Medial temporal lobe atrophy and memory dysfunction as predictors for dementia in subjects with mild cognitive impairment

The interactive effect of intra-beat and inter-beat blood pressure variability on neurodegeneration in older adults

Blood pressure variability (BPV) and arterial stiffness are age-related hemodynamic risk factors for neurodegenerative disease, but it remains unclear whether they exert independent or interactive effects on brain health. When combined with high inter-beat BPV, increased intra-beat BPV indicative of arterial stiffness could convey greater pressure wave fluctuations deeper into the cerebrovasculature, exacerbating neurodegeneration. This interactive effect was studied in older adults using multiple markers of neurodegeneration, including medial temporal lobe (MTL) volume, plasma neurofilament light (NfL) and glial fibrillary acidic protein (GFAP). Older adults (N=105) without major neurological or systemic disease were recruited and underwent brain MRI and continuous BP monitoring to quantify inter-beat BPV through systolic average real variability (ARV) and intra-beat variability through arterial stiffness index (ASI). Plasma NfL and GFAP were assessed. The interactive effect of ARV and ASI on MTL atrophy, plasma NfL, and GFAP was studied using hierarchical linear regression. Voxel-based morphometry (VBM) was used to confirm region-of-interest analysis findings. The interaction between higher ARV and higher ASI was significantly associated with left-sided MTL atrophy in both the region-of-interest and false discovery rate-corrected VBM analysis. The interactive effect was also significantly associated with increased plasma NfL, but not GFAP. The interaction between higher ARV and higher ASI is independently associated with increased neurodegenerative markers, including MTL atrophy and plasma NfL, in independently living older adults. Findings could suggest the increased risk for neurodegeneration associated with higher inter-beat BPV may be compounded by increased intra-beat variability due to arterial stiffness.

Constructing personalized characterizations of structural brain aberrations in patients with dementia using explainable artificial intelligence

Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine.

Brain-Cognition Associations in Older Patients With Remitted Major Depressive Disorder or Mild Cognitive Impairment: A Multivariate Analysis of Gray and White Matter Integrity

BACKGROUND

Almost half of older patients with major depressive disorder (MDD) present with cognitive impairment, and one-third meet diagnostic criteria for mild cognitive impairment (MCI). However, mechanisms linking MDD and MCI remain unclear. We investigated multivariate associations between brain structural alterations and cognition in 3 groups of older patients at risk for dementia, remitted MDD (rMDD), MCI, and rMDD+MCI, as well as cognitively healthy nondepressed control participants.

METHODS

We analyzed magnetic resonance imaging data and cognitive domain scores in participants from the PACt-MD (Prevention of Alzheimer's Disease With Cognitive Remediation Plus Transcranial Direct Current Stimulation in Mild Cognitive Impairment and Depression) study. Following quality control, we measured cortical thickness and subcortical volumes of selected regions from 283 T1-weighted scans and fractional anisotropy of white matter tracts from 226 diffusion-weighted scans. We assessed brain-cognition associations using partial least squares regressions in the whole sample and in each subgroup.

RESULTS

In the entire sample, atrophy in the medial temporal lobe and subregions of the motor and prefrontal cortex was associated with deficits in verbal and visuospatial memory, language skills, and, to a lesser extent, processing speed (p < .0001; multivariate r = 0.30, 0.34, 0.26, and 0.18, respectively). Widespread reduced white matter integrity was associated with deficits in executive functioning, working memory, and processing speed (p = .008; multivariate r = 0.21, 0.26, 0.35, respectively). Overall, associations remained significant in the MCI and rMDD+MCI groups, but not the rMDD or healthy control groups.

CONCLUSIONS

We confirm findings of brain-cognition associations previously reported in MCI and extend them to rMDD+MCI, but similar associations in rMDD are not supported. Early-onset and treated MDD might not contribute to structural alterations associated with cognitive impairment.

The Q/R editing site of AMPA receptor GluA2 subunit acts as an epigenetic switch regulating dendritic spines, neurodegeneration and cognitive deficits in Alzheimer's disease

BACKGROUND

RNA editing at the Q/R site of GluA2 occurs with ~99% efficiency in the healthy brain, so that the majority of AMPARs contain GluA2(R) instead of the exonically encoded GluA2(Q). Reduced Q/R site editing infcreases AMPA receptor calcium permeability and leads to dendritic spine loss, neurodegeneration, seizures and learning impairments. Furthermore, GluA2 Q/R site editing is impaired in Alzheimer's disease (AD), raising the possibility that unedited GluA2(Q)-containing AMPARs contribute to synapse loss and neurodegeneration in AD. If true, then inhibiting expression of unedited GluA2(Q), while maintaining expression of GluA2(R), may be a novel strategy of preventing synapse loss and neurodegeneration in AD.

METHODS

We engineered mice with the 'edited' arginine codon (CGG) in place of the unedited glutamine codon (CAG) at position 607 of the Gria2 gene. We crossbred this line with the J20 mouse model of AD and conducted anatomical, electrophysiological and behavioural assays to determine the impact of eliminating unedited GluA2(Q) expression on AD-related phenotypes.

RESULTS

Eliminating unedited GluA2(Q) expression in AD mice prevented dendritic spine loss and hippocampal CA1 neurodegeneration as well as improved working and reference memory in the radial arm maze. These phenotypes were improved independently of Aβ pathology and ongoing seizure susceptibility. Surprisingly, our data also revealed increased spine density in non-AD mice with exonically encoded GluA2(R) as compared to their wild-type littermates, suggesting an unexpected and previously unknown role for unedited GluA2(Q) in regulating dendritic spines.

CONCLUSION

The Q/R editing site of the AMPA receptor subunit GluA2 may act as an epigenetic switch that regulates dendritic spines, neurodegeneration and memory deficits in AD.

The Role of Clinical Assessment in the Era of Biomarkers

Hippocratic Medicine revolved around the three main principles of patient, disease, and physician and promoted the systematic observation of patients, rational reasoning, and interpretation of collected information. Although these remain the cardinal features of clinical assessment today, Medicine has evolved from a more physician-centered to a more patient-centered approach. Clinical assessment allows physicians to encounter, observe, evaluate, and connect with patients. This establishes the patient-physician relationship and facilitates a better understanding of the patient-disease relationship, as the ultimate goal is to diagnose, prognosticate, and treat. Biomarkers are at the core of the more disease-centered approach that is currently revolutionizing Medicine as they provide insight into the underlying disease pathomechanisms and biological changes. Genetic, biochemical, radiographic, and clinical biomarkers are currently used. Here, we define a seven-level theoretical construct for the utility of biomarkers in neurodegenerative diseases. Level 1-3 biomarkers are considered supportive of clinical assessment, capable of detecting susceptibility or risk factors, non-specific neurodegeneration or dysfunction, and/or changes at the individual level which help increase clinical diagnostic accuracy and confidence. Level 4-7 biomarkers have the potential to surpass the utility of clinical assessment through detection of early disease stages and prediction of underlying pathology. In neurodegenerative diseases, biomarkers can potentiate, but cannot substitute, clinical assessment. In this current era, aside from adding to the discovery, evaluation/validation, and implementation of more biomarkers, clinical assessment remains crucial to maintaining the personal, humanistic, and sociocultural aspects of patient care. We would argue that clinical assessment is a custom that should never go obsolete.

Decreased Brain Structural Network Connectivity in Patients with Mild Cognitive Impairment: A Novel Fractal Dimension Analysis

Mild cognitive impairment (MCI) is widely regarded to be the intermediate stage to Alzheimer's disease. Cerebral morphological alteration in cortical subregions can provide an accurate predictor for early recognition of MCI. Thirty patients with MCI and thirty healthy control subjects participated in this study. The Desikan-Killiany cortical atlas was applied to segment participants' cerebral cortex into 68 subregions. A complexity measure termed fractal dimension (FD) was applied to assess morphological changes in cortical subregions of participants. The MCI group revealed significantly decreased FD values in the bilateral temporal lobes, right parietal lobe including the medial temporal, fusiform, para hippocampal, and also the orbitofrontal lobes. We further proposed a novel FD-based brain structural network to compare network parameters, including intra- and inter-lobular connectivity between groups. The control group had five modules, and the MCI group had six modules in their brain networks. The MCI group demonstrated shrinkage of modular sizes with fewer components integrated, and significantly decreased global modularity in the brain network. The MCI group had lower intra- and inter-lobular connectivity in all lobes. Between cerebral lobes, the MCI patients may maintain nodal connections between both hemispheres to reduce connectivity loss in the lateral hemispheres. The method and results presented in this study could be a suitable tool for early detection of MCI.

Diagnostic performance of hippocampal volumetry in Alzheimer's disease or mild cognitive impairment: a meta-analysis

OBJECTIVE

To evaluate the diagnostic performance of hippocampal volumetry for Alzheimer's disease (AD) or mild cognitive impairment (MCI).

METHODS

The MEDLINE and Embase databases were searched for articles that evaluated the diagnostic performance of hippocampal volumetry in differentiating AD or MCI from normal controls, published up to March 6, 2022. The quality of the articles was evaluated by the QUADAS-2 tool. A bivariate random-effects model was used to pool sensitivity, specificity, and area under the curve. Sensitivity analysis and meta-regression were conducted to explain study heterogeneity. The diagnostic performance of entorhinal cortex volumetry was also pooled.

RESULTS

Thirty-three articles (5157 patients) were included. The pooled sensitivity and specificity for AD were 82% (95% confidence interval [CI], 77-86%) and 87% (95% CI, 82-91%), whereas those for MCI were 60% (95% CI, 51-69%) and 75% (95% CI, 67-81%), respectively. No difference in the diagnostic performance was observed between automatic and manual segmentation (p = 0.11). MMSE scores, study design, and the reference standard being used were associated with study heterogeneity (p < 0.01). Subgroup analysis demonstrated a higher diagnostic performance of entorhinal cortex volumetry for both AD (pooled sensitivity: 88% vs. 79%, specificity: 92% vs. 89%, p = 0.07) and MCI (pooled sensitivity: 71% vs. 55%, specificity: 83% vs. 68%, p = 0.06).

CONCLUSIONS

Our meta-analysis demonstrated good diagnostic performance of hippocampal volumetry for AD or MCI. Entorhinal cortex volumetry might have superior diagnostic performance to hippocampal volumetry. However, due to a small number of studies, the diagnostic performance of entorhinal cortex volumetry is yet to be determined.

KEY POINTS

• The pooled sensitivity and specificity of hippocampal volumetry for Alzheimer's disease were 82% and 87%, whereas those for mild cognitive impairment were 60% and 75%, respectively. • No significant difference in the diagnostic performance was observed between automatic and manual segmentation. • Subgroup analysis demonstrated superior diagnostic performance of entorhinal cortex volumetry for AD (pooled sensitivity: 88%, specificity: 92%) and MCI (pooled sensitivity: 71%, specificity: 83%).

Classification of Alzheimer’s disease progression based on sMRI using gray matter volume and lateralization index

Note that identifying Mild Cognitive Impairment (MCI) is crucial to early detection and diagnosis of Alzheimer’s disease (AD). This work explores how classification features and experimental algorithms influence classification performances on the ADNI database. Based on structural Magnetic Resonance Images (sMRI), two features including gray matter (GM) volume and lateralization index (LI) are firstly extracted through hypothesis testing. Afterward, several classifier algorithms including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor(KNN) and Support Vector Machine (SVM) with RBF kernel, Linear kernel or Polynomial kernel are established to realize binary classification among Normal Control (NC), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI) and AD groups. The main experimental results are as follows. (1) The classification performance in the feature of LI is poor compared with those in the feature of GM volume or the combined feature of LI and GM volume, i.e., the classification accuracies in the feature of LI are relatively low and unstable for most classifier models and subject groups. (2) Comparing with the classification performances in the feature of GM volume and the combined feature of LI and GM volume, the classification accuracy of NC group versus AD group is relatively stable for different classifier models, moreover, the accuracy of AD group versus NC group is almost the highest, with the most classification accuracy of 98.0909%. (3) For different subject groups, the SVM classifier algorithm with Polynomial kernel and the KNN classifier algorithm show relatively stable and high classification accuracy, while DT classifier algorithm shows relatively unstable and lower classification accuracy. (4) Except the groups of EMCI versus LMCI and NC versus EMCI, the classification accuracies are significantly enhanced by emerging the LI into the original feature of GM volume, with the maximum accuracy increase of 5.6364%. These results indicate that various factors of subject data, feature types and experimental algorithms influence classification performances remarkably, especially the newly introduced feature of LI into the feature of GM volume is helpful to improve classification results in some certain extent.

Effects of Alzheimer's disease of varying severity on cardiac and autonomic function

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases in the elderly. The aim of this study was to explore the effects of AD on cardiac function and autonomic nervous function, and the feasibility of electrocardiogram (ECG) in monitoring the development of AD. APP/PS1 double transgenic mice were used in the Morris water maze (MWM) experiment to evaluate the changes of cognitive ability of AD mice, then the non-invasive ECG acquisition system was used and the changes of ECG intervals and heart rate variability (HRV) were analyzed. AD mice already had cognitive dysfunction at the age of 5 months, reaching the level of mild dementia, and the degree of dementia increased with the course of disease. There were no significant changes in ECG intervals in the AD group at each month. The mean square of successive RR interval differences, percentage of intervals >6 ms different from preceding interval, and normalized high frequency power component in the AD group were decreased and low-to-high frequency power ratio and normalized low frequency power component were increased. Combined with the results of the MWM, it was shown that the regulation mechanism of sympathetic and parasympathetic nerves in mice was already imbalanced in early stage AD, which was manifested as the increase of excessive activity of sympathetic nerves and the inhibition of parasympathetic activities. Therefore, ECG-based analysis of HRV may become a means of daily monitoring of AD and provide an auxiliary basis for clinical diagnosis.

Association of Homocysteine Levels With Medial Temporal Lobe Atrophy Among Carriers and Non-carriers of APOE ε4 in MCI Subjects

BACKGROUND

Different clinical subtypes of mild cognitive impairment (MCI) involve heterogeneous underlying etiologies. This study investigated the association between demographics, neuropsychological performance, apolipoprotein E (APOE) genotype and magnetic resonance imaging (MRI) measures in patients with MCI (amnestic [aMCI] and non-amnestic [naMCI]).

METHODS

This case-control study included 130 aMCI patients, 58 naMCI patients, and 1,106 healthy controls (HCs). APOE genotypes, medial temporal lobe atrophy (MTA), neurological evaluation results, and white matter hyperintensities (WMH) were investigated. Serum folate and vitamin B12 concentrations were analyzed by radioimmunoassay, and plasma hyperhomocysteinemia (Hcy) was assessed by a high-performance liquid chromatography-fluorescence method.

RESULTS

Serum folate levels were significantly lower, but plasma Hcy levels were higher, in patients with aMCI and naMCI than in healthy controls. There were significantly higher MTA scores in the aMCI group than the healthy control group. Multiple linear regression showed that serum Hcy and folate concentrations were positively associated with MTA (p < 0.05), while APOE4 showed a significant negative association with MTA in the aMCI group (p < 0.01). In addition, moderate/severe WMH showed a significant negative association with MTA in the naMCI and HC groups (p < 0.01).

CONCLUSION

The combined presence of APOE4 and Hcy is associated with aMCI in elderly individuals, while moderate/severe WMH is related to naMCI, which suggests etiological differences across MCI subtypes.

Agreement Between Automated and Manual MRI Volumetry in Alzheimer's Disease: A Systematic Review and Meta-Analysis

BACKGROUND

Automated magnetic resonance imaging (MRI) volumetry is a promising tool to evaluate regional brain volumes in dementia and especially Alzheimer's disease (AD).

PURPOSE

To compare automated methods and the gold standard manual segmentation in measuring regional brain volumes on MRI across healthy controls, patients with mild cognitive impairment, and patients with dementia due to AD.

STUDY TYPE

Systematic review and meta-analysis.

DATA SOURCES

MEDLINE, Embase, and PsycINFO were searched through October 2021.

FIELD STRENGTH

1.0 T, 1.5 T, or 3.0 T.

ASSESSMENT

Two review authors independently identified studies for inclusion and extracted data. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2).

STATISTICAL TESTS

Standardized mean differences (SMD; Hedges' g) were pooled using random-effects meta-analysis with robust variance estimation. Subgroup analyses were undertaken to explore potential sources of heterogeneity. Sensitivity analyses were conducted to examine the impact of the within-study correlation between effect estimates on the meta-analysis results.

RESULTS

Seventeen studies provided sufficient data to evaluate the hippocampus, lateral ventricles, and parahippocampal gyrus. The pooled SMD for the hippocampus, lateral ventricles, and parahippocampal gyrus were 0.22 (95% CI -0.50 to 0.93), 0.12 (95% CI -0.13 to 0.37), and -0.48 (95% CI -1.37 to 0.41), respectively. For the hippocampal data, subgroup analyses suggested that the pooled SMD was invariant across clinical diagnosis and field strength. Subgroup analyses could not be conducted on the lateral ventricles data and the parahippocampal gyrus data due to insufficient data. The results were robust to the selected within-study correlation value.

DATA CONCLUSION

While automated methods are generally comparable to manual segmentation for measuring hippocampal, lateral ventricle, and parahippocampal gyrus volumes, wide 95% CIs and large heterogeneity suggest that there is substantial uncontrolled variance. Thus, automated methods may be used to measure these regions in patients with AD but should be used with caution.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 3.

Spatial Navigation and Visuospatial Strategies in Typical and Atypical Aging

Age-related spatial navigation decline is more pronounced in patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) dementia. We used a realistic-looking virtual navigation test suite to analyze different aspects of visuospatial processing in typical and atypical aging. A total of 219 older adults were recruited from the Czech Brain Aging Study cohort. Cognitively normal older adults (CN; n = 78), patients with amnestic MCI (n = 75), and those with mild AD dementia (n = 66) underwent three navigational tasks, cognitive assessment, and brain MRI. Route learning and wayfinding/perspective-taking tasks distinguished the groups as performance and learning declined and specific visuospatial strategies were less utilized with increasing cognitive impairment. Increased perspective shift and utilization of non-specific strategies were associated with worse task performance across the groups. Primacy and recency effects were observed across the groups in the route learning and the wayfinding/perspective-taking task, respectively. In addition, a primacy effect was present in the wayfinding/perspective-taking task in the CN older adults. More effective spatial navigation was associated with better memory and executive functions. The results demonstrate that a realistic and ecologically valid spatial navigation test suite can reveal different aspects of visuospatial processing in typical and atypical aging.

Association between Self-Reported Pain, Cognition, and Neuropathology in Older Adults Admitted to an Outpatient Memory Clinic-A Cross-Sectional Study

Cognitive impairment has been linked to reduced self-reporting of pain. However, it is unclear whether the various cognitive functions are similarly and/or independently associated with such pain report measures. In the present study, we explored how executive functioning (EF), memory, and global cognition relate to self-reported pain and investigated whether underlying neuropathology partially accounts for these results. We used Lasso categorical regression to analyze data from 179 individuals visiting a memory clinic. The data included the self-reported pain occurrence, intensity, severity and frequency, clinical diagnoses, neuropsychological scores, white matter hyperintensities, medial temporal lobe atrophy, depressive symptoms, and demographics. Our results showed that worse memory and EF performance predicted a lower pain occurrence. In those individuals who did report pain, worse memory predicted lower pain intensity, severity, and frequency levels, but for EF reversed effects were found, with worse EF predicting higher pain scores. These relationships were only partially explained by reductions in white matter and medial temporal lobe integrity. Similar effects were found for depressive symptoms. Our findings highlight the distinct associations of EF and memory with self-reported pain. A similar pattern of relationships found for both self-reported pain and depressive symptoms may reflect shared latent affective components.

Magnetic Resonance Imaging Findings Among Individuals With Olfactory and Cognitive Impairment

OBJECTIVES

The underlying mechanism of the association between olfactory impairment and dementia may be explained by neurodegenerative changes detected on magnetic resonance imaging (MRI). The purpose of this systematic review is to describe neurodegenerative changes on MRI in patients with olfactory impairment and mild cognitive impairment (MCI) or dementia.

STUDY DESIGN

Systematic review.

METHODS

A literature search encompassing PubMed, Embase, Cochrane Library, Web of Science, Scopus, and Google Scholar for studies with MRI and olfactory testing among participants diagnosed with MCI or dementia was performed. Sample size, study design, cognitive impairment type, olfactory testing, and MRI findings were abstracted. Two investigators independently reviewed all articles.

RESULTS

The search yielded 556 nonduplicate abstracts, from which 86 articles were reviewed and 24 were included. Seventeen (71%) of 24 studies reported hippocampal volume findings, with 14 studies reporting a relationship between hippocampal volume and olfactory performance. Two (50%) of four prospective studies reported the potential utility of baseline hippocampal volume as a marker of dementia conversion from MCI. Five (21%) of 24 studies reporting olfactory functional MRI (fMRI) findings highlighted the utility of olfactory fMRI to identify individuals in the early stages of cognitive decline.

CONCLUSION

Current evidence suggests hippocampal volume correlates with olfactory performance in individuals with cognitive impairment, and that olfactory fMRI may improve early detection of AD. However, the predictive utility of these imaging markers is limited in prospective studies. MRI may be a useful modality for selecting patients at high risk of future cognitive decline for enrollment in early treatment trials. Laryngoscope, 2021.

3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI

We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans of 331 participants, we obtained functional 3-dimensional (3-D) independent component spatial maps for use as features in classification and regression tasks. A 3-D convolutional neural network (CNN) architecture was developed for the classification task. MMSE scores were predicted using: linear least square regression (LLSR), support vector regression, bagging-based ensemble regression, and tree regression with group independent component analysis (gICA) features. To improve MMSE regression performance, we applied feature optimization methods including least absolute shrinkage and selection operator and support vector machine-based recursive feature elimination (SVM-RFE). The mean balanced test accuracy was 85.27% for the classification of AD versus healthy controls. The medial visual, default mode, dorsal attention, executive, and auditory related networks were mainly associated with AD. The maximum clinical MMSE score prediction accuracy with the LLSR method applied on gICA combined with SVM-RFE features had the lowest root mean square error (3.27 ± 0.58) and the highest R² value (0.63 ± 0.02). Classification of AD and healthy controls can be successfully achieved using only rs-fMRI and MMSE scores can be accurately predicted using functional independent component features. In the absence of trained clinicians, AD disease status and clinical MMSE scores can be jointly predicted using 3-D deep learning and regression learning approaches with rs-fMRI data.

Polygenic risk for Alzheimer's disease shapes hippocampal scene-selectivity

Preclinical models of Alzheimer's disease (AD) suggest APOE modulates brain function in structures vulnerable to AD pathophysiology. However, genome-wide association studies now demonstrate that AD risk is shaped by a broader polygenic architecture, estimated via polygenic risk scoring (AD-PRS). Despite this breakthrough, the effect of AD-PRS on brain function in young individuals remains unknown. In a large sample (N = 608) of young, asymptomatic individuals, we measure the impact of both (i) APOE and (ii) AD-PRS on a vulnerable cortico-limbic scene-processing network heavily implicated in AD pathophysiology. Integrity of this network, which includes the hippocampus (HC), is fundamental for maintaining cognitive function during ageing. We show that AD-PRS, not APOE, selectively influences activity within the HC in response to scenes, while other perceptual nodes remained intact. This work highlights the impact of polygenic contributions to brain function beyond APOE, which could aid potential therapeutic/interventional strategies in the detection and prevention of AD.

Dynamic association between AT(N) profile and cognition mediated by cortical thickness in Alzheimer's continuum

BACKGROUND

The recently-proposed National Institute on Aging and Alzheimer's Association research framework organizes Alzheimer's disease (AD) biomarkers based on amyloid/tau/neurodegeneration (AT(N)). This study investigated the mediating effect of structural change in brain MRI on changes in cognitive function according to initial AT(N) profiles.

METHODS

We included 576 subjects (cognitively unimpaired (N = 136), mild cognitive impairment (N = 294), dementia (N = 146)) from the Alzheimer's disease Neuroimaging Initiative study. The parallel-process latent growth curve model was applied to test the mediational effect of cortical thickness growth trajectory between the initial AT(N) profiles and cognitive growth trajectory.

RESULTS

In Alzheimer's continuum, only the A + T + (N)+ profile showed a mediational effect of the cortical thickness growth trajectory. A + T - (N)- was not sufficient to induce direct or indirect effects on cognitive dysfunction, and A + T + (N)- showed a significant direct path from an altered cortical thickness to cognitive decline.

CONCLUSION

The sequential effect between changes in brain MRI and cognition varied by baseline AT(N) profile, suggesting the dynamic changes in the relationships among biomarkers in the current cascade model.

Functional magnetic resonance imaging of the trail-making test in older adults

The trail-making test (TMT) is a popular neuropsychological test, which is used extensively to measure cognitive impairment associated with neurodegenerative disorders in older adults. Behavioural performance on the TMT has been investigated in older populations, but there is limited research on task-related brain activity in older adults. The current study administered a naturalistic version of the TMT to a healthy older-aged population in an MRI environment using a novel, MRI-compatible tablet. Functional MRI was conducted during task completion, allowing characterization of the brain activity associated with the TMT. Performance on the TMT was evaluated using number of errors and seconds per completion of each link. Results are reported for 36 cognitively healthy older adults between the ages of 52 and 85. Task-related activation was observed in extensive regions of the bilateral frontal, parietal, temporal and occipital lobes as well as key motor areas. Increased age was associated with reduced brain activity and worse task performance. Specifically, older age was correlated with decreased task-related activity in the bilateral occipital, temporal and parietal lobes. These results suggest that healthy older aging significantly affects brain function during the TMT, which consequently may result in performance decrements. The current study reveals the brain activation patterns underlying TMT performance in a healthy older aging population, which functions as an important, clinically-relevant control to compare to pathological aging in future investigations.

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year.

OBJECTIVES

To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI.

SEARCH METHODS

On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches.

SELECTION CRITERIA

We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter.

DATA COLLECTION AND ANALYSIS

Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available.

MAIN RESULTS

We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate.

AUTHORS' CONCLUSIONS

The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

Hippocampal atrophy based Alzheimer's disease diagnosis via machine learning methods

Alzheimer's disease is the most common form of dementia and is a serious health problem. The disease is expected to increase further in the upcoming years with the increase of the elderly population. Developing new treatments and diagnostic methods is getting more important. In this study, we focused on the early diagnosis of dementia in Alzheimer's disease via analysis of neuroimages. We analyzed the data diagnosed by the Alzheimer's Disease Neuroimaging Initiative (ADNI) protocol. The analyzed data were T1-weighted magnetic resonance images of 159 patients with Alzheimer's disease, 217 patients with mild cognitive impairment and 109 cognitively healthy older people. In this study, we propose that the volumetric reduction in the hippocampus is the most important indicator of Alzheimer's disease. There is not much research about the relationship between the volumetric reduction in the hippocampus and Alzheimer's disease. This volume information was calculated through semi-automatic segmentation software ITK-SNAP and a data set was created based on age, gender, diagnosis, and right and left hippocampal volume values. The diagnosis via hippocampal volume information was made by using machine learning techniques. By using this approach, we conclude that brain MRIs can be used to distinguish the patients with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) from each other; while most of the studies were only able to distinguish AD from CN. Our results have revealed that our approach improves the performance of the computer-aided diagnosis of Alzheimer's disease.

Cognitive Correlates of Hippocampal Atrophy and Ventricular Enlargement in Adults with or without Mild Cognitive Impairment

We analyzed structural magnetic resonance imaging data from 58 cognitively normal and 101 mild cognitive impairment subjects. We used a general linear regression model to study the association between cognitive performance with hippocampal atrophy and ventricular enlargement using the radial distance method. Bilateral hippocampal atrophy was associated with baseline and longitudinal memory performance. Left hippocampal atrophy predicted longitudinal decline in visuospatial function. The multidomain ventricular analysis did not reveal any significant predictors.

Development of a Stand-Alone Independent Graphical User Interface for Neurological Disease Prediction with Automated Extraction and Segmentation of Gray and White Matter in Brain MRI Images

This research presents an independent stand-alone graphical computational tool which functions as a neurological disease prediction framework for diagnosis of neurological disorders to assist neurologists or researchers in the field to perform automatic segmentation of gray and white matter regions in brain MRI images. The tool was built in collaboration with neurologists and neurosurgeons and many of the features are based on their feedback. This tool provides the user automatized functionality to perform automatic segmentation and extract the gray and white matter regions of patient brain image data using an algorithm called adapted fuzzy c-means (FCM) membership-based clustering with preprocessing using the elliptical Hough transform and postprocessing using connected region analysis. Dice coefficients for several patient brain MRI images were calculated to measure the similarity between the manual tracings by experts and automatic segmentations obtained in this research. The average Dice coefficients are 0.86 for gray matter, 0.88 for white matter, and 0.87 for total cortical matter. Dice coefficients of the proposed algorithm were also the highest when compared with previously published standard state-of-the-art brain MRI segmentation algorithms in terms of accuracy in segmenting the gray matter, white matter, and total cortical matter.

A comparative study: visual rating scores and the voxel-based specific regional analysis system for Alzheimer's disease on magnetic resonance imaging among subjects with Alzheimer's disease, mild cognitive impairment, and normal cognition

AIM

Hippocampal atrophy shown on magnetic resonance imaging can differentiate Alzheimer's disease (AD) patients from subjects with normal cognition (NC). Simplified automated methods that use volumetric analysis, such as as the voxel-based specific regional analysis system for AD, have become widely used in Japan. However, the diagnostic value of the voxel-based specific regional analysis system compared with visual rating scores for clinical diagnosis is unclear.

METHODS

Study participants consisted of 37 AD patients, 29 mild cognitive impairment (MCI) patients, and 21 NC subjects. All participants underwent neuropsychological testing and magnetic resonance imaging. The imaging was scored visually for regional brain atrophy by two raters based on a newly developed visual rating score. The voxel-based specific regional analysis system for AD scores were calculated with the analysis system's advanced software. We analyzed whether these scores aid in discriminating among AD, MCI, and NC.

RESULTS

The AD group had significantly different visual rating scores, regional analysis scores, and all neuropsychological test scores than the NC group. The AD group had significantly different visual rating scores than the MCI group, and a significant difference was observed between the MCI and NC groups on regional analysis scores. Both the visual rating and regional analysis scores showed equivalent correlations with the neuropsychological test scores.

CONCLUSIONS

Both the visual rating and regional analysis scores are clinically useful tools for differentiating among AD, MCI, and NC.

Usefulness of Discriminability and Response Bias Indices for the Evaluation of Recognition Memory in Mild Cognitive Impairment and Alzheimer Disease

BACKGROUND

Most studies examining episodic memory in Alzheimer disease (AD) have focused on patients' impaired ability to remember information. This approach provides only a partial picture of memory deficits since other factors involved are not considered.

OBJECTIVE

To evaluate the recognition memory performance by using a yes/no procedure to examine the effect of discriminability and response bias measures in amnestic mild cognitive impairment (a-MCI), AD dementia, and normal-aging subjects.

METHODS

We included 43 controls and 45 a-MCI and 51 mild AD dementia patients. Based on the proportions of correct responses (hits) and false alarms from the Rey Auditory Verbal Learning Test (RAVLT), discriminability (d') and response bias (C) indices from signal detection theory (SDT) were calculated.

RESULTS

Results showed significant group differences for d' (F (2) = 83.26, p < 0.001), and C (F (2) = 6.05, p = 0.00). The best predictors of group membership were delayed recall and d' scores. The d' measure correctly classified subjects with 82.98% sensitivity and 91.11% specificity.

CONCLUSIONS

a-MCI and AD dementia subjects exhibit less discrimination accuracy and more liberal response bias than controls. Furthermore, combined indices of delayed recall and discriminability from the RAVLT are effective in defining early AD. SDT may help enhance diagnostic specificity.

Visual Evaluation of Medial Temporal Lobe Atrophy as a Clinical Marker of Conversion from Mild Cognitive Impairment to Dementia and for Predicting Progression in Patients with Mild Cognitive Impairment and Mild Alzheimer's Disease

BACKGROUND/AIMS

To evaluate whether visual assessment of medial temporal lobe atrophy (vaMTA) can predict 2-year conversion from mild cognitive impairment (MCI) to dementia and progression of MCI and Alzheimer's disease dementia as measured by the Clinical Dementia Rating Scale Sum of Boxes score (CDR-SB).

METHODS

vaMTA was performed in 94 patients with MCI according to the Winblad criteria and in 124 patients with AD according to ICD-10 and NINCDS-ADRDA criteria. Demographic data, the Consortium to Establish a Registry for Alzheimer's Disease 10-word delayed recall, APOE ɛ4 status, Cornell Scale for Depression in Dementia, and comorbid hypertension were used as covariates.

RESULTS

vaMTA was associated with MCI conversion in an unadjusted model but not in an adjusted model (p = 0.075), where delayed recall and APOE ɛ4 status were significant predictors. With CDR-SB change as the outcome, an interaction between vaMTA and diagnosis was found, but in the adjusted model only delayed recall and age were significant predictors. For vaMTA below 2, the association between vaMTA and CDR-SB change differed between diagnostic groups. Similar results were found based on a trajectory analysis.

CONCLUSION

In adjusted models, memory function, APOE ɛ4 status and age were significant predictors of disease progression, not vaMTA. The association between vaMTA and CDR-SB change was different in patients with MCI and Alzheimer's disease dementia.

Gender-Specific Degeneration of Dementia-Related Subcortical Structures Throughout the Lifespan

Age-related changes in brain structure are a question of interest to a broad field of research. Structural decline has been consistently, but not unambiguously, linked to functional consequences, including cognitive impairment and dementia. One of the areas considered of crucial importance throughout this process is the medial temporal lobe, and primarily the hippocampal region. Gender also has a considerable effect on volume deterioration of subcortical grey matter (GM) structures, such as the hippocampus. The influence of age×gender interaction on disproportionate GM volume changes might be mediated by hormonal effects on the brain. Hippocampal volume loss appears to become accelerated in the postmenopausal period. This decline might have significant influences on neuroplasticity in the CA1 region of the hippocampus highly vulnerable to pathological influences. Additionally, menopause has been associated with critical pathobiochemical changes involved in neurodegeneration. The micro- and macrostructural alterations and consequent functional deterioration of critical hippocampal regions might result in clinical cognitive impairment-especially if there already is a decline in the cognitive reserve capacity. Several lines of potential vulnerability factors appear to interact in the menopausal period eventually leading to cognitive decline, mild cognitive impairment, or Alzheimer's disease. This focused review aims to delineate the influence of unmodifiable risk factors of neurodegenerative processes, i.e., age and gender, on critical subcortical GM structures in the light of brain derived estrogen effects. The menopausal period appears to be of key importance for the risk of cognitive decline representing a time of special vulnerability for molecular, structural, and functional influences and offering only a narrow window for potential protective effects.

Presenilin1/γ-secretase protects neurons from glucose deprivation-induced death by regulating miR-212 and PEA15

Reduced cerebral glucose utilization is found in aged individuals and often is an early sign of neurodegeneration. Here, we show that under glucose deprivation (GD) conditions, decreased expression of presenilin 1 (PS1) results in decreased neuronal survival, whereas increased PS1 increases neuronal survival. Inhibition of γ-secretase also decreases neuronal survival under GD conditions, which suggests the PS1/γ-secretase system protects neurons from GD-induced death. We also show that neuronal levels of the survival protein, phosphoprotein enriched in astrocytes at ∼15 kDa (PEA15), and its mRNA are regulated by PS1/γ-secretase. Furthermore, down-regulation of PEA15 decreases neuronal survival under reduced glucose conditions, whereas exogenous PEA15 increases neuronal survival even in the absence of PS1, which indicates that PEA15 promotes neuronal survival under GD conditions. The absence or reduction of PS1, as well as γ-secretase inhibitors, increases neuronal miR-212, which targets PEA15 mRNA. PS1/γ-secretase activates the transcription factor, cAMP response element-binding protein, regulating miR-212, which targets PEA15 mRNA. Taken together, our data show that under conditions of reduced glucose, the PS1/γ-secretase system decreases neuronal losses by suppressing miR-212 and increasing its target survival factor, PEA15. These observations have implications for mechanisms of neuronal death under conditions of reduced glucose and may provide targets for intervention in neurodegenerative disorders.-Huang, Q., Voloudakis, G., Ren, Y., Yoon, Y., Zhang, E., Kajiwara, Y., Shao, Z., Xuan, Z., Lebedev, D., Georgakopoulos, A., Robakis, N. K. Presenilin1/γ-secretase protects neurons from glucose deprivation-induced death by regulating miR-212 and PEA15.

Role of physical exercise on cognitive function in healthy older adults: A systematic review of randomized clinical trials

Cognitive impairment has a harmful effect on quality of life, is associated with functional limitations and disability in older adults. Physical activity (PA) has shown to have beneficial effects on cognition but the results and conclusions of randomized controlled trials (RCTs) are less consistent. Update of knowledge was necessary to examine the effects on cognitive function of new training modalities developed in recent years, such as multicomponent exercise training. Therefore, the purpose of this review was to examine the role of multicomponent training versus aerobic or resistance training alone on cognition in healthy older adults (>65 years) without known cognitive impairment. The mean differences (MD) of the parameters from pre-intervention to post-intervention between groups were pooled using a random-effects model. Twenty-one RCTs published between 2002 and 2016 were included. Multicomponent exercise training may have the most positive effects on cognitive function in older adults. The small number of included studies and the large variability in study populations, study design, exercise protocols, adherence rates and outcome measures complicate the interpretation of the results and contribute to discrepancies within the exercise research literature.

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985-June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions.

Divergent regional patterns of cerebral hypoperfusion and gray matter atrophy in mild cognitive impairment patients

Reductions of cerebral blood flow and gray matter structure have been implicated in early pathogenesis of Alzheimer's disease, potentially providing complementary information. The present study evaluated regional patterns of cerebral hypoperfusion and atrophy in patients with mild cognitive impairment and healthy older adults. In each participant, cerebral perfusion and gray matter structure were extracted within selected brain regions vulnerable to Alzheimer's disease using magnetic resonance imaging. Measures were compared between diagnostic groups with/without adjustment for covariates. In mild cognitive impairment patients, cerebral blood flow was significantly reduced in comparison with healthy controls in temporo-parietal regions and the basal ganglia in the absence of local gray matter atrophy. By contrast, gray matter structure was significantly reduced in the hippocampus in the absence of local hypoperfusion. Both, cerebral perfusion and gray matter structure were significantly reduced in the entorhinal and isthmus cingulate cortex in mild cognitive impairment patients compared with healthy older adults. Our results demonstrated partly divergent patterns of temporo-parietal hypoperfusion and medial-temporal atrophy in mild cognitive impairment patients, potentially indicating biomarker sensitivity to dissociable pathological mechanisms. The findings support applicability of cerebral perfusion and gray matter structure as complementary magnetic resonance imaging-based biomarkers in early Alzheimer's disease detection, a hypothesis to be further evaluated in longitudinal studies.

Functional-structural degeneration in dorsal and ventral attention systems for Alzheimer's disease, amnestic mild cognitive impairment

Growing evidence of attention related failures in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) has already been proposed by previous studies. However, previous studies lacked of systematic investigation on the functional and structural substrates for attention function for patients with AD and aMCI. In this work, we investigated the functional connectivity and gray matter density in dorsal and ventral attention networks (DAN, VAN) of normal participants (n = 15) and patients with aMCI (n = 12) and AD (n = 16) by applying group independent component analysis (ICA) and voxel-based morphometry (VBM) analysis. Using ICA, we extracted the functional patterns of DAN and VAN which are respectively responsible for the "top-down" attention process and "bottom-up" process. One-way analysis of variance (ANOVA) revealed significant group-differed functional connectivity in bilateral frontal eye fields (FEF) area and intraparietal sulcus (IPS) area, as well as posterior cingulate cortex and precuneus in the dorsal system. With regard to the ventral system, group-effects were significantly focused in right orbital superior/middle frontal gyrus, right inferior parietal lobule, angular gyrus, and supramarginal gyrus around the temporal-parietal junction area. Post hoc cluster-level comparisons revealed totally impaired functional substrates for both attentional networks for patients with AD, whereas selectively impaired attention systems for patients with aMCI with impaired functional patent of DAN but preserved functional pattern of VAN. Correspondingly, VBM analysis revealed gray matter loss in right ventral and dorsal frontal cortex was in the AD group, whereas preserved gray matter density was in aMCI, even a little extent of expansion of gray matter density in several participants. Using multivariate regression analysis we found discrepant couplings of functional-structural degenerations between both patient groups. Specifically, positive coupling of structural-functional degeneration was found in right dorsal and ventral frontal cortex in the AD group, whereas inverse coupling in dorsal frontal cortex was found in the aMCI group. These findings suggested discrepant functional-structural degenerations in both attention systems between both patient groups, widening avenues to better understanding the attentional deficits in patients with aMCI and AD.

Does MRI Increase the Diagnostic Confidence of Physicians in an Outpatient Memory Clinic

Background and Aim

Data showing the usefulness of MRI to improve the accuracy of the diagnostic process in cognitive disorders were derived from studies in tertiary referral centers. MRI is widely used as a diagnostic tool in everyday practice, but it is unknown what the actual added value of MRI is. We studied the usefulness of MRI in the diagnostic process by measuring the change of confidence of the physician.

Methods

Physicians indicated confidence in their diagnosis before and after presentation of MR images using a visual analogue scale from 0-100%.

Results

Use of MRI increased the level of confidence by 3% in experienced clinicians and by 9% in inexperienced physicians. In 2/125 cases, MRI showed an unexpected finding.

Conclusion

MRI is a useful diagnostic tool in everyday practice of diagnosing cognitive disorders.

Alzheimer's Disease Cerebrospinal Fluid and Neuroimaging Biomarkers: Diagnostic Accuracy and Relationship to Drug Efficacy

Widely researched Alzheimer's disease (AD) biomarkers include in vivo brain imaging with PET and MRI, imaging of amyloid plaques, and biochemical assays of Aβ 1 - 42, total tau, and phosphorylated tau (p-tau-181) in cerebrospinal fluid (CSF). In this review, we critically evaluate these biomarkers and discuss their clinical utility for the differential diagnosis of AD. Current AD biomarker tests are either highly invasive (requiring CSF collection) or expensive and labor-intensive (neuroimaging), making them unsuitable for use in the primary care, clinical office-based setting, or to assess drug efficacy in clinical trials. In addition, CSF and neuroimaging biomarkers continue to face challenges in achieving required sensitivity and specificity and minimizing center-to-center variability (for CSF-Aβ 1 - 42 biomarkers CV = 26.5% ; http://www.alzforum.org/news/conference-coverage/paris-standardization-hurdle-spinal-fluid-imaging-markers). Although potentially useful for selecting patient populations for inclusion in AD clinical trials, the utility of CSF biomarkers and neuroimaging techniques as surrogate endpoints of drug efficacy needs to be validated. Recent trials of β- and γ-secretase inhibitors and Aβ immunization-based therapies in AD showed no significant cognitive improvements, despite changes in CSF and neuroimaging biomarkers. As we learn more about the dysfunctional cellular and molecular signaling processes that occur in AD, and how these processes are manifested in tissues outside of the brain, new peripheral biomarkers may also be validated as non-invasive tests to diagnose preclinical and clinical AD.

Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI

Brain network alterations in patients with Alzheimer's disease (AD) has been the subject of much investigation, but the biological mechanisms underlying these alterations remain poorly understood. Here, we aim to identify the changes in brain networks in patients with AD and mild cognitive impairment (MCI), and provide an accurate algorithm for classification of these patients from healthy control subjects (HC) by using a graph theoretical approach and advanced machine learning methods. Multivariate Granger causality analysis was performed on resting-state functional magnetic resonance imaging (rs-fMRI) data of 34 AD, 89 MCI, and 45 HC to calculate various directed graph measures. The graph measures were used as the original feature set for the machine learning algorithm. Filter and wrapper feature selection methods were applied to the original feature set to select an optimal subset of features. An accuracy of 93.3% was achieved for classification of AD, MCI, and HC using the optimal features and the naïve Bayes classifier. We also performed a hub node analysis and found that the number of hubs in HC, MCI, and AD were 12, 10, and 9, respectively, suggesting that patients with AD experience disturbance of critical communication areas in their brain network as AD progresses. The findings of this study provide insight into the neurophysiological mechanisms underlying MCI and AD. The proposed classification method highlights the potential of directed graph measures of rs-fMRI data for identification of the early stage of AD.

Removing inter-subject technical variability in magnetic resonance imaging studies

Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect, and other technical artifacts is still present after standard intensity normalization in large multi-site neuroimaging studies. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. As proposed by SVA and RUV [Leek and Storey, 2007, 2008, Gagnon-Bartsch and Speed, 2012], two batch effect correction tools largely used in genomics, we decompose the voxel intensities of images registered to a template into a biological component and an unwanted variation component. The unwanted variation component is estimated from a control region obtained from the cerebrospinal fluid (CSF), where intensities are known to be unassociated with disease status and other clinical covariates. We perform a singular value decomposition (SVD) of the control voxels to estimate factors of unwanted variation. We then estimate the unwanted factors using linear regression for every voxel of the brain and take the residuals as the RAVEL-corrected intensities. We assess the performance of RAVEL using T1-weighted (T1-w) images from more than 900 subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI), as well as healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compare RAVEL to two intensity-normalization-only methods: histogram matching and White Stripe. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to be most associated with AD, and that these regions are significantly more present in structures impacted by AD (hippocampus, amygdala, parahippocampal gyrus, enthorinal area, and fornix stria terminals). In addition, we show that the RAVEL-corrected intensities have the best performance in distinguishing between MCI subjects and healthy subjects using the mean hippocampal intensity (AUC=67%), a marked improvement compared to results from intensity normalization alone (AUC=63% and 59% for histogram matching and White Stripe, respectively). RAVEL is promising for many other imaging modalities.

Heterogeneity of Regional Brain Atrophy Patterns Associated with Distinct Progression Rates in Alzheimer's Disease

We aimed to identify and characterize subtypes of Alzheimer’s disease (AD) exhibiting different patterns of regional brain atrophy on MRI using age- and gender-specific norms of regional brain volumes. AD subjects included in the Alzheimer's Disease Neuroimaging Initiative study were classified into subtypes based on standardized values (Z-scores) of hippocampal and regional cortical volumes on MRI with reference to age- and gender-specific norms obtained from 222 cognitively normal (CN) subjects. Baseline and longitudinal changes of clinical characteristics over 2 years were compared across subtypes. Whole-brain-level gray matter (GM) atrophy pattern using voxel-based morphometry (VBM) and cerebrospinal fluid (CSF) biomarkers of the subtypes were also investigated. Of 163 AD subjects, 58.9% were classified as the “both impaired” subtype with the typical hippocampal and cortical atrophy pattern, whereas 41.1% were classified as the subtypes with atypical atrophy patterns: “hippocampal atrophy only” (19.0%), “cortical atrophy only” (11.7%), and “both spared” (10.4%). Voxel-based morphometric analysis demonstrated whole-brain-level differences in overall GM atrophy across the subtypes. These subtypes showed different progression rates over 2 years; and all subtypes had significantly lower CSF amyloid-β_1–42 levels compared to CN. In conclusion, we identified four AD subtypes exhibiting heterogeneous atrophy patterns on MRI with different progression rates after controlling the effects of aging and gender on atrophy with normative information. CSF biomarker analysis suggests the presence of Aβ neuropathology irrespective of subtypes. Such heterogeneity of MRI-based neuronal injury biomarker and related heterogeneous progression patterns should be considered in clinical trials and practice with AD patients.

Using magnetic resonance imaging in diagnosing dementia: a Dutch outpatient memory clinics survey

BACKGROUND

In the Netherlands, dementia syndromes are diagnosed in specialized memory outpatient clinics (MC). Many radiologists are not trained to assess magnetic resonance imaging (MRI) scans with respect to possible radiological changes that may indicate neurodegenerative disease.

METHODS

This is a cross-sectional descriptive study. A survey was sent to all Dutch MC and included questions as to how MRI scans are assessed by radiologists and how these assessments are used in the diagnostic process.

RESULTS

In most MC, radiologists report on typical Alzheimer pathology and large vessel disease. Small vessel disease and other anatomical changes signifying neurodegenerative disease frequently are not assessed. In the majority of MC, the radiological assessment is not standardized, and physicians assess MRI for themselves to use this information to discuss the consensus diagnosis subsequently.

CONCLUSION

MRI assessment by radiologists in Dutch MC probably underestimates the presence of cerebrovascular and neurodegenerative disease. The validity of standardized assessment protocols in routine clinical practice deserves further study, as the implementation of standardization outside research settings could improve diagnostic accuracy.

Association of change in brain structure to objectively measured physical activity and sedentary behavior in older adults: Age, Gene/Environment Susceptibility-Reykjavik Study

Many studies have examined the hypothesis that greater participation in physical activity (PA) is associated with less brain atrophy. Here we examine, in a sub-sample (n=352, mean age 79.1 years) of the Age, Gene/Environment Susceptibility-Reykjavik Study cohort, the association of the baseline and 5-year change in magnetic resonance imaging (MRI)-derived volumes of gray matter (GM) and white matter (WM) to active and sedentary behavior (SB) measured at the end of the 5-year period by a hip-worn accelerometer for seven consecutive days. More GM (β=0.11; p=0.044) and WM (β=0.11; p=0.030) at baseline was associated with more total physical activity (TPA). Also, when adjusting for baseline values, the 5-year change in GM (β=0.14; p=0.0037) and WM (β=0.11; p=0.030) was associated with TPA. The 5-year change in WM was associated with SB (β=-0.11; p=0.0007). These data suggest that objectively measured PA and SB late in life are associated with current and prior cross-sectional measures of brain atrophy, and that change over time is associated with PA and SB in expected directions.

Changes of gray matter volume and amplitude of low-frequency oscillations in amnestic MCI: An integrative multi-modal MRI study

BACKGROUND

Numerous studies have reported that the amnestic-type mild cognitive impairment (aMCI) patients have impaired brain structural integrity and functional alterations separately.

PURPOSE

To investigate the changes of gray matter and amplitude of low-frequency oscillations in patients with aMCI by combining structural and functional magnetic resonance imaging (fMRI).

MATERIAL AND METHODS

Thirty-four patients with aMCI and 34 controls were recruited. We adopted optimized voxel-based morphometry to detect regions with gray matter volume (GMV) loss induced by aMCI. Then regional differences in amplitude of slow-4 band (0.027-0.073 Hz) oscillations among these regions between patients and healthy controls were examined. Both slow-4 amplitude of low-frequency fluctuations (ALFF) and slow-4 fractional ALFF (fALFF; the relative amplitude that resides in the low frequencies) were employed.

RESULTS

Patients with aMCI demonstrated significant GMV loss in the ventral medial prefrontal cortex (vMPFC), posterior cingulate cortex (PCC), bilateral hippocampus, right superior parietal gyrus, left insula and left middle temporal gyrus (P < 0.01). The patients exhibited significant decreases of slow-4 ALFF in the left hippocampus (P = 0.05) and PCC (P = 0.02), while the decreased slow-4 fALFF was detected in PCC (P = 0.01) and increased slow-4 fALFF in vMPFC (P = 0.03). In PCC, aMCI and controls exhibited significant different GMV-fALFF correlation (P < 0.05), with opposite correlation trend.

CONCLUSION

The correlates between anatomical deficits and functional alterations in aMCI suggest that anatomical and functional deficits are linked to each other. The differences of GMV-fALFF correlations demonstrated altered anatomical-functional relationship in aMCI.

Coalition Against Major Diseases/European Medicines Agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer's disease

BACKGROUND

Regulatory qualification of a biomarker for a defined context of use provides scientifically robust assurances to sponsors and regulators that accelerate appropriate adoption of biomarkers into drug development.

METHODS

The Coalition Against Major Diseases submitted a dossier to the Scientific Advice Working Party of the European Medicines Agency requesting a qualification opinion on the use of hippocampal volume as a biomarker for enriching clinical trials in subjects with mild cognitive impairment, incorporating a scientific rationale, a literature review and a de novo analysis of Alzheimer's Disease Neuroimaging Initiative data.

RESULTS

The literature review and de novo analysis were consistent with the proposed context of use, and the Committee for Medicinal Products for Human Use released an opinion in November 2011.

CONCLUSIONS

We summarize the scientific rationale and the data that supported the first qualification of an imaging biomarker by the European Medicines Agency.

Voxel Level Survival Analysis of Grey Matter Volume and Incident Mild Cognitive Impairment or Alzheimer's Disease

The purpose of this study was to identify, at the voxel level, brain regions associated with the time to develop mild cognitive impairment (MCI) or Alzheimer's disease (AD) from normal cognition. We analyzed incident MCI (n = 58) or AD (n = 151) in 292 cognitively normal participants in the Cardiovascular Health Study-Cognition Study (mean age = 79.2 ± 3.6 years). We used segmented, modulated grey matter maps from 3D (spoiled gradient echo) MRI scans obtained in 1998/99 (with clinical follow-up through 2012) that were smoothed with a 3-D 4 mm Gaussian filter. We fit approximately 1.92 million voxel-level Cox proportional hazard models to examine the grey matter volume effect on time to event, adjusting for age, sex, and diabetes. We used the significance threshold of p <  0.005 with contiguity threshold of at least 68 voxels (false detection probability <2.5×10 -8). Areas within the mesial temporal lobe (MTL), anterior temporal lobe, hippocampus, and posterior cingulate gyrus were associated with time to MCI or AD. The presence of white matter lesions (a marker of small vessel disease in the brain) was associated with the volumes of the MTL and precuneus; MRI-identified infarcts also predicted MTL volume. These findings are important because we identified critical brain regions that predict a person's increased likelihood of developing MCI or AD over a decade prior to the onset of clinical symptoms; these critical brain regions were themselves affected by the presence of vascular disease.

The Road Ahead to Cure Alzheimer's Disease: Development of Biological Markers and Neuroimaging Methods for Prevention Trials Across all Stages and Target Populations

Alzheimer's disease (AD) is a slowly progressing non-linear dynamic brain disease in which pathophysiological abnormalities, detectable in vivo by biological markers, precede overt clinical symptoms by many years to decades. Use of these biomarkers for the detection of early and preclinical AD has become of central importance following publication of two international expert working group's revised criteria for the diagnosis of AD dementia, mild cognitive impairment (MCI) due to AD, prodromal AD and preclinical AD. As a consequence of matured research evidence six AD biomarkers are sufficiently validated and partly qualified to be incorporated into operationalized clinical diagnostic criteria and use in primary and secondary prevention trials. These biomarkers fall into two molecular categories: biomarkers of amyloid-beta (Aβ) deposition and plaque formation as well as of tau-protein related hyperphosphorylation and neurodegeneration. Three of the six gold-standard ("core feasible) biomarkers are neuroimaging measures and three are cerebrospinal fluid (CSF) analytes. CSF Aβ1-42 (Aβ1-42), also expressed as Aβ1-42 : Aβ1-40 ratio, T-tau, and P-tau Thr181 & Thr231 proteins have proven diagnostic accuracy and risk enhancement in prodromal MCI and AD dementia. Conversely, having all three biomarkers in the normal range rules out AD. Intermediate conditions require further patient follow-up. Magnetic resonance imaging (MRI) at increasing field strength and resolution allows detecting the evolution of distinct types of structural and functional abnormality pattern throughout early to late AD stages. Anatomical or volumetric MRI is the most widely used technique and provides local and global measures of atrophy. The revised diagnostic criteria for "prodromal AD" and "mild cognitive impairment due to AD" include hippocampal atrophy (as the fourth validated biomarker), which is considered an indicator of regional neuronal injury. Advanced image analysis techniques generate automatic and reproducible measures both in regions of interest, such as the hippocampus and in an exploratory fashion, observer and hypothesis-indedendent, throughout the entire brain. Evolving modalities such as diffusion-tensor imaging (DTI) and advanced tractography as well as resting-state functional MRI provide useful additionally useful measures indicating the degree of fiber tract and neural network disintegration (structural, effective and functional connectivity) that may substantially contribute to early detection and the mapping of progression. These modalities require further standardization and validation. The use of molecular in vivo amyloid imaging agents (the fifth validated biomarker), such as the Pittsburgh Compound-B and markers of neurodegeneration, such as fluoro-2-deoxy-D-glucose (FDG) (as the sixth validated biomarker) support the detection of early AD pathological processes and associated neurodegeneration. How to use, interpret, and disclose biomarker results drives the need for optimized standardization. Multimodal AD biomarkers do not evolve in an identical manner but rather in a sequential but temporally overlapping fashion. Models of the temporal evolution of AD biomarkers can take the form of plots of biomarker severity (degree of abnormality) versus time. AD biomarkers can be combined to increase accuracy or risk. A list of genetic risk factors is increasingly included in secondary prevention trials to stratify and select individuals at genetic risk of AD. Although most of these biomarker candidates are not yet qualified and approved by regulatory authorities for their intended use in drug trials, they are nonetheless applied in ongoing clinical studies for the following functions: (i) inclusion/exclusion criteria, (ii) patient stratification, (iii) evaluation of treatment effect, (iv) drug target engagement, and (v) safety. Moreover, novel promising hypothesis-driven, as well as exploratory biochemical, genetic, electrophysiological, and neuroimaging markers for use in clinical trials are being developed. The current state-of-the-art and future perspectives on both biological and neuroimaging derived biomarker discovery and development as well as the intended application in prevention trials is outlined in the present publication.

Effect of brain structure and function on reward anticipation in children and adults with attention deficit hyperactivity disorder combined subtype

Attention deficit hyperactivity disorder (ADHD) is associated with decreased ventral-striatal responsiveness during reward anticipation. However, previous research mostly focused on adults with heterogeneous ADHD subtype and divers drug treatment status while studies in children with ADHD are sparse. Moreover, it remains unclear to what degree ADHD is characterized by a delay of normal brain structure or function maturation. We therefore attempt to determine whether results from structural and functional magnetic resonance imaging (fMRI) are associated with childhood and adult ADHD combined subtype (ADHD-CT). This study used fMRI to compare VS structure and function of 30 participants with ADHD-CT (16 adults, 14 children) and 30 controls (20 adults, 10 children), using a monetary incentive delay task. Joint analyses of structural and functional imaging data were conducted with Biological Parametric Mapping. Reward anticipation elicited decreased ventral-striatal responsiveness in adults but not in children with ADHD-CT. Children and adults with ADHD showed reduced ventral-striatal volume. Taking these gray matter differences into account, the results remained the same. These results suggest that decreased ventral-striatal responsiveness during reward anticipation is present in adults but not in children with ADHD-CT, irrespective of structural characteristics. The question arises whether ventral-striatal hypoactivity is an ADHD correlate that develops during the course of illness.

Early neuropsychological detection of Alzheimer's disease

Lifestyle modification offers a promising way of preventing or delaying Alzheimer's disease (AD). In particular, nutritional interventions can contribute to decrease the risk of dementia. The efficacy of such interventions should be assessed in individuals thought to be prone to AD. It is therefore necessary to identify markers that may help detecting AD as early as possible. This review will focus on subtle neuropsychological changes that may already exist in the predementia phase, and that could point to individuals at risk of dementia. Episodic memory decline appears consistently as the earliest sign of incipient typical AD. An episodic memory test that ensures deep encoding of information and assesses retrieval with free as well as cued recall appears as a useful tool to detect patients at an early stage of AD. Beyond the memory domain, category verbal fluency has been shown to decline early and to predict progression to AD. Moreover, in line with current diagnosis criteria for prodromal AD, combining neuropsychological scores and neuroimaging data allows a better discrimination of future AD patients than neuroimaging or neuropsychological data alone. Altogether, the detection of cognitive changes that are predictive of the typical form of probable AD already in the predementia stage points to at risk people who are the best target for therapeutic interventions, such as nutrition or physical exercise counseling or dietary interventions.

Hippocampal atrophy in people with memory deficits: results from the population-based IPREA study

BACKGROUND

Clinical studies have shown that hippocampal atrophy is present before dementia in people with memory deficits and can predict dementia development. The question remains whether this association holds in the general population. This is of interest for the possible use of hippocampal atrophy to screen population for preventive interventions. The aim of this study was to assess hippocampal volume and shape abnormalities in elderly adults with memory deficits in a cross-sectional population-based study.

METHODS

We included individuals participating in the Italian Project on the Epidemiology of Alzheimer Disease (IPREA) study: 75 cognitively normal individuals (HC), 31 individuals with memory deficits (MEM), and 31 individuals with memory deficits not otherwise specified (MEMnos). Hippocampal volumes and shape were extracted through manual tracing and the growing and adaptive meshes (GAMEs) shape-modeling algorithm. We investigated between-group differences in hippocampal volume and shape, and correlations with memory deficits.

RESULTS

In MEM participants, hippocampal volumes were significantly smaller than in HC and were mildly associated with worse memory scores. Memory-associated shape changes mapped to the anterior hippocampus. Shape-based analysis detected no significant difference between MEM and HC, while MEMnos showed shape changes in the posterior hippocampus compared with HC and MEM groups.

CONCLUSIONS

These findings support the discriminant validity of hippocampal volumetry as a biomarker of memory impairment in the general population. The detection of shape changes in MEMnos but not in MEM participants suggests that shape-based biomarkers might lack sensitivity to detect Alzheimer's-like pathology in the general population.