Standardization and Validation of the Flash Visual Evoked Potential-P2 Conversion Scores in the Diagnosis of Amnestic Mild Cognitive Impairment and Alzheimer's Dementia

Amnestic mild cognitive impairment (aMCI), which is characterized by normal daily activity, but a significant decline in episodic memory, is now widely accepted as a risk factor for the development of Alzheimer's dementia (AD). Research suggests that many of the same neuropathological changes associated with AD also occur in patients diagnosed with aMCI. A recent review of the literature revealed that the latency of the flash visual-evoked potential-P2 (FVEP-P2) may possess pathognomonic information that may assist in the early detection of aMCI. While standards exist for the recording of FVEP-P2, individual clinics often use recording parameters that may differ, resulting in latencies that may not generalize beyond the clinic that produced them. The present article illustrates the process by which the FVEP-P2 latency can be standardized across clinics using FVEP-P2 Conversion Scores. We then demonstrate the diagnostic accuracy of the newly developed scores. Method: In the present investigation, we used the previously unpublished data containing the FVEP-P2 latencies of 45 AD and 60 controls. Result: We were able to demonstrate the process by which individual clinics may first standardize FVEP-P2 latencies and then examine patient performance using FVEP-P2 Conversion Scores, providing clinicians with a richer context from which to examine the patient performance. Conclusion: Consistent with the findings of previous research, the findings of the present investigation support the use of the FVEP-P2 Conversion Scores in the diagnosis of AD. Future directions, including the modification of recording parameters associated with the FVEP-P2, are also discussed.

Characteristics of Dysregulated Proinflammatory Cytokines and Cognitive Dysfunction in Late-Life Depression and Amnestic Mild Cognitive Impairment

BackgroundLate-life depression (LLD) and amnestic mild cognitive impairment (aMCI) are two different diseases associated with a high risk of developing Alzheimer’s disease (AD). Both diseases are accompanied by dysregulation of inflammation. However, the differences and similarities of peripheral inflammatory parameters in these two diseases are not well understood.MethodsWe used Luminex assays to measure 29 cytokines simultaneously in the plasma of two large cohorts of subjects at high risk for AD (23 LLD and 23 aMCI) and 23 normal controls (NCs) in the community. Demographics and lifestyle factors were also collected. Cognitive function was evaluated with the Chinese versions of the Montreal Cognitive Assessment (C-MoCA) and neuropsychological test battery (NTB).ResultsWe observed a remarkably increased level of IL-6 in the plasma and reduced levels of chemokines (CXCL11 and CCL13) in the LLD group compared with the aMCI group. The LLD group also showed lower levels of CXCL16 than the NC group. Furthermore, altered cytokine levels were associated with abnormal results in neuropsychological testing and Geriatric Depression Scale scores in both the LLD and aMCI groups. Notably, combinations of cytokines (IL-6 and CCL13) and two subitems of C-MoCA (orientation and short-term memory) generated the best area under the receiver operating characteristic curve (AUROC = 0.974).ConclusionA novel model based on proinflammatory cytokines and brief screening tests performs with fair accuracy in the discrimination between LLD and aMCI. These findings will give clues to provide new therapeutic targets for interventions or markers for two diseases with similar predementia syndromes.

Comparison of MR Perfusion and FDG-PET Brain Studies in Patients With Alzheimer’s Disease and Amnestic Mild Cognitive Impairment

Background: The aim of this study was to compare Dynamic Susceptibility Contrast Enhanced MRI (DSC-MRI) and PET with flurodeoxyglucose (FDG-PET) in the diagnosis of Alzheimer’s Disease (AD) and amnestic Mild Cognitive Impairment (aMCI).Methods: Age and sex matched 27 patients with AD, 39 with aMCI and 16 controls underwent brain DSC-MRI followed by FDG-PET. Values of relative Cerebral Blood Volume (rCBV) and rCBV z-scores from frontal, temporal, parietal and PCG cortices were correlated with the rate of glucose metabolism from PET. Sensitivity, specificity and accuracy of DSC-MRI and FDG-PET in the diagnosis of AD and aMCI were assessed and compared.Results: In AD hypoperfusion was found within all examined locations, while in aMCI in both parietal and temporal cortices and left PCG. FDG-PET showed the greatest hypometabolism in parietal, temporal and left PCG regions in both AD and aMCI. FDG-PET was more accurate in distinguishing aMCI from controls than DSC-MRI. In AD and combined group (AD + aMCI ) there were numerous correlations between DSC-MRI and FDG-PET results. Conclusions: In AD the patterns of hypoperfusion and glucose hypometabolism are similar thus DSC-MRI may be a competitive method to FDG-PET. FDG-PET is a more accurate method in the diagnosis of aMCI.

Association Between Motor Task Performance and Hippocampal Atrophy Across Cognitively Unimpaired, Amnestic Mild Cognitive Impairment, and Alzheimer’s Disease Individuals

Hippocampal atrophy is a widely used biomarker for Alzheimer’s disease (AD), but the cost, time, and contraindications associated with magnetic resonance imaging (MRI) limit its use. Recent work has shown that a low-cost upper extremity motor task has potential in identifying AD risk. Fifty-four older adults (15 cognitively unimpaired, 24 amnestic mild cognitive impairment, and 15 AD) completed six motor task trials and a structural MRI. Several measures of motor task performance significantly predicted bilateral hippocampal volume, controlling for age, sex, education, and memory. Thus, this motor task may be an affordable, non-invasive screen for AD risk and progression.

Hippocampal-Subregion Mechanisms of Repetitive Transcranial Magnetic Stimulation Causally Associated with Amelioration of Episodic Memory in Amnestic Mild Cognitive Impairment

Background: Altered hippocampal subregions (HIPsub) and their network connectivity relate to episodic memory decline in amnestic mild cognitive impairment (aMCI), which is significantly limited by over-dependence on correlational associations. Objective: To identify whether restoration of HIPsub and its network connectivity using repetitive transcranial magnetic stimulation (rTMS) is causally linked to amelioration of episodic memory in aMCI. Methods: In the first cohort, analysis of HIPsub grey matter (GM) and its functional connectivity was performed to identify an episodic memory-related circuit in aMCI by using a pattern classification approach. In the second cohort, this circuit was experimentally modulated with rTMS. Structural equation modeling was employed to investigate rTMS regulatory mechanism in amelioration of episodic memory. Results: First, in the first cohort, this study identified HIPsub circuit pathology of episodic memory decline in aMCI patients. Second, in the second cohort, restoration of HIPc GM and its connectivity with left middle temporal gyrus (MTG.L) are causally associated with amelioration of episodic memory in aMCI after 4 weeks of rTMS. Especially important, the effects of HIPc GM changes on the improvement of episodic memory were significantly mediated by HIPc connectivity with MTG.L changes in aMCI. Conclusion: This study provides novel experimental evidence about a biological substrate for the treatment of the disabling episodic memory in aMCI patients. Correction of breakdown in HIPc structure and its connectivity with MTG can causally ameliorate episodic memory in aMCI.

Multimodal Classification of Alzheimer’s Disease and Amnestic Mild Cognitive Impairment: Integrated 18F-FDG PET and DTI Study

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and memory impairment. Amnestic mild cognitive impairment (aMCI) is the intermediate stage between normal cognitive aging and early dementia caused by AD. It can be challenging to differentiate aMCI patients from healthy controls (HC) and mild AD patients. Objective: To validate whether the combination of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) and diffusion tensor imaging (DTI) will improve classification performance compared with that based on a single modality. Methods: A total of thirty patients with AD, sixty patients with aMCI, and fifty healthy controls were included. AD was diagnosed according to the National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable. aMCI diagnosis was based on Petersen’s criteria. The 18F-FDG PET and DTI measures were each used separately or in combination to evaluate sensitivity, specificity, and accuracy for differentiating HC, aMCI, and AD using receiver operating characteristic analysis together with binary logistic regression. The rate of accuracy was based on the area under the curve (AUC). Results: For classifying AD from HC, we achieve an AUC of 0.96 when combining two modalities of biomarkers and 0.93 when using 18F-FDG PET individually. For classifying aMCI from HC, we achieve an AUC of 0.79 and 0.76 using the best individual modality of biomarkers. Conclusion: Our results show that the combination of two modalities improves classification performance, compared with that using any individual modality.

Baseline Neuroimaging Predicts Decline to Dementia From Amnestic Mild Cognitive Impairment

Background and Objectives: Prediction of decline to dementia using objective biomarkers in high-risk patients with amnestic mild cognitive impairment (aMCI) has immense utility. Our objective was to use multimodal MRI to (1) determine whether accurate and precise prediction of dementia conversion could be achieved using baseline data alone, and (2) generate a map of the brain regions implicated in longitudinal decline to dementia.Methods: Participants meeting criteria for aMCI at baseline (N = 55) were classified at follow-up as remaining stable/improved in their diagnosis (N = 41) or declined to dementia (N = 14). Baseline T1 structural MRI and resting-state fMRI (rsfMRI) were combined and a semi-supervised support vector machine (SVM) which separated stable participants from those who decline at follow-up with maximal margin. Cross-validated model performance metrics and MRI feature weights were calculated to include the strength of each brain voxel in its ability to distinguish the two groups.Results: Total model accuracy for predicting diagnostic change at follow-up was 92.7% using baseline T1 imaging alone, 83.5% using rsfMRI alone, and 94.5% when combining T1 and rsfMRI modalities. Feature weights that survived the p < 0.01 threshold for separation of the two groups revealed the strongest margin in the combined structural and functional regions underlying the medial temporal lobes in the limbic system.Discussion: An MRI-driven SVM model demonstrates accurate and precise prediction of later dementia conversion in aMCI patients. The multi-modal regions driving this prediction were the strongest in the medial temporal regions of the limbic system, consistent with literature on the progression of Alzheimer’s disease.