Hippocampal and entorhinal cortex atrophy in frontotemporal dementia and Alzheimer's disease

A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI

Automated analysis of MRI data of the subregions of the hippocampus requires computational atlases built at a higher resolution than those that are typically used in current neuroimaging studies. Here we describe the construction of a statistical atlas of the hippocampal formation at the subregion level using ultra-high resolution, ex vivo MRI. Fifteen autopsy samples were scanned at 0.13 mm isotropic resolution (on average) using customized hardware. The images were manually segmented into 13 different hippocampal substructures using a protocol specifically designed for this study; precise delineations were made possible by the extraordinary resolution of the scans. In addition to the subregions, manual annotations for neighboring structures (e.g., amygdala, cortex) were obtained from a separate dataset of in vivo, T1-weighted MRI scans of the whole brain (1mm resolution). The manual labels from the in vivo and ex vivo data were combined into a single computational atlas of the hippocampal formation with a novel atlas building algorithm based on Bayesian inference. The resulting atlas can be used to automatically segment the hippocampal subregions in structural MRI images, using an algorithm that can analyze multimodal data and adapt to variations in MRI contrast due to differences in acquisition hardware or pulse sequences. The applicability of the atlas, which we are releasing as part of FreeSurfer (version 6.0), is demonstrated with experiments on three different publicly available datasets with different types of MRI contrast. The results show that the atlas and companion segmentation method: 1) can segment T1 and T2 images, as well as their combination, 2) replicate findings on mild cognitive impairment based on high-resolution T2 data, and 3) can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy in standard resolution (1mm) T1 data, significantly outperforming the atlas in FreeSurfer version 5.3 (86% accuracy) and classification based on whole hippocampal volume (82% accuracy).

Boosting diagnosis accuracy of Alzheimer's disease using high dimensional recognition of longitudinal brain atrophy patterns

OBJECTIVE

Boosting accuracy in automatically discriminating patients with Alzheimer's disease (AD) and normal controls (NC), based on multidimensional classification of longitudinal whole brain atrophy rates and their intermediate counterparts in analyzing magnetic resonance images (MRI).

METHOD

Longitudinal percentage of brain volume changes (PBVC) in two-year follow up and its intermediate counterparts in early 6-month and late 18-month are used as features in supervised and unsupervised classification procedures based on K-mean, fuzzy clustering method (FCM) and support vector machine (SVM). The most relevant features for classification are selected using discriminative analysis (DA) of features and their principal components (PC). Accuracy of the proposed method is evaluated in a group of 30 patients with AD (16 males, 14 females, age±standard-deviation (SD)=75±1.36 years) and 30 normal controls (15 males, 15 females, age±SD=77±0.88 years) using leave-one-out cross-validation.

RESULTS

Results indicate superiority of supervised machine learning techniques over unsupervised ones in diagnosing AD and withal, predominance of RBF kernel over lineal one. Accuracies of 83.3%, 83.3%, 90% and 91.7% are achieved in classification by K-mean, FCM, linear SVM and SVM with radial based function (RBF) respectively.

CONCLUSION

Evidence that SVM classification of longitudinal atrophy rates may results in high accuracy is given. Additionally, it is realized that use of intermediate atrophy rates and their principal components improves diagnostic accuracy.

Using structural and diffusion magnetic resonance imaging to differentiate the dementias

Dementia is one of the major causes of personal, societal and financial dependence in older people and in today's ageing society there is a pressing need for early and accurate markers of cognitive decline. There are several subtypes of dementia but the four most common are Alzheimer's disease, Lewy body dementia, vascular dementia and frontotemporal dementia. These disorders can only be diagnosed at autopsy, and ante-mortem assessments of "probable dementia (e.g. of Alzheimer type)" are traditionally driven by clinical symptoms of cognitive or behavioural deficits. However, owing to the overlapping nature of symptoms and age of onset, a significant proportion of dementia cases remain incorrectly diagnosed. Misdiagnosis can have an extensive impact, both at the level of the individual, who may not be offered the appropriate treatment, and on a wider scale, by influencing the entry of patients into relevant clinical trials. Magnetic resonance imaging (MRI) may help to improve diagnosis by providing non-invasive and detailed disease-specific markers of cognitive decline. MRI-derived measurements of grey and white matter structural integrity are potential surrogate markers of disease progression, and may also provide valuable diagnostic information. This review summarises the latest evidence on the use of structural and diffusion MRI in differentiating between the four major dementia subtypes.

Odor identification in frontotemporal lobar degeneration subtypes

Odor identification impairment is a feature of several neurodegenerative disorders. Although neurodegenerative changes in the frontotemporal lobar degeneration (FTLD) subtypes involve areas important for olfactory processing, data on olfactory function in these patients are limited. An 18-item, multiple-choice odor identification test developed at our memory clinic, the Motol Hospital smell test, was administered to 9 patients with behavioral variant frontotemporal dementia, 13 patients with the language variants, primary nonfluent aphasia (n = 7) and semantic dementia (n = 6), and 8 patients with progressive supranuclear palsy. Compared to the control group (n = 15), all FTLD subgroups showed significant impairment of odor identification (P < .05). The differences between the FTLD subgroups were not significant. No correlation between odor identification and neuropsychological tests results was found. Our data suggest that odor identification impairment is a symptom common to FTLD syndromes, and it seems to be based on olfactory structure damage rather than cognitive decline.

Logopenic aphasia or Alzheimer's disease: Different phases of the same disease?

The logopenic variant of Primary Progressive Aphasia, or logopenic aphasia, is a the most recently described variant of Primary Progressive Aphasia and also the least well defined. This variant can present clinical findings that are also common to Alzheimer's disease, given they both share the same cytopathologic findings. This article reports the clinical case of a patient for whom it proved difficult to define a clinical diagnosis, being split between the logopenic variant and Alzheimer's disease at different phases of the disease. Using this case as an example and drawing on the latest evidence from the literature on the logopenic variant, we postulate the hypothesis that this variant may present as an initial symptom of Alzheimer's disease in some atypical cases.

Two distinct amnesic profiles in behavioral variant frontotemporal dementia

BACKGROUND

Whether or not episodic memory deficit is a characteristic of behavioral variant frontotemporal dementia (bvFTD) is a crucial question for its diagnosis and management.

METHODS

We compared the episodic memory performance profile of bvFTD patients with healthy control subjects and patients with Alzheimer's disease (AD) as defined by clinical and biological criteria. Episodic memory was assessed with the Free and Cued Selective Reminding Test, which controls for effective encoding and identifies memory storage ability resulting from consolidation processing. One hundred thirty-four participants were evaluated: 56 patients with typical clinical presentation of AD and pathophysiological evidence as defined by cerebrospinal fluid AD biomarker profile and/or significant amyloid retention on Pittsburgh Compound B positron emission tomography; 56 patients diagnosed with bvFTD with no evidence of AD-cerebrospinal fluid biomarkers when a profile was available (28/56), including 44 progressive (bvFTD) and 12 nonprogressive (phenocopies) patients; and 22 control subjects with negative amyloid imaging.

RESULTS

Memory scores could not differentiate bvFTD from AD patients (sensitivity and specificity <50%). Taking into account the individual distribution of Free and Cued Selective Reminding Test scores, half of bvFTD patients had a deficit of free recall, total (free + cued) recall, and delayed recall as severe as AD patients. The other half had subnormal scores similar to phenocopies and a delayed recall score similar to control subjects.

CONCLUSIONS

We observed two distinct amnesic profiles in bvFTD patients that could reflect two types of hippocampal structure and Papez circuit involvement. These findings on episodic memory profiles could contribute to discussions on the recent international consensus criteria for bvFTD.

Frontal presentation of Alzheimer's disease: a series of patients with biological evidence by CSF biomarkers

Besides its typical amnesic presentation, focal atypical presentations of Alzheimer's disease (AD) have been described in neuropathological studies. These phenotypical variants of AD (so-called "atypical AD") do not follow the typical amnestic pattern and include non-amnestic focal cortical syndromes, such as posterior cortical atrophy and frontal variant AD. These variants exhibit characteristic histological lesions of Alzheimer pathology at post-mortem exam. By using physiopathological markers, such as cerebrospinal fluid markers, it is now possible to establish in vivo a biological diagnosis of AD in these focal cortical syndromes. We report a series of eight patients who were diagnosed with behavioural variant frontotemporal dementia based on their clinical, neuropsychological and neuroimaging findings, while CSF biomarkers showed an AD biological profile, thus supporting a diagnosis of frontal variant of AD.

Hippocampal volume reduction in elderly patients at risk for postoperative cognitive dysfunction

PURPOSE

Postoperative cognitive dysfunction (POCD) is a formidable public health issue, which would not only affect the quality of life among elderly patients but also lead to pulmonary infection and increased mortality. While, there is a lack of an effective indicator in predicting POCD. As one pivotal part of the limbic system in brain, hippocampus is associated with cognitive function. Hippocampal atrophy could indicate the degree of changes in cognitive function.

METHODS

Forty-one ASA II or III patients (23 male, 18 female) aged ≥65 years undergoing open gastrointestinal tract surgery were enrolled in this study. MRI was performed to measure the volume of hippocampal formation before surgery and the results were standardized according to individual intracranial volume. All patients underwent a battery of neuropsychological tests including sensitive tests on the Wechsler adult memory scale and Wechsler adult intelligence scale, trail making test and the grooved pegboard test. We used the Z score to identify POCD as recommended by ISPOCD. All patients were then divided into POCD group and non-POCD group according to the results of the neuropsychological tests. The results of the tests were correlated with the volume of hippocampal formation measured by MRI. The value of MRI measurement of hippocampal volume in predicting POCD was analyzed. Multivariate linear correlation analyses of compositive Z score using potential contributing factors such as age, duration of anesthesia, education and hippocampal volume was carried out.

RESULTS

Thirty-six patients completed the whole battery of neuropsychological tests after surgery. Thirteen of the 36 patients were found to have POCD (36 %) on the postoperative 4th day. The hippocampal volume was significantly smaller in POCD group (4.75 ± 0.23) than in non-POCD group (5.06 ± 0.31). Hippocampal volume had great influence on Z score, and had negative correlation with Z score.

CONCLUSION

The MRI measurement of hippocampal volume is suggested to be valuable as a predictor of POCD in the elderly.

Structural MRI in frontotemporal dementia: comparisons between hippocampal volumetry, tensor-based morphometry and voxel-based morphometry

BACKGROUND

MRI is an important clinical tool for diagnosing dementia-like diseases such as Frontemporal Dementia (FTD). However there is a need to develop more accurate and standardized MRI analysis methods.

OBJECTIVE

To compare FTD with Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) with three automatic MRI analysis methods - Hippocampal Volumetry (HV), Tensor-based Morphometry (TBM) and Voxel-based Morphometry (VBM), in specific regions of interest in order to determine the highest classification accuracy.

METHODS

Thirty-seven patients with FTD, 46 patients with AD, 26 control subjects, 16 patients with progressive MCI (PMCI) and 48 patients with stable MCI (SMCI) were examined with HV, TBM for shape change, and VBM for gray matter density. We calculated the Correct Classification Rate (CCR), sensitivity (SS) and specificity (SP) between the study groups.

RESULTS

We found unequivocal results differentiating controls from FTD with HV (hippocampus left side) (CCR = 0.83; SS = 0.84; SP = 0.80), with TBM (hippocampus and amygdala (CCR = 0.80/SS = 0.71/SP = 0.94), and with VBM (all the regions studied, especially in lateral ventricle frontal horn, central part and occipital horn) (CCR = 0.87/SS = 0.81/SP = 0.96). VBM achieved the highest accuracy in differentiating AD and FTD (CCR = 0.72/SS = 0.67/SP = 0.76), particularly in lateral ventricle (frontal horn, central part and occipital horn) (CCR = 0.73), whereas TBM in superior frontal gyrus also achieved a high accuracy (CCR = 0.71/SS = 0.68/SP = 0.73). TBM resulted in low accuracy (CCR = 0.62) in the differentiation of AD from FTD using all regions of interest, with similar results for HV (CCR = 0.55).

CONCLUSION

Hippocampal atrophy is present not only in AD but also in FTD. Of the methods used, VBM achieved the highest accuracy in its ability to differentiate between FTD and AD.

Reduced hippocampal volume in healthy young ApoE4 carriers: an MRI study

The E4 allele of the ApoE gene has consistently been shown to be related to an increased risk of Alzheimer's disease (AD). The E4 allele is also associated with functional and structural grey matter (GM) changes in healthy young, middle-aged and older subjects. Here, we assess volumes of deep grey matter structures of 22 healthy younger ApoE4 carriers and 22 non-carriers (20-38 years). Volumes of the nucleus accumbens, amygdala, caudate nucleus, hippocampus, pallidum, putamen, thalamus and brain stem were calculated by FMRIB's Integrated Registration and Segmentation Tool (FIRST) algorithm. A significant drop in volume was found in the right hippocampus of ApoE4 carriers (ApoE4+) relative to non-carriers (ApoE4-), while there was a borderline significant decrease in the volume of the left hippocampus of ApoE4 carriers. The volumes of no other structures were found to be significantly affected by genotype. Atrophy has been found to be a sensitive marker of neurodegenerative changes, and our results show that within a healthy young population, the presence of the ApoE4+ carrier gene leads to volume reduction in a structure that is vitally important for memory formation. Our results suggest that the hippocampus may be particularly vulnerable to further degeneration in ApoE4 carriers as they enter middle and old age. Although volume reductions were noted bilaterally in the hippocampus, atrophy was more pronounced in the right hippocampus. This finding relates to previous work which has noted a compensatory increase in right hemisphere activity in ApoE4 carriers in response to preclinical declines in memory function. Possession of the ApoE4 allele may lead to greater predilection for right hemisphere atrophy even in healthy young subjects in their twenties.

Hippocampal shape analysis in Alzheimer's disease and frontotemporal lobar degeneration subtypes

Hippocampal pathology is central to Alzheimer's disease (AD) and other forms of dementia such as frontotemporal lobar degeneration (FTLD). Autopsy studies have shown that certain hippocampal subfields are more vulnerable than others to AD and FTLD pathology, in particular the subiculum and cornu ammonis 1 (CA1). We conducted shape analysis of hippocampi segmented from structural T1 MRI images on clinically diagnosed dementia patients and controls. The subjects included 19 AD and 35 FTLD patients [13 frontotemporal dementia (FTD), 13 semantic dementia (SD), and 9 progressive nonfluent aphasia (PNFA)] and 21 controls. Compared to controls, SD displayed severe atrophy of the whole left hippocampus. PNFA and FTD also displayed atrophy on the left side, restricted to the hippocampal head in FTD. Finally, AD displayed most atrophy in left hippocampal body with relative sparing of the hippocampal head. Consistent with neuropathological studies, most atrophic deformation was found in CA1 and subiculum areas in FTLD and AD.

Do Alzheimer-specific microstructural changes in mild cognitive impairment predict conversion?

Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that provides information on the fiber architecture of the brain by measuring water diffusion. Prior work has shown that neuronal degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI) alters this architecture. Since the conversion rate to AD is much higher for MCI patients than for normal healthy people, it is important to identify biomarkers with a predictive value on this conversion. In this study, we applied tract-based spatial statistics (TBSS) on datasets of 15 healthy controls, 15 AD patients, and 17 MCI patients. Of these MCI patients eight remained stable, whereas nine developed AD within the first 12-18 months of follow-up investigations. Analysis using TBSS combined with a maximum likelihood regression with random effects of the fornix, the corpus callosum, and the cingulum identified significant differences between these two types of MCI patients in fractional anisotropy (FA) and radial diffusivity (DR). Thus, DTI reveals Alzheimer-specific changes in those MCI subjects that later convert, although they were clinically identical to the other MCI-patients at the time the data were acquired. This finding could lead to early identification of AD and thereby aid early clinical intervention.

Episodic memory of APOE ε4 carriers is correlated with fractional anisotropy, but not cortical thickness, in the medial temporal lobe

The ε4 allele of apolipoprotein E (apoE, protein; APOE, gene) is the most important genetic risk factor for the development of Alzheimer's disease (AD). Cortical structures in the medial temporal lobe (MTL) are important for memory function and are affected early in AD. Both gray matter (GM) and white matter (WM) structures in the MTL have been reported to display AD related changes in healthy APOE ε4 carriers, but the effects are relatively small and somewhat deviating. Still, there is a lack of studies directly linking structural measures with performance on psychometric tests in ε4+ individuals. We hypothesized that intact WM integrity in the MTL facilitates episodic memory, and predicted a higher correlation between WM integrity and memory performance in APOE ε4 carriers due to a possible limiting effect of WM microstructure. In the present study of 92 healthy (MMSE>27) participants we acquired T1 3D and DTI images from a 1.5T MRI scanner, and tested the participants with California Verbal Learning Test II (CVLT-II). The study had two main aims: 1) to relate verbal memory performance to entorhinal WM (EWM) integrity in APOE ε4 carriers and non-carriers, and 2) to investigate APOE ε4 effects on EWM and EC thickness. We observed a strong, positive correlation between FA in the EWM and memory performance, which was driven solely by APOE ε4 carriers. These effects were significant while controlling for age, sex, EWM volume and EC thickness. Although EC thickness was significantly reduced in ε4 carriers, we did not find a relationship between EC thickness and memory performance. Thus, increased susceptibility of the WM structures underpinning the entorhinal-hippocampal network, offers a plausible explanation for the earlier onset of cognitive decline previously reported in APOE ε4 carriers.

White matter differences between healthy young ApoE4 carriers and non-carriers identified with tractography and support vector machines

The apolipoprotein E4 (ApoE4) is an established risk factor for Alzheimer's disease (AD). Previous work has shown that this allele is associated with functional (fMRI) changes as well structural grey matter (GM) changes in healthy young, middle-aged and older subjects. Here, we assess the diffusion characteristics and the white matter (WM) tracts of healthy young (20-38 years) ApoE4 carriers and non-carriers. No significant differences in diffusion indices were found between young carriers (ApoE4+) and non-carriers (ApoE4-). There were also no significant differences between the groups in terms of normalised GM or WM volume. A feature selection algorithm (ReliefF) was used to select the most salient voxels from the diffusion data for subsequent classification with support vector machines (SVMs). SVMs were capable of classifying ApoE4 carrier and non-carrier groups with an extremely high level of accuracy. The top 500 voxels selected by ReliefF were then used as seeds for tractography which identified a WM network that included regions of the parietal lobe, the cingulum bundle and the dorsolateral frontal lobe. There was a non-significant decrease in volume of this WM network in the ApoE4 carrier group. Our results indicate that there are subtle WM differences between healthy young ApoE4 carriers and non-carriers and that the WM network identified may be particularly vulnerable to further degeneration in ApoE4 carriers as they enter middle and old age.

Hippocampal atrophy in Alzheimer’s disease

SUMMARY New research criteria for Alzheimer’s disease (AD) and mild cognitive impairment (MCI) emphasize the use of imaging biomarkers in clinical diagnosis of these disorders. The volume loss of medial temporal lobe structures, especially hippocampal atrophy, is the best validated marker of AD. Manual tracing on MRI is the present gold standard for evaluating hippocampal volume; however, it is laborious and tracer-dependent. We categorized the most recent full- or semi-automated methods by the nature of the output of the method: size and shape of subcortical structures, cortical thickness, atrophy-rate and voxel- and region-based characteristics. The features of each method are introduced. The findings in structural MRI studies, especially in those studies utilizing the most recent methods, and the accuracies of those new methods in differentiating AD from healthy controls and stable MCI from progressive MCI are reviewed.

Identification of brain regions responsible for Alzheimer's disease using a Self-adaptive Resource Allocation Network

In this paper, we present a novel approach for the identification of brain regions responsible for Alzheimer's disease using the Magnetic Resonance (MR) images. The approach incorporates the recently developed Self-adaptive Resource Allocation Network (SRAN) for Alzheimer's disease classification using voxel-based morphometric features of MR images. SRAN classifier uses a sequential learning algorithm, employing self-adaptive thresholds to select the appropriate training samples and discard redundant samples to prevent over-training. These selected training samples are then used to evolve the network architecture efficiently. Since, the number of features extracted from the MR images is large, a feature selection scheme (to reduce the number of features needed) using an Integer-Coded Genetic Algorithm (ICGA) in conjunction with the SRAN classifier (referred to here as the ICGA-SRAN classifier) have been developed. In this study, different healthy/Alzheimer's disease patient's MR images from the Open Access Series of Imaging Studies data set have been used for the performance evaluation of the proposed ICGA-SRAN classifier. We have also compared the results of the ICGA-SRAN classifier with the well-known Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers. The study results clearly show that the ICGA-SRAN classifier produces a better generalization performance with a smaller number of features, lower misclassification rate and a compact network. The ICGA-SRAN selected features clearly indicate that the variations in the gray matter volume in the parahippocampal gyrus and amygdala brain regions may be good indicators of the onset of Alzheimer's disease in normal persons.

Frontotemporal lobar degeneration-related proteins induce only subtle memory-related deficits when bilaterally overexpressed in the dorsal hippocampus

Frontotemporal lobar degeneration (FTLD) is a neurodegenerative disease that involves cognitive decline and dementia. To model the hippocampal neurodegeneration and memory-related behavioral impairment that occurs in FTLD and other tau and TDP-43 proteinopathy diseases, we used an adeno-associated virus serotype 9 (AAV9) vector to induce bilateral expression of either microtubule-associated protein tau or transactive response DNA binding protein 43 kDa (TDP-43) in adult rat dorsal hippocampus. Human wild-type forms of tau or TDP-43 were expressed. The vectors/doses were designed for moderate expression levels within neurons. Rats were evaluated for acquisition and retention in the Morris water task over 12 weeks after gene transfer. Neither vector altered acquisition performance compared to controls. In measurements of retention, there was impairment in the TDP-43 group. Histological examination revealed specific loss of dentate gyrus granule cells and concomitant gliosis proximal to the injection site in the TDP-43 group, with shrinkage of the dorsal hippocampus. Despite specific tau pathology, the tau gene transfer surprisingly did not cause obvious neuronal loss or behavioral impairment. The data demonstrate that TDP-43 produced mild behavioral impairment and hippocampal neurodegeneration in rats, whereas tau did not. The models could be of value for studying mechanisms of FTLD and other diseases with tau and TDP-43 pathology in the hippocampus including Alzheimer's disease, with relevance to early stage mild impairment.

Automatic hippocampal segmentation in temporal lobe epilepsy: impact of developmental abnormalities

In drug-resistant temporal lobe epilepsy (TLE), detecting hippocampal atrophy on MRI is important as it allows defining the surgical target. The performance of automatic segmentation in TLE has so far been considered unsatisfactory. In addition to atrophy, about 40% of patients present with developmental abnormalities (referred to as malrotation) characterized by atypical morphologies of the hippocampus and collateral sulcus. Our purpose was to evaluate the impact of malrotation and atrophy on the performance of three state-of-the-art automated algorithms. We segmented the hippocampus in 66 patients and 35 sex- and age-matched healthy subjects using a region-growing algorithm constrained by anatomical priors (SACHA), a freely available atlas-based software (FreeSurfer) and a multi-atlas approach (ANIMAL-multi). To quantify malrotation, we generated 3D models from manual hippocampal labels and automatically extracted collateral sulci. The accuracy of automated techniques was evaluated relative to manual labeling using the Dice similarity index and surface-based shape mapping, for which we computed vertex-wise displacement vectors between automated and manual segmentations. We then correlated segmentation accuracy with malrotation features and atrophy. ANIMAL-multi demonstrated similar accuracy in patients and healthy controls (p > 0.1), whereas SACHA and FreeSurfer were less accurate in patients (p < 0.05). Surface-based analysis of contour accuracy revealed that SACHA over-estimated the lateral border of malrotated hippocampi (r = 0.61; p < 0.0001), but performed well in the presence of atrophy (|r |< 0.34; p > 0.2). Conversely, FreeSurfer and ANIMAL-multi were affected by both malrotation (FreeSurfer: r = 0.57; p = 0.02, ANIMAL-multi: r = 0.50; p = 0.05) and atrophy (FreeSurfer: r = 0.78, p < 0.0001, ANIMAL-multi: r = 0.61; p < 0.0001). Compared to manual volumetry, automated procedures underestimated the magnitude of atrophy (Cohen's d: manual: 1.68; ANIMAL-multi: 1.11; SACHA: 1.10; FreeSurfer: 0.90, p < 0.0001). In addition, they tended to lateralize the seizure focus less accurately in the presence of malrotation (manual: 64%; ANIMAL-multi: 55%, p = 0.4; SACHA: 50%, p = 0.1; FreeSurfer: 41%, p = 0.05). Hippocampal developmental anomalies and atrophy had a negative impact on the segmentation performance of three state-of-the-art automated methods. These shape variants should be taken into account when designing segmentation algorithms.

Simultaneous segmentation and grading of anatomical structures for patient's classification: application to Alzheimer's disease

In this paper, we propose an innovative approach to robustly and accurately detect Alzheimer's disease (AD) based on the distinction of specific atrophic patterns of anatomical structures such as hippocampus (HC) and entorhinal cortex (EC). The proposed method simultaneously performs segmentation and grading of structures to efficiently capture the anatomical alterations caused by AD. Known as SNIPE (Scoring by Non-local Image Patch Estimator), the novel proposed grading measure is based on a nonlocal patch-based frame-work and estimates the similarity of the patch surrounding the voxel under study with all the patches present in different training populations. In this study, the training library was composed of two populations: 50 cognitively normal subjects (CN) and 50 patients with AD, randomly selected from the ADNI database. During our experiments, the classification accuracy of patients (CN vs. AD) using several biomarkers was compared: HC and EC volumes, the grade of these structures and finally the combination of their volume and their grade. Tests were completed in a leave-one-out framework using discriminant analysis. First, we showed that biomarkers based on HC provide better classification accuracy than biomarkers based on EC. Second, we demonstrated that structure grading is a more powerful measure than structure volume to distinguish both populations with a classification accuracy of 90%. Finally, by adding the ages of subjects in order to better separate age-related structural changes from disease-related anatomical alterations, SNIPE obtained a classification accuracy of 93%.

High dimensional classification of structural MRI Alzheimer's disease data based on large scale regularization

In this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status. Its performance is illustrated using sMRI data from the Alzheimer Disease Neuroimaging Initiative (ADNI) clinical database. We downloaded sMRI data from 98 subjects (49 cognitive normal and 49 patients) matched by age and sex from the ADNI website. Images were segmented and normalized using SPM8 and ANTS software packages. Classification was performed using GLMNET library implementation of penalized logistic regression based on coordinate-wise descent optimization techniques. To avoid optimistic estimates classification accuracy, sensitivity, and specificity were determined based on a combination of three-way split of the data with nested 10-fold cross-validations. One of the main features of this approach is that classification is performed based on large scale regularization. The methodology presented here was highly accurate, sensitive, and specific when automatically classifying sMRI images of cognitive normal subjects and Alzheimer disease (AD) patients. Higher levels of accuracy, sensitivity, and specificity were achieved for gray matter (GM) volume maps (85.7, 82.9, and 90%, respectively) compared to white matter volume maps (81.1, 80.6, and 82.5%, respectively). We found that GM and white matter tissues carry useful information for discriminating patients from cognitive normal subjects using sMRI brain data. Although we have demonstrated the efficacy of this voxel-wise classification method in discriminating cognitive normal subjects from AD patients, in principle it could be applied to any clinical population.

The APOE ɛ4 allele modulates brain white matter integrity in healthy adults

The Apolipoprotein E (APOE) ɛ4 allele is the best-established genetic risk factor for sporadic Alzheimer's disease, and is also associated with structural gray matter and functional brain changes in healthy young, middle-aged and elderly subjects. Because APOE is implicated in brain mechanisms associated with white matter (WM) development and repair, we investigated the potential role played by the APOE polymorphism on WM structure in healthy younger (aged 20-35 years) and older (aged 50-78 years) adults using diffusion tensor imaging. General reduction of fractional anisotropy and increase in mean diffusivity values was found in carriers of the APOE ɛ4 allele relative to non-carriers. No significant interactions between genotype and age were observed, suggesting that differences in WM structure between APOE ɛ4-carriers and non-carriers do not undergo significant differential changes with age. This result was not explained by differences in brain morphology or cognitive measures. The APOE ɛ4 allele modulates brain WM structure before any clinical or neurophysiological expression of impending disease.

Reversibility of Tau-related cognitive defects in a regulatable FTD mouse model

The accumulation of proteins such as Tau is a hallmark of several neurodegenerative diseases, e.g., frontotemporal dementia (FTD). So far, many mouse models of tauopathies have been generated by the use of mutated or truncated human Tau isoforms in order to enhance the amyloidogenic character of Tau and to mimic pathological processes similar to those in FTD patients. Our inducible mice express the repeat domain of human Tau (Tau(RD)) carrying the FTDP-17 mutation ΔK280 in a "pro-aggregant" and an "anti-aggregant" version. Based on the enhanced tendency of Tau to aggregate, only the "pro-aggregant" Tau(RD) mice develop Tau pathology (hyperphosphorylation, coassembly of human and mouse Tau, synaptic loss, and neuronal degeneration). We have now carried out behavioral and electrophysiological analyses showing that only the pro-aggregant Tau(RD) mice have impaired learning/memory and a distinct loss of LTP. Remarkably, after suppressing the pro-aggregant human Tau(RD), memory and LTP recover, while neuronal loss persists. Aggregates persist as well but change their composition from mixed human/mouse to mouse Tau only. The rescue of cognition and synaptic plasticity is explained by a partial recovery of spine synapses in the hippocampus. These results indicate a tight relationship between the amyloidogenic character of Tau and brain malfunction, and suggest that the cognitive impairment is caused by toxic human Tau(RD) species rather than by mouse Tau aggregates.

Sex and age differences in atrophic rates: an ADNI study with n=1368 MRI scans

We set out to determine factors that influence the rate of brain atrophy in 1-year longitudinal magnetic resonance imaging (MRI) data. With tensor-based morphometry (TBM), we mapped the 3-dimensional profile of progressive atrophy in 144 subjects with probable Alzheimer's disease (AD) (age: 76.5 +/- 7.4 years), 338 with amnestic mild cognitive impairment (MCI; 76.0 +/- 7.2), and 202 healthy controls (77.0 +/- 5.1), scanned twice, 1 year apart. Statistical maps revealed significant age and sex differences in atrophic rates. Brain atrophic rates were about 1%-1.5% faster in women than men. Atrophy was faster in younger than older subjects, most prominently in mild cognitive impairment, with a 1% increase in the rates of atrophy and 2% in ventricular expansion, for every 10-year decrease in age. TBM-derived atrophic rates correlated with reduced beta-amyloid and elevated tau levels (n = 363) at baseline, baseline and progressive deterioration in clinical measures, and increasing numbers of risk alleles for the ApoE4 gene. TBM is a sensitive, high-throughput biomarker for tracking disease progression in large imaging studies; sub-analyses focusing on women or younger subjects gave improved sample size requirements for clinical trials.

Early-onset versus late-onset Alzheimer's disease: the case of the missing APOE ɛ4 allele

Some patients with early-onset Alzheimer's disease (AD) present with a distinct phenotype. Typically, the first and most salient characteristic of AD is episodic memory impairment. A few patients, however, present with focal cortical, non-memory symptoms, such as difficulties with language, visuospatial, or executive functions. These presentations are associated with specific patterns of atrophy and frequently with a young age at onset. Age is not, however, the only determinant of phenotype; underlying factors, especially genetic factors, seem also to affect phenotype and predispose patients to younger or older age at onset. Importantly, patients with atypical early-onset disease seldom carry the APOE ɛ4 allele, which is the most important risk factor for lowering the age of onset in patients with AD. Additionally, theAPOE ɛ4 genotype seems to predispose patients to vulnerability in the medial temporal areas, which leads to memory loss. Conversely, patients negative for the APOE ɛ4 allele and with early-onset AD are more likely to be predisposed to vulnerability of cerebral networks beyond the medial temporal lobes. Other factors are probably involved in determining the pattern of atrophy, but these are currently unknown.

Patterns of brain atrophy on magnetic resonance imaging and the boundary between ageing and Alzheimer's disease

Clinicians are increasingly faced with the problem of interpreting subtle, early cognitive symptoms. Enhanced awareness of Alzheimer's disease (AD) and available treatments has led to a growing demand for early assessment. Although it is known that a proportion of individuals with mild cognitive impairment will progress to dementia in following years, our ability to identify these individuals and predict individual cognitive trajectories is limited. The emergence of disease-modifying treatments would make these problems more acute. In this review, the potential role of magnetic resonance imaging (MRI) in aiding the clinician in early diagnosis of AD will be considered. The changes in grey matter structure that accompany ‘normal’ ageing will be described briefly, before moving on to studies that have attempted to distinguish the onset of disease from this background of structural change. Volumetric methods range from measurements of single key structures, such as the hippocampus, to methods based on computational neuroanatomy, which evaluate subtle structural alterations across the whole brain simultaneously. Computational methods are rapidly evolving and already perform as well as radiologists in distinguishing AD from normal ageing at an individual level. This article aims to provide a practical knowledge of how and why these methods work, point out the main advantages and disadvantages and sketch out outstanding issues and possible future directions.

Positron emission tomography and magnetic resonance imaging in the diagnosis and prediction of dementia

BACKGROUND

The diagnosis of dementia, along with the prediction of who will develop dementia, has been assisted by the development of the brain imaging techniques of magnetic resonance imaging (MRI) and positron emission tomography (PET).

METHODS

This paper reviews the brain imaging technologies of structural MRI and PET scanning as they have been applied to both the diagnosis of dementia and prediction of who will develop dementia.

RESULTS

Diagnosis has long been enhanced by the use of structural imaging techniques like MRI to rule out non-degenerative causes of disease. More recently, PET imaging with the glucose metabolic tracer [(18)F]Fluorodeoxyglucose (FDG) may be useful in providing information on the cause of dementia during life, most specifically in differentiating Alzheimer's disease from frontotemporal lobar degeneration. In addition to diagnosis, potential therapeutic advances have increased interest in prediction of dementia. Both MR and FDG-PET have shown evidence of change in brain structure and metabolism in several models of individuals at-risk for dementia, including those with mild cognitive impairment and genetic risk factors.

CONCLUSIONS

While these studies have not yet advanced to the level of prospective individual-subject predictive ability, the pattern of data emerging suggests likely candidate approaches for such studies. The advent of newer techniques such as amyloid imaging with PET and functional MRI may ultimately have relevance for both diagnosis and prediction.

Subregional neuroanatomical change as a biomarker for Alzheimer's disease

Regions of the temporal and parietal lobes are particularly damaged in Alzheimer's disease (AD), and this leads to a predictable pattern of brain atrophy. In vivo quantification of subregional atrophy, such as changes in cortical thickness or structure volume, could lead to improved diagnosis and better assessment of the neuroprotective effects of a therapy. Toward this end, we have developed a fast and robust method for accurately quantifying cerebral structural changes in several cortical and subcortical regions using serial MRI scans. In 169 healthy controls, 299 subjects with mild cognitive impairment (MCI), and 129 subjects with AD, we measured rates of subregional cerebral volume change for each cohort and performed power calculations to identify regions that would provide the most sensitive outcome measures in clinical trials of disease-modifying agents. Consistent with regional specificity of AD, temporal-lobe cortical regions showed the greatest disease-related changes and significantly outperformed any of the clinical or cognitive measures examined for both AD and MCI. Global measures of change in brain structure, including whole-brain and ventricular volumes, were also elevated in AD and MCI, but were less salient when compared to changes in normal subjects. Therefore, these biomarkers are less powerful for quantifying disease-modifying effects of compounds that target AD pathology. The findings indicate that regional temporal lobe cortical changes would have great utility as outcome measures in clinical trials and may also have utility in clinical practice for aiding early diagnosis of neurodegenerative disease.

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.

Markers of Alzheimer's disease in a population attending a memory clinic

BACKGROUND

New marker-based criteria for the diagnosis of Alzheimer's disease (AD) were recently proposed. We describe their operational translation in 144 consecutive patients referred to our Memory Clinic.

METHODS

Visual ratings of hippocampal atrophy and of cortical glucose hypometabolism in magnetic resonance imaging and positron emission tomography, and concentrations of total tau and Abeta1-42 in cerebrospinal fluid were assessed in 12 patients with subjective memory complaints (SMCs) (Mini-Mental State Examination [MMSE] score, 28.0 +/- 1.1 [mean +/- SD]), 37 with mild cognitive impairment (MCI) (MMSE, 25.1 +/- 3.6), 55 with AD (MMSE, 21.1 +/- 3.5), and 40 with non-AD dementia (MMSE, 21.6 +/- 5.5).

RESULTS

The sensitivity for AD of each individual biomarker was higher (65% to 87%) than for MCI (18% to 50%). Each biomarker's specificity for SMC and non-AD dementias was good to moderate (83% and 53%). Positivity for at least one marker increased the probability 38 times of belonging to the AD group (P < 0.0001).

CONCLUSION

The new diagnostic criteria can be operationalized in clinical routines, but longitudinal studies of MCI patients will need to assess the criteria's prognostic value.

Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI

The hippocampus is among the first structures affected in Alzheimer's disease (AD). Hippocampal magnetic resonance imaging volumetry is a potential biomarker for AD but is hindered by the limitations of manual segmentation. We proposed a fully automatic method using probabilistic and anatomical priors for hippocampus segmentation. Probabilistic information is derived from 16 young controls and anatomical knowledge is modeled with automatically detected landmarks. The results were previously evaluated by comparison with manual segmentation on data from the 16 young healthy controls, with a leave-one-out strategy, and eight patients with AD. High accuracy was found for both groups (volume error 6 and 7%, overlap 87 and 86%, respectively). In this article, the method was used to segment 145 patients with AD, 294 patients with mild cognitive impairment (MCI), and 166 elderly normal subjects from the Alzheimer's Disease Neuroimaging Initiative database. On the basis of a qualitative rating protocol, the segmentation proved acceptable in 94% of the cases. We used the obtained hippocampal volumes to automatically discriminate between AD patients, MCI patients, and elderly controls. The classification proved accurate: 76% of the patients with AD and 71% of the MCI converting to AD before 18 months were correctly classified with respect to the elderly controls, using only hippocampal volume.

Patterns of structural complexity in Alzheimer's disease and frontotemporal dementia

The goal of this project was to utilize an information theoretic formalism for medical image analysis initially proposed in [Young et al. (2005): Phys Rev Lett 94:098701-1] to detect and quantify subtle global and regional differences in spatial patterns in patients suffering from Alzheimer's disease (AD) and frontotemporal dementia (FTD) by estimating the structural complexity of anatomical brain MRI. The sensitivity and specificity of the results are compared with those of a recent analysis, currently considered state of the art for MR studies of neurodegeneration. The previous study used regional estimates of cortical thinning and/or volume loss to differentiate between normal aging, AD, and FTD. The analysis illustrates that the structural complexity estimation method, a general multivariate approach to the study of variation in brain structure which does not depend on highly specialized volumetric and thickness estimates, is capable of providing sensitive and interpretable diagnostic information.

Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging

We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi, which are segmented from magnetic resonance images (MRI) using a fully automatic method that we previously developed. SPHARM coefficients are used as features in a classification procedure based on support vector machines (SVM). The most relevant features for classification are selected using a bagging strategy. We evaluate the accuracy of our method in a group of 23 patients with AD (10 males, 13 females, age+/-standard-deviation (SD)=73+/-6 years, mini-mental score (MMS)=24.4+/-2.8), 23 patients with amnestic MCI (10 males, 13 females, age+/-SD=74+/-8 years, MMS=27.3+/-1.4) and 25 elderly healthy controls (13 males, 12 females, age+/-SD=64+/-8 years), using leave-one-out cross-validation. For AD vs controls, we obtain a correct classification rate of 94%, a sensitivity of 96%, and a specificity of 92%. For MCI vs controls, we obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of 84%. This accuracy is superior to that of hippocampal volumetry and is comparable to recently published SVM-based whole-brain classification methods, which relied on a different strategy. This new method may become a useful tool to assist in the diagnosis of Alzheimer's disease.

Neuropsychological patterns in magnetic resonance imaging-defined subgroups of patients with degenerative dementia

We hypothesized that specific neuropsychological deficits were associated with specific patterns of atrophy. A magnetic resonance imaging volumetric study and a neuropsychological protocol were obtained for patients with several frontotemporal lobar dementia phenotypes including a social/dysexecutive (SOC/EXEC, n = 17), progressive nonfluent aphasia (n = 9), semantic dementia (n = 7), corticobasal syndrome (n = 9), and Alzheimer's disease (n = 21). Blinded to testing results, patients were partitioned according to pattern of predominant cortical atrophy; our partitioning algorithm had been derived using seriation, a hierarchical classification technique. Neuropsychological test scores were regressed versus these atrophy patterns as fixed effects using the covariate total atrophy as marker for disease severity. The results showed the model accounted for substantial variance. Furthermore, the "large-scale networks" associated with each neuropsychological test conformed well to the known literature. For example, bilateral prefrontal cortical atrophy was exclusively associated with SOC/EXEC dysfunction. The neuropsychological principle of "double dissociation" was supported not just by such active associations but also by the "silence" of locations not previously implicated by the literature. We conclude that classifying patients with degenerative dementia by specific pattern of cortical atrophy has the potential to predict individual patterns of cognitive deficits.

The P2X7 receptor drives microglial activation and proliferation: a trophic role for P2X7R pore

Microglial activation is an integral part of neuroinflammation associated with many neurodegenerative conditions. Interestingly, a number of neurodegenerative conditions exhibit enhanced P2X(7) receptor (P2X(7)R) expression in the neuroinflammatory foci where activated microglia are a coexisting feature. Whether P2X(7)R overexpression is driving microglial activation or, conversely, P2X(7)R overexpression is a consequence of microglial activation is not known. We report that overexpression alone of a purinergic P2X(7)R, in the absence of pathological insults, is sufficient to drive the activation and proliferation of microglia in rat primary hippocampal cultures. The trophic responses observed in microglia were found to be P2X(7)R specific as the P2X(7)R antagonist, oxidized ATP (oxATP), was effective in markedly attenuating microgliosis. oxATP treatment of primary hippocampal cultures expressing exogenous P2X(7)Rs resulted in a significant decrease in the number of activated microglia. P2X(7)R is unusual in exhibiting two conductance states, a cation channel and a plasma membrane pore, and there are no pharmacological agents capable of cleanly discriminating between these two states. We used a point mutant of P2X(7)R (P2X7RG345Y) with intact channel function but ablated pore-forming capacity to establish that the trophic effects of increased P2X(7)R expression are exclusively mediated by the pore conductance. Collectively, and contrary to previous reports describing P2X(7)R as a "death receptor," we provide evidence for a novel trophic role for P2X(7)R pore in microglia.

Alzheimer's disease neuroimaging initiative: a one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition

Tensor-based morphometry can recover three-dimensional longitudinal brain changes over time by nonlinearly registering baseline to follow-up MRI scans of the same subject. Here, we compared the anatomical distribution of longitudinal brain structural changes, over 12 months, using a subset of the ADNI dataset consisting of 20 patients with Alzheimer's disease (AD), 40 healthy elderly controls, and 40 individuals with mild cognitive impairment (MCI). Each individual longitudinal change map (Jacobian map) was created using an unbiased registration technique, and spatially normalized to a geometrically-centered average image based on healthy controls. Voxelwise statistical analyses revealed regional differences in atrophy rates, and these differences were correlated with clinical measures and biomarkers. Consistent with prior studies, we detected widespread cerebral atrophy in AD, and a more restricted atrophic pattern in MCI. In MCI, temporal lobe atrophy rates were correlated with changes in mini-mental state exam (MMSE) scores, clinical dementia rating (CDR), and logical/verbal learning memory scores. In AD, temporal atrophy rates were correlated with several biomarker indices, including a higher CSF level of p-tau protein, and a greater CSF tau/beta amyloid 1-42 (ABeta42) ratio. Temporal lobe atrophy was significantly faster in MCI subjects who converted to AD than in non-converters. Serial MRI scans can therefore be analyzed with nonlinear image registration to relate ongoing neurodegeneration to a variety of pathological biomarkers, cognitive changes, and conversion from MCI to AD, tracking disease progression in 3-dimensional detail.

MRI correlates of protein deposition and disease severity in postmortem frontotemporal lobar degeneration

BACKGROUND

Frontotemporal lobar degeneration (FTLD) can be classified based on the presence of the microtubule-associated protein tau and the TAR DNA binding protein-43 (TDP-43). Future treatments will likely target these proteins, therefore it is important to identify biomarkers to help predict protein biochemistry.

OBJECTIVE

To determine whether there is an MRI signature pattern of tau or TDP-43 using a large cohort of FTLD subjects and to investigate how patterns of atrophy change according to disease severity using a large autopsy-confirmed cohort of FTLD subjects.

METHODS

Patterns of gray matter loss were assessed using voxel-based morphometry in 37 tau-positive and 44 TDP-43-positive subjects compared to 35 age and gender-matched controls, and compared to each other. Comparisons were also repeated in behavioral variant frontotemporal dementia (bvFTD) subjects (n = 15 tau-positive and n = 30 TDP-43-positive). Patterns of atrophy were also assessed according to performance on the Clinical Dementia Rating (CDR) scale and Mini-Mental State Examination (MMSE).

RESULTS

The tau-positive and TDP-43-positive groups showed patterns of frontotemporal gray matter loss compared to controls with no differences observed between the groups, for all subjects and for bvFTD subjects. Patterns of gray matter loss increased in a graded manner by CDR and MMSE with loss in the frontal lobes, insula and hippocampus in mild subjects, spreading to the temporal and parietal cortices and striatum in more advanced disease.

CONCLUSION

There is no signature pattern of atrophy for tau or TDP-43; however, patterns of atrophy in FTLD progress with measures of clinical disease severity.

Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI

PURPOSE

We present and evaluate a new automated method based on support vector machine (SVM) classification of whole-brain anatomical magnetic resonance imaging to discriminate between patients with Alzheimer's disease (AD) and elderly control subjects.

MATERIALS AND METHODS

We studied 16 patients with AD [mean age +/- standard deviation (SD) = 74.1 +/- 5.2 years, mini-mental score examination (MMSE) = 23.1 +/- 2.9] and 22 elderly controls (72.3 +/- 5.0 years, MMSE = 28.5 +/- 1.3). Three-dimensional T1-weighted MR images of each subject were automatically parcellated into regions of interest (ROIs). Based upon the characteristics of gray matter extracted from each ROI, we used an SVM algorithm to classify the subjects and statistical procedures based on bootstrap resampling to ensure the robustness of the results.

RESULTS

We obtained 94.5% mean correct classification for AD and control subjects (mean specificity, 96.6%; mean sensitivity, 91.5%).

CONCLUSIONS

Our method has the potential in distinguishing patients with AD from elderly controls and therefore may help in the early diagnosis of AD.

3D MR Models and Volumetric Measurements of the Brain in Patients with Alzheimer's Disease

This study aimed to describe the procedure we use to create 3D models of the brain parenchyma from MRI images and calculate the volume of the whole brain and different compartments of the brain in patients with Alzheimer's disease. The utility of the 3D models and volumetric measurements of the whole brain parenchyma and different brain structures is discussed. Thirty-six patients with Alzheimer's disease were examined during the last six months with MRI. Fourteen of them were men and 22 were women. The patients were between 53 and 67 years old. MR images were studied using an automatic algorithm. The images from MRI were segmented and then three-dimensional models of brain were produced to calculate the brain volume and the volume of the white matter, gray matter and CSF separately. The whole procedure was completed successfully in 34 patients. The procedure was unsuccessful in two patients due to movement artifacts in MR images. It is relatively easy to create 3D models of MR images and to obtain volumetric studies. If this procedure is adjusted in patients with Alzheimer's disease, we can provide information more clearly and accurately than single images alone. The information obtained can be used in daily clinical practice such as pharmaceutical treatment planning and results or in basic clinical research.

Evidence for an association between KIBRA and late-onset Alzheimer's disease

We recently reported evidence for an association between the individual variation in normal human episodic memory and a common variant of the KIBRA gene, KIBRA rs17070145 (T-allele). Since memory impairment is a cardinal clinical feature of Alzheimer's disease (AD), we investigated the possibility of an association between the KIBRA gene and AD using data from neuronal gene expression, brain imaging studies, and genetic association tests. KIBRA was significantly over-expressed and three of its four known binding partners under-expressed in AD-affected hippocampal, posterior cingulate and temporal cortex regions (P<0.010, corrected) in a study of laser-capture microdissected neurons. Using positron emission tomography in a cohort of cognitively normal, late-middle-aged persons genotyped for KIBRA rs17070145, KIBRA T non-carriers exhibited lower glucose metabolism than did carriers in posterior cingulate and precuneus brain regions (P<0.001, uncorrected). Lastly, non-carriers of the KIBRA rs17070145 T-allele had increased risk of late-onset AD in an association study of 702 neuropathologically verified expired subjects (P=0.034; OR=1.29) and in a combined analysis of 1026 additional living and expired subjects (P=0.039; OR=1.26). Our findings suggest that KIBRA is associated with both individual variation in normal episodic memory and predisposition to AD.

Neuronal gene expression in non-demented individuals with intermediate Alzheimer's Disease neuropathology

While the clinical and neuropathological characterization of Alzheimer's Disease (AD) is well defined, our understanding of the progression of pathologic mechanisms in AD remains unclear. Post-mortem brains from individuals who did not fulfill clinical criteria for AD may still demonstrate measurable levels of AD pathologies to suggest that they may have presented with clinical symptoms had they lived longer or are able to stave off disease progression. Comparison between such individuals and those clinically diagnosed and pathologically confirmed to have AD will be key in delineating AD pathogenesis and neuroprotection. In this study, we expression profiled laser capture microdissected non-tangle bearing neurons in 6 post-mortem brain regions that are differentially affected in the AD brain from 10 non-demented individuals demonstrating intermediate AD neuropathologies (NDAD; Braak stage of II through IV and CERAD rating of moderate to frequent) and evaluated this data against that from individuals who have been diagnosed with late onset AD as well as healthy elderly controls. We identified common statistically significant expression changes in both NDAD and AD brains that may establish a degenerative link between the two cohorts, in addition to NDAD specific transcriptomic changes. These findings pinpoint novel targets for developing earlier diagnostics and preventative therapies for AD prior to diagnosis of probable AD. We also provide this high-quality, low post-mortem interval (PMI), cell-specific, and region-specific NDAD/AD reference data set to the community as a public resource.

Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus

PURPOSE

To prospectively evaluate the accuracy of automated hippocampal volumetry to help distinguish between patients with Alzheimer disease (AD), patients with mild cognitive impairment (MCI), and elderly controls, by using established criteria for patients with AD and MCI as the reference standard.

MATERIALS AND METHODS

The regional ethics committee approved the study and written informed consent was obtained from all participants. The study included 25 patients with AD (11 men, 14 women; mean age +/- standard deviation [SD], 73 years +/- 6; Mini-Mental State Examination (MMSE) score, 24.4 +/- 2.7), 24 patients with amnestic MCI (10 men, 14 women; mean age +/- SD, 74 years +/- 8; MMSE score, 27.2 +/- 1.4) and 25 elderly healthy controls (13 men, 12 women; mean age +/- SD, 64 years +/- 8). For each participant, the hippocampi were automatically segmented on three-dimensional T1-weighted magnetic resonance (MR) images with high spatial resolution. Segmentation was performed by using recently developed software that allows fast segmentation with minimal user input. Group differences in hippocampal volume were assessed by using Student t tests. To obtain robust estimates of P values, the correct classification rate, sensitivity, and specificity, bootstrap methods were used.

RESULTS

Significant hippocampal volume reductions were detected in all groups of patients (-32% in AD patients vs controls, P < .001; -19% in MCI patients vs controls, P < .001; and -15% in AD patients vs MCI patients, P < .01). Individual classification on the basis of hippocampal volume resulted in 84% correct classification (sensitivity, 84%; specificity, 84%) between AD patients and controls and 73% correct classification (sensitivity, 75%; specificity, 70%) between MCI patients and controls.

CONCLUSION

This automated method can serve as an alternative to manual tracing and may thus prove useful in assisting with the diagnosis of AD.

Longitudinal studies of semantic dementia: the relationship between structural and functional changes over time

The pattern of brain atrophy in semantic dementia and its associated cognitive effects have attracted a considerable body of research, but the nature of core impairments remains disputed. A key issue is whether the disease encompasses one neurocognitive network (semantics) or two (language and semantics). In order to address these conflicting perspectives, we conducted a longitudinal investigation of two semantic dementia patients, in which behavioural performance across a range of measures of language and semantic performance was assessed and interpreted in the context of annually acquired MRI scans. Our results indicated a core semantic impairment in early stages of the disease, associated with atrophy of the inferior, anterior temporal cortex. Linguistic impairments emerged later, and were contingent on atrophy having spread into areas widely believed to subserve core language processes (left posterior perisylvian, inferior frontal and insular cortex). We claim, therefore, that phonological, syntactic and morphological processing deficits in semantic dementia reflect damage to core language areas. Further, we propose that much of the current controversy over the nature of deficits in semantic dementia reflect a tendency in the literature to adopt a static perspective on what is a progressive disease. An approach in which the relationship between progressive neural changes and behavioural change over time is carefully mapped, offers a more constraining data-set from which to draw inferences about the relationship between language, semantics and the brain.

Altered neuronal gene expression in brain regions differentially affected by Alzheimer's disease: a reference data set

Alzheimer's Disease (AD) is the most widespread form of dementia during the later stages of life. If improved therapeutics are not developed, the prevalence of AD will drastically increase in the coming years as the world's population ages. By identifying differences in neuronal gene expression profiles between healthy elderly persons and individuals diagnosed with AD, we may be able to better understand the molecular mechanisms that drive AD pathogenesis, including the formation of amyloid plaques and neurofibrillary tangles. In this study, we expression profiled histopathologically normal cortical neurons collected with laser capture microdissection (LCM) from six anatomically and functionally discrete postmortem brain regions in 34 AD-afflicted individuals, using Affymetrix Human Genome U133 Plus 2.0 microarrays. These regions include the entorhinal cortex, hippocampus, middle temporal gyrus, posterior cingulate cortex, superior frontal gyrus, and primary visual cortex. This study is predicated on previous parallel research on the postmortem brains of the same six regions in 14 healthy elderly individuals, for which LCM neurons were similarly processed for expression analysis. We identified significant regional differential expression in AD brains compared with control brains including expression changes of genes previously implicated in AD pathogenesis, particularly with regard to tangle and plaque formation. Pinpointing the expression of factors that may play a role in AD pathogenesis provides a foundation for future identification of new targets for improved AD therapeutics. We provide this carefully phenotyped, laser capture microdissected intraindividual brain region expression data set to the community as a public resource.