Lower cerebral blood flow predicts cognitive decline in patients with vascular cognitive impairment.

INTRODUCTION: Chronic cerebral hypoperfusion is one of the assumed pathophysiological mechanisms underlying vascular cognitive impairment (VCI). We investigated the association between baseline cerebral blood flow (CBF) and cognitive decline after 2 years in patients with VCI and reference participants. METHODS: One hundred eighty-one participants (mean age 66.3 ± 7.4 years, 43.6% women) underwent arterial spin labeling (ASL) magnetic resonance imaging (MRI) and neuropsychological assessment at baseline and at 2-year follow-up. We determined the association between baseline global and lobar CBF and cognitive decline with multivariable regression analysis. RESULTS: Lower global CBF at baseline was associated with more global cognitive decline in VCI and reference participants. This association was most profound in the domain of attention/psychomotor speed. Lower temporal and frontal CBF at baseline were associated with more cognitive decline in memory. DISCUSSION: Our study supports the role of hypoperfusion in the pathophysiological and clinical progression of VCI. HIGHLIGHTS: Impaired cerebral blood flow (CBF) at baseline is associated with faster cognitive decline in VCI and normal aging. Our results suggest that low CBF precedes and contributes to the development of vascular cognitive impairment. CBF determined by ASL might be used as a biomarker to monitor disease progression or treatment responses in VCI.

Evolution from a first clinical demyelinating event to multiple sclerosis in the REFLEX trial: Regional susceptibility in the conversion to multiple sclerosis at disease onset and its amenability to subcutaneous interferon beta-1a.

BACKGROUND AND PURPOSE: In the REFLEX trial (ClinicalTrials.gov identifier: NCT00404352), patients with a first clinical demyelinating event (FCDE) displayed significantly delayed onset of multiple sclerosis (MS; McDonald criteria) when treated with subcutaneous interferon beta-1a (sc IFN ß-1a) versus placebo. This post hoc analysis evaluated the effect of sc IFN ß-1a on spatio-temporal evolution of disease activity, assessed by changes in T2 lesion distribution, in specific brain regions of such patients and its relationship with conversion to MS. METHODS: Post hoc analysis of baseline and 24-month magnetic resonance imaging data from FCDE patients who received sc IFN ß-1a 44 µg once or three times weekly, or placebo in the REFLEX trial. Patients were grouped according to McDonald MS status (converter/non-converter) or treatment (sc IFN ß-1a/placebo). For each patient group, a baseline lesion probability map (LPM) and longitudinal new/enlarging and shrinking/disappearing LPMs were created. Lesion location/frequency of lesion occurrence were assessed in the white matter. RESULTS: At Month 24, lesion frequency was significantly higher in the anterior thalamic radiation (ATR) and corticospinal tract (CST) of converters versus non-converters (p < 0.05). Additionally, the overall distribution of new/enlarging lesions across the brain at Month 24 was similar in placebo- and sc IFN ß-1a-treated patients (ratio: 0.95). Patients treated with sc IFN ß-1a versus placebo showed significantly lower new lesion frequency in specific brain regions (cluster corrected): ATR (p = 0.025), superior longitudinal fasciculus (p = 0.042), CST (p = 0.048), and inferior longitudinal fasciculus (p = 0.048). CONCLUSIONS: T2 lesion distribution in specific brain locations predict conversion to McDonald MS and show significantly reduced new lesion occurrence after treatment with sc IFN ß-1a in an FCDE population.

EASE: Clinical Implementation of Automated Tumor Segmentation and Volume Quantification for Adult Low-Grade Glioma.

The growth rate of non-enhancing low-grade glioma has prognostic value for both malignant progression and survival, but quantification of growth is difficult due to the irregular shape of the tumor. Volumetric assessment could provide a reliable quantification of tumor growth, but is only feasible if fully automated. Recent advances in automated tumor segmentation have made such a volume quantification possible, and this work describes the clinical implementation of automated volume quantification in an application named EASE: Erasmus Automated SEgmentation. The visual quality control of segmentations by the radiologist is an important step in this process, as errors in the segmentation are still possible. Additionally, to ensure patient safety and quality of care, protocols were established for the usage of volume measurements in clinical diagnosis and for future updates to the algorithm. Upon the introduction of EASE into clinical practice, we evaluated the individual segmentation success rate and impact on diagnosis. In its first 3 months of usage, it was applied to a total of 55 patients, and in 36 of those the radiologist was able to make a volume-based diagnosis using three successful consecutive measurements from EASE. In all cases the volume-based diagnosis was in line with the conventional visual diagnosis. This first cautious introduction of EASE in our clinic is a valuable step in the translation of automatic segmentation methods to clinical practice.

Cerebral Blood Flow in Patients with Severe Aortic Valve Stenosis Undergoing Transcatheter Aortic Valve Implantation.

BACKGROUND: Transcatheter aortic valve implantation (TAVI) is a minimally invasive, life-saving treatment for patients with severe aortic valve stenosis that improves quality of life. We examined cardiac output and cerebral blood flow in patients undergoing TAVI to test the hypothesis that improved cardiac output after TAVI is associated with an increase in cerebral blood flow. DESIGN: Prospective cohort study. SETTING: European high-volume tertiary multidisciplinary cardiac care. PARTICIPANTS: Thirty-one patients (78.3 ± 4.6 years; 61% female) with severe symptomatic aortic valve stenosis. MEASUREMENTS: Noninvasive prospective assessment of cardiac output (L/min) by inert gas rebreathing and cerebral blood flow of the total gray matter (mL/100 g per min) using arterial spin labeling magnetic resonance imaging in resting state less than 24 hours before TAVI and at 3-month follow-up. Cerebral blood flow change was defined as the difference relative to baseline. RESULTS: On average, cardiac output in patients with severe aortic valve stenosis increased from 4.0 ± 1.1 to 5.4 ± 2.4 L/min after TAVI (P = .003). The increase in cerebral blood flow after TAVI strongly varied between patients (7% ± 24%; P = .41) and related to the increase in cardiac output, with an 8.2% (standard error = 2.3%; P = .003) increase in cerebral blood flow per every additional liter of cardiac output following the TAVI procedure. CONCLUSION: Following TAVI, there was an association of increase in cardiac output with increase in cerebral blood flow. These findings encourage future larger studies to determine the influence of TAVI on cerebral blood flow and cognitive function.

Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multi-center cohort.

BACKGROUND: Deep grey matter (DGM) atrophy in multiple sclerosis (MS) and its relation to cognitive and clinical decline requires accurate measurements. MS pathology may deteriorate the performance of automated segmentation methods. Accuracy of DGM segmentation methods is compared between MS and controls, and the relation of performance with lesions and atrophy is studied. METHODS: On images of 21 MS subjects and 11 controls, three raters manually outlined caudate nucleus, putamen and thalamus; outlines were combined by majority voting. FSL-FIRST, FreeSurfer, Geodesic Information Flow and volBrain were evaluated. Performance was evaluated volumetrically (intra-class correlation coefficient (ICC)) and spatially (Dice similarity coefficient (DSC)). Spearman's correlations of DSC with global and local lesion volume, structure of interest volume (ROIV), and normalized brain volume (NBV) were assessed. RESULTS: ICC with manual volumes was mostly good and spatial agreement was high. MS exhibited significantly lower DSC than controls for thalamus and putamen. For some combinations of structure and method, DSC correlated negatively with lesion volume or positively with NBV or ROIV. Lesion-filling did not substantially change segmentations. CONCLUSIONS: Automated methods have impaired performance in patients. Performance generally deteriorated with higher lesion volume and lower NBV and ROIV, suggesting that these may contribute to the impaired performance.

Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study.

BACKGROUND AND PURPOSE: In vivoidentification of white matter lesions plays a key-role in evaluation of patients with multiple sclerosis (MS). Automated lesion segmentation methods have been developed to substitute manual outlining, but evidence of their performance in multi-center investigations is lacking. In this work, five research-domain automated segmentation methods were evaluated using a multi-center MS dataset. METHODS: 70 MS patients (median EDSS of 2.0 [range 0.0-6.5]) were included from a six-center dataset of the MAGNIMS Study Group (www.magnims.eu) which included 2D FLAIR and 3D T1 images with manual lesion segmentation as a reference. Automated lesion segmentations were produced using five algorithms: Cascade; Lesion Segmentation Toolbox (LST) with both the Lesion growth algorithm (LGA) and the Lesion prediction algorithm (LPA); Lesion-Topology preserving Anatomical Segmentation (Lesion-TOADS); and k-Nearest Neighbor with Tissue Type Priors (kNN-TTP). Main software parameters were optimized using a training set (N = 18), and formal testing was performed on the remaining patients (N = 52). To evaluate volumetric agreement with the reference segmentations, intraclass correlation coefficient (ICC) as well as mean difference in lesion volumes between the automated and reference segmentations were calculated. The Similarity Index (SI), False Positive (FP) volumes and False Negative (FN) volumes were used to examine spatial agreement. All analyses were repeated using a leave-one-center-out design to exclude the center of interest from the training phase to evaluate the performance of the method on 'unseen' center. RESULTS: Compared to the reference mean lesion volume (4.85 ± 7.29 mL), the methods displayed a mean difference of 1.60 ± 4.83 (Cascade), 2.31 ± 7.66 (LGA), 0.44 ± 4.68 (LPA), 1.76 ± 4.17 (Lesion-TOADS) and -1.39 ± 4.10 mL (kNN-TTP). The ICCs were 0.755, 0.713, 0.851, 0.806 and 0.723, respectively. Spatial agreement with reference segmentations was higher for LPA (SI = 0.37 ± 0.23), Lesion-TOADS (SI = 0.35 ± 0.18) and kNN-TTP (SI = 0.44 ± 0.14) than for Cascade (SI = 0.26 ± 0.17) or LGA (SI = 0.31 ± 0.23). All methods showed highly similar results when used on data from a center not used in software parameter optimization. CONCLUSION: The performance of the methods in this multi-center MS dataset was moderate, but appeared to be robust even with new datasets from centers not included in training the automated methods.

Reproducibility of hippocampal atrophy rates measured with manual, FreeSurfer, AdaBoost, FSL/FIRST and the MAPS-HBSI methods in Alzheimer's disease.

The purpose of this study is to assess the reproducibility of hippocampal atrophy rate measurements of commonly used fully-automated algorithms in Alzheimer disease (AD). The reproducibility of hippocampal atrophy rate for FSL/FIRST, AdaBoost, FreeSurfer, MAPS independently and MAPS combined with the boundary shift integral (MAPS-HBSI) were calculated. Back-to-back (BTB) 3D T1-weighted MPRAGE MRI from the Alzheimer's Disease Neuroimaging Initiative (ADNI1) study at baseline and year one were used. Analysis on 3 groups of subjects was performed - 562 subjects at 1.5T, a 75 subject group that also had manual segmentation and 111 subjects at 3T. A simple and novel statistical test based on the binomial distribution was used that handled outlying data points robustly. Median hippocampal atrophy rates were -1.1%/year for healthy controls, -3.0%/year for mildly cognitively impaired and -5.1%/year for AD subjects. The best reproducibility was observed for MAPS-HBSI (1.3%), while the other methods tested had reproducibilities at least 50% higher at 1.5T and 3T which was statistically significant. For a clinical trial, MAPS-HBSI should require less than half the subjects of the other methods tested. All methods had good accuracy versus manual segmentation. The MAPS-HBSI method has substantially better reproducibility than the other methods considered.

Postmortem validation of MRI cortical volume measurements in MS.

Grey matter (GM) atrophy is a prominent aspect of multiple sclerosis pathology and an important outcome in studies. GM atrophy measurement requires accurate GM segmentation. Several methods are used in vivo for measuring GM volumes in MS, but assessing their validity in vivo remains challenging. In this postmortem study, we evaluated the correlation between postmortem MRI cortical volume or thickness and the cortical thickness measured on histological sections. Sixteen MS brains were scanned in situ using 3DT1-weighted MRI and these images were used to measure regional cortical volume using FSL-SIENAX, FreeSurfer, and SPM, and regional cortical thickness using FreeSurfer. Subsequently, cortical thickness was measured histologically in 5 systematically sampled cortical areas. Linear regression analyses were used to evaluate the relation between MRI regional cortical volume or thickness and histological cortical thickness to determine which postprocessing technique was most valid. After correction for multiple comparisons, we observed a significant correlation with the histological cortical thickness for FSL-SIENAX cortical volume with manual editing (std. ß = 0.345, adjusted R(2) = 0.105, P = 0.005), and FreeSurfer cortical volume with manual editing (std. ß = 0.379, adjusted R(2) = 0.129, P = 0.003). In addition, there was a significant correlation between FreeSurfer cortical thickness with manual editing and histological cortical thickness (std. ß = 0.381, adjusted R(2) = 0.130, P = 0.003). The results support the use of FSL-SIENAX and FreeSurfer in cases of severe MS pathology. Interestingly none of the methods were significant in automated mode, which supports the use of manual editing to improve the automated segmentation. Hum Brain Mapp 37:2223-2233, 2016. © 2016 Wiley Periodicals, Inc.

Different patterns of cortical gray matter loss over time in behavioral variant frontotemporal dementia and Alzheimer's disease.

We examined patterns of cortical thickness loss and cognitive decline over time in 19 patients with Alzheimer's disease (AD), 10 with behavioral variant frontotemporal dementia (bvFTD), and 34 controls with a mean interval of 2.1 ± 0.4 years. We measured vertexwise and regional cortical thickness changes of 6 lobar regions of interest between groups with the longitudinal FreeSurfer pipeline. Compared with controls, AD and bvFTD had a steeper rate of cognitive decline and showed faster cortical thinning per year. Decrease of thickness over time was highest in AD and generalized throughout the whole brain, most pronounced posteriorly, whereas bvFTD patients had a more selective loss in frontal cortex and in anterior parts of the temporal lobes. In a direct comparison, AD patients showed faster cortical thinning in the insula, temporal, and parietal regions, whereas bvFTD patients only showed faster cortical thinning in the orbitofrontal gyrus. Decline of cognitive performances was in line with cortical thinning and deteriorated the most in AD patients.

Grey Matter Atrophy in Multiple Sclerosis: Clinical Interpretation Depends on Choice of Analysis Method.

BACKGROUND: Studies disagree on the location of grey matter (GM) atrophy in the multiple sclerosis (MS) brain. AIM: To examine the consistency between FSL, FreeSurfer, SPM for GM atrophy measurement (for volumes, patient/control discrimination, and correlations with cognition). MATERIALS AND METHODS: 127 MS patients and 50 controls were included and cortical and deep grey matter (DGM) volumetrics were performed. Consistency of volumes was assessed with Intraclass Correlation Coefficient/ICC. Consistency of patients/controls discrimination was assessed with Cohen's d, t-tests, MANOVA and a penalized double-loop logistic classifier. Consistency of association with cognition was assessed with Pearson correlation coefficient and ANOVA. Voxel-based morphometry (SPM-VBM and FSL-VBM) and vertex-wise FreeSurfer were used for group-level comparisons. RESULTS: The highest volumetry ICC were between SPM and FreeSurfer for cortical regions, and the lowest between SPM and FreeSurfer for DGM. The caudate nucleus and temporal lobes had high consistency between all software, while amygdala had lowest volumetric consistency. Consistency of patients/controls discrimination was largest in the DGM for all software, especially for thalamus and pallidum. The penalized double-loop logistic classifier most often selected the thalamus, pallidum and amygdala for all software. FSL yielded the largest number of significant correlations. DGM yielded stronger correlations with cognition than cortical volumes. Bilateral putamen and left insula volumes correlated with cognition using all methods. CONCLUSION: GM volumes from FreeSurfer, FSL and SPM are different, especially for cortical regions. While group-level separation between MS and controls is comparable, correlations between regional GM volumes and clinical/cognitive variables in MS should be cautiously interpreted.

Thinner temporal and parietal cortex is related to incident clinical progression to dementia in patients with subjective cognitive decline.

INTRODUCTION: We aimed to investigate if thinner cortex of the Alzheimer's disease (AD)-signature region was related to clinical progression in patients with subjective cognitive decline (SCD). METHODS: We included 302 SCD patients with clinical follow-up (≥1 year) and three-dimensional T1 magnetic resonance imaging. We estimated AD-signature cortical thickness, consisting of nine frontal, parietal, and temporal gyri and hippocampal volume. We used Cox proportional hazard models (hazard ratios and 95% confidence intervals) to evaluate cortical thickness in relation to clinical progression to mild cognitive impairment (MCI) or dementia. RESULTS: After a follow-up of the mean (standard deviation) 3 (2) years, 49 patients (16%) showed clinical progression to MCI (n = 32), AD (n = 9), or non-AD dementia (n = 8). Hippocampal volumes, thinner cortex of the AD-signature (hazard ratio [95% confidence interval], 5 [2-17]) and various AD-signature subcomponents were associated with increased risk of clinical progression. Stratified analyses showed that thinner AD-signature cortex was specifically predictive for clinical progression to dementia but not to MCI. DISCUSSION: In SCD patients, thinner regional cortex is associated with clinical progression to dementia.

Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis.

Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD and those with bvFTD as compared with normal aging by using common T1-weighted structural MR imaging. (©) RSNA, 2015.

Joint assessment of white matter integrity, cortical and subcortical atrophy to distinguish AD from behavioral variant FTD: A two-center study.

We investigated the ability of cortical and subcortical gray matter (GM) atrophy in combination with white matter (WM) integrity to distinguish behavioral variant frontotemporal dementia (bvFTD) from Alzheimer's disease (AD) and from controls using voxel-based morphometry, subcortical structure segmentation, and tract-based spatial statistics. To determine which combination of MR markers differentiated the three groups with the highest accuracy, we conducted discriminant function analyses. Adjusted for age, sex and center, both types of dementia had more GM atrophy, lower fractional anisotropy (FA) and higher mean (MD), axial (L1) and radial diffusivity (L23) values than controls. BvFTD patients had more GM atrophy in orbitofrontal and inferior frontal areas than AD patients. In addition, caudate nucleus and nucleus accumbens were smaller in bvFTD than in AD. FA values were lower; MD, L1 and L23 values were higher, especially in frontal areas of the brain for bvFTD compared to AD patients. The combination of cortical GM, hippocampal volume and WM integrity measurements, classified 97-100% of controls, 81-100% of AD and 67-75% of bvFTD patients correctly. Our results suggest that WM integrity measures add complementary information to measures of GM atrophy, thereby improving the classification between AD and bvFTD.

More atrophy of deep gray matter structures in frontotemporal dementia compared to Alzheimer's disease.

BACKGROUND: The involvement of frontostriatal circuits in frontotemporal dementia (FTD) suggests that deep gray matter structures (DGM) may be affected in this disease. OBJECTIVE: We investigated whether volumes of DGM structures differed between patients with behavioral variant FTD (bvFTD), Alzheimer's disease (AD), and subjective complaints (SC) and explored relationships between DGM structures, cognition, and neuropsychiatric functioning. METHODS: For this cross-sectional study, we included 24 patients with FTD and matched them based on age, gender, and education at a ratio of 1:3 to 72 AD patients and 72 patients with SC who served as controls. Volumes of hippocampus, amygdala, thalamus, caudate nucleus, putamen, globus pallidus, and nucleus accumbens were estimated by automated segmentation of 3D T1-weighted MRI. MANOVA with Bonferroni adjusted post-hoc tests was used to compare volumes between groups. Relationships between volumes, cognition, and neuropsychiatric functioning were examined using multivariate linear regression and Spearman correlations. RESULTS: Nucleus accumbens and caudate nucleus discriminated all groups, with most severe atrophy in FTD. Globus pallidus volumes were smallest in FTD and discriminated FTD from AD and SC. Hippocampus, amygdala, thalamus, and putamen were smaller in both dementia groups compared to SC. Associations between amygdala and memory were found to be different in AD and FTD. Globus pallidus and nucleus accumbens were related to attention and executive functioning in FTD. CONCLUSION: Nucleus accumbens, caudate nucleus, and globus pallidus were more severely affected in FTD than in AD and SC. The associations between cognition and DGM structures varied between the diagnostic groups. The observed difference in volume of these DGM structures supports the idea that next to frontal cortical atrophy, DGM structures, as parts of the frontal circuits, are damaged in FTD rather than in AD.

Brain volume and white matter hyperintensities as determinants of cerebral blood flow in Alzheimer's disease.

To better understand whether decreased cerebral blood flow (CBF) in patients with Alzheimer's disease (AD) reflects neurodegeneration or cerebral small vessel disease, we investigated the associations of normalized brain volume (NBV) and white matter hyperintensity (WMH) volume with CBF. We included 129 patients with AD (66 ± 7 years, 53% female) and 61 age-matched controls (64 ± 5 years, 43% female). CBF was measured with pseudocontinuous arterial spin labeling at 3T in the whole brain and in partial volume corrected cortical maps. When NBV and WMH were simultaneously entered in age and sex adjusted models, smaller NBV was associated with lower whole brain (Stß: 0.29; p < 0.01) and cortical CBF (Stß: 0.28; p < 0.01) in patients with AD. Larger WMH volume was also associated with lower whole brain (Stß: -0.22; p < 0.05) and cortical CBF (Stß: -0.24; p < 0.05) in AD. Additional adjustments did not change these results. In controls, neither NBV nor WMH was associated with CBF. Our results indicate that in AD, lower CBF as measured using pseudocontinuous arterial spin labeling, reflects the combined disease burden of both neurodegeneration and small vessel disease.

Long-term effects of amyloid, hypometabolism, and atrophy on neuropsychological functions.

OBJECTIVE: To assess how amyloid deposition, glucose hypometabolism, and cerebral atrophy affect neuropsychological performance in patients with Alzheimer disease (AD) dementia, patients with mild cognitive impairment (MCI), and controls over time. METHODS: A total of 41 patients with AD dementia, 28 patients with MCI, and 19 controls underwent [(11)C]-Pittsburgh compound B ((11)C-PiB) and [(18)F]-2-fluoro-2-deoxy-d-glucose ((18)F-FDG)-PET and MRI scans at baseline. We extracted global binding potential for (11)C-PiB, the number of abnormal voxels for (18)F-FDG, and gray matter volumes using SIENAX for MRI as measures of amyloid, hypometabolism, and atrophy. In addition, repeat neuropsychological testing was performed, including memory, attention, language, and executive tasks (mean follow-up 2.2 ± 0.7 years). Cross-sectional and longitudinal relationships between imaging markers and cognition were assessed using linear mixed models, including terms for the imaging markers, time, sex, age, diagnosis, and interactions for imaging marker × time and imaging marker × time × diagnosis. RESULTS: Linear mixed models showed that baseline hypometabolism and atrophy were associated with poorer baseline performance on attention and executive functions (p < 0.05), whereas amyloid was not related to baseline cognition. Hypometabolism and amyloid were strongly associated with longitudinal decline in essentially all cognitive domains (pinteraction < 0.05), whereas atrophy was related specifically to future decline in Mini-Mental State Examination and memory (pinteraction < 0.05). CONCLUSION: Glucose hypometabolism and brain atrophy were associated with concurrent cognitive function, whereas brain amyloid was not. Amyloid deposition and glucose hypometabolism were predictors for decline of a wide variety of cognitive functions, while brain atrophy specifically predicted memory deterioration.

Quantitative regional validation of the visual rating scale for posterior cortical atrophy.

OBJECTIVES: Validate the four-point visual rating scale for posterior cortical atrophy (PCA) on magnetic resonance images (MRI) through quantitative grey matter (GM) volumetry and voxel-based morphometry (VBM) to justify its use in clinical practice. METHODS: Two hundred twenty-nine patients with probable Alzheimer's disease and 128 with subjective memory complaints underwent 3T MRI. PCA was rated according to the visual rating scale. GM volumes of six posterior structures and the total posterior region were extracted using IBASPM and compared among PCA groups. To determine which anatomical regions contributed most to the visual scores, we used binary logistic regression. VBM compared local GM density among groups. RESULTS: Patients were categorised according to their PCA scores: PCA-0 (n = 122), PCA-1 (n = 143), PCA-2 (n = 79), and PCA-3 (n = 13). All structures except the posterior cingulate differed significantly among groups. The inferior parietal gyrus volume discriminated the most between rating scale levels. VBM showed that PCA-1 had a lower GM volume than PCA-0 in the parietal region and other brain regions, whereas between PCA-1 and PCA-2/3 GM atrophy was mostly restricted to posterior regions. CONCLUSIONS: The visual PCA rating scale is quantitatively validated and reliably reflects GM atrophy in parietal regions, making it a valuable tool for the daily radiological assessment of dementia. KEY POINTS: • Visual rating scale reflects grey matter atrophy in posterior brain regions. • Different PCA scores corresponded well to different quantitative degrees of atrophy. • Inferior parietal gyrus volume influenced assessment based on the visual rating scale. • This simple visual rating scale makes it useful for radiological dementia assessment.

Different patterns of gray matter atrophy in early- and late-onset Alzheimer's disease.

We assessed patterns of gray matter atrophy according to-age-at-onset in a large sample of 215 Alzheimer's disease (AD) patients and 129 control subjects with voxel-based morphometry using 3-Tesla 3D T1-weighted magnetic resonance imaging. Local gray matter amounts were compared between late- and early-onset AD patients and older and younger control subjects, taking into account the effect of apolipoprotein E. Additionally, combined effects of age and diagnosis on volumes of hippocampus and precuneus were assessed. Compared with age-matched control subjects, late-onset AD patients exhibited atrophy of the hippocampus, right temporal lobe, and cerebellum, whereas early-onset AD patients showed gray matter atrophy in hippocampus, temporal lobes, precuneus, cingulate gyrus, and inferior frontal cortex. Direct comparisons between late- and early-onset AD patients revealed more pronounced atrophy of precuneus in early-onset AD patients and more severe atrophy in medial temporal lobe in late-onset AD patients. Age and diagnosis independently affected the hippocampus; moreover, the interaction between age and diagnosis showed that precuneus atrophy was most prominent in early-onset AD patients. Our results suggest that patterns of atrophy might vary in the spectrum of AD.

Cognitive impairment in MS: impact of white matter integrity, gray matter volume, and lesions.

OBJECTIVE: To investigate whether extent and severity of white matter (WM) damage, as measured with diffusion tensor imaging (DTI), can distinguish cognitively preserved (CP) from cognitively impaired (CI) multiple sclerosis (MS) patients. METHODS: Conventional MRI and DTI data were acquired from 55 MS patients (35 CP, 20 CI) and 30 healthy controls (HC). Voxelwise analyses were used to investigate fractional anisotropy (FA), mean diffusivity, radial diffusivity, and axial diffusivity of a WM skeleton. Regional gray matter volume was quantified and lesion probability maps were generated. RESULTS: Compared to HCs, decreased FA was found in 49% of the investigated WM skeleton in CP patients and in 76% of the investigated WM in CI patients. Several brain areas that showed reduced FA in both patient groups were significantly worse in CI patients, i.e, corpus callosum, superior and inferior longitudinal fasciculus, corticospinal tracts, forceps major, cingulum, and fornices. In CI patients, WM integrity damage was additionally seen in cortical brain areas, thalamus, uncinate fasciculus, brainstem, and cerebellum. These findings were independent of lesion location and regional gray matter volume, since no differences were found between the groups. CONCLUSION: CI patients diverged from CP patients only on DTI metrics. WM integrity changes were found in areas that are highly relevant for cognition in the CI patients but not in the CP patients. These WM changes are therefore thought to be related to the cognitive deficits and suggest that DTI might be a powerful tool when monitoring cognitive impairment in MS.