Curcumin and Novel Synthetic Analogs in Cell-Based Studies of Alzheimer's Disease

Alzheimer's disease (AD) is a chronic neurodegenerative disorder that is associated with the most common type of dementia and is characterized by the presence of deposits of the protein fragment amyloid beta (A\u3b2) in the brain. The natural product mixture of curcuminoids that improves certain defects in innate immune cells of AD patients may selectively enhance A\u3b2 phagocytosis by alteration of gene transcription. In this work, we evaluated the protective effects of curcuminoids in cells from AD patients by investigating the effect on NF-\u3baB and BACE1 signaling pathways. These results were compared to the gene expression profile of the clearance of A\u3b2. The minor curcumin constituent, bisdemethoxycurcumin (BDC) showed the most potent protective action to decrease levels of NF-\u3baB and BACE1, decrease the inflammatory cascade and diminish A\u3b2 aggregates in cells from AD patients. Moreover, mannosyl-glycoprotein 4-beta-N-acetylglucosaminyltransferase (MGAT3) and vitamin D receptor (VDR) gene mRNAs were up-regulated in peripheral blood mononuclear cells from AD patients treated with BDC. BDC treatment impacts both gene expression including Mannosyl (Beta-1,4-)-Glycoprotein Beta-1,4-N-Acetylglucosaminyltransferase, Vitamin D and Toll like receptor mRNA and A\u3b2 phagocytosis. The observation of down-regulation of BACE1 and NF-\u3baB following administration of BDC to cells from AD patients as a model system may have utility in the treatment of asymptomatic AD patients

DTI and MR Volumetry of Hippocampus-PC/PCC Circuit: In Search of Early Micro- and Macrostructural Signs of Alzheimers's Disease

Hippocampal damage, by DTI or MR volumetry, and PET hypoperfusion of precuneus/posterior cingulate cortex (PC/PCC) were proposed as biomarkers of conversion from preclinical (MCI) to clinical stage of Alzheimer's disease (AD). This study evaluated structural damage, by DTI and MR volumetry, of hippocampi and tracts connecting hippocampus to PC/PCC (hipp-PC/PCC) in 10 AD, 10 MCI, and 18 healthy controls (CTRL). Normalized volumes, mean diffusivity (MD), and fractional anisotropy (FA) were obtained for grey matter (GM), white matter (WM), hippocampi, PC/PCC, and hipp-PC/PCC tracts. In hippocampi and hipp-PC/PCC tracts, decreased volumes and increased MD were found in AD versus CTRL (P < .001). The same results with lower significance (P < .05) were found in MCI versus CTRL. Verbal memory correlated (P < .05) in AD with left hippocampal and hipp-PC/PCC tract MD, and in MCI with FA of total WM. Both DTI and MR volumetry of hippocampi and hipp-PC/PCC tracts detect early signs of AD in MCI patients

Unsuspected Involvement of Spinal Cord in Alzheimer Disease

OBJECTIVE: Brain atrophy is an established biomarker for dementia, yet spinal cord involvement has not been investigated to date. As the spinal cord is relaying sensorimotor control signals from the cortex to the peripheral nervous system and vice-versa, it is indeed a very interesting question to assess whether it is affected by atrophy due to a disease that is known for its involvement of cognitive domains first and foremost, with motor symptoms being clinically assessed too. We, therefore, hypothesize that in Alzheimer’s disease (AD), severe atrophy can affect the spinal cord too and that spinal cord atrophy is indeed an important in vivo imaging biomarker contributing to understanding neurodegeneration associated with dementia. METHODS: 3DT1 images of 31 AD and 35 healthy control (HC) subjects were processed to calculate volume of brain structures and cross-sectional area (CSA) and volume (CSV) of the cervical cord [per vertebra as well as the C2-C3 pair (CSA23 and CSV23)]. Correlated features (ρ > 0.7) were removed, and the best subset identified for patients’ classification with the Random Forest algorithm. General linear model regression was used to find significant differences between groups (p ≤ 0.05). Linear regression was implemented to assess the explained variance of the Mini-Mental State Examination (MMSE) score as a dependent variable with the best features as predictors. RESULTS: Spinal cord features were significantly reduced in AD, independently of brain volumes. Patients classification reached 76% accuracy when including CSA23 together with volumes of hippocampi, left amygdala, white and gray matter, with 74% sensitivity and 78% specificity. CSA23 alone explained 13% of MMSE variance. DISCUSSION: Our findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD from HC, together with more established features. The results show that CSA23, calculated from the same 3DT1 scan as all other brain volumes (including right and left hippocampi), has a considerable weight in classification tasks warranting further investigations. Together with recent studies revealing that AD atrophy is spread beyond the temporal lobes, our result adds the spinal cord to a number of unsuspected regions involved in the disease. Interestingly, spinal cord atrophy explains also cognitive scores, which could significantly impact how we model sensorimotor control in degenerative diseases with a primary cognitive domain involvement. Prospective studies should be purposely designed to understand the mechanisms of atrophy and the role of the spinal cord in AD

Unsuspected Involvement of Spinal Cord in Alzheimer Disease

Objective: Brain atrophy is an established biomarker for dementia, yet spinal cord involvement has not been investigated to date. As the spinal cord is relaying sensorimotor control signals from the cortex to the peripheral nervous system and vice-versa, it is indeed a very interesting question to assess whether it is affected by atrophy due to a disease that is known for its involvement of cognitive domains first and foremost, with motor symptoms being clinically assessed too. We, therefore, hypothesize that in Alzheimer’s disease (AD), severe atrophy can affect the spinal cord too and that spinal cord atrophy is indeed an important in vivo imaging biomarker contributing to understanding neurodegeneration associated with dementia. Methods: 3DT1 images of 31 AD and 35 healthy control (HC) subjects were processed to calculate volume of brain structures and cross-sectional area (CSA) and volume (CSV) of the cervical cord [per vertebra as well as the C2-C3 pair (CSA23 and CSV23)]. Correlated features (ρ > 0.7) were removed, and the best subset identified for patients’ classification with the Random Forest algorithm. General linear model regression was used to find significant differences between groups (p ≤ 0.05). Linear regression was implemented to assess the explained variance of the Mini-Mental State Examination (MMSE) score as a dependent variable with the best features as predictors. Results: Spinal cord features were significantly reduced in AD, independently of brain volumes. Patients classification reached 76% accuracy when including CSA23 together with volumes of hippocampi, left amygdala, white and gray matter, with 74% sensitivity and 78% specificity. CSA23 alone explained 13% of MMSE variance. Discussion: Our findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD from HC, together with more established features. The results show that CSA23, calculated from the same 3DT1 scan as all other brain volumes (including right and left hippocampi), has a considerable weight in classification tasks warranting further investigations. Together with recent studies revealing that AD atrophy is spread beyond the temporal lobes, our result adds the spinal cord to a number of unsuspected regions involved in the disease. Interestingly, spinal cord atrophy explains also cognitive scores, which could significantly impact how we model sensorimotor control in degenerative diseases with a primary cognitive domain involvement. Prospective studies should be purposely designed to understand the mechanisms of atrophy and the role of the spinal cord in AD

A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features

Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD–AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD–AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations

Frontal and Cerebellar Atrophy Supports FTSD-ALS Clinical Continuum

Background: Frontotemporal Spectrum Disorder (FTSD) and Amyotrophic Lateral Sclerosis (ALS) are neurodegenerative diseases often considered as a continuum from clinical, epidemiologic, and genetic perspectives. We used localized brain volume alterations to evaluate common and specific features of FTSD, FTSD-ALS, and ALS patients to further understand this clinical continuum. Methods: We used voxel-based morphometry on structural magnetic resonance images to localize volume alterations in group comparisons: patients (20 FTSD, seven FTSD-ALS, and 18 ALS) versus healthy controls (39 CTR), and patient groups between themselves. We used mean whole-brain cortical thickness (CT¯¯¯¯¯) to assess whether its correlations with local brain volume could propose mechanistic explanations of the heterogeneous clinical presentations. We also assessed whether volume reduction can explain cognitive impairment, measured with frontal assessment battery, verbal fluency, and semantic fluency. Results: Common (mainly frontal) and specific areas with reduced volume were detected between FTSD, FTSD-ALS, and ALS patients, confirming suggestions of a clinical continuum, while at the same time defining morphological specificities for each clinical group (e.g., a difference of cerebral and cerebellar involvement between FTSD and ALS). CT¯¯¯¯¯ values suggested extensive network disruption in the pathological process, with indications of a correlation between cerebral and cerebellar volumes and CT¯¯¯¯¯ in ALS. The analysis of the neuropsychological scores indeed pointed toward an important role for the cerebellum, along with fronto-temporal areas, in explaining impairment of executive, and linguistic functions. Conclusion: We identified common elements that explain the FTSD-ALS clinical continuum, while also identifying specificities of each group, partially explained by different cerebral and cerebellar involvement

Sleep-Related Falling Out of Bed in Parkinson's Disease

Background and purposeSleep-related falling out of bed (SFOB), with its potential for significant injury, has not been a strong focus of investigation in Parkinson's disease (PD) to date. We describe the demographic and clinical characteristics of PD patients with and without SFOB.MethodsWe performed a retrospective analysis of 50 consecutive PD patients, who completed an REM sleep behavior disorder screening questionnaire (RBDSQ), questionnaires to assess for RBD clinical mimickers and questions about SFOB and resulting injuries. Determination of high risk for RBD was based on an RBDSQ score of 5 or greater.ResultsThirteen patients reported history of SFOB (26%). Visual hallucinations, sleep-related injury, quetiapine and amantadine use were more common in those patients reporting SFOB. Twenty-two patients (44%) fulfilled criteria for high risk for RBD, 12 of which (55%) reported SFOB. Five patients reported injuries related to SFOB. SFOB patients had higher RBDSQ scores than non-SFOB patients (8.2±3.0 vs. 3.3±2.0, p&lt;0.01). For every one unit increase in RBDSQ score, the likelihood of SFOB increased two-fold (OR 2.4, 95% CI 1.3-4.2, p&lt;0.003).ConclusionsSFOB may be a clinical marker of RBD in PD and should prompt confirmatory polysomnography and pharmacologic treatment to avoid imminent injury. Larger prospective studies are needed to identify risk factors for initial and recurrent SFOB in PD