Quantitative MR imaging in Alzheimer disease

Zero-DeepSub: Zero-shot deep subspace reconstruction for rapid multiparametric quantitative MRI using 3D-QALAS

PURPOSE

To develop and evaluate methods for (1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) time-series images using a low-rank subspace method, which enables accurate and rapid T1 and T2 mapping, and (2) improving the fidelity of subspace QALAS by combining scan-specific deep-learning-based reconstruction and subspace modeling.

THEORY AND METHODS

A low-rank subspace method for 3D-QALAS (i.e., subspace QALAS) and zero-shot deep-learning subspace method (i.e., Zero-DeepSub) were proposed for rapid and high fidelity T1 and T2 mapping and time-resolved imaging using 3D-QALAS. Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the T1 and T2 maps estimated using the proposed methods were evaluated by comparing them with reference techniques. The reconstruction performance of the proposed subspace QALAS using Zero-DeepSub was evaluated in vivo and compared with conventional QALAS at high reduction factors of up to nine-fold.

RESULTS

Phantom experiments showed that subspace QALAS had good linearity with respect to the reference methods while reducing biases and improving precision compared to conventional QALAS, especially for T2 maps. Moreover, in vivo results demonstrated that subspace QALAS had better g-factor maps and could reduce voxel blurring, noise, and artifacts compared to conventional QALAS and showed robust performance at up to nine-fold acceleration with Zero-DeepSub, which enabled whole-brain T1, T2, and PD mapping at 1 mm isotropic resolution within 2 min of scan time.

CONCLUSION

The proposed subspace QALAS along with Zero-DeepSub enabled high fidelity and rapid whole-brain multiparametric quantification and time-resolved imaging.

Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach

BACKGROUND

MRI magnetization-prepared rapid acquisition (MPRAGE) is an easily available imaging modality for dementia diagnosis. Previous studies suggested that volumetric analysis plays a crucial role in various stages of dementia classification. In this study, volumetry, radiomics and demographics were integrated as inputs to develop an artificial intelligence model for various stages, including Alzheimer's disease (AD), mild cognitive decline (MCI) and cognitive normal (CN) dementia classifications.

METHOD

The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset was separated into training and testing groups, and the Open Access Series of Imaging Studies (OASIS) dataset was used as the second testing group. The MRI MPRAGE image was reoriented via statistical parametric mapping (SPM12). Freesurfer was employed for brain segmentation, and 45 regional brain volumes were retrieved. The 3D Slicer software was employed for 107 radiomics feature extractions from within the whole brain. Data on patient demographics were collected from the datasets. The feed-forward neural network (FFNN) and the other most common artificial intelligence algorithms, including support vector machine (SVM), ensemble classifier (EC) and decision tree (DT), were used to build the models using various features.

RESULTS

The integration of brain regional volumes, radiomics and patient demographics attained the highest overall accuracy at 76.57% and 73.14% in ADNI and OASIS testing, respectively. The subclass accuracies in MCI, AD and CN were 78.29%, 89.71% and 85.14%, respectively, in ADNI testing, as well as 74.86%, 88% and 83.43% in OASIS testing. Balanced sensitivity and specificity were obtained for all subclass classifications in MCI, AD and CN.

CONCLUSION

The FFNN yielded good overall accuracy for MCI, AD and CN categorization, with balanced subclass accuracy, sensitivity and specificity. The proposed FFNN model is simple, and it may support the triage of patients for further confirmation of the diagnosis.

SSL-QALAS: Self-Supervised Learning for rapid multiparameter estimation in quantitative MRI using 3D-QALAS

PURPOSE

To develop and evaluate a method for rapid estimation of multiparametric T1 , T2 , proton density, and inversion efficiency maps from 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) measurements using self-supervised learning (SSL) without the need for an external dictionary.

METHODS

An SSL-based QALAS mapping method (SSL-QALAS) was developed for rapid and dictionary-free estimation of multiparametric maps from 3D-QALAS measurements. The accuracy of the reconstructed quantitative maps using dictionary matching and SSL-QALAS was evaluated by comparing the estimated T1 and T2 values with those obtained from the reference methods on an International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. The SSL-QALAS and the dictionary-matching methods were also compared in vivo, and generalizability was evaluated by comparing the scan-specific, pre-trained, and transfer learning models.

RESULTS

Phantom experiments showed that both the dictionary-matching and SSL-QALAS methods produced T1 and T2 estimates that had a strong linear agreement with the reference values in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. Further, SSL-QALAS showed similar performance with dictionary matching in reconstructing the T1 , T2 , proton density, and inversion efficiency maps on in vivo data. Rapid reconstruction of multiparametric maps was enabled by inferring the data using a pre-trained SSL-QALAS model within 10 s. Fast scan-specific tuning was also demonstrated by fine-tuning the pre-trained model with the target subject's data within 15 min.

CONCLUSION

The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data.

Motion-corrected 3D-EPTI with efficient 4D navigator acquisition for fast and robust whole-brain quantitative imaging

PURPOSE

To develop a motion estimation and correction method for motion-robust three-dimensional (3D) quantitative imaging with 3D-echo-planar time-resolved imaging.

THEORY AND METHODS

The 3D-echo-planar time-resolved imaging technique was designed with additional four-dimensional navigator acquisition (x-y-z-echoes) to achieve fast and motion-robust quantitative imaging of the human brain. The four-dimensional-navigator is inserted into the relaxation-recovery deadtime of the sequence in every pulse TR (∼2 s) to avoid extra scan time, and to provide continuous tracking of the 3D head motion and B0 -inhomogeneity changes. By using an optimized spatiotemporal encoding combined with a partial-Fourier scheme, the navigator acquires a large central k-t data block for accurate motion estimation using only four small-flip-angle excitations and readouts, resulting in negligible signal-recovery reduction to the 3D-echo-planar time-resolved imaging acquisition. By incorporating the estimated motion and B0 -inhomogeneity changes into the reconstruction, multi-contrast images can be recovered with reduced motion artifacts.

RESULTS

Simulation shows the cost to the SNR efficiency from the added navigator acquisitions is <1%. Both simulation and in vivo retrospective experiments were conducted, that demonstrate the four-dimensional navigator provided accurate estimation of the 3D motion and B0 -inhomogeneity changes, allowing effective reduction of image artifacts in quantitative maps. Finally, in vivo prospective undersampling acquisition was performed with and without head motion, in which the motion corrupted data after correction show close image quality and consistent quantifications to the motion-free scan, providing reliable quantitative measurements even with head motion.

CONCLUSION

The proposed four-dimensional navigator acquisition provides reliable tracking of the head motion and B0 change with negligible SNR cost, equips the 3D-echo-planar time-resolved imaging technique for motion-robust and efficient quantitative imaging.

Radiomics-Based Artificial Intelligence Differentiation of Neurodegenerative Diseases with Reference to the Volumetry

This study aimed to build automated detection models—one by brain regional volume (V-model), and the other by radiomics features of the whole brain (R-model)—to differentiate mild cognitive impairment (MCI) from cognitive normal (CN), and Alzheimer’s Disease (AD) from mild cognitive impairment (MCI). The objectives are to compare the models and identify whether radiomics or volumetry can provide a better prediction for differentiating different types of dementia. Method: 582 MRI T1-weighted images were retrieved from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which is a multicenter operating open source database for AD. In total, 97 images of AD, 293 images of MCI patient and 192 images of cognitive normal were divided into a training, a validation and a test group at a ratio of 70:15:15. For each T1-weighted image, volumetric segmentation was performed with the image analysis software FreeSurfer, and radiomics features were retrieved by imaging research software 3D slicers. Brain regional volume and radiomics features were used to build the V-model and R-model, respectively, using the random forest algorithm by R. The receiver operating characteristics (ROC) curve of both models were used to evaluate their diagnostic accuracy and reliability to differentiate AD, MCI and CN. Results: To differentiate MCI and CN, both V-model and R-model achieved excellent performance, with an AUC of 0.9992 ± 0.0022 and 0.9850 ± 0.0032, respectively. No significant difference was found between the two AUCs, indicating both models attained similar good performance. In MCI and AD differentiation, the V-model and R-model yielded AUC of 0.9986 ± 0.0013 and 0.9714 ± 0.0175, respectively. The best performance was to differentiate AD from CN, where the V-model and R-model yielded AUC of 0.9994 ± 0.0019 and 0.9830 ± 0.009, respectively. The results suggested that both volumetry and radiomics approaches could be used in differentiating AD, MCI and CN, based on T1 weighted MR images using random forest algorithm successfully. Conclusion: This study showed that the radiomics features from T1-weighted MR images achieved excellence performance in differentiating AD, MCI and CN. Compared to the volumetry method, the accuracy, sensitivity and specificity are slightly lower in using radiomics, but still attained very good and reliable classification of the three stages of neurodegenerations. In view of the convenience and operator independence in feature extraction, radiomics can be a quantitative biomarker to differentiate the disease groups.

3D Echo Planar Time-resolved Imaging (3D-EPTI) for ultrafast multi-parametric quantitative MRI

Multi-parametric quantitative MRI has shown great potential to improve the sensitivity and specificity of clinical diagnosis and to enhance our understanding of complex brain processes, but suffers from long scan time especially at high spatial resolution. To address this longstanding challenge, we introduce a novel approach termed 3D Echo Planar Time-resolved Imaging (3D-EPTI), which significantly increases the acceleration capacity of MRI sampling, and provides high acquisition efficiency for multi-parametric MRI. This is achieved by exploiting the spatiotemporal correlation of MRI data at multiple timescales through new encoding strategies within and between efficient continuous readouts. Specifically, an optimized spatiotemporal CAIPI encoding within the readouts combined with a radial-block sampling strategy across the readouts enables an acceleration rate of 800 fold in the k-t space. A subspace reconstruction was employed to resolve thousands of high-quality multi-contrast images. We have demonstrated the ability of 3D-EPTI to provide robust and repeatable whole-brain simultaneous T₁, T₂, T₂*, PD and B₁⁺ mapping at high isotropic resolution within minutes (e.g., 1-mm isotropic resolution in 3 minutes), and to enable submillimeter multi-parametric imaging to study detailed brain structures.

The effect of beta-amyloid and tau protein aggregations on magnetic susceptibility of anterior hippocampal laminae in Alzheimer's diseases

Previous studies have reported the changes of magnetic susceptibility induced by iron deposition in hippocampus of Alzheimer's disease (AD) brains. It is well-known that hippocampus is divided into well-defined laminar architecture, which, however, is difficult to be resolved with in-vivo MRI due to the limited imaging resolution. The present study aims to investigate layer-specific magnetic susceptibility in the hippocampus of AD patients using high-resolution ex-vivo MRI, and elucidate its relationship with beta amyloid (Aβ) and tau protein histology. We performed quantitative susceptibility mapping (QSM) and T2* mapping on postmortem anterior hippocampus samples from four AD, four Primary Age-Related Tauopathy (PART), and three control brains. We manually segmented each sample into seven layers, including four layers in the cornu ammonis1 (CA1) and three layers in the dentate gyrus (DG), and then evaluated AD-related alterations of susceptibility and T2* values and their correlations with Aβ and tau in each hippocampal layer. Specifically, we found (1) layer-specific variations of susceptibility and T2* measurements in all samples; (2) the heterogeneity of susceptibility were higher in all layers of AD patients compared with the age- and gender-matched PART cases while the heterogeneity of T2* values were lower in four layers of CA1; and (3) voxel-wise MRI-histological correlation revealed both susceptibility and T2* values in the stratum molecular (SM) and stratum lacunosum (SL) layers were correlated with the Aβ content in AD, while the T2* values in the stratum radiatum (SR) layer were correlated with the tau content in the PART but not AD. These findings suggest a selective effect of the Aβ- and tau-pathology on the susceptibility and T2* values in the different layers of anterior hippocampus. Particularly, the alterations of magnetic susceptibility in the SM and SL layers may be associated with Aβ aggregation, while those in the SR layermay reflect the age-related tau protein aggregation.

Hippocampal and Amygdalar Morphological Abnormalities in Alzheimer's Disease Based on Three Chinese MRI Datasets

BACKGROUND

Early detection of Alzheimer's disease (AD) and its early stage, the mild cognitive impairment (MCI), has important scientific, clinical and social significance. Magnetic resonance imaging (MRI) based statistical shape analysis provides an opportunity to detect regional structural abnormalities of brain structures caused by AD and MCI.

OBJECTIVE

In this work, we aimed to employ a well-established statistical shape analysis pipeline, in the framework of large deformation diffeomorphic metric mapping, to identify and quantify the regional shape abnormalities of the bilateral hippocampus and amygdala at different prodromal stages of AD, using three Chinese MRI datasets collected from different domestic hospitals.

METHODS

We analyzed the region-specific shape abnormalities at different stages of the neuropathology of AD by comparing the localized shape characteristics of the bilateral hippocampi and amygdalas between healthy controls and two disease groups (MCI and AD). In addition to group comparison analyses, we also investigated the association between the shape characteristics and the Mini Mental State Examination (MMSE) of each structure of interest in the disease group (MCI and AD combined) as well as the discriminative power of different morphometric biomarkers.

RESULTS

We found the strongest disease pathology (regional atrophy) at the subiculum and CA1 subregions of the hippocampus and the basolateral, basomedial as well as centromedial subregions of the amygdala. Furthermore, the shape characteristics of the hippocampal and amygdalar subregions exhibiting the strongest AD related atrophy were found to have the most significant positive associations with the MMSE. Employing the shape deformation marker of the hippocampus or the amygdala for automated MCI or AD detection yielded a significant accuracy boost over the corresponding volume measurement.

CONCLUSION

Our results suggested that the amygdalar and hippocampal morphometrics, especially those of shape morphometrics, can be used as auxiliary indicators for monitoring the disease status of an AD patient.

MR Imaging of SCA3/MJD

Spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) is a progressive autosomal dominantly inherited cerebellar ataxia characterized by the aggregation of polyglutamine-expanded protein within neuronal nuclei in the brain, which can lead to brain damage that precedes the onset of clinical manifestations. Magnetic resonance imaging (MRI) techniques such as morphometric MRI, diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), and magnetic resonance spectroscopy (MRS) have gained increasing attention as non-invasive and quantitative methods for the assessment of structural and functional alterations in clinical SCA3/MJD patients as well as preclinical carriers. Morphometric MRI has demonstrated typical patterns of atrophy or volume loss in the cerebellum and brainstem with extensive lesions in some supratentorial areas. DTI has detected widespread microstructural alterations in brain white matter, which indicate disrupted brain anatomical connectivity. Task-related fMRI has presented unusual brain activation patterns within the cerebellum and some extracerebellar tissue, reflecting the decreased functional connectivity of these brain regions in SCA3/MJD subjects. MRS has revealed abnormal neurochemical profiles, such as the levels or ratios of N-acetyl aspartate, choline, and creatine, in both clinical cases and preclinical cases before the alterations in brain anatomical structure. Moreover, a number of studies have reported correlations of MR imaging alterations with clinical and genetic features. The utility of these MR imaging techniques can help to identify preclinical SCA3/MJD carriers, monitor disease progression, evaluate response to therapeutic interventions, and illustrate the pathophysiological mechanisms underlying the occurrence, development, and prognosis of SCA3/MJD.

Projection to Latent Spaces Disentangles Pathological Effects on Brain Morphology in the Asymptomatic Phase of Alzheimer's Disease

Alzheimer's disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers, such as cerebrospinal fluid (CSF) levels of Aβ, p-tau, and t-tau. In parallel, brain anatomy can be characterized through imaging techniques, such as magnetic resonance imaging (MRI). In this work we relate both sets of measurements and seek associations between biomarkers and the brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we used the projection to latent structures (PLS) method. Using PLS, we found a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We looked for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we used a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We applied this technique to the study of subjects in the whole AD continuum, from the pre-clinical asymptomatic stages all the way through to the symptomatic groups. Subsequent analyses involved splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitively impaired subjects (MCI), and subjects with dementia (AD-dementia), where all symptoms were due to AD.

Brain MR Radiomics to Differentiate Cognitive Disorders

OBJECTIVE

Subtle and gradual changes occur in the brain years before cognitive impairment due to age-related neurodegenerative disorders. The authors examined the utility of hippocampal texture analysis and volumetric features extracted from brain magnetic resonance (MR) data to differentiate between three cognitive groups (cognitively normal individuals, individuals with mild cognitive impairment, and individuals with Alzheimer's disease) and neuropsychological scores on the Clinical Dementia Rating (CDR) scale.

METHODS

Data from 173 unique patients with 3-T T₁-weighted MR images from the Alzheimer's Disease Neuroimaging Initiative database were analyzed. A variety of texture and volumetric features were extracted from bilateral hippocampal regions and were used to perform binary classification of cognitive groups and CDR scores. The authors used diagonal quadratic discriminant analysis in a leave-one-out cross-validation scheme. Sensitivity, specificity, and area under the receiver operating characteristic curve were used to assess the performance of models.

RESULTS

The results show promise for hippocampal texture analysis to distinguish between no impairment and early stages of impairment. Volumetric features were more successful at differentiating between no impairment and advanced stages of impairment.

CONCLUSIONS

MR radiomics may be a promising tool to classify various cognitive groups.

Multimodal Magnetic Resonance Imaging in Alzheimer's Disease Patients at Prodromal Stage

One objective of modern neuroimaging is to identify markers that can aid in diagnosis, monitor disease progression, and impact long-term drug analysis. In this study, physiopathological modifications in seven subcortical structures of patients with mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) were characterized by simultaneously measuring quantitative magnetic resonance parameters that are sensitive to complementary tissue characteristics (e.g., volume atrophy, shape changes, microstructural damage, and iron deposition). Fourteen MCI patients and fourteen matched, healthy subjects underwent 3T-magnetic resonance imaging with whole-brain, T1-weighted, T2^*-weighted, and diffusion-tensor imaging scans. Volume, shape, mean R2^*, mean diffusivity (MD), and mean fractional anisotropy (FA) in the thalamus, hippocampus, putamen, amygdala, caudate nucleus, pallidum, and accumbens were compared between MCI patients and healthy subjects. Comparisons were then performed using voxel-based analyses of R2^*, MD, FA maps, and voxel-based morphometry to determine which subregions showed the greatest difference for each parameter. With respect to the micro- and macro-structural patterns of damage, our results suggest that different and distinct physiopathological processes are present in the prodromal phase of AD. MCI patients had significant atrophy and microstructural changes within their hippocampi and amygdalae, which are known to be affected in the prodromal stage of AD. This suggests that the amygdala is affected in the same, direct physiopathological process as the hippocampus. Conversely, atrophy alone was observed within the thalamus and putamen, which are not directly involved in AD pathogenesis. This latter result may reflect another mechanism, whereby atrophy is linked to indirect physiopathological processes.

Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry

This paper presents a brain T1-weighted structural magnetic resonance imaging (MRI) biomarker that combines several individual MRI biomarkers (cortical thickness measurements, volumetric measurements, hippocampal shape, and hippocampal texture). The method was developed, trained, and evaluated using two publicly available reference datasets: a standardized dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the imaging arm of the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL). In addition, the method was evaluated by participation in the Computer-Aided Diagnosis of Dementia (CADDementia) challenge. Cross-validation using ADNI and AIBL data resulted in a multi-class classification accuracy of 62.7% for the discrimination of healthy normal controls (NC), subjects with mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD). This performance generalized to the CADDementia challenge where the method, trained using the ADNI and AIBL data, achieved a classification accuracy 63.0%. The obtained classification accuracy resulted in a first place in the challenge, and the method was significantly better (McNemar's test) than the bottom 24 methods out of the total of 29 methods contributed by 15 different teams in the challenge. The method was further investigated with learning curve and feature selection experiments using ADNI and AIBL data. The learning curve experiments suggested that neither more training data nor a more complex classifier would have improved the obtained results. The feature selection experiment showed that both common and uncommon individual MRI biomarkers contributed to the performance; hippocampal volume, ventricular volume, hippocampal texture, and parietal lobe thickness were the most important. This study highlights the need for both subtle, localized measurements and global measurements in order to discriminate NC, MCI, and AD simultaneously based on a single structural MRI scan. It is likely that additional non-structural MRI features are needed to further improve the obtained performance, especially to improve the discrimination between NC and MCI.

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The algorithm that won the CADDementia challenge is described and analyzed.

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Evaluation on data from ADNI, AIBL and the CADDementia challenge.

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Hippocampal texture is shown to be an important feature in the algorithm.

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Structural MRI intensity variations may include so far unused information.

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It is conjectured that additional features are needed in order to improve diagnostic performance.

Structural MR Imaging in the Diagnosis of Alzheimer's Disease and Other Neurodegenerative Dementia: Current Imaging Approach and Future Perspectives

With the rise of aging population, clinical concern and research attention has shifted towards neuroimaging of dementia. The advent of 3T, magnetic resonance imaging (MRI) has permitted the anatomical imaging of neurodegenerative disease, specifically dementia, with improved resolution. Furthermore, more powerful techniques such as diffusion tensor imaging, quantitative susceptibility mapping, and magnetic transfer imaging have successfully emerged for the detection of micro-structural abnormalities. In the present review article, we provide a brief overview of Alzheimer's disease and explore recent neuroimaging developments in the field of dementia with an emphasis on structural MR imaging in order to propose a simple and easily applicable systematic approach to the imaging diagnosis of dementia.

Detection of Alzheimer disease in MR images using structure tensor

Alzheimer's disease (AD) is the most common progressive neurodegenerative disorder. Therefore, early detection and evaluation of prognosis of AD is an important issue in contemporary brain research. Magnetic Resonance Imaging (MRI) provides valuable diagnostic information about AD. In this work, brain tissue is extracted using phase-based level set method. Structure tensor analysis is used to visualize and quantify structural features of the brain from MRI. Further, quantitative measures are derived to classify different stages of AD. Normal and AD subjects were classified up to an accuracy of 88% using these features. It is observed that structural changes in brain can be characterized using this technique and therefore can be helpful in tracking the progression of AD and aid in classification between normal and AD subjects.

Quantitative Neuroimaging Software for Clinical Assessment of Hippocampal Volumes on MR Imaging

BACKGROUND

Multiple neurological disorders including Alzheimer's disease (AD), mesial temporal sclerosis, and mild traumatic brain injury manifest with volume loss on brain MRI. Subtle volume loss is particularly seen early in AD. While prior research has demonstrated the value of this additional information from quantitative neuroimaging, very few applications have been approved for clinical use. Here we describe a US FDA cleared software program, NeuroreaderTM, for assessment of clinical hippocampal volume on brain MRI.

OBJECTIVE

To present the validation of hippocampal volumetrics on a clinical software program.

METHOD

Subjects were drawn (n = 99) from the Alzheimer Disease Neuroimaging Initiative study. Volumetric brain MR imaging was acquired in both 1.5 T (n = 59) and 3.0 T (n = 40) scanners in participants with manual hippocampal segmentation. Fully automated hippocampal segmentation and measurement was done using a multiple atlas approach. The Dice Similarity Coefficient (DSC) measured the level of spatial overlap between NeuroreaderTM and gold standard manual segmentation from 0 to 1 with 0 denoting no overlap and 1 representing complete agreement. DSC comparisons between 1.5 T and 3.0 T scanners were done using standard independent samples T-tests.

RESULTS

In the bilateral hippocampus, mean DSC was 0.87 with a range of 0.78-0.91 (right hippocampus) and 0.76-0.91 (left hippocampus). Automated segmentation agreement with manual segmentation was essentially equivalent at 1.5 T (DSC = 0.879) versus 3.0 T (DSC = 0.872).

CONCLUSION

This work provides a description and validation of a software program that can be applied in measuring hippocampal volume, a biomarker that is frequently abnormal in AD and other neurological disorders.

Early detection of Alzheimer's disease using MRI hippocampal texture

Cognitive impairment in patients with Alzheimer's disease (AD) is associated with reduction in hippocampal volume in magnetic resonance imaging (MRI). However, it is unknown whether hippocampal texture changes in persons with mild cognitive impairment (MCI) that does not have a change in hippocampal volume. We tested the hypothesis that hippocampal texture has association to early cognitive loss beyond that of volumetric changes. The texture marker was trained and evaluated using T1-weighted MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and subsequently applied to score independent data sets from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) and the Metropolit 1953 Danish Male Birth Cohort (Metropolit). Hippocampal texture was superior to volume reduction as predictor of MCI-to-AD conversion in ADNI (area under the receiver operating characteristic curve [AUC] 0.74 vs. 0.67; DeLong test, p = 0.005), and provided even better prognostic results in AIBL (AUC 0.83). Hippocampal texture, but not volume, correlated with Addenbrooke's cognitive examination score (Pearson correlation, r = -0.25, p < 0.001) in the Metropolit cohort. The hippocampal texture marker correlated with hippocampal glucose metabolism as indicated by fluorodeoxyglucose-positron emission tomography (Pearson correlation, r = -0.57, p < 0.001). Texture statistics remained significant after adjustment for volume in all cases, and the combination of texture and volume did not improve diagnostic or prognostic AUCs significantly. Our study highlights the presence of hippocampal texture abnormalities in MCI, and the possibility that texture may serve as a prognostic neuroimaging biomarker of early cognitive impairment.

Baseline shape diffeomorphometry patterns of subcortical and ventricular structures in predicting conversion of mild cognitive impairment to Alzheimer's disease

In this paper, we propose a novel predictor for the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). This predictor is based on the shape diffeomorphometry patterns of subcortical and ventricular structures (left and right amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, and lateral ventricle) of 607 baseline scans from the Alzheimer's Disease Neuroimaging Initiative database, including a total of 210 healthy control subjects, 222 MCI subjects, and 175 AD subjects. The optimal predictor is obtained via a feature selection procedure applied to all of the 14 sets of shape features via linear discriminant analysis, resulting in a combination of the shape diffeomorphometry patterns of the left hippocampus, the left lateral ventricle, the right thalamus, the right caudate, and the bilateral putamen. Via 10-fold cross-validation, we substantiate our method by successfully differentiating 77.04% (104/135) of the MCI subjects who converted to AD within 36 months and 71.26% (62/87) of the non-converters. To be specific, for the MCI-converters, we are capable of correctly predicting 82.35% (14/17) of subjects converting in 6 months, 77.5% (31/40) of subjects converting in 12 months, 74.07% (20/27) of subjects converting in 18 months, 78.13% (25/32) of subjects converting in 24 months, and 73.68% (14/19) of subject converting in 36 months. Statistically significant correlation maps were observed between the shape diffeomorphometry features of each of the 14 structures, especially the bilateral amygdala, hippocampus, lateral ventricle, and two neuropsychological test scores--the Alzheimer's Disease Assessment Scale-Cognitive Behavior Section and the Mini-Mental State Examination.

T2 signal intensity and volume abnormalities of hippocampal subregions in patients with amnestic mild cognitive impairment by magnetic resonance imaging

BACKGROUND

The volumetry of the hippocampal subregion may provide additional information in the early investigation of amnestic mild cognitive impairment (aMCI) and the T2 signal intensity (T2-SI) of the hippocampal subregion has not been well studied quantitatively by magnetic resonance imaging (MRI) in aMCI.

METHODS

Using combined MRI-based hippocampal volumetry and T2-SI at the levels of the whole hippocampus and hippocampal subregion, 18 patients with aMCI and 18 age-matched controls were investigated.

RESULTS

Significantly lower left whole hippocampal and hippocampal head volumes and higher T2-SI in the bilateral whole hippocampus and hippocampal head were shown, whereas atrophy of the right whole hippocampus and hippocampal subregion was not significant in aMCI. Additionally, correlations were found among the hippocampal volume, T2-SI and Mini-Mental State Examination (MMSE) scores for aMCI in the whole hippocampus and some hippocampal subregions and an almost perfect correlation was found between T2-SI of the left hippocampal head and MMSE scores regarding aMCI (r = -0.831, P = 0.000).

CONCLUSION

Abnormalities of the hippocampal volume and T2-SI were documented in aMCI, whereas T2-SI was implied to be more susceptible than the volume in the pathohistological progression in aMCI. Additionally, T2-SI in the left hippocampal head may be a potential biomarker to facilitate the early diagnosis of aMCI.

Automated Classification to Predict the Progression of Alzheimer's Disease Using Whole-Brain Volumetry and DTI

OBJECTIVE

This study proposes an automated diagnostic method to classify patients with Alzheimer's disease (AD) of degenerative etiology using magnetic resonance imaging (MRI) markers.

METHODS

Twenty-seven patients with subjective memory impairment (SMI), 18 patients with mild cognitive impairment (MCI), and 27 patients with AD participated. MRI protocols included three dimensional brain structural imaging and diffusion tensor imaging to assess the cortical thickness, subcortical volume and white matter integrity. Recursive feature elimination based on support vector machine (SVM) was conducted to determine the most relevant features for classifying abnormal regions and imaging parameters, and then a factor analysis for the top-ranked factors was performed. Subjects were classified using nonlinear SVM.

RESULTS

Medial temporal regions in AD patients were dominantly detected with cortical thinning and volume atrophy compared with SMI and MCI patients. Damage to white matter integrity was also accredited with decreased fractional anisotropy and increased mean diffusivity (MD) across the three groups. The microscopic damage in the subcortical gray matter was reflected in increased MD. Classification accuracy between pairs of groups (SMI vs. MCI, MCI vs. AD, SMI vs. AD) and among all three groups were 84.4% (±13.8), 86.9% (±10.5), 96.3% (±4.6), and 70.5% (±11.5), respectively.

CONCLUSION

This proposed method may be a potential tool to diagnose AD pathology with the current clinical criteria.

A review of β-amyloid neuroimaging in Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of dementia worldwide. As advancing age is the greatest risk factor for developing AD, the number of those afflicted is expected to increase markedly with the aging of the world's population. The inability to definitively diagnose AD until autopsy remains an impediment to establishing effective targeted treatments. Neuroimaging has enabled in vivo visualization of pathological changes in the brain associated with the disease, providing a greater understanding of its pathophysiological development and progression. However, neuroimaging biomarkers do not yet offer clear advantages over current clinical diagnostic criteria for them to be accepted into routine clinical use. Nonetheless, current insights from neuroimaging combined with the elucidation of biochemical and molecular processes in AD are informing the ongoing development of new imaging techniques and their application. Much of this research has been greatly assisted by the availability of transgenic mouse models of AD. In this review we summarize the main efforts of neuroimaging in AD in humans and in mouse models, with a specific focus on β-amyloid, and discuss the potential of new applications and novel approaches.

Progression of brain atrophy in spinocerebellar ataxia type 2: a longitudinal tensor-based morphometry study

Spinocerebellar ataxia type 2 (SCA2) is the second most frequent autosomal dominant inherited ataxia worldwide. We investigated the capability of magnetic resonance imaging (MRI) to track in vivo progression of brain atrophy in SCA2 by examining twice 10 SCA2 patients (mean interval 3.6 years) and 16 age- and gender-matched healthy controls (mean interval 3.3 years) on the same 1.5 T MRI scanner. We used T1-weighted images and tensor-based morphometry (TBM) to investigate volume changes and the Inherited Ataxia Clinical Rating Scale to assess the clinical deficit. With respect to controls, SCA2 patients showed significant higher atrophy rates in the midbrain, including substantia nigra, basis pontis, middle cerebellar peduncles and posterior medulla corresponding to the gracilis and cuneatus tracts and nuclei, cerebellar white matter (WM) and cortical gray matter (GM) in the inferior portions of the cerebellar hemisphers. No differences in WM or GM volume loss were observed in the supratentorial compartment. TBM findings did not correlate with modifications of the neurological deficit. In conclusion, MRI volumetry using TBM is capable of demonstrating the progression of pontocerebellar atrophy in SCA2, supporting a possible role of MRI as biomarker in future trials.

Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer's disease: detecting, quantifying, and predicting

This article assesses the feasibility of using shape information to detect and quantify the subcortical and ventricular structural changes in mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients. We first demonstrate structural shape abnormalities in MCI and AD as compared with healthy controls (HC). Exploring the development to AD, we then divide the MCI participants into two subgroups based on longitudinal clinical information: (1) MCI patients who remained stable; (2) MCI patients who converted to AD over time. We focus on seven structures (amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, and lateral ventricles) in 754 MR scans (210 HC, 369 MCI of which 151 converted to AD over time, and 175 AD). The hippocampus and amygdala were further subsegmented based on high field 0.8 mm isotropic 7.0T scans for finer exploration. For MCI and AD, prominent ventricular expansions were detected and we found that these patients had strongest hippocampal atrophy occurring at CA1 and strongest amygdala atrophy at the basolateral complex. Mild atrophy in basal ganglia structures was also detected in MCI and AD. Stronger atrophy in the amygdala and hippocampus, and greater expansion in ventricles was observed in MCI converters, relative to those MCI who remained stable. Furthermore, we performed principal component analysis on a linear shape space of each structure. A subsequent linear discriminant analysis on the principal component values of hippocampus, amygdala, and ventricle leads to correct classification of 88% HC subjects and 86% AD subjects.

Magnetization transfer contrast imaging reveals amyloid pathology in Alzheimer's disease transgenic mice

The presence of amyloid plaques in the brain is one of the pathological hallmarks of Alzheimer's disease, which might already be present in the early stage of the disease. Therefore it is important to track amyloid plaques as early as possible. In this paper, we report magnetization transfer contrast magnetic resonance imaging (MTC MRI) as a novel approach to detect amyloid plaques in vivo. Two mice models, APP/PS1 and BRI, developing amyloid pathology were investigated with MTC MRI, T2 relaxation measurements and immunohistochemistry (IHC). MT-ratios of several brain regions were compared to T2-values and correlated with quantitative IHC, revealing amyloid load and gliosis in different brain regions. APP/PS1 mice develop large compact plaques, resembling late stage Alzheimer's disease, while rather small and diffuse plaques are deposited in BRI mice, reflecting early stage of Alzheimer's disease. We found significantly higher MT-ratio's in the brain of APP/PS1 mice as compared to their controls and similar trends in BRI mice. A region based MT-ratio and IHC analysis and correlations between MT-ratios and quantitative IHC indicate amyloid plaques as the main substrate for altered MT-ratios in transgenic animals. We additionally demonstrated the improved sensitivity of MTC MRI to amyloid pathology as compared to traditional T2 relaxation measurements. Our results suggest that MTC MRI reveals extensive, and potentially even early amyloid pathology. Further unraveling the MT-effect of each pathological feature during each stage of AD might indicate MTC MRI as a useful diagnostic technique.

Regional analysis of the magnetization transfer ratio of the brain in mild Alzheimer disease and amnestic mild cognitive impairment

BACKGROUND AND PURPOSE

Manually drawn VOI-based analysis shows a decrease in magnetization transfer ratio in the hippocampus of patients with Alzheimer disease. We investigated with whole-brain voxelwise analysis the regional changes of the magnetization transfer ratio in patients with mild Alzheimer disease and patients with amnestic mild cognitive impairment.

MATERIALS AND METHODS

Twenty patients with mild Alzheimer disease, 27 patients with amnestic mild cognitive impairment, and 30 healthy elderly control subjects were examined with high-resolution T1WI and 3-mm-thick magnetization transfer images. Whole-brain voxelwise analysis of magnetization transfer ratio maps was performed by use of Statistical Parametric Mapping 8 software and was supplemented by the analysis of the magnetization transfer ratio in FreeSurfer parcellation-derived VOIs.

RESULTS

Voxelwise analysis showed 2 clusters of significantly decreased magnetization transfer ratio in the left hippocampus and amygdala and in the left posterior mesial temporal cortex (fusiform gyrus) of patients with Alzheimer disease as compared with control subjects but no difference between patients with amnestic mild cognitive impairment and either patients with Alzheimer disease or control subjects. VOI analysis showed that the magnetization transfer ratio in the hippocampus and amygdala was significantly lower (bilaterally) in patients with Alzheimer disease when compared with control subjects (ANOVA with Bonferroni correction, at P < .05). Mean magnetization transfer ratio values in the hippocampus and amygdala in patients with amnestic mild cognitive impairment were between those of healthy control subjects and those of patients with mild Alzheimer disease. Support vector machine-based classification demonstrated improved classification performance after inclusion of magnetization transfer ratio-related features, especially between patients with Alzheimer disease versus healthy subjects.

CONCLUSIONS

Bilateral but asymmetric decrease of magnetization transfer ratio reflecting microstructural changes of the residual GM is present not only in the hippocampus but also in the amygdala in patients with mild Alzheimer disease.

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.

Regional cerebral perfusion alterations in patients with mild cognitive impairment and Alzheimer disease using dynamic susceptibility contrast MRI

RATIONALE AND OBJECTIVES

The purpose of this study was to assess regional cerebral perfusion distribution in patients with Alzheimer disease (AD) or mild cognitive impairment (MCI) using dynamic susceptibility contrast magnetic resonance imaging.

MATERIALS AND METHODS

Regional changes of perfusion were evaluated in 34 patients with AD, 51 patients with MCI, and 23 healthy controls (HCs). Using region of interest analyses, regional cerebral blood flow (CBF), cerebral blood volume, and mean transit time were measured bilaterally in the hippocampus; the temporal, temporoparietal, frontal, and sensomotoric cortices; the anterior and posterior cingulate gyri; the lentiform nucleus; and the cerebellum.

RESULTS

A significant reduction of CBF in patients with AD compared to HCs was shown in the frontal and temporoparietal cortices bilaterally, the lentiform nuclei bilaterally, the left posterior cingulate gyrus, and the cerebellum. Compared with patients with MCI, patients with AD presented a reduction of CBF in the frontal cortices bilaterally, the left temporoparietal cortex, and the left anterior and posterior cingulate gyrus. In both hippocampi and the posterior cingulate gyrus, a trend to a slight increase of CBF in patients with MCI was noticed with a decrease in patients with AD.

CONCLUSIONS

Using dynamic susceptibility contrast magnetic resonance imaging, pathologic alterations of regional brain perfusion can be demonstrated in patients with AD compared to patients with MCI or HCs.

High field magnetic resonance microscopy of the human hippocampus in Alzheimer's disease: quantitative imaging and correlation with iron

We report R(2) and R(2) in human hippocampus from five unfixed post-mortem Alzheimer's disease (AD) and three age-matched control cases. Formalin-fixed tissues from opposing hemispheres in a matched AD and control were included for comparison. Imaging was performed in a 600MHz (14T) vertical bore magnet at MR microscopy resolution to obtain R(2) and R(2) (62 μm×62 μm in-plane, 80 μm slice thickness), and R(1) at 250 μm isotropic resolution. R(1), R(2) and R(2) maps were computed for individual slices in each case, and used to compare subfields between AD and controls. The magnitudes of R(2) and R(2) changed very little between AD and control, but their variances in the Cornu Ammonis and dentate gyrus were significantly higher in AD compared for controls (p<0.001). To investigate the relationship between tissue iron and MRI parameters, each tissue block was cryosectioned at 30 μm in the imaging plane, and iron distribution was mapped using synchrotron microfocus X-ray fluorescence spectroscopy. A positive correlation of R(2) and R(2)* with iron was demonstrated. While studies with fixed tissues are more straightforward to conduct, fixation can alter iron status in tissues, making measurement of unfixed tissue relevant. To our knowledge, these data represent an advance in quantitative imaging of hippocampal subfields in unfixed tissue, and the methods facilitate direct analysis of the relationship between MRI parameters and iron. The significantly increased variance in AD compared for controls warrants investigation at lower fields and in-vivo, to determine if this parameter is clinically relevant.

Comprehensive brain analysis with automated high-resolution magnetization transfer measurements

PURPOSE

To enhance the reliability and spatial resolution of magnetization transfer ratio (MTR) measurements for interrogation of subcortical brain regions with an automated volume of interest (VOI) approach.

MATERIALS AND METHODS

A 3D magnetization transfer (MT) sequence was acquired using a scan-rescan imaging protocol in nine healthy volunteers. VOI definition masks for the MTR measurements were generated using FreeSurfer and compared to a manual region of interest (ROI) approach. (The longitudinal stability of MTR was monitored using agar gel phantom over a 5-month period.) Intraclass correlation coefficients (ICCs), coefficients of variation (CVs), and instrumental standard deviation (ISD) were determined.

RESULTS

CVs ranged from 1.29%-2.64% (automated) vs. 1.30%-3.40% (manual). ISDs ranged from 0.62-1.10 pu (automated) vs. 0.68-1.67 pu (manual). The SD of the running difference was 1.70% for the phantom scans. The Bland-Altman method indicated interchangeability of the automated VOI and manual ROI measurements.

CONCLUSION

The automated VOI approach for MTR measurement yielded higher ICCs, lower CVs, and lower ISDs compared to the manual method, supporting the utility of this strategy. These results demonstrate the feasibility of obtaining reliable MTR measurements in hippocampus and other critical subcortical regions.

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

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

Autism, Alzheimer disease, and fragile X: APP, FMRP, and mGluR5 are molecular links

The present review highlights an association between autism, Alzheimer disease (AD), and fragile X syndrome (FXS). We propose a conceptual framework involving the amyloid-β peptide (Aβ), Aβ precursor protein (APP), and fragile X mental retardation protein (FMRP) based on experimental evidence. The anabolic (growth-promoting) effect of the secreted α form of the amyloid-β precursor protein (sAPPα) may contribute to the state of brain overgrowth implicated in autism and FXS. Our previous report demonstrated that higher plasma sAPPα levels associate with more severe symptoms of autism, including aggression. This molecular effect could contribute to intellectual disability due to repression of cell-cell adhesion, promotion of dense, long, thin dendritic spines, and the potential for disorganized brain structure as a result of disrupted neurogenesis and migration. At the molecular level, APP and FMRP are linked via the metabotropic glutamate receptor 5 (mGluR5). Specifically, mGluR5 activation releases FMRP repression of APP mRNA translation and stimulates sAPP secretion. The relatively lower sAPPα level in AD may contribute to AD symptoms that significantly contrast with those of FXS and autism. Low sAPPα and production of insoluble Aβ would favor a degenerative process, with the brain atrophy seen in AD. Treatment with mGluR antagonists may help repress APP mRNA translation and reduce secretion of sAPP in FXS and perhaps autism.

[Mild cognitive impairment: diagnostic value of different MR techniques]

In view of an increasingly aging population the prevalence of dementia is also expected to increase rapidly. As well as clinical, neuropsychological and laboratory procedures magnetic resonance imaging (MRI) plays an important role in the early diagnosis of dementia which is important in the precursor stage of mild cognitive impairment (MCI). On the one hand this stage is associated with an increased risk of dementia and on the other hand an early treatment in this stage could attenuate development of the disease. In addition to morphological changes different functional MRI techniques can help in the early diagnosis of dementia and the precursor stages. Moreover, it is important to detect those MCI patients who are at particularly risk for developing dementia. In the differentiation of converters to non-converters initial studies suggest that particularly voxel-based morphometry, MR spectroscopy and diffusion tensor imaging can provide important additional information.

Nonlinear registration of longitudinal images and measurement of change in regions of interest

We describe here a method, Quarc, for accurately quantifying structural changes in organs, based on serial MRI scans. The procedure can be used to measure deformations globally or in regions of interest (ROIs), including large-scale changes in the whole organ, and subtle changes in small-scale structures. We validate the method with model studies, and provide an illustrative analysis using the brain. We apply the method to the large, publicly available ADNI database of serial brain scans, and calculate Cohen's d effect sizes for several ROIs. Using publicly available derived-data, we directly compare effect sizes from Quarc with those from four existing methods that quantify cerebral structural change. Quarc produced a slightly improved, though not significantly different, whole brain effect size compared with the standard KN-BSI method, but in all other cases it produced significantly larger effect sizes.

Measurement of precuneal and hippocampal volumes using magnetic resonance volumetry in Alzheimer's disease

Background and Purpose

Alzheimer's disease (AD) is associated with structural alterations in the medial temporal lobe (MTL) and functional alterations in the posterior cortical region, especially in the early stages. However, it is unclear what mechanisms underlie these regional discrepancies or whether the posterior cortical hypometabolism reflects disconnection from the MTL lesion or is the result of local pathology. The precuneus, an area of the posteromedial cortex that is involved in the early stages of AD, has recently received a great deal of attention in functional neuroimaging studies. To assess the relationship between the precuneus and hippocampus in AD, we investigated the volumes of these two areas using a magnetic resonance volumetric method.

Methods

Twenty-three subjects with AD and 14 healthy age-matched controls underwent T1-weighted three-dimensional volumetric brain magnetic resonance imaging. Volumetric measurements were performed in the precuneus and hippocampus.

Results

Compared to controls, AD patients exhibited a significant reduction in total precuneal volume, which was more prominent on the right side, and significant bilateral reductions in hippocampal volume. No correlation was found between the total volumes of the precuneus and hippocampus in the AD group.

Conclusions

These results suggest that volumetric measurements of both the precuneus and hippocampus are useful radiological indices for the diagnosis of AD. Furthermore, the lack of correlation is attributable to local pathology rather than being a secondary consequence of MTL pathology.

Relative capability of MR imaging and FDG PET to depict changes associated with prodromal and early Alzheimer disease

PURPOSE

To quantify the effect sizes of regional metabolic and morphometric measures in patients with preclinical and mild Alzheimer disease (AD) to aid in the identification of noninvasive biomarkers for the early detection of AD.

MATERIALS AND METHODS

The study was conducted with institutional review board approval and in compliance with HIPAA regulations. Written informed consent was obtained from each participant or participant's legal guardian. Fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) and magnetic resonance (MR) imaging data were analyzed from 80 healthy control (HC) subjects, 68 individuals with AD, and 156 with amnestic mild cognitive impairment (MCI), 69 of whom had single-domain amnestic MCI. Regions of interest (ROIs) were derived after coregistering FDG PET and MR images by using high-throughput, subject-specific procedures. The Cohen d effect sizes were calculated for 42 predefined ROIs across the brain. Statistical comparison of the largest overall effect sizes for MR imaging and PET was performed. Metabolic effect sizes were determined with and without accounting for regional atrophy. Discriminative accuracy of ROIs showing the largest effect sizes were compared by calculating receiver operating characteristic curves.

RESULTS

For all disease groups, the hippocampus showed the largest morphometric effect size and the entorhinal cortex showed the largest metabolic effect size. In mild AD, the Cohen d effect size for hippocampal volume (1.92) was significantly larger (P < .05) than that for entorhinal metabolism (1.43). Regression of regional atrophy substantially reduced most metabolic effects. For all group comparisons, the areas under the receiver operating characteristic curves were significantly larger for hippocampal volume than for entorhinal metabolism.

CONCLUSION

The current results show no evidence that FDG PET is more sensitive than MR imaging to the degeneration occurring in preclinical and mild AD, suggesting that an MR imaging finding may be a more practical clinical biomarker for early detection of AD.

Monte Carlo study of a two-compartment exchange model of diffusion

Multisite exchange models have been applied frequently to quantify measurements of transverse relaxation and diffusion in living tissues. Although the simplicity of such models is attractive, the precise relationship of the model parameters to tissue properties may be difficult to ascertain. Here, we investigate numerically a two-compartment exchange (Kärger) model as applied to diffusion in a system of randomly packed identical parallel cylinders with permeable walls, representing cells with permeable membranes, that may serve particularly as a model for axons in the white matter of the brain. By performing Monte Carlo simulations of restricted diffusion, we show that the Kärger model may provide a reasonable coarse-grained description of the diffusion-weighted signal in the long time limit, as long as the cell membranes are sufficiently impermeable, i.e. whenever the residence time in a cell is much longer than the time it takes to diffuse across it. For larger permeabilities, the exchange time obtained from fitting to the Kärger model overestimates the actual exchange time, leading to an underestimated value of cell membrane permeability.

Hippocampus avoidance with fan beam and volumetric arc radiotherapy for base of skull tumours

Radiosensitive neurogenic stem cells reside in the hippocampi, suggesting that avoidance of the hippocampi may be an important strategy to reduce potential radiation-related cognitive effects. Six patients treated for base of skull tumours were re-planned using co-planar helical fan beam arc therapy (tomotherapy) and co-planar and non-coplanar volumetric arc techniques (RapidArc). The hippocampi were contoured as avoidance structures with the specific goal of minimising the dose. Two gross target volume (GTV) to planning target volume (PTV) expansions (10 and 2 mm) were considered to evaluate the impact of margin selection on organ at risk (OAR) sparing. The dose prescription was 50 Gy to >95% of the PTV. Comparison of the hippocampus avoidance plans demonstrated the importance of non-coplanar delivery when the 10 mm margin was used. With the 2 mm margin, both co-planar and non-coplanar delivery provided similar degrees of sparing. A mean dose of 3–4 Gy and a V6Gy <5% to the hippocampi was realised with the hippocampus sparing techniques. Our comparisons suggest interventions to minimise GTV to PTV margins will have a more profound influence on multiple OAR sparing than the choice of intensity modulated arc delivery technique.

[MR spectroscopy in dementia]

With an increasingly aging population we are faced with the problem of an increasing number of dementia patients. In addition to clinical, neuropsychological and laboratory procedures, MRI plays an important role in the early diagnosis of dementia. In addition to various morphological changes functional changes can also help in the diagnosis and differential diagnosis of dementia. Overall the diagnosis of dementia can be improved by using parameters from MR spectroscopy. This article focuses on MR spectroscopic changes in the physiological aging process as well as on changes in mild cognitive impairment a precursor of Alzheimer's dementia, in Alzheimer's dementia, frontotemporal dementia, vascular dementia and Lewy body dementia.

Automated MR morphometry to predict Alzheimer's disease in mild cognitive impairment

PURPOSE

Prediction of progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is challenging but essential for early treatment. This study aims to investigate the use of hippocampal atrophy markers for the automatic detection of MCI converters and to compare the predictive value to manually obtained hippocampal volume and temporal horn width.

METHODS

A study was performed with 15 patients with Alzheimer and 18 patients with MCI (ten converted, eight remained stable in a 3-year follow-up) as well as 15 healthy subjects. MRI scans were obtained at baseline and evaluated with an automated system for scoring of hippocampal atrophy. The predictive value of the automated system was compared with manual measurements of hippocampal volume and temporal horn width in the same subjects.

RESULTS

The conversion to AD was correctly predicted in 77.8% of the cases (sensitivity 70%, specificity 87.5%) in the MCI group using automated morphometry and a plain linear classifier that was trained on the AD and healthy groups. Classification was improved by limiting analysis to the left cerebral hemisphere (accuracy 83.3%, sensitivity 70%, specificity 100%). The manual linear and volumetric approaches reached rates of 66.7% (40/100%) and 72.2% (60/87.5%), respectively.

CONCLUSION

The automatic approach fulfills many important preconditions for clinical application. Contrary to the manual approaches, it is not observer-dependent and reduces human resource requirements. Automated assessment may be useful for individual patient assessment and for predicting progression to dementia.

The clinical use of structural MRI in Alzheimer disease

Structural imaging based on magnetic resonance is an integral part of the clinical assessment of patients with suspected Alzheimer dementia. Prospective data on the natural history of change in structural markers from preclinical to overt stages of Alzheimer disease are radically changing how the disease is conceptualized, and will influence its future diagnosis and treatment. Atrophy of medial temporal structures is now considered to be a valid diagnostic marker at the mild cognitive impairment stage. Structural imaging is also included in diagnostic criteria for the most prevalent non-Alzheimer dementias, reflecting its value in differential diagnosis. In addition, rates of whole-brain and hippocampal atrophy are sensitive markers of neurodegeneration, and are increasingly used as outcome measures in trials of potentially disease-modifying therapies. Large multicenter studies are currently investigating the value of other imaging and nonimaging markers as adjuncts to clinical assessment in diagnosis and monitoring of progression. The utility of structural imaging and other markers will be increased by standardization of acquisition and analysis methods, and by development of robust algorithms for automated assessment.

Robust atrophy rate measurement in Alzheimer's disease using multi-site serial MRI: tissue-specific intensity normalization and parameter selection

We describe an improved method of measuring brain atrophy rates from serial MRI for multi-site imaging studies of Alzheimer's disease (AD). The method (referred to as KN-BSI) improves an existing brain atrophy measurement technique-the boundary shift integral (classic-BSI), by performing tissue-specific intensity normalization and parameter selection. We applied KN-BSI to measure brain atrophy rates of 200 normal and 141 AD subjects using baseline and 1-year MRI scans downloaded from the Alzheimer's Disease Neuroimaging Initiative database. Baseline and repeat images were reviewed as pairs by expert raters and given quality scores. Including all image pairs, regardless of quality score, mean KN-BSI atrophy rates were 0.09% higher (95% CI 0.03% to 0.16%, p=0.007) than classic-BSI rates in controls and 0.07% higher (-0.01% to 0.16%, p=0.07) higher in ADs. The SD of the KN-BSI rates was 22% lower (15% to 29%, p<0.001) in controls and 13% lower (6% to 20%, p=0.001) in ADs, compared to classic-BSI. Using these results, the estimated sample size (needed per treatment arm) for a hypothetical trial of a treatment for AD (80% power, 5% significance to detect a 25% reduction in atrophy rate) would be reduced from 120 to 81 (a 32% reduction, 95% CI=18% to 45%, p<0.001) when using KN-BSI instead of classic-BSI. We concluded that KN-BSI offers more robust brain atrophy measurement than classic-BSI and substantially reduces sample sizes needed in clinical trials.

Serial susceptibility weighted MRI measures brain iron and microbleeds in dementia

A new iron sensitive MR sequence (susceptibility weighted imaging - SWI) enabling the simultaneous quantitation of regional brain iron levels and brain microbleeds (BMB) has been acquired serially to study dementia. Cohorts of mildly cognitively impaired (MCI) elderly (n = 73) and cognitively normal participants (n = 33) have been serially evaluated for up to 50 months. SWI phase values (putative iron levels) in 14 brain regions were measured and the number of BMB were counted for each SWI study. SWI phase values showed a left putaminal mean increase of iron (decrease of phase values) over the study duration in 27 participants who progressed to dementia compared to Normals (p = 0.035) and stable MCI (p = 0.01). BMB were detected in 9 out of 26 (38%) MCI participants who progressed to dementia and are a significant risk factor for cognitive failure in MCI participants [risk ratio = 2.06 (95% confidence interval 1.37-3.12)]. SWI is useful to measure regional iron changes and presence of BMB, both of which may be important MR-based biomarkers for neurodegenerative diseases.