Identifying Informative Imaging Biomarkers via Tree Structured Sparse Learning for AD Diagnosis

Neuroimaging provides a powerful tool to characterize neurodegenerative progression and therapeutic efficacy in Alzheimer’s disease (AD) and its prodromal stage—mild cognitive impairment (MCI). However, since the disease pathology might cause different patterns of structural degeneration, which is not pre-known, it is still a challenging problem to identify the relevant imaging markers for facilitating disease interpretation and classification. Recently, sparse learning methods have been investigated in neuroimaging studies for selecting the relevant imaging biomarkers and have achieved very promising results on disease classification. However, in the standard sparse learning method, the spatial structure is often ignored, although it is important for identifying the informative biomarkers. In this paper, a sparse learning method with tree-structured regularization is proposed to capture patterns of pathological degeneration from fine to coarse scale, for helping identify the informative imaging biomarkers to guide the disease classification and interpretation. Specifically, we first develop a new tree construction method based on the hierarchical agglomerative clustering of voxel-wise imaging features in the whole brain, by taking into account their spatial adjacency, feature similarity and discriminability. In this way, the complexity of all possible multi-scale spatial configurations of imaging features can be reduced to a single tree of nested regions. Second, we impose the tree-structured regularization on the sparse learning to capture the imaging structures, and then use them for selecting the most relevant biomarkers. Finally, we train a support vector machine (SVM) classifier with the selected features to make the classification. We have evaluated our proposed method by using the baseline MR images of 830 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which includes 198 AD patients, 167 progressive MCI (pMCI), 236 stable MCI (sMCI), and 229 normal controls (NC). Our experimental results show that our method can achieve accuracies of 90.2 %, 87.2 %, and 70.7 % for classifications of AD vs. NC, pMCI vs. NC, and pMCI vs. sMCI, respectively, demonstrating promising performance compared with other state-of-the-art methods.     

Diagnostic accuracy of markers for prodromal Alzheimer's disease in independent clinical series

ObjectiveTo capitalize on data from different clinical series to compare sensitivity and specificity of individual biomarkers for predicting mild cognitive impairment (MCI) progression to Alzheimer's disease (AD).MethodsMedial temporal atrophy, cortical hypometabolism, and cerebrospinal fluid biomarkers were assessed in 18 patients with mild cognitive impairment (MCI) with prodromal AD (pAD; conversion time, 26 ± 12 months) and 18 stable MCI (sMCI) patients from the Translational Outpatient Memory Clinic cohort, as well as in 24 pAD patients (conversion time, 36 ± 12 months) and 33 sMCI patients from the Alzheimer's Disease Neuroimaging Initiative cohort. Medial temporal atrophy was measured by manual, semi-automated, and automated hippocampal volumetry; cortical hypometabolism was measured using several indices of AD-related hypometabolism pattern; and cerebrospinal fluid markers were amyloid β (Aβ)42 and total tau protein concentrations. For each biomarker, sensitivity for pAD, specificity for sMCI, and diagnostic accuracy were computed.ResultsSensitivity to predict MCI conversion to AD in the Alzheimer's Disease Neuroimaging Initiative and Translational Outpatient Memory Clinic cohorts was 79% and 94% based on Aβ42, 46% and 28% based on hippocampal volumes, 33% to 66% and 56% to 78% based on different hypometabolism indices, and 46% and 61% based on total tau levels, respectively. Specificity to exclude sMCI was 27% and 50% based on Aβ42, 76% and 94% based on hippocampal volumes, 58% to 67% and 55% to 83% based on different hypometabolism indices, and 61% and 83% based on total tau levels, respectively.ConclusionsCurrent findings suggest that Aβ42 concentrations and hippocampal volumes may be used in combination to best identify pAD.     

Cortical changes in incipient Alzheimer's disease

Mild cognitive impairment (MCI) is defined by memory impairment with no impact on daily activities. 10 to 15% of MCI convert to Alzheimer's disease (AD) per year. While structural changes in the cortex of AD patients have been extensively investigated, fewer studies analyzed changes in the years preceding conversion. 46 MCI patients and 20 healthy controls underwent structural 1.0T-weighted high-resolution MR scans at baseline and after 1.4 (SD 0.3) years. All subjects were assessed yearly for up to 4 years with a comprehensive neuropsychological battery. Sixteen of the 46 patients converted to AD (cMCI) while 30 remained stable (sMCI). An accurate voxel-based statistical mesh-model technique (cortical pattern matching) with a related region-of-interest analysis based on networks defined from a Brodmann area atlas (BAs) were used to map gray matter changes over time. At baseline, cMCI patients had 10 to 30% less cortical gray matter volume than healthy controls in regions known to be affected by AD pathology (entorhinal, temporoparietal, posterior cingulate, and orbitofrontal cortex, p=0.0001). Over time, cMCI patients lost more gray matter than sMCI in all brain areas but mainly in the olfactory and in the polysynaptic hippocampal network (more than 8% gray matter loss, p<0.024). sMCI patients had 10 to 20% less volume than controls in the posterior cingulate and orbitofrontal cortex (p<0.008) although their progression over time was significantly slower than cMCI. AD patients in the MCI stage show greater gray matter loss in the olfactory and polysynaptic hippocampal network. These findings are in line with neuropathological knowledge.     

Spatial correlation maps of the hippocampus with cerebrospinal fluid biomarkers and cognition in Alzheimer's disease: A longitudinal study

This study is an observational study that takes the existing longitudinal data from Alzheimer''s disease Neuroimaging Initiative to examine the spatial correlation map of hippocampal subfield atrophy with CSF biomarkers and cognitive decline in the course of AD. This study included 421 healthy controls (HC), 557 patients of stable mild cognitive impairment (s‐MCI), 304 Alzheimer''s Disease (AD) patients, and 241 subjects who converted to be AD from MCI (c‐MCI), and 6,525 MRI scans in a period from 2004 to 2019. Our findings revealed that all the hippocampal subfields showed their accelerated atrophy rate from cognitively normal aging to stable MCI and AD. The presubiculum, dentate gyrus, and fimbria showed greater atrophy beyond the whole hippocampus in the HC, s‐MCI, and AD groups and corresponded to a greater decline of memory and attention in the s‐MCI group. Moreover, the higher atrophy rates of the subiculum and CA2/3, CA4 were also associated with a greater decline in attention in the s‐MCI group. Interestingly, patients with c‐MCI showed that the presubiculum atrophy was associated with CSF tau levels and corresponded to the onset age of AD and a decline in attention in patients with c‐MCI. These spatial correlation findings of the hippocampus suggested that the hippocampal subfields may not be equally impacted by normal aging, MCI, and AD, and their atrophy was selectively associated with declines in specific cognitive domains. The presubiculum atrophy was highlighted as a surrogate marker for the AD prognosis along with tau pathology and attention decline.     

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis

Accurate and early diagnosis of Alzheimer’s disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification. First, multiple deep 3D-CNNs are constructed on different local image patches to transform the local brain image into more compact high-level features. Then, an upper high-level 2D-CNN followed by softmax layer is cascaded to ensemble the high-level features learned from the multi-modality and generate the latent multimodal correlation features of the corresponding image patches for classification task. Finally, these learned features are combined by a fully connected layer followed by softmax layer for AD classification. The proposed method can automatically learn the generic multi-level and multimodal features from multiple imaging modalities for classification, which are robust to the scale and rotation variations to some extent. No image segmentation and rigid registration are required in pre-processing the brain images. Our method is evaluated on the baseline MRI and PET images of 397 subjects including 93 AD patients, 204 mild cognitive impairment (MCI, 76 pMCI +128 sMCI) and 100 normal controls (NC) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 93.26% for classification of AD vs. NC and 82.95% for classification pMCI vs. NC, demonstrating the promising classification performance.     

3D comparison of low,intermediate, and advanced hippocampal atrophy in MCI

We applied the hippocampal radial atrophy mapping technique to the baseline and follow‐up magnetic resonance image data of 169 amnestic mild cognitive impairment (MCI) participants in the imaging arm of the Alzheimer's Disease Cooperative Study MCI Donepezil/Vitamin E trial. Sixty percent of the subjects with none to mild hippocampal atrophy rated with the visual medial temporal atrophy rating scale (MTA score < 2) and 33.8% of the subjects with moderate to severe (MTA ≥ 2) hippocampal atrophy converted to Alzheimer's disease (AD) during 3‐year follow‐up. MTA ≥ 2 showed a trend for greater left sided hippocampal atrophy versus MTA < 2 groups at baseline (P_corrected = 0.08). Higher MTA scores were associated with progressive atrophy of the subiculum and the CA1‐3 subregions. The MTA < 2 group demonstrated significant bilateral atrophy progression at follow‐up (left P_corrected = 0.008; right P_corrected = 0.05). Relative to MTA < 2 nonconverters, MTA < 2 converters showed further involvement of the subiculum and CA1 and additional involvement of CA2‐3 at follow‐up. Right CA1 atrophy was significantly associated with conversion to dementia (for 1 mm greater right CA1 radial distance subjects had 50% reduced hazard for conversion). Greater CA1 and subicular atrophy can be demonstrated early and is predictive of future conversion to AD, whereas CA2‐3 involvement becomes more evident as the disease progresses. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.     

The Radial Width of the Temporal Horn in Mild Cognitive Impairment

BACKGROUND AND PURPOSE: The diagnosis of preclinical Alzheimer's disease (AD) (or mild cognitive impairment [MCI]) is loaded with a high degree of uncertainty. The aim was to test the accuracy of a computed tomography-based (CT-based) marker of medial temporal lobe atrophy, the radial width of the temporal horn (rWTH), in MCI. METHODS: Ten MCI and 42 AD patients and 29 nondemented controls underwent brain CT on the temporal lobe plane (slice thickness = 2 mm). The rWTH was taken on CT films with a precision caliper placed at the tip of the temporal horn radial to the curvature of the hippocampal head region. RESULTS: When specificity was fixed at 95%, the sensitivity for the detection of AD was 39/42 (93%) and that for the detection of MCI was 8/10 (80%). CONCLUSION: The rWTH is a measure sensitive to the regional brain atrophy common in early AD.     

White matter hyperintensities are associated with disproportionate progressive hippocampal atrophy

This study investigates relationships between white matter hyperintensity (WMH) volume, cerebrospinal fluid (CSF) Alzheimer's disease (AD) pathology markers, and brain and hippocampal volume loss. Subjects included 198 controls, 345 mild cognitive impairment (MCI), and 154 AD subjects with serial volumetric 1.5‐T MRI. CSF Aβ₄₂ and total tau were measured (n = 353). Brain and hippocampal loss were quantified from serial MRI using the boundary shift integral (BSI). Multiple linear regression models assessed the relationships between WMHs and hippocampal and brain atrophy rates. Models were refitted adjusting for (a) concurrent brain/hippocampal atrophy rates and (b) CSF Aβ₄₂ and tau in subjects with CSF data. WMH burden was positively associated with hippocampal atrophy rate in controls (P = 0.002) and MCI subjects (P = 0.03), and with brain atrophy rate in controls (P = 0.03). The associations with hippocampal atrophy rate remained following adjustment for concurrent brain atrophy rate in controls and MCIs, and for CSF biomarkers in controls (P = 0.007). These novel results suggest that vascular damage alongside AD pathology is associated with disproportionately greater hippocampal atrophy in nondemented older adults. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc.     

Quantitation of in vivo brain glutathione conformers in cingulate cortex among age‐matched control,MCI, and AD patients using MEGA‐PRESS

Oxidative stress (OS) plays an important role in Alzheimer's disease (AD) and glutathione (GSH) mitigates this effect by maintaining redox‐imbalance and free‐radical neutralization. Quantified brain GSH concentration provides distinct information about OS among age‐matched normal control (NC), mild cognitive impairment (MCI) and AD patients. We report alterations of in vivo GSH conformers, along with the choline, creatine, and N‐acetylaspartate levels in the cingulate cortex (CC) containing anterior (ACC) and posterior (PCC) regions of 64 (27 NC, 19 MCI, and 18 AD) participants using MEscher–GArwood‐Point‐RESolved spectroscopy sequence. Result indicated, tissue corrected GSH depletion in PCC among MCI (p = .001) and AD (p = .028) and in ACC among MCI (p = .194) and AD (p = .025) as compared to NC. Effects of the group, region, and group × region on GSH with age and gender as covariates were analyzed using a generalized linear model with Bonferroni correction for multiple comparisons. A significant effect of group with GSH depletion in AD and MCI was observed as compared to NC. Receiver operator characteristic (ROC) analysis of GSH level in CC differentiated between MCI and NC groups with an accuracy of 82.8% and 73.5% between AD and NC groups. Multivariate ROC analysis for the combined effect of the GSH alteration in both ACC and PCC regions provided improved diagnostic accuracy of 86.6% for NC to MCI conversion and 76.4% for NC to AD conversion. We conclude that only closed GSH conformer depletion in the ACC and PCC regions is critical and constitute a potential biomarker for AD.     

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

We investigated structural and functional changes in the medial temporal lobe (MTL) using magnetic resonance imaging (MRI) and compared the discriminative power of these measures with neuropsychological testing in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Functional MRI (fMRI) was performed in 21 elderly controls, 14 MCI subjects, and 15 mild AD patients during encoding and cued retrieval of word-picture pairs. A region-of-interest-based approach in SPM2 was used to extract the extent of hippocampal activation. The volumes of the hippocampus and entorhinal cortex (EC) were manually outlined from anatomical MR images. Discriminant analyses were conducted to assess the ability of hippocampal fMRI, MTL volumetry, and neuropsychological measures to classify subjects into clinical groups. Entorhinal but not hippocampal volumes differed significantly between the control and MCI subjects. Both entorhinal and hippocampal volumes differed between MCI and AD patients. There were no significant differences in the extent of hippocampal fMRI activation during encoding or retrieval between the groups. Entorhinal volume was the best discriminator with a discriminating accuracy of 85.7% between controls and MCI, 86.2% between MCI and AD, and 97.2% between controls and AD. Delayed recall of a wordlist classified the subjects, second best, with a discriminating accuracy of 81.8% between controls and MCI, 75% between MCI and AD and 93.5% between controls and AD. The accuracy of hippocampal volumetry ranged from 42.9 to 69.4%, and hippocampal fMRI activation during encoding and retrieval had a classification accuracy of only 41.4-57.7% between the groups. Our results suggest that evaluation of entorhinal atrophy, in addition to the prevailing diagnostic criteria, seems promising in the identification of prodromal AD. Future technical improvements may improve the utilization of hippocampal fMRI for early diagnostic purposes.     

Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer's disease,mild cognitive impairment,and elderly controls

We used a new method we developed for automated hippocampal segmentation, called the auto context model, to analyze brain MRI scans of 400 subjects from the Alzheimer's disease neuroimaging initiative. After training the classifier on 21 hand‐labeled expert segmentations, we created binary maps of the hippocampus for three age‐ and sex‐matched groups: 100 subjects with Alzheimer's disease (AD), 200 with mild cognitive impairment (MCI) and 100 elderly controls (mean age: 75.84; SD: 6.64). Hippocampal traces were converted to parametric surface meshes and a radial atrophy mapping technique was used to compute average surface models and local statistics of atrophy. Surface‐based statistical maps visualized links between regional atrophy and diagnosis (MCI versus controls: P = 0.008; MCI versus AD: P = 0.001), mini‐mental state exam (MMSE) scores, and global and sum‐of‐boxes clinical dementia rating scores (CDR; all P < 0.0001, corrected). Right but not left hippocampal atrophy was associated with geriatric depression scores (P = 0.004, corrected); hippocampal atrophy was not associated with subsequent decline in MMSE and CDR scores, educational level, ApoE genotype, systolic or diastolic blood pressure measures, or homocysteine. We gradually reduced sample sizes and used false discovery rate curves to examine the method's power to detect associations with diagnosis and cognition in smaller samples. Forty subjects were sufficient to discriminate AD from normal and correlate atrophy with CDR scores; 104, 200, and 304 subjects, respectively, were required to correlate MMSE with atrophy, to distinguish MCI from normal, and MCI from AD. Hum Brain Mapp 2009. © 2009 Wiley‐Liss, Inc.     

Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer's disease

OBJECTIVES: To explore volume changes of the entorhinal cortex (ERC) and hippocampus in mild cognitive impairment (MCI) and Alzheimer's disease (AD) compared with normal cognition (NC); to determine the powers of the ERC and the hippocampus for discrimination between these groups. METHODS: This study included 40 subjects with NC, 36 patients with MCI, and 29 patients with AD. Volumes of the ERC and hippocampus were manually measured based on coronal T1 weighted MR images. Global cerebral changes were assessed using semiautomatic image segmentation. RESULTS: Both ERC and hippocampal volumes were reduced in MCI (ERC 13%, hippocampus 11%, p<0.05) and AD (ERC 39%, hippocampus 27%, p<0.01) compared with NC. Furthermore, AD showed greater volume losses in the ERC than in the hippocampus (p<0.01). In addition, AD and MCI also had cortical grey matter loss (p< 0.01) and ventricular enlargement (p<0.01) when compared with NC. There was a significant correlation between ERC and hippocampal volumes in MCI and AD (both p<0.001), but not in NC. Using ERC and hippocampus together improved discrimination between AD and CN but did not improve discrimination between MCI and NC. The ERC was better than the hippocampus for distinguishing MCI from AD. In addition, loss of cortical grey matter significantly contributed to the hippocampus for discriminating MCI and AD from NC. CONCLUSIONS: Volume reductions in the ERC and hippocampus may be early signs of AD pathology that can be measured using MRI.     

Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer's disease

Background:

Histopathological studies and animal models suggest that hippocampal subfields may be differently affected by aging, Alzheimer's disease (AD), and other diseases. High‐resolution images at 4 Tesla depict details of the internal structure of the hippocampus allowing for in vivo volumetry of different subfields. The aims of this study were as follows: (1) to determine patterns of volume loss in hippocampal subfields in normal aging, AD, and amnestic mild cognitive impairment (MCI). (2) To determine if measurements of hippocampal subfields provide advantages over total hippocampal volume for differentiation between groups.

Methods:

Ninety‐one subjects (53 controls (mean age: 69.3 ± 7.3), 20 MCI (mean age: 73.6 ± 7.1), and 18 AD (mean age: 69.1 ± 9.5) were studied with a high‐resolution T2 weighted imaging sequence aimed at the hippocampus. Entorhinal cortex (ERC), subiculum, CA1, CA1‐CA2 transition zone (CA1‐2), CA3 & dentate gyrus (CA3&DG) were manually marked in the anterior third of the hippocampal body. Hippocampal volume was obtained from the Freesurfer and manually edited.

Results:

Compared to controls, AD had smaller volumes of ERC, subiculum, CA1, CA1‐2, and total hippocampal volumes. MCI had smaller CA1‐2 volumes. Discriminant analysis and power analysis showed that CA1‐2 was superior to total hippocampal volume for distinction between controls and MCI.

Conclusion:

The patterns of subfield atrophy in AD and MCI were consistent with patterns of neuronal cell loss/reduced synaptic density described by histopathology. These preliminary findings suggest that hippocampal subfield volumetry might be a better measure for diagnosis of early AD and for detection of other disease effects than measurement of total hippocampus. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.

     

Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps

BACKGROUND: While most patients with mild cognitive impairment (MCI) transition to Alzheimer disease (AD), others develop non-AD dementia, remain in the MCI state, or improve. OBJECTIVE: To test the following hypotheses: smaller hippocampal volumes predict conversion of MCI to AD, whereas larger hippocampal volumes predict cognitive stability and/or improvement; and patients with MCI who convert to AD have greater atrophy in the CA1 hippocampal subfield and subiculum. DESIGN: Prospective longitudinal cohort study. SETTING: University of California-Los Angeles Alzheimer's Disease Research Center. PATIENTS: We followed up 20 MCI subjects clinically and neuropsychologically for 3 years. MAIN OUTCOME MEASURE: Baseline regional hippocampal atrophy was analyzed with region-of-interest and 3-dimensional hippocampal mapping techniques. RESULTS: During the 3-year study, 6 patients developed AD (MCI-c), 7 remained stable (MCI-nc), and 7 improved (MCI-i). Patients with MCI-c had 9% smaller left and 13% smaller right mean hippocampal volumes compared with MCI-nc patients. Radial atrophy maps showed greater atrophy of the CA1 subregion in MCI-c. Patients with MCI-c had significantly smaller hippocampi than MCI-i patients (left, 24%; right, 27%). Volumetric analyses showed a trend for greater hippocampal atrophy in MCI-nc relative to MCI-i patients (eg, 16% volume loss). After permutation tests corrected for multiple comparison, the atrophy maps showed a significant difference on the right. Subicular differences were seen between MCI-c and MCI-i patients, and MCI-nc and MCI-i patients. Multiple linear regression analysis confirmed the group effect to be highly significant and independent of age, hemisphere, and Mini-Mental State Examination scores at baseline. CONCLUSIONS: Smaller hippocampi and specifically CA1 and subicular involvement are associated with increased risk for conversion from MCI to AD. Patients with MCI-i tend to have larger hippocampal volumes and relative preservation of both the subiculum and CA1.     

Longitudinal trajectory of Amyloid‐related hippocampal subfield atrophy in nondemented elderly

Hippocampal atrophy and abnormal β‐Amyloid (Aβ) deposition are established markers of Alzheimer's disease (AD). Nonetheless, longitudinal trajectory of Aβ‐associated hippocampal subfield atrophy prior to dementia remains unclear. We hypothesized that elevated Aβ correlated with longitudinal subfield atrophy selectively in no cognitive impairment (NCI), spreading to other subfields in mild cognitive impairment (MCI). We analyzed data from two independent longitudinal cohorts of nondemented elderly, including global PET‐Aβ in AD‐vulnerable cortical regions and longitudinal subfield volumes quantified with a novel auto‐segmentation method (FreeSurfer v.6.0). Moreover, we investigated associations of Aβ‐related progressive subfield atrophy with memory decline. Across both datasets, we found a converging pattern that higher Aβ correlated with faster CA1 volume decline in NCI. This pattern spread to other hippocampal subfields in MCI group, correlating with memory decline. Our results for the first time suggest a longitudinal focal‐to‐widespread trajectory of Aβ‐associated hippocampal subfield atrophy over disease progression in nondemented elderly.     

Hippocampal volume and asymmetry in mild cognitive impairment and Alzheimer's disease: Meta‐analyses of MRI studies

Numerous studies have reported a smaller hippocampal volume in Alzheimer's disease (AD) patients than in aging controls. However, in mild cognitive impairment (MCI), the results are inconsistent. Moreover, the left‐right asymmetry of the hippocampus receives less research attention. In this article, meta‐analyses are designed to determine the extent of hippocampal atrophy in MCI and AD, and to evaluate the asymmetry pattern of the hippocampal volume in control, MCI, and AD groups. From 14 studies including 365 MCI patients and 382 controls, significant atrophy is found in both the left [Effect size (ES), 0.92; 95% confidence interval (CI), 0.72–1.11] and right (ES, 0.78; 95% CI, 0.57–0.98) hippocampus, which is lower than that in AD (ES, 1.60, 95% CI, 1.37–1.84, in left; ES, 1.52, 95% CI, 1.31–1.72, in right). Comparing with aging controls, the average volume reduction weighted by sample size is 12.9% and 11.1% in left and right hippocampus in MCI, and 24.2% and 23.1% in left and right hippocampus in AD, respectively. The findings show a bilateral hippocampal volume loss in MCI and the extent of atrophy is less than that in AD. By comparing the left and right hippocampal volume, a consistent left‐less‐than‐right asymmetry pattern is found, but with different extents in control (ES, 0.39), MCI (ES, 0.56), and AD (ES, 0.30) group. © 2009 Wiley‐Liss, Inc.     

New MRI markers for Alzheimer's disease: a meta-analysis of diffusion tensor imaging and a comparison with medial temporal lobe measurements

The aim of the present study is to evaluate the diagnostic value of diffusion tensor imaging (DTI) for early Alzheimer's disease (AD) in comparison to widely accepted medial temporal lobe (MTL) atrophy measurements. A systematic literature research was performed into DTI and MTL atrophy in AD and mild cognitive impairment (MCI). We included seventy-six studies on MTL atrophy including 8,122 subjects and fifty-five DTI studies including 2,791 subjects. Outcome measure was the effect size (ES) expressed as Hedges g. In volumetric studies, atrophy of the MTL significantly differentiated between AD and controls (ES 1.32-1.98) and MCI and controls (ES 0.61-1.46). In DTI-Fractional anisotropy (FA) studies, the total cingulum differentiated best between AD and controls (ES = 1.73) and the parahippocampal cingulum between MCI and controls (ES = 0.97). In DTI-Mean diffusivity (MD) studies, the hippocampus differentiated best between AD and controls (ES = -1.17) and between MCI and controls (ES = -1.00). We can conclude that in general, the ES of volumetric MTL atrophy measurements was equal or larger than that of DTI measurements. However, for the comparison between controls and MCI-patients, ES of hippocampal MD was larger than ES of hippocampal volume. Furthermore, it seems that MD values have somewhat more discriminative power than FA values with higher ES in the frontal, parietal, occipital and temporal lobe.     

Combining a Patch-based Approach with a Non-rigid Registration-based Label Fusion Method for the Hippocampal Segmentation in Alzheimer’s Disease

We provide and evaluate an open-source software solution for automatically hippocampal segmentation from T1-weighted (T1w) magnetic resonance imaging (MRI). The method is applied for measuring the hippocampal volume, which allows discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and elderly controls (NC). The method is based on a fast patch-based label fusion method, whose selected patches and their weights are calculated from a combination of similarity measures between patches using intensity-based distances and labeling-based distances. These combined similarity measures produces better selection of the patches, and their weights are more robust. The algorithm is trained with the Harmonized Hippocampal Protocol (HarP). The proposal is compared with FreeSurfer and other label fusion methods. To evaluate the performance and the robustness of the proposed label fusion method, we employ two databases of T1w MRI of human brains. For AD vs NC, we obtain a high degree of accuracy, approximately 90 %. For MCI vs NC, we obtain accuracies around 75 %. The average time for the hippocampal segmentation from a T1w MRI is less than 17 minutes.     

Disruption of limbic white matter pathways in mild cognitive impairment and Alzheimer's disease: a DTI/FDG-PET study

Background: Alzheimer's disease (AD) and mild cognitive impairment (MCI) affect the limbic system, causing medial temporal lobe (MTL) atrophy and posterior cingulate cortex (PCC) hypometabolism. Additionally, diffusion tensor imaging (DTI) studies have demonstrated that MCI and AD involve alterations in cerebral white matter (WM) integrity. Objectives: To test if (1) patients with MCI and AD exhibit decreases in the integrity of limbic WM pathways; (2) disconnection between PCC and MTL, manifested as disruption of the cingulum bundle, contributes to PCC hypometabolism during incipient AD. Methods: We measured fractional anisotropy (FA) and volume of the fornix and cingulum using DTI in 23 individuals with MCI, 21 with mild‐to‐moderate AD, and 16 normal control (NC) subjects. We also measured PCC metabolism using ¹⁸F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) in AD and MCI patients. Results: Fornix FA and volume were reduced in MCI and AD to a similar extent. Descending cingulum FA was reduced in AD while volume was reduced in MCI and even more so in AD. Both FA and volume of the fornix and descending cingulum reliably discriminated between NC and AD. Fornix FA and descending cingulum volume also reliably discriminated between NC and MCI. Only descending cingulum volume reliably discriminated between MCI and AD. In the combined MCI‐AD cohort, PCC metabolism directly correlated with both FA and volume of the descending cingulum. Conclusions: Disruption of limbic WM pathways is evident during both MCI and AD. Disconnection of the PCC from MTL at the cingulum bundle contributes to PCC hypometabolism during incipient AD. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc     

Brain MRI hippocampal volume and prediction of clinical status in a mild cognitive impairment trial

Mild Cognitive Impairment (MCI) is considered a transitional stage in the pathogenesis of Alzheimer’s disease; however, not all MCI patients progress to clinically defined AD or decline at identical rates. Hippocampal atrophy, as measured by Magnetic Resonance Imaging (MRI), may be a marker for hippocampal pathology in patients with MCI and predict a more rapid deterioration to clinical AD. In this study, we used MRI data from an ongoing MCI clinical trial to determine whether MRI hippocampal volume at baseline was associated with cognitive and functional performance in MCI subjects and whether it predicted those individuals who were more likely to develop AD. We performed correlational analyses between the MRI hippocampal volumes at study entry and the subjects’ concurrent performance on neuropsychological measures and clinical ratings. Larger hippocampal volume was associated with better performance on tests of memory, general cognition, and overall clinical ratings. Further analyses suggested that a smaller baseline hippocampal volume may be associated with a higher risk of developing clinical AD. As the trial is still ongoing, these results require confirmation once the trial is completed. In summary, these data suggest that MRI hippocampal volume may be a useful correlate of disease severity in MCI subjects and a prognostic indicator of subsequent AD.