Early diagnosis of Alzheimer's disease: contribution of structural neuroimaging

Relations between brain tissue loss, CSF biomarkers, and the ApoE genetic profile: a longitudinal MRI study

Previously it was reported that Alzheimer's disease (AD) patients have reduced beta amyloid (Abeta(1-42)) and elevated total tau (t-tau) and phosphorylated tau (p-tau(181p)) in the cerebrospinal fluid (CSF), suggesting that these same measures could be used to detect early AD pathology in healthy elderly individuals and those with mild cognitive impairment (MCI). In this study, we tested the hypothesis that there would be an association among rates of regional brain atrophy, the CSF biomarkers Abeta(1-42), t-tau, and p-tau(181p) and apolipoprotein E (ApoE) epsilon4 status, and that the pattern of this association would be diagnosis-specific. Our findings primarily showed that lower CSF Abeta(1-42) and higher tau concentrations were associated with increased rates of regional brain tissue loss and the patterns varied across the clinical groups. Taken together, these findings demonstrate that CSF biomarker concentrations are associated with the characteristic patterns of structural brain changes in healthy elderly and mild cognitive impairment subjects that resemble to a large extent the pathology seen in AD. Therefore, the finding of faster progression of brain atrophy in the presence of lower Abeta(1-42) levels and higher tau levels supports the hypothesis that CSF Abeta(1-42) and tau are measures of early AD pathology. Moreover, the relationship among CSF biomarkers, ApoE epsilon4 status, and brain atrophy rates are regionally varying, supporting the view that the genetic predisposition of the brain to beta amyloid and tau mediated pathology is regional and disease stage specific.

White matter integrity in mild cognitive impairment: a tract-based spatial statistics study

Mild cognitive impairment (MCI) as a clinical diagnosis has limited specificity, and identifying imaging biomarkers may improve its predictive validity as a pre-dementia syndrome. This study used diffusion tensor imaging (DTI) to detect white matter (WM) structural alterations in MCI and its subtypes, and aimed to examine if DTI can serve as a potential imaging marker of MCI. We studied 96 amnestic MCI (aMCI), 69 non-amnestic MCI (naMCI), and 252 cognitively normal (CN) controls. DTI was performed to measure fractional anisotropy (FA), and tract-based spatial statistics (TBSS) were applied to investigate the characteristics of WM changes in aMCI and naMCI. The diagnostic utility of DTI in distinguishing MCI from CN was further evaluated by using a binary logistic regression model. We found that FA was significantly reduced in aMCI and naMCI when compared with CN. For aMCI subjects, decreased FA was seen in the frontal, temporal, parietal, and occipital WM, together with several commissural, association, and projection fibres. The best discrimination between aMCI and controls was achieved by combining FA measures of the splenium of corpus callosum and crus of fornix, with accuracy of 74.8% (sensitivity 71.0%, specificity 76.2%). For naMCI subjects, WM abnormality was more anatomically widespread, but the temporal lobe WM was relatively spared. These results suggest that aMCI is best characterized by pathology consistent with early Alzheimer's disease, whereas underlying pathology in naMCI is more heterogeneous, and DTI analysis of white matter structural integrity can serve as a potential biomarker of MCI and its subtypes.

Medial temporal lobe correlates of memory screening measures in normal aging, MCI, and AD

This article aimed to study the correlations for both the Memory Impairment Screen (MIS) and the Free and Cued Selective Reminding Test (FCSRT) with regard to the volumetric measures of hippocampal formation and entorhinal cortex and to explore the effect size of these measures.

METHODS

A total of 34 healthy controls, 24 participants with mild cognitive impairment (MCI), and 20 mild-to-moderate-staged Alzheimer disease (AD) participants underwent neuropsychological testing and magnetic resonance imaging (MRI). Global volumetric measures were obtained and hippocampal and entorhinal volumes were calculated. Spearman correlations were calculated between memory scores and brain volumes and an effect size analysis was performed.

RESULTS

No significant correlations with global brain volumes were found. There were dissimilar correlations among groups regarding memory and hippocampal and entorhinal volumes. No significant relationships were observed in healthy controls. The MCI group reached the higher correlation indexes, up to r = .55. In AD, only one significant correlation was observed between the delayed score of the FCSRT and the left hippocampus. Effect size values were higher for memory tests than for MRI measures, reaching d = 4.3 for the delayed score of the FCSRT.

CONCLUSIONS

Although the MIS did not reach the strong results of the FCSRT, it demonstrated a similar pattern to the FCSRT in correlational analysis. These results support the validity and usefulness of the MIS despite its brevity of application. Memory testing showed better discrimination among healthy controls, MCI, and AD participants than MRI measures by means of effect size analysis.

Correlations between Z-scores of VSRAD and regional cerebral blood flow of SPECT in patients with Alzheimer's disease and mild cognitive impairment

AIMS

The purpose of the present study was to investigate whether there were correlations between atrophy of the entorhinal cortex and individual regional cerebral blood flow (rCBF) in patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (MCI) to better clarify the relationships between morphological and functional changes in AD.

METHODS

Twenty-six patients including sixteen AD and 10 amnestic MCI patients were enrolled. Z scores of voxel-based specific regional analysis system for AD (VSRAD) were determined to assess the degree of atrophy of the entorhinal cortex. Single-photon emission computed tomography (SPECT) and 3-D stereotaxic region of interest template (3DSRT) were used to quantify absolute rCBF.

RESULTS

The Z scores of the entorhinal cortex were found to have significant negative correlations with the absolute rCBF in the bilateral hippocampus, thalamus and temporal regions. A negative correlation between Z scores and rCBF of the cerebellum region, especially on the right side, was also noted.

CONCLUSIONS

Atrophy of the entorhinal cortex had an obvious functional relationship with rCBF changes in the hippocampus, thalamus, temporal lobe and cerebellum in AD and MCI patients, which was attributed to their close anatomical and physiological connections.

Histochemical visualization and diffusion MRI at 7 Tesla in the TgCRND8 transgenic model of Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that has been characterized by gross cortical atrophy, cellular neurodegeneration, reactive gliosis, and the presence of microscopic extracellular amyloid plaques and intracellular neurofibrillary tangles. Earlier diagnoses of AD would be in the best interest of managing the patient and would allow for earlier therapeutic intervention. By measuring the apparent diffusion coefficient (ADC) using diffusion-weighted imaging (DWI), a type of magnetic resonance imaging (MRI), one can quantify alterations in water diffusivity resulting from microscopic structural changes in the cell at early stages that are associated with pathophysiological processes of brain injury and/or disease progression. Whether or not this methodology is useful for AD is a question under examination. For example, DWI in suspected AD patients has shown increases in mean ADC values in the hippocampus and diminished diffusion anisotropy in the posterior white matter. However, in some cases, hippocampal ADC values appear not to change in AD patients. Moreover, to our knowledge, all DWI studies in suspected AD patients to date are technically incomplete in experimental design, because corresponding histological sections demonstrating actual plaque deposition are lacking and so it is not clear that ADC changes actually correspond to plaque deposition. In our study, we used DWI in the TgCRND8 transgenic model of Alzheimer's disease in conjunction with histological techniques and found robust plaque deposition in the transgenic strain in older animals (12-16 months old). However, we did not find statistically significant changes (p > 0.05) in ADC values (although ADC values in TgCRND8 mice did decrease in all regions examined) in mice 12-16 months old. Collectively, recent results from human studies and in rodent AD transgenic models support our findings and suggest that amyloid beta plaque load is not likely the major or primary component contributing to diffusional changes, if they occur.

Preserved hippocampus activation in normal aging as revealed by fMRI

The hippocampus is deteriorated in various pathologies such as Alzheimer's disease (AD) and such deterioration has been linked to memory impairment. By contrast, the structural and functional effects of normal aging on the hippocampus is a matter of debate, with some findings suggesting deterioration and others providing evidence of preservation. This constitutes a crucial question since many investigations on AD are based on the assumption that the deterioration of the hippocampus is the breaking point between normal and pathological aging. A growing number of fMRI studies specifically aimed at investigating hippocampal engagement in various cognitive tasks, notably memory tasks, but the results have been inconclusive. Here, we optimized the episodic face-name paired-associates task in order to test the functioning of the hippocampus in normal aging. Critically, we found no difference in the activation of the hippocampus between the young and a group of older participants. Analysis of individual patterns of activation substantiated this impression. Collectively, these findings provide evidence of preserved hippocampal functioning in normal aging.

Object recognition memory and BDNF expression are reduced in young TgCRND8 mice

The TgCRND8 mouse model of Alzheimer's disease exhibits progressive cortical and hippocampal β-amyloid accumulation, resulting in plaque pathology and spatial memory impairment by 3 months of age. We tested whether TgCRND8 cognitive function is disrupted prior to the appearance of macroscopic plaques in an object recognition task. We found profound deficits in 8-week-old mice. Animals this age were not impaired on the Morris water maze task. TgCRND8 and littermate controls did not differ in their duration of object exploration or optokinetic responses. Thus, visual and motor dysfunction did not confound the phenotype. Object memory deficits point to the frontal cortex and hippocampus as early targets of functional disruption. Indeed, we observed altered levels of brain-derived neurotrophic factor (BDNF) messenger ribonucleic acid (mRNA) in these brain regions of preplaque TgCRND8 mice. Our findings suggest that object recognition provides an early index of cognitive impairment associated with amyloid exposure and reduced brain-derived neurotrophic factor expression in the TgCRND8 mouse.

Gray matter in amnestic mild cognitive impairment: voxel-based morphometry

Multiple regression voxel-based morphometry analyses were used to examine the relationship between regional gray matter volumes and neurocognitive performance in 10 patients with amnestic mild cognitive impairment and 20 healthy age-matched controls. Cognitive functioning was assessed with seven standardized neuropsychological tests. Patients with amnestic mild cognitive impairment exhibited impaired cognitive performance (on the Mini Mental State Examination, tests of verbal fluency, verbal and spatial learning and memory, and visual-motor abilities) and reduced gray matter volume in the right temporal pole. Across all participants, better performance on several neuropsychological tests was associated with higher regional gray matter volumes. Voxel-based morphometry provides an operator-unbiased means to investigate volumetric differences, which may be related to impaired neuropsychological functioning.

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

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

Joint analysis of structural and perfusion MRI for cognitive assessment and classification of Alzheimer's disease and normal aging

Structural magnetic resonance imaging (MRI) of brain tissue loss and physiological imaging of regional cerebral blood flow (rCBF) can provide complimentary information for the characterization of brain disorders, such as Alzheimer's disease (AD) but studies into gains in classification power for AD using these image modalities jointly have been limited. Our aim in this study was to determine the joint contribution of structural and perfusion-weighted imaging for the classification of AD in a cross-sectional study using an integrated multimodality MRI processing framework and a cortical surface-based analysis approach. We used logistic regression analysis to determine sequentially the value of cortical thickness, rCBF, and cortical thickness and rCBF jointly for classification for diagnosis of AD compared to controls. We further tested the extent to which cortical thinning and reduced rCBF explain individually or together variability in dementia severity. Separate analysis of structural MRI and perfusion-weighted MRI data yielded the well-established pattern of cortical thinning and rCBF reduction in AD, affecting predominantly temporo-parietal brain regions. Using structural MRI and perfusion-weighted MRI jointly indicated that cortical thinning dominated the classification of AD and controls without significant contributions from rCBF. However there was also a positive interaction between reduced rCBF and cortical thinning in the right superior temporal sulcus, implying that structural and physiological brain alterations in AD can be complementary. Compared to reduced rCBF, regional cortical thinning better explained the variability in dementia severity. In conclusion, structural brain alterations compared to physiological variations are the dominant features of MRI in AD.

MRI predictors of cognitive change in a diverse and carefully characterized elderly population

BACKGROUND

Trajectories of cognitive decline among elderly individuals are heterogeneous, and markers that have high reliability for predicting cognitive trajectories across a broad spectrum of the elderly population have yet to be identified.

METHOD

This study examined the utility of a variety of MRI-based brain measures, obtained at baseline, as predictors of subsequent declines in domain-specific measures of cognitive function in a cohort of 307 community-dwelling elderly individuals with varying degrees of cognitive impairment who were diverse across several relevant demographic variables and were evaluated yearly. Psychometrically matched measures of cognition were used to assess episodic memory, semantic memory, and executive function. Relationships between baseline MRI measures, including the volumes of the brain, hippocampus, and white matter hyperintensities (WMH), and cognitive trajectories were assessed in mixed effects regression models that modeled MRI effects on cognitive performance at baseline and rate of change as well as interindividual variability in cognitive baseline and rate of change.

RESULTS

Greater baseline brain volume predicted slower subsequent rate of decline in episodic memory and smaller WMH volume predicted slower subsequent rate of decline in executive function and semantic memory. Baseline hippocampal volume, while strongly related to baseline cognitive function, was not predictive of subsequent change in any of the cognitive domains.

CONCLUSIONS

Baseline measures of brain structure and tissue pathology predicted rate of cognitive decline in a diverse and carefully characterized cohort, suggesting that they may provide summary measures of pre-existing neuropathological damage or the capacity of the brain to compensate for the impact of subsequent neuropathology on cognition. Conventional MRI measures may have use for predicting cognitive outcomes in highly heterogeneous elderly populations.

Manual validation of FreeSurfer's automated hippocampal segmentation in normal aging, mild cognitive impairment, and Alzheimer Disease subjects

Hippocampal volume is reduced in Alzheimer Disease (AD) and has been proposed as a possible surrogate biomarker to aid early diagnosis. Whilst automated methods to segment the hippocampus from magnetic resonance images are available, manual segmentation, in spite of being time-consuming and unsuitable for large samples, is still the standard. In order to study the validity of FreeSurfer's automated method, we compared hippocampal automated measures with manual tracing in a sample composed of healthy elderly (N=41), Mild Cognitive Impairment (MCI) (N=23), and AD (N=25) subjects. Percent volume overlap, percent volume difference, correlations, and Bland-Altman plots were studied. Automated measures were slightly larger than hand tracing ones (mean difference 10%). Percent volume overlap showed good results, but was far from perfect (78%). Manual and automated volume correlations were approximately 0.84 and the Bland-Altman analysis showed acceptable interchangeability of methods. Within-group analysis demonstrated that patient samples obtained smaller values in validity indexes than controls. Globally, FreeSurfer's automated hippocampal volumetry showed adequate validity when compared to manual tracing, with a tendency to overestimation. Nevertheless, the greater difference between automated and manual segmentation in atrophic brains suggests that studies in AD based on this software could be more likely to produce false negatives.

ERP generator anomalies in presymptomatic carriers of the Alzheimer's disease E280A PS-1 mutation

Although subtle anatomical anomalies long precede the onset of clinical symptoms in Alzheimer's disease, their impact on the reorganization of brain networks underlying cognitive functions has not been fully explored. A unique window into this reorganization is provided by presymptomatic cases of familial Alzheimer's disease (FAD). Here we studied neural circuitry related to semantic processing in presymptomatic FAD cases by estimating the intracranial sources of the N400 event-related potential (ERP). ERPs were obtained during a semantic-matching task from 24 presymptomatic carriers and 25 symptomatic carriers of the E280A presenilin-1 (PS-1) mutation, as well as 27 noncarriers (from the same families). As expected, the symptomatic-carrier group performed worse in the matching task and had lower N400 amplitudes than both asymptomatic groups, which did not differ from each other on these variables. However, N400 topography differed in mutation carrier groups with respect to the noncarriers. Intracranial source analysis evinced that the presymptomatic-carriers presented a decrease of N400 generator strength in right inferior-temporal and medial cingulate areas and increased generator strength in the left hippocampus and parahippocampus compared to the controls. This represents alterations in neural function without translation into behavioral impairments. Compared to controls, the symptomatic-carriers presented a similar anatomical shift in the distribution of N400 generators to that found in presymptomatic-carriers, albeit with a larger reduction in generator strength. The redistribution of N400 generators in presymptomatic-carriers indicates that early focal degeneration associated with the mutation induces neural reorganization, possibly contributing to a functional compensation that enables normal performance in the semantic task.

Structural MRI discriminates individuals with Mild Cognitive Impairment from age-matched controls: a combined neuropsychological and voxel based morphometry study

BACKGROUND

Several previous studies have reported that amnestic mild cognitive impairment (aMCI), a significant risk factor for Alzheimer's disease (AD), is associated with greater atrophy in the medial temporal lobe (MTL) and posterior cingulate gyrus (PCG).

METHOD

In the present study, we examined the cross-sectional accuracy (i.e., the sensitivity and specificity) of voxel-based morphometry (VBM) in discriminating individuals with MCI (n =15) from healthy age-matched controls (n =15). In addition, we also sought to determine whether baseline GM volume predicted aMCI patients that converted to AD from those that did not approximately 2 years after the baseline visit.

RESULTS

MCI patients were found to display significantly less GM volume in several hypothesized regions including the MTL and PCG relative to the age-matched controls (p < 0.01). Logistic regression analysis and receiver operating characteristic (ROC) curves for GM volume in the anterior MTL and PCG revealed high discriminative accuracy of 87%. By contrast, baseline GM volume in anterior MTL and PCG did not appear to be sensitive to changes in clinical status at the follow-up visit.

CONCLUSION

These results suggest that VBM might be useful at characterizing GM volume reductions associated with the diagnosis of aMCI.

Neurostructural predictors of Alzheimer's disease: a meta-analysis of VBM studies

The identification of biological markers at early stages of Alzheimer's disease (AD) contributes to diagnostic accuracy and adds prognostic value. However, in spite of recent developments, results of neurostructural imaging studies on predicting conversion to AD are not uniform. We conducted a systematic review of voxel-based morphometry (VBM) studies about the neurostructural predictors of conversion to AD. Ten studies met inclusion criteria and nine reported baseline regional gray matter (GM) atrophy in mild cognitive impairment (MCI) or healthy subjects who progressed to AD. Using the method of Activation Likelihood Estimation, we meta-analyzed the coordinates from the six longitudinal VBM studies that enrolled subjects with amnestic MCI (aMCI) at baseline. These comprised a total of 429 aMCI subjects, of which 142 converted to AD. Meta-analysis yielded one significant cluster of GM volumetric reduction in aMCI patients who converted to AD, located in the left hippocampus and parahippocampal gyrus. In conclusion, left medial temporal lobe atrophy is the most consistent neurostructural biomarker to predict conversion from aMCI to AD.

A six stage approach for the diagnosis of the Alzheimer's disease based on fMRI data

The aim of this work is to present an automated method that assists in the diagnosis of Alzheimer's disease and also supports the monitoring of the progression of the disease. The method is based on features extracted from the data acquired during an fMRI experiment. It consists of six stages: (a) preprocessing of fMRI data, (b) modeling of fMRI voxel time series using a Generalized Linear Model, (c) feature extraction from the fMRI data, (d) feature selection, (e) classification using classical and improved variations of the Random Forests algorithm and Support Vector Machines, and (f) conversion of the trees, of the Random Forest, to rules which have physical meaning. The method is evaluated using a dataset of 41 subjects. The results of the proposed method indicate the validity of the method in the diagnosis (accuracy 94%) and monitoring of the Alzheimer's disease (accuracy 97% and 99%).

Automatic analysis of medial temporal lobe atrophy from structural MRIs for the early assessment of Alzheimer disease

The purpose of this study is to develop a software for the extraction of the hippocampus and surrounding medial temporal lobe (MTL) regions from T1-weighted magnetic resonance (MR) images with no interactive input from the user, to introduce a novel statistical indicator, computed on the intensities in the automatically extracted MTL regions, which measures atrophy, and to evaluate the accuracy of the newly developed intensity-based measure of MTL atrophy to (a) distinguish between patients with Alzheimer disease (AD), patients with amnestic mild cognitive impairment (aMCI), and elderly controls by using established criteria for patients with AD and aMCI as the reference standard and (b) infer about the clinical outcome of aMCI patients. For the development of the software, the study included 61 patients with mild AD (17 men, 44 women; mean age +/- standard deviation (SD), 75.8 years +/- 7.8; Mini Mental State Examination (MMSE) score, 24.1 +/- 3.1), 42 patients with aMCI (11 men, 31 women; mean age +/- SD, 75.2 years +/- 4.9; MMSE score, 27.9 +/- 1.9), and 30 elderly healthy controls (10 men, 20 women; mean age +/- SD, 74.7 years +/- 5.2; MMSE score, 29.1 +/- 0.8). For the evaluation of the statistical indicator, 150 patients with mild AD (62 men, 88 women; mean age +/- SD, 76.3 years +/- 5.8; MMSE score, 23.2 +/- 4.1), 247 patients with aMCI (143 men, 104 women; mean age +/- SD, 75.3 years +/- 6.7; MMSE score, 27.0 +/- 1.8), and 135 elderly healthy controls (61 men, 74 women; mean age +/- SD, 76.4 years +/- 6.1). Fifty aMCI patients were evaluated every 6 months over a 3 year period to assess conversion to AD. For each participant, two subimages of the MTL regions were automatically extracted from T1-weighted MR images with high spatial resolution. An intensity-based MTL atrophy measure was found to separate control, MCI, and AD cohorts. Group differences were assessed by using two-sample t test. Individual classification was analyzed by using receiver operating characteristic (ROC) curves. Compared to controls, significant differences in the intensity-based MTL atrophy measure were detected in both groups of patients (AD vs controls, 0.28 +/- 0.03 vs 0.34 +/- 0.03, P < 0.001; aMCI vs controls, 0.31 +/- 0.03 vs 0.34 +/- 0.03, P < 0.001). Moreover, the subgroup of aMCI converters was significantly different from controls (0.27 +/- 0.034 vs 0.34 +/- 0.03, P < 0.001). Regarding the ROC curve for intergroup discrimination, the area under the curve was 0.863 for AD patients vs controls, 0.746 for all aMCI patients vs controls, and 0.880 for aMCI converters vs controls. With specificity set at 85%, the sensitivity was 74% for AD vs controls, 45% for aMCI vs controls, and 83% for aMCI converters vs controls. The automated analysis of MTL atrophy in the segmented volume is applied to the early assessment of AD, leading to the discrimination of aMCI converters with an average 3 year follow-up. This procedure can provide additional useful information in the early diagnosis of AD.

In vivo quantification of hippocampal subfields using 4.7 T fast spin echo imaging

Several neuropsychiatric disorders involving hippocampal structural changes have been studied extensively using volumetric magnetic resonance imaging (MRI). These studies have mostly measured total hippocampal volume while the present study aimed to delineate and measure hippocampal subfields within the whole hippocampus and subdivisions along its longitudinal axis. Images were acquired at 4.7 T in 11 healthy subjects (5 males and 6 females, aged 23-56 years), using a fast spin echo (FSE) sequence with 0.52 x 0.68 x 1.0 mm(3) native resolution, collecting 90 contiguous coronal slices. Subiculum, cornu ammonis (CA1-3), and dentate gyrus were traced manually within the hippocampal head, body, and tail. We reported volumes for the subfields and demonstrated differences in the distribution within the hippocampus and its parts. The biggest part of the dentate gyrus was located in the hippocampal body, following the hippocampal head and tail. In contrast, the hippocampal head had the largest part of CA1-3, following the hippocampal body and tail. The hippocampal tail had the smallest portion of the subiculum compared to hippocampal head and tail. Subfield volumes were consistent between hemispheres and showed distributions within the longitudinal subdivisions that were consistent with histological data. Direct measurements of subfield distribution along the longitudinal axis of the hippocampus may be more sensitive to detecting disease effects than total volume measures and the differential distribution of subfield volumes may aid in the interpretation of measurements obtained at lower field strength and spatial resolution.

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

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

Spatially localized hippocampal shape analysis in late-life cognitive decline

We present a method for generating data-driven, concise, and spatially localized parameterizations of hippocampal (HP) shape, and use the method to analyze HP atrophy in late-life cognitive decline. The method optimizes a set of shape basis vectors (shape components) that strike a balance between spatial locality and compact representation of population shape characteristics. The method can be used for exploratory analysis of localized shape deformations in any population of HP on which point-to-point correspondence mappings have been established via anatomical landmarking or high-dimensional warping. Experiments combine the method with an automated HP to HP mapping method to analyze tracings of 101 elderly subjects with normal cognition, mild cognitive impairment, and Alzheimer's Disease (AD) from an AD Center population. Results suggest that shape components corresponding to atrophy to the CA1 and subiculum HP fields--where early AD pathology is located--correlate strongly with robust measures of the cognitive dysfunction that is typical of early AD. Furthermore, the energy function minimized by the shape component optimization technique is shown to be smooth with few local minima, suggesting that the method may be relatively easy to apply in practice.

Abnormal white matter independent of hippocampal atrophy in amnestic type mild cognitive impairment

Hippocampal atrophy is the key marker in the pathogenesis of Alzheimer's disease (AD), which is associated with white matter (WM) disruption. This type of WM disruption could partly explain AD-related pathology. However, relatively little attention has been directed toward WM disruption which may be independent of these fundamental gray matter (GM) changes in amnestic mild cognitive impairment (aMCI) which is associated with high risk of AD. To evaluate the differences of WM integrity between aMCI patients (N=32) and healthy controls (N=31), whole-brain voxel-based methods were applied to diffusion tensor imaging. To explore the possible independence of WM changes from GM loss, an index of hippocampal atrophy was used to partial out GM effects. aMCI patients showed WM disruption in frontal lobe, temporal lobe, internal capsule, cingulate gyrus and precuneus. The findings supported the evidence of independent patterns of degeneration in WM tracts which may co-act in the WM pathological process of aMCI patients. As aMCI is a putatively prodromal syndrome to AD, these data may assist with a better understanding of WM pathological change associated with the development of AD.

Abnormal functional connectivity of hippocampus during episodic memory retrieval processing network in amnestic mild cognitive impairment

BACKGROUND

Functional connectivity magnetic resonance imaging technique has revealed the importance of distributed network structures in higher cognitive processes in the human brain. The hippocampus has a key role in a distributed network supporting memory encoding and retrieval. Hippocampal dysfunction is a recurrent finding in memory disorders of aging such as amnestic mild cognitive impairment (aMCI) in which learning- and memory-related cognitive abilities are the predominant impairment. The functional connectivity method provides a novel approach in our attempts to better understand the changes occurring in this structure in aMCI patients.

METHODS

Functional connectivity analysis was used to examine episodic memory retrieval networks in vivo in twenty 28 aMCI patients and 23 well-matched control subjects, specifically between the hippocampal structures and other brain regions.

RESULTS

Compared with control subjects, aMCI patients showed significantly lower hippocampus functional connectivity in a network involving prefrontal lobe, temporal lobe, parietal lobe, and cerebellum, and higher functional connectivity to more diffuse areas of the brain than normal aging control subjects. In addition, those regions associated with increased functional connectivity with the hippocampus demonstrated a significantly negative correlation to episodic memory performance.

CONCLUSIONS

aMCI patients displayed altered patterns of functional connectivity during memory retrieval. The degree of this disturbance appears to be related to level of impairment of processes involved in memory function. Because aMCI is a putative prodromal syndrome to Alzheimer's disease (AD), these early changes in functional connectivity involving the hippocampus may yield important new data to predict whether a patient will eventually develop AD.

Regional shape abnormalities in mild cognitive impairment and Alzheimer's disease

Magnetic resonance (MR) based shape analysis provides an opportunity to detect regional specificity of volumetric changes that may distinguish mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy elderly controls (CON), and predict future conversion to AD. We assessed the surface deformation of seven structures (amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, body and temporal horn of the lateral ventricles) in 383 MRI volumes, based on data shared through the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI), to identify regionally-specific shape abnormalities in MCI and AD. Large deformation diffeomorphic metric mapping (LDDMM) was used to generate the shapes of seven structures based on template shapes injected into segmented subcortical volumes. LDDMM then constructed the surface deformation maps encoding the local shape variation of each subject relative to the template. Hierarchical models were developed to detect differences in local shape in MCI and AD relative to CON. Our findings revealed that surface inward-deformation in MCI and AD is most prominent in the anterior hippocampal segment and the basolateral complex of the amygdala. Most pronounced surface outward-deformation in MCI and AD occurs in the lateral ventricles. Mild surface inward-deformation in MCI and AD occurs in the anterior-lateral and ventral-lateral aspects of the thalamus, with no evidence of regionally-specific deformation in the putamen or globus pallidus. Although the locations of the shape abnormalities in MCI and AD are primarily within the mesial temporal region, analyses support distinct components of correlated shape variation that may help predict future MCI conversion.

Relating one-year cognitive change in mild cognitive impairment to baseline MRI features

BACKGROUND

We propose a completely automated methodology to investigate the relationship between magnetic resonance image (MRI) features and changes in cognitive estimates, applied to the study of Mini-Mental State Examination (MMSE) changes in mild cognitive impairment (MCI).

SUBJECTS

A reference group composed of 75 patients with clinically probable Alzheimer's Disease (AD) and 75 age-matched controls; and a study group composed of 49 MCI, 20 having progressed to clinically probable AD and 29 having remained stable after a 48 month follow-up.

METHODS

We created a pathology-specific reference space using principal component analysis of MRI-based features (intensity, local volume changes) within the medial temporal lobe of T1-weighted baseline images for the reference group. We projected similar data from the study group and identified a restricted set of image features highly correlated with one-year change in MMSE, using a bootstrap sampling estimation. We used robust linear regression models to predict one-year MMSE changes from baseline MRI, baseline MMSE, age, gender, and years of education.

RESULTS

All experiments were performed using a leave-one-out paradigm. We found multiple image-based features highly correlated with one-year MMSE changes (/r/>0.425). The model for all N=49 MCI subjects had a correlation of r=0.31 between actual and predicted one-year MMSE change values. A second model only for MCI subjects with MMSE loss larger than 1 U had a pairwise correlation r=0.80 with an adjusted coefficient of determination r(2)=0.61.

FINDINGS

Our automated MRI-based technique revealed a strong relationship between baseline MRI features and one-year cognitive changes in a sub-group of MCI subjects. This technique should be generalized to other aspects of cognitive evaluation and to a wider scope of dementias.

RABBIT: rapid alignment of brains by building intermediate templates

A brain image registration algorithm, referred to as RABBIT, is proposed to achieve fast and accurate image registration with the help of an intermediate template generated by a statistical deformation model. The statistical deformation model is built by principal component analysis (PCA) on a set of training samples of brain deformation fields that warp a selected template image to the individual brain samples. The statistical deformation model is capable of characterizing individual brain deformations by a small number of parameters, which is used to rapidly estimate the brain deformation between the template and a new individual brain image. The estimated deformation is then used to warp the template, thus generating an intermediate template close to the individual brain image. Finally, the shape difference between the intermediate template and the individual brain is estimated by an image registration algorithm, e.g., HAMMER. The overall registration between the template and the individual brain image can be achieved by directly combining the deformation fields that warp the template to the intermediate template, and the intermediate template to the individual brain image. The algorithm has been validated for spatial normalization of both simulated and real magnetic resonance imaging (MRI) brain images. Compared with HAMMER, the experimental results demonstrate that the proposed algorithm can achieve over five times speedup, with similar registration accuracy and statistical power in detecting brain atrophy.

Biomarkers for evaluation of clinical efficacy of multipotential neuroprotective drugs for Alzheimer's and Parkinson's diseases

During the last century, the world population has shown a staggering increase in its proportion of elderly members and thus neurodegenerative diseases like Alzheimer's disease (AD) and Parkinson's disease (PD), respectively, are becoming an increasing burden on society. Among the diverse, significant challenges facing clinicians, is the improvement of diagnostic measures to detect early and subtle symptoms, a phase in which prevention efforts might be expected to have their greatest impact and provide a measure of disease progression that can be evaluated during the course of drug treatment. At present, clinical diagnosis of AD and PD is based on a constellation of symptoms and manifestations, although the disease originated several years earlier. Given the multiple etiological nature of AD and PD, it is reasonable to assume that the initial causative pathobiological processes may differ between the affected individuals. Therefore, the availability of biological markers or biomarkers will help not only early disease diagnosis, but also delineate the pathological mechanisms more definitively and reliably than the traditional cognitive and neurological phenotypes. In the current article, we review the literature on biochemical, genetic, and neuroimaging biomarkers and discuss their predictive value as indicative for disease vulnerability to detect individuals at risk for PD and AD, and to determine the clinical efficacy of novel, disease-modifying (neuroprotective) strategies.

Temporoparietal MR imaging measures of atrophy in subjects with mild cognitive impairment that predict subsequent diagnosis of Alzheimer disease

BACKGROUND AND PURPOSE

Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer disease (AD). Our goal was to determine if specific temporoparietal regions can predict the time to progress from MCI to AD.

MATERIALS AND METHODS

MR images from 129 individuals with MCI were analyzed to identify the volume of 14 neocortical and 2 non-neocortical brain regions, comprising the temporal and parietal lobes. In addition, 3 neuropsychological test scores were included to determine whether they would provide independent information. After a mean follow-up time of 5 years, 44 of these individuals had progressed to a diagnosis of AD.

RESULTS

Cox proportional hazards models demonstrated significant effects for 6 MR imaging regions with the greatest differences being the following: the entorhinal cortex (hazard ratio [HR] = 0.54, P < .001), inferior parietal lobule (hazard ratio [HR] = 0.64, P < .005), and middle temporal gyrus (HR = 0.64, P < .004), indicating decreased risk with larger volumes. A multivariable model showed that a combination of the entorhinal cortex (HR = 0.60, P < .001) and the inferior parietal lobule (HR = 0.62, P < .01) was the best predictor of time to progress to AD. A multivariable model reiterated the importance of including both MR imaging and neuropsychological variables in the final model.

CONCLUSIONS

These findings reaffirm the importance of the entorhinal cortex and present evidence for the importance of the inferior parietal lobule as a predictor of time to progress from MCI to AD. The inclusion of neuropsychological performance in the final model continues to highlight the importance of using these measures in a complementary fashion.

Validation of a fully automated hippocampal segmentation method on patients with dementia

We describe a fully automated method for hippocampal segmentation. The method uses SPM5 (http://www.fil.ion.ucl.ac.uk/spm/) software to segment the brain into grey/white matter, and spatially normalize the images to standard space. Grey matter pixels within a predefined hippocampal region in standard space are identified to segment the hippocampi. The method was validated on 36 subjects (9 each of Alzheimer's disease, dementia with Lewy bodies, vascular dementia, and healthy controls). The mean absolute difference in volume compared with manual segmentation was 11% (SD 9%). Linear regression between manual and automated volume gave V(auto) = V(manual) x 0.83 + 401 ml. The method provides an acceptable automated alternative to manual segmentation which may be of value in large studies.

Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI

High-dimensional pattern classification was applied to baseline and multiple follow-up MRI scans of the Alzheimer's Disease Neuroimaging Initiative (ADNI) participants with mild cognitive impairment (MCI), in order to investigate the potential of predicting short-term conversion to Alzheimer's Disease (AD) on an individual basis. MCI participants that converted to AD (average follow-up 15 months) displayed significantly lower volumes in a number of grey matter (GM) regions, as well as in the white matter (WM). They also displayed more pronounced periventricular small-vessel pathology, as well as an increased rate of increase of such pathology. Individual person analysis was performed using a pattern classifier previously constructed from AD patients and cognitively normal (CN) individuals to yield an abnormality score that is positive for AD-like brains and negative otherwise. The abnormality scores measured from MCI non-converters (MCI-NC) followed a bimodal distribution, reflecting the heterogeneity of this group, whereas they were positive in almost all MCI converters (MCI-C), indicating extensive patterns of AD-like brain atrophy in almost all MCI-C. Both MCI subgroups had similar MMSE scores at baseline. A more specialized classifier constructed to differentiate converters from non-converters based on their baseline scans provided good classification accuracy reaching 81.5%, evaluated via cross-validation. These pattern classification schemes, which distill spatial patterns of atrophy to a single abnormality score, offer promise as biomarkers of AD and as predictors of subsequent clinical progression, on an individual patient basis.

Episodic, but not semantic, autobiographical memory is reduced in amnestic mild cognitive impairment

Amnestic mild cognitive impairment (aMCI) is characterized by decline in anterograde memory as measured by the ability to learn and remember new information. We investigated whether retrograde memory for autobiographical information was affected by aMCI. Eighteen control (age 66-84 years) and 17 aMCI (age 66-84 years) participants described a personal event from each of the five periods across the lifespan. These events were transcribed and scored according to procedures that separate episodic (specific happenings) from semantic (general knowledge) elements of autobiographical memory. Although both groups generated protocols of similar length, the composition of autobiographical recall differentiated the groups. The aMCI group protocols were characterized by reduced episodic and increased semantic information relative to the control group. Both groups showed a similar pattern of recall across time periods, with no evidence that the aMCI group had more difficulty recalling recent, rather than remote, life events. These results indicate that episodic and semantic autobiographical memories are differentially affected by the early brain changes associated with aMCI. Reduced autobiographical episodic memories in aMCI may be the result of medial temporal lobe dysfunction, consistent with multiple trace theory, or alternatively, could be related to dysfunction of a wider related network of neocortical structures. In contrast, the preservation of autobiographical semantic memories in aMCI suggests neural systems, such as lateral temporal cortex, that support these memories, may remain relatively intact.

Amnestic MCI future clinical status prediction using baseline MRI features

Amnestic mild cognitive impairment (aMCI) individuals are known to be at risk for progression to clinically probable Alzheimer's disease (AD). The objective of this work is to measure the accuracy of an automated classification technique based on clinical-quality, single time-point structural magnetic resonance imaging (MRI) scans for the retrospective prediction of future clinical status in aMCI. Thirty-one aMCI research subjects were followed with annual clinical reassessment after baseline MRI. Twenty subjects progressed to probable AD within an average 2.2 (1.4) years [mean age 76.6 (4.7) years, MMSE 27.1 (2.3)], while 11 remained non-demented on average 5.6 (2.6) years after baseline [mean age 73.3 (7.2) years, MMSE 28.2 (1.8)]. Leave-one-out classification was performed within a multidimensional MRI feature space built from intensity and local volume estimate data of a reference group of 75 probable AD and 75 age-matched control subjects. Prediction using aMCI data reached 81% accuracy, 70% sensitivity and 100% specificity. This automated and objective method has potential in helping predict future clinical status in aMCI.

Brain imaging in drug R&D

Magnetic resonance imaging (MRI), used as a clinical diagnostic tool since the early 1980s, is rapidly gaining traction as an integral part of the drug development process. Brain imaging research spans a wide area, covering both structure and function, and ranging from the physics and physiology associated with novel acquisition techniques, to the development of sophisticated image processing algorithms. This paper briefly describes two methods on either end of this spectrum: the "pipeline" framework for the fully automated morphometric analysis of brain imaging data, and molecular MRI, which holds promise for the non-invasive detection of molecular targets of new pharmacological compounds. The potential use of these technologies is illustrated by examples of their applications in multiple sclerosis, Alzheimer's disease, and oncology.

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.

T1rho MRI of Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia in the elderly. Classic symptoms of the disease include memory loss and confusion associated with the hallmark neuro-pathologic lesions of neurofibrillary tangles (NFT) and senile plaques (SP) and their sequelae, gray matter atrophy. Volumetric assessment methods measure tissue atrophy, which typically follows early biochemical changes. An alternate MRI contrast mechanism to visualize the early pathological changes is T1rho (or "T-1-rho"), the spin lattice relaxation time constant in the rotating frame, which determines the decay of the transverse magnetization in the presence of a "spin-lock" radio-frequency field. Macromolecular changes (in plaques and tangles) that accompany early AD are expected to alter bulk water T1rho relaxation times. In this work, we measure T1rho MRI on patients with clinically diagnosed AD, MCI and in age-matched cognitively normal control subjects in order to compare T1rho values with changes in brain volume in the same regions of the brain and demonstrate that T1rho can potentially constitute an important biomarker of AD.

Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI

The purpose of this study is to determine the diagnostic accuracy of MRI-based high-dimensional pattern classification in differentiating between patients with Alzheimer's disease (AD), Frontotemporal Dementia (FTD), and healthy controls, on an individual patient basis. MRI scans of 37 patients with AD and 37 age-matched cognitively normal elderly individuals, as well as 12 patients with FTD and 12 age-matched cognitively normal elderly individuals, were analyzed using voxel-based analysis and high-dimensional pattern classification. Diagnostic sensitivity and specificity of spatial patterns of regional brain atrophy found to be characteristic of AD and FTD were determined via cross-validation and via split-sample methods. Complex spatial patterns of relatively reduced brain volumes were identified, including temporal, orbitofrontal, parietal and cingulate regions, which were predominantly characteristic of either AD or FTD. These patterns provided 100% diagnostic accuracy, when used to separate AD or FTD from healthy controls. The ability to correctly distinguish AD from FTD averaged 84.3%. All estimates of diagnostic accuracy were determined via cross-validation. In conclusion, AD- and FTD-specific patterns of brain atrophy can be detected with high accuracy using high-dimensional pattern classification of MRI scans obtained in a typical clinical setting.

Ventricular enlargement as a possible measure of Alzheimer's disease progression validated using the Alzheimer's disease neuroimaging initiative database

Ventricular enlargement may be an objective and sensitive measure of neuropathological change associated with mild cognitive impairment (MCI) and Alzheimer's disease (AD), suitable to assess disease progression for multi-centre studies. This study compared (i) ventricular enlargement after six months in subjects with MCI, AD and normal elderly controls (NEC) in a multi-centre study, (ii) volumetric and cognitive changes between Apolipoprotein E genotypes, (iii) ventricular enlargement in subjects who progressed from MCI to AD, and (iv) sample sizes for multi-centre MCI and AD studies based on measures of ventricular enlargement. Three dimensional T(1)-weighted MRI and cognitive measures were acquired from 504 subjects (NEC n = 152, MCI n = 247 and AD n = 105) participating in the multi-centre Alzheimer's Disease Neuroimaging Initiative. Cerebral ventricular volume was quantified at baseline and after six months using semi-automated software. For the primary analysis of ventricle and neurocognitive measures, between group differences were evaluated using an analysis of covariance, and repeated measures t-tests were used for within group comparisons. For secondary analyses, all groups were dichotomized for Apolipoprotein E genotype based on the presence of an epsilon 4 polymorphism. In addition, the MCI group was dichotomized into those individuals who progressed to a clinical diagnosis of AD, and those subjects that remained stable with MCI after six months. Group differences on neurocognitive and ventricle measures were evaluated by independent t-tests. General sample size calculations were computed for all groups derived from ventricle measurements and neurocognitive scores. The AD group had greater ventricular enlargement compared to both subjects with MCI (P = 0.0004) and NEC (P < 0.0001), and subjects with MCI had a greater rate of ventricular enlargement compared to NEC (P = 0.0001). MCI subjects that progressed to clinical AD after six months had greater ventricular enlargement than stable MCI subjects (P = 0.0270). Ventricular enlargement was different between Apolipoprotein E genotypes within the AD group (P = 0.010). The number of subjects required to demonstrate a 20% change in ventricular enlargement was substantially lower than that required to demonstrate a 20% change in cognitive scores. Ventricular enlargement represents a feasible short-term marker of disease progression in subjects with MCI and subjects with AD for multi-centre studies.

Small baseline volume of left hippocampus is associated with subsequent conversion of MCI into dementia: the Göteborg MCI study

BACKGROUND

Earlier studies have reported that hippocampal atrophy can to some extent predict which patients with mild cognitive impairment (MCI) will subsequently convert to dementia, and that converters have an enhanced rate of hippocampal volume loss.

OBJECTIVE

To further validate the hypothesis that hippocampal atrophy predicts conversion from MCI to dementia, to relate baseline hippocampal volume to different forms of dementia, and to investigate the role of hippocampal side differences and rate of volume loss over time.

PATIENTS

The subjects (N=68) include patients with MCI at baseline and progression to dementia at the two-year follow-up (N=21), stable MCI patients (N=21), and controls (N=26). Among the progressing patients, 13 were diagnosed as having AD.

METHODS

The Göteborg MCI study is a clinically based longitudinal study with biannual clinical assessments. Hippocampal volumetry was performed manually on the MRI investigations at baseline and at the two-year follow-up.

RESULTS

Hippocampal volumetry could predict conversion to dementia in both the AD and the non-AD subgroup of converters. Left hippocampal volume in particular discriminated between converting and stable MCI. Cut off points for individual discrimination were shown to be potentially useful. The converting MCI group had a significantly higher rate of hippocampal volume loss as compared to the stable MCI group.

CONCLUSIONS

In MCI patients, hippocampal volumetry at baseline gives prognostic information about possible development of AD and non-AD dementia. Contrary to earlier studies, we found that left hippocampal volume has the best predictive power. Reliable predictions appear to be possible in many individual cases.

Naming of objects, faces and buildings in mild cognitive impairment

Accruing evidence suggests that the cognitive deficits in very early Alzheimer's Disease (AD) are not confined to episodic memory, with a number of studies documenting semantic memory deficits, especially for knowledge of people. To investigate whether this difficulty in naming famous people extends to other proper names based information, three naming tasks - the Graded Naming Test (GNT), which uses objects and animals, the Graded Faces Test (GFT) and the newly designed Graded Buildings Test (GBT) - were administered to 69 participants (32 patients in the early prodromal stage of AD, so-called Mild Cognitive Impairment (MCI), and 37 normal control participants). Patients were found to be impaired on all three tests compared to controls, although naming of objects was significantly better than naming of faces and buildings. Discriminant analysis successfully predicted group membership for 100% controls and 78.1% of patients. The results suggest that even in cases that do not yet fulfil criteria for AD naming of famous people and buildings is impaired, and that both these semantic domains show greater vulnerability than general semantic knowledge. A semantic deficit together with the hallmark episodic deficit may be common in MCI, and that the use of graded tasks tapping semantic memory may be useful for the early identification of patients with MCI.

Brain tissue volumes in relation to cognitive function and risk of dementia

We investigated in a population-based cohort study the association of global and lobar brain tissue volumes with specific cognitive domains and risk of dementia. Participants (n=490; 60-90 years) were non-demented at baseline (1995-1996). From baseline brain MRI-scans we obtained global and lobar volumes of CSF, GM, normal WM, white matter lesions and hippocampus. We performed neuropsychological testing at baseline to assess information processing speed, executive function, memory function and global cognitive function. Participants were followed for incident dementia until January 1, 2005. Larger volumes of CSF and WML were associated with worse performance on all neuropsychological tests, and an increased risk of dementia. Smaller WM volume was related to poorer information processing speed and executive function. In contrast, smaller GM volume was associated with worse memory function and increased risk of dementia. When investigating lobar GM volumes, we found that hippocampal volume and temporal GM volume were most strongly associated with risk of dementia, even in persons without objective and subjective cognitive deficits at baseline, followed by frontal and parietal GM volumes.

Novel MRI techniques in the assessment of dementia

INTRODUCTION

Positive markers of Alzheimer's disease (AD) have been established in MRI that may allow early detection of AD in at-risk groups. In the near future, these markers will be of high relevance for the selection of at-risk subjects in secondary preventive trials.

METHODS

We describe the methodology and diagnostic value of manual volumetry of the hippocampus and entorhinal cortex, automated voxel-based morphometry, cortical thickness measurement, basal forebrain volumetry and deformation-based morphometry, implementing multivariate statistics and machine learning algorithms to improve group separation and prediction of AD in at-risk groups. We also describe the methodological basis and results obtained in AD using the recently developed technique of diffusion tensor-based morphometry (DTI). This technique gives access to the integrity of subcortical fibre systems in the human brain.

RESULTS

The best established structural biomarker of AD to date is hippocampus volume that already has been implemented as secondary endpoint in clinical trials on disease modification in AD. Automated approaches will gain an increasing role as endpoints of clinical trials in the near future given the interest in these techniques expressed by the regulatory authorities. DTI is still a developing field where analysis techniques are presently being devised to make optimal use of the multivariate data. Data on changes of fibre tract in preclinical AD are still limited, but the first results are promising in respect to a further enhancement of diagnostic accuracy by combining MRI and DTI.

CONCLUSION

Besides their diagnostic use, MRI and DTI will broaden our understanding of the pathophysiology of AD and the structural and functional basis of normal cognition.

Differential patterns of hypoperfusion in subtypes of mild cognitive impairment

In this study the regional cerebral blood flow (rCBF) pattern of three Mild Cognitive Impairment (MCI) subtypes was measured with SPECT in 60 patients (nineteen with an amnestic deficit, sixteen with disexecutive deficits, and twenty five with mild multidomain deficits) and compared with that of 15 healthy matched older adults. The amnestic MCI subgroup showed significant hypoperfusion in the left hippocampus, parahippocampal gyrus and fronto-parieto-temporal areas. The disexecutive subgroup had significant hypoperfusion of the left superior, medial frontal and cingulate cortex. The multidomain subgroup had similar perfusion deficits to the amnestic subgroup, with an additional deficit in the left posterior cingulate gyrus. This study found differential patterns of hypoperfusion in MCI subtypes. Since all patients who progressed to dementia converted to probable Alzheimer's disease, the different rCBF patterns most likely reflect the neuropathological heterogeneity at onset and differences in disease stage.

In Vivo 3D Digital Atlas Database of the Adult C57BL/6J Mouse Brain by Magnetic Resonance Microscopy

In this study, a 3D digital atlas of the live mouse brain based on magnetic resonance microscopy (MRM) is presented. C57BL/6J adult mouse brains were imaged in vivo on a 9.4 Tesla MR instrument at an isotropic spatial resolution of 100 μm. With sufficient signal-to-noise (SNR) and contrast-to-noise ratio (CNR), 20 brain regions were identified. Several atlases were constructed including 12 individual brain atlases, an average atlas, a probabilistic atlas and average geometrical deformation maps. We also investigated the feasibility of using lower spatial resolution images to improve time efficiency for future morphological phenotyping. All of the new in vivo data were compared to previous published in vitro C57BL/6J mouse brain atlases and the morphological differences were characterized. Our analyses revealed significant volumetric as well as unexpected geometrical differences between the in vivo and in vitro brain groups which in some instances were predictable (e.g. collapsed and smaller ventricles in vitro) but not in other instances. Based on these findings we conclude that although in vitro datasets, compared to in vivo images, offer higher spatial resolutions, superior SNR and CNR, leading to improved image segmentation, in vivo atlases are likely to be an overall better geometric match for in vivo studies, which are necessary for longitudinal examinations of the same animals and for functional brain activation studies. Thus the new in vivo mouse brain atlas dataset presented here is a valuable complement to the current mouse brain atlas collection and will be accessible to the neuroscience community on our public domain mouse brain atlas website.

Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

We report evidence that computer-based high-dimensional pattern classification of magnetic resonance imaging (MRI) detects patterns of brain structure characterizing mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's disease (AD). Ninety percent diagnostic accuracy was achieved, using cross-validation, for 30 participants in the Baltimore Longitudinal Study of Aging. Retrospective evaluation of serial scans obtained during prior years revealed gradual increases in structural abnormality for the MCI group, often before clinical symptoms, but slower increase for individuals remaining cognitively normal. Detecting complex patterns of brain abnormality in very early stages of cognitive impairment has pivotal importance for the detection and management of AD.