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Purpose Positron emission tomography (PET) with the first and only tau targeting radiotracer of 18 F-flortaucipir approved by FDA has been increasingly used in depicting tau pathology deposition and distribution in patients with cognitive impairment. The goal of this international consensus is to help nuclear medicine practitioners procedurally perform 18 F-flortaucipir PET imaging. Method A multidisciplinary task group formed by experts from various countries discussed and approved the consensus for 18 F-flortaucipir PET imaging in Alzheimer’s disease (AD), focusing on clinical scenarios, patient preparation, and administered activities, as well as image acquisition, processing, interpretation, and reporting. Conclusion This international consensus and practice guideline will help to promote the standardized use of 18 F-flortaucipir PET in patients with AD. It will become an international standard for this purpose in clinical practice.

Background To systematically examine the clinical utility of tau-PET and Braak-staging as prognostic markers of future cognitive decline in older adults with and without cognitive impairment. Methods In this longitudinal study, we included 396 cognitively normal to dementia subjects with 18 F-Florbetapir/ 18 F-Florbetaben-amyloid-PET, 18 F-Flortaucipir-tau-PET and ~ 2-year cognitive follow-up. Annual change rates in global cognition (i.e., MMSE, ADAS13) and episodic memory were calculated via linear-mixed models. We determined global amyloid-PET (Centiloid) plus global and Braak-stage-specific tau-PET SUVRs, which were stratified as positive( + )/negative( − ) at pre-established cut-offs, classifying subjects as Braak 0 /Braak I+ /Braak I–IV+ /Braak I–VI+ /Braak atypical+ . In bootstrapped linear regression, we assessed the predictive accuracy of global tau-PET SUVRs vs. Centiloid on subsequent cognitive decline. To test for independent tau vs. amyloid effects, analyses were further controlled for the contrary PET-tracer. Using ANCOVAs, we tested whether more advanced Braak-stage predicted accelerated future cognitive decline. All models were controlled for age, sex, education, diagnosis, and baseline cognition. Lastly, we determined Braak-stage-specific conversion risk to mild cognitive impairment (MCI) or dementia. Results Baseline global tau-PET SUVRs explained more variance (partial R 2 ) in future cognitive decline than Centiloid across all cognitive tests (Cohen’s d  ~ 2, all tests p  < 0.001) and diagnostic groups. Associations between tau-PET and cognitive decline remained consistent when controlling for Centiloid, while associations between amyloid-PET and cognitive decline were non-significant when controlling for tau-PET. More advanced Braak-stage was associated with gradually worsening future cognitive decline, independent of Centiloid or diagnostic group ( p  < 0.001), and elevated conversion risk to MCI/dementia. Conclusion Tau-PET and Braak-staging are highly predictive markers of future cognitive decline and may be promising single-modality estimates for prognostication of patient-specific progression risk in clinical settings.

Background In preclinical Alzheimer’s disease, it is unclear why some individuals with amyloid pathologic change are asymptomatic (stage 1), whereas others experience subjective cognitive decline (SCD, stage 2). Here, we examined the association of stage 1 vs. stage 2 with structural brain reserve in memory-related brain regions. Methods We tested whether the volumes of hippocampal subfields and parahippocampal regions were larger in individuals at stage 1 compared to asymptomatic amyloid-negative older adults (healthy controls, HCs). We also tested whether individuals with stage 2 would show the opposite pattern, namely smaller brain volumes than in amyloid-negative individuals with SCD. Participants with cerebrospinal fluid (CSF) biomarker data and bilateral volumetric MRI data from the observational, multi-centric DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) study were included. The sample comprised 95 amyloid-negative and 26 amyloid-positive asymptomatic participants as well as 104 amyloid-negative and 47 amyloid-positive individuals with SCD. Volumes were based on high-resolution T2-weighted images and automatic segmentation with manual correction according to a recently established high-resolution segmentation protocol. Results In asymptomatic individuals, brain volumes of hippocampal subfields and of the parahippocampal cortex were numerically larger in stage 1 compared to HCs, whereas the opposite was the case in individuals with SCD. MANOVAs with volumes as dependent data and age, sex, years of education, and DELCODE site as covariates showed a significant interaction between diagnosis (asymptomatic versus SCD) and amyloid status (Aß42/40 negative versus positive) for hippocampal subfields. Post hoc paired comparisons taking into account the same covariates showed that dentate gyrus and CA1 volumes in SCD were significantly smaller in amyloid-positive than negative individuals. In contrast, CA1 volumes were significantly ( p  = 0.014) larger in stage 1 compared with HCs. Conclusions These data indicate that HCs and stages 1 and 2 do not correspond to linear brain volume reduction. Instead, stage 1 is associated with larger than expected volumes of hippocampal subfields in the face of amyloid pathology. This indicates a brain reserve mechanism in stage 1 that enables individuals with amyloid pathologic change to be cognitively normal and asymptomatic without subjective cognitive decline.

Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer’s disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18–94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology. Alzheimer’s disease has been associated with increased structural brain aging. Here the authors describe a model that predicts brain aging from resting state functional connectivity data, and demonstrate this is accelerated in individuals with pre-clinical familial Alzheimer’s disease.

This study was designed to evaluate whether subjects with amyloid beta (Aβ) pathology, detected using florbetapir positron emission tomorgraphy (PET), demonstrated greater cognitive decline than subjects without Aβ pathology. Sixty-nine cognitively normal (CN) controls, 52 with recently diagnosed mild cognitive impairment (MCI) and 31 with probable Alzheimer’s disease (AD) dementia were included in the study. PET images obtained in these subjects were visually rated as positive (Aβ+) or negative (Aβ−), blind to diagnosis. Fourteen percent (10/69) of CN, 37% (19/52) of MCI and 68% (21/31) of AD were Aβ+. The primary outcome was change in ADAS-Cog score in MCI subjects after 36 months; however, additional outcomes included change on measures of cognition, function and diagnostic status. Aβ+ MCI subjects demonstrated greater worsening compared with Aβ− subjects on the ADAS-Cog over 36 months (5.66±1.47 vs −0.71±1.09, P =0.0014) as well as on the mini-mental state exam (MMSE), digit symbol substitution (DSS) test, and a verbal fluency test ( P <0.05). Similar to MCI subjects, Aβ+ CN subjects showed greater decline on the ADAS-Cog, digit-symbol-substitution test and verbal fluency ( P <0.05), whereas Aβ+ AD patients showed greater declines in verbal fluency and the MMSE ( P <0.05). Aβ+ subjects in all diagnostic groups also showed greater decline on the CDR-SB ( P <0.04), a global clinical assessment. Aβ+ subjects did not show significantly greater declines on the ADCS-ADL or Wechsler Memory Scale. Overall, these findings suggest that in CN, MCI and AD subjects, florbetapir PET Aβ+ subjects show greater cognitive and global deterioration over a 3-year follow-up than Aβ− subjects do.

Predicting the probability of converting from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is still a challenging task. This study aims at providing a personalized MCI-to-AD conversion estimation by using a multipredictor nomogram that integrates neuroimaging features, cerebrospinal fluid (CSF) biomarker, and clinical assessments. To do so, 290 MCI patients were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), of whom 76 has converted to AD and 214 remained with MCI. All subjects were randomly divided into a primary and validation cohort. Radiomics signature (Rad-sig) was obtained based on 17 cerebral cortex features selected by using Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Clinical factors and amyloid-beta peptide (Aβ) concentration were selected by using Spearman correlation between the converted and not-converted patients. Then, a nomogram that combines image features, clinical factor, and Aβ concentration was constructed and validated. Furthermore, we explored the associations between various predictors from the macro- to the microperspective by assessing gene expression patterns. Our results showed that the multipredictor nomogram (C-index 0.978 and 0.956 in both cohorts, respectively) outperformed the nomogram using either Rad-sig or Aβ concentration as individual predictors. Significant associations were found between neuropsychological scores, cerebral cortex features, Aβ levels, and underlying gene pathways. Our study may have a clinical impact as a powerful predictive tool for predicting the conversion probability of MCI and providing associations between cognitive impairment, structural changes, Aβ levels, and underlying biological patterns from the macro- to the microperspective.

Dynamic Functional Connectivity (dFC) in the resting state (rs) is considered as a correlate of cognitive processing. Describing dFC as a flow across morphing connectivity configurations, our notion of dFC speed quantifies the rate at which FC networks evolve in time. Here we probe the hypothesis that variations of rs dFC speed and cognitive performance are selectively interrelated within specific functional subnetworks. In particular, we focus on Sleep Deprivation (SD) as a reversible model of cognitive dysfunction. We found that whole-brain level (global) dFC speed significantly slows down after 24h of SD. However, the reduction in global dFC speed does not correlate with variations of cognitive performance in individual tasks, which are subtle and highly heterogeneous. On the contrary, we found strong correlations between performance variations in individual tasks –including Rapid Visual Processing (RVP, assessing sustained visual attention)– and dFC speed quantified at the level of functional sub-networks of interest. Providing a compromise between classic static FC (no time) and global dFC (no space), modular dFC speed analyses allow quantifying a different speed of dFC reconfiguration independently for sub-networks overseeing different tasks. Importantly, we found that RVP performance robustly correlates with the modular dFC speed of a characteristic frontoparietal module. •Sleep Deprivation (SD) slows down the random walk in FC space implemented by Dynamic Functional Connectivity (dFC) at rest.•Whole-brain level slowing of dFC speed does not selectively correlate with fine and task-specific changes in performance.•We quantify dFC speed separately for different link-based modules coordinated by distinct regional “meta-hubs”.•Modular dFC speed variations capture subtle and task-specific variations of cognitive performance induced by SD.

Transcranial direct current stimulation (tDCS) can improve cognitive function. However, it is not clear how high-definition tDCS (HD-tDCS) regulates the cognitive function and its neural mechanism, especially in individuals with mild cognitive impairment (MCI). This study aimed to examine whether HD-tDCS can modulate cognitive function in individuals with MCI and to determine whether the potential variety is related to spontaneous brain activity changes recorded by resting-state functional magnetic resonance imaging (rs-fMRI). Forty-three individuals with MCI were randomly assigned to receive either 10 HD-tDCS sessions or 10 sham sessions to the left dorsolateral prefrontal cortex (L-DLPFC). The fractional amplitude of low-frequency fluctuation (fALFF) and the regional homogeneity (ReHo) was computed using rs-fMRI data from all participants. The results showed that the fALFF and ReHo values changed in multiple areas following HD-tDCS. Brain regions with significant decreases in fALFF values include the Insula R, Precuneus R, Thalamus L, and Parietal Sup R, while the Temporal Inf R, Fusiform L, Occipital Sup L, Calcarine R, and Angular R showed significantly increased in their fALFF values. The brain regions with significant increases in ReHo values include the Temporal Inf R, Putamen L, Frontal Mid L, Precentral R, Frontal Sup Medial L, Frontal Sup R, and Precentral L. We found that HD-tDCS can alter the intensity and synchrony of brain activity, and our results indicate that fALFF and ReHo analysis are sensitive indicators for the detection of HD-tDCS during spontaneous brain activity. Interestingly, HD-tDCS increases the ReHo values of multiple brain regions, which may be related to the underlying mechanism of its clinical effects, these may also be related to a potential compensation mechanism involving the mobilization of more regions to complete a function following a functional decline.

Cognitive impairment is a core feature of schizophrenia, which is aggravated by antipsychotics-induced metabolic disturbance and lacks effective pharmacologic treatments in clinical practice. Our previous study demonstrated the efficiency of metformin in alleviating metabolic disturbance following antipsychotic administration. Here we report that metformin could ameliorate cognitive impairment and improve functional connectivity (FC) in prefrontal regions. This is an open-labeled, evaluator-blinded study. Clinically stable patients with schizophrenia were randomly assigned to receive antipsychotics plus metformin (N = 48) or antipsychotics alone (N = 24) for 24 weeks. The improvement in cognition was assessed by the MATRICS Consensus Cognitive Battery (MCCB). Its association with metabolic measurements, and voxel-wise whole-brain FC with dorsolateral prefrontal cortex (DLPFC) subregions as seeds were evaluated. When compared to the antipsychotics alone group, the addition of metformin resulted in significantly greater improvements in the MCCB composite score, speed of processing, working memory, verbal learning, and visual learning. A significant time × group interaction effect of increased FC between DLPFC and the anterior cingulate cortex (ACC)/middle cingulate cortex (MCC), and between DLPFC subregions were observed after metformin treatment, which was positively correlated with MCCB cognitive performance. Furthermore, the FC between left DLPFC A9/46d to right ACC/MCC significantly mediated metformin-induced speed of processing improvement; the FC between left A46 to right ACC significantly mediated metformin-induced verbal learning improvement. Collectively, these findings demonstrate that metformin can improve cognitive impairments in schizophrenia patients and is partly related to the FC changes in the DLPFC. Trial Registration: The trial was registered with ClinicalTrials.gov (NCT03271866). The full trial protocol is provided in Supplementary Material.

IntroductionSubjective cognitive decline (SCD) can represent a preclinical stage of Alzheimer’s disease. Diffusion tensor imaging (DTI) could aid an early diagnosis, yet only few monocentric DTI studies in SCD have been conducted, reporting heterogeneous results. We investigated microstructural changes in SCD in a larger, multicentric cohort.Methods271 participants with SCD, mild cognitive impairment (MCI) or Alzheimer’s dementia (AD) and healthy controls (CON) were included, recruited prospectively at nine centers of the observational DELCODE study. DTI was acquired using identical protocols. Using voxel-based analyses, we investigated fractional anisotropy (FA), mean diffusivity (MD) and mode (MO) in the white matter (WM). Discrimination accuracy was determined by cross-validated elastic-net penalized regression. Center effects were explored using variance analyses.ResultsMO and FA were lower in SCD compared to CON in several anterior and posterior WM regions, including the anterior corona radiata, superior and inferior longitudinal fasciculus, cingulum and splenium of the corpus callosum (p < 0.01, uncorrected). MD was higher in the superior and inferior longitudinal fasciculus, cingulum and superior corona radiata (p < 0.01, uncorrected). The cross-validated accuracy for discriminating SCD from CON was 67% (p < 0.01). As expected, the AD and MCI groups had higher MD and lower FA and MO in extensive regions, including the corpus callosum and temporal brain regions. Within these regions, center accounted for 3–15% of the variance.ConclusionsDTI revealed subtle WM alterations in SCD that were intermediate between those in MCI and CON and may be useful to detect individuals with an increased risk for AD in clinical studies.