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Given 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). We 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. W-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. The 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.

Highlights • Theta density increase is the earliest and most sensitive EEG marker of AD pathology. • Alpha2 density progressively decreases following the progression of AD pathology. • EEG graph analysis of ADD patients shows network derangement at theta and alpha2 band. • EEG/fMRI integration model empowered EEG diagnostic performances., Objective We evaluated the value of resting-state EEG source biomarkers to characterize mild cognitive impairment (MCI) subjects with an Alzheimer’s disease (AD)-like cerebrospinal fluid (CSF) profile and to track neurodegeneration throughout the AD continuum. We further applied a resting-state functional MRI (fMRI)-driven model of source reconstruction and tested its advantage in terms of AD diagnostic accuracy. Methods Thirty-nine consecutive patients with AD dementia (ADD), 86 amnestic MCI, and 33 healthy subjects enter the EEG study. All ADD subjects, 37 out of 86 MCI patients and a distinct group of 53 healthy controls further entered the fMRI study. MCI subjects were divided according to the CSF phosphorylated tau/β amyloid-42 ratio (MCIpos: ≥ 0.13, MCIneg: < 0.13). Using Exact low-resolution brain electromagnetic tomography (eLORETA), EEG lobar current densities were estimated at fixed frequencies and analyzed. To combine the two imaging techniques, networks mostly affected by AD pathology were identified using Independent Component Analysis applied to fMRI data of ADD subjects. Current density EEG analysis within ICA-based networks at selected frequency bands was performed. Afterwards, graph analysis was applied to EEG and fMRI data at ICA-based network level. Results ADD patients showed a widespread slowing of spectral density. At a lobar level, MCIpos subjects showed a widespread higher theta density than MCIneg and healthy subjects; a lower beta2 density than healthy subjects was also found in parietal and occipital lobes. Evaluating EEG sources within the ICA-based networks, alpha2 band distinguished MCIpos from MCIneg, ADD and healthy subjects with good accuracy. Graph analysis on EEG data showed an alteration of connectome configuration at theta frequency in ADD and MCIpos patients and a progressive disruption of connectivity at alpha2 frequency throughout the AD continuum. Conclusions Theta frequency is the earliest and most sensitive EEG marker of AD pathology. Furthermore, EEG/fMRI integration highlighted the role of alpha2 band as potential neurodegeneration biomarker.

Visual hallucinations (VH) are a core clinical feature of dementia with Lewy bodies (DLB), but their specific neural substrate remains elusive. We used 18F-FDG-PET to study the neural dysfunctional signature of VH in a group of 38 DLB patients (mean age±SD 72.9 ± 7.5) with available anamnestic records, cognitive and neurological examination and NeuroPsychiatric Inventory assessing VH. We tested the voxel-wise correlation between 18F-FDG-PET hypometabolism and VH NPI scores at the whole-group level, then adopting inter-regional correlation analysis to explore the resting-state networks (RSNs) metabolic connectivity in DLB patients with and without visual hallucinations, as compared to N = 38 age-matched healthy controls (HCs) (mean age±SD 71.5 ± 6.9). At the whole-group level, we found a negative correlation between VH NPI scores and 18F-FDG-PET hypometabolism in the right occipito-temporal cortex (p < .001 uncorrected, p < .05 Family-Wise Error cluster-corrected). Then, splitting the group according to VH presence, we found that DLB non-hallucinators presented a pattern of connectivity seeding from this occipito-temporal cluster and extending to the ventral visual stream. At difference, the DLB hallucinators showed a metabolic connectivity pattern limited to the occipital-dorsal parietal regions. As for RSNs, both the DLB subgroups showed a markedly reduced extent of attention and visual networks compared to HCs, with a variable alteration in the topography. DLB-VH patients showed a more pronounced shrinkage of the primary visual network, which was disconnected from the higher visual hubs, at difference with both HC and DLB non-hallucinators. These findings suggest that an altered brain metabolic connectivity within and beyond visual systems may promote VH in DLB. These results support the most recent neurocognitive models interpreting VH as the result of an inefficient recruitment of the ventral visual stream and of a large-scale multi-network derangement.

Co-existence of Alzheimer's disease (AD) in normal pressure hydrocephalus (NPH) is a frequent finding, thus a common pathophysiological basis between AD and NPH has been postulated. We measured CSF amyloid-β 42 (Aβ42), total tau (t-tau), and phosphorylated tau (p-tau) concentrations in a sample of 294 patients with different types of dementia and 32 subjects without dementia. We then compared scores on neuropsychological tests of NPH patients with pathological and normal CSF Aβ42 values. Aβ42 levels were significantly lower in NPH than in control patients, with no significant differences between AD and NPH. On the contrary, t-tau and p-tau levels were significantly lower in NPH than in AD, with no differences between NPH and controls. NPH patients with pathological Aβ42 levels did not perform worse than NPH patients with normal Aβ42 levels in any cognitive domains. Our data seem to support the hypothesis of amyloid accumulation in brains of NPH patients. Nevertheless, amyloid does not seem to play a pathogenetic role in the development of cognitive deficits in NPH.

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%

We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional brain structural MRI scan. Convolutional neural networks (CNNs) were applied on 3D T1-weighted images from ADNI and subjects recruited at our Institute (407 healthy controls [HC], 418 AD, 280 c-MCI, 533 stable MCI [s-MCI]). CNN performance was tested in distinguishing AD, c-MCI and s-MCI. High levels of accuracy were achieved in all the classifications, with the highest rates achieved in the AD vs HC classification tests using both the ADNI dataset only (99%) and the combined ADNI + non-ADNI dataset (98%). CNNs discriminated c-MCI from s-MCI patients with an accuracy up to 75% and no difference between ADNI and non-ADNI images. CNNs provide a powerful tool for the automatic individual patient diagnosis along the AD continuum. Our method performed well without any prior feature engineering and regardless the variability of imaging protocols and scanners, demonstrating that it is exploitable by not-trained operators and likely to be generalizable to unseen patient data. CNNs may accelerate the adoption of structural MRI in routine practice to help assessment and management of patients., HIGHLIGHTS • CNNs predict AD and MCI with high accuracy based on a single T1-weighted image • CNNs discriminate c-MCI from s-MCI patients with an accuracy up to 75% • CNNs are exploitable by not-trained operators • CNNs are likely to be generalizable to unseen patient data

Dark-Agouti rats were immunized with spinal cord homogenate to develop Experimental Autoimmune Encephalomyelitis, a model of multiple sclerosis. We assessed motor signs and recorded VEPs for five or eight weeks with epidural or epidermal electrodes, respectively, with final histopathology of optic nerves (ONs). Injected rats exhibited motor deficits a week after immunization. VEP delays arose from the 2nd to the 5th week, when a recovery occurred in epidermal-recorded rats. ON damage appeared in epidural-, but not in epidermal-recorded rats, probably due to a remyelination process. VEP could be exploited as neurophysiological marker to test novel treatments against neurodegeneration involving ONs.

Background: Mild cognitive impairment (MCI) is a heterogeneous syndrome resulting from Alzheimer's disease (AD) as well as to non-AD and non-neurodegenerative conditions. A subset of patients with amnestic MCI (aMCI) present with an unusually long-lasting course, a slow rate of clinical neuropsychological progression, and evidence of focal involvement of medial temporal lobe structures. In the present study, we explored positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers in a sample of subjects with aMCI with such clinical features in order to provide in vivo evidence to improve disease characterisation in this subgroup.Methods: Thirty consecutive subjects with aMCI who had long-lasting memory impairment (more than 4 years from symptom onset) and a very slow rate of cognitive progression were included. All subjects underwent fluorodeoxyglucose-positron emission tomography (FDG-PET) metabolic imaging. A measure of cerebral amyloid load, by PET and/or CSF, was obtained in 26 of 30 subjects. The mean clinical follow-up was 58.3 ± 10.1 months.Results: No patient progressed to dementia during the follow-up. The typical AD FDG-PET pattern of temporoparietal hypometabolism was not present in any of the subjects. In contrast, a selective medial temporal lobe hypometabolism was present in all subjects, with an extension to frontolimbic regions in some subjects. PET imaging showed absent or low amyloid load in the majority of samples. The values were well below those reported in prodromal AD, and they were slightly elevated in only two subjects, consistent with the CSF β-amyloid (1–42) protein values. Notably, no amyloid load was present in the hippocampal structures.Conclusions: FDG-PET and amyloid-PET together with CSF findings questioned AD pathology as a unique neuropathological substrate in this aMCI subgroup with long-lasting disease course. The possibility of alternative pathological conditions, such as argyrophilic grain disease, primary age-related tauopathy or age-related TDP-43 proteinopathy, known to spread throughout the medial temporal lobe and limbic system structures should be considered in these patients with MCI.

Background/aims In this multicentre study in clinical settings, we assessed the accuracy of optimized procedures for FDG-PET brain metabolism and CSF classifications in predicting or excluding the conversion to Alzheimer's disease (AD) dementia and non-AD dementias. Methods We included 80 MCI subjects with neurological and neuropsychological assessments, FDG-PET scan and CSF measures at entry, all with clinical follow-up. FDG-PET data were analysed with a validated voxel-based SPM method. Resulting single-subject SPM maps were classified by five imaging experts according to the disease-specific patterns, as “typical-AD”, “atypical-AD” (i.e. posterior cortical atrophy, asymmetric logopenic AD variant, frontal-AD variant), “non-AD” (i.e. behavioural variant FTD, corticobasal degeneration, semantic variant FTD; dementia with Lewy bodies) or “negative” patterns. To perform the statistical analyses, the individual patterns were grouped either as “AD dementia vs. non-AD dementia (all diseases)” or as “FTD vs. non-FTD (all diseases)”. Aβ42, total and phosphorylated Tau CSF-levels were classified dichotomously, and using the Erlangen Score algorithm. Multivariate logistic models tested the prognostic accuracy of FDG-PET-SPM and CSF dichotomous classifications. Accuracy of Erlangen score and Erlangen Score aided by FDG-PET SPM classification was evaluated. Results The multivariate logistic model identified FDG-PET “AD” SPM classification (Expβ = 19.35, 95% C.I. 4.8–77.8, p, Highlights • Appropriate biomarkers measures improve early dementia diagnosis in MCI. • FDG-PET-SPM maps and CSF Aβ42 are the best predictors of AD dementia conversion. • FDG-PET-SPM maps accurately predict conversion to different dementia conditions. • A negative FDG-PET-SPM pattern characterizes stable or reverter MCI cases.

[18F]FDG-PET imaging has been recognized as a crucial diagnostic marker in Mild Cognitive Impairment (MCI), supporting the presence or the exclusion of Alzheimer's Disease (AD) pathology. A clinical heterogeneity, however, underlies MCI definition. In this study, we aimed to evaluate the predictive role of single-subject voxel-based maps of [18F]FDG distribution generated through statistical parametric mapping (SPM) in the progression to different dementia subtypes in a sample of 45 MCI. Their scans were compared to a large normal reference dataset developed and validated for comparison at single-subject level. Additionally, Aβ42 and Tau CSF values were available in 34 MCI subjects. Clinical follow-up (mean 28.5 ± 7.8 months) assessed subsequent progression to AD or non-AD dementias. The SPM analysis showed: 1) normal brain metabolism in 14 MCI cases, none of them progressing to dementia; 2) the typical temporo-parietal pattern suggestive for prodromal AD in 15 cases, 11 of them progressing to AD; 3) brain hypometabolism suggestive of frontotemporal lobar degeneration (FTLD) subtypes in 7 and dementia with Lewy bodies (DLB) in 2 subjects (all fulfilled FTLD or DLB clinical criteria at follow-up); and 4) 7 MCI cases showed a selective unilateral or bilateral temporo-medial hypometabolism without the typical AD pattern, and they all remained stable. In our sample, objective voxel-based analysis of [18F]FDG-PET scans showed high predictive prognostic value, by identifying either normal brain metabolism or hypometabolic patterns suggestive of different underlying pathologies, as confirmed by progression at follow-up. These data support the potential usefulness of this SPM [18F]FDG PET analysis in the early dementia diagnosis and for improving subject selection in clinical trials based on MCI definition., Highlights • We used an optimized voxel-based single-subject [18F]FDG-PET analysis • We showed different hypometabolic patterns (AD and non-AD) underlying MCI condition • Heterogeneous PET profiles predicted progression into specific dementia subtypes. • Statistical analyses showed high positive and negative post-test probability values. • CSF findings agreed with [18F]FDG-PET imaging in single cases.

Purpose: Visual evoked potentials (VEPs) are a powerful tool to evaluate nervous conduction along the visual pathways, both in humans and in animal models. Traditionally, epidural screw electrodes are used to record VEPs in preclinical research. Here we tested the feasibility in the preclinical setting of the same noninvasive technique used for clinical VEP acquisition, by using epidermal cup electrodes with no surgical procedures. Methods: Monocular flash VEPs were recorded bilaterally under sevoflurane anesthesia once a week for 6 weeks in 14 dark Agouti rats, 7 with implanted epidural screws and 7 with epidermal 6 mm Ø Ag/AgCl cups. Results: VEP traces obtained with the two techniques were morphologically comparable. There were no significant differences in latency of the main visual component between screw-recorded VEPs (sVEPs) and cup-recorded VEPs (cVEPs). Amplitude values with epidermal cups were significantly lower than those with epidural screws. Both techniques provided latencies and amplitudes which were stable over time. Furthermore, with regard to latency both methods ensured highly repeatable measurements over time, with epidermal cups even providing slightly better results. On the other hand, considering amplitudes, cVEPs and sVEPs provided fairly acceptable repeatability. Conclusions: Epidermal cup electrodes can provide comparable results to those obtained with the “gold standard” epidural screws, while representing a simpler and less invasive technique to test nervous conduction along the visual pathways in the preclinical setting.

Severe cognitive dysfunction is a frequent feature of multiple sclerosis (MS), normally associated with later stages of the disease in adult population. Nevertheless, progressive cognitive and neuropsychiatric disturbances might rarely be the presenting and predominant symptom. In order to better characterize this peculiar phenotype of MS, we report on the case of a 38-year-old man who referred to our hospital with the suspect of hereditary leukodystrophy after 5 years of behavioral and mood abnormalities, global cognitive dysfunction, clumsiness, and very mild pyramidal and cerebellar signs. Brain and spinal magnetic resonance imaging (MRI) combined with cerebrospinal fluid (CSF) analysis prompted the diagnosis of MS. Severe cognitive dysfunction is a frequent feature of multiple sclerosis (MS), normally associated with later stages of the disease in adult population. Nevertheless, progressive cognitive and neuropsychiatric disturbances might rarely be the presenting and predominant symptom. In order to better characterize this peculiar phenotype of MS, we report on the case of a 38-year-old man who referred to our hospital with the suspect of hereditary leukodystrophy after 5 years of behavioral and mood abnormalities, global cognitive dysfunction, clumsiness, and very mild pyramidal and cerebellar signs. Brain and spinal magnetic resonance imaging (MRI) combined with cerebrospinal fluid (CSF) analysis prompted the diagnosis of MS.