Aging is the major risk factor for the development of human neurodegenerative maladies such as Alzheimer's, Huntington's and Parkinson's diseases (PDs) and prion disorders, all of which stem from ...toxic protein aggregation. All of these diseases are correlated with cognitive decline. Cognitive Decline is a dynamic state from normal cognition of aging to dementia. According to the original criteria for Alzheimer's Disease (AD) (1984), a clinical diagnosis was possible only when someone was already demented. The prevalence rates of Cognitive Decline (mild cognitive impairment plus dementia) are very high now and will be higher in future because of the increasing survival time of people. Many neurological and psychiatric diseases are correlated with cognitive decline. Diagnosis of cognitive decline is mostly clinical (clinical criteria), but there are multiple biomarkers that could help us mostly in research programs such as short or long, paper and pencil or computerized neuropsychological batteries for cognition, activities of daily living and behavior, electroencephalograph, event-related potentials, and imaging-structural magnetic resonance imaging (MRI) and functional (fMRI, Pittsburgh bound positron emission tomography, FDG-PET, single photon emission computerized tomography and imaging of tau pathology)-cerebrospinal fluid proteins (Abeta, tau and phospho-tau in AD and α-synuclein (αSyn) for PD). Blood biomarkers need more studies to confirm their usefulness. Genetic markers are also studied but until now are not used in clinical praxis. Finally, in everyday clinical praxis and in research workout for early detection of cognitive decline, the combination of biomarkers is useful.
People with mild cognitive impairment (MCI) need to prevent the further decline of their cognitive functions, and one way to do so is by learning a foreign language.
This study describes the ...development of a protocol for a novel, non-pharmacological intervention for people with MCI that seeks to prevent or reduce cognitive decline by teaching English through songs.
The development of this protocol follows a mixed-methodology approach, consisting of three stages: 1) development of the protocol of the intervention, 2) a randomized controlled trial study with two arms over six months that includes an intervention group and a control group, and 3) the evaluation of the protocol by trainers. In the second stage, we recruited a total of 128 people with MCI from the five participating countries of this study (Greece, Spain, Croatia, Slovenia, and Italy). This educational program will assess three main outcomes after 6 months of the English Lessons with the Use of Songs for People with Mild Cognitive Impairment (E.L.So.M.C.I.) workshops.
Our primary outcome will hopefully be an improvement in general cognition in the intervention group compared to the control group from baseline to 6 months follow-up. Secondary outcomes include a decrease in participants' anxiety and depression and an improvement in their quality of life. Development of English language skills is the last outcome.
Alzheimer's disease is biologically heterogeneous, and detailed understanding of the processes involved in patients is critical for development of treatments. CSF contains hundreds of proteins, with ...concentrations reflecting ongoing (patho)physiological processes. This provides the opportunity to study many biological processes at the same time in patients. We studied whether Alzheimer's disease biological subtypes can be detected in CSF proteomics using the dual clustering technique non-negative matrix factorization. In two independent cohorts (EMIF-AD MBD and ADNI) we found that 705 (77% of 911 tested) proteins differed between Alzheimer's disease (defined as having abnormal amyloid, n = 425) and controls (defined as having normal CSF amyloid and tau and normal cognition, n = 127). Using these proteins for data-driven clustering, we identified three robust pathophysiological Alzheimer's disease subtypes within each cohort showing (i) hyperplasticity and increased BACE1 levels; (ii) innate immune activation; and (iii) blood-brain barrier dysfunction with low BACE1 levels. In both cohorts, the majority of individuals were labelled as having subtype 1 (80, 36% in EMIF-AD MBD; 117, 59% in ADNI), 71 (32%) in EMIF-AD MBD and 41 (21%) in ADNI were labelled as subtype 2, and 72 (32%) in EMIF-AD MBD and 39 (20%) individuals in ADNI were labelled as subtype 3. Genetic analyses showed that all subtypes had an excess of genetic risk for Alzheimer's disease (all P > 0.01). Additional pathological comparisons that were available for a subset in ADNI suggested that subtypes showed similar severity of Alzheimer's disease pathology, and did not differ in the frequencies of co-pathologies, providing further support that found subtypes truly reflect Alzheimer's disease heterogeneity. Compared to controls, all non-demented Alzheimer's disease individuals had increased risk of showing clinical progression (all P < 0.01). Compared to subtype 1, subtype 2 showed faster clinical progression after correcting for age, sex, level of education and tau levels (hazard ratio = 2.5; 95% confidence interval = 1.2, 5.1; P = 0.01), and subtype 3 at trend level (hazard ratio = 2.1; 95% confidence interval = 1.0, 4.4; P = 0.06). Together, these results demonstrate the value of CSF proteomics in studying the biological heterogeneity in Alzheimer's disease patients, and suggest that subtypes may require tailored therapy.
Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of neuroimaging with strong potential to be used in practice. In this context, assessment of models' robustness to ...noise and imaging protocol differences together with post-processing and tuning strategies are key tasks to be addressed in order to move towards successful clinical applications. In this study, we investigated the efficacy of Random Forest classifiers trained using different structural MRI measures, with and without neuroanatomical constraints in the detection and prediction of AD in terms of accuracy and between-cohort robustness. From The ADNI database, 185 AD, and 225 healthy controls (HC) were randomly split into training and testing datasets. 165 subjects with mild cognitive impairment (MCI) were distributed according to the month of conversion to dementia (4-year follow-up). Structural 1.5-T MRI-scans were processed using Freesurfer segmentation and cortical reconstruction. Using the resulting output, AD/HC classifiers were trained. Training included model tuning and performance assessment using out-of-bag estimation. Subsequently the classifiers were validated on the AD/HC test set and for the ability to predict MCI-to-AD conversion. Models' between-cohort robustness was additionally assessed using the AddNeuroMed dataset acquired with harmonized clinical and imaging protocols. In the ADNI set, the best AD/HC sensitivity/specificity (88.6%/92.0% - test set) was achieved by combining cortical thickness and volumetric measures. The Random Forest model resulted in significantly higher accuracy compared to the reference classifier (linear Support Vector Machine). The models trained using parcelled and high-dimensional (HD) input demonstrated equivalent performance, but the former was more effective in terms of computation/memory and time costs. The sensitivity/specificity for detecting MCI-to-AD conversion (but not AD/HC classification performance) was further improved from 79.5%/75%-83.3%/81.3% by a combination of morphometric measurements with ApoE-genotype and demographics (age, sex, education). When applied to the independent AddNeuroMed cohort, the best ADNI models produced equivalent performance without substantial accuracy drop, suggesting good robustness sufficient for future clinical implementation.
Research highlights ► Sensitivity of a new neurophysiological index in AD diagnosis. ► N200 amplitude is more sensitive in identifying differences over time at the early stages of AD, whereas P300 ...latency at later stages. ► Marked difference was observed in the values of ERP parameters between MCI stable patients and AD converters.
Depression affects cognitive abilities, such as thinking, concentration and making decisions in both young adults and elders. However, financial capacity (which consists of multiple cognitive domains ...and specific skills) and depression in Parkinson's disease with dementia (PDD) are little investigated. Sixty participants divided into four groups (PDD with and without depressive symptoms, non-demented elders with and without depression) were examined with the Mini-Mental State Examination (MMSE), the Geriatric Depression Scale (GDS-15) and the Legal Capacity for Property Law Transactions Assessment Scale (LCPLTAS) - full and short form. Results indicated that PDD patients' performance in cognitive functioning and financial capacity is severely impaired, while there is a statistically significant difference between depressed and non-depressed PDD patients. Differences in financial capacity performance indicate that depression should not be disregarded. Further studies on larger PDD population are necessary in order to investigate the decisive role of depression on financial capacity impairment.
Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer's disease (AD). However, reported results across similar studies are ...often not consistent. In this paper we investigated the stability of the graph analysis measures clustering, path length, global efficiency and transitivity in a cohort of AD (N = 293) and control subjects (N = 293). More specifically, we studied the effect that group size and composition, choice of neuroanatomical atlas, and choice of cortical measure (thickness or volume) have on binary and weighted network properties and relate them to the magnitude of the differences between groups of AD and control subjects. Our results showed that specific group composition heavily influenced the network properties, particularly for groups with less than 150 subjects. Weighted measures generally required fewer subjects to stabilize and all assessed measures showed robust significant differences, consistent across atlases and cortical measures. However, all these measures were driven by the average correlation strength, which implies a limitation of capturing more complex features in weighted networks. In binary graphs, significant differences were only found in the global efficiency and transitivity measures when using cortical thickness measures to define edges. The findings were consistent across the two atlases, but no differences were found when using cortical volumes. Our findings merits future investigations of weighted brain networks and suggest that cortical thickness measures should be preferred in future AD studies if using binary networks. Further, studying cortical networks in small cohorts should be complemented by analyzing smaller, subsampled groups to reduce the risk that findings are spurious.
Background
Atrophy in the medial temporal lobe, frontal lobe and posterior cortex can be measured with visual rating scales such as the medial temporal atrophy (MTA), global cortical atrophy – ...frontal subscale (GCA‐F) and posterior atrophy (PA) scales, respectively. However, practical cut‐offs are urgently needed, especially now that different presentations of Alzheimer's disease (AD) are included in the revised diagnostic criteria.
Aims
The aim of this study was to generate a list of practical cut‐offs for the MTA, GCA‐F and PA scales, for both diagnosis of AD and determining prognosis in mild cognitive impairment (MCI), and to evaluate the influence of key demographic and clinical factors on these cut‐offs.
Methods
AddNeuroMed and ADNI cohorts were combined giving a total of 1147 participants (322 patients with AD, 480 patients with MCI and 345 control subjects). The MTA, GCA‐F and PA scales were applied and a broad range of cut‐offs was evaluated.
Results
The MTA scale showed better diagnostic and predictive performances than the GCA‐F and PA scales. Age, apolipoprotein E (ApoE) ε4 status and age at disease onset influenced all three scales. For the age ranges 45–64, 65–74, 75–84 and 85–94 years, the following cut‐offs should be used. MTA: ≥1.5, ≥1.5, ≥2 and ≥2.5; GCA‐F, ≥1, ≥1, ≥1 and ≥1; and PA, ≥1, ≥1, ≥1 and ≥1, respectively, with an adjustment for early‐onset ApoE ε4 noncarrier AD patients (MTA: ≥2, ≥2, ≥3 and ≥3; and GCA‐F: ≥1, ≥1, ≥2 and ≥2, respectively).
Conclusions
If successfully validated in clinical settings, the list of practical cut‐offs proposed here might be useful in clinical practice. Their use might also (i) promote research on atrophy subtypes, (ii) increase the understanding of different presentations of AD, (iii) improve diagnosis and prognosis and (iv) aid population selection and enrichment for clinical trials.
The EURO-D, a12-item self-report questionnaire for depression, was developed with the aim of facilitating cross-cultural research into late-life depression in Europe.
To describe the national ...variation in depression symptoms and syndrome prevalence across ten European countries.
The EURO-D was administered to cross-sectional nationally representative samples of non-institutionalised persons aged > or =50 years (n=22 777). The effects of age, gender, education and cognitive functioning on individual symptoms and EURO-D factor scores were estimated. Country-specific depression prevalence rates and mean factor scores were re-estimated, adjusted for these compositional effects.
The prevalence of all symptoms was higher in the Latin ethno-lingual group of countries, especially symptoms related to motivation. Women scored higher on affective suffering; older people and those with impaired verbal fluency scored higher on motivation.
The prevalence of individual EURO-D symptoms and of probable depression (cut-off score > or =4) varied consistently between countries. Standardising for effects of age, gender, education and cognitive function suggested that these compositional factors did not account for the observed variation.
Abstract Naming abilities seem to be affected in Alzheimer's disease (AD) patients, though MCI individuals tend to exhibit greater impairments in category fluency. In this study we: (1) detect ...language deficits of amnestic MCIs (aMCIs) and mild AD (mAD) participants and present their language performance (the Boston Diagnostic Aphasia Examination – BDAE scores) according to educational level, (2) study the diagnostic value of language deficits according to the cognitive state of the participants. One hundred nineteen participants, 38 normal controls (NC), 28 aMCIs and 53 mADs, were recruited randomly as outpatients of 2 clinical departments and administered clinical, neuropsychological and neuroimaging assessment. Language abilities were assessed by the adapted Greek edition of the BDAE (2nd edition). Our results indicate that verbal fluency, auditory, reading comprehension and narrative ability are the main language abilities to be affected in mADs, although they are almost intact in NCs and less vulnerable in aMCIs. Narrative ability seems to be significantly impaired in mADs but not so in aMCIs. Six language subtests of the BDAE assess safely the above deficits. This brief version of the BDAE discriminated mADs from the other 2 groups 92.5% of the time, NCs 86.8% and aMCI 67.9% of the time in order to save time and to be accurate in clinical practice.
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