Discrimination of normal aging, MCI and AD with multimodal imaging measures on the medial temporal lobe

Combined quantitative amyloid-β PET and structural MRI features improve Alzheimer's Disease classification in random forest model - A multicenter study

RATIONALE AND OBJECTIVES

Prior to clinical presentations of Alzheimer's Disease (AD), neuropathological changes, such as amyloid-β and brain atrophy, have accumulated at the earlier stages of the disease. The combination of such biomarkers assessed by multiple modalities commonly improves the likelihood of AD etiology. We aimed to explore the discriminative ability of Aβ PET features and whether combining Aβ PET and structural MRI features can improve the classification performance of the machine learning model in older healthy control (OHC) and mild cognitive impairment (MCI) from AD.

MATERIAL AND METHODS

We collected 94 AD patients, 82 MCI patients, and 85 OHC from three different cohorts. 17 global/regional Aβ features in Centiloid, 122 regional volume, and 68 regional cortical thickness were extracted as imaging features. Single or combined modality features were used to train the random forest model on the testing set. The top 10 features were sorted based on the Gini index in each binary classification.

RESULTS

The results showed that AUC scores were 0.81/0.86 and 0.69/0.68 using sMRI/Aβ PET features on the testing set in differentiating OHC and MCI from AD. The performance was improved while combining two-modality features with an AUC of 0.89 and an AUC of 0.71 in two classifications. Compared to sMRI features, particular Aβ PET features contributed more to differentiating AD from others.

CONCLUSION

Our study demonstrated the discriminative ability of Aβ PET features in differentiating AD from OHC and MCI. A combination of Aβ PET and structural MRI features can improve the RF model performance.

Glycemic variability correlates with medial temporal lobe atrophy and decreased cognitive performance in patients with memory deficits

Background

In the past, researchers have observed a significant link between glycemia and dementia. Medial temporal atrophy (MTA) is regarded as a common marker of dementia. The correlation between glycemic variability and MTA is unclear, and it has not been determined whether glycemic variability can be utilized as a biomarker of MTA and cognitive performance.

Methods

The patients in a memory clinic who underwent brain MRI scans and cognitive assessments within the first week of their hospital visit, were enrolled. All participants underwent three fasting blood glucose and one HBA1c assessments on three self-selected days within 1 week of their first visit. The variability independent of the mean (VIM) was employed. Validated visual scales were used to rate the MTA results. The mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA) scales were employed to assess the cognitive functions of the participants. Spearman's correlation and regression models were used to examine the relationship between the MMSE and MoCA scales, and also determine the link between the MRI characteristics and cognitive status, where vascular risk factors, educational status, age, gender, and mean glucose parameters served as covariates.

Results

Four hundred sixty-one subjects completed the MMSE scale, while 447 participants completed the MoCA scale. Data analysis revealed that 47.72% of the participants were men (220/461), and the median age of the patients was 69.87 ± 5.37 years. The findings of Spearman's correlation analysis exhibited a strong negative relationship between the VIM and MMSE score (r = -0.729, P < 0.01), and the MoCA score (r = -0.710, P < 0.01). The VIM was regarded as an independent risk factor for determining cognitive impairment in both the MMSE and MoCA assessments. The results were unaffected by sensitivity analysis. In addition, a non-linear relationship was observed between the VIM and MTA scores.

Conclusion

The variability in the blood glucose levels, which was presented as VIM, was related to the reduced cognitive function, which was reflected by MMSE and MoCA scales. The relationship between the VIM and the MTA score was non-linear. The VIM was positively related to the MTA score when the VIM was less than 2.42.

Characterization of diffusion magnetic resonance imaging revealing relationships between white matter disconnection and behavioral disturbances in mild cognitive impairment: a systematic review

White matter disconnection is the primary cause of cognition and affection abnormality in mild cognitive impairment (MCI). Adequate understanding of behavioral disturbances, such as cognition and affection abnormality in MCI, can help to intervene and slow down the progression of Alzheimer's disease (AD) promptly. Diffusion MRI is a non-invasive and effective technique for studying white matter microstructure. This review searched the relevant papers published from 2010 to 2022. Sixty-nine studies using diffusion MRI for white matter disconnections associated with behavioral disturbances in MCI were screened. Fibers connected to the hippocampus and temporal lobe were associated with cognition decline in MCI. Fibers connected to the thalamus were associated with both cognition and affection abnormality. This review summarized the correspondence between white matter disconnections and behavioral disturbances such as cognition and affection, which provides a theoretical basis for the future diagnosis and treatment of AD.

Differentiation of Subjective Cognitive Decline, Mild Cognitive Impairment, and Dementia Using qEEG/ERP-Based Cognitive Testing and Volumetric MRI in an Outpatient Specialty Memory Clinic

BACKGROUND

Distinguishing between subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia in a scalable, accessible way is important to promote earlier detection and intervention.

OBJECTIVE

We investigated diagnostic categorization using an FDA-cleared quantitative electroencephalographic/event-related potential (qEEG/ERP)-based cognitive testing system (eVox® by Evoke Neuroscience) combined with an automated volumetric magnetic resonance imaging (vMRI) tool (Neuroreader® by Brainreader).

METHODS

Patients who self-presented with memory complaints were assigned to a diagnostic category by dementia specialists based on clinical history, neurologic exam, neuropsychological testing, and laboratory results. In addition, qEEG/ERP (n = 161) and quantitative vMRI (n = 111) data were obtained. A multinomial logistic regression model was used to determine significant predictors of cognitive diagnostic category (SCD, MCI, or dementia) using all available qEEG/ERP features and MRI volumes as the independent variables and controlling for demographic variables. Area under the Receiver Operating Characteristic curve (AUC) was used to evaluate the diagnostic accuracy of the prediction models.

RESULTS

The qEEG/ERP measures of Reaction Time, Commission Errors, and P300b Amplitude were significant predictors (AUC = 0.79) of cognitive category. Diagnostic accuracy increased when volumetric MRI measures, specifically left temporal lobe volume, were added to the model (AUC = 0.87).

CONCLUSION

This study demonstrates the potential of a primarily physiological diagnostic model for differentiating SCD, MCI, and dementia using qEEG/ERP-based cognitive testing, especially when combined with volumetric brain MRI. The accessibility of qEEG/ERP and vMRI means that these tools can be used as adjuncts to clinical assessments to help increase the diagnostic certainty of SCD, MCI, and dementia.

Incremental diagnostic value of 18F-Fluetemetamol PET in differential diagnoses of Alzheimer's Disease-related neurodegenerative diseases from an unselected memory clinic cohort

To evaluate the incremental diagnostic value of 18F-Flutemetamol PET following MRI measurements on an unselected prospective cohort collected from a memory clinic. A total of 84 participants was included in this study. A stepwise study design was performed including initial analysis (based on clinical assessments), interim analysis (revision of initial analysis post-MRI) and final analysis (revision of interim analysis post-18F-Flutemetamol PET). At each time of evaluation, every participant was categorized into SCD, MCI or dementia syndromal group and further into AD-related, non-AD related or non-specific type etiological subgroup. Post 18F-Flutemetamol PET, the significant changes were seen in the syndromal MCI group (57%, p < 0.001) involving the following etiological subgroups: AD-related MCI (57%, p < 0.01) and non-specific MCI (100%, p < 0.0001); and syndromal dementia group (61%, p < 0.0001) consisting of non-specific dementia subgroup (100%, p < 0.0001). In the binary regression model, amyloid status significantly influenced the diagnostic results of interim analysis (p < 0.01). 18F-Flutemetamol PET can have incremental value following MRI measurements, particularly reflected in the change of diagnosis of individuals with unclear etiology and AD-related-suspected patients due to the role in complementing AD-related pathological information.

Fascicle- and Glucose-Specific Deterioration in White Matter Energy Supply in Alzheimer's Disease

BACKGROUND

White matter energy supply to oligodendrocytes and the axonal compartment is crucial for normal axonal function. Although gray matter glucose hypometabolism is extensively reported in Alzheimer's disease (AD), glucose and ketones, the brain's two main fuels, are rarely quantified in white matter in AD.

OBJECTIVE

Using a dual-tracer PET method combined with a fascicle-specific diffusion MRI approach, robust to white matter hyper intensities and crossing fibers, we aimed to quantify both glucose and ketone metabolism in specific white matter fascicles associated with mild cognitive impairment (MCI; n = 51) and AD (n = 13) compared to cognitively healthy age-matched controls (Controls; n = 14).

METHODS

Eight white matter fascicles of the limbic lobe and corpus callosum were extracted and analyzed into fascicle profiles of five sections. Glucose (18F-fluorodeoxyglucose) and ketone (11C-acetoacetate) uptake rates, corrected for partial volume effect, were calculated along each fascicle.

RESULTS

The only fascicle with significantly lower glucose uptake in AD compared to Controls was the left posterior cingulate segment of the cingulum (-22%; p = 0.016). Non-significantly lower glucose uptake in this fascicle was also observed in MCI. In contrast to glucose, ketone uptake was either unchanged or higher in sections of the fornix and parahippocampal segment of the cingulum in AD.

CONCLUSION

To our knowledge, this is the first report of brain fuel uptake calculated along white matter fascicles in humans. Energetic deterioration in white matter in AD appears to be specific to glucose and occurs first in the posterior cingulum.

White matter microstructural abnormalities in amnestic mild cognitive impairment: A meta-analysis of whole-brain and ROI-based studies

Studies that examined white matter (WM) alterations in amnestic mild cognitive impairment (aMCI) abound. This timely meta-analysis aims to synthesize the results of these studies. Seventy-seven studies (totalN_aMCI=1844) were included. Fourteen region-of-interest-based (ROI-based) (k≥8;N_aMCI≥284 per ROI) and two activation likelihood estimation (ALE) meta-analyses (fractional anisotropy [FA]: k=15;N_aMCI=463; mean diffusivity [MD]: k=8;N_aMCI=193) were carried out. Among the many significant ROI-related findings, reliable FA and MD alterations in the fornix, uncinate fasciculus, and parahippocampal cingulum were observed in aMCI. Larger effects were observed in MD relative to FA. The ALE meta-analysis revealed a significant FA decrease among aMCI subjects in the posterior corona radiata. These results provide robust evidence of the presence of WM abnormalities in aMCI. Our findings also highlight the importance of carrying out both ROI-based and whole-brain-based research to obtain a complete picture of WM microstructural alterations associated with the condition..

Screening Ability of Subjective Memory Complaints, Informant-Reports for Cognitive Decline, and Their Combination in Memory Clinic Setting

OBJECTIVE

This study aimed to compare the accuracy of subjective memory complaints, informant-reports for cognitive declines, and their combination for screening cognitive disorders in memory clinic setting.

METHODS

One-hundred thirtytwo cognitively normal (CN), 136 mild cognitive impairment (MCI), and 546 dementia who visited the memory clinic in the Seoul National University Hospital underwent standardized clinical evaluation and comprehensive neuropsychological assessment. The Subjective Memory Complaints Questionnaire (SMCQ) and the Seoul Informant Report Questionnaire for Dementia (SIRQD) were used to assess subjective memory complaints and informant-reports for cognitive declines, respectively.

RESULTS

Both SMCQ and SIRQD showed significant screening ability for MCI, dementia, and overall cognitive disorder (CDall: MCI plus dementia) (screening accuracy: 60.1-94.6%). The combination of SMCQ and SIRQD (SMCQ+SIRQD) was found to have significantly better screening accuracy compared to SMCQ alone for any cognitive disorders. SMCQ+SIRQD also significantly improved screening accuracy of SIRQD alone for MCI and CDall, but not for dementia.

CONCLUSION

Our findings suggest that the combined information of both subjective memory complaints and informant-reports for cognitive declines can improve MCI screening by each individual information, while such combination appears not better than informant-reports in regard of dementia screening.

Comparison of regional gray matter atrophy, white matter alteration, and glucose metabolism as a predictor of the conversion to Alzheimer's disease in mild cognitive impairment

We compared the predictive ability of the various neuroimaging tools and determined the most cost-effective, non-invasive Alzheimer's disease (AD) prediction model in mild cognitive impairment (MCI) individuals. Thirty-two MCI subjects were evaluated at baseline with [(18)F]-fluorodeoxyglucose positron emission tomography (FDG-PET), MRI, diffusion tensor imaging (DTI), and neuropsychological tests, and then followed up for 2 yr. After a follow up period, 12 MCI subjects converted to AD (MCIc) and 20 did not (MCInc). Of the voxel-based statistical comparisons of baseline neuroimaging data, the MCIc showed reduced cerebral glucose metabolism (CMgl) in the temporo-parietal, posterior cingulate, precuneus, and frontal regions, and gray matter (GM) density in multiple cortical areas including the frontal, temporal and parietal regions compared to the MCInc, whereas regional fractional anisotropy derived from DTI were not significantly different between the two groups. The MCIc also had lower Mini-Mental State Examination (MMSE) score than the MCInc. Through a series of model selection steps, the MMSE combined with CMgl model was selected as a final model (classification accuracy 93.8%). In conclusion, the combination of MMSE with regional CMgl measurement based on FDG-PET is probably the most efficient, non-invasive method to predict AD in MCI individuals after a two-year follow-up period.

Integrating retrogenesis theory to Alzheimer's disease pathology: insight from DTI-TBSS investigation of the white matter microstructural integrity

Microstructural abnormalities in white matter (WM) are often reported in Alzheimer's disease (AD) and may reflect primary or secondary circuitry degeneration (i.e., due to cortical atrophy). The interpretation of diffusion tensor imaging (DTI) eigenvectors, known as multiple indices, may provide new insights into the main pathological models supporting primary or secondary patterns of WM disruption in AD, the retrogenesis, and Wallerian degeneration models, respectively. The aim of this review is to analyze the current literature on the contribution of DTI multiple indices to the understanding of AD neuropathology, taking the retrogenesis model as a reference for discussion. A systematic review using MEDLINE, EMBASE, and PUBMED was performed. Evidence suggests that AD evolves through distinct patterns of WM disruption, in which retrogenesis or, alternatively, the Wallerian degeneration may prevail. Distinct patterns of WM atrophy may be influenced by complex interactions which comprise disease status and progression, fiber localization, concurrent risk factors (i.e., vascular disease, gender), and cognitive reserve. The use of DTI multiple indices in addition to other standard multimodal methods in dementia research may help to determine the contribution of retrogenesis hypothesis to the understanding of neuropathological hallmarks that lead to AD.

Beta-amyloid associated differential effects of APOE ε4 on brain metabolism in cognitively normal elderly

OBJECTIVE

Although apolipoprotein (APOE) ε4 allele is a well-established risk factor for late-onset Alzheimer disease (AD), the mechanism of its effects on AD pathogenesis is not fully understood. We aimed to investigate the effects of APOE genotype on regional cerebral glucose metabolism in cognitively normal (CN) elderly. We further tried to elucidate whether or not such effects are associated with beta-amyloid protein (Aβ) deposition.

METHODS

31 CN elderly participants underwent clinical examination, a range of neuropsychological tests, APOE genotyping, and Pittsburgh compound-B- and fluorodeoxyglucose-PET scans.

RESULTS

17 APOE ε4 carriers and 15 non-carriers were included. Both hypometabolic and hypermetabolic regions were observed in ε4 carriers compared with noncarriers when age, education, and sex were controlled. When the degree of global cerebral Aβ deposition was adjusted, the hypometabolic regions in the temporo-parietal area (i.e., BA 22 and 39) largely disappeared, whereas the hypermetabolic regions persisted in medial frontal and anterior temporal areas (i.e., BA 38, 11, and 39). Behaviorally, verbal episodic memory scores of APOE ε4 carriers were slightly lower than those of noncarriers, though still within normal range.

CONCLUSIONS

Our findings indicate that decreased cerebral glucose metabolism in the temporoparietal junction associated with APOE ε4 in CN elderly appears to be mediated by Aβ deposition, and the effect of APOE ε4 on hypermetabolism in the frontal and anterior temporal regions is independent of Aβ and may be associated with presence of compensatory mechanism in CN elderly with the ε4 allele.

High-Dimensional Medial Lobe Morphometry: An Automated MRI Biomarker for the New AD Diagnostic Criteria

Introduction. Medial temporal lobe atrophy assessment via magnetic resonance imaging (MRI) has been proposed in recent criteria as an in vivo diagnostic biomarker of Alzheimer's disease (AD). However, practical application of these criteria in a clinical setting will require automated MRI analysis techniques. To this end, we wished to validate our automated, high-dimensional morphometry technique to the hypothetical prediction of future clinical status from baseline data in a cohort of subjects in a large, multicentric setting, compared to currently known clinical status for these subjects. Materials and Methods. The study group consisted of 214 controls, 371 mild cognitive impairment (147 having progressed to probable AD and 224 stable), and 181 probable AD from the Alzheimer's Disease Neuroimaging Initiative, with data acquired on 58 different 1.5 T scanners. We measured the sensitivity and specificity of our technique in a hierarchical fashion, first testing the effect of intensity standardization, then between different volumes of interest, and finally its generalizability for a large, multicentric cohort. Results. We obtained 73.2% prediction accuracy with 79.5% sensitivity for the prediction of MCI progression to clinically probable AD. The positive predictive value was 81.6% for MCI progressing on average within 1.5 (0.3 s.d.) year. Conclusion. With high accuracy, the technique's ability to identify discriminant medial temporal lobe atrophy has been demonstrated in a large, multicentric environment. It is suitable as an aid for clinical diagnostic of AD.

Changes in topological organization of functional PET brain network with normal aging

Recent studies about brain network have suggested that normal aging is associated with alterations in coordinated patterns of the large-scale brain functional and structural systems. However, age-related changes in functional networks constructed via positron emission tomography (PET) data are still barely understood. Here, we constructed functional brain networks composed of regions in younger (mean age years) and older (mean age years) age groups with PET data. younger and older healthy individuals were separately selected for two age groups, from a physical examination database. Corresponding brain functional networks of the two groups were constructed by thresholding average cerebral glucose metabolism correlation matrices of regions and analysed using graph theoretical approaches. Although both groups showed normal small-world architecture in the PET networks, increased clustering and decreased efficiency were found in older subjects, implying a degeneration process that brain system shifts from a small-world network to regular one along with normal aging. Moreover, normal senescence was related to changed nodal centralities predominantly in association and paralimbic cortex regions, e.g. increasing in orbitofrontal cortex (middle) and decreasing in left hippocampus. Additionally, the older networks were about equally as robust to random failures as younger counterpart, but more vulnerable against targeted attacks. Finally, methods in the construction of the PET networks revealed reasonable robustness. Our findings enhanced the understanding about the topological principles of PET networks and changes related to normal aging.

Combining PET images and neuropsychological test data for automatic diagnosis of Alzheimer's disease

In recent years, several approaches to develop computer aided diagnosis (CAD) systems for dementia have been proposed. Some of these systems analyze neurological brain images by means of machine learning algorithms in order to find the patterns that characterize the disorder, and a few combine several imaging modalities to improve the diagnostic accuracy. However, they usually do not use neuropsychological testing data in that analysis. The purpose of this work is to measure the advantages of using not only neuroimages as data source in CAD systems for dementia but also neuropsychological scores. To this aim, we compared the accuracy rates achieved by systems that use neuropsychological scores beside the imaging data in the classification step and systems that use only one of these data sources. In order to address the small sample size problem and facilitate the data combination, a dimensionality reduction step (implemented using three different algorithms) was also applied on the imaging data. After each image is summarized in a reduced set of image features, the data sources were combined and classified using three different data combination approaches and a Support Vector Machine classifier. That way, by testing different dimensionality reduction methods and several data combination approaches, we aim not only highlighting the advantages of using neuropsychological scores in the classification, but also implementing the most accurate computer system for early dementia detention. The accuracy of the CAD systems were estimated using a database with records from 46 subjects, diagnosed with MCI or AD. A peak accuracy rate of 89% was obtained. In all cases the accuracy achieved using both, neuropsychological scores and imaging data, was substantially higher than the one obtained using only the imaging data.

Influence of APOE genotype on whole-brain functional networks in cognitively normal elderly

This study aimed to investigate the influence of apolipoprotein E (APOE) ε4 allele on whole-brain functional networks in cognitively normal (CN) elderly by applying graph theoretical analysis to brain glucose metabolism. Eighty-six CN elderly [28 APOE ε4 carriers (ε4+) and 58 non-carriers (ε4-)] underwent clinical evaluation and resting [(18)F] fluorodeoxyglucose positron emission tomography scan. Whole-brain functional networks were constructed from correlations of the 90 regions of interest using the automated anatomical labeling template, and analyzed using graph theoretical approaches. The overall small-world property seen in ε4- was preserved in ε4+. However, both local clustering and path length were lower in ε4+ compared to ε4-. In terms of the hubs of functional networks, ε4+ showed decreased centrality of the right hippocampus but increased centrality of several brain regions associated with the default mode network compared to ε4-. Our results indicate that genetic vulnerability to Alzheimer's disease may alter whole-brain functional networks even before clinical symptoms appear.

Discriminative analysis of early-stage Alzheimer's disease and normal aging with automatic segmentation technique in subcortical gray matter structures: a multicenter in vivo MRI volumetric and DTI study

BACKGROUND

Previous studies have revealed that amyloid depositions exist in not only the hippocampus but in other subcortical gray matter structures as well. Diffusion-tensor imaging (DTI) parameters might be more sensitive measures of early degeneration in Alzheimer's disease (AD) than conventional magnetic resonance imaging (MRI) techniques.

PURPOSE

To evaluate the significance of the volumes and the mean diffusivity (MD) values of subcortical gray matter structures in discrimination between early-stage AD and normal subjects using the Integrated Registration and Segmentation Tool in FMRIB's Software Library.

MATERIAL AND METHODS

Fifty-three cases of early-stage AD and 30 normal aging volunteers from two hospitals were scanned with 3D-FSPGRIR and SSSE-EPI sequences using two similar 1.5T MR systems. The mean relative volumes and mean MD values of subcortical gray matter structures were compared between early-stage AD and control groups. Binary logistic regression analysis and receiver-operating characteristic (ROC) curves were applied to assess the diagnostic significance of every structure's relative volume, MD value, and combination of both.

RESULTS

The relative volumes of the left hippocampus, right amygdala, bilateral thalamus, right caudate, left putamen, and bilateral pallidum were significantly lower in the early-stage AD group than in the control group (P < 0.05). The MD values of the bilateral hippocampus and pallidum, and of the right thalamus and caudate were significantly elevated in the early-stage AD group (P < 0.05). In binary logistic regression analysis, the relative volume of left hippocampus and age entered the final model of volumetric analysis. The MD values of bilateral hippocampi and pallidums entered the final model of MD analysis. The MD values of bilateral hippocampi and pallidums, and the relative volume of left pallidum, entered the final model of combination analysis. The accuracy of three models was 84.7%, 88.9%, and 93.1%, respectively.

CONCLUSION

Pathological changes takes place in the hippocampus and other subcortical gray matter structures in early-stage AD. Diffusive imaging has great diagnostic significance in early-stage AD. The combination of both imaging modalities can lead to better discrimination between early-stage AD and normal aging.

Discriminative analysis of mild Alzheimer's disease and normal aging using volume of hippocampal subfields and hippocampal mean diffusivity: an in vivo magnetic resonance imaging study

BACKGROUND

Studies discovered that the hippocampal subfields are differentially affected by pathological damage, and magnetic resonance (MR) diffusion tensor imaging parameters might be more sensitive measures of early degeneration in Alzheimer's disease (AD) than conventional MR imaging techniques. The purpose of this study was to evaluate the significance of the volume of hippocampal subfields and the mean diffusivity (MD) value of hippocampus in discrimination between mild AD and normal aging.

METHODS

A total of 29 patients with mild AD and 30 normal aging were scanned. Binary logistic regression analysis was applied to assess the diagnostic significance of the volumes of hippocampal subfields and the MD value of hippocampus.

RESULTS

All hippocampal subfields except right tail atrophied significantly in the mild AD group (P < .05). The relative volumes of right CA1 and subiculum subfields entered the binary logistic regression model. The accuracy was 91.8%, which was improved to 93.9% as the MD value of right hippocampus entered the model.

CONCLUSION

Atrophy was present in almost all hippocampal subfields at mild AD stage. The volumes of CA1 and subiculum were of the most diagnostic significance in discrimination of mild AD, which can be improved by the combination of volume and diffusivity analysis.

Meta-analysis of functional network alterations in Alzheimer's disease: toward a network biomarker

The increasing prevalence of Alzheimer's disease (AD) emphasizes the need for sensitive biomarkers. Memory, a core deficit in AD, involves the interaction of distributed brain networks. We propose that biomarkers should be sought at the level of disease-specific disturbances in large-scale neural networks instead of alterations in a single brain region. This is the first voxel-level quantitative meta-analysis of default mode connectivity and task-related activation in 1196 patients and 1255 controls to detect robust changes in components of large-scale neural networks. We show that with disease progression, specific components of networks are widespread altered. The medial parietal regions and the subcortical areas are differentially affected depending on the disease stage. Specific compensatory mechanisms are only seen in the earliest stages, before symptoms are evident, and could become a functional network biomarker or target for interventions. These results underline the need to further fine-grain these networks spatially and temporally across disease stages. To conclude, AD should indeed be considered as a syndrome involving neural network disruption before cognitive deficits are detectable.

Whole-brain functional networks in cognitively normal, mild cognitive impairment, and Alzheimer's disease

The conceptual significance of understanding functional brain alterations and cognitive deficits associated with Alzheimer's disease (AD) process has been widely established. However, the whole-brain functional networks of AD and its prodromal stage, mild cognitive impairment (MCI), are not well clarified yet. In this study, we compared the characteristics of the whole-brain functional networks among cognitively normal (CN), MCI, and AD individuals by applying graph theoretical analyses to [(18)F] fluorodeoxyglucose positron emission tomography (FDG-PET) data. Ninety-four CN elderly, 183 with MCI, and 216 with AD underwent clinical evaluation and FDG-PET scan. The overall small-world property as seen in the CN whole-brain network was preserved in MCI and AD. In contrast, individual parameters of the network were altered with the following patterns of changes: local clustering of networks was lower in both MCI and AD compared to CN, while path length was not different among the three groups. Then, MCI had a lower level of local clustering than AD. Subgroup analyses for AD also revealed that very mild AD had lower local clustering and shorter path length compared to mild AD. Regarding the local properties of the whole-brain networks, MCI and AD had significantly decreased normalized betweenness centrality in several hubs regionally associated with the default mode network compared to CN. Our results suggest that the functional integration in whole-brain network progressively declines due to the AD process. On the other hand, functional relatedness between neighboring brain regions may not gradually decrease, but be the most severely altered in MCI stage and gradually re-increase in clinical AD stages.

Temporal association tracts and the breakdown of episodic memory in mild cognitive impairment

OBJECTIVE

To examine the pattern of association between microstructure of temporal lobe connections and the breakdown of episodic memory that is a core feature of mild cognitive impairment (MCI).

METHODS

Twenty-five individuals with MCI and 20 matched controls underwent diffusion MRI and cognitive assessment. Three temporal pathways were reconstructed by tractography: fornix, parahippocampal cingulum (PHC), and uncinate fasciculus. Tissue volume fraction-a tract-specific measure of atrophy-and microstructural measures were derived for each tract. To test specificity of associations, a comparison tract (corticospinal tract) and control cognitive domains were also examined.

RESULTS

In MCI, tissue volume fraction was reduced in the fornix. Axial and radial diffusivity were increased in uncinate and PHC implying more subtle microstructural change. In controls, tissue volume fraction in the fornix was the predominant correlate of free recall. In contrast, in MCI, the strongest relationship was with left PHC. Microstructure of uncinate and PHC also correlated with recognition memory, and recognition confidence, in MCI.

CONCLUSIONS

Episodic memory in MCI is related to the structure of multiple temporal association pathways. These associations are not confined to the fornix, as they are in healthy young and older adults. In MCI, because of a compromised fornix, alternative pathways may contribute disproportionally to episodic memory performance.

Comparison of hippocampal volume measured using magnetic resonance imaging in Alzheimer's disease, vascular dementia, mild cognitive impairment and pseudodementia

OBJECTIVE

To examine the relationship between different types of dementia and hippocampal volume.

METHODS

Hippocampal volume was measured by magnetic resonance imaging in patients with Alzheimer's disease (n = 22), vascular dementia (n = 14), mild cognitive impairment (n = 12) or pseudodementia (n = 16), and in healthy control subjects (n = 11). The Mini Mental State Examination was used to stratify subjects according to cognitive function.

RESULTS

Hippocampal volume was reduced by 42% in Alzheimer's disease, 21% in vascular dementia, 15% in mild cognitive impairment and 13% in pseudodementia compared with controls. The severity of dementia increased in line with decreasing hippocampal volume.

CONCLUSIONS

Measurement of hippocampal volume may facilitate differentiation between dementia subtypes. There was a relationship between reduced hippocampal volume and the degree of cognitive impairment.

MULTIMODAL CLASSIFICATION OF DEMENTIA USING FUNCTIONAL DATA, ANATOMICAL FEATURES AND 3D INVARIANT SHAPE DESCRIPTORS

Multimodality classification of Alzheimer's disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), is of interest to the medical community. We improve on prior classification frameworks by incorporating multiple features from MRI and PET data obtained with multiple radioligands, fluorodeoxyglucose (FDG) and Pittsburg compound B (PIB). We also introduce a new MRI feature, invariant shape descriptors based on 3D Zernike moments applied to the hippocampus region. Classification performance is evaluated on data from 17 healthy controls (CTR), 22 MCI, and 17 AD subjects. Zernike significantly outperforms volume, accuracy (Zernike to volume): CTR/AD (90.7% to 71.6%), CTR/MCI (76.2% to 60.0%), MCI/AD (84.3% to 65.5%). Zernike also provides comparable and complementary performance to PET. Optimal accuracy is achieved when Zernike and PET features are combined (accuracy, specificity, sensitivity), CTR/AD (98.8%, 99.5%, 98.1%), CTR/MCI (84.3%, 82.9%, 85.9%) and MCI/AD (93.3%, 93.6%, 93.3%).

Role of hippocampal CA1 atrophy in memory encoding deficits in amnestic Mild Cognitive Impairment

Identifying the specific substrates of memory deficits in early Alzheimer's disease would help to develop clinically-relevant therapies. The present study assesses the relationships between encoding versus retrieval deficits in patients with amnestic Mild Cognitive Impairment (aMCI) and atrophy specifically within the hippocampus and throughout the white matter. Twenty-two aMCI patients underwent T1-weighted MRI scans and neuropsychological testing. Grey matter and white matter segments obtained from the MRI images were each entered in correlation analyses, assessed only in the hippocampus for grey matter segments, with encoding and retrieval memory performances. For the grey matter segments, the resulting spmT correlation maps were then superimposed onto a 3D surface view of the hippocampus to identify the relative involvement of the different subfields, a method already used and validated elsewhere. Memory encoding deficits specifically correlated with CA1 subfield atrophy, while no relationship was found with white matter atrophy. In contrast, retrieval deficits were weakly related to hippocampal atrophy and did not involve a particular subfield, while they strongly correlated with loss of white matter, specifically in medial parietal and frontal areas. In aMCI patients, encoding impairment appears specifically related to atrophy of the CA1 hippocampal subfield, consistent with the predominance of encoding deficits and CA1 atrophy in aMCI. In contrast, episodic retrieval deficits seem to be underlain by more distributed tissue losses, consistent with a disruption of a hippocampo-parieto-frontal network.

Brain activation disturbance for target detection in patients with mild cognitive impairment: an fMRI study

Functional brain imaging in mild cognitive impairment (MCI) reveals differences in activation of task-relevant brain areas between patients and age-matched healthy controls. However, some studies reported hyperactivation and others hypoactivation in MCI compared with controls. The inconsistencies may be explained by compensatory mechanisms due to high complexity of the applied tasks. The oddball task is a simple paradigm that is known to activate a widespread network in the brain, involving attentional and monitoring mechanisms. In the present study, we examined amnestic or amnestic multidomain MCI patients (n = 12) and healthy controls (n = 13) in an auditory oddball task. Participants had to respond to infrequent targets and inhibit response to infrequent novel-nontarget stimuli. Lower stimulus related activation was found in MCI patients compared with healthy controls in parts of the middle temporal gyrus, the temporal pole, regions along the superior temporal sulcus, in the left cuneus, the left supramarginal gyrus, the anterior cingulated cortex and in the left inferior and middle frontal gyrus. Activation for oddball stimuli is assumed to reflect an automatic reflexive engagement of many brain regions in response to potentially important changes in the environment as well as cognitive control to monitor responses. The mechanisms of attention and cognitive control may be severely impaired in MCI and thus, underlie the cognitive deficits of this clinical group.

Entorhinal cortex volume is associated with episodic memory related brain activation in normal aging and amnesic mild cognitive impairment

The present study examined the relationship between entorhinal cortex and hippocampal volume with fMRI activation during episodic memory function in elderly controls with no cognitive impairment and individuals with amnesic mild cognitive impairment (aMCI). Both groups displayed limited evidence for a relationship between hippocampal volume and fMRI activation. Smaller right entorhinal cortex volume was correlated with reduced activation in left and right medial frontal cortex (BA 8) during incidental encoding for both aMCI and elderly controls. However, during recognition, smaller left entorhinal cortex volume correlated with reduced activation in right BA 8 for the control group, but greater activation for the aMCI group. There was no significant relationship between entorhinal cortex volume and activation during intentional encoding in either group. The recognition-related dissociation in structure/function relationships in aMCI paralleled our behavioral findings, where individuals with aMCI displayed poorer performance relative to controls during recognition, but not encoding. Taken together, these results suggest that the relationship between entorhinal cortex volume and fMRI activation during episodic memory function is altered in individuals with aMCI.