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Background: Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents the prodromal stage of cit-synucleinopathies. Reliable biomarkers are needed to predict phenoconversion.Objective: The aim was to derive and validate a brain glucose metabolism pattern related to phenoconversion in iRBD (iRBDconvRP) using spatial covariance analysis (Scaled Subprofile Model and Principal Component Analysis [SSM-PCA]).Methods: Seventy-six consecutive iRBD patients (70 +/- 6 years, 15 women) were enrolled in two centers and prospectively evaluated to assess phenoconversion (30 converters, 73 +/- 6 years, 14 Parkinson's disease and 16 dementia with Lewy bodies, follow-up time: 21 +/- 14 months; 46 nonconverters, 69 +/- 6 years, follow-up time: 33 +/- 19 months). All patients underwent [F-18]FDG-PET (F-18-fluorodeoxyglucose positron emitting tomography) to investigate brain glucose metabolism at baseline. SSM-PCA was applied to obtain the iRBDconvRP; nonconverter patients were considered as the reference group. Survival analysis and Cox regression were applied to explore prediction power.Results: First, we derived and validated two distinct center-specific iRBDconvRP that were comparable and significantly able to predict phenoconversion. Then, SSM-PCA was applied to the whole set, identifying the iRBDconvRP. The iRBDconvRP included positive voxel weights in cerebellum; brainstem; anterior cingulate cortex; lentiform nucleus; and middle, mesial temporal, and postcentral areas. Negative voxel weights were found in posterior cingulate, precuneus, middle frontal gyrus, and parietal areas. Receiver operating characteristic analysis showed an area under the curve of 0.85 (sensitivity: 87%, specificity: 72%), discriminating converters from nonconverters. The iRBDconvRP significantly predicted phenoconversion (hazard ratio: 7.42, 95% confidence interval: 2.6-21.4).Conclusions: We derived and validated an iRBDconvRP to efficiently discriminate converter from nonconverter iRBD patients. [F-18]FDG-PET pattern analysis has potential as a phenoconversion biomarker in iRBD patients. (C) 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A cut-off of -2 z-score for striatal or putaminal SBR has been to date arbitrarily used to define an abnormal DaT SPECT in patients with suspected neurodegenerative parkinsonism. We aimed to experimentally identify the most accurate z-score cut-offs for SBR of striatal and substriatal regions to independently discriminate PD and DLB, with respect to essential tremor (ET) and Alzheimer's disease (AD) respectively.Two-hundred twenty-five patients undergoing DaT SPECT were enrolled (seventy-five de novo PD, eighty ET, fifty DLB, and twenty AD). Semiquantification was computed by means of Datquant® software which returns measures of striatal SBR and z-scores with respect to 118 healthy volunteers belonging to the Parkinson's Progression Markers Initiative (PPMI). ROC analysis was used to identify most accurate cut-offs for z-score for striatum and substriatal regions (clinical diagnosis at follow-up as gold standard).Posterior putamen of the most affected hemisphere (MAH) with a z-score cut-off of - 1.27 demonstrated the highest accuracy to differentiate between PD and ET (sensitivity 0.97, specificity 0.94). The whole putamen (z-score cut-off - 0.96) was the most accurate parameter to support the diagnosis of DLB (sensitivity 0.74, specificity 0.95). Putamen to caudate ratio was accurate to detect PD (especially in early stages) while not DLB patients.We experimentally demonstrated that different substriatal regions and cut-offs for z-score of SBR should be considered to support the diagnosis of either PD or DLB. The identified less conservative cut-offs showed higher sensitivity without a measurable reduction in specificity with respect to the arbitrary - 2 z-score.

Abnormalities in cortical sources of resting-state eyes closed electroencephalographic (rsEEG) rhythms recorded by hospital settings (10-20 montage) with 19 scalp electrodes characterized Alzheimer's disease (AD) from preclinical to dementia stages. An intriguing rsEEG application is the monitoring and evaluation of AD progression in large populations with few electrodes in low-cost devices. Here we evaluated whether the above-mentioned abnormalities can be observed from fewer scalp electrodes in patients with mild cognitive impairment due to AD (ADMCI). Clinical and rsEEG data acquired in hospital settings (10-20 montage) from 75 ADMCI participants and 70 age-, education-, and sex-matched normal elderly controls (Nold) were available in an Italian-Turkish archive (PDWAVES Consortium; www.pdwaves.eu ). Standard spectral fast fourier transform (FFT) analysis of rsEEG data for individual delta, theta, and alpha frequency bands was computed from 6 monopolar scalp electrodes to derive bipolar C3-P3, C4-P4, P3-O1, and P4-O2 markers. The ADMCI group showed increased delta and decreased alpha power density at the C3-P3, C4-P4, P3-O1, and P4-O2 bipolar channels compared to the Nold group. Increased theta power density for ADMCI patients was observed only at the C3-P3 bipolar channel. Best classification accuracy between the ADMCI and Nold individuals reached 81% (area under the receiver operating characteristic curve) using Alpha2/Theta power density computed at the C3-P3 bipolar channel. Standard rsEEG power density computed from six posterior bipolar channels characterized ADMCI status. These results may pave the way toward diffuse clinical applications in health monitoring of dementia using low-cost EEG systems with a strict number of electrodes in lower- and middle-income countries.

Background: Cognitive impairment is frequent in Parkinson’s disease (PD) and several neurotransmitter changes have been reported since the time of diagnosis, although seldom investigated altogether in the same patient cohort. Objective: Our aim was to evaluate the association between neurotransmitter impairment, brain metabolism, and cognition in a cohort of de novo, drug-naïve PD patients. Methods: We retrospectively selected 95 consecutive drug-naïve PD patients (mean age 71.89±7.53) undergoing at the time of diagnosis a brain [18F]FDG-PET as a marker of brain glucose metabolism and proxy measure of neurodegeneration, [123I]FP-CIT-SPECT as a marker and dopaminergic deafferentation in the striatum and frontal cortex, as well as a marker of serotonergic deafferentation in the thalamus, and quantitative electroencephalography (qEEG) as an indirect measure of cholinergic deafferentation. Patients also underwent a complete neuropsychological battery. Results: Positive correlations were observed between (i) executive functions and left cerebellar cortex metabolism, (ii) prefrontal dopaminergic tone and working memory (r = 0.304, p = 0.003), (iii) qEEG slowing in the posterior leads and both memory (r = 0.299, p = 0.004) and visuo-spatial functions (r = 0.357, p

Abstract Background A brain glucose metabolism pattern related to phenoconversion in patients with idiopathic/isolated REM sleep behaviour disorder (iRBDconvRP) was recently identified. However, the validation of the iRBDconvRP in an external, independent group of iRBD patients is needed to verify the reproducibility of such pattern, so to increase its importance in clinical and research settings. The aim of this work was to validate the iRBDconvRP in an independent group of iRBD patients. Methods Forty iRBD patients (70 ± 5.59 years, 19 females) underwent brain [18F]FDG-PET in Seoul National University. Thirteen patients phenoconverted at follow-up (7 Parkinson disease, 5 Dementia with Lewy bodies, 1 Multiple system atrophy; follow-up time 35 ± 20.56 months) and 27 patients were still free from parkinsonism/dementia after 62 ± 29.49 months from baseline. We applied the previously identified iRBDconvRP to validate its phenoconversion prediction power. Results The iRBDconvRP significantly discriminated converters from non-converters iRBD patients (p = 0.016; Area under the Curve 0.74, Sensitivity 0.69, Specificity 0.78), and it significantly predicted phenoconversion (Hazard ratio 4.26, C.I.95%: 1.18–15.39). Conclusions The iRBDconvRP confirmed its robustness in predicting phenoconversion in an independent group of iRBD patients, suggesting its potential role as a stratification biomarker for disease-modifying trials.

Background: Neuropsychological assessment is still the basis for the first evaluation of patients with cognitive complaints. The Free and Cued Selective Reminding Test (FCSRT) generates several indices that could have different accuracy in the differential diagnosis between Alzheimer’s disease (AD) and other disorders. Objective: In a consecutive series of naturalistic patients, the accuracy of the FCSRT indices in differentiating patients with either mild cognitive impairment (MCI) due to AD or AD dementia from other competing conditions was evaluated. Methods: We evaluated the accuracy of the seven FCSRT indices in differentiating patients with AD from other competing conditions in 434 consecutive outpatients, either at the MCI or at the early dementia stage. We analyzed these data through the receiver operating characteristics curve, and we then generated the odds-ratio map of the two indices with the best discriminative value between pairs of disorders. Results: The immediate and the delayed free total recall, the immediate total recall, and the index of sensitivity of cueing were the most useful indices and allowed to distinguish AD from dementia with Lewy bodies and psychiatric conditions with very high accuracy. Accuracy was instead moderate in distinguishing AD from behavioral variant frontotemporal dementia, vascular cognitive impairment, and other conditions. Conclusion: By using odd-ratio maps and comparison-customized cut-off scores, we confirmed that the FCSRT represents a useful tool to characterize the memory performance of patients with MCI and thus to assist the clinician in the diagnosis process, though with different accuracy values depending on the clinical hypothesis.

Purpose Hyposmia is a common feature of COVID-19 and Parkinson’s disease (PD). As parkinsonism has been reported after COVID-19, a link has been hypothesized between SARS-CoV2 infection and PD. We aimed to evaluate brain metabolic correlates of isolated persistent hyposmia after mild-to-moderate COVID-19 and to compare them with metabolic signature of hyposmia in drug-naïve PD patients. Methods Forty-four patients who experienced hyposmia after SARS-COV2 infection underwent brain [18F]-FDG PET in the first 6 months after recovery. Olfaction was assessed by means of the 16-item “Sniffin’ Sticks” test and patients were classified as with or without persistent hyposmia (COVID-hyposmia and COVID-no-hyposmia respectively). Brain [18F]-FDG PET of post-COVID subgroups were compared in SPM12. COVID-hyposmia patients were also compared with eighty-two drug-naïve PD patients with hyposmia. Multiple regression analysis was used to identify correlations between olfactory test scores and brain metabolism in patients’ subgroups. Results COVID-hyposmia patients (n = 21) exhibited significant hypometabolism in the bilateral gyrus rectus and orbitofrontal cortex with respect to COVID-non-hyposmia (n = 23) (p

Background Sleep disturbances are common non-motor symptoms of Parkinson’s Disease (PD). Methods The aim of this study was to investigate the polysomnographic correlates of sleep changes, as investigated by the Parkinson’s Disease Sleep Scale-2 (PDSS-2), in a cohort of sixty-two consecutive de novo, drug naïve PD patients (71.40 ± 7.84 y/o). Results PDSS-2 total score showed a direct correlation with stage shifts (p = 0.008). Fragmented sleep showed an inverse correlation with sleep efficiency (p = 0.012). Insomnia symptoms showed an inverse correlation with wake after sleep onset (p = 0.005) and direct correlation with periodic leg movements (p = 0.006) and stage shift indices (p = 0.003). Motor Symptoms showed a direct correlation with Apnoea-Hypopnoea (AHI; p = 0.02) and awakenings indices (p = 0.003). Dream distressing showed a direct correlation with REM without atonia (RWA, p = 0.042) and an inverse correlation with AHI (p = 0.012). Sleep quality showed an inverse correlation with RWA (p = 0.008). Conclusion PDSS-2 features are significantly correlated with polysomnography objective findings, thus further supporting its reliability to investigate sleep disturbances in PD patients.

Introduction: We assessed the influence of education as a proxy of cognitive reserve and age on the dementia with Lewy bodies (DLB) metabolic pattern. Methods: Brain 18F-fluorodeoxyglucose positron emission tomography and clinical/demographic information were available in 169 probable DLB patients included in the European DLB-consortium database. Principal component analysis identified brain regions relevant to local data variance. A linear regression model was applied to generate age- and education-sensitive maps corrected for Mini-Mental State Examination score, sex (and either education or age). Results: Age negatively covaried with metabolism in bilateral middle and superior frontal cortex, anterior and posterior cingulate, reducing the expression of the DLB-typical cingulate island sign (CIS). Education negatively covaried with metabolism in the left inferior parietal cortex and precuneus (making the CIS more prominent). Discussion: These findings point out the importance of tailoring interpretation of DLB biomarkers considering the concomitant effect of individual, non-disease-related variables such as age and cognitive reserve.

Background: Mild cognitive impairment (MCI) is a heterogeneous condition. Idiopathic REM sleep behavior disorder (iRBD) can be associated with MCI (MCI-RBD). Objective: To investigate neuropsychological and brain metabolism features of patients with MCI-RBD by comparison with matched MCI-AD patients. To explore their predictive value toward conversion to a full-blown neurodegenerative disease. Methods: Seventeen MCI-RBD patients (73.6±6.5 years) were enrolled. Thirty-four patients with MCI-AD were matched for age (74.8±4.4 years), Mini-Mental State Exam score and education with a case-control criterion. All patients underwent a neuropsychological assessment and brain 18F-FDG-PET. Images were compared between groups to identify hypometabolic volumes of interest (MCI-RBD-VOI and MCI-AD-VOI). The dependency of whole-brain scaled metabolism levels in MCI-RBD-VOI and MCI-AD-VOI on neuropsychological test scores was explored with linear regression analyses in both groups, adjusting for age and education. Survival analysis was performed to investigate VOIs phenoconversion prediction power. Results: MCI-RBD group scored lower in executive functions and higher in verbal memory compared to MCI-AD group. Also, compared with MCI-AD, MCI-RBD group showed relative hypometabolism in a posterior brain area including cuneus, precuneus, and occipital regions while the inverse comparison revealed relative hypometabolism in the hippocampus/parahippocampal areas in MCI-AD group. MCI-RBD-VOI metabolism directly correlated with executive functions in MCI-RBD (p = 0.04). MCI-AD-VOI metabolism directly correlated with verbal memory in MCI-AD (p = 0.001). MCI-RBD-VOI metabolism predicted (p = 0.03) phenoconversion to an alpha-synucleinopathy. MCI-AD-VOI metabolism showed a trend (p = 0.07) in predicting phenoconversion to dementia. Conclusion: MCI-RBD and MCI-AD showed distinct neuropsychological and brain metabolism profiles, that may be helpful for both diagnosis and prognosis purposes.

Clinical decision support systems based on machine-learning algorithms are largely applied in the context of the diagnosis of neurodegenerative diseases (NDDs). While recent models yield robust classifications in supervised two classes-problems accurately separating Parkinson’s disease (PD) from healthy control (HC) subjects, few works looked at prodromal stages of NDDs. Idiopathic Rapid-eye Movement (REM) sleep behavior disorder (iRBD) is considered a prodromal stage of PD with a high chance of phenoconversion but with heterogeneous symptoms that hinder accurate disease prediction. Machine learning (ML) based methods can be used to develop personalized trajectory models, but these require large amounts of observational points with homogenous features significantly reducing the possible imaging modalities to non-invasive and cost-effective techniques such as high-density electrophysiology (hdEEG). In this work, we aimed at quantifying the increase in accuracy and robustness of the classification model with the inclusion of network-based metrics compared to the classical Fourier-based power spectral density (PSD). We performed a series of analyses to quantify significance in cohort-wise metrics, the performance of classification tasks, and the effect of feature selection on model accuracy.We report that amplitude correlation spectral profiles show the largest difference between iRBD and HC subjects mainly in delta and theta bands. Moreover, the inclusion of amplitude correlation and phase synchronization improves the classification performance by up to 11% compared to using PSD alone. Our results show that hdEEG features alone can be used as potential biomarkers in classification problems using iRBD data and that large-scale network metrics improve the performance of the model. This evidence suggests that large-scale brain network metrics should be considered important tools for investigating prodromal stages of NDD as they yield more information without harming the patient, allowing for constant and frequent longitudinal evaluation of patients at high risk of phenoconversion.HighlightsNetwork-based features are important tools to investigate prodromal stages of PDAmplitude correlation shows the largest difference between two groups in 9/30 bandsAmplitude correlation improved up to 11% the performance compared to PSD aloneClassification robustness increases when we use both network-based EEG featuresClassifier performance worsens when PSD is added to network-based EEG features

This scientific commentary refers to ‘Brain atrophy in prodromal synucleinopathy is shaped by structural connectivity and gene expression’ by Rahayel et al. (https://doi.org/10.1093/brain/awac187).

The study of dementia and epilepsy may provide particular insight into behavioural alterations. We describe a rare case of ictal aggressive behaviour in a patient with focal epilepsy associated with a non-dominant dorso-lateral prefrontal lesion. During focal seizures, our patient showed intense agitation and anger, for a long time misinterpreted as psychogenic attacks, which disappeared after epilepsy surgery. The defined anatomical origin of such ictal emotional behaviour is not fully understood, however, the dorso-lateral prefrontal area appears to correlate less frequently with aggressiveness compared to the antero-mesial area. We describe the electroclinical data of our patient and provide a brief review of the mechanisms underlying aggressive conduct in epilepsy and dementia. An understanding of this mechanism could help to clarify the neural basis and treatment of violence associated with these and other neurological disorders. [Published with video sequence].

Background: Patients with amnesic mild cognitive impairment due to Alzheimer’s disease (ADMCI) typically show a “slowing” of cortical resting-state eyes-closed electroencephalographic (rsEEG) rhythms. Some of them also show subclinical, non-convulsive, and epileptiform EEG activity (EEA) with an unclear relationship with that “slowing.” Objective: Here we tested the hypothesis that the “slowing” of rsEEG rhythms is related to EEA in ADMCI patients. Methods: Clinical and instrumental datasets in 62 ADMCI patients and 38 normal elderly (Nold) subjects were available in a national archive. No participant had received a clinical diagnosis of epilepsy. The eLORETA freeware estimated rsEEG cortical sources. The area under the receiver operating characteristic curve (AUROCC) indexed the accuracy of eLORETA solutions in the classification between ADMCI-EEA and ADMCI-noEEA individuals. Results: EEA was observed in 15% (N = 8) of the ADMCI patients. The ADMCI-EEA group showed: 1) more abnormal Aβ42 levels in the cerebrospinal fluid as compared to the ADMCI-noEEA group and 2) higher temporal and occipital delta (

A classic approach to estimate individual theta-to-alpha transition frequency (TF) requires two electroencephalographic (EEG) recordings, one acquired in a resting state condition and one showing alpha desynchronisation due, for example, to task execution. This translates into long recording sessions that may be cumbersome in studies involving patients. Moreover, an incomplete desynchronisation of the alpha rhythm may compromise TF estimates. Here we present transfreq, a publicly available Python library that allows TF computation from resting state data by clustering the spectral profiles associated to the EEG channels based on their content in alpha and theta bands. A detailed overview of transfreq core algorithm and software architecture is provided. Its effectiveness and robustness across different experimental setups are demonstrated on a publicly available EEG data set and on in-house recordings, including scenarios where the classic approach fails to estimate TF. We conclude with a proof of concept of the predictive power of transfreq TF as a clinical marker. Specifically, we present a scenario where transfreq TF shows a stronger correlation with the mini mental state examination score than other widely used EEG features, including individual alpha peak and median/mean frequency. The documentation of transfreq and the codes for reproducing the analysis of the article with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/. Motivated by the results showed in this article, we believe our method will provide a robust tool for discovering markers of neurodegenerative diseases.

Insomnia affects one-third of the adult population and is associated with multiple medical conditions. We conducted an observational epidemiological survey to assess (1) the prevalence of insomnia in an Italian group of patients aged over 50 years, presenting directly to the general physician (GP); (2) the association of insomnia with sleepiness and comorbidities; and (3) the pharmacological treatment. The study was carried out by GPs. Each GP was asked to enroll the first patient over 50 years old spontaneously presenting for any medical problems for 5 consecutive days. The Italian version of the Sleep Condition Indicator (SCI) was administered; daytime sleepiness was evaluated by a visual analogic scale (VAS). For every patient, GPs collected information regarding comorbidities and pharmacological treatment for insomnia and evaluated the severity of insomnia using the Clinical Global Impression Severity (CGI-S) scale. A total of 748 patients (mean age 65.12 ± 9.45 years) were enrolled by 149 GPs. Prevalence of insomnia was 55.3%. SCI, VAS, and CGI-S scores were highly correlated between each other (p 0.0001). At general linear model analysis, the comorbidities more associated with the presence of insomnia were anxiety-depressive disorder (p 0.001), other psychiatric disorders (p = 0.017), cardiovascular disorders (p = 0.006), and dementia (p = 0.027). A statistically significant correlation was found between SCI score and the use of benzodiazepines (p 0.001), z-drugs (p = 0.012), antidepressants (p 0.001), and melatonin-prolonged release (p 0.001). Insomnia affects half of Italian primary care patients over 50 years and is frequently associated with different medical conditions, sleepiness, and use of multiple-often off-label-drugs.

Introduction. Prospective memory (PM) is the ability of forming and maintaining an intention in memory over time while performing another task. From previous studies in healthy volunteers the prefrontal regions are deeply involved in PM, although little is known in early Alzheimer's disease (AD). Hence, we investigated the functional neural basis of PM in patients with mild cognitive impairment due to AD (MCI-AD) by assessing brain metabolism (18F-FDG PET). Methods. Eighteen patients (10 males, age:74±4.9; MMSE score:27.7±1.6) with intermediate or high likelihood of MCI-AD and a control group (HC) of 23 healthy subjects (11 males, age:71.5±5.7) underwent FDG-PET, neuropsychological evaluation and the PM-specific Shum and Ungvari paradigm test. Brain metabolism was correlated with the PM score in the two groups separately to find those brain areas correlated with PM performance ('PM cluster'), which were then used as central hub for the subsequent interregional metabolic connectivity analyses (IRCA). Results. In MCI-AD patients PM score positively correlated with metabolic levels in the right anterior prefrontal cortex (aPFC, middle and inferior frontal gyri); no correlation was found in HC. The IRCA showed in MCI-AD patients a loss of the interhemispheric connectivity of the PM cluster. Conclusion. The functioning of the right aPFC and its metabolic interhemispheric connectivity is crucial in early AD to sustain PM performance which deteriorates along with progressive metabolic failure in these regions.

Please modify the Abstract as follows:Here we tested if the reactivity of posterior resting-state electroencephalographic (rsEEG) alpha rhythms from the eye-closed to the eyes-open condition may differ in patients with dementia due to Lewy Bodies (DLB) and Alzheimer's disease (ADD) as a functional probe of the dominant neural synchronization mechanisms regulating the vigilance in posterior visual systems.We used clinical, demographical, and rsEEG datasets in 28 older adults (Healthy), 42 DLB, and 48 ADD participants. The eLORETA freeware was used to estimate cortical rsEEG sources.Results showed a substantial (> -10%) reduction in the posterior alpha activities during the eyes-open condition in 24 Healthy, 26 ADD, and 22 DLB subjects. There were lower reductions in the posterior alpha activities in the ADD and DLB groups than in the Healthy group. That reduction in the occipital region was lower in the DLB than in the ADD group.These results suggest that DLB patients may suffer from a greater alteration in the neural synchronization mechanisms regulating vigilance in occipital cortical systems compared to ADD patients. H2020 Marie S. Curie ITN-ETN project Italian Ministry of Health to the IRCCS SDN of Naples, IRCCS OASI of Troina, and IRCCS San Raffaele Pisana of Rome

BACKGROUND Dopamine transporter single photon-emission computed tomography (DAT-SPECT) is the strongest risk factor for phenoconversion in patients with idiopathic rapid eye movement (REM)-sleep behavior disorder (iRBD). However, it might be used as a second-line stratification tool in clinical trials, because it is expensive and mini-invasive. OBJECTIVE Aim of the study is to investigate whether other cost-effective and non-invasive biomarkers may be proposed as first-line stratification tools. METHODS Forty-seven consecutive iRBD patients (68.53 ± 7.16 years, 40 males) underwent baseline clinical and neuropsychological assessment, olfaction test, resting electroencephalogram (EEG), and DAT-SPECT. All patients underwent 6 month-based clinical follow-up to investigate the emergence of parkinsonism and/or dementia. Survival analysis and Cox regression were used to estimate conversion risk. RESULTS Seventeen patients developed an overt synucleinopathy (eight Parkinsonism and nine dementia) 32.8 ± 22 months after diagnosis. The strongest risk factors were putamen specific to non-displaceable binding ratio (SBR) (hazard ratio [HR], 7.3), attention/working memory cognitive function (NPS-AT/WM) (HR, 5.9), EEG occipital mean frequency (HR, 2.7) and clinical motor assessment (HR, 2.3). On multivariate Cox-regression analysis, only putamen SBR and NPS-AT/WM significantly contributed to the model (HR, 6.2, 95% confidence interval [CI], 1.9-19.8). At post-hoc analysis, the trail-making test B (TMT-B) was the single most efficient first-line stratification tool that allowed to reduce the number of eligible subjects to 76.6% (sensitivity 1, specificity 0.37). Combining TMT-B and DAT-SPECT further reduced the sample to 66% (sensitivity 0.88, specificity 0.47). CONCLUSION The TMT-B seems to be a cost-effective and efficient first-line screening tool, to be used to select patients that deserve DAT-SPECT as second-line screening tool for disease-modifying clinical trials. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Purpose: FDG-PET is an established supportive biomarker in dementia with Lewy bodies (DLB), but its diagnostic accuracy is unknown at the mild cognitive impairment (MCI-LB) stage when the typical metabolic pattern may be difficultly recognized at the individual level. Semiquantitative analysis of scans could enhance accuracy especially in less skilled readers, but its added role with respect to visual assessment in MCI-LB is still unknown. Methods: We assessed the diagnostic accuracy of visual assessment of FDG-PET by six expert readers, blind to diagnosis, in discriminating two matched groups of patients (40 with prodromal AD (MCI-AD) and 39 with MCI-LB), both confirmed by in vivo biomarkers. Readers were provided in a stepwise fashion with (i) maps obtained by the univariate single-subject voxel-based analysis (VBA) with respect to a control group of 40 age- and sex-matched healthy subjects, and (ii) individual odds ratio (OR) plots obtained by the volumetric regions of interest (VROI) semiquantitative analysis of the two main hypometabolic clusters deriving from the comparison of MCI-AD and MCI-LB groups in the two directions, respectively. Results: Mean diagnostic accuracy of visual assessment was 76.8 ± 5.0% and did not significantly benefit from adding the univariate VBA map reading (77.4 ± 8.3%) whereas VROI-derived OR plot reading significantly increased both accuracy (89.7 ± 2.3%) and inter-rater reliability (ICC 0.97 [0.96-0.98]), regardless of the readers' expertise. Conclusion: Conventional visual reading of FDG-PET is moderately accurate in distinguishing between MCI-LB and MCI-AD, and is not significantly improved by univariate single-subject VBA but by a VROI analysis built on macro-regions, allowing for high accuracy independent of reader skills.

Theory of mind (ToM) deficit is a frequent finding in subjects with neurological and psychiatric conditions. While a number of brain regions play a role in ToM, to date the contribution of the diffuse projection systems is less understood. Here, we explored the topographical and neurochemical bases of ToM using multi-tracer molecular imaging and quantitative electroencephalography (qEEG) in a group of 30 drug-naive, de novo Parkinson's Disease (PD) patients (mean age 73.39 ± 8.93 years, 11 females). ToM was assessed using the “Reading the Mind in the Eyes Task” (RMET), while general cognition with the MMSE. We acquired FDG-PET images (as a marker of regional neurodegeneration), I-123 Ioflupane Single Photon Emission Computed Tomography (123 I-FP-CIT-SPECT, as a marker of dopaminergic impairment in the basal ganglia and in the cortex and as a proxy marker of serotoninergic deafferentation in the thalamus), and qEEG recordings (using the Theta/Alpha power ratio as marker of cholinergic deafferentation). PD presented with a significantly worse RMET score compared to 60 controls (20.7 ± 5.5 vs 27.5 ± 3.0 p = .001) while there was no difference between the two groups in age, education or MMSE. The voxel-wise analysis of total RMET score and regional metabolism showed a positive correlation in the superior temporal gyrus and in the insula. Among the proxy markers of dopaminergic degeneration, serotoninergic and cholinergic deafferentation, ToM presented only an inverse correlation with 123 I-FP-CIT thalamic specific binding ratio (SBR) values -a proxy serotoninergic marker-which remained significant after correction for FDG metabolism in the areas associated with ToM. On the other hand, MMSE only correlated with qEEG posterior Theta/Alpha power. These findings point to the presence of a specific cortical and neurochemical signature of ToM in PD, to the independence of ToM from general cognition, and suggest possible therapeutic targets to treat social cognition deficits.

Background Striatal dopamine deficiency and metabolic changes are well-known phenomena in dementia with Lewy bodies and can be quantified in vivo by I-123-Ioflupane brain single-photon emission computed tomography of dopamine transporter and F-18-fluorodesoxyglucose PET. However, the linkage between both biomarkers is ill-understood. Objective We used the hitherto largest study cohort of combined imaging from the European consortium to elucidate the role of both biomarkers in the pathophysiological course of dementia with Lewy bodies. Methods We compared striatal dopamine deficiency and glucose metabolism of 84 dementia with Lewy body patients and comparable healthy controls. After normalization of data, we tested their correlation by region-of-interest-based and voxel-based methods, controlled for study center, age, sex, education, and current cognitive impairment. Metabolic connectivity was analyzed by inter-region coefficients stratified by dopamine deficiency and compared to healthy controls. Results There was an inverse relationship between striatal dopamine availability and relative glucose hypermetabolism, pronounced in the basal ganglia and in limbic regions. With increasing dopamine deficiency, metabolic connectivity showed strong deteriorations in distinct brain regions implicated in disease symptoms, with greatest disruptions in the basal ganglia and limbic system, coincident with the pattern of relative hypermetabolism. Conclusions Relative glucose hypermetabolism and disturbed metabolic connectivity of limbic and basal ganglia circuits are metabolic correlates of dopamine deficiency in dementia with Lewy bodies. Identification of specific metabolic network alterations in patients with early dopamine deficiency may serve as an additional supporting biomarker for timely diagnosis of dementia with Lewy bodies. (c) 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. Funding Agencies|Velox Foundation [project 1123]; Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen Forschung [SNF 320030_169876]

Background An ideal imaging biomarker for a neurodegenerative disorder should be able to measure abnormalities in the earliest stages of the disease. Objective We investigated metabolic network changes in two independent cohorts of drug-naive Parkinson's disease (PD) patients who have not been exposed to dopaminergic medication. Methods We scanned 85 de novo, drug-naive PD patients and 85 age-matched healthy control subjects from Italy (n = 96) and the United States (n = 74) with [18 F]-fluorodeoxyglucose PET. All patients had clinical follow-ups to verify the diagnosis of idiopathic PD. Spatial covariance analysis was used to identify and validate de novo PD-related metabolic patterns in the Italian and U.S. cohorts. We compared the de novo PD-related metabolic patterns to the original PD-related pattern that was identified in more advanced patients who had been on chronic dopaminergic treatment. Results De novo PD-related metabolic patterns were identified in each of the two independent cohorts of drug-naive PD patients, and each differentiated PD patients from healthy control subjects. Expression values for these disease patterns were elevated in drug-naive PD patients relative to healthy controls in the identification as well as in each of the validation subgroups. The two de novo PD-related metabolic patterns were topographically very similar to each other and to the original PD-related pattern. Conclusions Reproducible PD-related patterns are expressed in de novo, drug-naive PD patients. In PD, disease-related metabolic patterns have stereotyped topographies that develop independently of chronic levodopa treatment. © 2019 International Parkinson and Movement Disorder Society.

Rationale In Parkinson’s disease (PD), spatial covariance analysis of 18F-FDG PET data has consistently revealed a characteristic PD-related brain pattern (PDRP). By quantifying PDRP expression on a scan-by-scan basis, this technique allows objective assessment of disease activity in individual subjects. We provide a further validation of the PDRP by applying spatial covariance analysis to PD cohorts from the Netherlands (NL), Italy (IT), and Spain (SP). Methods The PDRPNL was previously identified (17 controls, 19 PD) and its expression was determined in 19 healthy controls and 20 PD patients from the Netherlands. The PDRPIT was identified in 20 controls and 20 “de-novo” PD patients from an Italian cohort. A further 24 controls and 18 “de-novo” Italian patients were used for validation. The PDRPSP was identified in 19 controls and 19 PD patients from a Spanish cohort with late-stage PD. Thirty Spanish PD patients were used for validation. Patterns of the three centers were visually compared and then cross-validated. Furthermore, PDRP expression was determined in 8 patients with multiple system atrophy. Results A PDRP could be identified in each cohort. Each PDRP was characterized by relative hypermetabolism in the thalamus, putamen/pallidum, pons, cerebellum, and motor cortex. These changes co-varied with variable degrees of hypometabolism in posterior parietal, occipital, and frontal cortices. Frontal hypometabolism was less pronounced in “de-novo” PD subjects (Italian cohort). Occipital hypometabolism was more pronounced in late-stage PD subjects (Spanish cohort). PDRPIT, PDRPNL, and PDRPSP were significantly expressed in PD patients compared with controls in validation cohorts from the same center (P < 0.0001), and maintained significance on cross-validation (P < 0.005). PDRP expression was absent in MSA. Conclusion The PDRP is a reproducible disease characteristic across PD populations and scanning platforms globally. Further study is needed to identify the topography of specific PD subtypes, and to identify and correct for center-specific effects.

We explored the brain metabolism correlates of emergent cerebrospinal fluid (CSF) biomarkers in a group of 26 patients with prodromal Alzheimer's disease (AD). Distinct volumes of interest (VOIs) expressed the sites of correlation between CSF biomarkers and brain metabolism as determined on [18F]FDG-PET images, as well as of significant hypometabolism in patients compared to healthy controls. Neurogranin- and α-synuclein-VOIs included left precuneus and/or posterior cingulate cortex (PC and/or PCC) and partially overlapped hypometabolism at those sites. β-synuclein- and neurofilament light chain (NfL)-VOIs regarded either left or right lateral temporal areas, respectively, with partial overlap with hypometabolism only for the β-synuclein-VOI, whereas the NfL-VOI did not include hypometabolic regions. We speculate that CSF neurogranin and α-synuclein express an already established hippocampal damage leading to PC and/or PCC deafferentation and hypometabolism. β-synuclein may represent the progression of synaptopathy in the temporal lobe, while NfL the axonal injury in right temporal regions where neuronal loss is not yet evident.

A classic approach to estimate the individual theta-to-alpha transition frequency requires two electroencephalographic (EEG) recordings, one acquired in restingstate condition and one showing an alpha de-synchronisation due e.g. to task execution. This translates into longer recording sessions that my be cumbersome in studies involving patients. Moreover, incomplete de-synchronisation of the alpha rhythm may compromise the final estimation of the transition frequency. Here we present transfreq, a Python library that allows the computation of the transition frequency from resting-state data by clustering the spectral profiles at different EEG channels based on their content in the alpha and theta bands. We first provide an overview of the transfreq core algorithm and of the software architecture. Then we demonstrate its feasibility and robustness across different experimental setups on a publicly available EEG data set and on in-house recordings. A detailed documentation of transfreq and the codes for reproducing the analysis of the paper with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/