Innovative diagnostic tools for early detection of Alzheimer's disease

Aβ and tau structure-based biomarkers for a blood- and CSF-based two-step recruitment strategy to identify patients with dementia due to Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) diagnosis requires invasive CSF analysis or expensive brain imaging. Therefore, a minimal-invasive reliable and cost-effective blood test is requested to power large clinical AD trials at reduced screening failure.

METHODS

We applied an immuno-infrared sensor to measure the amyloid-β (Aβ) and tau secondary structure distribution in plasma and CSF as structure-based biomarkers for AD (61 disease controls, 39 AD cases).

RESULTS

Within a first diagnostic screening step, the structure-based Aβ blood biomarker supports AD identification with a sensitivity of 90%. In a second diagnostic validation step, the combined use of the structure-based CSF biomarkers Aβ and tau excluded false-positive cases which offers an overall specificity of 97%.

DISCUSSION

The primary Aβ-based blood biomarker funnels individuals with suspected AD for subsequent validation of the diagnosis by structure-based combined analysis of the CSF biomarkers Aβ and tau. Our novel two-step recruitment strategy substantiates the diagnosis of AD with a likelihood of 29.

Interactive Effect of Depression and Cognitive Impairment on Olfactory Identification in Elderly People

Olfactory identification (OI) deficits have been regarded as an indicator of cognitive impairment in the elderly, but few studies have analyzed the mixed effect of depression on OI. Since depression is common in the elderly and strongly associated with OI, we aimed to explore whether the comorbidity of depression and cognitive impairment may be associated with worse outcomes. In total, 153 elderly patients with depression and 154 normal elderly were recruited. Subjects underwent assessments of depression, cognitive function, and OI. Information on the factors that may affect OI performance was collected (age, sex, smoking history, diabetes, etc.). Correlation analysis showed that several factors had a significant influence on OI performance in the elderly, including severity of depression, cognitive scores, age, sex, and years of education (p < 0.05). Among the different cognitive domains, OI was positively associated with global cognition, memory, language, executive function, and attention performance (p < 0.05). The multiple linear regression analysis indicated that memory scores, age, HAMD scores, and sex were the most relevant factors to OI scores across all elderly participants. The factorial analysis suggested that elderly with comorbidity of depression and cognitive impairment (memory deficits or language deficits) had worse OI impairment, and there was an interactive effect of depression and memory deficits on OI in elderly people. The present study suggested that the coexistence of depressive symptoms and cognitive impairment was associated with worse OI in the elderly. Studies exploring the association between OI and cognitive function should include an assessment of depression and adjust the interactive effects of depression.

Narrative impairment, white matter damage and CSF biomarkers in the Alzheimer's disease spectrum

BACKGROUND

Narrative discourse (ND) refers to one's ability to verbally reproduce a sequence of temporally and logically-linked events. Impairments in ND may occur in subjects with Amnestic Mild Cognitive Impairment (aMCI) and Alzheimer's Disease (AD), but correlates across this function, neuroimaging and cerebrospinal fluid (CSF) AD biomarkers remain understudied.

OBJECTIVES

We sought to measure correlates among ND, Diffusion Tensor Imaging (DTI) indexes and AD CSF biomarkers in patients within the AD spectrum.

RESULTS

Groups differed in narrative production (NProd) and comprehension. aMCI and AD presented poorer inference abilities than controls. AD subjects were more impaired than controls and aMCI regarding WB (p<0.01). ROIs DTI assessment distinguished the three groups. Mean Diffusivity (MD) in the uncinate, bilateral parahippocampal cingulate and left inferior occipitofrontal fasciculi negatively correlated with NProd. Changes in specific tracts correlated with T-tau/Aβ1-42 ratio in CSF.

CONCLUSIONS

AD and aMCI patients presented more ND impairments than controls. Those findings were associated with changes in ventral language-associated and in the inferior parahippocampal pathways. The latest were correlated with biomarkers' levels in the CSF.

METHODS

AD (N=14), aMCI (N=31) and Control (N=39) groups were compared for whole brain (WB) and regions of interest (ROI) DTI parameters, ND and AD CSF biomarkers.

Wearable Sensor-Based Daily Life Walking Assessment of Gait for Distinguishing Individuals With Amnestic Mild Cognitive Impairment

Objectives: To characterize gait disorders in patients with amnestic mild cognitive impairment (aMCIs) and determine the association between the performance of the gait function and cognition.

Methodology: In this study, we enrolled 38 patients with aMCI and 30 cognitively normal individuals normal controls (NC). Neuropsychological assessments included tests of memory, executive function, language, and attention. Using an inertial-sensor-based wearable instrument, we collected the gait data dynamically for at least 1 h/day for 2 weeks. The gait parameters included walking velocity, stride length, stride time, cadence, and stride time variability.

Results: The aMCI patients had reduced walking velocity and stride length and increased stride time variability compared with the NCs. The total number of steps, stride time, and cadence did not differ between the two groups. For all the subjects, walking velocity and stride length was positively associated with memory and executive function. Stride time variability was negatively correlated with the cognitive domains including memory, executive function and attention.

Conclusion: This study suggested that cognitive impairment-related gait disorders occur (reduced gait speed, gait length, and gait stability) in daily life walking among the aMCI patients. A sensor-based wearable device for gait measurement may be an alternative and convenient tool for screening cognitive impairment.

A novel kit for early diagnosis of Alzheimer's disease using a fluorescent nanoparticle imaging

Alzheimer's disease (AD) is a progressive neurodegenerative disease and chronic illness with long preclinical phases and a long clinical duration. Until recently, a lack of potential therapeutic agents against AD was the primary focus of research, which resulted in less effort directed towards developing useful diagnostic approaches. In this study, we developed a WO2002/088706 kit that is composed of fluorescent nanoparticles for the early detection of AD. We provided a fluorescent nanoparticle for detecting markers and a kit for the early diagnosis of AD. The kit consists of a probe molecule comprising an oligonucleotide capable of detecting one or more AD-specific microRNAs (miRNAs) and biomarkers related to AD. Through screening, we selected miR-106b, miR-146b, miR-181a, miR-200a, miR-34a, miR-124b, miR-153, miR-155, Aβ_1-42 monomer (mAβ), Aβ_1-42 oligomer (oAβ), UCHL1, NLRP3, Tau, STAT3, SORL1, Clusterin, APOE3, APOE4, Nogo-A, IL-13, and Visfatin to serve as AD- and inflammation-related markers. For detection of kit-binding properties, we checked the expression levels of amyloid beta (Aβ), tau protein, and inflammatory mediators in APP/PS/ApoE knockdown (KD) mice and a control group using co-localisation analysis conducted with a confocal microscope. Using a similar approach, we checked the expression levels of miRNAs in HT22 cells. Finally, we used the plasma from AD patients to confirm that our fluorescent nanoparticles and the WO2002/088706 kit will provide a possible early diagnosis to serve as an AD detector that can be further improved for future studies on targeting AD.

Risk estimation before progression to mild cognitive impairment and Alzheimer's disease: an AD resemblance atrophy index

To realize an individual-level risk evaluation of progression of early Alzheimer’s disease (AD), we applied an AD resemblance atrophy index (AD-RAI) to differentiate the subjects at risk of progression from normal subjects (NC) to mild cognitive impairment (MCI) and from MCI to AD. We included 183 subjects with a two-year follow-up: 50 NC stable (NCs), 23 NC-to-MCI converters (NCc), 50 MCI stable (MCIs), 35 MCI-to-AD converters (MCIc), 25 AD stable (ADs). ANCOVA analyses were used to identify baseline brain atrophy in converters compared with non-converters. To explore the relative merits of AD-RAI over individual regional volumetric measures in prediction of disease progression, we searched for the optimal cutoff for each measure in logistic regressions and plotted the longitudinal trajectories of these brain volumetric measures in converters and non-converters. Baseline AD-RAI performed the best in differentiating NCc from NCs (odds ratio 26.35, AUC 0.740) and MCIc from MCIs (odds ratio 8.91, AUC 0.771). The AD-RAI presented greater increase in the second year for NCc vs. NCs but not for MCIc vs. MCIs. Baseline AD-RAIs were also associated with CSF-based and PET-based AD biomarkers. These results showed the potential of AD-RAI in early risk estimation before progression to MCI/AD at an individual-level.

Connected Speech Features from Picture Description in Alzheimer's Disease: A Systematic Review

The language changes that occur over the course of Alzheimer's disease (AD) can impact communication abilities and have profound functional consequences. Picture description tasks can be used to approximate everyday communication abilities of AD patients. As various methods and variables have been studied over the years, current knowledge about the most affected features of AD discourse in the context of picture descriptions is difficult to summarize. This systematic review aims to provide researchers with an overview of the most common areas of impairment in AD discourse as they appear in picture description tasks. Based on the 44 articles fulfilling inclusion criteria, our findings reflect a multidimensional pattern of changes in the production (speech rate), syntactic (length of utterance), lexical (word-frequency and use of pronouns), fluency (repetitions and word-finding difficulties), semantic (information units), and discourse (efficiency) domains. We discuss our findings in the light of current research and point to potential scientific and clinical uses of picture description tasks in the context of AD.

Validity of screening instruments for the detection of dementia and mild cognitive impairment in hospital inpatients: A systematic review of diagnostic accuracy studies

INTRODUCTION

As the population ages, Alzheimer's disease and other subtypes of dementia are becoming increasingly prevalent. However, in recent years, diagnosis has often been delayed or not made at all. Thus, improving the rate of diagnosis has become an integral part of national dementia strategies. Although screening for dementia remains controversial, the case is strong for screening for dementia and other forms of cognitive impairment in hospital inpatients. For this reason, the objective of this systematic review was to provide clinicians, who wish to implement screening, an up-to-date choice of cognitive tests with the most extensive evidence base for the use in elective hospital inpatients.

METHODS

For this systematic review, PubMed, PsycINFO and Cochrane Library were searched by using a multi-concept search strategy. The databases were accessed on April 10, 2019. All cross-sectional studies that utilized brief, multi-domain cognitive tests as index test and a reference standard diagnosis of dementia or mild cognitive impairment as comparator were included. Only studies conducted in the hospital setting, sampling from unselected, elective inpatients older than 64 were considered.

RESULTS

Six studies met the inclusion criteria, with a total of 2112 participants. Diagnostic accuracy data for the Six-Item Cognitive Impairment Test, Cognitive Performance Scale, Clock-Drawing Test, Mini-Mental Status Examination, and Time & Change test were extracted and descriptively analyzed. Clinical and methodological heterogeneity between the studies precluded performing a meta-analysis.

DISCUSSION

This review found only a small number of instruments and was not able to recommend a single best instrument for use in a hospital setting. Although it was not possible to estimate the pooled operating characteristics, the included description of instrument characteristics, the descriptive analysis of performance measures, and the critical evaluation of the reporting studies may contribute to clinician's choice of the screening instrument that fits best their purpose.

A Systematic Review and Implementation of IoT-Based Pervasive Sensor-Enabled Tracking System for Dementia Patients

In today's world, 46.8 million people suffer from brain related diseases. Dementia is most prevalent of all. In general scenario, a dementia patient lacks proper guidance in searching out the way to return back at his/her home. Thus, increasing the risk of getting damaged at individual-health level. Therefore, it is important to track their movement in more sophisticated manner as possible. With emergence of wearables, GPS sensors and Internet of Things (IoT), such devices have become available in public domain. Smartphone apps support caregiver to locate the dementia patients in real-time. RF, GSM, 3G, Wi-Fi and 4G technology fill the communication gap between patient and caregiver to bring them closer. In this paper, we incorporated 7 most popular wearables for investigation to seek appropriateness for dementia tracking in recent times in systematic manners. We performed an in-depth review of these wearables as per the cost, technology wise and application wise characteristics. A case novel study i.e. IoT-based Force Sensor Resistance enabled System-FSRIoT, has been proposed and implemented to validate the effectiveness of IoT in the domain of smarter dementia patient tracking in wearable form factor. The results show promising aspect of a whole new notion to leverage efficient assistive physio-medical healthcare to the dementia patients and the affected family members to reduce life risks and achieve a better social life.

Distinct neural correlates of episodic memory among apolipoprotein E alleles in cognitively normal elderly

The apolipoprotein E (APOE) ε4 and ε2 alleles are acknowledged genetic factors modulating Alzheimer's disease (AD) risk and episodic memory (EM) deterioration in an opposite manner. Mounting neuroimaging studies describe EM-related brain activity differences among APOE alleles but remain limited in elucidating the underlying mechanism. Here, we hypothesized that the APOE ε2, ε3, and ε4 alleles have distinct EM neural substrates, as a manifestation of degeneracy, underlying their modulations on EM-related brain activity and AD susceptibility. To test the hypothesis, we identified neural correlates of EM function by correlating intrinsic hippocampal functional connectivity networks with neuropsychological EM performances in a voxelwise manner, with 129 cognitively normal elderly subjects (36 ε2 carriers, 44 ε3 homozygotes, and 49 ε4 carriers). We demonstrated significantly different EM neural correlates among the three APOE allele groups. Specifically, in the ε3 homozygotes, positive EM neural correlates were characterized in the Papez circuit regions; in the ε4 carriers, positive EM neural correlates involved the lateral temporal cortex, premotor cortex/sensorimotor cortex/superior parietal lobule, and cuneus; and in the ε2 carriers, negative EM neural correlates appeared in the bilateral frontopolar, posteromedial, and sensorimotor cortex. Further, in the ε4 carriers, the interaction between age and EM function occurred in the temporoparietal junction and prefrontal cortex. Our findings suggest that the underlying mechanism of APOE polymorphism modulations on EM function and AD susceptibility is genetically related to the neural degeneracy of EM function across APOE alleles.

A new diagnostic approach for the identification of patients with neurodegenerative cognitive complaints

Neurodegenerative diseases causing dementia are known to affect a person's speech and language. Part of the expert assessment in memory clinics therefore routinely focuses on detecting such features. The current outpatient procedures examining patients' verbal and interactional abilities mainly focus on verbal recall, word fluency, and comprehension. By capturing neurodegeneration-associated characteristics in a person's voice, the incorporation of novel methods based on the automatic analysis of speech signals may give us more information about a person's ability to interact which could contribute to the diagnostic process. In this proof-of-principle study, we demonstrate that purely acoustic features, extracted from recordings of patients' answers to a neurologist's questions in a specialist memory clinic can support the initial distinction between patients presenting with cognitive concerns attributable to progressive neurodegenerative disorders (ND) or Functional Memory Disorder (FMD, i.e., subjective memory concerns unassociated with objective cognitive deficits or a risk of progression). The study involved 15 FMD and 15 ND patients where a total of 51 acoustic features were extracted from the recordings. Feature selection was used to identify the most discriminating features which were then used to train five different machine learning classifiers to differentiate between the FMD/ND classes, achieving a mean classification accuracy of 96.2%. The discriminative power of purely acoustic approaches could be integrated into diagnostic pathways for patients presenting with memory concerns and are computationally less demanding than methods focusing on linguistic elements of speech and language that require automatic speech recognition and understanding.

Usability study and pilot validation of a computer-based emotion recognition test for older adults with Alzheimer's disease and amnestic mild cognitive impairment

OBJECTIVES

This study aimed to carry out a pilot validation of Affect-GRADIOR, a computer-based emotion recognition test, with older adults. The study evaluated its usability, reliability and validity for the screening of people with Alzheimer´s disease (AD) and amnestic mild cognitive impairment (aMCI).

METHODS

The test was administered to 212 participants (76.37 ± 6.20 years) classified into three groups (healthy controls, n = 69; AD, n = 84; and aMCI, n = 59) on the basis of detailed neurological, neuropsychological, laboratory and neuro-imaging evidence. Data on usability were collected by means of a questionnaire and automated evaluation.

RESULTS

The validated test comprised 53 stimuli and 7 practice items (one per emotion). Participants reported that Affect-GRADIOR was accessible and user-friendly. It had high internal consistency (ordinal Cronbach's α = 0.96). Test-retest reliability correlations were significant and robust (r = 0.840, p < 0.001). Exploratory factor analysis supported a seven-factor model of the emotions assessed (neutral expression, happiness, surprise, disgust, sadness, anger and fear). Receiver operating characteristic curve analyses suggested that the test discriminated healthy older adults from AD and aMCI cases. Correct answer score improved MMSE predictive power from 0.547 to 0.560 (Cox & Snell R², p = 0.012), and Affect-GRADIOR speed of processing score improved MMSE predictive power from 0.547 to 0.563 (Cox & Snell R², p = 0.010).

CONCLUSIONS

Affect-GRADIOR is a valid instrument for the assessment of the facial recognition of emotions in older adults with and without cognitive impairment.

Cerebrovascular Reactivity Impairment in Preclinical Alzheimer's Disease

BACKGROUND AND PURPOSE

A substantial overlap exists between declines in cerebral vasoreactivity (CVR) and symptomatic Alzheimer's disease (AD). CVR can be quantified using transcranial Doppler (TCD) measurement of cerebral blood flow velocities (CBFV) in the middle cerebral artery (MCA) with CO₂ as a vasodilatory stimulus. The breath-hold acceleration index (BHAI) is a new, more reliable measure of CVR developed recently in our laboratory. Our primary goal is to explore the possibility of using TCD for asymptomatic AD screening.

METHODS

A pilot study population was divided into three groups: 9 healthy control subjects, 8 subjects identified as preclinical AD, and 10 patients diagnosed with prodromal or mild AD. Control subjects had a Clinical Dementia Rating (CDR) score of 0 without elevated amyloid-β (Aβ) on amyloid positron emission tomography (PET) imaging, preclinical AD subjects had CDR = 0 with elevated Aβ, and prodromal to mild AD subjects had CDR scores ≥.5 and elevated Aβ. CVR was calculated using two indices: the conventional breath-holding index (BHI) and the new BHAI. TCD parameters between the three groups were compared.

RESULTS

BHAI was able to distinguish between 9 normal control subjects and 8 preclinical-AD subjects with high statistical significance (P < .001). BHI and pulsatility index were able only to distinguish AD from healthy and preclinical subjects (P < .001).

CONCLUSIONS

In this exploratory pilot study, CVR was significantly decreased in preclinical, prodromal, and mild AD subjects as compared to the healthy group. Lower CVR in the preclinical AD group was detected using the new BHAI index but not the conventional BHI index.

Discrete Cosine Transform for the Analysis of Essential Tremor

Essential tremor (ET) is the most common movement disorder. In fact, its prevalence is about 20 times higher than that of Parkinson's disease. In addition, studies have shown that a high percentage of cases, between 50 and 70%, are estimated to be of genetic origin. The gold standard test for diagnosis, monitoring and to differentiate between both pathologies is based on the drawing of the Archimedes' spiral. Our major challenge is to develop the simplest system able to correctly classify Archimedes' spirals, therefore we will exclusively use the information of the x and y coordinates. This is the minimum information provided by any digitizing device. We explore the use of features from drawings related to the Discrete Cosine Transform as part of a wider cross-study for the diagnosis of essential tremor held at Biodonostia. We compare the performance of these features against other classic and already analyzed ones. We outperform previous results using a very simple system and a reduced set of features. Because the system is simple, it will be possible to implement it in a portable device (microcontroller), which will receive the x and y coordinates and will issue the classification result. This can be done in real time, and therefore without needing any extra job from the medical team. In future works these new drawing-biomarkers will be integrated with the ones obtained in the previous Biodonostia study. Undoubtedly, the use of this technology and user-friendly tools based on indirect measures could provide remarkable social and economic benefits.

Mobile Technology for Cognitive Assessment of Older Adults: A Scoping Review

BACKGROUND AND OBJECTIVES

The number of people diagnosed with dementia is rising appreciably as the population ages. In an effort to improve outcomes, many have called for facilitating early detection of cognitive decline. Increased use of mobile technology by older adults provides the opportunity to deliver convenient, cost-effective assessments for earlier detection of cognitive impairment. This article presents a review of the literature on how mobile platforms-smartphones and tablets-are being used for cognitive assessment of older adults along with benefits and opportunities associated with using mobile platforms for cognitive assessment.

RESEARCH DESIGN AND METHODS

We searched MEDLINE, Web of Science, PsycInfo, CINAHL, EMBASE, and Cochrane Central Register of Controlled Trials in October 2018. This search returned 7,024 articles. After removing 1,464 duplicates, we screened titles and abstracts then screened full-text for those articles meeting inclusion and exclusion criteria.

RESULTS

Twenty-nine articles met our inclusion criteria and were categorized into 3 groups as follows: (a) mobile versions of existing article or computerized neuropsychological tests; (b) new cognitive tests developed specifically for mobile platforms; and (c) the use of new types of data for cognitive assessment. This scoping review confirms the considerable potential of mobile assessment.

DISCUSSION AND IMPLICATIONS

Mobile technologies facilitate repeated and continuous assessment and support unobtrusive collection of auxiliary behavioral markers of cognitive impairment, thus allowing users to view trends and detect acute changes that have traditionally been difficult to identify. Opportunities include using new mobile sensors and wearable devices, improving reliability and validity of mobile assessments, determining appropriate clinical use of mobile assessment information, and incorporating person-centered assessment principles and digital phenotyping.

Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task

BACKGROUND

Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment.

METHODS

SVF data were collected from 95 older people with MCI (n = 47), Alzheimer's or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD.

RESULTS

Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758).

CONCLUSION

The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline.

Connected speech and language in mild cognitive impairment and Alzheimer's disease: A review of picture description tasks

INTRODUCTION

The neuropsychological profile of people with mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia includes a history of decline in memory and other cognitive domains, including language. While language impairments have been well described in AD dementia, language features of MCI are less well understood. Connected speech and language analysis is the study of an individual's spoken discourse, usually elicited by a target stimulus, the results of which can facilitate understanding of how language deficits typical of MCI and AD dementia manifest in everyday communication. Among discourse genres, picture description is a constrained task that relies less on episodic memory and more on semantic knowledge and retrieval, within the cognitive demands of a communication context. Understanding the breadth of evidence across the continuum of cognitive decline will help to elucidate the areas of strength and need in terms of using this method as an evaluative tool for both cognitive changes and everyday functional communication.

METHOD

We performed an extensive literature search of peer-reviewed journal articles that focused on the use of picture description tasks for evaluating language in persons with MCI or AD dementia. We selected articles based on inclusion and exclusion criteria and described the measures assessed, the psychometric properties that were reported, the findings, and the limitations of the included studies.

RESULTS

36 studies were selected and reviewed. Across all 36 studies, there were 1, 127 patients with AD dementia and 274 with MCI or early cognitive decline. Multiple measures were examined, including those describing semantic content, syntactic complexity, speech fluency, vocal parameters, and pragmatic language. Discriminant validity widely reported and distinct differences in language were observable between adults with dementia and controls; fewer studies were able to distinguish language differences between typically aging adults and those with MCI.

DISCUSSION

Our review shows that picture description tasks are useful tools for detecting differences in a wide variety of language and communicative measures. Future research should expand knowledge about subtle changes to language in preclinical AD and Mild Cognitive Impairment (MCI) which may improve the utility of this method as a clinically meaningful screening tool.

Visual Features in Alzheimer's Disease: From Basic Mechanisms to Clinical Overview

Alzheimer's disease (AD) is the leading cause of dementia worldwide. It compromises patients' daily activities owing to progressive cognitive deterioration, which has elevated direct and indirect costs. Although AD has several risk factors, aging is considered the most important. Unfortunately, clinical diagnosis is usually performed at an advanced disease stage when dementia is established, making implementation of successful therapeutic interventions difficult. Current biomarkers tend to be expensive, insufficient, or invasive, raising the need for novel, improved tools aimed at early disease detection. AD is characterized by brain atrophy due to neuronal and synaptic loss, extracellular amyloid plaques composed of amyloid-beta peptide (Aβ), and neurofibrillary tangles of hyperphosphorylated tau protein. The visual system and central nervous system share many functional components. Thus, it is plausible that damage induced by Aβ, tau, and neuroinflammation may be observed in visual components such as the retina, even at an early disease stage. This underscores the importance of implementing ophthalmological examinations, less invasive and expensive than other biomarkers, as useful measures to assess disease progression and severity in individuals with or at risk of AD. Here, we review functional and morphological changes of the retina and visual pathway in AD from pathophysiological and clinical perspectives.

Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment

Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.

Encoding deficits in low-educated individuals with non-amnestic Mild Cognitive Impairment. Analysis of memory processes using the Item Specific Deficit Approach

This work aims to analyze encoding impairments using new assessment scores in patients with naMCI who present to memory clinics with subjective cognitive complaints. The sample included 102 participants, of whom 28 were classified as healthy controls (HC), 24 as amnestic MCI (aMCI), 24 as naMCI and 26 patients as Alzheimer's disease (AD). Research outcomes were the Encoding, Consolidation and Retrieval deficit indices from the Item Specific Deficit Approach, and traditional indices (immediate total recall, delayed cued recall, delayed total recall) derived from the Free and Cued Selective Reminding Test (FCSRT). We found no differences in immediate recall or delayed recall between HC and naMCI on the FCSRT, both scoring higher than aMCI and AD. naMCI showed encoding deficits in between HC and aMCI, with no differences between naMCI and HC on consolidation or retrieval deficit indices. The ISDA indices were better than traditional indices to discriminate between HC and naMCI (sensitivity: 70.8%, specificity: 78.6%), whereas the opposite pattern was found between naMCI and aMCI (sensitivity: 70.8%, specificity: 91.7%). New indices derived from neuropsychological tests may help to identify objective memory impairments in naMCI. Whether these new indices are useful for predicting conversion to AD needs further research.

Early diagnosis of mild cognitive impairment and Alzheimer's with event-related potentials and event-related desynchronization in N-back working memory tasks

BACKGROUND AND OBJECTIVE

In this study we investigate whether or not event-related potentials (ERP) and/or event-related (de)synchronization (ERD/ERS) can be used to differentiate between 27 healthy elderly (HE), 21 subjects diagnosed with mild cognitive impairment (MCI) and 15 mild Alzheimer's disease (AD) patients.

METHODS

Using 32-channel EEG recordings, we measured ERP responses to a three-level (N-back, N = 0,1,2) visual working memory task. We also performed ERD analysis over the same EEG data, dividing the full-band signal into the well-known delta, theta, alpha, beta and gamma bands. Both ERP and ERD analyses were followed by cluster analysis with correction for multicomparisons whenever significant differences were found between groups.

RESULTS

Regarding ERP (full-band analysis), our findings have shown both patient groups (MCI and AD) with reduced P450 amplitude (compared to HE controls) in the execution of the non-match 1-back task at many scalp electrodes, chiefly at parietal and centro-parietal areas. However, no significant differences were found between MCI and AD in ERP analysis whatever was the task. As for sub-band analyses, ERD/ERS measures revealed that HE subjects elicited consistently greater alpha ERD responses than MCI and AD patients during the 1-back task in the match condition, with all differences located at frontal, central and occipital regions. Moreover, in the non-match condition, it was possible to distinguish between MCI and AD patients when they were performing the 0-back task, with MCI presenting more desynchronization than AD on the theta band at temporal and fronto-temporal areas. In summary, ERD analyses have revealed themselves more valuable than ERP, since they showed significant differences in all three group comparisons: HE vs. MCI, HE vs. AD, and MCI vs. AD.

CONCLUSIONS

Based on these findings, we conclude that ERD responses to working memory (N-back) tasks could be useful not only for early MCI diagnosis or for improved AD diagnosis, but probably also for assessing the likelihood of MCI progression to AD, after further validated by a longitudinal study.

A Pilot Study Investigating a Novel Non-Linear Measure of Eyes Open versus Eyes Closed EEG Synchronization in People with Alzheimer’s Disease and Healthy Controls

Background: The incidence of Alzheimer disease (AD) is increasing with the ageing population. The development of low cost non-invasive diagnostic aids for AD is a research priority. This pilot study investigated whether an approach based on a novel dynamic quantitative parametric EEG method could detect abnormalities in people with AD. Methods: 20 patients with probable AD, 20 matched healthy controls (HC) and 4 patients with probable fronto temporal dementia (FTD) were included. All had detailed neuropsychology along with structural, resting state fMRI and EEG. EEG data were analyzed using the Error Reduction Ratio-causality (ERR-causality) test that can capture both linear and nonlinear interactions between different EEG recording areas. The 95% confidence intervals of EEG levels of bi-centroparietal synchronization were estimated for eyes open (EO) and eyes closed (EC) states. Results: In the EC state, AD patients and HC had very similar levels of bi-centro parietal synchronization; but in the EO resting state, patients with AD had significantly higher levels of synchronization (AD = 0.44; interquartile range (IQR) 0.41 vs. HC = 0.15; IQR 0.17, p < 0.0001). The EO/EC synchronization ratio, a measure of the dynamic changes between the two states, also showed significant differences between these two groups (AD ratio 0.78 versus HC ratio 0.37 p < 0.0001). EO synchronization was also significantly different between AD and FTD (FTD = 0.075; IQR 0.03, p < 0.0001). However, the EO/EC ratio was not informative in the FTD group due to very low levels of synchronization in both states (EO and EC). Conclusion: In this pilot work, resting state quantitative EEG shows significant differences between healthy controls and patients with AD. This approach has the potential to develop into a useful non-invasive and economical diagnostic aid in AD.

Automatic Analysis of Archimedes' Spiral for Characterization of Genetic Essential Tremor Based on Shannon's Entropy and Fractal Dimension

Among neural disorders related to movement, essential tremor has the highest prevalence; in fact, it is twenty times more common than Parkinson's disease. The drawing of the Archimedes' spiral is the gold standard test to distinguish between both pathologies. The aim of this paper is to select non-linear biomarkers based on the analysis of digital drawings. It belongs to a larger cross study for early diagnosis of essential tremor that also includes genetic information. The proposed automatic analysis system consists in a hybrid solution: Machine Learning paradigms and automatic selection of features based on statistical tests using medical criteria. Moreover, the selected biomarkers comprise not only commonly used linear features (static and dynamic), but also other non-linear ones: Shannon entropy and Fractal Dimension. The results are hopeful, and the developed tool can easily be adapted to users; and taking into account social and economic points of view, it could be very helpful in real complex environments.

Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study

Background

Up to half of patients with dementia may not receive a formal diagnosis, limiting access to appropriate services. It is hypothesised that it may be possible to identify undiagnosed dementia from a profile of symptoms recorded in routine clinical practice.

Aim

The aim of this study is to develop a machine learning-based model that could be used in general practice to detect dementia from routinely collected NHS data. The model would be a useful tool for identifying people who may be living with dementia but have not been formally diagnosed.

Design & setting

The study involved a case-control design and analysis of primary care data routinely collected over a 2-year period. Dementia diagnosed during the study period was compared to no diagnosis of dementia during the same period using pseudonymised routinely collected primary care clinical data.

Method

Routinely collected Read-encoded data were obtained from 18 consenting GP surgeries across Devon, for 26 483 patients aged >65 years. The authors determined Read codes assigned to patients that may contribute to dementia risk. These codes were used as features to train a machine-learning classification model to identify patients that may have underlying dementia.

Results

The model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively.

Conclusion

The results show that routinely collected primary care data may be used to identify undiagnosed dementia. The methodology is promising and, if successfully developed and deployed, may help to increase dementia diagnosis in primary care.

Reliability of the Flash Visual Evoked Potential P2: Double-Stimulation Study

The flash visual evoked potential P2 (FVEP-P2) has been identified as a potentially useful clinical, diagnostic tool for Alzheimer's dementia (AD) and mild cognitive impairment (MCIa) due to its association with cholinergic functioning in the brain. The FVEP-P2 is the second positive component of the VEP waveform elicited by a single strobe flash. Despite finding a selective delay in the latency of the FVEP-P2 in AD and MCIa groups, adequate levels of sensitivity and specificity have not been achieved due to natural group differences and inter-individual variability. In response, Fix and colleagues introduced a novel, double-stimulation paradigm that contained two strobe flashes (i.e., stimulations). The first stimulation served as a visual challenge while the second stimulation produced the recorded FVEP-P2 component. The results of that investigation indicated that the latency of the FVEP-P2 could be used to reliably discriminate between aMCI and healthy controls when the ISI of the double-stimulation condition was 100 ms or higher. Unfortunately, very little is known regarding the psychometric properties of the FVEP-P2 when produced by a double-stimulation condition. Consequently, we assessed the test-retest reliability of the FVEP-P2 latency produced by a single- and twelve double-stimulation conditions in a sample of young, healthy individuals (N = 20). Results indicated that while the FVEP-P2 latencies produced by the single- and double-stimulation paradigm were reliable, the intra-individual variability continued to be too high for the FVEP-P2 latency to be used clinically. Methods of reducing the intra-individual variability are discussed, including the use of monochromatic light.

On the analysis of speech and disfluencies for automatic detection of Mild Cognitive Impairment

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.

Neutral merocyanine dyes: for in vivo NIR fluorescence imaging of amyloid-β plaques

Two neutral merocyanine-based near-infrared fluorescent probes were for the first time developed through rational engineering of the classical cationic cyanine scaffold IR-780 for in vivo imaging of amyloid-β plaques. In vivo NIRF imaging revealed that the probe could penetrate the blood-brain barrier and efficiently differentiate the living transgenic and wild-type mice.

A sensitive detection assay based on signal amplification technology for Alzheimer's disease's early biomarker in exosome

Alzheimer's disease (AD) considered as the third health "killer" has seriously threatened the health of the elderly. However, the modern diagnostic strategies of AD present several disadvantages: the low accuracy and specificity resulting in some false-negative diagnoses, and the poor sensitivity leading to a delayed treatment. In view of this situation, a enzyme-free and target-triggered signal amplification strategy, based on graphene oxide (GO) and entropy-driven strand displacement reaction (ESDR) principle, was proposed. In this strategy, when the hairpin structure probes (H)specially binds with beta-amyloid-(1-42) oligomers (Aβ42 oligomers), it's structure will be opened, causing the bases complementary to FAM-labeled replacement probes R (R1 and R2) exposed. At this time, R1 and R2 will hybridize with H, resulting in the bound Aβ42 oligomers released. The released Aβ42 oligomers would participate in the next cycle reaction, making the signal amplified. As a quencher, GO could absorb the free single-stranded DNA R1 and R2 and quench their fluorescence; however, the DNA duplex still exists free and keeps its signal-on. Through the detection of Aβ42 oligomers in exosomes, this ultrasensitive detection method with the advantages of low limit of detection (LOD, 20 pM), great accuracy, excellent precision and convenience provides an excellent prospect for AD's early diagnosis.

Cognitive Impairment and Structural Abnormalities in Late Life Depression with Olfactory Identification Impairment: an Alzheimer's Disease-Like Pattern

BACKGROUND

Late-life depression patients are at a high risk of developing Alzheimer's disease, and diminished olfactory identification is an indicator in early screening for Alzheimer's disease in the elderly. However, whether diminished olfactory identification is associated with risk of developing Alzheimer's disease in late-life depression patients remains unclear.

METHODS

One hundred and twenty-five late-life depression patients, 50 Alzheimer's disease patients, and 60 normal controls were continuously recruited. The participants underwent a clinical evaluation, olfactory test, neuropsychological assessment, and neuroimaging assessment.

RESULTS

The olfactory identification impairment in late-life depression patients was milder than that in Alzheimer's disease patients. Diminished olfactory identification was significantly correlated with worse cognitive performance (global function, memory language, executive function, and attention) and reduced grey matter volume (olfactory bulb and hippocampus) in the late-life depression patients. According to a multiple linear regression analysis, olfactory identification was significantly associated with the memory scores in late-life depression group (B=1.623, P<.001). The late-life depression with olfactory identification impairment group had worse cognitive performance (global, memory, language, and executive function) and more structural abnormalities in Alzheimer's disease-related regions than the late-life depression without olfactory identification impairment group, and global cognitive function and logical memory in the late-life depression without olfactory identification impairment group was intact. Reduced volume observed in many areas (hippocampus, precuneus, etc.) in the Alzheimer's disease group was also observed in late-life depression with olfactory identification impairment group but not in the late-life depression without olfactory identification impairment group.

CONCLUSION

The patterns of cognitive impairment and structural abnormalities in late-life depression with olfactory identification impairment patients were similar to those in Alzheimer's disease; olfactory identification may help identify late-life depression patients who are at a high risk of developing Alzheimer's disease.

Computer-based evaluation of Alzheimer's disease and mild cognitive impairment patients during a picture description task

Introduction

We present a methodology to automatically evaluate the performance of patients during picture description tasks.

Methods

Transcriptions and audio recordings of the Cookie Theft picture description task were used. With 25 healthy elderly control (HC) samples and an information coverage measure, we automatically generated a population-specific referent. We then assessed 517 transcriptions (257 Alzheimer's disease [AD], 217 HC, and 43 mild cognitively impaired samples) according to their informativeness and pertinence against this referent. We extracted linguistic and phonetic metrics which previous literature correlated to early-stage AD. We trained two learners to distinguish HCs from cognitively impaired individuals.

Results

Our measures significantly (P < .001) correlated with the severity of the cognitive impairment and the Mini–Mental State Examination score. The classification sensitivity was 81% (area under the curve of receiver operating characteristics = 0.79) and 85% (area under the curve of receiver operating characteristics = 0.76) between HCs and AD and between HCs and AD and mild cognitively impaired, respectively.

Discussion

An automated assessment of a picture description task could assist clinicians in the detection of early signs of cognitive impairment and AD.

Digital ELISA for the quantification of attomolar concentrations of Alzheimer's disease biomarker protein Tau in biological samples

The close correlation between Tau pathology and Alzheimer's disease (AD) progression makes this protein a suitable biomarker for diagnosis and monitoring of the disorder evolution. However, the use of Tau in diagnostics has been hampered, as it currently requires collection of cerebrospinal fluid (CSF), which is an invasive clinical procedure. Although measuring Tau-levels in blood plasma would be favorable, the concentrations are below the detection limit of a conventional ELISA. In this work, we developed a digital ELISA for the quantification of attomolar protein Tau concentrations in both buffer and biological samples. Individual Tau molecules were first captured on the surface of magnetic particles using in-house developed antibodies and subsequently isolated into the femtoliter-sized wells of a 2 × 2 mm² microwell array. Combination of high-affinity antibodies, optimal assay conditions and a digital quantification approach resulted in a 24 ± 7 aM limit of detection (LOD) in buffer samples. Additionally, a dynamic range of 6 orders of magnitude was achieved by combining the digital readout with an analogue approach, allowing quantification from attomolar to picomolar levels of Tau using the same platform. This proves the compatibility of the presented assay with the wide range of Tau concentrations encountered in different biological samples. Next, the developed digital assay was applied to detect total Tau levels in spiked blood plasma. A similar LOD (55 ± 29 aM) was obtained compared to the buffer samples, which was 5000-fold more sensitive than commercially available ELISAs and even outperformed previously reported digital assays with 10-fold increase in sensitivity. Finally, the performance of the developed digital ELISA was assessed by quantifying protein Tau in three clinical CSF samples. Here, a high correlation (i.e. Pearson coefficient of 0.99) was found between the measured percentage of active particles and the reference protein Tau values. The presented digital ELISA technology has great capacity in unlocking the potential of Tau as biomarker for early AD diagnosis.

A Potential VEP Biomarker for Mild Cognitive Impairment: Evidence from Selective Visual Deficit of Higher-Level Dorsal Pathway

Visual dysfunctions are common in Alzheimer's disease (AD). Our aim was to establish a neurophysiological biomarker for amnestic mild cognitive impairment (aMCI). Visual evoked potentials (VEPs) were recorded in aMCI patients who later developed AD (n = 15) and in healthy older (n = 15) and younger controls (n = 15). Visual stimuli were optimized to separately activate lower and higher levels of the ventral and dorsal streams. We compared VEP parameters across the three groups of participants and conducted a linear correlation analysis between VEPs and data from neuropsychological tests. We then used a receiver operating characteristic (ROC) analysis to discriminate those with aMCI from those who were healthy older adults. The latency and phase of VEPs to lower-level stimuli (chromatic and achromatic gratings) were significantly affected by age but not by cognitive decline. Conversely, VEP latencies for higher-ventral (faces and kanji-words) and dorsal (kana-words and optic flow motion) stimuli were not affected by age, but they were significantly prolonged in aMCI patients. Interestingly, VEPs for higher-dorsal stimuli were related to outcomes of neuropsychological tests. Furthermore, the ROC analysis showed that the highest areas under the curve were obtained for VEP latencies in response to higher-dorsal stimuli. These results suggest aMCI-related functional impairment specific to higher-level visual processing. Further, dysfunction in the higher-level of the dorsal stream could be an early indicator of cognitive decline. Therefore, we conclude that VEPs associated with higher-level dorsal stream activity can be a sensitive biomarker for early detection of aMCI.

Declines in Connected Language Are Associated with Very Early Mild Cognitive Impairment: Results from the Wisconsin Registry for Alzheimer's Prevention

Changes to everyday spoken language ("connected language") are evident in persons with AD dementia, yet little is known about when these changes are first detectable on the continuum of cognitive decline. The aim of this study was to determine if participants with very early, subclinical memory declines were also showing declines in connected language. We analyzed connected language samples obtained from a simple picture description task at two time points in 264 participants from the Wisconsin Registry for Alzheimer's Prevention (WRAP). In parallel, participants were classified as either "Cognitively Healthy" or "Early Mild Cognitive Impairment" based on longitudinal neuropsychological test performance. Linear mixed effects models were used to analyze language parameters that were extracted from the connected language samples using automated feature extraction. Participants with eMCI status declined faster in features of speech fluency and semantic content than those who were cognitively stable. Measures of lexical diversity and grammatical complexity were not associated with eMCI status in this group. These findings provide novel insights about the relationship between cognitive decline and everyday language, using a quick, inexpensive, and performance-based method.

Time Trends in the Prevalence of Neurocognitive Disorders and Cognitive Impairment in the United States: The Effects of Disease Severity and Improved Ascertainment

BACKGROUND

Trends in the prevalence of cognitive impairment (CI) based on cognitive assessment instruments are often inconsistent with those of neurocognitive disorders (ND) based on Medicare claims records.

OBJECTIVE

We hypothesized that improved ascertainment and resulting decrease in disease severity at the time of diagnosis are responsible for this phenomenon.

METHODS

Using Medicare data linked to the Health and Retirement Study (1992-2012), we performed a joint analysis of trends in CI and ND to test our hypothesis.

RESULTS

We identified two major contributors to the divergent directions in CI and ND trends: reductions in disease severity explained more than 60% of the differences between CI and ND prevalence over the study period; the remaining 40% was explained by a decrease in the fraction of undiagnosed individuals.

DISCUSSION

Improvements in the diagnoses of ND diseases were a major contributor to reported trends in ND and CI. Recent forecasts of CI and ND trends in the U.S. may be overly pessimistic.

A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech

BACKGROUND

Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI.

METHODS

Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech signals, first manually (using the Praat software), and then automatically, with an automatic speech recognition (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features.

RESULTS

The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process - that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%.

CONCLUSION

The temporal analysis of spontaneous speech can be exploited in implementing a new, automatic detection-based tool for screening MCI for the community.

Differential associations between retinal signs and CMBs by location: The AGES-Reykjavik Study

OBJECTIVE

To test the hypothesis that age-related macular degeneration (AMD) and retinal microvascular signs are differentially associated with lobar and deep cerebral microbleeds (CMBs).

METHODS

CMBs in lobar regions indicate cerebral amyloid angiopathy (CAA). β-Amyloid deposits are implicated in both CAA and AMD. Deep CMBs are associated with hypertension, a major risk factor for retinal microvascular damage. This population-based cohort study included 2,502 participants in the Age, Gene/Environment Susceptibility (AGES)-Reykjavik Study who undertook binocular digital retinal photographs at baseline (2002-2006) to assess retinal microvascular signs and AMD and brain MRI scan at both baseline and follow-up (2007-2011) to assess CMBs. We assessed retinal microvascular lesion burden by counting the 3 retinal microvascular signs (focal arteriolar narrowing, arteriovenous nicking, and retinopathy) concurrently present in the participant. We used multiple logistic models to examine the association of baseline retinal pathology to incident CMBs detected at follow-up.

RESULTS

During an average 5.2 years of follow-up, 461 people (18.3%) developed new CMBs, including 293 in exclusively lobar regions and 168 in deep regions. Pure geographic atrophy was significantly associated with strictly lobar CMBs (multivariable-adjusted odds ratio 2.59, 95% confidence interval [CI] 1.01-6.65) but not with deep CMBs. Concurrently having ≥2 retinal microvascular signs was associated with a 3-fold (95% CI 1.73-5.20) increased likelihood for deep CMBs but not exclusively lobar CMBs.

CONCLUSIONS

Retinal microvascular signs and pure geographic atrophy may be associated with deep and exclusively lobar CMBs, respectively, in older people. These results have implications for further research to define the role of small vessel disease in cognitive impairment.

Detecting Dementia Through Interactive Computer Avatars

This paper proposes a new approach to automatically detect dementia. Even though some works have detected dementia from speech and language attributes, most have applied detection using picture descriptions, narratives, and cognitive tasks. In this paper, we propose a new computer avatar with spoken dialog functionalities that produces spoken queries based on the mini-mental state examination, the Wechsler memory scale-revised, and other related neuropsychological questions. We recorded the interactive data of spoken dialogues from 29 participants (14 dementia and 15 healthy controls) and extracted various audiovisual features. We tried to predict dementia using audiovisual features and two machine learning algorithms (support vector machines and logistic regression). Here, we show that the support vector machines outperformed logistic regression, and by using the extracted features they classified the participants into two groups with 0.93 detection performance, as measured by the areas under the receiver operating characteristic curve. We also newly identified some contributing features, e.g., gap before speaking, the variations of fundamental frequency, voice quality, and the ratio of smiling. We concluded that our system has the potential to detect dementia through spoken dialog systems and that the system can assist health care workers. In addition, these findings could help medical personnel detect signs of dementia.

Nanoparticles as contrast agents for brain nuclear magnetic resonance imaging in Alzheimer's disease diagnosis

Nuclear Magnetic Resonance Imaging (MRI) of amyloid plaques is a powerful non-invasive approach for the early and accurate diagnosis of Alzheimer's disease (AD) along with clinical observations of behavioral changes and cognitive impairment. The present article aims at giving a critical and comprehensive review of recent advances in the development of nanoparticle-based contrast agents for brain MRI. Nanoparticles considered for the MRI of AD must comply with a highly stringent set of requirements including low toxicity and the ability to cross the blood-brain-barrier. In addition, to reach an optimal signal-to-noise ratio, they must exhibit a specific ability to target amyloid plaques, which can be achieved by grafting antibodies, peptides or small molecules. Finally, we propose to consider new directions for the future of MRI in the context of Alzheimer's disease, in particular by enhancing the performances of contrast agents and by including therapeutic functionalities following a theranostic strategy.

Memory Alteration Test to Detect Amnestic Mild Cognitive Impairment and Early Alzheimer's Dementia in Population with Low Educational Level

Background/Aims: Short tests to early detection of the cognitive impairment are necessary in primary care setting, particularly in populations with low educational level. The aim of this study was to assess the performance of Memory Alteration Test (M@T) to discriminate controls, patients with amnestic Mild Cognitive Impairment (aMCI) and patients with early Alzheimer’s Dementia (AD) in a sample of individuals with low level of education.

Methods: Cross-sectional study to assess the performance of the M@T (study test), compared to the neuropsychological evaluation (gold standard test) scores in 247 elderly subjects with low education level from Lima-Peru. The cognitive evaluation included three sequential stages: (1) screening (to detect cases with cognitive impairment); (2) nosological diagnosis (to determinate specific disease); and (3) classification (to differentiate disease subtypes). The subjects with negative results for all stages were considered as cognitively normal (controls). The test performance was assessed by means of area under the receiver operating characteristic (ROC) curve. We calculated validity measures (sensitivity, specificity and correctly classified percentage), the internal consistency (Cronbach’s alpha coefficient), and concurrent validity (Pearson’s ratio coefficient between the M@T and Clinical Dementia Rating (CDR) scores).

Results: The Cronbach’s alpha coefficient was 0.79 and Pearson’s ratio coefficient was 0.79 (p < 0.01). The AUC of M@T to discriminate between early AD and aMCI was 99.60% (sensitivity = 100.00%, specificity = 97.53% and correctly classified = 98.41%) and to discriminate between aMCI and controls was 99.56% (sensitivity = 99.17%, specificity = 91.11%, and correctly classified = 96.99%).

Conclusions: The M@T is a short test with a good performance to discriminate controls, aMCI and early AD in individuals with low level of education from urban settings.

A Comparison of Lysosomal Enzymes Expression Levels in Peripheral Blood of Mild- and Severe-Alzheimer's Disease and MCI Patients: Implications for Regenerative Medicine Approaches

The association of lysosomal dysfunction and neurodegeneration has been documented in several neurodegenerative diseases, including Alzheimer's Disease (AD). Herein, we investigate the association of lysosomal enzymes with AD at different stages of progression of the disease (mild and severe) or with mild cognitive impairment (MCI). We conducted a screening of two classes of lysosomal enzymes: glycohydrolases (β-Hexosaminidase, β-Galctosidase, β-Galactosylcerebrosidase, β-Glucuronidase) and proteases (Cathepsins S, D, B, L) in peripheral blood samples (blood plasma and PBMCs) from mild AD, severe AD, MCI and healthy control subjects. We confirmed the lysosomal dysfunction in severe AD patients and added new findings enhancing the association of abnormal levels of specific lysosomal enzymes with the mild AD or severe AD, and highlighting the difference of AD from MCI. Herein, we showed for the first time the specific alteration of β-Galctosidase (Gal), β-Galactosylcerebrosidase (GALC) in MCI patients. It is notable that in above peripheral biological samples the lysosomes are more sensitive to AD cellular metabolic alteration when compared to levels of Aβ-peptide or Tau proteins, similar in both AD groups analyzed. Collectively, our findings support the role of lysosomal enzymes as potential peripheral molecules that vary with the progression of AD, and make them useful for monitoring regenerative medicine approaches for AD.

Identification of blood biomarkers for use in point of care diagnosis tool for Alzheimer's disease

Early diagnosis of Alzheimer's Disease (AD) is widely regarded as necessary to allow treatment to be started before irreversible damage to the brain occur and for patients to benefit from new therapies as they become available. Low-cost point-of-care (PoC) diagnostic tools that can be used to routinely diagnose AD in its early stage would facilitate this, but such tools require reliable and accurate biomarkers. However, traditional biomarkers for AD use invasive cerebrospinal fluid (CSF) analysis and/or expensive neuroimaging techniques together with neuropsychological assessments. Blood-based PoC diagnostics tools may provide a more cost and time efficient way to assess AD to complement CSF and neuroimaging techniques. However, evidence to date suggests that only a panel of biomarkers would provide the diagnostic accuracy needed in clinical practice and that the number of biomarkers in such panels can be large. In addition, the biomarkers in a panel vary from study to study. These issues make it difficult to realise a PoC device for diagnosis of AD. An objective of this paper is to find an optimum number of blood biomarkers (in terms of number of biomarkers and sensitivity/specificity) that can be used in a handheld PoC device for AD diagnosis. We used the Alzheimer's disease Neuroimaging Initiative (ADNI) database to identify a small number of blood biomarkers for AD. We identified a 6-biomarker panel (which includes A1Micro, A2Macro, AAT, ApoE, complement C3 and PPP), which when used with age as covariate, was able to discriminate between AD patients and normal subjects with a sensitivity of 85.4% and specificity of 78.6%.

Web Camera Based Eye Tracking to Assess Visual Memory on a Visual Paired Comparison Task

Background: Web cameras are increasingly part of the standard hardware of most smart devices. Eye movements can often provide a noninvasive "window on the brain," and the recording of eye movements using web cameras is a burgeoning area of research. Objective: This study investigated a novel methodology for administering a visual paired comparison (VPC) decisional task using a web camera.To further assess this method, we examined the correlation between a standard eye-tracking camera automated scoring procedure [obtaining images at 60 frames per second (FPS)] and a manually scored procedure using a built-in laptop web camera (obtaining images at 3 FPS). Methods: This was an observational study of 54 clinically normal older adults.Subjects completed three in-clinic visits with simultaneous recording of eye movements on a VPC decision task by a standard eye tracker camera and a built-in laptop-based web camera. Inter-rater reliability was analyzed using Siegel and Castellan's kappa formula. Pearson correlations were used to investigate the correlation between VPC performance using a standard eye tracker camera and a built-in web camera. Results: Strong associations were observed on VPC mean novelty preference score between the 60 FPS eye tracker and 3 FPS built-in web camera at each of the three visits (r = 0.88-0.92). Inter-rater agreement of web camera scoring at each time point was high (κ = 0.81-0.88). There were strong relationships on VPC mean novelty preference score between 10, 5, and 3 FPS training sets (r = 0.88-0.94). Significantly fewer data quality issues were encountered using the built-in web camera. Conclusions: Human scoring of a VPC decisional task using a built-in laptop web camera correlated strongly with automated scoring of the same task using a standard high frame rate eye tracker camera.While this method is not suitable for eye tracking paradigms requiring the collection and analysis of fine-grained metrics, such as fixation points, built-in web cameras are a standard feature of most smart devices (e.g., laptops, tablets, smart phones) and can be effectively employed to track eye movements on decisional tasks with high accuracy and minimal cost.

Multivariate analyses of peripheral blood leukocyte transcripts distinguish Alzheimer's, Parkinson's, control, and those at risk for developing Alzheimer's

The need for a reliable, simple, and inexpensive blood test for Alzheimer's disease (AD) suitable for use in a primary care setting is widely recognized. This has led to a large number of publications describing blood tests for AD, which have, for the most part, not been replicable. We have chosen to examine transcripts expressed by the cellular, leukocyte compartment of blood. We have used hypothesis-based cDNA arrays and quantitative PCR to quantify the expression of selected sets of genes followed by multivariate analyses in multiple independent samples. Rather than a single study with no replicates, we chose an experimental design in which there were multiple replicates using different platforms and different sample populations. We have divided 177 blood samples and 27 brain samples into multiple replicates to demonstrate the ability to distinguish early clinical AD (Clinical Dementia Rating scale 0.5), Parkinson's disease (PD), and cognitively unimpaired APOE4 homozygotes, as well as to determine persons at risk for future cognitive impairment with significant accuracy. We assess our methods in a training/test set and also show that the variables we use distinguish AD, PD, and control brain. Importantly, we describe the variability of the weights assigned to individual transcripts in multivariate analyses in repeated studies and suggest that the variability we describe may be the cause of inability to repeat many earlier studies. Our data constitute a proof of principle that multivariate analysis of the transcriptome related to cell stress and inflammation of peripheral blood leukocytes has significant potential as a minimally invasive and inexpensive diagnostic tool for diagnosis and early detection of risk for AD.

Predicting dementia using socio-demographic characteristics and the Free and Cued Selective Reminding Test in the general population

BACKGROUND

Our study aimed to determine whether the consideration of socio-demographic features improves the prediction of Alzheimer's dementia (AD) at 5 years when using the Free and Cued Selective Reminding Test (FCSRT) in the general older population.

METHODS

Our analyses focused on 2558 subjects from the prospective Three-City Study, a cohort of community-dwelling individuals aged 65 years and over, with FCSRT scores. Four "residual scores" and "risk scores" were built that included the FCSRT scores and socio-demographic variables. The predictive performance of crude, residual and risk scores was analyzed by comparing the areas under the ROC curve (AUC).

RESULTS

In total, 1750 subjects were seen 5 years after completing the FCSRT. AD was diagnosed in 116 of them. Compared with the crude free-recall score, the predictive performances of the residual score and of the risk score were not significantly improved (AUC: 0.83 vs 0.82 and 0.88 vs 0.89 respectively).

CONCLUSION

Using socio-demographic features in addition to the FCSRT does not improve its predictive performance for dementia or AD.

Glucose-6-phosphate dehydrogenase a novel hope on a blood-based diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is a multi-factorial neurodegenerative disorder that numerous factors have key properties in the development of this proteopathy. Glucose-6-phosphate dehydrogenase (G6PD) is the most common form of enzymopathy. We have examined G6PD enzyme activity levels in the serum of newly diagnosed AD patients compared with control subjects without dementia from the both sexes. Serum G6PD levels were found to be significantly higher (approximately two times) in AD patients compared to control geriatric subjects in both sexes. We have concluded that G6PD seems to play an integral role in the progress and/or prevention of AD.