Detection and localisation of hesitant steps in people with Alzheimer's disease navigating routes of varying complexity

Behavioral Disorders of Spatial Cognition in Patients with Mild Cognitive Impairment due to Alzheimer's Disease: Preliminary Findings from the BDSC-MCI Project

(1) Background: Spatial cognition (SC) is one of the earliest cognitive domains to be impaired in the course of Alzheimer's disease (AD), resulting in spatial disorientation and becoming lost even in familiar surroundings as later dementia symptoms. To date, few studies have identified initial alterations of spatial navigation (SN) in the premorbid AD phase by real-world paradigms, and none have adopted an innovative technological apparatus to better detect gait alterations as well as physiological aspects correlated to spatial disorientation (SD). The present study aimed at exploring initial SN defects in patients with prodromal AD via a naturalistic task by using a sensory garment. (2) Methods: 20 community-dwelling patients with Mild Cognitive Impairment (MCI) due to AD and 20 age/education controls were assessed on their sequential egocentric and allocentric navigation abilities by using a modified version of the Detour Navigation Test (DNT-mv). (3) Results: When compared to controls, patients with MCI due to AD exhibited higher wrong turns (WT) and moments of hesitation (MsH) in the DNT-mv, reflecting difficulties both in sequential egocentric and allocentric navigation, depending on hippocampal deterioration. Moreover, they reported more complaints about their SN competencies and lower long-term visuospatial memory abilities than controls. Remarkably, WTs and MsH manifested in the allocentric naturalistic task of the DNT-mv were associated with autonomic nervous system alteration pertaining to cardiac functioning in the whole sample. (4) Conclusions: Naturalistic navigation tests of hippocampal function using a continuous non-invasive monitoring device can provide early markers of spatial disorientation in patients with MCI due to AD. Future studies should develop cognitive remediation techniques able to enhance SC residual abilities in patients at high risk of conversion into dementia and ecological paradigms to be replicated on a large scale.

Diagnosis and Management of Posterior Cortical Atrophy

Purpose of review

The study aims to provide a summary of recent developments for diagnosing and managing posterior cortical atrophy (PCA). We present current efforts to improve PCA characterisation and recommendations regarding use of clinical, neuropsychological and biomarker methods in PCA diagnosis and management and highlight current knowledge gaps.

Recent findings

Recent multi-centre consensus recommendations provide PCA criteria with implications for different management strategies (e.g. targeting clinical features and/or disease). Studies emphasise the preponderance of primary or co-existing Alzheimer's disease (AD) pathology underpinning PCA. Evidence of approaches to manage PCA symptoms is largely derived from small studies.

Summary

PCA diagnosis is frequently delayed, and people are likely to receive misdiagnoses of ocular or psychological conditions. Current treatment of PCA is symptomatic - pharmacological and non-pharmacological - and the use of most treatment options is based on small studies or expert opinion. Recommendations for non-pharmacological approaches include interdisciplinary management tailored to the PCA clinical profile - visual-spatial - rather than memory-led, predominantly young onset - and psychosocial implications. Whilst emerging disease-modifying treatments have not been tested in PCA, an accurate and timely diagnosis of PCA and determining underlying pathology is of increasing importance in the advent of disease-modifying therapies for AD and other albeit rare causes of PCA.

A review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images

Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder that degrades the memory and cognitive ability in elderly people. The main reason for memory loss and reduction in cognitive ability is the structural changes in the brain that occur due to neuronal loss. These structural changes are most conspicuous in the hippocampus, cortex, and grey matter and can be assessed by using neuroimaging techniques viz. Positron Emission Tomography (PET), structural Magnetic Resonance Imaging (MRI) and functional MRI (fMRI), etc. Out of these neuroimaging techniques, structural MRI has evolved as the best technique as it indicates the best soft tissue contrast and high spatial resolution which is important for AD detection. Currently, the focus of researchers is on predicting the conversion of Mild Cognitive Impairment (MCI) into AD. MCI represents the transition state between expected cognitive changes with normal aging and Alzheimer's disease. Not every MCI patient progresses into Alzheimer's disease. MCI can develop into stable MCI (sMCI, patients are called non-converters) or into progressive MCI (pMCI, patients are diagnosed as MCI converters). This paper discusses the prognosis of MCI to AD conversion and presents a review of structural MRI-based studies for AD detection. AD detection framework includes feature extraction, feature selection, and classification process. This paper reviews the studies for AD detection based on different feature extraction methods and machine learning algorithms for classification. The performance of various feature extraction methods has been compared and it has been observed that the wavelet transform-based feature extraction method would give promising results for AD classification. The present study indicates that researchers are successful in classifying AD from Normal Controls (N_rmC) but, it still requires a lot of work to be done for MCI/ N_rmC and MCI/AD, which would help in detecting AD at its early stage.

Predicting real world spatial disorientation in Alzheimer's disease patients using virtual reality navigation tests

Spatial navigation impairments in Alzheimer's disease (AD) have been suggested to underlie patients experiencing spatial disorientation. Though many studies have highlighted navigation impairments for AD patients in virtual reality (VR) environments, the extent to which these impairments predict a patient's risk for spatial disorientation in the real world is still poorly understood. The aims of this study were to (a) investigate the spatial navigation abilities of AD patients in VR environments as well as in a real world community setting and (b) explore whether we could predict patients at a high risk for spatial disorientation in the community based on their VR navigation. Sixteen community-dwelling AD patients and 21 age/gender matched controls were assessed on their egocentric and allocentric navigation abilities in VR environments using the Virtual Supermarket Test (VST) and Sea Hero Quest (SHQ) as well as in the community using the Detour Navigation Test (DNT). When compared to controls, AD patients exhibited impairments on the VST, SHQ, and DNT. For patients, only SHQ wayfinding distance and wayfinding duration significantly predicted composite disorientation score on the DNT (β = 0.422, p = 0.034, R² = 0.299 and β = 0.357, p = 0.046, R² = 0.27 respectively). However, these same VR measures could not reliably predict which patients were at highest risk of spatial disorientation in the community (p > 0.1). Future studies should focus on developing VR-based tests which can predict AD patients at high risk of getting spatially disorientated in the real world.

STEP-UP: Enabling Low-Cost IMU Sensors to Predict the Type of Dementia During Everyday Stair Climbing

Posterior Cortical Atrophy is a rare but significant form of dementia which affects people's visual ability before their memory. This is often misdiagnosed as an eyesight rather than brain sight problem. This paper aims to address the frequent, initial misdiagnosis of this disease as a vision problem through the use of an intelligent, cost-effective, wearable system, alongside diagnosis of the more typical Alzheimer's Disease. We propose low-level features constructed from the IMU data gathered from 35 participants, while they performed a stair climbing and descending task in a real-world simulated environment. We demonstrate that with these features the machine learning models predict dementia with 87.02% accuracy. Furthermore, we investigate how system parameters, such as number of sensors, affect the prediction accuracy. This lays the groundwork for a simple clinical test to enable detection of dementia which can be carried out in the wild.

Effects of lighting variability on locomotion in posterior cortical atrophy

INTRODUCTION

Clinical reports describe patients with Alzheimer's disease (AD) exhibiting atypical adaptive walking responses to the visual environment; however, there is limited empirical investigation of such behaviors or factors modulating their expression. We aim to evaluate effects of lighting-based interventions and clinical presentation (visual- vs memory-led) on walking function in participants with posterior cortical atrophy (PCA) and typical AD (tAD).

METHODS

Participants with PCA (n = 10), tAD (n = 9), and healthy controls (n = 12) walked to visible target destinations under different lighting conditions within two pilot repeated-measures design investigations (Experiment 1: 32 trials per participant; Experiment 2: 36 trials per participant). Participants walked to destinations with the floorpath interrupted by shadows varying in spatial extent (Experiment 1: no, medium, high shadow) or with different localized parts of the environment illuminated (Experiment 2: target, middle, or distractor illuminated). The primary study outcome for both experimental tasks was completion time; secondary kinematic outcomes were proportions of steps identified as outliers (Experiment 1) and walking path directness (Experiment 2).

RESULTS

In Experiment 1, PCA participants overall demonstrated modest reductions in time taken to reach destinations when walking to destinations uninterrupted by shadows compared to high shadow conditions (7.1% reduction [95% confidence interval 2.5, 11.5; P = .003]). Experiment 2 found no evidence of differences in task performance for different localized lighting conditions in PCA participants overall. Neither experiment found evidence of differences in task performance between conditions in tAD or control participants overall. Completion time in both patient groups was longer relative to controls, and longer in PCA relative to tAD groups.

DISCUSSION

Findings represent a quantitative characterization of a clinical phenomenon involving patients misperceiving shadows, implicating dementia-related cortico-visual impairments. Results contribute to evidence-based design guidelines for dementia-friendly environments.

Smartphone technology for fostering goal-directed ambulation and object use in people with moderate Alzheimer's disease

Purpose: This study was aimed at assessing a smartphone-based intervention to help 11 individuals with moderate Alzheimer's disease and ambulation problems to manage goal-directed, walker-assisted ambulation and object use (i.e., to reach specific destinations and put away objects at those destinations independently).Method: The study was carried out according to a non-concurrent multiple baseline design across participants. Two measures were recorded, that is, the number of target responses performed correctly (with each target response consisting of reaching a destination and putting away an object), and the number of observation intervals with indices of enjoyment/appreciation (e.g., smiles and positive comments). During baseline, the participants were provided with a walker and three objects that were to be transported to and put away at specific destinations. During the intervention, the participants also had the smartphone-based technology that provided them with instructions about the destinations and objects, praise, and preferred stimulation.Results: During baseline, the mean frequency of correct target responses was virtually zero. The mean frequency of intervals with indices of enjoyment/appreciation ranged from zero to close to one. During the intervention, the mean frequencies for the two measures were slightly below three and just over three, respectively. The maximum frequencies possible were three and four, respectively.Conclusions: A smartphone-based intervention may be suitable to foster goal-directed, walker-assisted ambulation and object use as well as enjoyment/appreciation in participants like those involved in this study.IMPLICATIONS FOR REHABILITATIONA smartphone-based intervention may be used to promote goal-directed, walker-assisted ambulation and object use as well as indices of enjoyment/appreciation in persons with moderate Alzheimer disease.The technology for such intervention might involve (a) a smartphone with Android operating system, light sensor, Bluetooth v4.0, and MacroDroid, (b) Bluetooth headphones or earpieces, and (c) battery-powered light sources.The technology may provide the participants with instructions about the destinations to reach and the objects to put away at those destinations, and with praise and brief periods of preferred stimulation at the target destinations.The technology may be considered easy to access, friendly for the participants, and suitable for use by staff within daily contexts.