Discriminating accuracy of medial temporal lobe volumetry and fMRI in mild cognitive impairment

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%).

Use of the Montreal Cognitive Assessment Thai Version to Discriminate Amnestic Mild Cognitive Impairment from Alzheimer's Disease and Healthy Controls: Machine Learning Results

BACKGROUND

The Montreal Cognitive Assessment (MoCA) is an effective and applicable screening instrument to confirm the diagnosis of amnestic mild cognitive impairment (aMCI) from patients with Alzheimer's disease (AD) and healthy controls (HCs).

OBJECTIVES

This study aimed to determine the reliability and validity of the following: (a) Thai translation of the MoCA (MoCA-Thai) and (b) delineate the key features of aMCI based on the MoCA subdomains.

METHODS

This study included 60 HCs, 61 aMCI patients, and 60 AD patients. The MoCA-Thai shows adequate psychometric properties including internal consistency, concurrent validity, test-retest validity, and inter-rater reliability.

RESULTS

The MoCA-Thai may be employed as a diagnostic criterion to make the diagnosis of aMCI, whereby aMCI patients are discriminated from HC with an area under the receiver-operating characteristic (AUC-ROC) curve of 0.813 and from AD patients with an AUC-ROC curve of 0.938. The best cutoff scores of the MoCA-Thai to discriminate aMCI from HC is ≤24 and from AD > 16. Neural network analysis showed that (a) aberrations in recall was the most important feature of aMCI versus HC with impairments in language and orientation being the second and third most important features and (b) aberrations in visuospatial skills and executive functions were the most important features of AD versus aMCI and that impairments in recall, language, and orientation but not attention, concentration, and working memory, further discriminated AD from aMCI.

CONCLUSIONS

The MoCA-Thai is an appropriate cognitive assessment tool to be used in the Thai population for the diagnosis of aMCI and AD.

Modeling Large Sparse Data for Feature Selection: Hospital Admission Predictions of the Dementia Patients Using Primary Care Electronic Health Records

A growing elderly population suffering from incurable, chronic conditions such as dementia present a continual strain on medical services due to mental impairment paired with high comorbidity resulting in increased hospitalization risk. The identification of at risk individuals allows for preventative measures to alleviate said strain. Electronic health records provide opportunity for big data analysis to address such applications. Such data however, provides a challenging problem space for traditional statistics and machine learning due to high dimensionality and sparse data elements. This article proposes a novel machine learning methodology: entropy regularization with ensemble deep neural networks (ECNN), which simultaneously provides high predictive performance of hospitalization of patients with dementia whilst enabling an interpretable heuristic analysis of the model architecture, able to identify individual features of importance within a large feature domain space. Experimental results on health records containing 54,647 features were able to identify 10 event indicators within a patient timeline: a collection of diagnostic events, medication prescriptions and procedural events, the highest ranked being essential hypertension. The resulting subset was still able to provide a highly competitive hospitalization prediction (Accuracy: 0.759) as compared to the full feature domain (Accuracy: 0.755) or traditional feature selection techniques (Accuracy: 0.737), a significant reduction in feature size. The discovery and heuristic evidence of correlation provide evidence for further clinical study of said medical events as potential novel indicators. There also remains great potential for adaption of ECNN within other medical big data domains as a data mining tool for novel risk factor identification.

Anterolateral entorhinal cortex thickness as a new biomarker for early detection of Alzheimer's disease

INTRODUCTION

Loss of entorhinal cortex (EC) layer II neurons represents the earliest Alzheimer's disease (AD) lesion in the brain. Research suggests differing functional roles between two EC subregions, the anterolateral EC (aLEC) and the posteromedial EC (pMEC).

METHODS

We use joint label fusion to obtain aLEC and pMEC cortical thickness measurements from serial magnetic resonance imaging scans of 775 ADNI-1 participants (219 healthy; 380 mild cognitive impairment; 176 AD) and use linear mixed-effects models to analyze longitudinal associations among cortical thickness, disease status, and cognitive measures.

RESULTS

Group status is reliably predicted by aLEC thickness, which also exhibits greater associations with cognitive outcomes than does pMEC thickness. Change in aLEC thickness is also associated with cerebrospinal fluid amyloid and tau levels.

DISCUSSION

Thinning of aLEC is a sensitive structural biomarker that changes over short durations in the course of AD and tracks disease severity-it is a strong candidate biomarker for detection of early AD.

Parametric mode regression for bounded responses

We propose new parametric frameworks of regression analysis with the conditional mode of a bounded response as the focal point of interest. Covariate effects estimation and prediction based on the maximum likelihood method under two new classes of regression models are demonstrated. We also develop graphical and numerical diagnostic tools to detect various sources of model misspecification. Predictions based on different central tendency measures inferred using various regression models are compared using synthetic data in simulations. Finally, we conduct regression analysis for data from the Alzheimer's Disease Neuroimaging Initiative to demonstrate practical implementation of the proposed methods. Supporting Information that contain technical details and additional simulation and data analysis results are available online.

Automated Multi-Atlas Segmentation of Hippocampal and Extrahippocampal Subregions in Alzheimer's Disease at 3T and 7T: What Atlas Composition Works Best?

BACKGROUND

Multi-atlas segmentation, a popular technique implemented in the Automated Segmentation of Hippocampal Subfields (ASHS) software, utilizes multiple expert-labelled images ("atlases") to delineate medial temporal lobe substructures. This multi-atlas method is increasingly being employed in early Alzheimer's disease (AD) research, it is therefore becoming important to know how the construction of the atlas set in terms of proportions of controls and patients with mild cognitive impairment (MCI) and/or AD affects segmentation accuracy.

OBJECTIVE

To evaluate whether the proportion of controls in the training sets affects the segmentation accuracy of both controls and patients with MCI and/or early AD at 3T and 7T.

METHODS

We performed cross-validation experiments varying the proportion of control subjects in the training set, ranging from a patient-only to a control-only set. Segmentation accuracy of the test set was evaluated by the Dice similarity coeffiecient (DSC). A two-stage statistical analysis was applied to determine whether atlas composition is linked to segmentation accuracy in control subjects and patients, for 3T and 7T.

RESULTS

The different atlas compositions did not significantly affect segmentation accuracy at 3T and for patients at 7T. For controls at 7T, including more control subjects in the training set significantly improves the segmentation accuracy, but only marginally, with the maximum of 0.0003 DSC improvement per percent increment of control subject in the training set.

CONCLUSION

ASHS is robust in this study, and the results indicate that future studies investigating hippocampal subfields in early AD populations can be flexible in the selection of their atlas compositions.

Automated segmentation of medial temporal lobe subregions on in vivo T1-weighted MRI in early stages of Alzheimer's disease

Medial temporal lobe (MTL) substructures are the earliest regions affected by neurofibrillary tangle pathology-and thus are promising biomarkers for Alzheimer's disease (AD). However, automatic segmentation of the MTL using only T1-weighted (T1w) magnetic resonance imaging (MRI) is challenging due to the large anatomical variability of the MTL cortex and the confound of the dura mater, which is commonly segmented as gray matter by state-of-the-art algorithms because they have similar intensity in T1w MRI. To address these challenges, we developed a novel atlas set, consisting of 15 cognitively normal older adults and 14 patients with mild cognitive impairment with a label explicitly assigned to the dura, that can be used by the multiatlas automated pipeline (Automatic Segmentation of Hippocampal Subfields [ASHS-T1]) for the segmentation of MTL subregions, including anterior/posterior hippocampus, entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36, and parahippocampal cortex on T1w MRI. Cross-validation experiments indicated good segmentation accuracy of ASHS-T1 and that the dura can be reliably separated from the cortex (6.5% mislabeled as gray matter). Conversely, FreeSurfer segmented majority of the dura mater (62.4%) as gray matter and the degree of dura mislabeling decreased with increasing disease severity. To evaluate its clinical utility, we applied the pipeline to T1w images of 663 ADNI subjects and significant volume/thickness loss is observed in BA35, ERC, and posterior hippocampus in early prodromal AD and all subregions at later stages. As such, the publicly available new atlas and ASHS-T1 could have important utility in the early diagnosis and monitoring of AD and enhancing brain-behavior studies of these regions.

Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities, and Challenges

Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever-continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing for patients with dementia due to advances in information technology, such as genetics, neuroimaging, cognitive assessment, free texts, routine electronic health records, etc. Making the best use of these diverse and strategic resources will lead to high-quality care of patients with dementia. As such, machine learning becomes a crucial factor in achieving this objective. The aim of this paper is to provide a state-of-the-art review of machine learning methods applied to health informatics for dementia care. We collate and review the existing scientific methodologies and identify the relevant issues and challenges when faced with big health data. Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less effort has been made to make use of integrated heterogeneous data via advanced machine learning approaches. We further indicate future potential and research directions in applying advanced machine learning, such as deep learning, to dementia informatics.

Neuroprotective, antidiabetic and antioxidant effect of Hedera nepalensis and lupeol against STZ + AlCl3 induced rats model

PURPOSE

This study was aimed to evaluate the effect of Hedera nepalensis crude extract (HNC) and its isolated compound lupeol on antioxidant defence system, biochemical parameters and behavioural indices of Alzheimer disease generated in diabetic rats.

METHODS

To evaluate the effect of the plant extract and lupeol, symptoms of Alzheimer and diabetes were induced in rats by STZ + AlCl3 treatment. Glucose level was measured with glucometer followed by antioxidant and biochemical assessment of the treated and untreated animals. Behavioural response of the rats was determined by Elevated Plus Maze (EPM) test and Morris Water Maze (MWM) test followed by determination of brain neurotransmitters by HPLC.

RESULTS

HNC significantly reduced blood glucose level in a time dependent manner and elevated liver function markers were significantly (P < 0.05) reinstated to normal levels. HNC showed increase in level of catalase (CAT), superoxide dismutase (SOD) and reduced glutathione (GSH). HPLC quantification revealed that HNC treatment led to significant (p < 0.001) elevation in the level of neurotransmitters (dopamine and serotonin) in the midbrain region as compared to Alzheimer control (AC) group. EPM and MWM test showed decrease in cognitive and memory impairment in a rat group treated with HNC as compared to AC group.

CONCLUSION

Overall, results showed that H. nepalensis has therapeutic potential for the treatment of diseases like Alzheimer and diabetes. Graphical abstract Therapeutic effect of Hedera nepalensis K. Koch and lupeol against STZ + AICI3 induced diabetic rats model.

Dysregulation of Autophagy, Mitophagy, and Apoptotic Genes in the Medial Temporal Lobe Cortex in an Ischemic Model of Alzheimer's Disease

Ischemic brain damage is a pathological incident that is often linked with medial temporal lobe cortex injury and finally its atrophy. Post-ischemic brain injury associates with poor prognosis since neurons of selectively vulnerable ischemic brain areas are disappearing by apoptotic program of neuronal death. Autophagy has been considered, after brain ischemia, as a guardian against neurodegeneration. Consequently, we have examined changes in autophagy (BECN 1), mitophagy (BNIP 3), and apoptotic (caspase 3) genes in the medial temporal lobe cortex with the use of quantitative reverse-transcriptase PCR following transient 10-min global brain ischemia in rats with survival 2, 7, and 30 days. The intense significant overexpression of BECN 1 gene was noted on the 2nd day, while on days 7-30 the expression of this gene was still upregulated. BNIP 3 gene was downregulated on the 2nd day, but on days 7-30 post-ischemia, there was a significant reverse tendency. Caspase 3 gene, associated with apoptotic neuronal death, was induced in the same way as BNIP 3 gene after brain ischemia. Thus, the demonstrated changes indicate that the considerable dysregulation of expression of BECN 1, BNIP 3, and caspase 3 genes may be connected with a response of neuronal cells in medial temporal lobe cortex to transient complete brain ischemia.

The anteroposterior and primary-to-posterior limbic ratios as MRI-derived volumetric markers of Alzheimer's disease

BACKGROUND/AIMS

Alzheimer's disease (AD) shows a characteristic pattern of brain atrophy, with predominant involvement of posterior limbic structures, and relative preservation of rostral limbic and primary cortical regions. We aimed to investigate the diagnostic utility of two gray matter volume ratios based on this pattern, and to develop a fully automated method to calculate them from unprocessed MRI files.

PATIENTS AND METHODS

Cross-sectional study of 118 subjects from the ADNI database, including normal controls and patients with mild cognitive impairment (MCI) and AD. Clinical variables and 3T T1-weighted MRI files were analyzed. Regional gray matter and total intracranial volumes were calculated with a shell script (gm_extractor) based on FSL. Anteroposterior and primary-to-posterior limbic ratios (APL and PPL) were calculated from these values. Diagnostic utility of variables was tested in logistic regression models using Bayesian model averaging for variable selection. External validity was evaluated with bootstrap sampling and a test set of 60 subjects.

RESULTS

gm_extractor showed high test-retest reliability and high concurrent validity with FSL's FIRST. Volumetric measurements agreed with the expected anatomical pattern associated with AD. APL and PPL ratios were significantly different between groups, and were selected instead of hippocampal and entorhinal volumes to differentiate normal from MCI or cognitively impaired (MCI plus AD) subjects.

CONCLUSION

APL and PPL ratios may be useful components of models aimed to differentiate normal subjects from patients with MCI or AD. These values, and other gray matter volumes, may be reliably calculated with gm_extractor.

Discrepancy in Expression of β-Secretase and Amyloid-β Protein Precursor in Alzheimer-Related Genes in the Rat Medial Temporal Lobe Cortex Following Transient Global Brain Ischemia

Brain ischemia may be causally related with Alzheimer's disease. Presumably, β-secretase and amyloid-β protein precursor gene expression changes may be associated with Alzheimer's disease neuropathology. Consequently, we have examined quantitative changes in both β-secretase and amyloid-β protein precursor genes in the medial temporal lobe cortex with the use of quantitative rtPCR analysis following 10-min global brain ischemia in rats with survival of 2, 7, and 30 days. The greatest significant overexpression of β-secretase gene was noted on the 2nd day, while on days 7-30 the expression of this gene was only modestly downregulated. Amyloid-β protein precursor gene was downregulated on the 2nd day, but on days 7-30 postischemia, there was a significant reverse tendency. Thus, the demonstrated alterations indicate that the considerable changes of expression of β-secretase and amyloid-β protein precursor genes may be connected with a response of neurons in medial temporal lobe cortex to transient global brain ischemia. Finally, the ischemia-induced gene changes may play a key role in a late and slow onset of Alzheimer-type pathology.

First magenetic resonance imaging studies on aluminium maltolate-treated aged New Zealand rabbits: an Alzheimer's animal model

BACKGROUND

Alzheimer's disease is a devastative neurodegenerative disorder. To date, there has been no animal model that could unravel the complete disease pathology. Magnetic resonance imaging has played a pivotal role in the quantitative assessment of brain tissue atrophy for a few decades. In particular, temporal lobe atrophy and ventricular dilatation have been found to be sensitive in Alzheimer's disease.

METHODS

The present study focused on the replication of these crucial pathological events to enable disease progression to be diagnosed at an early stage and stopped through the use of potential therapeutic strategies.

RESULT

The objective of this study was to show temporal lobe atrophy and ventricular dilatation in aluminium maltolate-treated aged New Zealand rabbit, and our study was able to demonstrate this for the first time.

CONCLUSION

The present study makes this animal model a substantial one for further molecular level studies and opens up new targets for potential therapeutic strategies.

Radiological Evaluation of Strategic Structures in Patients with Mild Cognitive Impairment and Early Alzheimer's Disease

Background

The aim of the study was to evaluate the diagnostic value of two measurement techniques in patients with cognitive impairment – automated volumetry of the hippocampus, entorhinal cortex, parahippocampal gyrus, posterior cingulate gyrus, cortex of the temporal lobes and corpus callosum, and fractional anisotropy (FA) index measurement of the corpus callosum using diffusion tensor imaging.

Material/Methods

A total number of 96 patients underwent magnetic resonance imaging study of the brain – 33 healthy controls (HC), 33 patients with diagnosed mild cognitive impairment (MCI) and 30 patients with Alzheimer’s disease (AD) in early stage. The severity of the dementia was evaluated with neuropsychological test battery. The volumetric measurements were performed automatically using FreeSurfer imaging software. The measurements of FA index were performed manually using ROI (region of interest) tool.

Results

The volumetric measurement of the temporal lobe cortex had the highest correct classification rate (68.7%), whereas the lowest was achieved with FA index measurement of the corpus callosum (51%).

The highest sensitivity and specificity in discriminating between the patients with MCI vs. early AD was achieved with the volumetric measurement of the corpus callosum – the values were 73% and 71%, respectively, and the correct classification rate was 72%.

The highest sensitivity and specificity in discriminating between HC and the patients with early AD was achieved with the volumetric measurement of the entorhinal cortex – the values were 94% and 100%, respectively, and the correct classification rate was 97%.

The highest sensitivity and specificity in discriminating between HC and the patients with MCI was achieved with the volumetric measurement of the temporal lobe cortex – the values were 90% and 93%, respectively, and the correct classification rate was 92%.

Conclusions

The diagnostic value varied depending on the measurement technique. The volumetric measurement of the atrophy proved to be the best imaging biomarker, which allowed the distinction between the groups of patients. The volumetric assessment of the corpus callosum proved to be a useful tool in discriminating between the patients with MCI vs. early AD.

Plastic modulation of episodic memory networks in the aging brain with cognitive decline

Social-cognitive processing has been posited to underlie general functions such as episodic memory. Episodic memory impairment is a recognized hallmark of amnestic mild cognitive impairment (aMCI) who is at a high risk for dementia. Three canonical networks, self-referential processing, executive control processing and salience processing, have distinct roles in episodic memory retrieval processing. It remains unclear whether and how these sub-networks of the episodic memory retrieval system would be affected in aMCI. This task-state fMRI study constructed systems-level episodic memory retrieval sub-networks in 28 aMCI and 23 controls using two computational approaches: a multiple region-of-interest based approach and a voxel-level functional connectivity-based approach, respectively. These approaches produced the remarkably similar findings that the self-referential processing network made critical contributions to episodic memory retrieval in aMCI. More conspicuous alterations in self-referential processing of the episodic memory retrieval network were identified in aMCI. In order to complete a given episodic memory retrieval task, increases in cooperation between the self-referential processing network and other sub-networks were mobilized in aMCI. Self-referential processing mediate the cooperation of the episodic memory retrieval sub-networks as it may help to achieve neural plasticity and may contribute to the prevention and treatment of dementia.

Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art

In the last 15 years, many articles have studied brain connectivity in Mild Cognitive Impairment patients with fMRI techniques, seemingly using different connectivity statistical models in each investigation to identify complex connectivity structures so as to recognize typical behavior in this type of patient. This diversity in statistical approaches may cause problems in results comparison. This paper seeks to describe how researchers approached the study of brain connectivity in MCI patients using fMRI techniques from 2002 to 2014. The focus is on the statistical analysis proposed by each research group in reference to the limitations and possibilities of those techniques to identify some recommendations to improve the study of functional connectivity. The included articles came from a search of Web of Science and PsycINFO using the following keywords: f MRI, MCI, and functional connectivity. Eighty-one papers were found, but two of them were discarded because of the lack of statistical analysis. Accordingly, 79 articles were included in this review. We summarized some parts of the articles, including the goal of every investigation, the cognitive paradigm and methods used, brain regions involved, use of ROI analysis and statistical analysis, emphasizing on the connectivity estimation model used in each investigation. The present analysis allowed us to confirm the remarkable variability of the statistical analysis methods found. Additionally, the study of brain connectivity in this type of population is not providing, at the moment, any significant information or results related to clinical aspects relevant for prediction and treatment. We propose to follow guidelines for publishing fMRI data that would be a good solution to the problem of study replication. The latter aspect could be important for future publications because a higher homogeneity would benefit the comparison between publications and the generalization of results.

Update on the core and developing cerebrospinal fluid biomarkers for Alzheimer disease

Alzheimer disease (AD) is a complex neurodegenerative disorder, whose prevalence will dramatically rise by 2050. Despite numerous clinical trials investigating this disease, there is still no effective treatment. Many trials showed negative or inconclusive results, possibly because they recruited only patients with severe disease, who had not undergone disease-modifying therapies in preclinical stages of AD before severe degeneration occurred. Detection of AD in asymptomatic at risk individuals (and a few presymptomatic individuals who carry an autosomal dominant monogenic AD mutation) remains impractical in many of clinical situations and is possible only with reliable biomarkers. In addition to early diagnosis of AD, biomarkers should serve for monitoring disease progression and response to therapy. To date, the most promising biomarkers are cerebrospinal fluid (CSF) and neuroimaging biomarkers. Core CSF biomarkers (amyloid β_1-42, total tau, and phosphorylated tau) showed a high diagnostic accuracy but were still unreliable for preclinical detection of AD. Hence, there is an urgent need for detection and validation of novel CSF biomarkers that would enable early diagnosis of AD in asymptomatic individuals. This article reviews recent research advances on biomarkers for AD, focusing mainly on the CSF biomarkers. In addition to core CSF biomarkers, the potential usefulness of novel CSF biomarkers is discussed.

Visual rating and volumetric measurement of medial temporal atrophy in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort: baseline diagnosis and the prediction of MCI outcome

OBJECTIVE

This study aims to determine the clinical utility of visual ratings and volumetric measurements of medial temporal atrophy among subjects from the Alzheimer's Disease Neurorimaging Initiative (ADNI) cohort.

METHODS

A sample of 189 subjects from the ADNI, Phase 1 (ADNI-1), was chosen as follows: 49 cognitively normal (CN), 89 with mild cognitive impairment (MCI), and 50 with Alzheimer's disease (AD). Structural MRI images were downloaded from the ADNI website, and a visual rating system (VRS) was used to obtain semi-quantitative ratings of the hippocampus (HPC) and entorhinal cortex (ERC). VRS ratings and FreeSurfer measures of the HPC and ERC were used to predict (i) baseline diagnosis and (ii) progression to AD among subjects with MCI at baseline.

RESULTS

VRS and FreeSurfer measures of ERC were equivalent in classifying subjects at baseline, but FreeSurfer measures of HPC were superior to VRS measures for classifying CN versus MCI subjects. VRS and FreeSurfer measures of both HPC and ERC were significant predictors of progression from MCI to AD. However, VRS ratings of ERC were superior to other MRI measures. MCI subjects with minimal ERC atrophy by VRS had a threefold lower progression rate to AD at 3.2 years compared with those with mild, moderate, or severe atrophy (23% vs 63%, 69%, and 87%, respectively).

CONCLUSIONS

Visual ratings of HPC and ERC provide useful information to a physician in a clinical setting. Visual ratings of ERC may be especially useful in following patients with MCI.

MRI markers for mild cognitive impairment: comparisons between white matter integrity and gray matter volume measurements

The aim of the study was to evaluate the value of assessing white matter integrity using diffusion tensor imaging (DTI) for classification of mild cognitive impairment (MCI) and prediction of cognitive impairments in comparison to brain atrophy measurements using structural MRI. Fifty-one patients with MCI and 66 cognitive normal controls (CN) underwent DTI and T1-weighted structural MRI. DTI measures included fractional anisotropy (FA) and radial diffusivity (DR) from 20 predetermined regions-of-interest (ROIs) in the commissural, limbic and association tracts, which are thought to be involved in Alzheimer's disease; measures of regional gray matter (GM) volume included 21 ROIs in medial temporal lobe, parietal cortex, and subcortical regions. Significant group differences between MCI and CN were detected by each MRI modality: In particular, reduced FA was found in splenium, left isthmus cingulum and fornix; increased DR was found in splenium, left isthmus cingulum and bilateral uncinate fasciculi; reduced GM volume was found in bilateral hippocampi, left entorhinal cortex, right amygdala and bilateral thalamus; and thinner cortex was found in the left entorhinal cortex. Group classifications based on FA or DR was significant and better than classifications based on GM volume. Using either DR or FA together with GM volume improved classification accuracy. Furthermore, all three measures, FA, DR and GM volume were similarly accurate in predicting cognitive performance in MCI patients. Taken together, the results imply that DTI measures are as accurate as measures of GM volume in detecting brain alterations that are associated with cognitive impairment. Furthermore, a combination of DTI and structural MRI measurements improves classification accuracy.

Staging neurodegenerative disorders: structural, regional, biomarker, and functional progressions

The notion of staging in the neurodegenerative disorders is modulated by the constant and progressive loss of several aspects of brain structural integrity, circuitry, and neuronal processes. These destructive processes eventually remove individuals' abilities to perform at sufficient and necessary functional capacity at several levels of disease severity. The classification of (a) patients on the basis of diagnosis, risk prognosis, and intervention outcome, forms the basis of clinical staging, and (b) laboratory animals on the basis of animal model of brain disorder, extent of insult, and dysfunctional expression, provides the components for the clinical staging and preclinical staging, respectively, expressing associated epidemiological, biological, and genetic characteristics. The major focus of clinical staging in the present account stems from the fundamental notions of Braak staging as they describe the course and eventual prognosis for Alzheimer's disease, Lewy Body dementia, and Parkinson's disease. Mild cognitive impairment, which expresses the decline in episodic and semantic memory performance below the age-adjusted normal range without marked loss of global cognition or activities of daily living, and the applications of longitudinal magnetic resonance imaging, major instruments for the monitoring of either disease progression in dementia, present important challenges for staging concepts. Although Braak notions present the essential basis for further developments, current staging conceptualizations seem inadequate to comply with the massive influx of information dealing with neurodegenerative processes in brain, advanced both under clinical realities, and discoveries in the laboratory setting. The contributions of various biomarkers of disease progression, e.g., amyloid precursor protein, and neurotransmitter system imbalances, e.g., dopamine receptor supersensitivity and interactive propensities, await their incorporation into the existing staging models thereby underlining the ongoing, dynamic feature of the staging of brain disorders.

Brain activity patterns in stable and progressive mild cognitive impairment during working memory as evidenced by magnetoencephalography

It has been reported that mild cognitive impairment (MCI) patients, when compared with controls, show increased activity in different brain regions within the ventral pathway during memory tasks. A key question is whether this profile of increased activity could be useful to predict which patients will develop dementia. Herein, we present profiles of brain magnetic activity during a memory task recorded with magnetoencephalography from MCI patients (N = 10), Alzheimer's disease (AD) patients (N = 10), and healthy volunteers (N = 17). After 2½ years of follow-up, five of the MCI patients developed AD. Patients who progressed to AD (PMCI) showed higher activity than those who remained stable (SMCI), AD patients and controls. This increased activity in PMCI patients involves regions within the ventral and dorsal pathways. In contrast, SMCI patients showed higher activation than controls only along the ventral pathway. This increase in both the ventral and dorsal pathways in PMCI patients may reflect a compensatory mechanism for the loss in efficiency in memory networks, which would be absent in AD patients as they showed lower activity levels than the rest of the groups.

Novel age-dependent learning deficits in a mouse model of Alzheimer's disease: implications for translational research

Computational modeling predicts that the hippocampus plays an important role in the ability to apply previously learned information to novel problems and situations (referred to as the ability to generalize information or simply as 'transfer learning'). These predictions have been tested in humans using a computer-based task on which individuals with hippocampal damage are able to learn a series of complex discriminations with two stimulus features (shape and color), but are impaired in their ability to transfer this information to newly configured problems in which one of the features is altered. This deficit occurs despite the fact that the feature predictive of the reward (the relevant information) is not changed. The goal of the current study was to develop a mouse analog of transfer learning and to determine if this new task was sensitive to pathological changes in a mouse model of AD. We describe a task in which mice were able to learn a series of concurrent discriminations that contained two stimulus features (odor and digging media) and could transfer this learned information to new problems in which the irrelevant feature in each discrimination pair was altered. Moreover, we report age-dependent deficits specific to transfer learning in APP+PS1 mice relative to non-transgenic littermates. The robust impairment in transfer learning may be more sensitive to AD-like pathology than traditional cognitive assessments in that no deficits were observed in the APP+PS1 mice on the widely used Morris water maze task. These data describe a novel and sensitive paradigm to evaluate mnemonic decline in AD mouse models that has unique translational advantages over standard species-specific cognitive assessments (e.g., water maze for rodent and delayed paragraph recall for humans).

Biomarkers in Alzheimer's disease drug development

Developing new therapies for Alzheimer's disease (AD) is critically important to avoid the impending public health disaster imposed by this common disorder. Means must be found to prevent, delay the onset, or slow the progression of AD. These goals will be achieved by identifying disease-modifying therapies and testing them in clinical trials. Biomarkers play an increasingly important role in AD drug development. In preclinical testing, they assist in decisions to develop an agent. Biomarkers in phase I provide insights into toxic responses and drug metabolism and in Phase II proof-of-concept trials they facilitate go/no-go decisions and dose finding. Biomarkers can play a role in identifying presymptomatic patients or specific patient subgroups. They can provide evidence of target engagement before clinical changes can be expected. Brain imaging can serve as a primary outcome in Phase II trials and as a key secondary outcome in Phase III trials. Magnetic resonance imaging is currently best positioned for use in large multicenter clinical trials. Cerebrospinal fluid (CSF) measures of amyloid beta protein (Aβ), tau protein, and hyperphosphorylated tau (p-tau) protein are sensitive and specific to the diagnosis of AD and may serve as inclusion criteria and possibly as outcomes in clinical trials targeting relevant pathways. Plasma measures of Aβ are of limited diagnostic value but may provide important information as a measure of treatment response. A wide variety of measures of detectable products of cellular processes are being developed as possible biomarkers accessible in the cerebrospinal fluid and plasma or serum. Surrogate markers that can function as outcomes in pivotal trials and reliably predict clinical outcomes are needed to facilitate primary prevention trials of asymptomatic persons where clinical measures may be of limited value. Fit-for-purpose biomarkers are increasingly available to guide AD drug development decisions.

Minimal atrophy of the entorhinal cortex and hippocampus: progression of cognitive impairment

BACKGROUND

In Alzheimer's disease, neurodegenerative atrophy progresses from the entorhinal cortex (ERC) to the hippocampus (HP), limbic system and neocortex. The significance of very mild atrophy of the ERC and HP on MRI scans among elderly subjects is unknown.

METHODS

A validated visual rating system on coronal MRI scans was used to identify no atrophy of the HP or ERC (HP(0); ERC(0)), or minimal atrophy of the HP or ERC (HP(ma); ERC(ma)), among 414 participants. Subjects fell into the following groups: (1) ERC(0)/HP(0), (2) ERC(ma)/HP(0), (3) ERC(0)/HP(ma), and (4) ERC(ma)/HP(ma). HP volume was independently measured using volumetric methods.

RESULTS

In comparison to ERC(0)/HP(0) subjects, those with ERC(0)/HP(ma) had impairment on 1 memory test, ERC(ma)/HP(0) subjects had impairment on 2 memory tests and the Mini Mental State Examination (MMSE), while ERC(ma)/HP(ma) subjects had impairment on 3 memory tests, the MMSE and Clinical Dementia Rating. Progression rates of cognitive and functional impairment were significantly greater among subjects with ERC(ma).

CONCLUSION

Minimal atrophy of the ERC results in greater impairment than minimal atrophy of the HP, and the combination is additive when measured by cognitive and functional tests. Rates of progression to greater impairment were higher among ERC(ma) subjects.

Functional response in ventral temporal cortex differentiates mild cognitive impairment from normal aging

This study sought to identify altered brain activation patterns in amnestic mild cognitive impairment (MCI) that could precede frank task impairment and neocortical atrophy. A high-accuracy lexical decision (LD) task was therefore employed. Both MCI and normal seniors (NS) groups completed the LD task while functional magnetic resonance imaging (fMRI) was performed. Accuracy on the LD task was high (> or =89% correct for both groups), and both groups activated a network of occipitotemporal regions and inferior frontal cortex. However, compared with the NS group, the MCI group showed reduced fMRI activation in these regions and increased activation in bilateral portions of anterior cingluate cortex. The results from a voxel-based morphometry analysis indicated that altered activations in the MCI group were not within regions of atrophy. Receiver operating characteristic curves demonstrated that reduced fMRI response in the left and right midfusiform gyri accurately discriminated MCI from NS. When activation magnitude in both fusiform gyri were included in a single logistic regression model, group classification accuracy was very high (area under the curve = 0.93). These results showed that a disrupted functional response in the ventral temporal lobe accurately distinguishes individuals with MCI from NS, a finding which may have implications for identifying seniors at risk for cognitive decline.

Atrophy in the parahippocampal gyrus as an early biomarker of Alzheimer's disease

The main aim of the present study was to compare volume differences in the hippocampus and parahippocampal gyrus as biomarkers of Alzheimer's disease (AD). Based on the previous findings, we hypothesized that there would be significant volume differences between cases of healthy aging, amnestic mild cognitive impairment (aMCI), and mild AD. Furthermore, we hypothesized that there would be larger volume differences in the parahippocampal gyrus than in the hippocampus. In addition, we investigated differences between the anterior, middle, and posterior parts of both structures. We studied three groups of participants: 18 healthy participants without memory decline, 18 patients with aMCI, and 18 patients with mild AD. 3 T T1-weighted MRI scans were acquired and gray matter volumes of the anterior, middle, and posterior parts of both the hippocampus and parahippocampal gyrus were measured using a manual tracing approach. Volumes of both the hippocampus and parahippocampal gyrus were significantly different between the groups in the following order: healthy>aMCI>AD. Volume differences between the groups were relatively larger in the parahippocampal gyrus than in the hippocampus, in particular, when we compared healthy with aMCI. No substantial differences were found between the anterior, middle, and posterior parts of both structures. Our results suggest that parahippocampal volume discriminates better than hippocampal volume between cases of healthy aging, aMCI, and mild AD, in particular, in the early phase of the disease. The present results stress the importance of parahippocampal atrophy as an early biomarker of AD.

Machine-learning techniques for building a diagnostic model for very mild dementia

Many researchers have sought to construct diagnostic models to differentiate individuals with very mild dementia (VMD) from healthy elderly people, based on structural magnetic-resonance (MR) images. These models have, for the most part, been based on discriminant analysis or logistic regression, with few reports of alternative approaches. To determine the relative strengths of different approaches to analyzing structural MR data to distinguish people with VMD from normal elderly control subjects, we evaluated seven different classification approaches, each of which we used to generate a diagnostic model from a training data set acquired from 83 subjects (33 VMD and 50 control). We then evaluated each diagnostic model using an independent data set acquired from 30 subjects (13 VMD and 17 controls). We found that there were significant performance differences across these seven diagnostic models. Relative to the diagnostic models generated by discriminant analysis and logistic regression, the diagnostic models generated by other high-performance diagnostic-model-generation algorithms manifested increased generalizability when diagnostic models were generated from all atlas structures.

Hippocampal dysfunction in amnestic-type mild cognitive impairment: implications for predicting Alzheimer’s risk

Functional MRI is an attractive method for studying cognitive task-related and resting-state patterns of brain activation and connectivity. Since hippocampal dysfunction has been widely reported in patients with amnestic-type mild cognitive impairment (MCI) with Alzheimer’s risk, a number of studies have focused on this region of the brain; these studies are reviewed here. Three principle findings are highlighted: first, impaired hippocampal function relates to disturbances in episodic memory encoding and retrieval in MCI, but possibly in different ways; second, there is evidence of a nonlinear relationship between memory function and hippocampal activity as one progresses through the stages of MCI to Alzheimer’s disease; and third, hippocampal function is intimately related to default mode network mechanisms. Future work should be directed toward extending our understanding of the relationships between hippocampal function in MCI and pathological and cognitive disturbance. This may be a valuable neuroimaging marker in the objective of early detection of the disease processes that presage the development of Alzheimer’s disease.