results and thus we will investigate the prognostic value of amyloid‐T cell response in the future.

Quantitative MRI assessment methods have limited utility due to a lack of standardized methods and measures for Alzheimer disease (AD) and amnestic mild cognitive impairment (aMCI). To employ a relatively new and easy-to-use quantitative assessment method to reveal volumetric changes in subcortical gray matter (GM) regions, hippocampus, and global intracranial structures as well as the diagnostic performance and best thresholds of total hippocampal volumetry in individuals with AD and those with aMCI. A total of 74 individuals-37 with mild to moderate AD, 19 with aMCI, and 18 with normal cognition (NC)-underwent a 3T MRI. Fully automated segmentation and volumetric measurements were performed. The AD and aMCI groups had smaller volumes of amygdala, nucleus accumbens, and hippocampus compared with the NC group. These same two groups had significantly smaller total white matter volume than the NC group. The AD group had smaller total GM volume compared with the aMCI and NC groups. The thalamus in the AD group showed a subtle atrophy. There were no significant volumetric differences in the caudate nucleus, putamen, or globus pallidus between the groups. The amygdala and nucleus accumbens showed atrophy comparable to the hippocampal atrophy in both the AD and aMCI groups, which may contribute to cognitive impairment. Hippocampal volumetry is a reliable tool for differentiating between AD and NC groups but has substantially less power in differentiating between AD and aMCI groups. The loss of total GM volume differentiates AD from aMCI and NC.

BackgroundPredicting brain age from neuroimaging data is an emerging field. The age gap (AG), the difference between chronological age (CA) and brain age (BA), is crucial for indicating individual neuroanatomical aging. Previous deep learning models faced challenges in generalizability and neuroanatomical interpretability. We incorporated patients with different dementia types, including dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD), alongside mild cognitive impairment (MCI) and cognitive normal (CN) individuals. This inclusive strategy is essential for comprehensive mapping of neurocognitive trajectories and understanding distinct aging patterns across various cognitive conditions.MethodUtilizing T1‐weighted MRI images of n = 3,859 subjects (Table 1) from the CamCAN, NACC, and ADNI databases, this study aimed to predict brain age in four groups (CN, MCI, AD, and DLB). Structural MRI data were spatial normalized and skull‐striped. Then a 3D Convolutional Neural Network (CNN) based on the skull‐striped data was used for age prediction. The model’s architecture includes three convolutional layers with ReLU activation, max‐pooling, batch normalization, and dropout for regularization, ending with global average pooling and dense layers. The model was trained and validated on CN subjects. The trained model was used to predict age in MCI, DLB, and AD patients as well as the test set of CN subjects.ResultThe 3D CNN model accurately predicted brain age in the CN test set with an AG of 0.64 ± 2.74 years and an absolute AG of 1.86 ± 2.11 years (Figure 1 and Table 1). In DLB and AD patients, the average AG was 3.81 and 2.90 years, respectively, and significantly larger than 0 (P &lt; 10‐5), indicating accelerated aging patterns in these groups. The average AG of MCI was 0.09 years which was significantly smaller than that of both DLB and AD (P &lt; 10‐3), indicating the early stage of impairment in MCI patients.ConclusionOur 3D CNN model accurately predicted brain age in cognitively normal individuals and identified accelerated aging in DLB and AD patients. The model's precision highlights its potential for early detection and understanding of neurocognitive trajectories, contributing to advancements in neurological research and clinical diagnostics.

ObjectiveTo investigate the associations between cerebral microhemorrhages (CMH) and cognitive decline across the Alzheimer’s dementia continuum.MethodsUsing the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, we studied 619 participants, categorized into 221 cognitively normal (CN) participants, 281 patients with mild cognitive impairment (MCI), and 117 patients with Alzheimer’s disease (AD). CMH prevalence and distribution were determined using T2-weighted magnetic resonance imaging (MRI), focusing on the frontal, occipital, and parietal subcortical regions of interest (ROIs).Clinical dementia rating scale sum of boxes (CDR-SB) and mini-mental state examination (MMSE) were used for diagnosis and composite cognitive scores regarding visuospatial abilities, language, memory, and executive functions were used as outcome variables. Age, gender, and APOE ε4 positivity status were used as covariates.ResultsThe AD group displayed significantly elevated tau and P-tau levels compared to MCI and CN groups (p < 0.001). APOE ε4 positivity was 67.5% in the AD group, surpassing the 50.2% in MCI and 29% in CN individuals (p < 0.001). Cognitive assessments revealed that the AD group’s CDR-SB score and MMSE both significantly differed from these scores in the MCI and CN groups (p < 0.001). Overall, CMH prevalence was 27.7%, with a predominant distribution in the frontal subcortical ROIs. MCI subjects with CMH showed notably diminished ADNI Visuospatial Composite Scores compared to those without CMH. Age significantly predicted CMH in CN and MCI (p < 0.05). In AD participants, APOE ε4 heterozygotes (p = 0.02) and homozygotes (p = 0.01) hadincreased CMH likelihood.ConclusionCMHs are significantly associated with cognitive decline in patients with MCI. This association is more prominent in regard to the decline in visuospatial abilities.

The pathological effects of amyloid β oligomers (Aβo) may be mediated through the metabotropic glutamate receptor subtype 5 (mGluR5), leading to synaptic loss in Alzheimer's disease (AD). Positron emission tomography (PET) studies of mGluR5 using [18F]FPEB indicate a reduction of receptor binding that is focused in the medial temporal lobe in AD. Synaptic loss due to AD measured through synaptic vesicle glycoprotein 2A (SV2A) quantification with [11C]UCB-J PET is also focused in the medial temporal lobe, but with clear widespread reductions is commonly AD-affected neocortical regions. In this study, we used [18F]FPEB and [11C]UCB-J PET to investigate the relationship between mGluR5 and synaptic density in early AD. Fifteen amyloid positive participants with early AD and 12 amyloid negative, cognitively normal (CN) participants underwent PET scans with both [18F]FPEB to measure mGluR5 and [11C]UCB-J to measure synaptic density. Parametric DVR images using equilibrium methods were generated from dynamic. For [18F]FPEB PET, DVR was calculated using equilibrium methods and a cerebellum reference region. For [11C]UCB-J PET, DVR was calculated with a simplified reference tissue model - 2 and a whole cerebellum reference region.. A strong positive correlation between mGluR5 and synaptic density was present in the hippocampus for participants with AD (r = 0.81, p < 0.001) and in the CN group (r = 0.74, p = 0.005). In the entorhinal cortex, there was a strong positive correlation between mGluR5 and synaptic in the AD group (r = 0.85, p <0.001), but a weaker non-significant correlation in the CN group (r = 0.36, p = 0.245). Exploratory analyses within and between other brain regions suggested significant positive correlations between mGluR5 in the medial temporal lobe and synaptic density in a broader set of commonly AD-affected regions. Medial temporal loss of mGluR5 in AD is associated with synaptic loss in both medial temporal regions and more broadly in association cortical regions, indicating that mGluR5 mediated Aβo toxicity may lead to early synaptic loss more broadly in AD-affected networks. In CN individuals, an isolated strong association between lower mGluR5 and lower synaptic density may indicate non-AD related synaptic loss.

BackgroundEarly prevention of Alzheimer’s disease (AD) is a feasible way to delay AD onset and progression. Information on AD prediction at the individual patient level will be useful in AD prevention. In this study, we aim to develop risk models for predicting AD onset at individual level using optimal set of predictors from multiple features.MethodsA total of 487 cognitively normal (CN) individuals and 796 mild cognitive impairment (MCI) patients were included from Alzheimer's Disease Neuroimaging Initiative. All the participants were assessed for clinical, cognitive, magnetic resonance imaging and cerebrospinal fluid (CSF) markers and followed for mean periods of 5.6 years for CN individuals and 4.6 years for MCI patients to ascertain progression from CN to incident prodromal stage of AD or from MCI to AD dementia. Least Absolute Shrinkage and Selection Operator Cox regression was applied for predictors selection and model construction.ResultsDuring the follow-up periods, 139 CN participants had progressed to prodromal AD (CDR ≥ 0.5) and 321 MCI patients had progressed to AD dementia. In the prediction of individual risk of incident prodromal stage of AD in CN individuals, the AUC of the final CN model was 0.81 within 5 years. The final MCI model predicted individual risk of AD dementia in MCI patients with an AUC of 0.92 within 5 years. The models were also associated with longitudinal change of Mini-Mental State Examination (p < 0.001 for CN and MCI models). An Alzheimer’s continuum model was developed which could predict the Alzheimer’s continuum for individuals with normal AD biomarkers within 3 years with high accuracy (AUC = 0.91).ConclusionsThe risk models were able to provide personalized risk for AD onset at each year after evaluation. The models may be useful for better prevention of AD.

Background:Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) are often misdiagnosed with each other because of similar symptoms including progressive memory loss. The metabolic network topology that describes inter-regional metabolic connections can be generated using fluorodeoxyglucose positron emission tomography (FDG-PET) data with the graph-theoretical method. We hypothesized that different metabolic connectivity underlies the symptoms of AD patients, DLB patients, and cognitively normal (CN) individuals.Objective:This study aimed to generate metabolic connectivity using FDG-PET data and assess the network topology to differentiate AD patients, DLB patients, and CN individuals.Methods:This study included 45 AD patients, 18 DLB patients, and 142 CN controls. We analyzed FDG-PET data using the graph-theoretical method and generated the network topology in AD patients, DLB patients, and CN individuals. We statistically assessed the topology with global and nodal parameters.Results:The whole metabolic network was preserved in CN; however, diffusely decreased connection was found in AD and partially but more deeply decreased connection was observed in DLB. The metabolic topology revealed that the right posterior cingulate and the left transverse temporal gyrus were significantly different between AD and DLB.Conclusion:The present findings indicate that metabolic connectivity decreased in both AD and DLB, compared with CN. DLB was characterized restricted but deeper stereotyped network disruption compared with AD. The right posterior cingulate and the left transverse temporal gyrus are significant regions in the metabolic connectivity for differentiating AD from DLB.

Background: The Hopkins Verbal Learning Test (HVLT) has been validated for detecting dementia in English-speaking populations. However, no studies have examined the Chinese version of the HVLT scale, and appropriate cut-off scores for dementia in the Chinese population remain unclear. Methods: 631 subjects aged 60 and over were recruited at a memory clinic at Dongzhimen Hospital in Beijing. Of these, 249 were classified as exhibiting normal cognition (NC), 134 were diagnosed with mild cognitive impairment (MCI), 97 were diagnosed with Alzheimer’s disease (AD), 14 met the diagnosis for vascular dementia (VaD), and 50 were diagnosed with other types of dementia, including mixed dementia. The discriminative capacity of the HVLT total learning score, recognition score and total score were calculated to determine their sensitivity and specificity for detecting MCI, AD and other dementias, and various cut-off scores. Results: HVLT scores were affected by age, education and sex. The HVLT total learning score exhibited an optimal balance between sensitivity and specificity using a cut-off score of 15.5 for distinguishing AD and other types of dementia from NC using the ROC curve, with sensitivity of 94.7% for distinguishing AD and all types of dementia, and specificity of 92.5% for detecting AD and 93.4% for detecting all types of dementias. We stratified the AD and MCI groups by age, and calculated the validity in each age group. In the 50–64 years age group, when the cutoff score was 18.5, the sensitivity of 0.955 and specificity of 0.921 were obtained for discriminating the NC and AD groups, and in the 65–80 years group, and optimal sensitivity and specificity values (0.948 and 0.925, respectively) were obtained with a cutoff score of 14.5. When the cutoff score was 21.5 in HVLT total recall, an optimal balance was obtained between sensitivity and specificity (69.1% and 70.7%, respectively) in distinguishing MCI from NC. Conclusion: A cut-off score of 15.5 in the HVLT total learning score led to high discriminative capacity between the dementia and NC groups. This suggests that the HVLT total learning score can provide a useful tool for discriminating dementia, but not MCI, from NC in clinical and epidemiological practice.