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Imaging and histopathological studies have demonstrated that structural changes of the retina affect subjects with Alzheimer’s disease (AD) or mild cognitive impairment (MCI). The aim of this study was to quantitatively investigate the retinal vessels in these disorders, using dynamic vessel analyzer (DVA) and optical coherence tomography angiography (OCTA) analysis. Twelve subjects with AD, 12 subjects with MCI, and 32 gender- and age-matched controls were prospectively enrolled. Mean ± SD age was 72.9 ± 7.2 years in the AD group, 76.3 ± 6.9 years in the MCI group, and 71.6 ± 5.9 years in the control group (p = 0.104). In the DVA dynamic analysis, the arterial dilation was decreased in the AD group (0.77 ± 2.06%), in the comparison with the control group (3.53 ± 1.25%, p = 0.002). The reaction amplitude was decreased both in AD (0.21 ± 1.80%, <0.0001) and MCI (2.29 ± 1.81%, p = 0.048) subjects, compared with controls (3.86 ± 1.94%). OCTA variables did not differ among groups. In the Pearson correlation analysis, amyloid β level in the cerebrospinal fluid was directly correlated with the arterial dilation (R = 0.441, p = 0.040) and reaction amplitude (R = 0.580, p = 0.005). This study demonstrate that Alzheimer’s and MCI subjects are characterized by a significant impairment of the retinal neurovascular coupling. This impairment is inversely correlated with the level of amyloid β in the cerebrospinal fluid.

The aim of this study was two-fold: (i) to investigate structural and functional brain network architecture in patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI), stratified in converters (c-aMCI) and non-converters (nc-aMCI) to AD; and to assess the relationship between healthy brain network functional connectivity and the topography of brain atrophy in patients along the AD continuum. Ninety-four AD patients, 47 aMCI patients (25 c-aMCI within 36 months) and 53 age- and sex-matched healthy controls were studied. Graph analysis and connectomics assessed global and local, structural and functional topological network properties and regional connectivity. Healthy topological features of brain regions were assessed based on their connectivity with the point of maximal atrophy (epicenter) in AD and aMCI patients. Brain network graph analysis properties were severely altered in AD patients. Structural brain network was already altered in c-aMCI patients relative to healthy controls in particular in the temporal and parietal brain regions, while functional connectivity did not change. Structural connectivity alterations distinguished c-aMCI from nc-aMCI cases. In both AD and c-aMCI, the point of maximal atrophy was located in left hippocampus (disease-epicenter). Brain regions most strongly connected with the disease-epicenter in the healthy functional connectome were also the most atrophic in both AD and c-aMCI patients. Progressive degeneration in the AD continuum is associated with an early breakdown of anatomical brain connections and follows the strongest connections with the disease-epicenter. These findings support the hypothesis that the topography of brain connectional architecture can modulate the spread of AD through the brain.

PurposeGiven the challenges posed by the clinical diagnosis of atypical Alzheimer’s disease (AD) variants and the limited imaging evidence available in the prodromal phases of atypical AD, we assessed brain hypometabolism patterns at the single-subject level in the AD variants spectrum. Specifically, we tested the accuracy of [18F]FDG-PET brain hypometabolism, as a biomarker of neurodegeneration, in supporting the differential diagnosis of atypical AD variants in individuals with dementia and mild cognitive impairment (MCI).MethodsWe retrospectively collected N = 67 patients with a diagnosis of typical AD and AD variants according to the IWG-2 criteria (22 typical-AD, 15 frontal variant-AD, 14 logopenic variant-AD and 16 posterior variant-AD). Further, we included N = 11 MCI subjects, who subsequently received a clinical diagnosis of atypical AD dementia at follow-up (21 ± 11 months). We assessed brain hypometabolism patterns at group- and single-subject level, using W-score maps, measuring their accuracy in supporting differential diagnosis. In addition, the regional prevalence of cerebral hypometabolism was computed to identify the most vulnerable core regions.ResultsW-score maps pointed at distinct, specific patterns of hypometabolism in typical and atypical AD variants, confirmed by the assessment of core hypometabolism regions, showing that each variant was characterized by specific regional vulnerabilities, namely in occipital, left-sided, or frontal brain regions. ROC curves allowed discrimination among AD variants and also non-AD dementia (i.e., dementia with Lewy bodies and behavioral variant of frontotemporal dementia), with high sensitivity and specificity. Notably, we provide preliminary evidence that, even in AD prodromal phases, these specific [18F]FDG-PET patterns are already detectable and predictive of clinical progression to atypical AD variants at follow-up.ConclusionsThe AD variant-specific patterns of brain hypometabolism, highly consistent at single-subject level and already evident in the prodromal stages, represent relevant markers of disease neurodegeneration, with highly supportive diagnostic and prognostic role.

Background Early-onset Alzheimer’s disease (EOAD) is characterized by young age of onset (< 65 years), severe neurodegeneration, and rapid disease progression, thus differing significantly from typical late-onset Alzheimer’s disease. Growing evidence suggests a primary role of neuroinflammation in AD pathogenesis. However, the role of microglia activation in EOAD remains a poorly explored field. Investigating microglial activation and its influence on the development of synaptic dysfunction and neuronal loss in EOAD may contribute to the understanding of its pathophysiology and to subject selection in clinical trials. In our study, we aimed to assess the amount of neuroinflammation and neurodegeneration and their relationship in EOAD patients, through positron emission tomography (PET) measures of microglia activation and brain metabolic changes. Methods We prospectively enrolled 12 EOAD patients, classified according to standard criteria, who underwent standard neurological and neuropsychological evaluation, CSF analysis, brain MRI, and both [ 18 F]-FDG PET and [ 11 C]-(R)-PK11195 PET. Healthy controls databases were used for statistical comparison. [ 18 F]-FDG PET brain metabolism in single subjects and as a group was assessed by an optimized SPM voxel-wise single-subject method. [ 11 C]-PK11195 PET binding potentials were obtained using reference regions selected with an optimized clustering procedure followed by a parametric analysis. We performed a topographic interaction analysis and correlation analysis in AD-signature metabolic dysfunctional regions and regions of microglia activation. A network connectivity analysis was performed using the interaction regions of hypometabolism and [ 11 C]-PK11195 PET BP increases. Results EOAD patients showed a significant and extended microglia activation, as [ 11 C]-PK11195 PET binding potential increases, and hypometabolism in typical AD-signature brain regions, i.e., temporo-parietal cortex, with additional variable frontal and occipital hypometabolism in the EOAD variants. There was a spatial concordance in the interaction areas and significant correlations between the two biological changes. The network analysis showed a disruption of frontal connectivity induced by the metabolic/microglia effects. Conclusion The severe microglia activation characterizing EOAD and contributing to neurodegeneration may be a marker of rapid disease progression. The coupling between brain glucose hypometabolism and local immune response in AD-signature regions supports their biological interaction.

Background Early recognition and treatment of autoimmune encephalitis (AE) are crucial for patients, but diagnosis is often difficult and time-consuming. For this purpose, a syndrome-based diagnostic approach was published by Graus et al. (Lancet Neurol 15:391–404, 2016), but very little is known in the literature about its application in clinical practice. Aim Our aims are to test the feasibility of such approach in a real-world single-centre setting and to analyse the most relevant factors in criteria fulfilment. Methods We retrospectively applied these criteria to our cohort of patients discharged from our hospital with diagnosis of autoimmune encephalitis ( n  = 33, 58% antibody-positive). Results All the subjects fulfilled criteria for possible AE (pAE), with EEG and MRI playing a central role in diagnosis, while CSF was useful mainly to rule out other conditions. Three patients respected criteria for probable anti-NMDA-R encephalitis (pNMDA). Definite anti-NMDAR encephalitis was diagnosed in 4 patients with detection of the autoantibody but, surprisingly, none of these subjects had fulfilled criteria for pNMDA. 18 patients were diagnosed with definite limbic AE (15 patients were antibody-positive, three antibody-negative). Need for MRI bilateral involvement in antibody-negative limbic AE limited diagnosis. One patient fulfilled criteria for probable antibody-negative AE, while ten patients remained classified as pAE. Conclusion From our retrospective analysis, some suggestions for a better definition of the criteria may emerge. Larger studies on prospective cohorts may be more helpful to explore possible important issues.

PurposeTo know whether mild cognitive impairment (MCI) patients will develop Alzheimer’s disease (AD) dementia in very short time or remain stable is of crucial importance, also considering new experimental drugs usually tested within very short time frames. Here we combined cerebrospinal fluid (CSF) AD biomarkers and a neurodegeneration marker such as brain FDG-PET to define an objective algorithm, suitable not only to reliably detect MCI converters to AD dementia but also to predict timing of conversion.MethodsWe included 77 consecutive MCI patients with neurological/neuropsychological assessment, brain 18F-FDG-PET and CSF analysis available at diagnosis and a neuropsychological/neurological evaluation every 6 months for a medium- to a long-term follow-up (at least 2 and up to 8 years). Binomial logistic regression models and Kaplan-Meier survival analyses were performed to determine the best biomarker (or combination of biomarkers) in detecting MCI converters to AD dementia and then, among the converters, those who converted in short time frames.ResultsThirty-five out of 77 MCI patients (45%) converted to AD dementia, with an average conversion time since MCI diagnosis of 26.07 months. CSF p-tau/Aβ42 was the most accurate predictor of conversion from MCI to AD dementia (82.9% sensitivity; 90% specificity). CSF p-tau/Aβ42 and FDG-PET-positive MCIs converted to AD dementia significantly earlier than the CSF-positive-only MCIs (median conversion time, 17.1 vs 31.3 months).ConclusionsCSF p-tau/Aβ42 ratio and brain FDG-PET may predict both occurrence and timing of MCI conversion to full-blown AD dementia. MCI patients with both biomarkers suggestive for AD will likely develop an AD dementia shortly, thus representing the ideal target for any new experimental drug requiring short periods to be tested for.

Background [18F]FDG-PET hypometabolism patterns are indicative of different neurodegenerative conditions, even from the earliest disease phase. This makes [18F]FDG-PET a valuable tool in the diagnostic workup of neurodegenerative diseases. The utility of [18F]FDG-PET in dementia with Lewy bodies (DLB) needs further validation by considering large samples of patients and disease comparisons and applying state-of-the-art statistical methods. Here, we aimed to provide an extensive validation of the [18F]FDG-PET metabolic signatures in supporting DLB diagnosis near the first clinical assessment, which is characterized by high diagnostic uncertainty, at the single-subject level. Methods In this retrospective study, we included N  = 72 patients with heterogeneous clinical classification at entry (mild cognitive impairment, atypical parkinsonisms, possible DLB, probable DLB, and other dementias) and an established diagnosis of DLB at a later follow-up. We generated patterns of [18F]FDG-PET hypometabolism in single cases by using a validated voxel-wise analysis ( p  < 0.05, FWE-corrected). The hypometabolism patterns were independently classified by expert raters blinded to any clinical information. The final clinical diagnosis at follow-up (2.94 ± 1.39 [0.34–6.04] years) was considered as the diagnostic reference and compared with clinical classification at entry and with [18F]FDG-PET classification alone. In addition, we calculated the diagnostic accuracy of [18F]FDG-PET maps in the differential diagnosis of DLB with Alzheimer’s disease dementia (ADD) ( N  = 60) and Parkinson’s disease (PD) ( N  = 36). Results The single-subject [18F]FDG-PET hypometabolism pattern, showing temporo-parietal and occipital involvement, was highly consistent across DLB cases. Clinical classification at entry produced several misclassifications with an agreement of only 61.1% with the diagnostic reference. On the contrary, [18F]FDG-PET hypometabolism maps alone accurately predicted diagnosis of DLB at follow-up (88.9%). The high power of the [18F]FDG-PET hypometabolism signature in predicting the final clinical diagnosis allowed a ≈ 50% increase in accuracy compared to the first clinical assessment alone. Finally, [18F]FDG-PET hypometabolism maps yielded extremely high discriminative power, distinguishing DLB from ADD and PD conditions with an accuracy of > 90%. Conclusion The present validation of the diagnostic and prognostic accuracy of the disease-specific brain metabolic signature in DLB at the single-subject level argues for the consideration of [18F]FDG-PET in the early phase of the DLB diagnostic flowchart. The assessment of the [18F]FDG-PET hypometabolism pattern at entry may shorten the diagnostic time, resulting in benefits for treatment options and management of patients.

Purpose The aim of this study was to evaluate the supportive role of molecular and structural biomarkers (CSF protein levels, FDG PET and MRI) in the early differential diagnosis of dementia in a large sample of patients with neurodegenerative dementia, and in determining the risk of disease progression in subjects with mild cognitive impairment (MCI). Methods We evaluated the supportive role of CSF Aβ 42 , t-Tau, p-Tau levels, conventional brain MRI and visual assessment of FDG PET SPM t-maps in the early diagnosis of dementia and the evaluation of MCI progression. Results Diagnosis based on molecular biomarkers showed the best fit with the final diagnosis at a long follow-up. FDG PET SPM t-maps had the highest diagnostic accuracy in Alzheimer’s disease and in the differential diagnosis of non-Alzheimer’s disease dementias. The p-tau/Aβ 42 ratio was the only CSF biomarker providing a significant classification rate for Alzheimer’s disease. An Alzheimer’s disease-positive metabolic pattern as shown by FDG PET SPM in MCI was the best predictor of conversion to Alzheimer’s disease. Conclusion In this clinical setting, FDG PET SPM t-maps and the p-tau/Aβ 42 ratio improved clinical diagnostic accuracy, supporting the importance of these biomarkers in the emerging diagnostic criteria for Alzheimer’s disease dementia. FDG PET using SPM t-maps had the highest predictive value by identifying hypometabolic patterns in different neurodegenerative dementias and normal brain metabolism in MCI, confirming its additional crucial exclusionary role.