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Abstract Background The specific binding ratio (SBR) of 123I-FP-CIT in the putamen is widely used to support the interpretation of dopamine transporter (DAT) SPECT. Automatic methods for computation of the putamen SBR often include stereotactical normalization of the individual DAT-SPECT image to an anatomical standard space. This study compared using a single 123I-FP-CIT template image as target for stereotactical normalization versus multiple templates representative of normal and different levels of Parkinson-typical reduction of striatal 123I-FP-CIT uptake. Methods 1702 clinical 123I-FP-CIT SPECT images were stereotactically normalized (affine) to the anatomical space of the Montreal Neurological Institute (MNI) with SPM12 either using a single custom-made 123I-FP-CIT template representative of normal striatal uptake or using eight different templates representative of normal and different levels of Parkinson-typical reduction of striatal FP-CIT uptake with and without attenuation and scatter correction. In the latter case, SPM finds the linear combination of the multiple templates that best matches the patient’s image. The putamen SBR was obtained using hottest voxels analysis in large unilateral regions-of-interest predefined in MNI space. The histogram of the putamen SBR in the whole sample was fitted by the sum of two Gaussians. The power to differentiate between reduced and normal SBR was estimated by the effect size of the distance between the two Gaussians computed as the differences between their mean values scaled to their pooled standard deviation. Results The effect size of the distance between the two Gaussians was 3.83 with the single template versus 3.96 with multiple templates for stereotactical normalization. Conclusions Multiple templates representative of normal and different levels of Parkinson-typical reduction for stereotactical normalization of DAT-SPECT might provide improved separation between normal and reduced putamen SBR that could result in slightly improved power for the detection of nigrostriatal degeneration.

Abstract Background The aims of this study were to establish a normal database (NDB) for semiquantification of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) with [123I]FP-CIT on a cadmium zinc telluride (CZT) camera, test the preexisting NaI-derived NDB for use in CZT scans, and compare the diagnostic findings in subjects imaged with a CZT scanner with either the preexisting NaI-based NDB or our newly defined CZT NDB. Methods The sample comprised 73 subjects with clinically uncertain parkinsonian syndrome (PS) who prospectively underwent [123I]FP-CIT SPECT on a CZT camera according to standard guidelines with identical acquisition and reconstruction protocols (DaTQUANT). Two experienced readers visually assessed the images and binarized the subjects into “non-neurodegenerative PS” and “neurodegenerative PS”. Twenty-five subjects from the “non-neurodegenerative PS” subgroup were randomly selected to establish a CZT NDB. The remaining 48 subjects were defined as “test group”. DaTQUANT was used to determine the specific binding ratio (SBR). For the test group, SBR values were transformed to z-scores for the putamen utilizing both the CZT NDB and the manufacturer-provided NaI-based NDB (GE NDB). A predefined fixed cut-off of -2 was used for dichotomization of z-scores to classify neurodegenerative and non-neurodegenerative PS. Performance of semiquantification using the two NDB to identify subjects with neurodegenerative PS was assessed in comparison with the visual rating. Furthermore, a randomized head-to-head comparison of both detector systems was performed semiquantitatively in a subset of 32 out of all 73 subjects. Results Compared to the visual rating as reference, semiquantification based on the dedicated CZT NDB led to fewer discordant ratings than the GE NDB in CZT scans (3 vs. 8 out of 48 subjects). This can be attributed to the putaminal z-scores being consistently higher with the GE NDB on a CZT camera (median absolute difference of 1.68), suggesting an optimal cut-off of -0.5 for the GE NDB instead of -2.0. Average binding ratios and z-scores were significantly lower in CZT compared to NaI data. Conclusions Use of a dedicated, CZT-derived NDB is recommended in [123I]FP-CIT SPECT with a CZT camera since it improves agreement between semiquantification and visual assessment.

Abstract Introduction Both elevated cortisol and hippocampal volume have been linked to an increased risk for the development of Alzheimer's disease (AD). This longitudinal study assessed the effects of plasma cortisol on hippocampal atrophy and clinical progression rates in patients with mild cognitive impairment (MCI). Methods Patients with amnestic MCI (n = 304) were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) based on availability of baseline plasma cortisol and hippocampal volume measures, assessed at baseline and during follow‐ups. We investigated associations between plasma cortisol, hippocampal volume, and risk of clinical progression to AD over a study period of up to 100 months (mean follow‐up time 36.8 months) using linear mixed models, Cox proportional hazards models, and Kaplan‐Meier estimators. Results Plasma cortisol predicted greater hippocampal atrophy, such that participants with higher cortisol showed faster decline in hippocampal volume over time (interaction: β = ‐0.15, p = 0.004). Small hippocampal volume predicted a higher risk of clinical progression to AD (haard ratio [HR] = 2.15; confidence in terval [CI], 1.64–2.80; p

BackgroundWhite matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research.MethodsWe used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS).ResultsAcross tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions.ConclusionTo conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia.

Abstract Background Previous research has described distinct subtypes of Alzheimer’s disease (AD) based on the differences in regional patterns of brain atrophy on MRI. We conducted a data-driven exploration of distinct AD neurodegeneration subtypes using FDG-PET as a sensitive molecular imaging marker of neurodegenerative processes. Methods Hierarchical clustering of voxel-wise FDG-PET data from 177 amyloid-positive patients with AD dementia enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to identify distinct hypometabolic subtypes of AD, which were then further characterized with respect to clinical and biomarker characteristics. We then classified FDG-PET scans of 217 amyloid-positive patients with mild cognitive impairment (“prodromal AD”) according to the identified subtypes and studied their domain-specific cognitive trajectories and progression to dementia over a follow-up interval of up to 72 months. Results Three main hypometabolic subtypes were identified: (i) “typical” (48.6%), showing a classic posterior temporo-parietal hypometabolic pattern; (ii) “limbic-predominant” (44.6%), characterized by old age and a memory-predominant cognitive profile; and (iii) a relatively rare “cortical-predominant” subtype (6.8%) characterized by younger age and more severe executive dysfunction. Subtypes classified in the prodromal AD sample demonstrated similar subtype characteristics as in the AD dementia sample and further showed differential courses of cognitive decline. Conclusions These findings complement recent research efforts on MRI-based identification of distinct AD atrophy subtypes and may provide a potentially more sensitive molecular imaging tool for early detection and characterization of AD-related neurodegeneration variants at prodromal disease stages.

Abstract Background White matter hyperintensities (WMH) are frequently found in Alzheimer’s disease (AD). Commonly considered as a marker of cerebrovascular disease, regional WMH may be related to pathological hallmarks of AD, including beta-amyloid (Aβ) plaques and neurodegeneration. The aim of this study was to examine the regional distribution of WMH associated with Aβ burden, glucose hypometabolism, and gray matter volume reduction. Methods In a total of 155 participants (IMAP+ cohort) across the cognitive continuum from normal cognition to AD dementia, FLAIR MRI, AV45-PET, FDG-PET, and T1 MRI were acquired. WMH were automatically segmented from FLAIR images. Mean levels of neocortical Aβ deposition (AV45-PET), temporo-parietal glucose metabolism (FDG-PET), and medial-temporal gray matter volume (GMV) were extracted from processed images using established AD meta-signature templates. Associations between AD brain biomarkers and WMH, as assessed in region-of-interest and voxel-wise, were examined, adjusting for age, sex, education, and systolic blood pressure. Results There were no significant associations between global Aβ burden and region-specific WMH. Voxel-wise WMH in the splenium of the corpus callosum correlated with greater Aβ deposition at a more liberal threshold. Region- and voxel-based WMH in the posterior corpus callosum, along with parietal, occipital, and frontal areas, were associated with lower temporo-parietal glucose metabolism. Similarly, lower medial-temporal GMV correlated with WMH in the posterior corpus callosum in addition to parietal, occipital, and fontal areas. Conclusions This study demonstrates that local white matter damage is correlated with multimodal brain biomarkers of AD. Our results highlight modality-specific topographic patterns of WMH, which converged in the posterior white matter. Overall, these cross-sectional findings corroborate associations of regional WMH with AD-typical Aß deposition and neurodegeneration.

BackgroundPoor vascular health may impede brain functioning in older adults, thus possibly increasing the risk of cognitive decline and Alzheimer’s disease (AD). The emerging link between vascular risk factors (VRF) and longitudinal decline in resting-state functional connectivity (RSFC) within functional brain networks needs replication and further research in independent cohorts.MethodWe examined 95 non-demented older adults using the IMAP+ cohort (Caen, France). VRF were assessed at baseline through systolic and diastolic blood pressure, body-mass-index, and glycated hemoglobin (HbA1c) levels. Brain pathological burden was measured using white matter hyperintensity (WMH) volumes, derived from FLAIR images, and cortical β-Amyloid (Aβ) deposition, derived from florbetapir-PET imaging. RSFC was estimated from functional MRI scans within canonical brain networks at baseline and up to 3 years of follow-up. Linear mixed-effects models evaluated the independent predictive value of VRF on longitudinal changes in network-specific and global RSFC as well as a potential association between these RSFC changes and cognitive decline.ResultsWe replicate that RSFC increased over time in global RSFC and in the default-mode, salience/ventral-attention and fronto-parietal networks. In contrast, higher diastolic blood pressure levels were independently associated with a decrease of RSFC over time in the default-mode, salience/ventral-attention, and fronto-parietal networks. Moreover, higher HbA1c levels were independently associated with a reduction of the observed RSFC increase over time in the salience/ventral-attention network. Both of these associations were independent of brain pathology related to Aβ load and WMH volumes. The VRF-related changes in RSFC over time were not significantly associated with longitudinal changes in cognitive performance.ConclusionOur longitudinal findings corroborate that VRF promote RSFC alterations over time within higher-order brain networks, irrespective of pathological brain burden. Altered RSFC in large-scale cognitive networks may eventually increase the vulnerability to aging and AD.

Abstract Background This study investigated the impact of the size of the normal database on the classification performance of the specific binding ratio (SBR) in dopamine transporter (DAT) SPECT with [123I]FP-CIT in different settings. Methods The first subject sample comprised 645 subjects from the Parkinson’s Progression Marker Initiative (PPMI), 207 healthy controls (HC), and 438 Parkinson’s disease (PD) patients. The second sample comprised 372 patients from clinical routine patient care, 186 with non-neurodegenerative parkinsonian syndrome (PS) and 186 with neurodegenerative PS. Single-photon emission computed tomography (SPECT) images of the clinical sample were reconstructed with two different reconstruction algorithms (filtered backprojection, iterative ordered subsets expectation maximization (OSEM) reconstruction with resolution recovery). The putaminal specific binding ratio (SBR) was computed using an anatomical region of interest (ROI) predefined in standard (MNI) space in the Automated Anatomic Labeling (AAL) atlas or using hottest voxels (HV) analysis in large predefined ROIs. SBR values were transformed to z-scores using mean and standard deviation of the SBR in a normal database of varying sizes (n = 5, 10, 15,…, 50) randomly selected from the HC subjects (PPMI sample) or the patients with non-neurodegenerative PS (clinical sample). Accuracy, sensitivity, and specificity for identifying patients with PD or neurodegenerative PS were determined as performance measures using a predefined fixed cutoff on the z-score. This was repeated for 10,000 randomly selected normal databases, separately for each size of the normal database. Mean and 5th percentile of the performance measures over the 10,000 realizations were computed. Accuracy, sensitivity, and specificity when using the whole set of HC or non-neurodegenerative PS subjects as normal database were used as benchmark. Results Mean loss of accuracy of the putamen SBR z-score was below 1% when the normal database included at least 15 subjects, independent of subject sample (PPMI or clinical), reconstruction method (filtered backprojection or OSEM), and ROI method (AAL or HV). However, the variability of the accuracy of the putamen SBR z-score decreased monotonically with increasing size of normal database and was still considerable at size 15. In order to achieve less than 5% “maximum” loss of accuracy (defined by the 5th percentile) in all settings required at least 25 to 30 subjects in the normal database. Reduction of mean and “maximum” loss of accuracy of the putamen SBR z-score by further increasing the size of the normal database was very small beyond size 40. Conclusions The results of this study suggest that 25 to 30 is the minimum size of the normal database to reliably achieve good performance of semi-quantitative analysis in dopamine transporter (DAT) SPECT, independent of the algorithm used for image reconstruction and the ROI method used to estimate the putaminal SBR.

Subjective cognitive decline (SCD) is considered an early risk stage for dementia due to Alzheimer's disease (AD) and the development of pathological brain changes, such as the aggregation of amyloid-beta (amyloid-β) plaques. This study evaluates the association between specific features of SCD and cerebral amyloid-β load measured by positron emission tomography (PET) with 18F-florbetaben in 40 cognitively normal older individuals. Global amyloid-β, as well as regional amyloid-β load for the frontal, temporal, parietal, and cingulate cortex, was quantified. Specific features of SCD, such as subjective cognitive complaints and worry, were assessed using the 39-item Everyday Cognition Scales and the 16-item Penn State Worry Questionnaire. Spearman's rank partial correlation analyses, adjusted for age and apolipoprotein E ε4 status, were conducted to test the associations between specific features of SCD and cerebral amyloid-β load. The severity of subjective cognitive complaints in everyday memory and organization was positively correlated with amyloid-β load in the frontal cortex. In addition, the severity of subjective cognitive complaints in everyday planning was positively correlated with amyloid-β load in the parietal cortex. Higher levels of worry were associated with higher amyloid-β load in the frontal cortex. After correction of the PET data for partial volume effects, these associations were reduced to trend level. In conclusion, the severity of subjective cognitive complaints and the level of trait worry were positively associated with cortical amyloid-β burden, particularly in the frontal and parietal cortex. Further studies are required to elucidate the direction of these associations in order to develop strategies to prevent amyloid deposition and cognitive decline.

Abstract Background Given the global increase in the aging population and age-related diseases, the promotion of healthy aging is one of the most crucial public health issues. This trial aims to contribute to the establishment of effective approaches to promote cognitive and brain health in older individuals with subjective cognitive decline (SCD). Presence of SCD is known to increase the risk of objective cognitive decline and progression to dementia due to Alzheimer’s disease. Therefore, it is our primary goal to determine whether spermidine supplementation has a positive impact on memory performance in this at-risk group, as compared with placebo. The secondary goal is to examine the effects of spermidine intake on other neuropsychological, behavioral, and physiological parameters. Methods The SmartAge trial is a monocentric, randomized, double-blind, placebo-controlled phase IIb trial. The study will investigate 12 months of intervention with spermidine-based nutritional supplementation (target intervention) compared with 12 months of placebo intake (control intervention). We plan to recruit 100 cognitively normal older individuals with SCD from memory clinics, neurologists and general practitioners in private practice, and the general population. Participants will be allocated to one of the two study arms using blockwise randomization stratified by age and sex with a 1:1 allocation ratio. The primary outcome is the change in memory performance between baseline and post-intervention visits (12 months after baseline). Secondary outcomes include the change in memory performance from baseline to follow-up assessment (18 months after baseline), as well as changes in neurocognitive, behavioral, and physiological parameters (including blood and neuroimaging biomarkers), assessed at baseline and post-intervention. Discussion The SmartAge trial aims to provide evidence of the impact of spermidine supplementation on memory performance in older individuals with SCD. In addition, we will identify possible neurophysiological mechanisms of action underlying the anticipated cognitive benefits. Overall, this trial will contribute to the establishment of nutrition intervention in the prevention of Alzheimer’s disease. Trial registration ClinicalTrials.gov, NCT03094546. Registered 29 March 2017—retrospectively registered. Protocol version Based on EA1/250/16 version 1.5

Abstract Introduction The specific uptake size index (SUSI) of striatal FP-CIT uptake is independent of spatial resolution in the SPECT image, in contrast to the specific binding ratio (SBR). This suggests that the SUSI is particularly appropriate for multi-site/multi-camera settings in which camera-specific effects increase inter-subject variability of spatial resolution. However, the SUSI is sensitive to inter-subject variability of striatum size. Furthermore, it might be more sensitive to errors of the estimate of non-displaceable FP-CIT binding. This study compared SUSI and SBR in the multi-site/multi-camera (MULTI) setting of a prospective multi-center study and in a mono-site/mono-camera (MONO) setting representative of clinical routine. Methods The MULTI setting included patients with Parkinson’s disease (PD, n = 438) and healthy controls (n = 207) from the Parkinson Progression Marker Initiative. The MONO setting included 122 patients from routine clinical patient care in whom FP-CIT SPECT had been performed with the same double-head SPECT system according to the same acquisition and reconstruction protocol. Patients were categorized as “neurodegenerative” (n = 84) or “non-neurodegenerative” (n = 38) based on follow-up data. FP-CIT SPECTs were stereotactically normalized to MNI space. SUSI and SBR were computed for caudate, putamen, and whole striatum using unilateral ROIs predefined in MNI space. SUSI analysis was repeated in native patient space in the MONO setting. The area (AUC) under the ROC curve for identification of PD/“neurodegenerative” cases was used as performance measure. Results In both settings, the highest AUC was achieved by the putamen (minimum over both hemispheres), independent of the semi-quantitative method (SUSI or SBR). The putaminal SUSI provided slightly better performance with ROI analysis in MNI space compared to patient space (AUC = 0.969 vs. 0.961, p = 0.129). The SUSI (computed in MNI space) performed slightly better than the SBR in the MULTI setting (AUC = 0.993 vs. 0.991, p = 0.207) and slightly worse in the MONO setting (AUC = 0.969 vs. AUC = 0.976, p = 0.259). There was a trend toward larger AUC difference between SUSI and SBR in the MULTI setting compared to the MONO setting (p = 0.073). Variability of voxel intensity in the reference region was larger in misclassified cases compared to correctly classified cases for both SUSI and SBR (MULTI setting: p = 0.007 and p = 0.012, respectively). Conclusions The SUSI is particularly useful in MULTI settings. SPECT images should be stereotactically normalized prior to SUSI analysis. The putaminal SUSI provides better diagnostic performance than the SUSI of the whole striatum. Errors of the estimate of non-displaceable count density in the reference region can cause misclassification by both SUSI and SBR, particularly in borderline cases. These cases might be identified by visual checking FP-CIT uptake in the reference region for particularly high variability.

Abstract Background Cerebrovascular pathology, quantified by white matter lesions (WML), is known to affect cognition in aging, and is associated with an increased risk of dementia. The present study aimed to investigate whether higher functional connectivity in cognitive control networks mitigates the detrimental effect of WML on cognition. Methods Nondemented older participants (≥ 50 years; n = 230) underwent cognitive evaluation, fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI), and resting state functional magnetic resonance imaging (fMRI). Total WML volumes were quantified algorithmically. Functional connectivity was assessed in preselected higher-order resting state networks, namely the fronto-parietal, the salience, and the default mode network, using global and local measures. Latent moderated structural equations modeling examined direct and interactive relationships between WML volumes, functional connectivity, and cognition. Results Larger WML volumes were associated with worse cognition, having a greater impact on executive functions (β = −0.37, p

PURPOSE: Attenuation correction (AC) based on low-dose computed tomography (CT) could be more accurate in brain single-photon emission computed tomography (SPECT) than the widely used Chang method, and, therefore, has the potential to improve both semi-quantitative analysis and visual image interpretation. The present study evaluated CT-based AC for dopamine transporter SPECT with I-123-ioflupane. MATERIALS AND METHODS: Sixty-two consecutive patients in whom I-123-ioflupane SPECT including low-dose CT had been performed were recruited retrospectively at 3 centres. For each patient, 3 different SPECT images were reconstructed: without AC, with Chang AC and with CT-based AC. Distribution volume ratio (DVR) images were obtained by scaling voxel intensities using the whole brain without striata as reference. For assessing the impact of AC on semi-quantitative analysis, specific-to-background ratios (SBR) in caudate and putamen were obtained by fully automated SPM8-based region of interest (ROI) analysis and tested for their diagnostic power using receiver-operator-characteristic (ROC) analysis. For assessing the impact of AC on visual image reading, screenshots of stereotactically normalized DVR images presented in randomized order were interpreted independently by two raters at each centre. RESULTS: CT-based AC resulted in intermediate SBRs about half way between no AC and Chang. Maximum area under the ROC curve was achieved by the putamen SBR, with negligible impact of AC (0.924, 0.935 and 0.938 for no, CT-based and Chang AC). Diagnostic accuracy of visual interpretation also did not depend on AC. CONCLUSIONS: The impact of CT-based versus Chang AC on the interpretation of I-123-ioflupane SPECT is negligible. Therefore, CT-based AC cannot be recommended for routine use in clinical patient care, not least because of the additional radiation exposure.