MRI hippocampal and entorhinal cortex mapping in predicting conversion to Alzheimer's disease

Longitudinal changes in the brain in mild cognitive impairment: a magnetic resonance imaging study using the visual rating method and tensor-based morphometry

Background Brain atrophy is associated with mild cognitive impairment (MCI), and by using volumetric and visual analyzing methods, it is possible to differentiate between individuals with progressive MCI (MCIp) and stable MCI (MCIs). Automated analysis methods detect degenerative changes in the brain earlier and more reliably than visual methods. Purpose To detect and evaluate structural brain changes between and within the MCIs, MCIp, and control groups during a two-year follow-up period. Material and Methods Brain magnetic resonance imaging (MRI) scans of 11 participants with MCIs, 18 participants with MCIp, and 84 controls were analyzed by the visual rating method (VRM) and tensor-based morphometry (TBM). Results At baseline, both VRM and TBM differentiated the whole MCI group (combined MCIs and MCIp) and the MCIp group from the control group, but they did not differentiate the MCIs group from the control group. At follow-up, both methods differentiated the MCIp group from the control group, but minor differences between the MCIs and control groups were only seen by TBM. Neuropsychological tests did not find differences between the MCIs and control groups at follow-up. Neither method revealed relevant signs of brain atrophy progression within or between MCI subgroups during the follow-up time. Conclusion Both methods are equally good in the evaluation of structural brain changes in MCI if the groups are sufficiently large and the disease progresses to AD. Only TBM disclosed minor atrophic changes in the MCIs group compared to controls at follow-up. The results need to be confirmed with a large patient group and longer follow-up time.

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.

The Relational and Item-Specific Encoding Task in Mild Cognitive Impairment and Alzheimer Disease

BACKGROUND

The Relational and Item-Specific Encoding task (RISE) measures episodic memory subcomponents, including item-specific and relational encoding of to-be-remembered stimuli. These memory components are neurobiologically relevant because they may engage distinct subregions of the medial temporal lobe, perirhinal and entorhinal cortices, parahippocampus, and hippocampus.

METHODS

A total of 125 participants, including 84 healthy controls (HC), 22 mild cognitive impairment-diagnosed and 19 Alzheimer disease (AD)-diagnosed participants, were administered the RISE and neuropsychological measures. Stepwise linear regressions assessed prediction of functional ability from RISE d' measures. ANOVAs and logistic regressions determined the ability of the RISE to discriminate between the diagnostic groups. In addition, the psychometric properties of the RISE were examined.

RESULTS

RISE measures predicted diagnosis with pseudo R2 values in the range of 0.25-0.30. Receiver operating characteristic curves demonstrated adequate sensitivity and specificity with areas under the curve in the range of 0.78-0.98. Memory following relational encoding was a significant predictor of everyday functional competence. The RISE had acceptable psychometric properties, with the exception of floor effects in the AD group.

CONCLUSION

The RISE measures significantly predicted diagnosis and predicted everyday functional competence. The RISE offers unique advantages in the assessment of HC and individuals with preclinical AD.

Continuous measurement of object location memory is sensitive to effects of age and mild cognitive impairment and related to medial temporal lobe volume

Introduction

We present findings of a novel and ecologically relevant associative memory test, the Object Location Touchscreen Test (OLTT), which was posited as sensitive to early medial temporal lobe compromise associated with mild cognitive impairment (MCI).

Methods

A total of 114 participants, including healthy young and older controls and patients with MCI, completed the OLTT and standard neuropsychological testing. The OLTT required participants to recall the location of objects under free and cued recall conditions, with accuracy evaluated using distance measures (i.e., a continuous error score), and a standard recognition format. Correlations between performance and volumetric data were evaluated from a subset of 77 participants.

Results

Significant age effects were dwarfed by MCI effects across all test conditions. OLTT Cued Recall was strongly and specifically related to the volume of disease-relevant medial temporal lobe regions, generally more than traditional memory tests.

Discussion

The OLTT may be sensitive to early structural compromise in regions affected by Alzheimer's disease.

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Evaluated age and mild cognitive impairment effects using ecologically relevant object location (OL) task.

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Performance evaluated using both continuous and dichotomous measures of accuracy.

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Greater decline in OL memory with mild cognitive impairment than with “healthy” aging.

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Performance, especially continuous measure, reflected medial temporal integrity.

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Novel OL memory task may be sensitive to early structural compromise.

Hippocampal Deformations and Entorhinal Cortex Atrophy as an Anatomical Signature of Long-Term Cognitive Impairment: from the MCAO Rat Model to the Stroke Patient

Stroke patients have an elevated risk of developing long-term cognitive disorders or dementia. The latter is often associated with atrophy of the medial temporal lobe. However, it is not clear whether hippocampal and entorhinal cortex atrophy is the sole predictor of long-term post-stroke dementia. We hypothesized that hippocampal deformation (rather than atrophy) is a predictive marker of long-term post-stroke dementia on a rat model and tested this hypothesis in a prospective cohort of stroke patients.Male Wistar rats were subjected to transient middle cerebral artery occlusion and assessed 6 months later. Ninety initially dementia-free patients having suffered a first-ever ischemic stroke were prospectively included in a clinical study. In the rat model, significant impairments in hippocampus-dependent memories were observed. MRI studies did not reveal significant atrophy of the hippocampus volume, but significant deformations were indeed observed-particularly on the ipsilateral side. There, the neuronal surface area was significantly lower in ischemic rats and was associated with a lower tissue density and a markedly thinner entorhinal cortex. At 6 months post-stroke, 49 of the 90 patients displayed cognitive impairment (males 55.10%). Shape analysis revealed marked deformations of their left hippocampus, a significantly lower entorhinal cortex surface area, and a wider rhinal sulcus but no hippocampal atrophy. Hence, hippocampal deformations and entorhinal cortex atrophy were associated with long-term impaired cognitive abilities in a stroke rat model and in stroke patients. When combined with existing biomarkers, these markers might constitute sensitive new tools for the early prediction of post-stroke dementia.

Entorhinal and transentorhinal atrophy in mild cognitive impairment using longitudinal diffeomorphometry

INTRODUCTION

Autopsy findings have shown the entorhinal cortex and transentorhinal cortex are among the earliest sites of accumulation of pathology in patients developing Alzheimer's disease.

METHODS

Here, we study this region in subjects with mild cognitive impairment (n = 36) and in control subjects (n = 16). The cortical areas are manually segmented, and local volume and shape changes are quantified using diffeomorphometry, including a novel mapping procedure that reduces variability in anatomic definitions over time.

RESULTS

We find significant thickness and volume changes localized to the transentorhinal cortex through high field strength atlasing.

DISCUSSION

This demonstrates that in vivo neuroimaging biomarkers can detect these early changes among subjects with mild cognitive impairment.

Structural MRI and Amyloid PET Imaging for Prediction of Conversion to Alzheimer's Disease in Patients with Mild Cognitive Impairment: A Meta-Analysis

OBJECTIVE

The aim of this study was to explore the prognostic values of biomarkers of neurodegeneration as measured by magnetic resonance imaging (MRI) and amyloid burden as measured by amyloid positron emission tomography (PET) in predicting conversion to Alzheimer's disease (AD) in patients with mild cognitive impairment (MCI).

METHODS

PubMed and EMBASE databases were searched for structural MRI or amyloid PET imaging studies published between January 2000 and July 2014 that reported conversion to AD in patients with MCI. Means and standard deviations or individual numbers of biomarkers with positive or negative status at baseline and corresponding numbers of patients who had progressed to AD at follow-up were retrieved from each study. The effect size of each biomarker was expressed as Hedges's g.

RESULTS

Twenty-four MRI studies and 8 amyloid PET imaging studies were retrieved. 674 of the 1741 participants (39%) developed AD. The effect size for predicting conversion to AD was 0.770 [95% confidence interval (CI) 0.607-0.934] for across MRI and 1.316 (95% CI 0.920-1.412) for amyloid PET imaging (p<0.001). The effect size was 1.256 (95% CI 0.902-1.609) for entorhinal cortex volume from MRI.

CONCLUSION

Our study suggests that volumetric MRI measurement may be useful for the early detection of AD.

Prediction of Mild Cognitive Impairment Conversion Using a Combination of Independent Component Analysis and the Cox Model

Mild cognitive impairment (MCI) represents a transitional stage from normal aging to Alzheimer’s disease (AD) and corresponds to a higher risk of developing AD. Thus, it is necessary to explore and predict the onset of AD in MCI stage. In this study, we propose a combination of independent component analysis (ICA) and the multivariate Cox proportional hazards regression model to investigate promising risk factors associated with MCI conversion among 126 MCI converters and 108 MCI non-converters from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Using structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data, we extracted brain networks from AD and normal control groups via ICA and then constructed Cox models that included network-based neuroimaging factors for the MCI group. We carried out five separate Cox analyses and the two-modality neuroimaging Cox model identified three significant network-based risk factors with higher prediction performance (accuracy = 73.50%) than those in either single-modality model (accuracy = 68.80%). Additionally, the results of the comprehensive Cox model, including significant neuroimaging factors and clinical variables, demonstrated that MCI individuals with reduced gray matter volume in a temporal lobe-related network of structural MRI [hazard ratio (HR) = 8.29E-05 (95% confidence interval (CI), 5.10E- 07 ~ 0.013)], low glucose metabolism in the posterior default mode network based on FDG-PET [HR = 0.066 (95% CI, 4.63E-03 ~ 0.928)], positive apolipoprotein E ε4-status [HR = 1. 988 (95% CI, 1.531 ~ 2.581)], increased Alzheimer’s Disease Assessment Scale-Cognitive Subscale scores [HR = 1.100 (95% CI, 1.059 ~ 1.144)] and Sum of Boxes of Clinical Dementia Rating scores [HR = 1.622 (95% CI, 1.364 ~ 1.930)] were more likely to convert to AD within 36 months after baselines. These significant risk factors in such comprehensive Cox model had the best prediction ability (accuracy = 84.62%, sensitivity = 86.51%, specificity = 82.41%) compared to either neuroimaging factors or clinical variables alone. These results suggested that a combination of ICA and Cox model analyses could be used successfully in survival analysis and provide a network-based perspective of MCI progression or AD-related studies.

Utility of Molecular and Structural Brain Imaging to Predict Progression from Mild Cognitive Impairment to Dementia

This project compares three neuroimaging biomarkers to predict progression to dementia in subjects with mild cognitive impairment (MCI). Eighty-eight subjects with MCI and 40 healthy controls (HCs) were recruited. Subjects had a 3T magnetic resonance imaging (MRI) scan, and two positron emission tomography (PET) scans, one with Pittsburgh compound B ([11C]PIB) and one with fluorodeoxyglucose ([18F]FDG). MCI subjects were followed for up to 4 y and progression to dementia was assessed on an annual basis. MCI subjects had higher [11C]PIB binding potential (BPND) than HCs in multiple brain regions, and lower hippocampus volumes. [11C]PIB BPND, [18F]FDG standard uptake value ratio (SUVR), and hippocampus volume were associated with time to progression to dementia using a Cox proportional hazards model. [18F]FDG SUVR demonstrated the most statistically significant association with progression, followed by [11C]PIB BPND and then hippocampus volume. [11C]PIB BPND and [18F]FDG SUVR were independently predictive, suggesting that combining these measures is useful to increase accuracy in the prediction of progression to dementia. Hippocampus volume also had independent predictive properties to [11C]PIB BPND, but did not add predictive power when combined with the [18F]FDG SUVR data. This work suggests that PET imaging with both [11C]PIB and [18F]FDG may help to determine which MCI subjects are likely to progress to AD, possibly directing future treatment options.

The Primacy Effect in Amnestic Mild Cognitive Impairment: Associations with Hippocampal Functional Connectivity

Background: The “primacy effect,” i.e., increased memory recall for the first items of a series compared to the following items, is reduced in amnestic mild cognitive impairment (aMCI). Memory task-fMRI studies demonstrated that primacy recall is associated with higher activation of the hippocampus and temporo-parietal and frontal cortical regions in healthy subjects. Functional magnetic resonance imaging (fMRI) at resting state revealed that hippocampus functional connectivity (FC) with neocortical brain areas, including regions of the default mode network (DMN), is altered in aMCI. The present study aimed to investigate whether resting state fMRI FC between the hippocampus and cortical brain regions, especially the DMN, is associated with primacy recall performance in aMCI.

Methods: A number of 87 aMCI patients underwent resting state fMRI and verbal episodic memory assessment. FC between the left or right hippocampus, respectively, and all other voxels in gray matter was mapped voxel-wise and used in whole-brain regression analyses, testing whether FC values predicted delayed primacy recall score. The delayed primacy score was defined as the number of the first four words recalled on the California Verbal Learning Test. Additionally, a partial least squares (PLS) analysis was performed, using DMN regions as seeds to identify the association of their functional interactions with delayed primacy recall.

Results: Voxel-based analyses indicated that delayed primacy recall was mainly (positively) associated with higher FC between the left and right hippocampus. Additionally, significant associations were found for higher FC between the left hippocampus and bilateral temporal cortex, frontal cortical regions, and for higher FC between the right hippocampus and right temporal cortex, right frontal cortical regions, left medial frontal cortex and right amygdala (p < 0.01, uncorr.). PLS analysis revealed positive associations of delayed primacy recall with FC between regions of the DMN, including the left and right hippocampus, as well as middle cingulate cortex and thalamus (p < 0.04). In conclusion, in the light of decreased hippocampus function in aMCI, inter-hemispheric hippocampus FC and hippocampal FC with brain regions predominantly included in the DMN may contribute to residual primacy recall in aMCI.

Identify the Atrophy of Alzheimer's Disease, Mild Cognitive Impairment and Normal Aging Using Morphometric MRI Analysis

Quantitatively assessing the medial temporal lobe (MTL) structures atrophy is vital for early diagnosis of Alzheimer's disease (AD) and accurately tracking of the disease progression. Morphometry characteristics such as gray matter volume (GMV) and cortical thickness have been proved to be valuable measurements of brain atrophy. In this study, we proposed a morphometric MRI analysis based method to explore the cross-sectional differences and longitudinal changes of GMV and cortical thickness in patients with AD, MCI (mild cognitive impairment) and the normal elderly. High resolution 3D MRI data was obtained from ADNI database. SPM8 plus DARTEL was carried out for data preprocessing. Two kinds of z-score map were calculated to, respectively, reflect the GMV and cortical thickness decline compared with age-matched normal control database. A volume of interest (VOI) covering MTL structures was defined by group comparison. Within this VOI, GMV, and cortical thickness decline indicators were, respectively, defined as the mean of the negative z-scores and the sum of the normalized negative z-scores of the corresponding z-score map. Kruskal-Wallis test was applied to statistically identify group wise differences of the indicators. Support vector machines (SVM) based prediction was performed with a leave-one-out cross-validation design to evaluate the predictive accuracies of the indicators. Linear least squares estimation was utilized to assess the changing rate of the indicators for the three groups. Cross-sectional comparison of the baseline decline indicators revealed that the GMV and cortical thickness decline were more serious from NC, MCI to AD, with statistic significance. Using a multi-region based SVM model with the two indicators, the discrimination accuracy between AD and NC, MCI and NC, AD and MCI was 92.7, 91.7, and 78.4%, respectively. For three-way prediction, the accuracy was 74.6%. Furthermore, the proposed two indicators could also identify the atrophy rate differences among the three groups in longitudinal analysis. The proposed method could serve as an automatic and time-sparing approach for early diagnosis and tracking the progression of AD.

Hippocampal Volumetry as a Biomarker for Dementia in People with Low Education

Background/Aims

To evaluate the relationship between hippocampal volume and cognitive decline in patients with dementia due to probable Alzheimer's disease (AD), amnestic mild cognitive impairment (aMCI) and education, and the possible relationship between cognitive reserve and education in this population.

Methods

From February 2013 to October 2015, 76 patients (25 men, 51 women) were classified according to the NIA-AA diagnostic criteria. We used two 3.0-tesla MRI scanners and performed manual hippocampal volumetry.

Results

Twenty-six patients were found to have AD, 20 aMCI and 30 had normal aging (NA). The mean normalized hippocampal volume in age-, sex- and education (years)-matched subjects was 2.38 ± 0.51 cm³ in AD (p < 0.001), 2.91 ± 0.78 cm³ in aMCI (p = 0.019) and 3.07 ± 0.76 cm³ in NA.

Conclusion

Psychometric test (MMSE and MoCA) scores had a good to strong positive correlation with statistically significant differences in the entire population and healthy subjects but not among dementia patients and lower educational level groups. The patients with low education had greater hippocampal volumes, which is in line with the cognitive reserve theory; lower-educated individuals can tolerate less neuropathology and will thus show less atrophy at a similar level of cognitive performance than higher-educated subjects.

The Utilization of Retinal Nerve Fiber Layer Thickness to Predict Cognitive Deterioration

Our previous studies have shown that longitudinal reduction in retinal nerve fiber layer (RNFL) thickness is associated with cognitive deterioration. However, whether the combination of longitudinal reduction in RNFL thickness with baseline episodic memory performance can better predict cognitive deterioration remains unknown. Therefore, we set out to re-analyze the data obtained from our previous studies with 78 elderly adults (mean age 74.4 ± 3.83 years, 48.7% male) in the community over a 25-month period. The participants were categorized as either stable participants whose cognitive status did not change (n = 60) or converted participants whose cognitive status deteriorated (n = 18). A logistic regression analysis was applied to determine a conversion score for predicting the cognitive deterioration in the participants. We found that the area under the receiver operating characteristic curve (AUC) for the multivariable model was 0.854 (95% CI 0.762-0.947) using baseline story recall as a predictor, but the AUC increased to 0.915 (95% CI 0.849-0.981) with the addition of the longitudinal reduction of RNFL thickness in the inferior quadrant. The conversion score was significantly higher for the converted participants than the stable participants (0.59 ± 0.30 versus 0.12 ± 0.19, p <  0.001). Finally, the optimal cutoff value of the conversion score (0.134) was determined by the analysis of receiver operating characteristic curve, and this conversion score generated a sensitivity of 0.944 and a specificity of 0.767 in predicting the cognitive deterioration. These findings have established a system to perform a larger scale study to further test whether the longitudinal reduction in RNFL thickness could serve as a biomarker of Alzheimer's disease.

Brain atrophy in Alzheimer's Disease and aging

Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the pathophysiology of neurodegenerative disorders such as Alzheimer's disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and normal aging over the past twenty years, focusing on the patterns of gray and white matter changes assessed in vivo using MRI. Major progresses in the field concern the segmentation of the hippocampus with novel manual and automatic segmentation approaches, which might soon enable to assess also hippocampal subfields. Advancements in quantification of hippocampal volumetry might pave the way to its broader use as outcome marker in AD clinical trials. Patterns of cortical atrophy have been shown to accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts. Recent studies have investigated two areas often overlooked in AD, such as the striatum and basal forebrain, reporting significant atrophy, although the impact of these changes on cognition is still unclear. Future integration of different MRI modalities may further advance the field by providing more powerful biomarkers of disease onset and progression.

Prediction of Cognitive and Functional Decline Using the Telephone-Administered Minnesota Cognitive Acuity Screen

OBJECTIVES

To examine whether a telephone-based cognitive assessment-the Minnesota Cognitive Acuity Screen (MCAS)-is effective in predicting cognitive and functional decline in older adults with mild cognitive impairment (MCI) and conversion to dementia.

DESIGN

Longitudinal.

SETTING

Academic medical center.

PARTICIPANTS

Individuals aged 60 to 84 with MCI (N = 61).

MEASUREMENTS

An initial office visit consisting of a neurological examination, Clinical Dementia Rating Scale (CDR), and neuropsychological testing using the Dementia Rating Scale-2 (DRS-2), followed by the MCAS within 1 month. Participants completed up to three follow-up in-office neuropsychological assessments, originally scheduled 1 year apart. A multidisciplinary consensus group determined diagnosis (MCI, dementia) at each assessment.

RESULTS

Higher baseline MCAS total scores emerged as a significant predictor of slower functional decline (P = .002) and dementia conversion (P = .02). An increase in score from 43 to 50 points (1st to 3rd quartile) was associated with a 0.59-point (95% confidence interval (CI) = 0.23-0.95) lower CDR score at follow-up, and a 71% (95% CI = 1.11-2.63) increase in median time to dementia conversion from 2 years to 3.5 years. Of the MCAS subscales, delayed word recall predicted functional decline alone (P < .001), whereas computation was nominally associated with cognitive (P = .01) and functional (P = .01) decline.

CONCLUSION

The brief telephone-administered MCAS provides valuable information about future cognitive and functional decline in older adults with MCI and predicted conversion from MCI to dementia. These findings provide additional support for use of MCAS in clinical and research settings. The instrument may be particularly valuable in settings in which an office visit is difficult.

Basal Forebrain and Hippocampus as Predictors of Conversion to Alzheimer's Disease in Patients with Mild Cognitive Impairment - A Multicenter DTI and Volumetry Study

BACKGROUND

Hippocampal grey matter (GM) atrophy predicts conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Pilot data suggests that mean diffusivity (MD) in the hippocampus, as measured with diffusion tensor imaging (DTI), may be a more accurate predictor of conversion than hippocampus volume. In addition, previous studies suggest that volume of the cholinergic basal forebrain may reach a diagnostic accuracy superior to hippocampal volume in MCI.

OBJECTIVE

The present study investigated whether increased MD and decreased volume of the hippocampus, the basal forebrain and other AD-typical regions predicted time to conversion from MCI to AD dementia.

METHODS

79 MCI patients with DTI and T1-weighted magnetic resonance imaging (MRI) were retrospectively included from the European DTI Study in Dementia (EDSD) dataset. Of these participants, 35 converted to AD dementia after 6-46 months (mean: 21 months). We used Cox regression to estimate the relative conversion risk predicted by MD values and GM volumes, controlling for age, gender, education and center.

RESULTS

Decreased GM volume in all investigated regions predicted an increased risk for conversion. Additionally, increased MD in the right basal forebrain predicted increased conversion risk. Reduced volume of the right hippocampus was the only significant predictor in a stepwise model combining all predictor variables.

CONCLUSION

Volume reduction of the hippocampus, the basal forebrain and other AD-related regions was predictive of increased risk for conversion from MCI to AD. In this study, volume was superior to MD in predicting conversion.

A semi-mechanism approach based on MRI and proteomics for prediction of conversion from mild cognitive impairment to Alzheimer's disease

Mild cognitive impairment (MCI) is a precursor phase of Alzheimer’s disease (AD). As current treatments may be effective only at the early stages of AD, it is important to track MCI patients who will convert to AD. The aim of this study is to develop a high performance semi-mechanism based approach to predict the conversion from MCI to AD and improve our understanding of MCI-to-AD conversion mechanism. First, analysis of variance (ANOVA) test and lasso regression are employed to identify the markers related to the conversion. Then the Bayesian network based on selected markers is established to predict MCI-to-AD conversion. The structure of Bayesian network suggests that the conversion may start with fibrin clot formation, verbal memory impairment, eating pattern changing and hyperinsulinemia. The Bayesian network achieves a high 10-fold cross-validated prediction performance with 96% accuracy, 95% sensitivity, 65% specificity, area under the receiver operating characteristic curve of 0.82 on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The semi-mechanism based approach provides not only high prediction performance but also clues of mechanism for MCI-to-AD conversion.

Multimodal imaging in Alzheimer's disease: validity and usefulness for early detection

Alzheimer's disease is a progressive neurodegenerative disease that typically manifests clinically as an isolated amnestic deficit that progresses to a characteristic dementia syndrome. Advances in neuroimaging research have enabled mapping of diverse molecular, functional, and structural aspects of Alzheimer's disease pathology in ever increasing temporal and regional detail. Accumulating evidence suggests that distinct types of imaging abnormalities related to Alzheimer's disease follow a consistent trajectory during pathogenesis of the disease, and that the first changes can be detected years before the disease manifests clinically. These findings have fuelled clinical interest in the use of specific imaging markers for Alzheimer's disease to predict future development of dementia in patients who are at risk. The potential clinical usefulness of single or multimodal imaging markers is being investigated in selected patient samples from clinical expert centres, but additional research is needed before these promising imaging markers can be successfully translated from research into clinical practice in routine care.

Hippocampal (subfield) volume and shape in relation to cognitive performance across the adult lifespan

Newer approaches to characterizing hippocampal morphology can provide novel insights regarding cognitive function across the lifespan. We comprehensively assessed the relationships among age, hippocampal morphology, and hippocampal-dependent cognitive function in 137 healthy individuals across the adult lifespan (18-86 years of age). They underwent MRI, cognitive assessments and genotyping for Apolipoprotein E status. We measured hippocampal subfield volumes using a new multiatlas segmentation tool (MAGeT-Brain) and assessed vertex-wise (inward and outward displacements) and global surface-based descriptions of hippocampus morphology. We examined the effects of age on hippocampal morphology, as well as the relationship among age, hippocampal morphology, and episodic and working memory performance. Age and volume were modestly correlated across hippocampal subfields. Significant patterns of inward and outward displacement in hippocampal head and tail were associated with age. The first principal shape component of the left hippocampus, characterized by a lengthening of the antero-posterior axis was prominently associated with working memory performance across the adult lifespan. In contrast, no significant relationships were found among subfield volumes and cognitive performance. Our findings demonstrate that hippocampal shape plays a unique and important role in hippocampal-dependent cognitive aging across the adult lifespan, meriting consideration as a biomarker in strategies targeting the delay of cognitive aging.

Mouse brain magnetic resonance microscopy: Applications in Alzheimer disease

Over the past two decades, various Alzheimer's disease (AD) trangenetic mice models harboring genes with mutation known to cause familial AD have been created. Today, high-resolution magnetic resonance microscopy (MRM) technology is being widely used in the study of AD mouse models. It has greatly facilitated and advanced our knowledge of AD. In this review, most of the attention is paid to fundamental of MRM, the construction of standard mouse MRM brain template and atlas, the detection of amyloid plaques, following up on brain atrophy and the future applications of MRM in transgenic AD mice. It is believed that future testing of potential drugs in mouse models with MRM will greatly improve the predictability of drug effect in preclinical trials.

Can multi-modal neuroimaging evidence from hippocampus provide biomarkers for the progression of amnestic mild cognitive impairment?

Impaired structure and function of the hippocampus is a valuable predictor of progression from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD). As a part of the medial temporal lobe memory system, the hippocampus is one of the brain regions affected earliest by AD neuropathology, and shows progressive degeneration as aMCI progresses to AD. Currently, no validated biomarkers can precisely predict the conversion from aMCI to AD. Therefore, there is a great need of sensitive tools for the early detection of AD progression. In this review, we summarize the specific structural and functional changes in the hippocampus from recent aMCI studies using neurophysiological and neuroimaging data. We suggest that a combination of advanced multi-modal neuroimaging measures in discovering biomarkers will provide more precise and sensitive measures of hippocampal changes than using only one of them. These will potentially affect early diagnosis and disease-modifying treatments. We propose a new sequential and progressive framework in which the impairment spreads from the integrity of fibers to volume and then to function in hippocampal subregions. Meanwhile, this is likely to be accompanied by progressive impairment of behavioral and neuropsychological performance in the progression of aMCI to AD.

Mild cognitive impairment, poor episodic memory, and late-life depression are associated with cerebral cortical thinning and increased white matter hyperintensities

In various independent studies to date, cerebral cortical thickness and white matter hyperintensity (WMH) volume have been associated with episodic memory, depression, and mild cognitive impairment (MCI). The aim of this study was to uncover variations in cortical thickness and WMH volume in association with episodic memory, depressive state, and the presence of MCI simultaneously in a single study population. The participants were 186 individuals with MCI (clinical dementia rating [CDR] of 0.5) and 136 healthy elderly controls (HCs; CDR of 0) drawn from two community-based cohort studies in northern Japan. We computed cerebral cortical thickness and WMH volume by using MR scans and statistically analyzed differences in these indices between HCs and MCI participants. We also assessed the associations of these indices with memory performance and depressive state in participants with MCI. Compared with HCs, MCI participants exhibited thinner cortices in the temporal and inferior parietal lobes and greater WMH volumes in the corona radiata and semioval center. In MCI participants, poor episodic memory was associated with thinner cortices in the left entorhinal region and increased WMH volume in the posterior periventricular regions. Compared with non-depressed MCI participants, depressed MCI participants showed reduced cortical thickness in the anterior medial temporal lobe and gyrus adjacent to the amygdala bilaterally, as well as greater WMH volume as a percentage of the total intracranial volume (WMHr). A higher WMHr was associated with cortical thinning in the frontal, temporal, and parietal regions in MCI participants. These results demonstrate that episodic memory and depression are associated with both cortical thickness and WMH volume in MCI participants. Additional longitudinal studies are needed to clarify the dynamic associations and interactions among these indices.

Structural imaging biomarkers of Alzheimer's disease: predicting disease progression

Optimized magnetic resonance imaging (MRI)-based biomarkers of Alzheimer's disease (AD) may allow earlier detection and refined prediction of the disease. In addition, they could serve as valuable tools when designing therapeutic studies of individuals at risk of AD. In this study, we combine (1) a novel method for grading medial temporal lobe structures with (2) robust cortical thickness measurements to predict AD among subjects with mild cognitive impairment (MCI) from a single T1-weighted MRI scan. Using AD and cognitively normal individuals, we generate a set of features potentially discriminating between MCI subjects who convert to AD and those who remain stable over a period of 3 years. Using mutual information-based feature selection, we identify 5 key features optimizing the classification of MCI converters. These features are the left and right hippocampi gradings and cortical thicknesses of the left precuneus, left superior temporal sulcus, and right anterior part of the parahippocampal gyrus. We show that these features are highly stable in cross-validation and enable a prediction accuracy of 72% using a simple linear discriminant classifier, the highest prediction accuracy obtained on the baseline Alzheimer's Disease Neuroimaging Initiative first phase cohort to date. The proposed structural features are consistent with Braak stages and previously reported atrophic patterns in AD and are easy to transfer to new cohorts and to clinical practice.

Mild cognitive impairment

Mild cognitive impairment is the term applied to the cognitive state that lies between normal aging and dementia. There has been significant controversy around describing, defining and characterizing mild cognitive impairment. This review will cover current understanding of the condition and discuss clinical features, research strategies and future directions.

Using neuroimaging to inform clinical practice for the diagnosis and treatment of mild cognitive impairment

Advances in structural and functional neuroimaging techniques have unquestionably improved understanding of the development and progression of Alzheimer disease (AD), with evidence supporting regional (and network) change that underlies cognitive decline across the "healthy" aging/mild cognitive impairment (MCI)/AD spectrum. This review focuses on visual rating scales and volumetric analyses that could be easily integrated into clinical practice, followed by a review of functional neuroimaging findings suggesting that widespread cerebral dysfunction underlies the learning and memory deficits in MCI. Evidence of preserved neuroplasticity in this population and that cognitive rehabilitation techniques may capitalize on this plasticity to improve cognition in those with MCI is also discussed.

Potential benefits of mindfulness-based interventions in mild cognitive impairment and Alzheimer's disease: an interdisciplinary perspective

The present article is based on the premise that the risk of developing Alzheimer's disease (AD) from its prodromal phase (mild cognitive impairment; MCI) is higher when adverse factors (e.g., stress, depression, and metabolic syndrome) are present and accumulate. Such factors augment the likelihood of hippocampal damage central in MCI/AD aetiology, as well as compensatory mechanisms failure triggering a switch toward neurodegeneration. Because of the devastating consequences of AD, there is a need for early interventions that can delay, perhaps prevent, the transition from MCI to AD. We hypothesize that mindfulness-based interventions (MBI) show promise with regard to this goal. The present review discusses the associations between modifiable adverse factors and MCI/AD decline, MBI's impacts on adverse factors, and the mechanisms that could underlie the benefits of MBI. A schematic model is proposed to illustrate the course of neurodegeneration specific to MCI/AD, as well as the possible preventive mechanisms of MBI. Whereas regulation of glucocorticosteroids, inflammation, and serotonin could mediate MBI's effects on stress and depression, resolution of the metabolic syndrome might happen through a reduction of inflammation and white matter hyperintensities, and normalization of insulin and oxidation. The literature reviewed in this paper suggests that the main reach of MBI over MCI/AD development involves the management of stress, depressive symptoms, and inflammation. Future research must focus on achieving deeper understanding of MBI's mechanisms of action in the context of MCI and AD. This necessitates bridging the gap between neuroscientific subfields and a cross-domain integration between basic and clinical knowledge.

When optimism hurts: inflated predictions in psychiatric neuroimaging

The ability to predict outcomes from neuroimaging data has the potential to answer important clinical questions such as which depressed patients will respond to treatment, which abstinent drug users will relapse, or which patients will convert to dementia. However, many prediction analyses require methods and techniques, not typically required in neuroimaging, to accurately assess a model's predictive ability. Regression models will tend to fit to the idiosyncratic characteristics of a particular sample and consequently will perform worse on unseen data. Failure to account for this inherent optimism is especially pernicious when the number of possible predictors is high relative to the number of participants, a common scenario in psychiatric neuroimaging. We show via simulated data that models can appear predictive even when data and outcomes are random, and we note examples of optimistic prediction in the literature. We provide some recommendations for assessment of model performance.

Development of a brain MRI-based hidden Markov model for dementia recognition

BACKGROUND

Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition.

METHODS

Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range.

RESULTS

The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia.

CONCLUSION

The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia.

Comparison of medial temporal measures between Binswanger's disease and Alzheimer's disease

Binswanger's disease (BD) is a common cause of vascular dementia in elderly patients; however, few studies have investigated the medial temporal lobe (MTL) atrophy in BD, and the differences in the atrophic patterns between BD and Alzheimer's disease (AD) remain largely unknown. Such knowledge is essential for understanding the pathologic basis of dementia. In this study, we collected structural magnetic resonance imaging (MRI) data from 16 normal controls, 14 patients with AD and 14 patients with BD. The volumes of the hippocampus and amygdala, and morphologic parameters (volume, surface area, cortical thickness and mean curvature) of the entorhinal cortex (ERC) and perirhinal cortex (PRC) were calculated using an automated approach. Volume reduction of the hippocampus, amygdala and ERC, and disturbance of the PRC curvature was found in both AD and BD patients compared with the controls (p<0.05, uncorrected). There were no significant differences among all the structural measures between the AD and BD patients. Finally, partial correlation analyses revealed that cognitive decline could be attributed to ERC thinning in AD and volume reduction of PRC in BD. We conclude that AD and BD exhibit similar atrophy patterns in the medial temporal cortices and deep gray matter but have distinct pathologic bases for cognitive impairments. Although atrophy of the MTL structures is a sensitive biomarker for AD, it is not superior for discrimination between AD and BD.

Alzheimer's disease susceptibility genes APOE and TOMM40, and hippocampal volumes in the Lothian birth cohort 1936

The APOE ε and TOMM40 rs10524523 (‘523’) variable length poly-T repeat gene loci have been significantly and independently associated with Alzheimer’s disease (AD) related phenotypes such as age of clinical onset. Hippocampal atrophy has been significantly associated with memory impairment, a characteristic of AD. The current study aimed to test for independent effects of APOE ε and TOMM40 ‘523’ genotypes on hippocampal volumes as assessed by brain structural MRI in a relatively large sample of community-dwelling older adults. As part of a longitudinal study of cognitive ageing, participants in the Lothian Birth Cohort 1936 underwent genotyping for APOE ε2/ε3/ε4 status and TOMM40 ‘523’ poly-T repeat length, and detailed structural brain MRI at a mean age of 72.7 years (standard deviation = 0.7, N range = 624 to 636). No significant effects of APOE ε or TOMM40 523 genotype were found on hippocampal volumes when analysed raw, or when adjusted for either intracranial or total brain tissue volumes. In summary, in a large community-dwelling sample of older adults, we found no effects of APOE ε or TOMM40 523 genotypes on hippocampal volumes. This is discrepant with some previous reports of significant association between APOE and left/right hippocampal volumes, and instead echoes other reports that found no association. Previous significant findings may partly reflect type 1 error. Future studies should carefully consider: 1) their specific techniques in adjusting for brain size; 2) assessing more detailed sub-divisions of the hippocampal formation; and 3) testing whether significant APOE-hippocampal associations are independent of generalised brain atrophy.

Olfactory identification and cognitive performance in community-dwelling older adults with mild cognitive impairment

Olfactory impairment constitutes one of the earliest signs of Alzheimer's disease in older adults with mild cognitive impairment. We investigated which aspects of neuropsychological measures are correlated with olfactory identification performance among older adults with mild cognitive impairment. Total of 220 participants with mild cognitive impairment (mean age 71.7 years) were examined. Odor identification was assessed using the Open Essence test. Participants underwent comprehensive neurocognitive evaluation, including measures of verbal memory, visual memory, working memory, attention/executive function, and processing speed. We examined associations between olfactory function and cognitive performance scores. Participants with severe hyposmia exhibited significantly poor verbal and visual memory performance, attention/executive function, and slower processing speed scores compared with those without severe hyposmia. In multivariable logistic regression models, better performance scores on verbal and visual memory were significantly associated with decreased likelihood of severe hyposmia after adjusting for age, sex, education, and other cognitive performance scores. These findings suggest that olfactory impairment might be more closely associated with memory loss compared with other aspects of cognitive functioning in mild cognitive impairment subjects.

Decreased white matter integrity in neuropsychologically defined mild cognitive impairment is independent of cortical thinning

Improved understanding of the pattern of white matter changes in early and prodromal Alzheimer’s disease (AD) states such as mild cognitive impairment (MCI) is necessary to support earlier preclinical detection of AD, and debate remains whether white matter changes in MCI are secondary to gray matter changes. We applied neuropsychologically based MCI criteria to a sample of normally aging older adults; 32 participants met criteria for MCI and 81 participants were classified as normal control (NC) subjects. Whole-head high resolution T1 and diffusion tensor imaging scans were completed. Tract-Based Spatial Statistics was applied and a priori selected regions of interest were extracted. Hippocampal volume and cortical thickness averaged across regions with known vulnerability to AD were derived. Controlling for corticalthic kness, the MCI group showed decreased average fractional anisotropy (FA) and decreased FA in parietal white matter and in white matter underlying the entorhinal and posterior cingulate cortices relative to the NC group. Statistically controlling for cortical thickness, medial temporal FA was related to memory and parietal FA was related to executive functioning. These results provide further support for the potential role of white matter integrity as an early biomarker for individuals at risk for AD and highlight that changes in white matter may be independent of gray matter changes.

Confirming the diversity of the brain after normalization: an approach based on identity authentication

During the development of neuroimaging, numerous analyses were performed to identify population differences, such as studies on age, gender, and diseases. Researchers first normalized the brain image and then identified features that represent key differences between groups. In these studies, the question of whether normalization (a pre-processing step widely used in neuroimaging studies) reduces the diversity of brains was largely ignored. There are a few studies that identify the differences between individuals after normalization. In the current study, we analyzed brain diversity on an individual level, both qualitatively and quantitatively. The main idea was to utilize brain images for identity authentication. First, the brain images were normalized and registered. Then, a pixel-level matching method was developed to compute the identity difference between different images for matching. Finally, by analyzing the performance of the proposed brain recognition strategy, the individual differences in brain images were evaluated. Experimental results on a 150-subject database showed that the proposed approach could achieve a 100% identification ratio, which indicated distinct differences between individuals after normalization. Thus, the results proved that after the normalization stage, brain images retain their main distinguishing information and features. Based on this result, we suggest that diversity (individual differences) should be considered when conducting group analysis, and that this approach may facilitate group pattern classification.

A review of neuroimaging biomarkers of Alzheimer's disease

Neuroimaging biomarkers have potential role in the early diagnosis as well as periodic follow-up of neurodegenerative diseases such as Alzheimer's disease (AD). Structural imaging biomarkers can be used to predict those who are at risk or in preclinical stages of AD. It could possibly be useful even in predicting the conversion of Mild Cognitive Impairment (MCI) an early stage of AD to AD. In addition there has been a lot of progress in molecular imaging in AD. This article presents a review of recent progress in selected imaging biomarkers for early diagnosis, classification, and progression, of AD. A comprehensive integrative strategy initiated early in the cognitive decline is perhaps the most effective method of controlling progression to Alzheimer's disease.

Spatial navigation-a unique window into physiological and pathological aging

Spatial navigation is a skill of determining and maintaining a trajectory from one place to another. Mild progressive decline of spatial navigation develops gradually during the course of physiological ageing. Nevertheless, severe spatial navigation deficit can be the first sign of incipient Alzheimer's disease (AD), occurring in the stage of mild cognitive impairment (MCI), preceding the development of a full blown dementia. Patients with amnestic MCI, especially those with the hippocampal type of amnestic syndrome, are at very high risk of AD. These patients present with the same pattern of spatial navigation impairment as do the patients with mild AD. Spatial navigation testing of elderly as well as computer tests developed for routine clinical use thus represents a possibility for further investigation of this cognitive domain, but most of all, an opportunity for making early diagnosis of AD.