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OBJECTIVES We studied a sample of cognitively unimpaired individuals, with and without subjective cognitive decline (SCD), in order to investigate accelerated long-term forgetting (ALF) and to explore the relationships between objective and subjective cognitive performance and cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers. METHODS Fifty-two individuals were included and SCD was quantified through the Subjective Cognitive Decline Questionnaire (SCD-Q), using its validated cutoff to classify participants as Low SCD-Q (n = 21) or High SCD-Q (n = 31). These groups were further subdivided according to the presence or absence of abnormal levels of CSF Aβ42 . Objective cognitive performance was assessed with the Ancient Farming Equipment Test (AFE-T), a new highly-demanding test that calls for acquisition and retention of novel object/name pairs and allows measuring ALF over a 6-month period. RESULTS The High SCD-Q group showed a significantly higher free forgetting rate at 3 months compared to the Low SCD-Q (F [1,44] = 4.72; p

Background: Abnormal cholesterol metabolism changes the neuronal membrane and may promote amyloidogenesis. Oxysterols in cerebrospinal fluid (CSF) are related to Alzheimer's disease (AD) biomarkers in mild cognitive impairment and dementia. Cholesterol turnover is important for axonal and white matter (WM) microstructure maintenance. Objective: We aim to demonstrate that the association of oxysterols, AD biomarkers, and WM microstructure occurs early in asymptomatic individuals. Methods: We studied the association of inter-individual variability of CSF 24-hydroxycholesterol (24-OHC), 27-hydroxycholesterol (27-OHC), 7-ketocholesterol (7-KC), 7 beta-hydroxycholesterol (7 beta-OHC), amyloid-beta(42) (A beta(42)), total-tau (t-tau), phosphorylated-tau (p-tau), neurofilament (NfL), and WM microstructure using diffusion tensor imaging, generalized linear models and moderation/mediation analyses in 153 healthy adults. Results: Higher 7-KC levels were related to lower A beta(42), indicative of greater AD pathology (p = 0.041). Higher 7-KC levels were related to lower fractional anisotropy (FA) and higher mean (MD), axial (AxD), and radial (RD) diffusivity. 7-KC modulated the association between AxD and NfL in the corpus callosum splenium (B = 39.39, p = 0.017), genu (B = 68.64, p = 0.000), and fornix (B = 10.97, p = 0.000). Lower A beta(42) levels were associated to lower FA and higher MD, AxD, and RD in the fornix, corpus callosum, inferior longitudinal fasciculus, and hippocampus. The association between AxD and A beta(42) was moderated by 7K-C (p = 0.048). Conclusion: This study adds clinical evidence to support the role of 7K-C on axonal integrity and the involvement of cholesterol metabolism in the A beta(42) generation process.

Alzheimer's disease (AD) is one the main causes of dementia in the world and the patients develop severe disability and sometime full dependence. In previous stages Mild Cognitive Impairment (MCI) produces cognitive loss but not severe enough to interfere with daily life. This work, on selection of biomarkers from speech for the detection of AD, is part of a wide-ranging cross study for the diagnosis of Alzheimer. Specifically in this work a task for detection of MCI has been used. The task analyzes Categorical Verbal Fluency. The automatic classification is carried out by SVM over classical linear features, Castiglioni fractal dimension and Permutation Entropy. Finally the most relevant features are selected by ANOVA test. The promising results are over 50% for MCI, Comment: 4 pages, published in 2015 4th International Work Conference on Bioinspired Intelligence (IWOBI), pp. 101-104

Alzheimer's disease is characterized by a progressive and irreversible cognitive deterioration. In a previous stage, the so-called Mild Cognitive Impairment or cognitive loss appears. Nevertheless, this previous stage does not seem sufficiently severe to interfere in independent abilities of daily life, so it is usually diagnosed inappropriately. Thus, its detection is a crucial challenge to be addressed by medical specialists. This paper presents a novel proposal for such early diagnosis based on automatic analysis of speech and disfluencies, and Deep Learning methodologies. The proposed tools could be useful for supporting Mild Cognitive Impairment diagnosis. The Deep Learning approach includes Convolutional Neural Networks and nonlinear multifeature modeling. Additionally, an automatic hybrid methodology is used in order to select the most relevant features by means of nonparametric Mann-Whitney U test and Support Vector Machine Attribute evaluation. This work has been supported by FEDER and MICINN, TEC2016-77,791-C4-2-R, and UPV/EHU-Basque Research Groups IT11156 and Basque Country EleKin Research Group

Dislipidemia is a risk factor for cognitive impairment. We studied the association between interindividual variability of plasma lipids and white matter (WM) microstructure, using diffusion tensor imaging (DTI) in 273 healthy adults. Special focus was placed on 7 regions of interest (ROI) which are structural components of cognitive neurocircuitry. We also investigated the effect of plasma lipids on cerebrospinal fluid (CSF) neurofilament light chain (NfL), an axonal degeneration marker. Low density lipoprotein (LDL) and triglyceride (TG) levels showed a negative association with axial diffusivity (AxD) in multiple regions. High density lipoproteins (HDL) showed a positive correlation. The association was independent of Apolipoprotein E (APOE) genotype, blood pressure or use of statins. LDL moderated the relation between NfL and AxD in the body of the corpus callosum (p = 0.041), right cingulum gyrus (p = 0.041), right fornix/stria terminalis (p = 0.025) and right superior longitudinal fasciculus (p = 0.020) and TG in the right inferior longitudinal fasciculus (p = 0.004) and left fornix/stria terminalis (p = 0.001). We conclude that plasma lipids are associated to WM microstructural changes and axonal degeneration and might represent a risk factor in the transition from healthy aging to disease.

ObjectiveTo investigate the cognitive profile of healthy individuals with increased Cardiovascular Risk Factors, Aging and Dementia (CAIDE) dementia risk score and to explore whether this association is related to vascular burden and CSF biomarkers of amyloidosis and neurodegeneration.MethodCognitively normal participants (mean age 57.6 years) from the Gipuzkoa Alzheimer Project study were classified as having high risk (HR; n = 82) or low risk (LR; n = 293) for dementia according to a CAIDE score cutoff of 9. Cognitive composites were compared between groups. We explored using generalized linear models the role of APOE genotype, MRI white matter hyperintensities (WMH), and CSF (n = 218) levels of β-amyloid1-42 (Aβ1-42), total tau (t-tau), and phosphorylated tau (p-tau) in the association between CAIDE score and cognition.ResultsHR participants obtained lower scores on executive function (EF) (p = 0.001) and visual perception and construction (VPC) (p < 0.001) composites. EF composite was associated with CAIDE score × p-tau (p = 0.001), CAIDE score × t-tau (p = 0.001), and WMH (p = 0.003). VPC composite was associated with APOE (p = 0.001), Aβ1–42 (p = 0.004), the interaction APOE × Aβ1–42 (p = 0.003), and WMH (p = 0.004). Performance on global memory was associated with Aβ1–42 (p = 0.006), APOE (p = 0.008), and their interaction (p = 0.006). Analyses were adjusted for age, education, sex, premorbid intelligence, and stress.ConclusionHealthy participants at increased dementia risk based on CAIDE scores show lower performance in EF and VPC. This difference is related to APOE, WMH, and Alzheimer biomarkers.

We investigated whether amyloid-β (Aβ) and tau affected cognition in cognitively normal (CN) individuals, and whether norms for neuropsychological tests based on biomarker-negative individuals would improve early detection of dementia. We included 907 CN individuals from 8 European cohorts and from the Alzheimer's disease Neuroimaging Initiative. All individuals were aged above 40, had Aβ status and neuropsychological data available. Linear mixed models were used to assess the associations of Aβ and tau with five neuropsychological tests assessing memory (immediate and delayed recall of Auditory Verbal Learning Test, AVLT), verbal fluency (Verbal Fluency Test, VFT), attention and executive functioning (Trail Making Test, TMT, part A and B). All test except the VFT were associated with Aβ status and this influence was augmented by age. We found no influence of tau on any of the cognitive tests. For the AVLT Immediate and Delayed recall and the TMT part A and B, we calculated norms in individuals without Aβ pathology (Aβ- norms), which we validated in an independent memory-clinic cohort by comparing their predictive accuracy to published norms. For memory tests, the Aβ- norms rightfully identified an additional group of individuals at risk of dementia. For non-memory test we found no difference. We confirmed the relationship between Aβ and cognition in cognitively normal individuals. The Aβ- norms for memory tests in combination with published norms improve prognostic accuracy of dementia. ispartof: FRONTIERS IN AGING NEUROSCIENCE vol:10 ispartof: location:Switzerland status: published

The so-called Mild Cognitive Impairment (MCI) or cognitive loss appears in a previous stage before Alzheimer's Disease (AD), but it does not seem sufficiently severe to interfere in independent abilities of daily life, so it usually does not receive an appropriate diagnosis. Its detection is a challenging issue to be addressed by medical specialists. This work presents a novel proposal based on automatic analysis of speech and disfluencies aimed at supporting MCI diagnosis. The approach includes deep learning by means of Convolutional Neural Networks (CNN) and non-linear multifeature modelling. Moreover, to select the most relevant features non-parametric Mann-Whitney U-testt and Support Vector Machine Attribute (SVM) evaluation are used., Comment: 5 pages, published in 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI), 2017, pp. 1-4, 10-12 July Funchal (Portugal)

We employed a highly demanding experimental associative learning test (the AFE-T) to explore memory functioning in Preclinical Alzheimer's Disease stage 1 (PreAD-1) and stage 2 (PreAD-2). The task consisted in the learning of unknown object/name pairs and our comprehensive setup allowed the analysis of learning curves, immediate recall, long-term forgetting rates at one week, three months, and six months, and relearning curves. Forty-nine cognitively healthy subjects were included and classified according to the presence or absence of abnormal CSF biomarkers (Control, n = 31; PreAD-1, n = 14; PreAD-2, n = 4). Control and PreAD-1 performances on the experimental test were compared by controlling for age and education. These analyses showed clear learning difficulties in PreAD-1 subjects (F = 6.98; p = 0.01). Between-group differences in long-term forgetting rates were less notable, reaching statistical significance only for the three-month cued forgetting rate (F = 4.83; p = 0.03). Similarly, relearning sessions showed only statistical trends between the groups (F = 3.22; p = 0.08). In the whole sample, significant correlations between CSF Aβ42/tau ratio and the AFE-T were found, both in the total learning score (r = 0.52; p

Objective: Nowadays proper detection of cognitive impairment has become a challenge for the scientific community. Alzheimer's Disease (AD), the most common cause of dementia, has a high prevalence that is increasing at a fast pace towards epidemic level. In the not-so-distant future this fact could have a dramatic social and economic impact. In this scenario, an early and accurate diagnosis of AD could help to decrease its effects on patients, relatives and society. Over the last decades there have been useful advances not only in classic assessment techniques, but also in novel non-invasive screening methodologies. Methods: Among these methods, automatic analysis of speech -one of the first damaged skills in AD patients- is a natural and useful low cost tool for diagnosis. Results: In this paper a non-linear multi-task approach based on automatic speech analysis is presented. Three tasks with different language complexity levels are analyzed, and promising results that encourage a deeper assessment are obtained. Automatic classification was carried out by using classic Multilayer Perceptron (MLP) and Deep Learning by means of Convolutional Neural Networks (CNN) (biologically- inspired variants of MLPs) over the tasks with classic linear features, perceptual features, Castiglioni fractal dimension and Multiscale Permutation Entropy. Conclusion: Finally, the most relevant features are selected by means of the non-parametric Mann- Whitney U-test.

Introduction: Cortical mean diffusivity (MD) and free water fraction (FW) changes are proposed biomarkers for Alzheimer's disease (AD). Methods: We included healthy control subjects (N = 254), mild cognitive impairment (N = 41), and AD dementia (N = 31) patients. Participants underwent a lumbar puncture and a 3 T magnetic resonance imaging. Healthy control subjects were classified following National Institute on Aging-Alzheimer's Association stages (stage 0, N = 220; stage 1, N = 25; and stage 2/3, N = 9). We assessed the cortical MD, cortical FW, and cortical thickness (CTh) changes along the AD continuum. Results: Microstructural and macrostructural changes show a biphasic trajectory. Stage 1 subjects showed increased CTh and decreased MD and FW with respect the stage 0 subjects. Stage 2/3 subjects showed decreased CTh and increased cortical MD and FW, changes that were more widespread in symptomatic stages. Discussion: These results support a biphasic model of changes in AD, which could affect the selection of patients for clinical trials and the use of magnetic resonance imaging as a surrogate marker of disease modification. (C) 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Bilingualism as a component of cognitive reserve has been claimed to delay the onset of Alzheimer's disease (AD). However, its effect on cerebrospinal fluid (CSF) AD-biomarkers has not been investigated. We assessed cognitive performance and CSF AD-biomarkers, and potential moderation effect of bilingualism on the association between age, CSF AD-biomarkers, and cognition. Cognitively healthy middle-aged participants classified as monolinguals (n = 100, nCSF = 59), early (n = 81, nCSF = 55) and late bilinguals (n = 97, nCSF = 52) were evaluated. Models adjusted for confounders showed that bilinguals performed better than monolinguals on digits backwards (early-bilinguals p = 0.003), Judgment of Line Orientation (JLO) (early-bilinguals p = 0.018; late-bilinguals p = 0.004), and Trail Making Test-B (late-bilinguals p = 0.047). Early bilingualism was associated with lower CSF total-tau (p = 0.019) and lower prevalence of preclinical AD (NIA-AA classification) (p = 0.02). Bilingualism showed a moderation effect on the relationship between age and CSF AD-biomarkers and the relationship between age and executive function. We conclude that bilingualism contributes to cognitive reserve enhancing executive and visual-spatial functions. For the first time, this study reveals that early bilingualism is associated with more favorable CSF AD-biomarker profile.

Low levels of cell-free mitochondrial DNA (mtDNA) in the cerebrospinal fluid (CSF) of Alzheimer's disease (AD) patients have been identified and proposed as a novel biomarker for the disease. The lack of validation studies of previous results prompted us to replicate this finding in a comprehensive series of patients and controls. We applied droplet digital polymerase chain reaction in CSF specimens from 124 patients representing the AD spectrum and 140 neurologically healthy controls. The following preanalytical and analytical parameters were evaluated: the effect of freeze-thaw cycles on mtDNA, the linearity of mtDNA load across serial dilutions, and the mtDNA levels in the diagnostic groups. We found a wide range of mtDNA copies, which resulted in a high degree of overlap between groups. Although the AD group presented significantly higher mtDNA counts, the receiver-operating characteristic analysis disclosed an area under the curve of 0.715 to distinguish AD patients from controls. MtDNA was highly stable with low analytical variability. In conclusion, mtDNA levels in CSF show a high interindividual variability, with great overlap within phenotypes and presents low sensitivity for AD.

Introduction: Amyloid plaque deposition in the brain is an early pathological change in Alzheimer's disease (AD), causing disrupted synaptic connections. Brain network disruptions in AD have been demonstrated with eigenvector centrality (EC), a measure that identifies central regions within networks. Carrying an apolipoprotein (APOE)-ε4 allele is a genetic risk for AD, associated with increased amyloid deposition. We studied whether APOE-ε4 carriership is associated with EC disruptions in cognitively normal individuals. Methods: A total of 261 healthy middle-aged to older adults (mean age 56.6 years) were divided into high-risk (APOE-ε4 carriers) and low-risk (noncarriers) groups. EC was computed from resting-state functional MRI data. Clusters of between-group differences were assessed with a permutation-based method. Correlations between cluster mean EC with brain volume, CSF biomarkers, and psychological test scores were assessed. Results: Decreased EC in the visual cortex was associated with APOE-ε4 carriership, a genetic risk factor for AD. EC differences were correlated with age, CSF amyloid levels, and scores on the trail-making and 15-object recognition tests. Conclusion: Our findings suggest that the APOE-ε4 genotype affects brain connectivity in regions previously found to be abnormal in AD as a sign of very early disease-related pathology. These differences were too subtle in healthy elderly to use EC for single-subject prediction of APOE genotype.

Objective: To investigate CSF markers involved in amyloid precursor protein processing, neuronal damage, and neuroinflammation in the preclinical stages of Alzheimer disease (AD) and participants with suspected non-Alzheimer pathology (SNAP). Methods: We collected CSF from 266 cognitively normal volunteers participating in a cross-sectional multicenter study (the SIGNAL study) to investigate markers involved in amyloid precursor protein processing (A beta 42, sAPP beta, beta-secretase activity), neuronal damage (total-tau [t-tau], phospho-tau [p-tau]), and neuroinflammation (YKL-40). We analyzed the relationship among biomarkers, clinical variables, and the APOE genotype, and compared biomarker levels across the preclinical stages of the National Institute on Aging-Alzheimer's Association classification: stage 0, 1, 2, 3, and SNAP. Results: The median age in the whole cohort was 58.8 years (range 39.8-81.6). Participants in stages 2-3 and SNAP had higher levels of YKL-40 than those in stages 0 and 1. Participants with SNAP had higher levels of sAPP beta than participants in stage 0 and 1. No differences were found between stages 0, 1, and 2-3 in sAPP beta and beta-secretase activity in CSF. Age correlated with t-tau, p-tau, and YKL-40. It also correlated with A beta 42, but only in APOE epsilon 4 carriers. A beta 42 correlated positively with t-tau, sAPP beta, and YKL-40 in participants with normal A beta 42. Conclusions: Our findings suggest that inflammation in the CNS increases in normal aging and is intimately related to markers of neurodegeneration in the preclinical stages of AD and SNAP. sAPP beta and beta-secretase activity are not useful diagnostic or staging markers in preclinical AD.

Internet of things and smart cities are becoming a reality. Nowadays, more and more devices are interconnected and in order to deal with this new situation, data processing speeds are increasing to keep the pace. Smart devices like tablets and smartphones are accessible to a wide part of society in developed countries, and Internet connections for data exchange make it possible to handle large volumes of information in less time. This new reality has opened up the possibility of developing client-server architectures focused on clinical diagnosis in real time and at a very low cost. This paper illustrates the design and implementation of the ALZUMERIC system that is oriented to the diagnosis of Alzheimer’s disease (AD). It is a platform where the medical specialist can gather voice samples through non-invasive methods from patients with and without mild cognitive impairment (MCI), and the system automatically parameterizes the input signal to make a diagnose proposal. Although this type of impairment produces a cognitive loss, it is not severe enough to interfere with daily life. The present approach is based on the description of speech pathologies with regard to the following profiles: phonation, articulation, speech quality, analysis of the emotional response, language perception, and complex dynamics of the system. Privacy, confidentiality and information security have also been taken into consideration, as well as possible threats that the system could suffer, so this first prototype of services offered by ALZUMERIC has been targeted to a predetermined number of medical specialists.

letter (FAS) and semantic (fruits, animals) verbal fluency tests, Digit Span Test, Stroop Color-Word Interference Test, Boston Naming Test, Rule shift cards of BADS, DEX and VOSP. The genetic study consisted of three SNPs in PICALM (rs3851179), CLU (rs11136000) and CR1 (rs3818361) gene. Genotypingwas performed through Taqman 5 ’ nuclease Assays.Results: Several associations were found between these genes and cognitive tasks. In the PICALM analysis, those carrying the GG combination (vs AA+AG) performed worse on FAS-F (GG: 7.664.6; AA+AG: 9.364; p1⁄40.027) and FAS-A (GG: 7 64.5; AA+AG: 8.664.3; p1⁄40.05). For the CLU polymorphism, patients who carried the protective TT genotype (vs CC+CT) showed a better performance on TMT-B time (TT: 168.6691; CC+CT: 244.66112.7; p1⁄40.023), on FAS-A (TT: 1165.1; CC+CT: 7.564.3; p1⁄40.012) and on backwards digit span (TT: 5.361.8; CC+CT: 3.961.3; p1⁄40.021). Finally, for the CR1 gene, MCI subjects who carried the TT genotype (VS CC+CT) got lower scores in the semantic fluency task, both in fruits (TT: 764.2; CC+CT: 10.463.1; p1⁄40.029) and animals (TT: 11.560.6; CC+CT: 13.164.3; p1⁄40.023) sections. Conclusions: There is a gene-cognitive endophenotype association between PICALM, CLU and CR1 genes in patients with MCI, being PICAL and CLU associated with prefrontal dysfunction (executive functioning) and CR1 with frontotemporal lobe dysfunction (executive functioning and semantic memory).