Search

Cerebral atrophy is a key finding in patients with dementia and usually determined on MRI. We tested whether cerebral atrophy can be imaged with FDG PET by applying deformation-based morphometry (DBM). We retrospectively identified 26 patients with a biomarker-supported clinical diagnosis of Alzheimer's disease (AD) who had received FDG PET on a fully-digital PET/CT system and structural MRI and compared them to 13 healthy elderly controls (HEC). We performed DBM with FDG PET data (FDG-DBM). As a reference standard for determining atrophy we used voxel-based morphometry of MRI data (MRI-VBM). For conventional analysis of hypometabolism, scaled FDG PET scans (reference: brain parenchyma) were compared between groups. Receiver operating characteristic (ROC) analyses were performed. ROI read-outs were tested for associations with cognitive test performance. FDG-DBM showed abnormalities in AD mainly in the bilateral hippocampi. Similarly, MRI-VBM showed hippocampal atrophy. By contrast, conventional FDG PET analysis revealed reduced bilateral temporo-parietal FDG uptake (all p < 0.05, FWE-corrected). FDG-DBM measures of the hippocampus significantly separated AD from HEC with an AUC of 0.81; MRI-VBM achieved an AUC of 0.87; the difference between the two ROC curves was not significant (p = 0.40). Whereas FDG uptake of the hippocampus did not separate AD from HEC, FDG uptake of the Landau Meta-ROI achieved an AUC of 0.88. Verbal memory was significantly associated with FDG-DBM measures of the hippocampus (p = 0.009), but not of the Landau Meta-ROI (p > 0.1). The opposite held true for conventional FDG uptake (p > 0.1 and p = 0.001, respectively). Hippocampal atrophy in AD can be detected by applying DBM to clinical, fully-digital FDG PET. It correlates with cognitive performance and might constitute a biomarker of neurodegeneration that is complementary to conventional FDG PET analysis of regional hypometabolism., (© 2024. The Author(s).)

Identification of patients with idiopathic normal pressure hydrocephalus (iNPH) in a collective with suspected neurodegenerative disease is essential. This study aimed to determine the metabolic spatial covariance pattern of iNPH on FDG PET using an established technique based on scaled subprofile model principal components analysis (SSM-PCA).We identified 11 patients with definite iNPH. By applying SSM-PCA to the FDG PET data, they were compared to 48 age-matched healthy controls to determine the whole-brain voxel-wise metabolic spatial covariance pattern of definite iNPH (iNPH-related pattern, iNPHRP). The iNPHRP score was compared between groups of patients with definite iNPH, possible iNPH (N = 34), Alzheimer's (AD, N = 38), and Parkinson's disease (PD, N = 35) applying pairwise Mann-Whitney U tests and correction for multiple comparisons.SSM-PCA of FDG PET revealed an iNPHRP that is characterized by relative negative voxel weights at the vicinity of the lateral ventricles and relative positive weights in the paracentral midline region. The iNPHRP scores of patients with definite iNPH were substantially higher than in patients with AD and PD (both p < 0.05) and non-significantly higher than those of patients with possible iNPH. Subject scores of the iNPHRP discriminated definite iNPH from AD and PD with 96% and 100% accuracy and possible iNPH from AD and PD with 83% and 86% accuracy.We defined a novel metabolic spatial covariance pattern of iNPH that might facilitate the differential diagnosis of iNPH versus other neurodegenerative disorders. The knowledge of iNPH-associated alterations in the cerebral glucose metabolism is of high relevance as iNPH constitutes an important differential diagnosis to dementia and movement disorders., (© 2023. The Author(s).)

Importance: One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design., Objective: To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates., Design, Setting, and Participants: This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria., Exposures: Alzheimer disease biomarkers detected on PET or in CSF., Main Outcomes and Measures: Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations., Results: Among the 19 097 participants (mean [SD] age, 69.1 [9.8] years; 10 148 women [53.1%]) included, 10 139 (53.1%) underwent an amyloid PET scan and 8958 (46.9%) had an amyloid CSF measurement. Using cohort-provided cutoffs, amyloid abnormality prevalences were similar to 2015 estimates for individuals without dementia and were similar across PET- and CSF-based estimates (24%; 95% CI, 21%-28%) in participants with normal cognition, 27% (95% CI, 21%-33%) in participants with subjective cognitive decline, and 51% (95% CI, 46%-56%) in participants with mild cognitive impairment, whereas for clinical AD dementia the estimates were higher for PET than CSF (87% vs 79%; mean difference, 8%; 95% CI, 0%-16%; P = .04). Gaussian mixture modeling-based cutoffs for amyloid measures on PET scans were similar to cohort-provided cutoffs and were not adjusted. Adjusted CSF cutoffs resulted in a 10% higher amyloid abnormality prevalence than PET-based estimates in persons with normal cognition (mean difference, 9%; 95% CI, 3%-15%; P = .004), subjective cognitive decline (9%; 95% CI, 3%-15%; P = .005), and mild cognitive impairment (10%; 95% CI, 3%-17%; P = .004), whereas the estimates were comparable in persons with clinical AD dementia (mean difference, 4%; 95% CI, -2% to 9%; P = .18)., Conclusions and Relevance: This study found that CSF-based estimates using adjusted data-driven cutoffs were up to 10% higher than PET-based estimates in people without dementia, whereas the results were similar among people with dementia. This finding suggests that preclinical and prodromal AD may be more prevalent than previously estimated, which has important implications for clinical trial recruitment strategies and health care planning policies.

Objective: To determine whether the Alzheimer disease (AD) dementia conversion-related pattern (ADCRP) on [ 18 F]FDG PET can serve as a valid predictor for the development of AD dementia, the individual expression of the ADCRP (subject score) and its prognostic value were examined in patients with mild cognitive impairment (MCI) and biologically defined AD., Methods: A total of 269 patients with available [ 18 F]FDG PET, [ 18 F]AV-45 PET, phosphorylated and total tau in CSF, and neurofilament light chain in plasma were included. Following the AT(N) classification scheme, where AD is defined biologically by in vivo biomarkers of β-amyloid (Aβ) deposition ("A") and pathologic tau ("T"), patients were categorized to the A-T-, A+T-, A+T+ (AD), and A-T+ groups., Results: The mean subject score of the ADCRP was significantly higher in the A+T+ group compared to each of the other group (all p < 0.05) but was similar among the latter (all p > 0.1). Within the A+T+ group, the subject score of ADCRP was a significant predictor of conversion to dementia (hazard ratio, 2.02 per z score increase; p < 0.001), with higher predictive value than of alternative biomarkers of neurodegeneration (total tau and neurofilament light chain). Stratification of A+T+ patients by the subject score of ADCRP yielded well-separated groups of high, medium, and low conversion risks., Conclusions: The ADCRP is a valuable biomarker of neurodegeneration in patients with MCI and biologically defined AD. It shows great potential for stratifying the risk and estimating the time to conversion to dementia in patients with MCI and underlying AD (A+T+)., Classification of Evidence: This study provides Class I evidence that [ 18 F]FDG PET predicts the development of AD dementia in individuals with MCI and underlying AD as defined by the AT(N) framework., (© 2021 American Academy of Neurology.)

Background: Amyloid-β (Aβ) PET is an established predictor of conversion from mild cognitive impairment (MCI) to Alzheimer's dementia (AD). We compared three PET (including an approach based on voxel-wise Cox regression) and one cerebrospinal fluid (CSF) outcome measures in their predictive power., Methods: Datasets were retrieved from the ADNI database. In a training dataset (N = 159), voxel-wise Cox regression and principal component analyses were used to identify conversion-related regions (Cox-VOI and AD conversion-related pattern (ADCRP), respectively). In a test dataset (N = 129), the predictive value of mean normalized 18 F-florbetapir uptake (SUVR) in AD-typical brain regions (composite SUVR) or the Cox-VOI and the pattern expression score (PES) of ADCRP and CSF Aβ 42 /Aβ 40 as predictors were compared by Cox models (corrected for age and sex)., Results: All four Aβ measures were significant predictors (p < 0.001). Prediction accuracies (Harrell's c) showed step-wise significant increases from Cox-SUVR (c = 0.71; HR = 1.84 per Z-score increase), composite SUVR (c = 0.73; HR = 2.18), CSF Aβ 42 /Aβ 40 (c = 0.75; HR = 3.89) to PES (c = 0.77; HR = 2.71)., Conclusion: The PES of ADCRP is the most predictive Aβ PET outcome measure, comparable to CSF Aβ 42 /Aβ 40 , with a slight but statistically significant advantage.

The present study examined the predictive values of amyloid PET, 18 F-FDG PET, and nonimaging predictors (alone and in combination) for development of Alzheimer dementia (AD) in a large population of patients with mild cognitive impairment (MCI). Methods: The study included 319 patients with MCI from the Alzheimer Disease Neuroimaging Initiative database. In a derivation dataset ( n = 159), the following Cox proportional-hazards models were constructed, each adjusted for age and sex: amyloid PET using 18 F-florbetapir (pattern expression score of an amyloid-β AD conversion-related pattern, constructed by principle-components analysis); 18 F-FDG PET (pattern expression score of a previously defined 18 F-FDG-based AD conversion-related pattern, constructed by principle-components analysis); nonimaging (functional activities questionnaire, apolipoprotein E, and mini-mental state examination score); 18 F-FDG PET + amyloid PET; amyloid PET + nonimaging; 18 F-FDG PET + nonimaging; and amyloid PET + 18 F-FDG PET + nonimaging. In a second step, the results of Cox regressions were applied to a validation dataset ( n = 160) to stratify subjects according to the predicted conversion risk. Results: On the basis of the independent validation dataset, the 18 F-FDG PET model yielded a significantly higher predictive value than the amyloid PET model. However, both were inferior to the nonimaging model and were significantly improved by the addition of nonimaging variables. The best prediction accuracy was reached by combining 18 F-FDG PET, amyloid PET, and nonimaging variables. The combined model yielded 5-y free-of-conversion rates of 100%, 64%, and 24% for the low-, medium- and high-risk groups, respectively. Conclusion: 18 F-FDG PET, amyloid PET, and nonimaging variables represent complementary predictors of conversion from MCI to AD. Especially in combination, they enable an accurate stratification of patients according to their conversion risks, which is of great interest for patient care and clinical trials., (© 2020 by the Society of Nuclear Medicine and Molecular Imaging.)

Background and Objectives: To evaluate tau PET using C-pyridinyl-butadienyl-benzothiazole 3 (C-PBB3) in the 4-repeat (4R)-tauopathies progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS)., Methods: Retrospective analysis of C-PBB3 PET in 2, 7, and 2 patients with CBS, PSP, and Alzheimer dementia (AD), respectively. Normalized C-PBB3 uptake in clusters with significant hypometabolism on F-FDG-PET and corresponding atlas-based volumes of interest was compared between diagnostic groups., Results: In accordance with visually appreciable group differences, C-PBB3 uptake was significantly higher in dorsolateral frontal and motor cortex in CBS patients and frontal and temporal cortices in AD patients as compared with PSP patients. Patients with PSP showed mildly but significantly higher uptake in midbrain compared with AD patients., Conclusions: In line with known neuropathological changes, the spatial pattern and magnitude of C-PBB3 tau binding differ between CBS, PSP, and AD, which may be of diagnostic utility. Thus, C-PBB3 offers a promising lead structure for development of ligands for tau imaging, including 4R-tauopathies.

We evaluated the effect of a reduced acquisition time for 18 F-FDG PET studies of Alzheimer dementia (AD) and frontotemporal dementia (FTD) to derive a limit for reductions of acquisition time (improving patient compliance) and administered activity (lowering the radiation dose) with uncompromised diagnostic outcome. Methods: We included patients with a clinical diagnosis of AD ( n = 13) or FTD ( n = 12) who were examined with 18 F-FDG PET/CT after injection of 210 ± 9 MBq of 18 F-FDG. List-mode data were reconstructed over various time intervals simulating reduced acquisition times or administered activities. Volume-of-interest-based and voxelwise statistical analyses including group contrasts were performed for 15 different acquisition times ranging from 10 min to 2 s. In addition, masked visual reads were obtained from 3 readers independently for 7 different acquisition times down to 30 s, providing a diagnosis of either AD or FTD and the individual diagnostic certainty. Results: Regional mean uptake changed by less than 5% at a reduced acquisition time down to 1 min in all regions and patients except for the posterior cingulate cortex of 1 patient. Voxelwise group contrasts suggest a sufficient measurement time of only 2 min, for which the number of significant voxels decreased by merely 5% while maintaining their spatial pattern. In 450 visual reads at reduced times, no change in the original diagnosis was observed. The diagnostic certainty showed only a very slow and mild decline, with small effect sizes (Cohen's d) of 0.3, at acquisition times of 3 and 2 min compared with the original results at 10 min. Conclusion: Statistical results at a region and voxel level, as well as single-subject visual reads, reveal a considerable potential to reduce the typical 10-min acquisition time (by a factor of 4) without compromising diagnostic quality. Conversely, our data suggest that for a given acquisition time of 10 min and a similar effect size, the administered activity may be reduced to 50 MBq, resulting in an effective dose of less than 1 mSv for the PET examination., (© 2019 by the Society of Nuclear Medicine and Molecular Imaging.)

The value of 18 F-FDG PET for predicting conversion from mild cognitive impairment (MCI) to Alzheimer dementia (AD) is currently under debate. We used a principal components analysis (PCA) to identify a metabolic AD conversion-related pattern (ADCRP) and investigated the prognostic value of the resulting pattern expression score (PES). Methods: F-FDG PET scans of 544 MCI patients were obtained from the Alzheimer Disease Neuroimaging Initiative database and analyzed. We implemented voxel-based PCA and standard Statistical Parametric Mapping analysis (as a reference) to disclose cerebral metabolic patterns associated with conversion from MCI to AD. By Cox proportional hazards regression, we examined the prognostic value of candidate predictors. Also, we constructed prognostic models with clinical, imaging, and clinical and imaging variables in combination. 18 F-FDG PET scans of 544 MCI patients were obtained from the Alzheimer Disease Neuroimaging Initiative database and analyzed. We implemented voxel-based PCA and standard Statistical Parametric Mapping analysis (as a reference) to disclose cerebral metabolic patterns associated with conversion from MCI to AD. By Cox proportional hazards regression, we examined the prognostic value of candidate predictors. Also, we constructed prognostic models with clinical, imaging, and clinical and imaging variables in combination. Results: PCA revealed an ADCRP that involved regions with relative decreases in metabolism (temporoparietal, frontal, posterior cingulate, and precuneus cortices) and relative increases in metabolism (sensorimotor and occipital cortices, cerebellum, and left putamen). Among the predictor variables age, sex, Functional Activities Questionnaire, Mini-Mental State Examination, apolipoprotein E, PES, and normalized 18 F-FDG uptake (regions with significant hypo- and hypermetabolism in patients with conversion vs. those without conversion), PES was the best independent predictor of conversion (hazard ratio, 1.77, per z score increase; 95% CI, 1.24-2.52; P < 0.001). Moreover, adding PES to the model including the clinical variables significantly increased its prognostic value. Conclusion: The ADCRP expression score was a valid predictor of conversion. A combination of clinical variables and PES yielded a higher accuracy than each single tool in predicting conversion from MCI to AD, underlining the incremental utility of 18 F-FDG PET., (© 2019 by the Society of Nuclear Medicine and Molecular Imaging.)

Aim: The value of 18 F-fluorodeoxyglucose (FDG) PET for the prognosis of conversion from mild cognitive impairment (MCI) to Alzheimer's dementia (AD) is controversial. In the present work, the identification of cerebral metabolic patterns with significant prognostic value for conversion of MCI patients to AD is investigated with voxel-based Cox regression, which in contrast to common categorical comparisons also utilizes time information., Methods: FDG PET data of 544 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were randomly split into two equally-sized datasets (training and test). Within a median follow-up duration of 47 months (95% CI: 46-48 months) 181 patients developed AD. In the training dataset, voxel-wise Cox regressions were used to identify regions associated with conversion of MCI to AD. These were compared to regions identified by a classical group comparison (analysis of covariance (ANCOVA) with statistical parametric mapping (SPM) 8) between converters and non-converters (both adjusted for apolipoprotein E (APOE) genotype, mini-mental state examination (MMSE) score, age, sex and education). In the test dataset, normalized FDG uptake within significant brain regions from voxel-wise Cox- and ANCOVA analyses (Cox- and ANCOVA- regions of interest (ROI), respectively) and clinical variables APOE status, MMSE score and education were tested in different Cox models (adjusted for age, sex) including: (1) only clinical variables, (2) only normalized FDG uptake in ANCOVA-ROI, (3) only normalized FDG uptake from Cox-ROI, (4) clinical variables plus FDG uptake in ANCOVA-ROI, (5) clinical variables plus FDG uptake from Cox-ROI., Results: Conversion-related regions with relative hypometabolism comprised parts of the temporo-parietal and posterior cingulate cortex/precuneus for voxel-wise ANCOVA, plus frontal regions for voxel-wise Cox regression (both p < .01, false discovery rate (FDR) corrected). The clinical-only model (1) and the models based on normalized FDG uptake from Cox-ROI only (2) and ANCOVA-ROI only (3) all significantly predicted conversion to AD (Wald Test (WT): p < .001). The clinical model (1) was significantly improved by adding imaging information in model (4) (Akaike information criterion (AIC) relative likelihood (RL) (1) vs (4): RL < 0.018). There were no significant differences between models (2) and (3), as well as (4) and (5)., Conclusions: Voxel-wise Cox regression identifies conversion-related patterns of cerebral glucose metabolism, but is not superior to classical group contrasts in this regard. With imaging information from both FDG PET patterns, the prediction of conversion to AD was improved., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: The value of imaging regional glucose metabolism with [ 18 F]FDG PET for the prediction of progression from mild cognitive impairment (MCI) to Alzheimer's dementia (AD) is controversial. The predictive value of imaging with [ 18 F]FDG PET was therefore tested and compared with that of imaging beta-amyloid load with [ 11 C]PIB PET in the same memory clinic population of MCI patients., Methods: Thirty-nine patients with MCI who had undergone [ 18 F]FDG as well as [ 11 C]PIB PET were identified from a single-centre clinical registry. [ 18 F]FDG and [ 11 C]PIB PET images were rated as positive or negative for the presence of regional hypometabolism typical of AD and beta-amyloid deposition, respectively. Raters were blinded to the clinical information. Patients were followed clinically for 2.7 ± 1.2 years after PET. Cox proportional hazards models, adjusted for age and sex, were used to test the predictive value of [ 18 F]FDG PET, [ 11 C]PIB PET, and both in combination., Results: [ 18 F]FDG PET did not significantly predict conversion to AD (p > 0.1). By contrast, models including [ 11 C]PIB PET only (p < 0.05) or both [ 18 F]FDG and [ 11 C]PIB PET (p < 0.05) significantly predicted conversion to AD. The hazard ratio for AD in patients with a positive [ 11 C]PIB scan was 10.2 (95% confidence interval 1.3-78.1). The results were confirmed by analysis of semiquantitative measures using normalized [ 18 F]FDG uptake and [ 11 C]PIB standardized uptake value ratios in AD-typical regions as continuous predictors., Conclusion: In contrast to [ 11 C]PIB PET, [ 18 F]FDG PET did not predict conversion from MCI to AD in this clinical patient sample. Therefore, amyloid PET should be preferred for individual prediction and patient counselling in clinical practice.

Background: Positron emission tomography (PET) with the glucose analog F-18-fluorodeoxyglucose (FDG) is widely used in the diagnosis of neurodegenerative diseases. Guidelines recommend voxel-based statistical testing to support visual evaluation of the PET images. However, the performance of voxel-based testing strongly depends on each single preprocessing step involved., Objective: To optimize the processing pipeline of voxel-based testing for the prognosis of dementia in subjects with amnestic mild cognitive impairment (MCI)., Methods: The study included 108 ADNI MCI subjects grouped as 'stable MCI' (n = 77) or 'MCI-to-AD converter' according to their diagnostic trajectory over 3 years. Thirty-two ADNI normals served as controls. Voxel-based testing was performed with the statistical parametric mapping software (SPM8) starting with default settings. The following modifications were added step-by-step: (i) motion correction, (ii) custom-made FDG template, (iii) different reference regions for intensity scaling, and (iv) smoothing was varied between 8 and 18 mm. The t-sum score for hypometabolism within a predefined AD mask was compared between the different settings using receiver operating characteristic (ROC) analysis with respect to differentiation between 'stable MCI' and 'MCI-to-AD converter'. The area (AUC) under the ROC curve was used as performance measure., Results: The default setting provided an AUC of 0.728. The modifications of the processing pipeline improved the AUC up to 0.832 (p = 0.046). Improvement of the AUC was confirmed in an independent validation sample of 241 ADNI MCI subjects (p = 0.048)., Conclusion: The prognostic value of voxel-based single subject analysis of brain FDG PET in MCI subjects can be improved considerably by optimizing the processing pipeline.

Naming impairment in Alzheimer's disease dementia (AD) is associated with atrophy of the left anterior temporal lobe (ATL). We aimed to elucidate if regional cerebral glucose metabolism, as a biomarker of synaptic dysfunction and neurodegeneration, of the left ATL predicts naming impairment, and if amyloid-beta (Aβ) deposition, a pathological hallmark of AD, contributes to the prediction. Twenty-nine patients with AD underwent combined [(11)C]PIB and [(18)F]FDG PET examinations for assessment of Aβ load and regional cerebral glucose metabolism. An a priori defined region of interest was used for regional PET analyses of the left ATL. In linear stepwise regression analyses, glucose metabolism of the left ATL was the only significant predictor of naming performance, independent of sex, age, and education. Neither regional nor global Aβ load contributed to the prediction. Left ATL glucose metabolism predicts naming impairment in AD. By contrast, Aβ deposition does not predict naming impairment.

Clinical Alzheimer's disease affects both cerebral hemispheres to a similar degree in clinically typical cases. However, in atypical variants like logopenic progressive aphasia, neurodegeneration often presents asymmetrically. Yet, no in vivo imaging study has investigated whether lateralized neurodegeneration corresponds to lateralized amyloid-β burden. Therefore, using combined (11)C-Pittsburgh compound B and (18)F-fluorodeoxyglucose positron emission tomography, we explored whether asymmetric amyloid-β deposition in Alzheimer's disease is associated with asymmetric hypometabolism and clinical symptoms. From our database of patients who underwent positron emission tomography with both (11)C-Pittsburgh compound B and (18)F-fluorodeoxyglucose (n = 132), we included all amyloid-positive patients with prodromal or mild-to-moderate Alzheimer's disease (n = 69). The relationship between (11)C-Pittsburgh compound B binding potential and (18)F-fluorodeoxyglucose uptake was assessed in atlas-based regions of interest covering the entire cerebral cortex. Lateralizations of amyloid-β and hypometabolism were tested for associations with each other and with type and severity of cognitive symptoms. Positive correlations between asymmetries of Pittsburgh compound B binding potential and hypometabolism were detected in 6 of 25 regions (angular gyrus, middle frontal gyrus, middle occipital gyrus, superior parietal gyrus, inferior and middle temporal gyrus), i.e. hypometabolism was more pronounced on the side of greater amyloid-β deposition (range: r = 0.41 to 0.53, all P < 0.001). Stronger leftward asymmetry of amyloid-β deposition was associated with more severe language impairment (P < 0.05), and stronger rightward asymmetry with more severe visuospatial impairment (at trend level, P = 0.073). Similarly, patients with predominance of language deficits showed more left-lateralized amyloid-β burden and hypometabolism than patients with predominant visuospatial impairment and vice versa in several cortical regions. Associations between amyloid-β deposition and hypometabolism or cognitive impairment were predominantly observed in brain regions with high amyloid-β load. The relationship between asymmetries of amyloid-β deposition and hypometabolism in cortical regions with high amyloid-β load is in line with the detrimental effect of amyloid-β burden on neuronal function. Asymmetries were also concordant with lateralized cognitive symptoms, indicating their clinical relevance., (© The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Posterior cortical atrophy (PCA) is a rare neurodegenerative syndrome with visuospatial deficits. PET studies have identified hypometabolism of the occipital cortex in PCA. There is, however, a huge overlap in clinical presentation and involvement of the occipital cortex between PCA, dementia with Lewy bodies (DLB), and Alzheimer's disease (AD). Syndrome-specific patterns of metabolism have not yet been demonstrated that allow for a reliable differentiation with [F-18]-FDG-PET., Methods: A total of 33 dementia patients (PCA n = 6, DLB n = 12, AD n = 15) who underwent [F-18]-FDG-PET imaging and a neuropsychological examination were retrospectively analyzed. Group comparisons of regional cerebral glucose metabolism were calculated with statistical parametric mapping. Extracted clusters were used to evaluate discrimination accuracy by logistic regression., Results: PCA patients showed a syndrome-specific area of hypometabolism in the right lateral temporooccipital cortex. DLB patients showed specific hypometabolism predominantly in the left occipital cortex. Logistic regression based on these two regions correctly separated patients with a sensitivity/specificity of 83/93% for PCA, 75/86% for DLB and 67/78% for AD. Overall accuracy was 73%., Conclusion: [F-18]-FDG-PET could reveal syndrome-specific patterns of glucose metabolism in PCA and DLB. Accurate group discrimination in the differential diagnosis of dementia with visuospatial impairment is feasible., (Copyright © 2014 by the American Society of Neuroimaging.)

Introduction: I-FP-CIT SPECT is increasingly used to differentiate between Alzheimer's dementia (AD) and dementia with Lewy bodies (DLB). The role of I-FP-CIT SPECT in frontotemporal dementia (FTD) is rather unclear, albeit nigrostriatal involvement may occur. The aim of this study was to evaluate its role in the differentiation of FTD, DLB, and AD., Methods: We analyzed 34 patients with clinical diagnosis of FTD (n = 13), DLB (n = 12), and AD (n = 9) undergoing combined F-FDG PET and I-FP-CIT SPECT. We performed a semiquantitative region of interest-based analysis to determine the binding potential values in caudate nucleus, putamen, and whole striatum including the caudate/putamen binding potential ratio and asymmetry indices. The receiver operating characteristic analyses and multinomial logistic regression were conducted to assess discrimination accuracy., Results: The putaminal binding potential separated DLB from AD with high accuracy (area under the receiver operating characteristic curve [AUC], 0.94). It also discriminated FTD from DLB with high accuracy (AUC, 0.92), whereas differentiation between FTD and AD was less accurate (AUC, 0.74). The binding potential ratio also provided high accuracy for differentiation of FTD and DLB (AUC, 0.91). Combination of these 2 parameters yielded slightly higher results for differentiation of FTD and DLB (AUC, 0.97). In a group including all patients, accuracy remained very high for DLB (AUC, 0.95), whereas values for FTD (AUC, 0.81) and AD (AUC, 0.80) were lower., Conclusions: Semiquantitative assessment of striatal dopamine transporter availability can differentiate between FTD and DLB as well as DLB and AD with high accuracy, whereas discrimination between AD and FTD is limited.

Several studies have demonstrated that fully automated pattern recognition methods applied to structural magnetic resonance imaging (MRI) aid in the diagnosis of dementia, but these conclusions are based on highly preselected samples that significantly differ from that seen in a dementia clinic. At a single dementia clinic, we evaluated the ability of a linear support vector machine trained with completely unrelated data to differentiate between Alzheimer's disease (AD), frontotemporal dementia (FTD), Lewy body dementia, and healthy aging based on 3D-T1 weighted MRI data sets. Furthermore, we predicted progression to AD in subjects with mild cognitive impairment (MCI) at baseline and automatically quantified white matter hyperintensities from FLAIR-images. Separating additionally recruited healthy elderly from those with dementia was accurate with an area under the curve (AUC) of 0.97 (according to Fig. 4). Multi-class separation of patients with either AD or FTD from other included groups was good on the training set (AUC >  0.9) but substantially less accurate (AUC = 0.76 for AD, AUC = 0.78 for FTD) on 134 cases from the local clinic. Longitudinal data from 28 cases with MCI at baseline and appropriate follow-up data were available. The computer tool discriminated progressive from stable MCI with AUC = 0.73, compared to AUC = 0.80 for the training set. A relatively low accuracy by clinicians (AUC = 0.81) illustrates the difficulties of predicting conversion in this heterogeneous cohort. This first application of a MRI-based pattern recognition method to a routine sample demonstrates feasibility, but also illustrates that automated multi-class differential diagnoses have to be the focus of future methodological developments and application studies.

Behavioural variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD) dementia are characterised by progressive brain atrophy. Longitudinal MRI volumetry may help to characterise ongoing structural degeneration and support the differential diagnosis of dementia subtypes. Automated, observer-independent atlas-based MRI volumetry was applied to analyse 102 MRI data sets from 15 bvFTD, 14 AD, and 10 healthy elderly control participants with consecutive scans over at least 12 months. Anatomically defined targets were chosen a priori as brain structures of interest. Groups were compared regarding volumes at clinic presentation and annual change rates. Baseline volumes, especially of grey matter compartments, were significantly reduced in bvFTD and AD patients. Grey matter volumes of the caudate and the gyrus rectus were significantly smaller in bvFTD than AD. The bvFTD group could be separated from AD on the basis of caudate volume with high accuracy (79% cases correct). Annual volume decline was markedly larger in bvFTD and AD than controls, predominantly in white matter of temporal structures. Decline in grey matter volume of the lateral orbitofrontal gyrus separated bvFTD from AD and controls. Automated longitudinal MRI volumetry discriminates bvFTD from AD. In particular, greater reduction of orbitofrontal grey matter and temporal white matter structures after 12 months is indicative of bvFTD.

Unlabelled: Amyloid-β (Aβ) deposition is a pathologic hallmark of Alzheimer disease (AD). Although the typical spatial distribution pattern of Aβ deposition in early AD mainly involves regions distant from the hippocampus, the predominant clinical feature is impairment of hippocampus-dependent memory. We aimed at elucidating the relationship between neocortical Aβ load, regional neuronal function, and memory impairment., Methods: Thirty patients with early AD underwent combined (11)C-Pittsburgh compound B ((11)C-PIB) and (18)F-FDG PET and memory assessments. Composite measures of hemispheric Aβ load were calculated by volume-weighted mean values of neocortical (11)C-PIB binding. Voxelwise (18)F-FDG uptake was used as a measure of regional glucose metabolism reflecting neuronal activity. We investigated the relationship between left- and right-hemispheric Aβ load and regional glucose metabolism (voxelwise analyses). In addition, we assessed the correlations of hemispheric Aβ load (region-of-interest-based analyses) and regional glucose metabolism (voxelwise analysis) with memory performance. Analyses were corrected for age and sex., Results: Higher Aβ load in the left hemisphere was associated with reduced glucose metabolism of the left medial temporal lobe (MTL; r(2) = 0.38) and correlated with worse wordlist recall (r = -0.37; partial correlation controlled for sex and age). Furthermore, wordlist recall correlated with regional glucose metabolism in the bilateral MTL and precuneus-posterior cingulate cortex and right lingual gyrus (r(2) = 0.24)., Conclusion: We demonstrated an association between the left-hemispheric Aβ load and impairment of the left MTL in AD at 2 different levels: regional hypometabolism and verbal memory. This correlation suggests that neocortical amyloid deposition is connected to or even drives neuronal dysfunction and neurodegeneration of the MTL, which is associated with impaired episodic memory processing as a clinical core symptom of AD.

Cognitive decline in degenerative dementia is paralleled by progressive brain atrophy, with the localization of atrophy reflecting specific cognitive impairment. Confrontation naming deficits are frequently observed in dementia across etiologies. In this study we aimed to identify the brain regions underlying this deficit. In patients with clinically diagnosed dementia or mild cognitive impairment (MCI) we investigated the relationship between gray matter volume (GMV) and performance on a standardized confrontation naming test. 268 patients with one of three probable etiologies were included: Alzheimer's Dementia (AD), AD with signs of cerebrovascular pathology, and frontotemporal dementia. Applying voxel-based morphometry using a diffeomorphic registration algorithm we contrasted GMV of patients performing within the normal range with those of patients with pathological performance. Further, differential effects of gray matter atrophy on impaired performance in AD versus MCI of AD type were investigated. Results revealed significantly reduced GMV in the left anterior temporal lobe (ATL) in pathological performers compared to normal performers. The subgroup analysis confined to MCI of AD type and AD patients confirmed this relationship. While left ATL atrophy is known to be implicated in naming deficits in semantic dementia, our data confirm the same in AD and MCI of AD type.

The precuneus is part of the default network of the human brain, which exhibits a high level of activity during the resting state and lower activity during task-related behavior. Typically, the posterior midline areas show this characteristic response in functional magnetic resonance imaging (fMRI) studies. In Alzheimer's disease (AD) and mild cognitive impairment (MCI), subjects exhibit a lack of this typical deactivation. The interpretation of these findings, however, is obfuscated by the presence of local pathology and atrophy in AD. In contrast to AD, in patients with early frontotemporal lobar degeneration (FTLD), the precuneus is virtually free of local neuropathology. In this study, we demonstrate reduced fMRI signal in the precuneus in a group of patients with FTLD during a confrontation naming task. We show that this effect in FTLD patients was (1) similar to that observed in AD and MCI and (2) not related to the degree of gray matter atrophy in the precuneus. We hypothesize that reduced deactivation of the default network is not related to local pathology but to a lack of connectivity, which decreases in both FTLD and AD, the major cortical dementias., (Copyright © 2010 S. Karger AG, Basel.)