Automatic quantification of REM sleep without atonia reliably identifies patients with REM sleep behavior disorder: a possible screening tool?

BACKGROUND: REM Sleep Behavior Disorder (RBD) is characterized by absence of physiological muscle atonia during REM sleep (REM sleep without atonia, RWA). Nigro-striatal dopaminergic impairment is a feature of Parkinson disease (PD) and can be identified in prodromal stages as well, such as idiopathic RBD (iRBD). Aims of this study are to explore the efficacy of an automatic RWA quantification in identifying RBD patients and the correlation between RWA and nigro-striatal dopaminergic function. METHODS: Forty-five iRBD, 46 PD with RBD, 24 PD without RBD patients and 11 healthy controls were enrolled in the Genoa Center (group A) and 25 patients with iRBD (group B) were enrolled in the Danish Center. Group A underwent brain [123I]FP-CIT-SPECT and group B underwent brain [18F]PE2I-PET as measures of nigro-striatal dopaminergic function. Chin muscle activity was recorded in all subjects and analyzed by applying a published automatic algorithm. Correlations between RWA and nigro-striatal dopaminergic function were explored. RESULTS: The automatic quantification of RWA significantly differentiated RBD from non-RBD subjects (AUC = 0.86), although with lower accuracy compared with conventional visual scoring (AUC = 0.99). No significant correlation was found between RWA and nigro-striatal dopaminergic function. CONCLUSION: The automatic quantification of RWA is a reliable tool to identify subjects with RBD and may be used as a first-line screening tool, but without correlations with nigro-striatal dopaminergic functioning.

Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy.

OBJECTIVE: To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB). METHODS: In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.3 years, 70.5% males) with polysomnography-confirmed RBD who eventually phenoconverted to overt alpha-synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non-phenoconvertor patients (67.6 ± 7.1 years, 78.4% males) and 160 controls (68.2 ± 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion. RESULTS: Machine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD-converters from DLB-converters. Quantification of presynaptic dopaminergic imaging showed that an empirical z-score cutoff of -1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of -1.0 at the most affected hemisphere putamen/caudate ratio characterized PD-converters. INTERPRETATION: Clinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease-modifying trials. ANN NEUROL 2024;95:1178-1192.

EEG-based machine learning models for the prediction of phenoconversion time and subtype in isolated rapid eye movement sleep behavior disorder.

STUDY OBJECTIVES: Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of α-synucleinopathies and eventually phenoconverts to overt neurodegenerative diseases including Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). Associations of baseline resting-state electroencephalography (EEG) with phenoconversion have been reported. In this study, we aimed to develop machine learning models to predict phenoconversion time and subtype using baseline EEG features in patients with iRBD. METHODS: At baseline, resting-state EEG and neurological assessments were performed on patients with iRBD. Calculated EEG features included spectral power, weighted phase lag index, and Shannon entropy. Three models were used for survival prediction, and four models were used for α-synucleinopathy subtype prediction. The models were externally validated using data from a different institution. RESULTS: A total of 236 iRBD patients were followed up for up to 8 years (mean 3.5 years), and 31 patients converted to α-synucleinopathies (16 PD, 9 DLB, 6 MSA). The best model for survival prediction was the random survival forest model with an integrated Brier score of 0.114 and a concordance index of 0.775. The K-nearest neighbor model was the best model for subtype prediction with an area under the receiver operating characteristic curve of 0.901. Slowing of the EEG was an important feature for both models. CONCLUSIONS: Machine learning models using baseline EEG features can be used to predict phenoconversion time and its subtype in patients with iRBD. Further research including large sample data from many countries is needed to make a more robust model.

Incident anti-LGI1 autoimmune encephalitis during dementia with Lewy bodies: when Occam razor is a double-edged sword.

In Dementia with Lewy bodies (DLB), rapid cognitive decline and seizures seldom complicate the typical clinical course. Nevertheless, concurrent, treatable conditions may be responsible. We report a case of DLB with superimposed anti-LGI1 encephalitis, emphasizing the importance of thorough diagnostic reasoning beyond the simplest explanation amid distinct clinical cues.

Prodromal dementia with Lewy bodies in REM sleep behavior disorder: A multicenter study.

INTRODUCTION: Isolated/idiopathic rapid eye movement sleep behavior disorder (iRBD) is a powerful early predictor of dementia with Lewy bodies (DLB) and Parkinson's disease (PD). This provides an opportunity to directly observe the evolution of prodromal DLB and to identify which cognitive variables are the strongest predictors of evolving dementia. METHODS: IRBD participants (n = 754) from 10 centers of the International RBD Study Group underwent annual neuropsychological assessment. Competing risk regression analysis determined optimal predictors of dementia. Linear mixed-effect models determined the annual progression of neuropsychological testing. RESULTS: Reduced attention and executive function, particularly performance on the Trail Making Test Part B, were the strongest identifiers of early DLB. In phenoconverters, the onset of cognitive decline began up to 10 years prior to phenoconversion. Changes in verbal memory best differentiated between DLB and PD subtypes. DISCUSSION: In iRBD, attention and executive dysfunction strongly predict dementia and begin declining several years prior to phenoconversion. HIGHLIGHTS: Cognitive decline in iRBD begins up to 10 years prior to phenoconversion. Attention and executive dysfunction are the strongest predictors of dementia in iRBD. Decline in episodic memory best distinguished dementia-first from parkinsonism-first phenoconversion.

Tracking the progression of Alzheimer's disease: Insights from metabolic patterns of SOMI stages.

BACKGROUND: SOMI (Stages of Objective Memory Impairment) is a novel classification that identifies six stages of memory decline in Alzheimer's Disease (AD) using the Free and Cued Selective Reminding Test (FCSRT). However, the relationship between SOMI stages and brain metabolism remains unexplored. This study aims to investigate the metabolic correlates of SOMI stages using FDG-PET in Mild Cognitive Impairment due to AD (MCI-AD) and early AD patients. METHODS: One hundred twenty-nine-patients (99 aMCI-AD and 30 AD), and 42 healthy controls (HCs) (MMSE = 29.2 ± .8; age:69.1 ± 8.6 years; education:10.7 ± 3.8 years) who underwent an extensive neuropsychological battery including FCSRT and brain FDG-PET were enrolled. According to their clinical relevance and available sample sizes, SOMI-4 (N = 24 subjects; MMSE score:26.6 ± 2.6: age:75.4 ± 3.2; education:9.9 ± 4.5) and SOMI-5 groups (N = 97; MMSE:25.3 ± 2.6; age:73.9 ± 5.8; education:9.4 ± 4.1) were investigated. RESULTS: Compared to HCs, SOMI-4 showed hypometabolism in the precuneus, medial temporal gyrus bilaterally, right pecuneus and angular gyrus. SOMI-5 exhibited broader hypometabolism, extending to the left posterior cingulate and medial frontal gyrus bilaterally. The conjunction analysis revealed overlapping areas in the precuneus, medial temporal gyrus bilaterally, and in the right angular gyrus and cuneus. The disjunction analysis identified SOMI-5 specific hypometabolism encompassing left inferior temporal gyrus, uncus and parahippocampal gyrus, and medial frontal gyrus bilaterally (p < .001, p-value (FWE) < .05). DISCUSSION: SOMI-4 relates to posterior hypometabolism, while SOMI-5 to more extensive hypometabolism further encompassing frontal cortices, suggesting SOMI as a biologically relevant classification system of memory decline. CONCLUSION: Memory decline staged with SOMI is associated with hypometabolism spreading in amnesic MCI-AD/AD, suggesting its usefulness as a clinical marker of increasing neurodegeneration.

Cerebrospinal fluid NPTX2 changes and relationship with regional brain metabolism metrics across mild cognitive impairment due to Alzheimer's disease.

BACKGROUND: Neuronal pentraxin-2 (NPTX2), crucial for synaptic functioning, declines in cerebrospinal fluid (CSF) as cognition deteriorates. The variations of CSF NPTX2 across mild cognitive impairment (MCI) due to Alzheimer's disease (AD) and its association with brain metabolism remain elusive, albeit relevant for patient stratification and pathophysiological insights. METHODS: We retrospectively analyzed 49 MCI-AD patients grouped by time until dementia (EMCI, n = 34 progressing within 2 years; LMCI, n = 15 progressing later/stable at follow-up). We analyzed demographic variables, cognitive status (MMSE score), and CSF NPTX2 levels using a commercial ELISA assay in EMCI, LMCI, and a control group of age-/sex-matched individuals with other non-dementing disorders (OND). Using [18F]FDG PET scans for voxel-based analysis, we explored correlations between regional brain metabolism metrics and CSF NPTX2 levels in MCI-AD patients, accounting for age. RESULTS: Baseline and follow-up MMSE scores were lower in LMCI than EMCI (p value = 0.006 and p < 0.001). EMCI exhibited significantly higher CSF NPTX2 values than both LMCI (p = 0.028) and OND (p = 0.006). We found a significant positive correlation between NPTX2 values and metabolism of bilateral precuneus in MCI-AD patients (p < 0.005 at voxel level, p < 0.05 with family-wise error correction at the cluster level). CONCLUSIONS: Higher CSF NPTX2 in EMCI compared to controls and LMCI suggests compensatory synaptic responses to initial AD pathology. Disease progression sees these mechanisms overwhelmed, lowering CSF NPTX2 approaching dementia. Positive CSF NPTX2 correlation with precuneus glucose metabolism links to AD-related metabolic changes across MCI course. These findings posit CSF NPTX2 as a promising biomarker for both AD staging and progression risk stratification.

Clinical and dopaminergic imaging characteristics of the FARPRESTO cohort of trial-ready idiopathic rapid eye movement sleep behavior patients.

INTRODUCTION: Idiopathic/isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is considered the prodromal stage of alpha-synucleinopathies. Thus, iRBD patients are the ideal target for disease-modifying therapy. The risk FActoRs PREdictive of phenoconversion in iRBD Italian STudy (FARPRESTO) is an ongoing Italian database aimed at identifying risk factors of phenoconversion, and eventually to ease clinical trial enrollment of well-characterized subjects. METHODS: Polysomnography-confirmed iRBD patients were retrospectively and prospectively enrolled. Baseline harmonized clinical and nigrostriatal functioning data were collected at baseline. Nigrostriatal functioning was evaluated by dopamine transporter-single-photon emission computed tomography (DaT-SPECT) and categorized with visual semi-quantification. Longitudinal data were evaluated to assess phenoconversion. Cox regressions were applied to calculate hazard ratios. RESULTS: 365 patients were enrolled, and 289 patients with follow-up (age 67.7 ± 7.3 years, 237 males, mean follow-up 40 ± 37 months) were included in this study. At follow-up, 97 iRBD patients (33.6%) phenoconverted to an overt synucleinopathy. Older age, motor and cognitive impairment, constipation, urinary and sexual dysfunction, depression, and visual semi-quantification of nigrostriatal functioning predicted phenoconversion. The remaining 268 patients are in follow-up within the FARPRESTO project. CONCLUSIONS: Clinical data (older age, motor and cognitive impairment, constipation, urinary and sexual dysfunction, depression) predicted phenoconversion in this multicenter, longitudinal, observational study. A standardized visual approach for semi-quantification of DaT-SPECT is proposed as a practical risk factor for phenoconversion in iRBD patients. Of note, non-converted and newly diagnosed iRBD patients, who represent a trial-ready cohort for upcoming disease-modification trials, are currently being enrolled and followed in the FARPRESTO study. New data are expected to allow better risk characterization.

Validation of the REM behaviour disorder phenoconversion-related pattern in an independent cohort.

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.

Progression of clinical markers in prodromal Parkinson's disease and dementia with Lewy bodies: a multicentre study.

The neurodegenerative synucleinopathies, including Parkinson's disease and dementia with Lewy bodies, are characterized by a typically lengthy prodromal period of progressive subclinical motor and non-motor manifestations. Among these, idiopathic REM sleep behaviour disorder is a powerful early predictor of eventual phenoconversion, and therefore represents a critical opportunity to intervene with neuroprotective therapy. To inform the design of randomized trials, it is essential to study the natural progression of clinical markers during the prodromal stages of disease in order to establish optimal clinical end points. In this study, we combined prospective follow-up data from 28 centres of the International REM Sleep Behavior Disorder Study Group representing 12 countries. Polysomnogram-confirmed REM sleep behaviour disorder subjects were assessed for prodromal Parkinson's disease using the Movement Disorder Society criteria and underwent periodic structured sleep, motor, cognitive, autonomic and olfactory testing. We used linear mixed-effect modelling to estimate annual rates of clinical marker progression stratified by disease subtype, including prodromal Parkinson's disease and prodromal dementia with Lewy bodies. In addition, we calculated sample size requirements to demonstrate slowing of progression under different anticipated treatment effects. Overall, 1160 subjects were followed over an average of 3.3 ± 2.2 years. Among clinical variables assessed continuously, motor variables tended to progress faster and required the lowest sample sizes, ranging from 151 to 560 per group (at 50% drug efficacy and 2-year follow-up). By contrast, cognitive, olfactory and autonomic variables showed modest progression with higher variability, resulting in high sample sizes. The most efficient design was a time-to-event analysis using combined milestones of motor and cognitive decline, estimating 117 per group at 50% drug efficacy and 2-year trial duration. Finally, while phenoconverters showed overall greater progression than non-converters in motor, olfactory, cognitive and certain autonomic markers, the only robust difference in progression between Parkinson's disease and dementia with Lewy bodies phenoconverters was in cognitive testing. This large multicentre study demonstrates the evolution of motor and non-motor manifestations in prodromal synucleinopathy. These findings provide optimized clinical end points and sample size estimates to inform future neuroprotective trials.

Right posterior hypometabolism in Pisa syndrome of Parkinson's disease: A key to explain body schema perception deficit?

BACKGROUND: Pisa syndrome (PS) is a trunk postural abnormality in Parkinson's disease (PD). Its pathophysiology is still debated: peripheral and central mechanisms have been hypothesized. OBJECTIVE: To investigate the role of nigrostriatal dopaminergic deafferentation and of brain metabolism impairment in the onset PS in PD patients. METHODS: We retrospectively selected 34 PD patients who developed PS (PS+) and who had previously undergone dopamine transporter (DaT)-SPECT and/or brain F-18 fluorodeoxyglucose PET (FDG-PET). PS + patients were divided considering leaning body side in left ((l)PS+) or right ((r)PS+). DaT-SPECT specific-to-non-displaceable binding ratio (SBR) of striatal regions (BasGan V2 software) were compared between 30 PS+ and 60 PD patients without PS (PS-) as well as between 16 (l)PS+ and 14 (r)PS + patients. Voxel-based analysis (SPM12) was used to compare FDG-PET among 22 PS+, 22 PS- and 42 healthy controls (HC) and between 9 (r)PS+ and 13 (l)PS+. RESULTS: No significant DaT-SPECT SBR differences were found between PS+ and PS- groups or between (r)PD+ and (l)PS + subgroups. Compared to HC, significant hypometabolism in PS+ was found in bilateral temporal-parietal regions, mainly in the right hemisphere, whereas the right Brodmann area 39 (BA39) was relatively hypometabolic both in the (r)PS+ and in the (l)PS+. BA39 and bilateral posterior cingulate cortex were significantly hypometabolic in PS + than in PS- group. CONCLUSIONS: As a hub of the network supervising the body schema perception, the involvement of the right posterior hypometabolism supports the hypothesis PS is a result of a somatosensory perceptive deficit rather than a nigrostriatal dopaminergic unbalance.

Different z-score cut-offs for striatal binding ratio (SBR) of DaT SPECT are needed to support the diagnosis of Parkinson's Disease (PD) and dementia with Lewy bodies (DLB).

PURPOSE: 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. METHODS: 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). RESULTS: 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. CONCLUSION: 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.

Derivation and Validation of a Phenoconversion-Related Pattern in Idiopathic Rapid Eye Movement Behavior Disorder.

BACKGROUND: Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents the prodromal stage of α-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 [18 F]FDG-PET (18 F-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. [18 F]FDG-PET pattern analysis has potential as a phenoconversion biomarker in iRBD patients. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Electric Source Imaging in Presurgical Evaluation of Epilepsy: An Inter-Analyser Agreement Study.

Electric source imaging (ESI) estimates the cortical generator of the electroencephalography (EEG) signals recorded with scalp electrodes. ESI has gained increasing interest for the presurgical evaluation of patients with drug-resistant focal epilepsy. In spite of a standardised analysis pipeline, several aspects tailored to the individual patient involve subjective decisions of the expert performing the analysis, such as the selection of the analysed signals (interictal epileptiform discharges and seizures, identification of the onset epoch and time-point of the analysis). Our goal was to investigate the inter-analyser agreement of ESI in presurgical evaluations of epilepsy, using the same software and analysis pipeline. Six experts, of whom five had no previous experience in ESI, independently performed interictal and ictal ESI of 25 consecutive patients (17 temporal, 8 extratemporal) who underwent presurgical evaluation. The overall agreement among experts for the ESI methods was substantial (AC1 = 0.65; 95% CI: 0.59-0.71), and there was no significant difference between the methods. Our results suggest that using a standardised analysis pipeline, newly trained experts reach similar ESI solutions, calling for more standardisation in this emerging clinical application in neuroimaging.

Cognitive and Brain Metabolism Profiles of Mild Cognitive Impairment in Prodromal Alpha-Synucleinopathy.

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.

The Role of Monoaminergic Tones and Brain Metabolism in Cognition in De Novo Parkinson's Disease.

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 < 0.001). CONCLUSIONS: In subjects with PD, the impact of regional metabolism and diffuse projection systems degeneration differs across cognitive domains. These findings suggest possible tailored approaches to the treatment of cognitive deficits in PD.

The Role of Hub and Spoke Regions in Theory of Mind in Early Alzheimer's Disease and Frontotemporal Dementia.

Theory of mind (ToM, the ability to attribute mental states to others) deficit is a frequent finding in neurodegenerative conditions, mediated by a diffuse brain network confirmed by 18F-FDG-PET and MR imaging, involving frontal, temporal and parietal areas. However, the role of hubs and spokes network regions in ToM performance, and their respective damage, is still unclear. To study this mechanism, we combined ToM testing with brain 18F-FDG-PET imaging in 25 subjects with mild cognitive impairment due to Alzheimer's disease (MCI−AD), 24 subjects with the behavioral variant of frontotemporal dementia (bvFTD) and 40 controls. Regions included in the ToM network were divided into hubs and spokes based on their structural connectivity and distribution of hypometabolism. The hubs of the ToM network were identified in frontal regions in both bvFTD and MCI−AD patients. A mediation analysis revealed that the impact of spokes damage on ToM performance was mediated by the integrity of hubs (p < 0.001), while the impact of hubs damage on ToM performance was independent from the integrity of spokes (p < 0.001). Our findings support the theory that a key role is played by the hubs in ToM deficits, suggesting that hubs could represent a final common pathway leading from the damage of spoke regions to clinical deficits.

Polysomnographic correlates of sleep disturbances in de novo, drug naïve Parkinson's Disease.

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.