CSF α-Synuclein Seed Amplification Assay in Patients With Atypical Parkinsonian Disorders.

BACKGROUND AND OBJECTIVES: There is no disease-modifying treatment of corticobasal syndrome (CBS) and progressive supranuclear palsy (PSP), 2 disorders characterized by their striking phenotypic, and, in CBS, pathologic heterogeneity. Seed amplification assays (SAAs) could enable the detection of neuropathologic processes, such as α-synuclein (αSyn) copathology, that affect the success of future disease-modifying treatment strategies. The primary objective was to assess possible αSyn copathology in CBS and PSP, as detected in CSF using an αSyn SAA (αSyn-SAA). Secondary objectives were to evaluate the association of αSyn-SAA positivity with other biomarkers including of Alzheimer disease (AD), and with clinical presentation. We hypothesized that αSyn-SAA positivity would be detectable in CBS and PSP and that it would be associated with AD biomarker positivity and ß-amyloid (Aß) 42 levels, neurodegeneration as assessed by neurofilament light chain (NfL) levels, and symptoms associated with synucleinopathies. METHODS: This cross-sectional observational study included patients clinically diagnosed with CBS and PSP who underwent a lumbar puncture between 2012 and 2021 (Toronto Western Hospital, Canada). CSF was tested for αSyn-SAA positivity, AD biomarkers, and NfL levels. Clinical data were derived from medical records. RESULTS: We tested the CSF of 40 patients with CBS (19 female patients, 65.9 ± 8.6 years) and 28 with PSP (13 female patients, 72.5 ± 8.7 years old), mostly White (n = 50) or Asian (n = 14). αSyn-SAA positivity was observed in 35.9% patients with CBS and 28.6% with PSP. In young-onset, but not late-onset patients, αSyn-SAA positivity and AD positivity were associated (odds ratio [OR] 8.8, 95% CI 1.2-82.6, p < 0.05). A multivariable linear regression analysis showed a significant interaction of αSyn-SAA status by age at onset on CSF Aß42 levels (ß = 0.3 ± 0.1, p < 0.05). Indeed, age at onset was positively related to Aß42 levels only in αSyn-SAA-positive patients, as shown by slope comparison. A logistic regression analysis also suggested that REM sleep behavior disorder was associated with αSyn-SAA positivity (OR 60.2, 95% CI 5.2-1,965.8; p < 0.01). DISCUSSION: We detected a frequency of αSyn-SAA positivity in CBS and PSP in line with pathologic studies, highlighting the usefulness of SAAs for in vivo detection of otherwise undetectable neuropathologic processes. Our results also suggest that AD status (specifically low Aß42) and older age at onset may contribute to αSyn-SAA positivity. This opens new perspectives for the stratification of patients in clinical trials.

The Longitudinal Assessment of Neuropsychiatric Symptoms in Mild Cognitive Impairment and Alzheimer's Disease and Their Association With White Matter Hyperintensities in the National Alzheimer's Coordinating Center's Uniform Data Set.

BACKGROUND: Neuropsychiatric symptoms (NPSs) are common in Alzheimer's disease (AD). NPSs contribute to patients' distress, caregiver burden, and institutionalization. White matter hyperintensities (WMHs) appear on magnetic resonance imaging, usually indicative of cerebrovascular disease. WMHs have been associated with certain NPSs. We aimed to assess the relationship between WMH and NPS severity in mild cognitive impairment (MCI) due to AD (MCI-AD) and in AD and to assess the ability of WMHs to predict NPS progression. Data were obtained from the National Alzheimer's Coordinating Center. METHODS: A total of 252 participants (114 with MCI-AD and 138 with AD) were used in this study. Baseline WMHs were quantified using an automated segmentation technique. NPSs were measured using the Neuropsychiatric Inventory. Mixed-effect models and correlations were used to determine the relationship between WMHs and NPSs. RESULTS: Longitudinal mixed-effect models revealed a significant relationship between increase in Neuropsychiatric Inventory total scores and baseline WMHs (p = .014). There was a significant relationship between baseline WMHs and an increase in delusions (p = .023), hallucinations (p = .040), agitation (p = .093), depression (p = .017), and irritability (p = .002). Correlation plot analysis showed that baseline whole-brain WMHs predicted change in future Neuropsychiatric Inventory total scores (r = .169, p = .008) and predicted change in future agitation severity scores (r = .165, p = .009). WMHs in the temporal lobes (r = .169, p = .008) and frontal lobes (r = .153, p = .016) contributed most to this change. CONCLUSIONS: Depression, irritability, and agitation are common NPSs and very distressful to patients and caregivers. Our findings of increased NPS severity over time in MCI-AD and AD with increased WMHs have important implications for treatment, arguing for aggressive treatment of vascular risk factors in patients with MCI-AD or AD.

Association Between Social Cognition Changes and Resting State Functional Connectivity in Frontotemporal Dementia, Alzheimer's Disease, Parkinson's Disease, and Healthy Controls.

OBJECTIVE: To determine the relationship between alterations in resting state functional connectivity and social cognition dysfunction among patients with frontotemporal dementia (FTD), Alzheimer's disease (AD), Parkinson's disease (PD), and healthy controls (HC). METHODS: Fifty-seven participants (FTD = 10, AD = 18, PD = 19, and HC = 10) underwent structural and functional imaging and completed the Awareness of Social Inference Test-Emotion Evaluation Test (TASIT-EET), Behavioral Inhibition System/Behavioral Activation System (BIS/BAS) scale, Revised Self-Monitoring Scale (RSMS), Interpersonal Reactivity Index (IRI), and Social Norms Questionnaire (SNQ). A multi-variate pattern analysis (MVPA) was carried out to determine activation differences between the groups. The clusters from the MVPA were used as seeds for the ROI-to-voxel analysis. Relationship between social cognition deficits and uncinate integrity was also investigated. RESULTS: BOLD signal activation differed among the four groups of AD, PD, FTD, and HC in the left inferior temporal gyrus-anterior division [L-ITG (ant)], right central opercular cortex (R-COp), right supramarginal gyrus, posterior division (R-SMG, post), right angular gyrus (R-AG), and R-ITG. The BOLD co-activation of the L-ITG (ant) with bilateral frontal pole (FP) and paracingulate gyrus was positively associated with IRI-perspective taking (PT) (r = 0.38, p = 0.007), SNQ total (r = 0.37, p = 0.009), and TASIT-EET (r = 0.47, p < 0.001). CONCLUSION: Patients with neurodegenerative diseases showed alterations in connectivity in brain regions important for social cognition compared with HCs. Functional connectivity correlated with performance on social cognition tasks and alterations could be responsible for some of the social cognition deficits observed in all neurodegenerative diseases.

Emotion Detection Deficits and Decreased Empathy in Patients with Alzheimer's Disease and Parkinson's Disease Affect Caregiver Mood and Burden.

Background: Changes in social cognition occur in patients with Alzheimer's disease (AD) and Parkinson's disease (PD) and can be caused by several factors, including emotion recognition deficits and neuropsychiatric symptoms (NPS). The aims of this study were to investigate: (1) group differences on emotion detection between patients diagnosed with AD or PD and their respective caregivers; (2) the association of emotion detection with empathetic ability and NPS in individuals with AD or PD; (3) caregivers' depression and perceived burden in relation to patients' ability to detect emotions, empathize with others, presence of NPS; and (4) caregiver's awareness of emotion detection deficits in patients with AD or Parkinson. Methods: In this study, patients with probable AD (N = 25) or PD (N = 17), and their caregivers (N = 42), performed an emotion detection task (The Awareness of Social Inference Test-Emotion Evaluation Test, TASIT-EET). Patients underwent cognitive assessment, using the Behavioral Neurology Assessment (BNA). In addition, caregivers completed questionnaires to measure empathy (Interpersonal Reactivity Index, IRI) and NPS (Neuropsychiatric Inventory, NPI) in patients and self-reported on depression (Geriatric Depression Scale, GDS) and burden (Zarit Burden Interview, ZBI). Caregivers were also interviewed to measure dementia severity (Clinical Dementia Rating (CDR) Scale) in patients. Results: The results suggest that individuals with AD and PD are significantly worse at recognizing emotions than their caregivers. Moreover, caregivers failed to recognize patients' emotion recognition deficits and this was associated with increased caregiver burden and depression. Patients' emotion recognition deficits, decreased empathy and NPS were also related to caregiver burden and depression. Conclusions: Changes in emotion detection and empathy in individuals with AD and PD has implications for caregiver burden and depression and may be amenable to interventions with both patients and caregivers.

Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging.

INTRODUCTION: White matter hyperintensities (WMHs) are areas of abnormal signal on magnetic resonance images (MRIs) that characterize various types of histopathological lesions. The load and location of WMHs are important clinical measures that may indicate the presence of small vessel disease in aging and Alzheimer's disease (AD) patients. Manually segmenting WMHs is time consuming and prone to inter-rater and intra-rater variabilities. Automated tools that can accurately and robustly detect these lesions can be used to measure the vascular burden in individuals with AD or the elderly population in general. Many WMH segmentation techniques use a classifier in combination with a set of intensity and location features to segment WMHs, however, the optimal choice of classifier is unknown. METHODS: We compare 10 different linear and nonlinear classification techniques to identify WMHs from MRI data. Each classifier is trained and optimized based on a set of features obtained from co-registered MR images containing spatial location and intensity information. We further assess the performance of the classifiers using different combinations of MRI contrast information. The performances of the different classifiers were compared on three heterogeneous multi-site datasets, including images acquired with different scanners and different scan-parameters. These included data from the ADC study from University of California Davis, the NACC database and the ADNI study. The classifiers (naïve Bayes, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, bagging, and boosting) were evaluated using a variety of voxel-wise and volumetric similarity measures such as Dice Kappa similarity index (SI), Intra-Class Correlation (ICC), and sensitivity as well as computational burden and processing times. These investigations enable meaningful comparisons between the performances of different classifiers to determine the most suitable classifiers for segmentation of WMHs. In the spirit of open-source science, we also make available a fully automated tool for segmentation of WMHs with pre-trained classifiers for all these techniques. RESULTS: Random Forests yielded the best performance among all classifiers with mean Dice Kappa (SI) of 0.66±0.17 and ICC=0.99 for the ADC dataset (using T1w, T2w, PD, and FLAIR scans), SI=0.72±0.10, ICC=0.93 for the NACC dataset (using T1w and FLAIR scans), SI=0.66±0.23, ICC=0.94 for ADNI1 dataset (using T1w, T2w, and PD scans) and SI=0.72±0.19, ICC=0.96 for ADNI2/GO dataset (using T1w and FLAIR scans). Not using the T2w/PD information did not change the performance of the Random Forest classifier (SI=0.66±0.17, ICC=0.99). However, not using FLAIR information in the ADC dataset significantly decreased the Dice Kappa, but the volumetric correlation did not drastically change (SI=0.47±0.21, ICC=0.95). CONCLUSION: Our investigations showed that with appropriate features, most off-the-shelf classifiers are able to accurately detect WMHs in presence of FLAIR scan information, while Random Forests had the best performance across all datasets. However, we observed that the performances of most linear classifiers and some nonlinear classifiers drastically decline in absence of FLAIR information, with Random Forest still retaining the best performance.

Neuropsychiatric Symptoms in Alzheimer Disease, Vascular Dementia, and Mixed Dementia.

BACKGROUND/AIMS: Neuropsychiatric symptoms (NPS) are common in Alzheimer disease (AD) and vascular dementia (VaD), and are distressful to patients and caregivers. NPS are likely related to the underlying pathology. Previous studies suggest that frontal lobe lesions and vascular changes such as white matter hyperintensities (WMH) have a significant association with specific NPS. The current study aimed to compare NPS in patients with AD, VaD, and mixed AD/VaD, and to evaluate the differences in the prevalence of NPS in relation to frontal WMH volume. METHODS: In total, 180 patients with NPS and MRI data (92 probable AD, 51%; 34 probable VaD, 19%; and 54 probable mixed AD/VaD, 30%) were included in the study. Regression analyses were performed to determine the relationships between NPS prevalence and diagnosis, and between NPS and frontal WMH. RESULTS: VaD patients had significantly more agitation (p < 0.05; 40 vs. 14%) and sleep disturbances (p < 0.05; 57 vs. 32%) than AD patients, and significantly more depression (p < 0.05; 48 vs. 20%) and aberrant motor behaviors (p < 0.05; 31 vs. 13%) than mixed AD/VaD patients. AD patients with delusions had significantly greater right frontal WMH volumes than those without (p < 0.05; delusions 1/0 = 314.8/112.6 mm3). CONCLUSION: Differences in NPS prevalence are likely related to the underlying pathology and warrant further study as they have implications for treatment.

Mutation analysis of C9orf72 in patients with corticobasal syndrome.

Corticobasal syndrome (CBS) is a neurodegenerative disease characterized by progressive asymmetrical rigidity and apraxia, cortical sensory loss, myoclonus, dystonia, and cognitive impairment. CBS is usually sporadic and associated with tau pathology but there are reports of TDP-43 pathology. We screened 39 CBS cases to determine if any of the cases could be explained by a G4C2-repeat expansion in a noncoding region of C9orf72 gene, the most common genetic cause of frontotemporal lobar degeneration and amyotrophic lateral sclerosis. One patient with CBS had a large (>50 repeats) expansion in C9orf72. Our case features a 63-year-old right-handed woman who developed mild apathy 9 years before presentation, which progressed to include behavioral symptoms, oral stereotypies, significant language impairment, parkinsonism and apraxia. A magnetic resonance imaging acquired at age 60 years, that is, 6 years after disease onset revealed significant asymmetric left > right frontotemporal atrophy, including orbitofrontal and parietal areas. Her father developed a behavioral syndrome and died at an early age. This case highlights the importance of genetic screening for C9orf72 in patients with CBS.