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Negative symptoms, such as lack of motivation or social withdrawal, are highly prevalent and debilitating in patients with schizophrenia. Underlying mechanisms of negative symptoms are incompletely understood, thereby preventing the development of targeted treatments. We hypothesized that in patients with schizophrenia during psychotic remission, impaired influences of both model-based and model-free reward predictions on decision-making (‘reward prediction influence’, RPI) underlie negative symptoms. We focused on psychotic remission, because psychotic symptoms might confound reward-based decision-making. Moreover, we hypothesized that impaired model-based/model-free RPIs depend on alterations of both associative striatum dopamine synthesis and storage (DSS) and executive functioning. Both factors influence RPI in healthy subjects and are typically impaired in schizophrenia. Twenty-five patients with schizophrenia with pronounced negative symptoms during psychotic remission and 24 healthy controls were included in the study. Negative symptom severity was measured by the Positive and Negative Syndrome Scale negative subscale, model-based/model-free RPI by the two-stage decision task, associative striatum DSS by 18F-DOPA positron emission tomography and executive functioning by the symbol coding task. Model-free RPI was selectively reduced in patients and associated with negative symptom severity as well as with reduced associative striatum DSS (in patients only) and executive functions (both in patients and controls). In contrast, model-based RPI was not altered in patients. Results provide evidence for impaired model-free reward prediction influence as a mechanism for negative symptoms in schizophrenia as well as for reduced associative striatum dopamine and executive dysfunction as relevant factors. Data suggest potential treatment targets for patients with schizophrenia and pronounced negative symptoms.

Therapeutic modulation of TREM2-dependent microglial function might provide an additional strategy to slow the progression of Alzheimer's disease. Although studies in animal models suggest that TREM2 is protective against Alzheimer's pathology, its effect on tau pathology and its potential beneficial role in people with Alzheimer's disease is still unclear. Our aim was to study associations between the dynamics of soluble TREM2, as a biomarker of TREM2 signalling, and amyloid β (Aβ) deposition, tau-related pathology, neuroimaging markers, and cognitive decline, during the progression of autosomal dominant Alzheimer's disease.We did a longitudinal analysis of data from the Dominantly Inherited Alzheimer Network (DIAN) observational study, which includes families with a history of autosomal dominant Alzheimer's disease. Participants aged over 18 years who were enrolled in DIAN between Jan 1, 2009, and July 31, 2019, were categorised as either carriers of pathogenic variants in PSEN1, PSEN2, and APP genes (n=155) or non-carriers (n=93). We measured amounts of cleaved soluble TREM2 using a novel immunoassay in CSF samples obtained every 2 years from participants who were asymptomatic (Clinical Dementia Rating [CDR]=0) and annually for those who were symptomatic (CDR>0). CSF concentrations of Aβ40, Aβ42, total tau (t-tau), and tau phosphorylated on threonine 181 (p-tau) were measured by validated immunoassays. Predefined neuroimaging measurements were total cortical uptake of Pittsburgh compound B PET (PiB-PET), cortical thickness in the precuneus ascertained by MRI, and hippocampal volume determined by MRI. Cognition was measured using a validated cognitive composite (including DIAN word list test, logical memory delayed recall, digit symbol coding test [total score], and minimental status examination). We based our statistical analysis on univariate and bivariate linear mixed effects models.In carriers of pathogenic variants, a high amyloid burden at baseline, represented by low CSF Aβ42 (β=-4·28 × 10-2 [SE 0·013], p=0·0012), but not high cortical uptake in PiB-PET (β=-5·51 × 10-3 [0·011], p=0·63), was the only predictor of an augmented annual rate of subsequent increase in soluble TREM2. Augmented annual rates of increase in soluble TREM2 were associated with a diminished rate of decrease in amyloid deposition, as measured by Aβ42 in CSF (r=0·56 [0·22], p=0·011), in presymptomatic carriers of pathogenic variants, and with diminished annual rate of increase in PiB-PET (r=-0·67 [0·25], p=0·0060) in symptomatic carriers of pathogenic variants. Presymptomatic carriers of pathogenic variants with annual rates of increase in soluble TREM2 lower than the median showed a correlation between enhanced annual rates of increase in p-tau in CSF and augmented annual rates of increase in PiB-PET signal (r=0·45 [0·21], p=0·035), that was not observed in those with rates of increase in soluble TREM2 higher than the median. Furthermore, presymptomatic carriers of pathogenic variants with rates of increase in soluble TREM2 above or below the median had opposite associations between Aβ42 in CSF and PiB-PET uptake when assessed longitudinally. Augmented annual rates of increase in soluble TREM2 in presymptomatic carriers of pathogenic variants correlated with decreased cortical shrinkage in the precuneus (r=0·46 [0·22]), p=0·040) and diminished cognitive decline (r=0·67 [0·22], p=0·0020).Our findings in autosomal dominant Alzheimer's disease position the TREM2 response within the amyloid cascade immediately after the first pathological changes in Aβ aggregation and further support the role of TREM2 on Aβ plaque deposition and compaction. Furthermore, these findings underpin a beneficial effect of TREM2 on Aβ deposition, Aβ-dependent tau pathology, cortical shrinkage, and cognitive decline. Soluble TREM2 could, therefore, be a key marker for clinical trial design and interpretation. Efforts to develop TREM2-boosting therapies are ongoing.German Research Foundation, US National Institutes of Health.

Despite growing interest in cognitive interventions from academia and industry, it remains unclear if working memory (WM) training, one of the most popular cognitive interventions, produces transfer effects. Transfer effects are training-induced gains in performance in untrained cognitive tasks, while practice effects are improvements in trained task. The goal of this study was to evaluate potential transfer effects by comprehensive cognitive testing and neuroimaging. In this prospective, randomized-controlled, and single-blind study, we administered an 8-week n-back training to 55 healthy middle-aged (50–64 years) participants. State-of-the-art multimodal neuroimaging was used to examine potential anatomic and functional changes. Relative to control subjects, who performed non-adaptive WM training, no near or far transfer effects were detected in experimental subjects, who performed adaptive WM training. Equivalently, no training-related changes were observed in white matter integrity, amplitude of low frequency fluctuations, glucose metabolism, functional and metabolic connectivity. Exploratory within-group comparisons revealed some gains in transfer tasks, which, however, cannot be attributed to an increased WM capacity. In conclusion, WM training produces transfer effects neither at the cognitive level nor in terms of neural structure or function. These results speak against a common view that training-related gains reflect an increase in underlying WM capacity. Instead, the presently observed practice effects may be a result of optimized task processing strategies, which do not necessarily engage neural plasticity.

Normative brain volume reports (NBVRs) are becoming more and more available for the workup of dementia patients in clinical routine. However, it is yet unknown how this information can be used in the radiological decision-making process. The present study investigates the diagnostic value of NBVRs for detection and differential diagnosis of distinct regional brain atrophy in several dementing neurodegenerative disorders. NBVRs were obtained for 81 consecutive patients with distinct dementing neurodegenerative diseases and 13 healthy controls (HC). Forty Alzheimer’s disease (AD; 18 with dementia, 22 with mild cognitive impairment (MCI), 11 posterior cortical atrophy (PCA)), 20 frontotemporal dementia (FTD), and ten semantic dementia (SD) cases were analyzed, and reports were tested qualitatively for the representation of atrophy patterns. Gold standard diagnoses were based on the patients’ clinical course, FDG-PET imaging, and/or cerebrospinal fluid (CSF) biomarkers following established diagnostic criteria. Diagnostic accuracy of pattern representations was calculated. NBVRs improved the correct identification of patients vs. healthy controls based on structural MRI for rater 1 (p

"Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.

The clinical presentations of early idiopathic Parkinson disease (IPD) substantially overlap with those of atypical parkinsonian syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). This study aimed to develop metabolic imaging indices based on deep learning to support the differential diagnosis of these conditions. Methods: A benchmark Huashan parkinsonian PET imaging (HPPI, China) database including 1,275 parkinsonian patients and 863 nonparkinsonian subjects with (18)F-FDG PET images was established to support artificial intelligence development. A 3-dimensional deep convolutional neural network was developed to extract deep metabolic imaging (DMI) indices and blindly evaluated in an independent cohort with longitudinal follow-up from the HPPI and an external German cohort of 90 parkinsonian patients with different imaging acquisition protocols. Results: The proposed DMI indices had less ambiguity space in the differential diagnosis. They achieved sensitivities of 98.1%, 88.5%, and 84.5%, and specificities of 90.0%, 99.2%, and 97.8%, respectively, for the diagnosis of IPD, MSA, and PSP in the blind-test cohort. In the German cohort, they resulted in sensitivities of 94.1%, 82.4%, and 82.1%, and specificities of 84.0%, 99.9%, and 94.1%, respectively. Using the PET scans independently achieved a performance comparable to the integration of demographic and clinical information into the DMI indices. Conclusion: The DMI indices developed on the HPPI database show the potential to provide an early and accurate differential diagnosis for parkinsonism and are robust when dealing with discrepancies between populations and imaging acquisitions.

PURPOSE Sparse inverse covariance estimation (SICE) is increasingly utilized to estimate inter-subject covariance of FDG uptake (FDGcov) as proxy of metabolic brain connectivity. However, this statistical method suffers from the lack of robustness in the connectivity estimation. Patterns of FDGcov were observed to be spatially similar with patterns of structural connectivity as obtained from DTI imaging. Based on this similarity, we propose to regularize the sparse estimation of FDGcov using the structural connectivity. METHODS We retrospectively analyzed the FDG-PET and DTI data of 26 healthy controls, 41 patients with Alzheimer's disease (AD), and 30 patients with frontotemporal lobar degeneration (FTLD). Structural connectivity matrix derived from DTI data was introduced as a regularization parameter to assign individual penalties to each potential metabolic connectivity. Leave-one-out cross validation experiments were performed to assess the differential diagnosis ability of structure weighted SICE approach. A few approaches of structure weighted were compared with the standard SICE. RESULTS Compared to the standard SICE, structural weighting has shown more stable performance in the supervised classification, especially in the differentiation AD vs. FTLD (accuracy of 89-90%, while unweighted SICE only 85%). There was a significant positive relationship between the minimum number of metabolic connection and the robustness of the classification accuracy (r = 0.57, P < 0.001). Shuffling experiments showed significant differences between classification score derived with true structural weighting and those obtained by randomized structure (P < 0.05). CONCLUSION The structure-weighted sparse estimation can enhance the robustness of metabolic connectivity, which may consequently improve the differentiation of pathological phenotypes.

Recent functional magnetic resonance imaging (fMRI) studies revealed lower neural activation during processing of an n-back task following working memory training, indicating a training-related increase in neural efficiency. In the present study, we asked if the training induced regional neural activation is accompanied by changes in glucose consumption. An active control and an experimental group of healthy middle-aged volunteers conducted 32 sessions of visual and verbal n-back trainings over 8 weeks. We analyzed data of 52 subjects (25 experimental and 27 control group) for practice effects underlying verbal working memory task and 50 subjects (24 experimental and 26 control group) for practice effects underlying visual WM task. The samples of these two tasks were nearly identical (data of 47 subjects were available for both verbal and visual tasks). Both groups completed neuroimaging sessions at a hybrid PET/MR system before and after training. Each session included criterion task fMRI and resting state positron emission tomography with FDG (FDG-PET). As reported previously, lower neural activation following n-back training was found in regions of the fronto-parieto-cerebellar circuitry during a verbal n-back task. Notably, these changes co-occurred spatially with a higher relative FDG-uptake. Decreased neural activation within regions of the fronto-parietal network during visual n-back task did not show co-occurring changes in relative FDG-uptake. There was no direct association between neuroimaging and behavioral measures, which could be due to the inter-subjects' variability in reaching capacity limits. Our findings provide new details for working memory training induced neural efficiency on a molecular level by integrating FDG-PET and fMRI measures.

Background Cerebrospinal fluid (CSF) analysis for detecting amyloid positivity may be as reliable as positron emission tomography (PET). We evaluated the performance of the amyloid beta (Aβ)42/40 ratio for predicting amyloid positivity by PET, compared with Aβ42 alone, and phosphorylated tau 181 (pTau181)/Aβ42 and total tau (tTau)/Aβ42 ratios, using fully automated CSF immunoassays (Roche Diagnostics International Ltd, Rotkreuz, Switzerland) in a heterogeneous cohort of patients with a range of cognitive disorders reflecting the typical population of a memory clinic. Methods CSF samples from 103 patients with known amyloid PET status (PET positive = 54; PET negative = 49) were retrospectively selected from one site in Germany; 71 patients were undergoing treatment for mild cognitive impairment (n = 44) or mild-to-moderate dementia (n = 27) due to Alzheimer’s disease (AD), and 32 patients were undergoing treatment for non-AD-related cognitive disorders. Aβ42, pTau181, and tTau concentrations were measured in CSF samples using the respective Elecsys® CSF immunoassays modified for use on the cobas e 411 analyzer; Aβ40 concentrations were measured using a non-commercially available robust prototype assay. Sensitivities/specificities for amyloid positivity cut-offs (Youden-derived and pre-defined) were calculated, and receiver operating characteristic analyses determined area under the curve (AUC) versus amyloid PET status. Limitations include a small sample size, use of a pre-analytical protocol not in accordance with the Elecsys CSF immunoassay method sheets, and the lack of a pre-defined cut-off for Aβ42/40. Results Point estimates for sensitivity and specificity of CSF biomarkers and biomarker ratios versus amyloid PET were 0.93 and 0.57 for Aβ42, 0.96 and 0.69 for pTau181/Aβ42, 0.92 and 0.69 for tTau/Aβ42, and 0.94 and 0.82 for Aβ42/40. For AUCs, point estimates (95% confidence intervals) versus amyloid PET were 0.78 (0.68−0.88) for Aβ42, 0.88 (0.81−0.95) for pTau181/Aβ42, 0.87 (0.80−0.95) for tTau/Aβ42, and 0.90 (0.83−0.97) for Aβ42/40. Conclusions CSF Aβ42/40 ratio can predict PET amyloid positivity with high accuracy in patients with a range of cognitive disorders when evaluating Aβ pathology independent of tau and neurodegeneration for research purposes. The performance of Aβ42/40 was comparable with pTau181/Aβ42 and tTau/Aβ42 used in clinical practice and better than Aβ42 alone.

Background: Neprilysin (NEP) cleaves amyloid-β 1–42 (Aβ42) in the brain. Hence, we aimed to elucidate the effect of NEP on Aβ42 in cerebrospinal fluid (CSF) and on in vivo brain amyloid load using amyloid positron emission tomography (PET) with [11C]PiB (Pittsburgh compound B). In addition, associations with the biomarkers for neuronal injury, CSF-tau and FDG-PET, were investigated. Methods: Associations were calculated using global and voxel-based (SPM8) linear regression analyses in the same cohort of 23 highly characterized Alzheimer’s disease patients. Results: CSF-NEP was significantly inversely associated with CSF-Aβ42 and positively with the extent of neuronal injury as measured by CSF-tau and FDG-PET. Conclusions: Our results on CSF-NEP are compatible with the assumption that local degradation, amongst other mechanisms of amyloid clearance, plays a role in the development of Alzheimer’s pathology. In addition, CSF-NEP is associated with the extent and the rate of neurodegeneration.

Recently, Jamadar et al. (2021, Metabolic and hemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study. Cereb Cortex. 31(6), 2855–2867) compared the patterns of brain connectivity or covariance as obtained from 3 neuroimaging measures: 1) functional connectivity estimated from temporal correlations in the functional magnetic resonance imaging blood oxygen level-dependent signal, metabolic connectivity estimated, 2) from temporal correlations in 16-s frames of dynamic [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET), which they designate as functional FDG-PET (fPET), and 3) from intersubject correlations in static FDG-PET images (sPET). Here, we discuss a number of fundamental issues raised by the Jamadar study. These include the choice of terminology, the interpretation of cross-modal findings, the issue of group- to single-subject level inferences, and the meaning of metabolic connectivity as a biomarker. We applaud the methodological approach taken by the authors, but wish to present an alternative perspective on their findings. In particular, we argue that sPET and fPET can both provide valuable information about brain connectivity. Certainly, resolving this conundrum calls for further experimental and theoretical efforts to advance the developing framework of PET-based brain connectivity indices.

Brain atrophy occurs in aging even in the absence of dementia, but it is unclear to what extent this is due to undetected preclinical Alzheimer disease. Here we examine a cross-sectional cohort (ages 18-88) free from confounding influence of preclinical Alzheimer disease, as determined by amyloid PET scans and three years of clinical evaluation post-imaging. We determine the regional strength of age-related atrophy using linear modeling of brain volumes and cortical thicknesses with age. Age-related atrophy was seen in nearly all regions, with greatest effects in the temporal lobe and subcortical regions. When modeling age with the estimated derivative of smoothed aging curves, we found that the temporal lobe declined linearly with age, subcortical regions declined faster at later ages, and frontal regions declined slower at later ages than during midlife. This age-derivative pattern was distinct from the linear measure of age-related atrophy and significantly associated with a measure of myelin. Atrophy did not detectably differ from a preclinical Alzheimer disease cohort when age ranges were matched.

Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDGWe retrospectively analyzed neuroimaging data from 303 subjects, both patients with suspected neurodegenerative disorders and healthy individuals. For each subject, structural magnetic resonance, diffusion tensor imaging, and FDG-PET data were available. The images were spatially normalized to a standard space and segmented into 62 anatomical regions using a probabilistic atlas. Sparse inverse covariance estimation was employed to estimate FDGFor the whole brain, 55% of detected connections were found to be convergent, i.e., present in both FDGStructural connectivity via white matter fiber tracts is a relevant substrate of FDG

To date, 3 18F-labeled PET tracers have been approved for assessing cerebral amyloid plaque pathology in the diagnostic workup of suspected Alzheimer disease (AD). Although scanning protocols are relatively similar across tracers, U.S. Food and Drug Administration- and the European Medicines Agency-approved visual rating protocols differ among the 3 tracers. This proof-of-concept study assessed the comparability of the 3 approved visual rating protocols to classify a scan as amyloid-positive or -negative, when applied by groups of experts and nonexperts to all 3 amyloid tracers. Methods: In an international multicenter approach, both expert (n = 4) and nonexpert raters (n = 3) rated scans acquired with 18F-florbetaben, 18F-florbetapir and 18F-flutemetamol. Scans obtained with each tracer were presented for reading according to all 3 approved visual rating protocols. In a randomized order, every single scan was rated by each reader according to all 3 protocols. Raters were blinded for the amyloid tracer used and asked to rate each scan as positive or negative, giving a confidence judgment after each response. Percentage of visual reader agreement, interrater reliability, and agreement of each visual read with binary quantitative measures (fixed SUV ratio threshold for positive or negative scans) were computed. These metrics were analyzed separately for expert and nonexpert groups. Results: No significant differences in using the different approved visual rating protocols were observed across the different metrics of agreement in the group of experts. Nominal differences suggested that the 18F-florbetaben visual rating protocol achieved the highest interrater reliability and accuracy especially under low confidence conditions. For the group of nonexpert raters, significant differences between the different visual rating protocols were observed with overall moderate-to-fair accuracy and with the highest reliability for the 18F-florbetapir visual rating protocol. Conclusion: We observed high interrater agreement despite applying different visual rating protocols for all 18F-labeled amyloid tracers. This implies that the results of the visual interpretation of amyloid imaging can be well standardized and do not depend on the rating protocol in experts. Consequently, the creation of a universal visual assessment protocol for all amyloid imaging tracers appears feasible, which could benefit especially the less-experienced readers. ispartof: JOURNAL OF NUCLEAR MEDICINE vol:62 issue:7 pages:999-1005 ispartof: location:United States status: published

Allelic variation in the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism moderates increases in cerebrospinal fluid (CSF) levels of tau and phosphorylated tau 181 (p-tau181), measured using immunoassay, and cognitive decline in presymptomatic dominantly inherited Alzheimer disease (DIAD). Advances in mass spectrometry show that CSF tau phosphorylation occupancy at threonine 181 and 217 (p-tau181/tau181, p-tau217/tau217) increases with initial β-amyloid (Aβ) aggregation, while phosphorylation occupancy at threonine 205 (p-tau205/tau205) and level of total tau increase when brain atrophy and clinical symptoms become evident.To determine whether site-specific tau phosphorylation occupancy (ratio of phosphorylated to unphosphorylated tau) is associated with BDNF Val66Met in presymptomatic and symptomatic DIAD.This cross-sectional cohort study included participants from the Dominantly Inherited Alzheimer Network (DIAN) and Aβ-positive cognitively normal older adults in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Data were collected from 2009 through 2018 at multicenter clinical sites in the United States, United Kingdom, and Australia, with no follow-up. DIAN participants provided a CSF sample and completed clinical and cognitive assessments. Data analysis was conducted between March 2020 and March 2021.Mass spectrometry analysis was used to determine site-specific tau phosphorylation level; tau levels were also measured using immunoassay. Episodic memory and global cognitive composites were computed.Of 374 study participants, 144 were mutation noncarriers, 156 were presymptomatic mutation carriers, and 74 were symptomatic carriers. Of the 527 participants in the network, 153 were excluded because their CSF sample, BDNF status, or both were unavailable. Also included were 125 Aβ-positive cognitively normal older adults in the ADNI. The mean (SD) age of DIAD participants was 38.7 (10.9) years; 43% were women. The mean (SD) age of participants with preclinical sporadic AD was 74.8 (5.6) years; 52% were women. In presymptomatic mutation carriers, compared with Val66 homozygotes, Met66 carriers showed significantly poorer episodic memory (d = 0.62; 95% CI, 0.28-0.95), lower hippocampal volume (d = 0.40; 95% CI, 0.09-0.71), and higher p-tau217/tau217 (d = 0.64; 95% CI, 0.30-0.97), p-tau181/tau181 (d = 0.65; 95% CI, 0.32-0.99), and mass spectrometry total tau (d = 0.43; 95% CI, 0.10-0.76). In symptomatic mutation carriers, Met66 carriers showed significantly poorer global cognition (d = 1.17; 95% CI, 0.65-1.66) and higher p-tau217/tau217 (d = 0.53; 95% CI, 0.05-1.01), mass spectrometry total tau (d = 0.78; 95% CI, 0.28-1.25), and p-tau205/tau205 (d = 0.97; 95% CI, 0.46-1.45), when compared with Val66 homozygotes. In preclinical sporadic AD, Met66 carriers showed poorer episodic memory (d = 0.39; 95% CI, 0.00-0.77) and higher total tau (d = 0.45; 95% CI, 0.07-0.84) and p-tau181 (d = 0.46; 95% CI, 0.07-0.85).In DIAD, clinical disease stage and BDNF Met66 were associated with cognitive impairment and levels of site-specific tau phosphorylation. This suggests that pharmacological strategies designed to increase neurotrophic support in the presymptomatic stages of AD may be beneficial.

Objective: The aims of our study were to describe the clinical phenotype and to characterize the cerebral glucose metabolism patterns as measured with fluordesoxyglucose-positron emission tomography (FDG-PET) in symptomatic FTLD-patients with different GRN variants. Methods: For this study, data were included from all patients (n = 10) of a single-center FTLD registry study who had a pathogenic GRN variant and who had undergone a cerebral FDG-PET scan. Results: An overt variability of clinical phenotypes was identified with half of the cases being not unambiguously classifiable into one of the clinical FTLD subtypes. Furthermore, GRN + patients showed a considerable inter-individual variability of FDG uptake pattern. In half of the GRN + patients, metabolic changes expanded from frontal and temporal brain regions to parietal brain regions including the posterior cingulate cortex. Striking asymmetry without a preference for either hemisphere was overt in half of GRN + cases. Conclusion: We conclude that GRN mutations cause variable patterns of neurodegeneration that often exceed the anatomical boundaries of the frontotemporal brain regions and produce clinical syndromes that cannot clearly be classified into one of the subtypes as defined by the diagnostic criteria.

To date, 3

Highlights • Imaging biomarkers of the metabolic trajectory from HC, iRBD and PD are identified. • Frontal, limbic and occipital brain regions as imaging biomarkers in PD. • Frontal, limbic and occipital brain regions as imaging biomarkers of the phenoconversion from iRBD to PD., Objective Idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal stage of synucleinopathies such as Parkinson’s disease (PD). Positron emission tomography (PET) with 18F-FDG reveals metabolic perturbations, which are scored by spatial covariance analysis. However, the resultant pattern scores do not capture the spatially heterogeneous trajectories of metabolic changes between individual brain regions. Assuming metabolic progression occurs as a continuum from the healthy control (HC) condition to iRBD and then PD, we investigated spatial dynamics of progressively perturbed glucose metabolism in a cross-sectional study. Methods 19 iRBD patients, 38 PD patients and 19 HC subjects underwent 18F-FDG PET. The images were spatially normalized, scaled to the global mean uptake, and automatically parcellated. We contrasted regional metabolism by group, and allocated the inferred progression to one of several possible trajectories. We further investigated the correlations between 18F-FDG uptake and the disease duration in the iRBD and PD groups, respectively. We also explored relationships between 18F-FDG uptake and the Unified Parkinson’s Disease Rating Scale motor (UPDRS III) scores in the PD group. Results PD patients exhibited more extensive relative hyper- and hypo-metabolism than iRBD patients. We identified three dynamic metabolic trajectories, cross-sectional hypo- or hypermetabolism, cross-sectionally unchanged hypo- or hypermetabolism, cross-sectionally late hypo- or hypermetabolism, appearing only in the contrast of PD with iRBD. No correlation was found between relative 18F-FDG metabolism and disease duration in the iRBD group. Regional hyper- and hypo-metabolism in the PD patients correlated with disease duration or clinical UPDRS III scores. Conclusion Cerebral metabolism changes heterogeneously in a continuum extending from HC to iRBD and PD groups in this preliminary study. The distinctive metabolic trajectories point towards a potential neuroimaging biomarker for conversion of iRBD to frank PD, which should be amenable to advanced pattern recognition analysis in future longitudinal studies.

Idiopathic Parkinsons disease and atypical parkinsonian syndromes have similar symptoms at early disease stages, which makes the early differential diagnosis difficult. Positron emission tomography with

Previous unimodal PET and fMRI studies in humans revealed a reproducible vestibular brain activation pattern, but with variations in its weighting and expansiveness. Hybrid studies minimizing methodological variations at baseline conditions are rare and still lacking for task-based designs. Thus, we applied for the first time hybrid 3T PET-MRI scanning (Siemens mMR) in healthy volunteers using galvanic vestibular stimulation (GVS) in healthy volunteers in order to directly compare H215O-PET and BOLD MRI responses. List mode PET acquisition started with the injection of 750 MBq H215O simultaneously to MRI EPI sequences. Group-level statistical parametric maps were generated for GVS vs. rest contrasts of PET, MR-onset (event-related), and MR-block. All contrasts showed a similar bilateral vestibular activation pattern with remarkable proximity of activation foci. Both BOLD contrasts gave more bilateral wide-spread activation clusters than PET; no area showed contradictory signal responses. PET still confirmed the right-hemispheric lateralization of the vestibular system, whereas BOLD-onset revealed only a tendency. The reciprocal inhibitory visual-vestibular interaction concept was confirmed by PET signal decreases in primary and secondary visual cortices, and BOLD-block decreases in secondary visual areas. In conclusion, MRI activation maps contained a mixture of CBF measured using H215O-PET and additional non-CBF effects, and the activation-deactivation pattern of the BOLD-block appears to be more similar to the H215O-PET than the BOLD-onset.

C-X-C chemokine receptor 4 (CXCR4) is a transmembrane chemokine receptor involved in growth, survival, and dissemination of cancer, including aggressive B-cell lymphoma. MRI is the standard imaging technology for central nervous system (CNS) involvement of B-cell lymphoma and provides high sensitivity but moderate specificity. Therefore, novel molecular and functional imaging strategies are urgently required. Methods: In this proof-of-concept study, 11 patients with lymphoma of the CNS (8 primary and 3 secondary involvement) were imaged with the CXCR4-directed PET tracer 68Ga-pentixafor. To evaluate the predictive value of this imaging modality, treatment response, as determined by MRI, was correlated with quantification of CXCR4 expression by 68Ga-pentixafor PET in vivo before initiation of treatment in 7 of 11 patients. Results:68Ga-pentixafor PET showed excellent contrast with the surrounding brain parenchyma in all patients with active disease. Furthermore, initial CXCR4 uptake determined by PET correlated with subsequent treatment response as assessed by MRI. Conclusion:68Ga-pentixafor PET represents a novel diagnostic tool for CNS lymphoma with potential implications for theranostic approaches as well as response and risk assessment.

Functional connectivity of blood oxygenation level dependent signal fluctuations (BOLD-FC) is decreased in Alzheimer’s disease (AD), and suggested to reflect reduced coherence in neural population activity; however, as both neuronal and vascular-hemodynamic processes underlie BOLD signals, impaired perfusion might also contribute to reduced BOLD-FC; 42 AD patients and 27 controls underwent simultaneous PET/MR imaging. Resting-state functional MRI assessed BOLD co-activity to quantify BOLD-FC, pulsed arterial spin labeling (pASL) assessed cerebral blood flow (CBF) as proxy for vascular hemodynamics, and 18F-fluorodeoxyglucose PET assessed glucose metabolism (GluMet) to index neuronal activity. Patients’ BOLD-FC, CBF, and GluMet were reduced within the same precuneal parietal regions. BOLD-FC was positively associated with mean CBF, specifically in patients and controlled for GluMet levels, suggesting that BOLD-FC reductions correlate with pASL-derived hypoperfusion in AD, independently from 18F-fluorodeoxyglucose PET-derived hypometabolism. Data indicate that impaired vascular hemodynamic processes contribute to reduced BOLD connectivity in AD.

PurposePositron emission tomography (PET) with [18F]fluorodeoxyglucose (FDG) is a powerful method for mapping cerebral glucose metabolism as a proxy of neural activity, assuming a steady-state during the recording interval. We asked if a clinical FDG-PET imaging protocol might also capture changes in neural activity associated with performance of a working memory (WM) task.MethodsTo test this concept, we examined hybrid PET/MR data for FDG-PET and simultaneous functional magnetic resonance imaging (fMRI) in a sample of healthy volunteers. The PET image acquisition started 30 min after a bolus injection of approximately 100 MBq FDG, and the WM task was undertaken starting at approximately 60 min post-injection. We reconstructed FDG-PET sum images corresponding to baseline (44–60 min p.i.) and WM tasks (63- 71 min p.i.), each with intensity scaling to the corresponding global mean.ResultsCompared to the baseline resting condition, relative FDG uptake increased during WM task performance in brain regions previously associated with WM. Furthermore, these metabolically active regions partly overlapped with the regions showing task-dependent increases in BOLD signal in simultaneous fMRI.ConclusionWe find evidence for WM task-induced neural activation using a clinical FDG-PET imaging protocol. These findings encourage the development of dedicated protocols for tracking neural correlates of cognitive function. published

Here, we present a case with a pacemaker due to an atrioventricular (AV) block 2 Mobitz type, in whom a gating failure resulted in a relevant underestimation of cardiac function in myocardial perfusion scintigraphy. A set of quality control steps for gating errors is proposed.

Purpose Imaging glioma biology holds great promise to unravel the complex nature of these tumors. Besides well-established imaging techniques such O-(2-[18F]fluoroethyl)-l-tyrosine (FET)-PET and dynamic susceptibility contrast (DSC) perfusion imaging, amide proton transfer–weighted (APTw) imaging has emerged as a promising novel MR technique. In this study, we aimed to better understand the relation between these imaging biomarkers and how well they capture cellularity and vascularity in newly diagnosed gliomas. Methods Preoperative MRI and FET-PET data of 46 patients (31 glioblastoma and 15 lower-grade glioma) were segmented into contrast-enhancing and FLAIR-hyperintense areas. Using established cutoffs, we calculated hot-spot volumes (HSV) and their spatial overlap. We further investigated APTw and CBV values in FET-HSV. In a subset of 10 glioblastoma patients, we compared cellularity and vascularization in 34 stereotactically targeted biopsies with imaging. Results In glioblastomas, the largest HSV was found for APTw, followed by PET and CBV (p Conclusions We found a relevant spatial overlap in glioblastomas between hotspots of APTw and FET both in contrast-enhancing and FLAIR-hyperintense tumor. As suggested by earlier studies, APTw was lower in lower-grade gliomas compared with glioblastomas. APTw meaningfully contributes to biological imaging of gliomas.

Background As investigations of disease modifying drugs aim to slow down progression of Alzheimer’ disease (AD) biomarkers to reliably track disease progression gain more importance. This is especially important as clinical symptoms, including psychometric measures, are only modestly associated with the underlying disease pathology, in particular at the pre-dementia stages. The decision which biomarkers to choose in clinical trials is crucial and depends on effect size. However, longitudinal studies of multiple biomarkers in parallel that allow direct comparison on effect size are scarce. Methods We calculated effect size and minimal sample size for three common imaging biomarkers of AD, namely amyloid deposition measured with PiB-PET, neuronal dysfunction measured with FDG-PET and cortical thickness measured with MRI in a prospective 24-month follow-up study in a monocentric cohort of early AD. Results Post hoc power calculation revealed large effect sizes of Cohen’s d for PiB-PET and cortical thickness and a small effect size for FDG-PET (1.315, 0.914, and 0.341, respectively). Accordingly, sample sizes for PiB-PET and cortical thickness required significantly smaller sample sizes than FDG-PET to reliably detect statistically significant changes after 24 months in early AD (n = 7, n = 12, and n = 70, respectively). Conclusion Amyloid imaging with PET and measuring cortical thickness with MRI are suitable biomarkers to detect disease progression in early AD within a small sample.

Functional MRI (fMRI) studies have reported altered integrity of large-scale neurocognitive networks (NCNs) in dementing disorders. However, findings on the specificity of these alterations in patients with Alzheimer disease (AD) and behavioral-variant frontotemporal dementia (bvFTD) are still limited. Recently, NCNs have been successfully captured using PET with 18F-FDG. Methods: Network integrity was measured in 72 individuals (38 male) with mild AD or bvFTD, and in healthy controls, using a simultaneous resting-state fMRI and 18F-FDG PET. Indices of network integrity were calculated for each subject, network, and imaging modality. Results: In either modality, independent-component analysis revealed 4 major NCNs: anterior default-mode network (DMN), posterior DMN, salience network, and right central executive network (CEN). In fMRI data, the integrity of the posterior DMN was found to be significantly reduced in both patient groups relative to controls. In the AD group the anterior DMN and CEN appeared to be additionally affected. In PET data, only the integrity of the posterior DMN in patients with AD was reduced, whereas 3 remaining networks appeared to be affected only in patients with bvFTD. In a logistic regression analysis, the integrity of the anterior DMN as measured with PET alone accurately differentiated between the patient groups. A correlation between indices of 2 imaging modalities was low overall. Conclusion: FMRI and 18F-FDG PET capture partly different aspects of network integrity. A higher disease specificity for NCNs as derived from PET data supports metabolic connectivity imaging as a promising diagnostic tool.

Background Augmenting the brain clearance of toxic oligomers with small molecule modulators constitutes a promising therapeutic concept against tau deposition. However, there has been no test of this concept in animal models of Alzheimer’s disease (AD) with initiation at a late disease stage. Thus, we aimed to investigate the effects of interventional late-stage Anle138b treatment, which previously indicated great potential to inhibit oligomer accumulation by binding of pathological aggregates, on the metabolic decline in transgenic mice with established tauopathy in a longitudinal 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) study. Methods Twelve transgenic mice expressing all six human tau isoforms (hTau) and ten controls were imaged by FDG-PET at baseline (14.5 months), followed by randomization into Anle138b treatment and vehicle groups for 3 months. FDG-PET was repeated after treatment for 3 months, and brains were analyzed by tau immunohistochemistry. Longitudinal changes of glucose metabolism were compared between study groups, and the end point tau load was correlated with individual FDG-PET findings. Results Tau pathology was significantly ameliorated by late-stage Anle138b treatment when compared to vehicle (frontal cortex − 53%, p

Meningiomas have an excellent survival prognosis, and radiotherapy (RT) is a central component of interdisciplinary treatment. During treatment planning, the definition of the target volume remains challenging using MR and CT imaging alone. This is the first study to analyze the impact of additional PET-imaging on local control (LC) and overall survival (OS) after high-precision RT. We analyzed 339 meningiomas treated between 2000 and 2018. For analyses, we divided the patients in low-grade (n = 276) and high-grade (n = 63) cases. We performed RT in an adjuvant setting due to subtotal resection or later due to recurrent tumor growth. The target volumes were delineated based on diagnostic CT and MRI and, if available, additional PET-imaging (low-grade: n = 164, 59.4%; high-grade: n = 39, 61.9%) with either 68Ga-Dotanoc/Dotatoc, 18F-fluoroethyltyrosine or 11C-methionine tracer. Patients were treated with fractionated stereotactic RT with a median total dose and dose per fraction of 54 Gy and 1.8 Gy, respectively. Median follow-up was 5.6 years. For low-grade meningiomas, mean OS was 15.6 years and mean LC was 16.9 years; for high-grade cases mean OS was 11.6 years, and mean LC was 11.1 years. In univariate analyses, PET-imaging had a significant impact on OS (p = 0.035) and LC (p = 0.041) for low-grade meningiomas and remained significant (p = 0.015) for LC in the multivariate analysis. For high-grade cases, PET did not influence both OS and LC. Further prognostic factors could be identified. For low-grade meningiomas, we showed that the addition of PET-imaging for target volume definition led to a significantly enhanced LC. Thus, PET improves the detection of tumor cells and helps distinguish between healthy tissue and meningioma tissue, especially during the treatment planning process.

In Alzheimer's disease (AD), a single-nucleotide polymorphism in the gene encoding brain-derived neurotrophic factor (BDNFVal66Met) is associated with worse impact of primary AD pathology (beta-amyloid, Aβ) on neurodegeneration and cognitive decline, rendering BDNFVal66Met an important modulating factor of cognitive impairment in AD. However, the effect of BDNFVal66Met on functional networks that may underlie cognitive impairment in AD is poorly understood. Using a cross-validation approach, we first explored in subjects with autosomal dominant AD (ADAD) from the Dominantly Inherited Alzheimer Network (DIAN) the effect of BDNFVal66Met on resting-state fMRI assessed functional networks. In seed-based connectivity analysis of six major large-scale networks, we found a stronger decrease of hippocampus (seed) to medial-frontal connectivity in the BDNFVal66Met carriers compared to BDNFVal homozogytes. BDNFVal66Met was not associated with connectivity in any other networks. Next, we tested whether the finding of more pronounced decrease in hippocampal-medial-frontal connectivity in BDNFVal66Met could be also found in elderly subjects with sporadically occurring Aβ, including a group with subjective cognitive decline (N = 149, FACEHBI study) and a group ranging from preclinical to AD dementia (N = 114, DELCODE study). In both of these independently recruited groups, BDNFVal66Met was associated with a stronger effect of more abnormal Aβ-levels (assessed by biofluid-assay or amyloid-PET) on hippocampal-medial-frontal connectivity decreases, controlled for hippocampus volume and other confounds. Lower hippocampal-medial-frontal connectivity was associated with lower global cognitive performance in the DIAN and DELCODE studies. Together these results suggest that BDNFVal66Met is selectively associated with a higher vulnerability of hippocampus-frontal connectivity to primary AD pathology, resulting in greater AD-related cognitive impairment.

Knowledge about spatial and temporal patterns of beta-amyloid (A beta) accumulation is essential for understanding Alzheimer disease (AD) and for design of antiamyloid drug trials. Here, we tested whether the regional pattern of longitudinal A beta accumulation can be predicted by baseline amyloid PET. Methods: Baseline and 2-y followup F-18-florbetapir PET data from 58 patients with incipient and manifest dementia due to AD were analyzed. With the determination of how fast amyloid deposits in a given region relative to the whole brain gray matter, a pseudotemporal accumulation rate for each region was calculated. The actual accumulation rate of 18F-florbetapir was calculated from follow-up data. Results: Pseudotemporal measurements from baseline PET data explained 87% (P < 0.001) of the variance in longitudinal accumulation rate across 62 regions. The method accurately predicted the top 10 fast and slow accumulating regions. Conclusion: Pseudotemporal analysis of baseline PET images is capable of predicting the regional pattern of longitudinal A beta accumulation in AD at a group level. This approach may be useful in exploring spatial patterns of A beta accumulation in other amyloidassociated disorders such as Lewy body disease and atypical forms of AD. In addition, the method allows identification of brain regions with a high accumulation rate of A beta, which are of particular interest for antiamyloid clinical trials.

Posterior cortical atrophy is dominated by progressive degradation of parieto-occipital grey and white matter, and represents in most cases a variant of Alzheimer's disease. Patients with posterior cortical atrophy are characterized by increasing higher visual and visuo-spatial impairments. In particular, a key symptom of posterior cortical atrophy is simultanagnosia i.e. the inability to perceive multiple visual objects at the same time. Two neuro-cognitive mechanisms have been suggested to underlie simultanagnosia, either reduced visual short-term memory capacity or decreased visual processing speed possibly resulting from white matter impairments over and above damage to cortical brain areas. To test these distinct hypotheses, we investigated a group of 12 patients suffering from posterior cortical atrophy with homogenous lesion sides in parieto-occipital cortices and varying severity of grey and white matter loss. More specifically, we (i) tested whether impaired short-term memory capacity or processing speed underlie symptoms of simultanagnosia; (ii) assessed the link to grey and white matter damage; and (iii) integrated those findings into a neuro-cognitive model of simultanagnosia in patients with posterior cortical atrophy. To this end, simultaneous perception of multiple visual objects was tested in patients with posterior cortical atrophy mostly with positive Alzheimer's disease biomarkers and healthy age-matched controls. Critical outcome measures were identification of overlapping relative to non-overlapping figures and visuo-spatial performance in tests sensitive to simultanagnosia. Using whole report of briefly presented letter arrays based on the mathematically formulated 'Theory of Visual Attention', we furthermore quantified parameters of visual short-term memory capacity and visual processing speed. Grey and white matter atrophy was assessed by voxel-based morphometry analyses of structural magnetic resonance data. All patients showed severe deficits of simultaneous perception. Compared to controls, we observed a specific slowing of visual processing speed, while visual short-term memory capacity was preserved. In a regression analysis, processing speed was identified as the only significant predictor of simultaneous perception deficits that explained a high degree of variance (70-82%) across simultanagnosia tasks. More severe slowing was also indicative for more severe impairments in reading and scene comprehension. Voxel-based morphometry yielded extensive reductions of grey and white matter in parieto-occipital and thalamic brain areas. Importantly, the degree of individual atrophy of white matter in left superior parietal lobe, but not of any grey matter region, was associated with processing speed. Based on these findings, we propose that atrophy of white matter commonly observed in posterior cortical atrophy leads to slowing of visual processing speed, which underlies the overt clinical symptoms of simultanagnosia.

Presently, visual and quantitative approaches for image-supported diagnosis of dementing disorders rely on regional intensity rather than on connectivity measurements. Here, we test metabolic connectivity for differentiation between Alzheimer’s disease and frontotemporal lobar degeneration. Positron emission tomography with 18F-fluorodeoxyglucose was conducted in 47 patients with mild Alzheimer’s disease, 52 patients with mild frontotemporal lobar degeneration, and 45 healthy elderly subjects. Sparse inverse covariance estimation and selection were used to identify patterns of metabolic, inter-subject covariance on the basis of 60 regional values. Relative to healthy subjects, significantly more pathological within-lobe connections were found in the parietal lobe of patients with Alzheimer’s disease, and in the frontal and temporal lobes of subjects with frontotemporal lobar degeneration. Relative to the frontotemporal lobar degeneration group, more pathological connections between the parietal and temporal lobe were found in the Alzheimer’s disease group. The obtained connectivity patterns differentiated between two patients groups with an overall accuracy of 83%. Linear discriminant analysis and univariate methods provided an accuracy of 74% and 69%, respectively. There are characteristic patterns of abnormal metabolic connectivity in mild Alzheimer’s disease and frontotemporal lobar degeneration. Such patterns can be utilized for single-subject analyses and might be more accurate in the differential diagnosis of dementing disorders than traditional intensity-based analyses.

Background: Volumetric quantification of structural MRI has been shown to increase the diagnostic accuracy of patients with mild cognitive impairment (MCI);however, its implementation in clinical routine is usually technically difficult and time-consuming. Objective: The purpose of this study was to investigate whether volumetric information obtained from the free and easy-to-use online tool volBrain can improve correct identification of MCI patients with Alzheimer's disease (AD) compared to visual reading. Methods: The study cohort consisted of 27 patients with MCI due to AD (AD positive) as determined by biomarker information and 26 cognitively normal controls (CN). Three blinded readers, 2 radiologists and 1 clinical dementia expert, assessed the patients' MRI regarding brain atrophy and probability of underlying AD two times, without and with supporting volumetric information from volBrain. To assess diagnostic accuracy of volBrain measures alone, a simple sum score based on basic volumetric measures was developed and tested. Results: Correct patient classification by readers 1, 2, and 3 without a volumetric report was 73.6%, 77.4%, and 83.0%. With a volumetric report, correct classification increased for the radiological readers to 77.4% and 81.1%, respectively and decreased to 77.4% for reader 3. Usage of the volumetric report alone yielded the highest diagnostic accuracy of 84.9%. Diagnostic confidence increased significantly for radiological readers. Conclusion: Volumetric information from volBrain increases the radiologist's diagnostic performance and confidence in identifying MCI patients with AD. We propose that such tools may be implemented in the routine diagnostic work-up of patients with suspected AD.

Converging evidence from structural, metabolic and functional connectivity MRI suggests that neurodegenerative diseases, such as Alzheimer's disease, target specific neural networks. However, age-related network changes commonly co-occur with neuropathological cascades, limiting efforts to disentangle disease-specific alterations in network function from those associated with normal ageing. Here we elucidate the differential effects of ageing and Alzheimer's disease pathology through simultaneous analyses of two functional connectivity MRI datasets: (i) young participants harbouring highly-penetrant mutations leading to autosomal-dominant Alzheimer's disease from the Dominantly Inherited Alzheimer's Network (DIAN), an Alzheimer's disease cohort in which age-related comorbidities are minimal and likelihood of progression along an Alzheimer's disease trajectory is extremely high; and (ii) young and elderly participants from the Harvard Aging Brain Study, a cohort in which imaging biomarkers of amyloid burden and neurodegeneration can be used to disambiguate ageing alone from preclinical Alzheimer's disease. Consonant with prior reports, we observed the preferential degradation of cognitive (especially the default and dorsal attention networks) over motor and sensory networks in early autosomal-dominant Alzheimer's disease, and found that this distinctive degradation pattern was magnified in more advanced stages of disease. Importantly, a nascent form of the pattern observed across the autosomal-dominant Alzheimer's disease spectrum was also detectable in clinically normal elderly with clear biomarker evidence of Alzheimer's disease pathology (preclinical Alzheimer's disease). At the more granular level of individual connections between node pairs, we observed that connections within cognitive networks were preferentially targeted in Alzheimer's disease (with between network connections relatively spared), and that connections between positively coupled nodes (correlations) were preferentially degraded as compared to connections between negatively coupled nodes (anti-correlations). In contrast, ageing in the absence of Alzheimer's disease biomarkers was characterized by a far less network-specific degradation across cognitive and sensory networks, of between- and within-network connections, and of connections between positively and negatively coupled nodes. We go on to demonstrate that formalizing the differential patterns of network degradation in ageing and Alzheimer's disease may have the practical benefit of yielding connectivity measurements that highlight early Alzheimer's disease-related connectivity changes over those due to age-related processes. Together, the contrasting patterns of connectivity in Alzheimer's disease and ageing add to prior work arguing against Alzheimer's disease as a form of accelerated ageing, and suggest multi-network composite functional connectivity MRI metrics may be useful in the detection of early Alzheimer's disease-specific alterations co-occurring with age-related connectivity changes. More broadly, our findings are consistent with a specific pattern of network degradation associated with the spreading of Alzheimer's disease pathology within targeted neural networks.

The established biomarkers of Alzheimer's disease (AD) require invasive endeavours or presuppose sophisticated technical equipment. Consequently, new biomarkers are needed. Here, we report that plasma levels of soluble amyloid precursor protein β (sAPPβ), a protein of the initial phase of the amyloid cascade, were significantly lower in patients with symptomatic AD (21 with mild cognitive impairment due to AD and 44 with AD dementia) with AD-typical cerebral hypometabolic pattern compared with 27 cognitively healthy elderly individuals without preclinical AD. These findings yield further evidence for the potential of sAPPβ in plasma as an AD biomarker candidate.

White matter alterations are present in the majority of patients with Alzheimer's disease type dementia. However, the spatiotemporal pattern of white matter changes preceding dementia symptoms in Alzheimer's disease remains unclear, largely due to the inherent diagnostic uncertainty in the preclinical phase and increased risk of confounding age-related vascular disease and stroke in late-onset Alzheimer's disease. In early-onset autosomal-dominantly inherited Alzheimer's disease, participants are destined to develop dementia, which provides the opportunity to assess brain changes years before the onset of symptoms, and in the absence of ageing-related vascular disease. Here, we assessed mean diffusivity alterations in the white matter in 64 mutation carriers compared to 45 non-carrier family non-carriers. Using tract-based spatial statistics, we mapped the interaction of mutation status by estimated years from symptom onset on mean diffusivity. For major atlas-derived fibre tracts, we determined the earliest time point at which abnormal mean diffusivity changes in the mutation carriers were detectable. Lastly, we assessed the association between mean diffusivity and cerebrospinal fluid biomarkers of amyloid, tau, phosphorylated-tau, and soluble TREM2, i.e. a marker of microglia activity. Results showed a significant interaction of mutations status by estimated years from symptom onset, i.e. a stronger increase of mean diffusivity, within the posterior parietal and medial frontal white matter in mutation carriers compared with non-carriers. The earliest increase of mean diffusivity was observed in the forceps major, forceps minor and long projecting fibres-many connecting default mode network regions-between 5 to 10 years before estimated symptom onset. Higher mean diffusivity in fibre tracts was associated with lower grey matter volume in the tracts' projection zones. Global mean diffusivity was correlated with lower cerebrospinal fluid levels of amyloid-β1-42 but higher levels of tau, phosphorylated-tau and soluble TREM2. Together, these results suggest that regionally selective white matter degeneration occurs years before the estimated symptom onset. Such white matter alterations are associated with primary Alzheimer's disease pathology and microglia activity in the brain.

Introduction This study examined a longitudinal trajectory of β-amyloid (Aβ) accumulation at the predementia stage of Alzheimer's disease in the context of clinical trials. Methods Analyzed were baseline (BL) and 2 years' follow-up 18F-florbetapir positron emission tomography data of 246 Aβ-positive subjects with normal cognition and mild cognitive impairment. We studied the relationship between annual accumulation rates of 18F-florbetapir and BL standard uptake value ratios in whole gray matter (SUVRGM). Results Subjects with BL SUVRGM of 0.56 to 0.92 (n = 134) appeared to accumulate Aβ approximately 1.5 times faster than remaining subjects. In subjects with SUVRGM above 0.95, most regions with the highest annual accumulation rate were outside the established set of Alzheimer's disease typical regions. Conclusion There are global and regional variations in annual accumulation rate at the predementia stage of Alzheimer's disease. When taken into account, the sample size in anti-amyloid trials can be substantially reduced. Critically, treated and placebo groups should be matched for BL SUVRGM.

Metabolic connectivity modelling aims to detect functionally interacting brain regions based on PET recordings with the glucose analogue [F]fluorodeoxyglucose (FDG). Here, we outline the most popular metabolic connectivity methods and summarize recent applications in clinical and basic neuroscience.Metabolic connectivity is modelled by various methods including a seed correlation, sparse inverse covariance estimation, independent component analysis and graph theory. Given its multivariate nature, metabolic connectivity possess added value relative to conventional univariate analyses of FDG-PET data. As such, metabolic connectivity provides valuable insights into pathophysiology and diagnosis of dementing, movement disorders, and epilepsy. Metabolic connectivity can also identify resting state networks resembling patterns of functional connectivity as derived from functional MRI data.Metabolic connectivity is a valuable concept in the fast-developing field of brain connectivity, at least as reasonable as functional connectivity of functional MRI. So far, the value of metabolic connectivity is best established in neurodegenerative disorders, but studies in other brain diseases as well as in the healthy state are emerging. Growing evidence indicates that metabolic connectivity may serve a marker of normal and pathological cognitive function. A relationship of metabolic connectivity with structural and functional connectivity is yet to be established.

In Alzheimer's disease (AD), amyloid-β (Aβ) pathology and intrinsic functional connectivity (iFC) interact. Across stages of AD, we expected individual spatial correspondence of Aβ and iFC to reveal both Aβ accumulation and its detrimental effects on iFC. We used resting-state functional magnetic imaging and Aβ imaging in a cross-sectional sample of 90 subjects across stages of AD and healthy older adults. Global and local correspondence of Aβ and iFC were assessed within the posterior default mode network (pDMN) by within-subject voxel-wise correlations. Beginning at preclinical stages, global Aβ-iFC correspondence was positive for the whole pDMN, showing that Aβ accumulates in areas of high connectivity, and reached a plateau at prodromal stages. Starting at preclinical stages, local correspondence was negative in network centers, indicating that Aβ reduces connectivity of the pDMN as a function of local plaque concentration, and peaked at prodromal stages. Positive global correspondence suggests that Aβ accumulation progresses along iFC, with this effect starting in preclinical stages, and being constant along clinical periods. Negative local correspondence suggests detrimental effects of Aβ on iFC in network centers, starting at preclinical stages, and peaking when first symptoms appear. Data reveal a complex trajectory of Aβ and iFC correspondence, affecting both Aβ accumulation and iFC impairments.

In patients with Alzheimer's disease (AD), prominent hypometabolism has been observed in brain regions with minor amyloid load. These hypometabolism-only (HO) areas cannot be explained merely as a consequence of local amyloid toxicity. The aim of this multimodal imaging study was to explore whether such HO phenomenon may be related to pathologies in functionally connected, remote brain regions. Nineteen AD patients and 15 matched controls underwent examinations with [(11)C]PiB-PET and [(18)F]FDG-PET. Voxel-based statistical group comparisons were performed to obtain maps of significantly elevated amyloid burden and reduced cerebral glucose metabolism, respectively, in patients. An HO area was identified by subtraction of equally thresholded result maps (hypometabolism minus amyloid burden). To identify the network typically functionally connected to this HO area, it was used as a seed region for a functional connectivity analysis in resting-state functional magnetic resonance imaging data of 17 elderly healthy controls. The resulting intrinsic connectivity network (HO-ICN) was retransferred into the brains of AD patients to be able to analyze pathologies within this network in the positron emission tomography (PET) datasets. The most prominent HO area was detected in the left middle frontal gyrus of AD patients. The HO-ICN in healthy controls showed a major overlap with brain areas significantly affected by both amyloid deposition and hypometabolism in patients. This association was substantiated by the results of region-of-interest-based and voxel-wise correlation analyses, which revealed strong correlations between the degree of hypometabolism within the HO region and within the HO-ICN. These results support the notion that hypometabolism in brain regions not strongly affected by locoregional amyloid pathology may be related to ongoing pathologies in remote but functionally connected regions, that is, by reduced neuronal input from these regions.

New diagnostic criteria for Alzheimer's disease (AD) treat different biomarkers of neuronal injury as equivalent. Here, we quantified the degree of agreement between hippocampal volume on structural magnetic resonance imaging, regional glucose metabolism on positron emission tomography, and levels of phosphorylated tau in cerebrospinal fluid (CSF) in 585 subjects from all phases of the AD Neuroimaging Initiative. The overall chance-corrected agreement was poor (Cohen κ, 0.24–0.34), in accord with a high rate of conflicting findings (26%–41%). Neither diagnosis nor APOE e4 status significantly influenced the distribution of agreement between the biomarkers. The degree of agreement tended to be higher in individuals with abnormal versus normal CSF β-amyloid (Aβ 1-42 ) levels. Prospective diagnostic criteria for AD should address the relative importance of markers of neuronal injury and elaborate a way of dealing with conflicting biomarker findings.

Background According to new diagnostic guidelines for Alzheimer’s disease (AD), biomarkers enable estimation of the individual likelihood of underlying AD pathophysiology and the associated risk of progression to AD dementia for patients with mild cognitive impairment (MCI). Nonetheless, how conflicting biomarker constellations affect the progression risk is still elusive. The present study explored the impact of different cerebrospinal fluid (CSF) biomarker constellations on the progression risk of MCI patients. Methods A multicentre cohort of 469 patients with MCI and available CSF biomarker results and clinical follow-up data was considered. Biomarker values were categorized as positive for AD, negative or borderline. Progression risk differences between patients with different constellations of total Tau (t-Tau), phosphorylated Tau at threonine 181 (p-Tau) and amyloid-beta 1–42 (Aβ42) were studied. Group comparison analyses and Cox regression models were employed. Results Patients with all biomarkers positive for AD (N = 145) had the highest hazard for progression to dementia due to AD, whilst patients with no positive biomarkers (N = 111) had the lowest. The risk of patients with only abnormal p-Tau and/or t-Tau (N = 49) or with positive Aβ42 in combination with positive t-Tau or p-Tau (N = 119) is significantly lower than that of patients with all biomarkers positive. Conclusions The risk of progression to dementia due to AD differs between patients with different CSF biomarker constellations.

There is a great deal of heterogeneity in the impact of aging on cognition and cerebral functioning. One potential factor contributing to individual differences among the elderly is the cognitive reserve, which designates the partial protection from the deleterious effects of aging that lifetime experience provides. Neuroimaging studies examining task-related activation in elderly people suggested that cognitive reserve takes the form of more efficient use of brain networks and/or greater ability to recruit alternative networks to compensate for age-related cerebral changes. In this exploratory multi-center study, we examined the relationships between cognitive reserve, as measured by education and verbal intelligence, and cerebral metabolism at rest (FDG-PET) in a sample of 74 healthy older participants. Higher degree of education and verbal intelligence was associated with less metabolic activity in the right posterior temporoparietal cortex and the left anterior intraparietal sulcus. Functional connectivity analyses of resting-state fMRI images in a subset of 41 participants indicated that these regions belong to the default mode network and the dorsal attention network respectively. Lower metabolism in the temporoparietal cortex was also associated with better memory abilities. The findings provide evidence for an inverse relationship between cognitive reserve and resting-state activity in key regions of two functional networks respectively involved in internal mentation and goal-directed attention.

Purpose: To evaluate the feasibility of multicenter tractography of the cingulate bundle (CB) in Alzheimer's disease (AD). Materials and Methods: Automated deterministic tractography of the CB was applied to scans of 45 patients with probable AD and 58 healthy controls (HC) acquired with Siemens Sonata (1.5T; 60 gradients), Trio (3T; 61 gradients), and Avanto (1.5T; 30 gradients). Diagnosis and center effects on the tracking indices fractional anisotropy (FA), mean diffusivity (MD), track density, and volume were estimated with analysis of variance. Results: The multicenter coefficients of variance (CVs) in HC and AD patients were 7% and 7% for FA, 10% and 8% for MD, 18% and 20% for density, and 21% and 21% for volume. Multicenter and single-center CVs were within a similar range. Significant center effects declined in the order MD > FA > density > volume. After adjustment for center and age, the AD group showed significantly higher MD (P < 0.001) and lower FA (P < 0.05) as compared with the HC group. Conclusion: Despite strong center effects, we detected significantly altered microstructural integrity of the CB in AD patients. Diffusion-tensor imaging indices of the CB as obtained by automated tractography might qualify as a biologically sustained surrogate marker for diagnostic and monitoring purposes in multicenter AD trials. J. Magn. Reson. Imaging 2012;36:84–91. © 2012 Wiley Periodicals Inc.

The combination of Positron Emission Tomography (PET) with magnetic resonance imaging (MRI) in hybrid PET/MRI scanners offers a number of advantages in investigating brain structure and function. A critical step of PET data reconstruction is attenuation correction (AC). Accounting for bone in attenuation maps (μ-map) was shown to be important in brain PET studies. While there are a number of MRI-based AC methods, no systematic comparison between them has been performed so far. The aim of this work was to study the different performance obtained by some of the recent methods presented in the literature. To perform such a comparison, we focused on [18F]-Fluorodeoxyglucose-PET/MRI neurodegenerative dementing disorders, which are known to exhibit reduced levels of glucose metabolism in certain brain regions. Four novel methods were used to calculate μ-maps from MRI data of 15 patients with Alzheimer’s dementia (AD). The methods cover two atlas-based methods, a segmentation method, and a hybrid template/segmentation method. Additionally, the Dixon-based and a UTE-based method, offered by a vendor, were included in the comparison. Performance was assessed at three levels: tissue identification accuracy in the μ-map, quantitative accuracy of reconstructed PET data in specific brain regions, and precision in diagnostic images at identifying hypometabolic areas. Quantitative regional errors of −20–−10 % were obtained using the vendor’s AC methods, whereas the novel methods produced errors in a margin of ±5 %. The obtained precision at identifying areas with abnormally low levels of glucose uptake, potentially regions affected by AD, were 62.9 and 79.5 % for the two vendor AC methods, the former ignoring bone and the latter including bone information. The precision increased to 87.5–93.3 % in average for the four new methods, exhibiting similar performances. We confirm that the AC methods based on the Dixon and UTE sequences provided by the vendor are inferior to alternative techniques. As a novel finding, there was no substantial difference between the recently proposed atlas-based, template-based and segmentation-based methods.

The hippocampus is among the first brain structures to be affected by Alzheimer's disease (AD) pathology. Microstructural alterations within this region have been quantified in vivo using diffusion tensor imaging (DTI), a relatively novel MRI-based technique for mapping diffusion properties of water. Existing evidence indicates that DTI-derived mean diffusivity (MD) of the anterior hippocampus is more predictive than ordinary volumetric indices of the degree of episodic memory impairment in patients with early AD. Thus, altered MD of the (anterior) hippocampus might be highly indicative of hippocampal dysfunction, thereby potentially qualifying this measure as a candidate marker for monitoring progression of AD. Longitudinal studies are needed to confirm this concept. DTI-based assessment of hippocampal microstructure might be also of value for early AD diagnosis and for predicting the course of cognitive decline in subjects at risk for Alzheimer's dementia. Mean diffusivity as microstructural and volume as macrostructural index of hippocampal integrity seem to reflect different, albeit overlapping, aspects of the neurodegenerative process. In contrast, fractional anisotropy is less efficient for quantifying microstructural integrity of the diseased hippocampus in the clinical context. Development of automatic algorithms, providing MD measurements of the hippocampus for routine use, is a task for future studies.

Hypometabolism of the posterior cingulate cortex (PCC) in early Alzheimer's disease (AD) is thought to arise in part due to AD-specific neuronal damage to the hippocampal formation. Here, we explored the association between microstructural alterations within the hippocampus and whole-brain glucose metabolism in subjects with AD, also in relation to episodic memory impairment.Twenty patients with early AD (Mini-Mental State Examination 25.7 ± 1.7) were studied with [(18)F]fluorodeoxyglucose (FDG) positron emission tomography and diffusion tensor imaging. Episodic memory performance was assessed using the free delayed verbal recall task (DVR). Voxel-wise relative FDG uptake was correlated to diffusivity indices of the hippocampus, followed by extraction of FDG uptake values from significant clusters. Linear regression analysis was performed to test for unique contributions of diffusivity and metabolic indices in the prediction of memory function.Diffusivity in the left anterior hippocampus negatively correlated with FDG uptake primarily in the left anterior hippocampus, parahippocampal gyrus and the PCC (p0.005). The same correlation pattern was found for right hippocampal diffusivity (p0.05). In linear regression analysis, left anterior hippocampal diffusivity and FDG uptake from the PCC cluster were the only significant predictors for performance on DVR, together explaining 60.6% of the variance. We found an inverse association between anterior hippocampal diffusivity and PCC glucose metabolism, which was in turn strongly related to episodic memory performance in subjects with early AD.These findings support the diaschisis hypothesis of AD and implicate a dysfunction of structures along the hippocampal output pathways as a significant contributor to the genesis of episodic memory impairment.

Abnormal microstructural integrity and glucose metabolism of the hippocampus are common in subjects with Alzheimer’s disease (AD) that typically manifest as episodic memory impairment. The above-tissue alterations can be captured in vivo using diffusion tensor imaging (DTI) and positron emission tomography with [18F]fluorodeoxyglucose (FDG-PET). Here, we explored relationships between the above neuroimaging and cognitive markers of early AD-specific hippocampal damage. Twenty patients with early AD (MMSE 25.7 ± 1.7) were studied using DTI and FDG-PET. Episodic memory performance was assessed using the free delayed verbal recall task (DVR). In the between-modality correlation analysis, FDG uptake was strongly associated with diffusivity in the left anterior hippocampus only (r = −0.81, p < 0.05 Bonferroni’s corrected for multiple tests). Performance on DVR significantly correlated with left anterior (r = −0.80, p < 0.05) and left mean (r = −0.72, p < 0.05) hippocampal diffusivity, while the correlation with left anterior FDG uptake did not reach statistical significance (r = 0.52, n.s.). DTI-derived diffusivity of the anterior hippocampus might be a sensitive early marker of hippocampal dysfunction as reflected at the synaptic and cognitive levels. This neurobiological distinction of the anterior hippocampus might be related to the disruption of the perforant pathway that is known to occur early in the course of AD.

Background: Neuropsychiatric symptoms (NPS), mainly cognitive deficits up to dementia and depressive syndromes have been described repeatedly in Fabry disease (FD). However, examinations regarding the pattern, extent, and frequency of the NPS in FD are still lacking. Moreover, the relationship between NPS and brain structural alterations in FD is unknown. The aim of this study was 1) to characterize NPS in a relatively large cohort of adult subjects with FD, and 2) to explore the association of cognitive performance and depressive syndromes with the FD-typical brain structural findings. Methods: Twenty-five Fabry patients (age 36.5 ± 11.0) with mild to moderate disease involvement and 20 age, gender-, and education-matched healthy controls were extensively studied by neuropsychiatric assessment, structural magnetic resonance imaging, magnetic resonance angiography, and diffusion-tensor imaging. Results: Patients with FD showed deficits only in the attention domain. Clinically relevant depressive syndromes were noted in 60% of the patients. The subgroup of patients with markedly elevated volumes of white matter lesions (not associated with actual stroke; n = 7) showed slightly more learning and memory deficits, but no higher depression rate compared to less affected patients. Conclusions: Against the prevailing assumption, Fabry patients, even those with marked brain structural alterations, showed only mild cognitive deficits. The high frequency of depression in FD is likely to be related to the burden of this chronic multiorganic hereditary disease, but not to the FD-typical brain structural alterations. Longitudinal studies are necessary to clear, if the mild cognitive deficits in FD might precede clinically relevant cognitive decline.