Alzheimer's disease and mild cognitive impairment deteriorate fine movement control

Cognitive activity analysis of Parkinson's patients using artificial intelligence techniques

PURPOSE

The development of modern Artificial Intelligence (AI) based models for the early diagnosis of Parkinson's disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, hand movements, gait, etc.) increases the variety of up-to-date models. Movement disorders and tremors are also among the most prominent symptoms of PD. The usage of drawings in the detection of PD can be a crucial decision-support approach that doctors can benefit from.

METHODS

A dataset was created by asking 40 PD and 40 Healthy Controls (HC) to draw spirals with and without templates using a special tablet. The patient-healthy distinction was achieved by classifying drawings of individuals using Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) algorithms. Prior to classification, the data were normalized by applying the min-max normalization method. Moreover, Leave-One-Subject-Out (LOSO) Cross-Validation (CV) approach was utilized to eliminate possible overfitting scenarios. To further improve the performances of classifiers, Principal Component Analysis (PCA) dimension reduction technique were also applied to the raw data and the results were compared accordingly.

RESULTS

The highest accuracy among machine learning based classifiers was obtained as 90% with SVM classifier using non-template drawings with PCA application.

CONCLUSION

The model can be used as a pre-evaluation system in the clinic as a non-invasive method that also minimizes environmental and educational level differences by using simple hand gestures such as hand drawing, writing numbers, words, and syllables. As a result of our study, preliminary preparation has been made so that hand drawing analysis can be used as an auxiliary system that can save time for health professionals. We plan to work on more comprehensive data in the future.

Discriminant Power of Smartphone-Derived Keystroke Dynamics for Mild Cognitive Impairment Compared to a Neuropsychological Screening Test: Cross-Sectional Study

BACKGROUND

Conventional neuropsychological screening tools for mild cognitive impairment (MCI) face challenges in terms of accuracy and practicality. Digital health solutions, such as unobtrusively capturing smartphone interaction data, offer a promising alternative. However, the potential of digital biomarkers as a surrogate for MCI screening remains unclear, with few comparisons between smartphone interactions and existing screening tools.

OBJECTIVE

This study aimed to investigate the effectiveness of smartphone-derived keystroke dynamics, captured via the Neurokeys keyboard app, in distinguishing patients with MCI from healthy controls (HCs). This study also compared the discriminant performance of these digital biomarkers against the Korean version of the Montreal Cognitive Assessment (MoCA-K), which is widely used for MCI detection in clinical settings.

METHODS

A total of 64 HCs and 47 patients with MCI were recruited. Over a 1-month period, participants generated 3530 typing sessions, with 2740 (77.6%) analyzed for this study. Keystroke metrics, including hold time and flight time, were extracted. Receiver operating characteristics analysis was used to assess the sensitivity and specificity of keystroke dynamics in discriminating between HCs and patients with MCI. This study also explored the correlation between keystroke dynamics and MoCA-K scores.

RESULTS

Patients with MCI had significantly higher keystroke latency than HCs (P<.001). In particular, latency between key presses resulted in the highest sensitivity (97.9%) and specificity (96.9%). In addition, keystroke dynamics were significantly correlated with the MoCA-K (hold time: r=-.468; P<.001; flight time: r=-.497; P<.001), further supporting the validity of these digital biomarkers.

CONCLUSIONS

These findings highlight the potential of smartphone-derived keystroke dynamics as an effective and ecologically valid tool for screening MCI. With higher sensitivity and specificity than the MoCA-K, particularly in measuring flight time, keystroke dynamics can serve as a noninvasive, scalable, and continuous method for early cognitive impairment detection. This novel approach could revolutionize MCI screening, offering a practical alternative to traditional tools in everyday settings.

TRIAL REGISTRATION

Thai Clinical Trials Registry TCTR20220415002; https://www.thaiclinicaltrials.org/show/TCTR20220415002.

Explainability of CNN-based Alzheimer's disease detection from online handwriting

With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer's disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning techniques for Alzheimer's disease detection, the widespread adoption of deep learning models raises concerns about their explainability. The lack of explainability in deep learning models for online handwriting analysis is a critical gap in the literature in the context of Alzheimer's disease detection. This paper addresses this challenge by interpreting predictions from a Convolutional Neural Network applied to multivariate time series data, generated by online handwriting data associated with continuous loop series handwritten on a graphical tablet. Our explainability methods reveal distinct motor behavior characteristics for healthy individuals and those diagnosed with Alzheimer's. Healthy subjects exhibited consistent, smooth movements, while Alzheimer's patients demonstrated erratic patterns marked by abrupt stops and direction changes. This emphasizes the critical role of explainability in translating complex models into clinically relevant insights. Our research contributes to the enhancement of early diagnosis, providing significant and reliable insights to stakeholders involved in patient care and intervention strategies. Our work bridges the gap between machine learning predictions and clinical insights, fostering a more effective and understandable application of advanced models for Alzheimer's disease assessment.

Alzheimer's disease biomarker burden in primary motor cortices is associated with poorer dexterity performance

INTRODUCTION

Motor function has correlated with longevity and functionality; however, there is limited research on those with Alzheimer's disease (AD). We studied the association between motor functionality and AD pathology in primary motor and medial temporal cortices.

METHODS

A total of 206 participants with a clinical diagnosis of cognitively healthy, AD, or mild cognitive impairment (MCI) underwent imaging and motor assessment. Linear regressions and analyses of variance were applied to test the prediction from AD imaging biomarkers to motor performance and the diagnosis group differences in motor performance.

RESULTS

Increased neurodegeneration was associated with worsening dexterity and lower walking speed, and increased amyloid and tau were associated with worsening dexterity. AD and MCI participants had lower motor performance than the cognitively healthy participants.

DISCUSSION

Increased AD pathology is associated with worsening dexterity performance. The decline in dexterity in those with AD pathology may offer an opportunity for non-pharmacological therapy intervention.

HIGHLIGHTS

Noted worsening dexterity performance was associated with greater Alzheimer's disease (AD) pathology (tau, amyloid beta, and neurodegeneration) in primary motor cortices. Similarly, increased neurodegeneration and tau pathology in parahippocampal, hippocampal, and entorhinal cortices is associated with worsening dexterity performance. Motor performance declined in those with clinical and preclinical AD among an array of motor assessments.

Machine Learning Classification of Patients with Amnestic Mild Cognitive Impairment and Non-Amnestic Mild Cognitive Impairment from Written Picture Description Tasks

Individuals with Mild Cognitive Impairment (MCI), a transitional stage between cognitively healthy aging and dementia, are characterized by subtle neurocognitive changes. Clinically, they can be grouped into two main variants, namely patients with amnestic MCI (aMCI) and non-amnestic MCI (naMCI). The distinction of the two variants is known to be clinically significant as they exhibit different progression rates to dementia. However, it has been particularly challenging to classify the two variants robustly. Recent research indicates that linguistic changes may manifest as one of the early indicators of pathology. Therefore, we focused on MCI's discourse-level writing samples in this study. We hypothesized that a written picture description task can provide information that can be used as an ecological, cost-effective classification system between the two variants. We included one hundred sixty-nine individuals diagnosed with either aMCI or naMCI who received neurophysiological evaluations in addition to a short, written picture description task. Natural Language Processing (NLP) and a BERT pre-trained language model were utilized to analyze the writing samples. We showed that the written picture description task provided 90% overall classification accuracy for the best classification models, which performed better than cognitive measures. Written discourses analyzed by AI models can automatically assess individuals with aMCI and naMCI and facilitate diagnosis, prognosis, therapy planning, and evaluation.

Proprioceptive and olfactory deficits in individuals with Parkinson disease and mild cognitive impairment

BACKGROUND

Individuals with neurodegenerative diseases such as Parkinson disease (PD) and Alzheimer's (AD) disease often present with perceptual impairments at an early clinical stage. Therefore, early identification and quantification of these impairments could facilitate diagnosis and early intervention.

OBJECTIVES

This study aimed to compare proprioceptive and olfactory sensitivities in individuals diagnosed with PD and mild cognitive impairment (MCI).

METHODS

Proprioception in the forearm and olfactory function were measured in neurotypical older adults, individuals with PD, and individuals with MCI. Position and passive motion senses were assessed using a passive motion apparatus. The traditional Chinese version of the University of Pennsylvania smell identification test (UPSIT-TC) and the smell threshold test (STT) were used to identify and discriminate smell, respectively.

RESULTS

Position sense threshold between the groups differed significantly (p < 0.001), with the PD (p < 0.001) and MCI (p = 0.004) groups showing significantly higher than the control group. The control group had significantly higher mean UPSIT-TC scores than the PD (p < 0.001) and MCI (p = 0.006) groups. The control group had a significantly lower mean STT threshold than the PD and MCI groups (p < 0.001 and p = 0.008, respectively). UPSIT-TC scores significantly correlated with disease progression in PD (r = - 0.50, p = 0.008) and MCI (r = 0.44, p = 0.04).

CONCLUSIONS

Proprioceptive and olfactory sensitivities were reduced in individuals with PD and MCI, and these deficits were related to disease severity. These findings support previous findings indicating that perceptual loss may be a potential biomarker for diagnosing and monitoring disease progression in individuals with neurodegenerative diseases.

Function of the hand as a predictor of early diagnosis and progression of Alzheimer's dementia: A systematic review

BACKGROUND

The dominant feature of Alzheimer's dementia (AD) is gradual cognitive decline, which can be reflected by reduced finger dexterity.

OBJECTIVE

This review analyzed reports on hand function in AD patients to determine the possibility of using it for an early diagnosis and for monitoring the disease progression of AD.

METHODS

PubMed, Web of Science, EMBASE, and Cochrane library were searched systematically (search dates: 2000-2022), and relevant articles were cross-checked for related and relevant publications.

RESULTS

Seventeen studies assessed the association of the handgrip strength or dexterity with cognitive performance. The hand dexterity was strongly correlated with the cognitive function in all studies. In the hand dexterity test using the pegboard, there was little difference in the degree of decline in hand function between the healthy elderly (HE) group and the mild cognitive impairment (MCI) group. On the other hand, there was a difference in the hand function between the HE group and the AD group. In addition, the decline in hand dexterity is likely to develop from moderate to severe dementia. In complex hand movements, movement speed variations were greater in the AD than in the HE group, and the automaticity, regularity, and rhythm were reduced.

CONCLUSIONS

HE and AD can be identified by a simple hand motion test using a pegboard. The data can be used to predict dementia progression from moderate dementia to severe dementia. An evaluation of complex hand movements can help predict the transition from MCI to AD and the progression from moderate to severe dementia.

Handwriting Markers for the Onset of Alzheimer's Disease

INTRODUCTION

Alzheimer's disease has an impact on handwriting (AD). Numerous researchers reported that fact. Therefore, examining handwriting characteristics could be a useful way to screen for AD. The aim of the article is to present the reliability and effectiveness of the AD-HS tool.

METHODS

Most of the existing studies examine either linguistic manifestations of writing or certain motor functions. However, handwriting is a complex of cognitive and motor activities. Since the influence of AD on handwriting is individual, it is important to analyze the complete set of handwriting features. The AD-HS instrument is based on this principle. Validation of the AD-HS instrument for revealing cognitive impairment in AD-diagnosed persons in comparison to the control group. The study is based on the evaluation of free handwritten texts. AD-HS includes 40 handwriting and 2 linguistic features of handwritten texts. It is based on the standard protocol for handwriting analysis. The cumulative evaluation of all features builds a quantitative AD-Indicator (ADI) as a marker of possible AD conditions. The analyzed experiment includes 53 AD-diagnosed persons and a control group of 192 handwriting specimens from the existing database.

RESULTS

AD-HS shows a distinct difference in evaluated ADI for the participants (the mean value equals 0.49) and the control group (the mean value equals 0.28).

CONCLUSION

The handwriting marker of AD could be an effective supplement instrument for earlier screening. It is also useful when traditional biomarkers and neurological tests could not be applied. AD-HS can accompany therapy as an indication of its effect on a person.

Toward Quantitative Neurology: Sensors to Assess Motor Deficits in Dementia

Background

Alzheimer's disease (AD) is the most common neurodegenerative disorder which primarily involves memory and cognitive functions. It is increasingly recognized that motor involvement is also a common and significant aspect of AD, contributing to functional decline and profoundly impacting quality of life. Motor impairment, either at early or later stages of cognitive disorders, can be considered as a proxy measure of cognitive impairment, and technological devices can provide objective measures for both diagnosis and prognosis purposes. However, compared to other neurodegenerative disorders, the use of technological tools in neurocognitive disorders, including AD, is still in its infancy.

Objective

This report aims to evaluate the role of technological devices in assessing motor involvement across the AD spectrum and in other dementing conditions, providing an overview of the existing devices that show promise in this area and exploring their clinical applications.

Methods

The evaluation involves a review of the existing literature in the PubMed, Web of Science, Scopus, and Cochrane databases on the effectiveness of these technologies. 21 studies were identified and categorized as: wearable inertial sensors/IMU, console/kinect, gait analysis, tapping device, tablet/mobile, and computer.

Results

We found several parameters, such as speed and stride length, that appear promising for detecting abnormal motor function in MCI or dementia. In addition, some studies have found correlations between these motor aspects and cognitive state.

Conclusions

Clinical application of technological tools to assess motor function in people with cognitive impairments of a neurodegenerative nature, such as AD, may improve early detection and stratification of patients.

The "Little Circles Test" (LCT): a dusted-off tool for assessing fine visuomotor function

BACKGROUND

The fine visuomotor function is commonly impaired in several neurological conditions. However, there is a scarcity of reliable neuropsychological tools to assess such a critical domain.

AIMS

The aim of this study is to explore the psychometric properties and provide normative data for the Visual-Motor Speed and Precision Test (VMSPT).

RESULTS

Our normative sample included 220 participants (130 females) aged 18-86 years (mean education = 15.24 years, SD = 3.98). Results showed that raw VMSPT scores were affected by higher age and lower education. No effect of sex or handedness was shown. Age- and education-based norms were provided. VMSPT exhibited weak-to-strong correlations with well-known neuropsychological tests, encompassing a wide range of cognitive domains of clinical relevance. By gradually intensifying the cognitive demands, the test becomes an indirect, performance-oriented measure of executive functioning. Finally, VMSPT seems proficient in capturing the speed-accuracy trade-off typically observed in the aging population.

CONCLUSIONS

This study proposes the initial standardization of a versatile, time-efficient, and cost-effective neuropsychological tool for assessing fine visuomotor coordination. We propose renaming the VMSPT as the more approachable "Little Circles Test" (LCT).

Linguistic, visuospatial, and kinematic writing characteristics in cognitively impaired patients with beta-amyloid deposition

Introduction

Beta-amyloid (Aβ) deposition, a hallmark of Alzheimer's disease (AD), begins before dementia and is an important factor in mild cognitive impairment (MCI). Aβ deposition is a recognized risk factor for various cognitive impairments and has been reported to affect motor performance as well. This study aimed to identify the linguistic, visuospatial, and kinematic characteristics evident in the writing performance of patients with cognitive impairment (CI) who exhibit Aβ deposition.

Methods

A total of 31 patients diagnosed with amnestic mild cognitive impairment (aMCI) with Aβ deposition, 26 patients with Alzheimer's-type dementia, and 33 healthy control (HC) participants without deposition were administered tasks involving dictation of 60 regular words, irregular words, and non-words consisting of 1-4 syllables. Responses from all participants were collected and analyzed through digitized writing tests and analysis tools.

Results

In terms of linguistic aspects, as cognitive decline progressed, performance in the dictation of irregular words decreased, with errors observed in substituting the target grapheme with other graphemes. The aMCI group frequently exhibited corrective aspects involving letter rewriting during the task. In terms of visuospatial aspects, the AD group displayed more errors in grapheme combination compared to the HC group. Lastly, in the kinematic aspects, both the aMCI group and the AD group exhibited slower writing speeds compared to the HC group.

Discussion

The findings suggest that individuals in the CI group exhibited lower performance in word dictation tasks than those in the HC group, and these results possibly indicate complex cognitive-language-motor deficits resulting from temporal-parietal lobe damage, particularly affecting spelling processing. These results provide valuable clinical insights into understanding linguistic-visuospatial-kinematic aspects that contribute to the early diagnosis of CI with Aβ deposition.

Alzheimer's early detection in post-acute COVID-19 syndrome: a systematic review and expert consensus on preclinical assessments

Introduction

The risk of developing Alzheimer's disease (AD) in older adults increasingly is being discussed in the literature on Post-Acute COVID-19 Syndrome (PACS). Remote digital Assessments for Preclinical AD (RAPAs) are becoming more important in screening for early AD, and should always be available for PACS patients, especially for patients at risk of AD. This systematic review examines the potential for using RAPA to identify impairments in PACS patients, scrutinizes the supporting evidence, and describes the recommendations of experts regarding their use.

Methods

We conducted a thorough search using the PubMed and Embase databases. Systematic reviews (with or without meta-analysis), narrative reviews, and observational studies that assessed patients with PACS on specific RAPAs were included. The RAPAs that were identified looked for impairments in olfactory, eye-tracking, graphical, speech and language, central auditory, or spatial navigation abilities. The recommendations' final grades were determined by evaluating the strength of the evidence and by having a consensus discussion about the results of the Delphi rounds among an international Delphi consensus panel called IMPACT, sponsored by the French National Research Agency. The consensus panel included 11 international experts from France, Switzerland, and Canada.

Results

Based on the available evidence, olfaction is the most long-lasting impairment found in PACS patients. However, while olfaction is the most prevalent impairment, expert consensus statements recommend that AD olfactory screening should not be used on patients with a history of PACS at this point in time. Experts recommend that olfactory screenings can only be recommended once those under study have reported full recovery. This is particularly important for the deployment of the olfactory identification subdimension. The expert assessment that more long-term studies are needed after a period of full recovery, suggests that this consensus statement requires an update in a few years.

Conclusion

Based on available evidence, olfaction could be long-lasting in PACS patients. However, according to expert consensus statements, AD olfactory screening is not recommended for patients with a history of PACS until complete recovery has been confirmed in the literature, particularly for the identification sub-dimension. This consensus statement may require an update in a few years.

Sex-dependent performance differences in curvilinear aiming arm movements in octogenarians

In an aging society, it is necessary to detect the cognitive decline of individuals at an early stage using simple measurement methods. This makes early health care possible for those affected. The aim of the study was to develop a classifier for cognitive state in older adults with and without mild cognitive impairment (MCI) based on kinematic parameters of linear and curvilinear aiming arm movements. In a group of 224 older adults over 80 years of age (cognitively healthy and MCI), the movement duration and intersegment intervals of linear and curvilinear arm movements of 20 cm were recorded. Movement duration was significantly longer in the curvilinear condition than in the straight movement, and MCI participants required significantly more time than cognitively healthy participants. Post-hoc analysis on the fluidity of movement in the curvilinear condition showed that MCI men had significantly longer inter-segmental intervals than non-MCI men. No difference was found in women. Based on the inter-segmental intervals, a simple classifier could be developed that correctly classified 63% of the men. In summary, aiming arm movements are only conditionally suitable as a classifier for cognitive states. For the construction of an ideal classifier, age-related degeneration of cortical and subcortical motor areas should be considered.

A study of auxiliary screening for Alzheimer’s disease based on handwriting characteristics

Background and objectives

Alzheimer’s disease (AD) has an insidious onset, the early stages are easily overlooked, and there are no reliable, rapid, and inexpensive ancillary detection methods. This study analyzes the differences in handwriting kinematic characteristics between AD patients and normal elderly people to model handwriting characteristics. The aim is to investigate whether handwriting analysis has a promising future in AD auxiliary screening or even auxiliary diagnosis and to provide a basis for developing a handwriting-based diagnostic tool.

Materials and methods

Thirty-four AD patients (15 males, 77.15 ± 1.796 years) and 45 healthy controls (20 males, 74.78 ± 2.193 years) were recruited. Participants performed four writing tasks with digital dot-matrix pens which simultaneously captured their handwriting as they wrote. The writing tasks consisted of two graphics tasks and two textual tasks. The two graphics tasks are connecting fixed dots (task 1) and copying intersecting pentagons (task 2), and the two textual tasks are dictating three words (task 3) and copying a sentence (task 4). The data were analyzed by using Student’s t-test and Mann–Whitney U test to obtain statistically significant handwriting characteristics. Moreover, seven classification algorithms, such as eXtreme Gradient Boosting (XGB) and Logistic Regression (LR) were used to build classification models. Finally, the Receiver Operating Characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Area Under Curve (AUC) were used to assess whether writing scores and kinematics parameters are diagnostic.

Results

Kinematic analysis showed statistically significant differences between the AD and controlled groups for most parameters (p &lt; 0.05, p &lt; 0.01). The results found that patients with AD showed slower writing speed, tremendous writing pressure, and poorer writing stability. We built statistically significant features into a classification model, among which the model built by XGB was the most effective with a maximum accuracy of 96.55%. The handwriting characteristics also achieved good diagnostic value in the ROC analysis. Task 2 had a better classification effect than task 1. ROC curve analysis showed that the best threshold value was 0.084, accuracy = 96.30%, sensitivity = 100%, specificity = 93.41%, PPV = 92.21%, NPV = 100%, and AUC = 0.991. Task 4 had a better classification effect than task 3. ROC curve analysis showed that the best threshold value was 0.597, accuracy = 96.55%, sensitivity = 94.20%, specificity = 98.37%, PPV = 97.81%, NPV = 95.63%, and AUC = 0.994.

Conclusion

This study’s results prove that handwriting characteristic analysis is promising in auxiliary AD screening or AD diagnosis.

Reduction of vowel space in Alzheimer's disease

Reduced vowel space area (VSA) is a known effect of neurodegenerative diseases such as Parkinson's disease (PD). Using large publicly available corpuses, two experiments were conducted comparing the vowel space of speakers with and without Alzheimer's disease (AD) during spontaneous and read speech. First, a comparison of vowel distance found reduced distance in AD for English spontaneous speech, but not Spanish read speech. Findings were then verified using an unsupervised learning approach to quantify VSA through cluster center detection. These results corroborate observations for PD that VSA reduction is task-dependent, but further experiments are necessary to quantify the effect of language.

Handwriting Changes in Alzheimer's Disease: A Systematic Review

BACKGROUND

Handwriting is a complex process involving fine motor skills, kinesthetic components, and several cognitive domains, often impaired by Alzheimer's disease (AD).

OBJECTIVE

Provide a systematic review of handwriting changes in AD, highlighting the effects on motor, visuospatial and linguistic features, and to identify new research topics.

METHODS

A search was conducted on PubMed, Scopus, and Web of Science to identify studies on AD and handwriting. The review followed PRISMA norms and analyzed 91 articles after screening and final selection.

RESULTS

Handwriting is impaired at all levels of the motor-cognitive hierarchy in AD, particularly in text, with higher preservation of signatures. Visuospatial and linguistic features were more affected. Established findings for motor features included higher variability in AD signatures, higher in-air/on-surface time ratio and longer duration in text, longer start time/reaction time, and lower fluency. There were conflicting findings for pressure and velocity in motor features, as well as size, legibility, and pen lifts in general features. For linguistic features, findings were contradictory for error patterns, as well as the association between agraphia and severity of cognitive deficits.

CONCLUSIONS

Further re-evaluation studies are needed to clarify the divergent results on motor, general, and linguistic features. There is also a lack of research on the influence of AD on signatures and the effect of AD variants on handwriting. Such research would have an impact on clinical management (e.g., for early detection and patient follow-up using handwriting tasks), or forensic examination aimed at signatory identification.

Classification of mild cognitive impairment based on handwriting dynamics and qEEG

Subtle changes in fine motor control and quantitative electroencephalography (qEEG) in patients with mild cognitive impairment (MCI) are important in screening for early dementia in primary care populations. In this study, an automated, non-invasive and rapid detection protocol for mild cognitive impairment based on handwriting kinetics and quantitative EEG analysis was proposed, and a classification model based on a dual fusion of feature and decision layers was designed for clinical decision-marking. Seventy-nine volunteers (39 healthy elderly controls and 40 patients with mild cognitive impairment) were recruited for this study, and the handwritten data and the EEG signals were performed using a tablet and MUSE under four designed handwriting tasks. Sixty-eight features were extracted from the EEG and handwriting parameters of each test. Features selected from both models were fused using a late feature fusion strategy with a weighted voting strategy for decision making, and classification accuracy was compared using three different classifiers under handwritten features, EEG features and fused features respectively. The results show that the dual fusion model can further improve the classification accuracy, with the highest classification accuracy for the combined features and the best classification result of 96.3% using SVM with RBF kernel as the base classifier. In addition, this not only supports the greater significance of multimodal data for differentiating MCI, but also tests the feasibility of using the portable EEG headband as a measure of EEG in patients with cognitive impairment.

Finger Tapping Test for Assessing the Risk of Mild Cognitive Impairment

Aim

A testing method for early diagnosis of Mild cognitive dementia (MCI) that can be easily applied in clinical practice was investigated in this study. We examined whether MCI risk can be determined through finger movements.

Methods

Between 2013 and 2020, 1097 individuals were screened. After applying propensity-score matching to adjust for variability between the groups, 173 individuals each in the mild cognitive impairment and control groups were selected. Thereafter, differences between groups in mean values of parameters extracted from finger tap movements were determined using unpaired t-test and effect size. Furthermore, area under the curve, sensitivity, and specificity were calculated from the receiver operating characteristic curve for parameters with significant difference.

Results

A significant difference was observed, especially in the number of taps in the MCI group compared with that in the control group (p < .001; 95% CI, -12.7 to -8.8; r = 0.51). A cut-off value of 30 taps was applied (sensitivity, 0.77; specificity, 0.67; AUC, 0.79). Significant differences were also observed in rhythm-related parameters.

Conclusions

These parameters might be useful for capturing MCI risk. Finger taps are easily measured and may be suitable for screening large populations. This tool might be used as a supplemental method to increase the sensitivity of traditional cognitive tests.

Reach-to-grasp kinematics and kinetics with and without visual feedback in early-stage Alzheimer’s disease

This study aimed to investigate the effects of early-stage Alzheimer’s disease (AD) on the reach-to-grasp kinematics and kinetics with and without visual supervision of the grasping arm and hand. Seventeen patients who had been diagnosed with early-stage AD and 17 age- and gender-matched, cognitive normal (CN) adults participated in the experiment. A mirror operating system was designed to block the visual feedback of their grasping hand and forearms but to virtually show grasped targets. The target for reach-to-grasp kinematics was a reflective marker installed on a base; and the target for reach-to-grasp kinetics was a custom-made apparatus installed with two six-component force/torque transducers. Kinematics and kinetic parameters were used to quantify the reach-to-grasp performances. Results showed that the early-stage AD remarkably decreased the reaching speed, reduced the grasping accuracy and increased the transportation variability for reach-to-grasp kinematics. For kinetic analysis, early-stage AD extended the preload duration, disturbed the grip and lift forces coordination, and increased the feedforward proportion in the grasping force control. The AD-related changes in the reach-to-grasp kinematic and kinetic parameters depended on visual feedback and were associated with nervous system function according to correlation analyses with the neuropsychological testing. These results suggest that the abnormal kinematic and kinetic characteristics may correlate with the neuropsychological status of early-stage AD, and that the reach-to-grasp kinematic and kinetic maneuver could potentially be used as a novel tool for non-invasive screening or evaluation of early-stage AD.

Differentiation of patients with mild cognitive impairment and healthy controls based on computer assisted hand movement analysis: a proof-of-concept study

Mild cognitive impairment (MCI) is the prodromal phase of dementia, and it is highly underdiagnosed in the community. We aimed to develop an automated, rapid (< 5 min), electronic screening tool for the recognition of MCI based on hand movement analysis. Sixty-eight individuals participated in our study, 46 healthy controls and 22 patients with clinically defined MCI. All participants underwent a detailed medical assessment including neuropsychology and brain MRI. Significant differences were found between controls and MCI groups in mouse movement characteristics. Patients showed higher level of entropy for both the left (F = 5.24; p = 0.001) and the right hand (F = 8.46; p < 0.001). Longer time was required in MCI to perform the fine motor task (p < 0.005). Furthermore, we also found significant correlations between mouse movement parameters and neuropsychological test scores. Correlation was the strongest between motor parameters and Clinical Dementia Rating scale (CDR) score (average r: - 0.36, all p's < 0.001). Importantly, motor parameters were not influenced by age, gender, or anxiety effect (all p's > 0.05). Our study draws attention to the utility of hand movement analysis, especially to the estimation of entropy in the early recognition of MCI. It also suggests that our system might provide a promising tool for the cognitive screening of large populations.

Reduction in manual asymmetry and decline in fine manual dexterity in right-handed older adults with mild cognitive impairment

Research in Parkinson's or Alzheimer's disease suggests that hand function is affected by neurodegenerative diseases. However, little is known about the relationship between hand function and mild cognitive impairment (MCI). Therefore, we conducted a kinematic analysis of unimanual hand movements in MCI patients to answer whether manual asymmetries and manual dexterity are affected or preserved in this condition. Forty-one MCI patients and fifty healthy controls were tested with the Purdue Pegboard test. All participants were right-handed. Kinematic analyses (by hand) were calculated for path length, angle, and linear and angular velocities during reaching, grasping, transport and inserting. Group differences were tested by with factorial MANOVAs and laterality indexes (LI) were assessed. Groups were compared on "Right-Left" hand correlations to identify kinematics that best single-out patients. Kinematics from grasping and inserting were significantly more deteriorated in the MCI group, while outcomes for reaching and transport denoted superior performance. LIs data showed symmetry of movements in the MCI group, during reaching and transport. Comparisons of "Right-Left" hand correlations revealed that kinematics in reaching and transport were more symmetrical in patients. This study showed a deterioration of fine manual dexterity, an enhancement in gross dexterity of upper-limbs, and symmetrical movements in MCI patients.

Characteristics of Drawing Process Differentiate Alzheimer’s Disease and Dementia with Lewy Bodies

Background: Early differential diagnosis of Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) is important for treatment and disease management, but it remains challenging. Although computer-based drawing analysis may help differentiate AD and DLB, it has not been extensively studied. Objective: We aimed to identify the differences in features characterizing the drawing process between AD, DLB, and cognitively normal (CN) individuals, and to evaluate the validity of using these features to identify and differentiate AD and DLB. Methods: We collected drawing data with a digitizing tablet and pen from 123 community-dwelling older adults in three clinical diagnostic groups of mild cognitive impairment or dementia due to AD (n = 47) or Lewy body disease (LBD; n = 27), and CN (n = 49), matched for their age, sex, and years of education. We then investigated drawing features in terms of the drawing speed, pressure, and pauses. Results: Reduced speed and reduced smoothness in speed and pressure were observed particularly in the LBD group, while increased pauses and total durations were observed in both the AD and LBD groups. Machine-learning models using these features achieved an area under the receiver operating characteristic curve (AUC) of 0.80 for AD versus CN, 0.88 for LBD versus CN, and 0.77 for AD versus LBD. Conclusion: Our results indicate how different types of drawing features were particularly discriminative between the diagnostic groups, and how the combination of these features can facilitate the identification and differentiation of AD and DLB.

On Extracting Digitized Spiral Dynamics’ Representations: A Study on Transfer Learning for Early Alzheimer’s Detection

This work proposes a decision-aid tool for detecting Alzheimer’s disease (AD) at an early stage, based on the Archimedes spiral, executed on a Wacom digitizer. Our work assesses the potential of the task as a dynamic gesture and defines the most pertinent methodology for exploiting transfer learning to compensate for sparse data. We embed directly in spiral trajectory images, kinematic time functions. With transfer learning, we perform automatic feature extraction on such images. Experiments on 30 AD patients and 45 healthy controls (HC) show that the extracted features allow a significant improvement in sensitivity and accuracy, compared to raw images. We study at which level of the deep network features have the highest discriminant capabilities. Results show that intermediate-level features are the best for our specific task. Decision fusion of experts trained on such descriptors outperforms low-level fusion of hybrid images. When fusing decisions of classifiers trained on the best features, from pressure, altitude, and velocity images, we obtain 84% of sensitivity and 81.5% of accuracy, achieving an absolute improvement of 22% in sensitivity and 7% in accuracy. We demonstrate the potential of the spiral task for AD detection and give a complete methodology based on off-the-shelf features.

fNIRS-based adaptive visuomotor task improves sensorimotor cortical activation

OBJECTIVE

Investigating how to promote the functional activation of the central sensorimotor system is an important goal in the neurorehabilitation research domain. We aim to validate the effectiveness of facilitating cortical excitability using a closed-loop visuomotor task, in which the task difficulty is adaptively adjusted based on an individual's sensorimotor cortical activation.

APPROACH

We developed a novel visuomotor task, in which subjects moved a handle of a haptic device along a specific path while exerting a constant force against a virtual surface under visual feedback. The difficulty levels of the task were adapted with the aim of increasing the activation of sensorimotor areas, measured non-invasively by functional near-infrared spectroscopy. The changes in brain activation of the bilateral prefrontal cortex, sensorimotor cortex, and the occipital cortex obtained during the adaptive visuomotor task (adaptive group), were compared to the brain activation pattern elicited by the same duration of task with random difficulties in a control group.

MAIN RESULTS

During one intervention session, the adaptive group showed significantly increased activation in the bilateral sensorimotor cortex, also enhanced effective connectivity between the prefrontal and sensorimotor areas compared to the control group.

SIGNIFICANCE

Our findings demonstrated that the fNIRS-based adaptive visuomotor task with high ecological validity can facilitate the neural activity in sensorimotor areas and thus has the potential to improve hand motor functions.

Automated Early Detection of Alzheimer's Disease by Capturing Impairments in Multiple Cognitive Domains with Multiple Drawing Tasks

BACKGROUND

Automatic analysis of the drawing process using a digital tablet and pen has been applied to successfully detect Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most studies focused on analyzing individual drawing tasks separately, and the question of how a combination of drawing tasks could improve the detection performance thus remains unexplored.

OBJECTIVE

We aimed to investigate whether analysis of the drawing process in multiple drawing tasks could capture different, complementary aspects of cognitive impairments, with a view toward combining multiple tasks to effectively improve the detection capability.

METHODS

We collected drawing data from 144 community-dwelling older adults (27 AD, 65 MCI, and 52 cognitively normal, or CN) who performed five drawing tasks. We then extracted motion- and pause-related drawing features for each task and investigated the statistical associations of the features with the participants' diagnostic statuses and cognitive measures.

RESULTS

The drawing features showed gradual changes from CN to MCI and then to AD, and the changes in the features for each task were statistically associated with cognitive impairments in different domains. For classification into the three diagnostic categories, a machine learning model using the features from all five tasks achieved a classification accuracy of 75.2%, an improvement by 7.8% over that of the best single-task model.

CONCLUSION

Our results demonstrate that a common set of drawing features from multiple drawing tasks can capture different, complementary aspects of cognitive impairments, which may lead to a scalable way to improve the automated detection of AD and MCI.

Automated Analysis of Drawing Process to Estimate Global Cognition in Older Adults: Preliminary International Validation on the US and Japan Data Sets

BACKGROUND

With the aging of populations worldwide, early detection of cognitive impairments has become a research and clinical priority, particularly to enable preventive intervention for dementia. Automated analysis of the drawing process has been studied as a promising means for lightweight, self-administered cognitive assessment. However, this approach has not been sufficiently tested for its applicability across populations.

OBJECTIVE

The aim of this study was to evaluate the applicability of automated analysis of the drawing process for estimating global cognition in community-dwelling older adults across populations in different nations.

METHODS

We collected drawing data with a digital tablet, along with Montreal Cognitive Assessment (MoCA) scores for assessment of global cognition, from 92 community-dwelling older adults in the United States and Japan. We automatically extracted 6 drawing features that characterize the drawing process in terms of the drawing speed, pauses between drawings, pen pressure, and pen inclinations. We then investigated the association between the drawing features and MoCA scores through correlation and machine learning-based regression analyses.

RESULTS

We found that, with low MoCA scores, there tended to be higher variability in the drawing speed, a higher pause:drawing duration ratio, and lower variability in the pen's horizontal inclination in both the US and Japan data sets. A machine learning model that used drawing features to estimate MoCA scores demonstrated its capability to generalize from the US dataset to the Japan dataset (R2=0.35; permutation test, P<.001).

CONCLUSIONS

This study presents initial empirical evidence of the capability of automated analysis of the drawing process as an estimator of global cognition that is applicable across populations. Our results suggest that such automated analysis may enable the development of a practical tool for international use in self-administered, automated cognitive assessment.

Associations of Lower Extremity Peripheral Nerve Impairment and Risk of Dementia in Black and White Older Adults

BACKGROUND AND OBJECTIVES

Peripheral nerve impairments and dementia are common among older adults and share risk factors. However, few studies have examined whether peripheral nerve function and dementia are associated. We evaluated whether lower extremity peripheral nerve impairments were associated with higher incidence of dementia and whether associations differed by comorbidity subgroups (diabetes, low vitamin B12, and APOE ε4 allele carriers).

METHODS

We studied Black and White Health, Aging, and Body Composition Study participants 70 to 79 years of age and without dementia at enrollment. Lower extremity sensory and motor peripheral nerve function was measured at year 4 (the analytic baseline of this study). Sensory nerve impairments were measured with monofilament (1.4 g, 10 g) and vibration threshold of the toe. Monofilament insensitivity was defined as unable to detect monofilament (3 of 4 touches), and vibration detection impairment was defined as >130 μm. Fibular motor impairments were defined as <1 mV compound motor action potential (CMAP) amplitude and slow nerve conduction velocity <40 m/s. Incident dementia over the following 11 years was determined from medical records, cognitive scores, and medications. Cox proportional hazard models adjusted for demographics and health conditions assessed associations of nerve impairments with incident dementia.

RESULTS

Among 2,174 participants (52% women, 35% Black), 45% could not detect monofilament 1.4 g, 9% could not detect monofilament 10 g, 6% could not feel vibration, 10% had low CMAP amplitude, and 24% had slow conduction velocity. Monofilament 10 g (hazard ratio [HR] 1.35, 95% CI 0.99-1.84) and vibration detection insensitivity (HR 1.73, 95% CI 1.24-2.40) were associated/borderline associated with a higher risk of dementia after covariate adjustment. Estimates were elevated but not significant for monofilament 1.4 g, CMAP amplitude, and conduction velocity (p > 0.05). Increasing number of peripheral nerve impairments was associated with higher risk of dementia in a graded fashion; for ≥3 impairments, the HR was 2.37 (95% CI 1.29-4.38). In subgroup analyses, effect estimates were generally higher among those with diabetes, low vitamin B12, and APOE ε4 allele except for vibration detection.

DISCUSSION

Peripheral nerve impairments, especially sensory, were associated with a higher risk of dementia even after adjustment for age and other health factors. These associations may represent a shared susceptibility to nervous system degeneration.

Correlations between Hand Dexterity and Bimanual Coordination on the Activities of Daily Living in Older Adults with Mild Cognitive Impairment

Background/Aims

Many motor impairments are present in older adults with cognitive decline. One of them is the impairment of hand dexterity and bimanual coordination that result in poor functional ability in the activities of daily living (ADL). This study investigated the effects of hand dexterity and bimanual coordination declination on the sub-domains of ADL in older adults with mild cognitive impairment (MCI).

Methods

Thirty-one senior individuals with MCI were recruited in this study. The Purdue Pegboard Test was used to measure hand dexterity, and bimanual coordination was assessed by the continuous circle-drawing task. Their ADL were assessed with the General Activity Daily Living questionnaire.

Results

The correlations analysis showed an association between the dominant hand and bimanual dexterity with the domestic domain of ADL and all conditions of hand dexterity with the complex domain of ADL. Moreover, the multiple regression analysis showed that the predictor of the greatest effect for domestic and complex domains was dominant hand dexterity.

Discussion/Conclusion

These results revealed that dominant hand dexterity strongly affected domestic and complex ADL in older adults with MCI. There were age-related changes regarding lateral asymmetrical motor reduction, especially in cognitive tasks. However, complex tasks involving cognitive function may need dominant, nondominant and bimanual hand dexterity.

Neuropsychology of posteromedial parietal cortex and conversion factors from Mild Cognitive Impairment to Alzheimer's disease: systematic search and state-of-the-art review

In the present review, we discuss the rationale and the clinical implications of assessing visuospatial working memory (VSWM), awareness of memory deficits, and visuomotor control in patients with mild cognitive impairment (MCI). These three domains are related to neural activity in the posteromedial parietal cortex (PMC) whose hypoactivation seems to be a significant predictor of conversion from MCI to Alzheimer’s disease (AD) as indicated by recent neuroimaging evidence. A systematic literature search was performed up to May 2021. Forty-eight studies were included: 42 studies provided analytical cross-sectional data and 6 studies longitudinal data on conversion rates. Overall, these studies showed that patients with MCI performed worse than healthy controls in tasks assessing VSWM, awareness of memory deficits, and visuomotor control; in some cases, MCI patients’ performance was comparable to that of patients with overt dementia. Deficits in VSWM and metamemory appear to be significant predictors of conversion. No study explored the relationship between visuomotor control and conversion. Nevertheless, it has been speculated that the assessment of visuomotor abilities in subjects at high AD risk might be useful to discriminate patients who are likely to convert from those who are not. Being able to indirectly estimate PMC functioning through quick and easy neuropsychological tasks in outpatient settings may improve diagnostic and prognostic accuracy, and therefore, the quality of the MCI patient’s management.

A new scheme for the automatic assessment of Alzheimer's disease on a fine motor task with Transfer Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:3823-3829. [PMID: 34892068 DOI: 10.1109/embc46164.2021.9630539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

We present a new scheme for Alzheimer's Disease (AD) automatic assessment, based on Archimedes spiral, drawn on a digitizing tablet. We propose to enrich spiral images generated from the raw sequence of pen coordinates with dynamic information (pressure, altitude, velocity) represented with a semi-global encoding in RGB images. By exploiting Transfer Learning, such hybrid images are given as input to a deep network for an automatic high-level feature extraction. Experiments on 30 AD patients and 45 Healthy Controls (HC) showed that the hybrid representations allow a considerable improvement of classification performance, compared to those obtained on raw spiral images. We reach, with SVM classifiers, an accuracy of 79% with pressure, 76% with velocity, and 70.5% with altitude. The analysis with PCA of internal features of the deep network, showed that dynamic information included in images explain a much higher amount of variance compared to raw images. Moreover, our study demonstrates the need for a semi-global description of dynamic parameters, for a better discrimination of AD and HC classes. This description allows uncovering specific trends on the dynamics for both classes. Finally, combining the decisions of the three SVMs leads to 81.5% of accuracy.Clinical Relevance- This work proposes a decision-aid tool for detecting AD at an early stage, based on a non-invasive simple graphic task, executed on a Wacom digitizer. This task can be considered in the battery of usual clinical tests.

Primary Progressive Aphasia: Use of Graphical Markers for an Early and Differential Diagnosis

Primary progressive aphasia (PPA) brings together neurodegenerative pathologies whose main characteristic is to start with a progressive language disorder. PPA diagnosis is often delayed in non-specialised clinical settings. With the technologies' development, new writing parameters can be extracted, such as the writing pressure on a touch pad. Despite some studies having highlighted differences between patients with typical Alzheimer's disease (AD) and healthy controls, writing parameters in PPAs are understudied. The objective was to verify if the writing pressure in different linguistic and non-linguistic tasks can differentiate patients with PPA from patients with AD and healthy subjects. Patients with PPA (n = 32), patients with AD (n = 22) and healthy controls (n = 26) were included in this study. They performed a set of handwriting tasks on an iPad® digital tablet, including linguistic, cognitive non-linguistic, and non-cognitive non-linguistic tasks. Average and maximum writing pressures were extracted for each task. We found significant differences in writing pressure, between healthy controls and patients with PPA, and between patients with PPA and AD. However, the classification of performances was dependent on the nature of the tasks. These results suggest that measuring writing pressure in graphical tasks may improve the early diagnosis of PPA, and the differential diagnosis between PPA and AD.

Anticipatory and Reactive Grip Force Control in Patients with Alzheimer's Disease: A Pilot Study

BACKGROUND

Alzheimer's disease (AD) affects several cognitive functions and causes altered motor function. Fine motor deficits during object manipulation are evident in other neurological conditions, but have not been assessed in dementia patients yet.

OBJECTIVE

Investigate reactive and anticipatory grip force control in response to unexpected and expected load force perturbation in AD.

METHODS

Reactive and anticipatory grip force was investigated using a grip-device with force sensors. In this pilot study, fifteen AD patients and fourteen healthy controls performed a catching task. They held the device with one hand while a sandbag was dropped into an attached receptacle either by the experimenter or by the participant.

RESULTS

In contrast to studies of other neurological conditions, the majority of AD patients exerted lower static grip force levels than controls. Interestingly, patients who were slow in the Luria's three-step test produced normal grip forces. The timing and magnitude of reactive grip force control were largely preserved in patients. In contrast, timing and extent of anticipatory grip forces were impaired in patients, although anticipatory control was generally preserved. These deficits were correlated with decreasing Mini-Mental State Examination scores. Apraxia scores, assessed by pantomime of tool-use, did not correlate with performance in the catching task.

CONCLUSION

We interpreted the decreased grip force in AD in the context of loss of strength and lethargy, typical for patients with AD. The lower static grip force during object manipulation may emerge as a potential biomarker for early stages of AD, but more studies with larger sample sizes are necessary.

From online handwriting to synthetic images for Alzheimer's disease detection using a deep transfer learning approach

Early diagnosis of neurodegenerative disorders, such as Alzheimer's Disease (AD), is very important to reduce their effects and to improve both quality and life expectancy of patients. In this context, it is generally agreed that handwriting is one of the first skills altered by the onset of such diseases. For this reason, the analysis of handwriting and the study of its alterations have become of great interest in order to formulate the diagnosis as soon as possible. A fundamental aspect for the use of these techniques is the definition of effective features, which allows the system to distinguish the natural alterations of handwriting due to age, from those caused by neurodegenerative disorders. Starting from these considerations, the aim of our study is to verify whether the combined use of both shape and dynamic features allows a decision support system to improve performance for AD diagnosis. To this purpose, starting from a database of on-line handwriting samples, we generated for each of them an off-line synthetic color image, where the color of each elementary trait encodes, in the three RGB channels, the dynamic information associated with that trait. In order to verify the importance and the specific role played by shape information, we also generated an off-line synthetic binary image for each handwriting sample, where background pixels have white color, while those corresponding to the traits have black color. Finally, we exploited the ability of Convolutional Neural Network (CNN) to automatically extract features on both color and binary images. We carried out a large set of experiments for comparing the results obtained by using on-line features with those obtained by using the off-line features provided by CNN on both color and binary images.

Emerging concepts on bradykinesia in non-parkinsonian conditions

BACKGROUND AND PURPOSE

Bradykinesia is one of the cardinal motor symptoms of Parkinson's disease. However, clinical and experimental studies indicate that bradykinesia may also be observed in various neurological diseases not primarily characterized by parkinsonism. These conditions include hyperkinetic movement disorders, such as dystonia, chorea, and essential tremor. Bradykinesia may also be observed in patients with neurological conditions that are not seen as "movement disorders," including those characterized by the involvement of the cerebellum and corticospinal system, dementia, multiple sclerosis, and psychiatric disorders.

METHODS

We reviewed clinical reports and experimental studies on bradykinesia in non-parkinsonian conditions and discussed the major findings.

RESULTS

Bradykinesia is a common motor abnormality in non-parkinsonian conditions. From a pathophysiological standpoint, bradykinesia in neurological conditions not primarily characterized by parkinsonism may be explained by brain network dysfunction.

CONCLUSION

In addition to the pathophysiological implications, the present paper highlights important terminological issues and the need for a new, more accurate, and more widely used definition of bradykinesia in the context of movement disorders and other neurological conditions.

Deterioration, Compensation and Motor Control Processes in Healthy Aging, Mild Cognitive Impairment and Alzheimer's Disease

Aging is associated with modifications of several brain structures and functions. These modifications then manifest as modified behaviors. It has been proposed that some brain function modifications may compensate for some other deteriorated ones, thus maintaining behavioral performance. Through the concept of compensation versus deterioration, this article reviews the literature on motor function in healthy and pathological aging. We first highlight mechanistic studies that used paradigms, allowing us to identify precise compensation mechanisms in healthy aging. Subsequently, we review studies investigating motor function in two often-associated neurological conditions, i.e., mild cognitive impairment and Alzheimer's disease. We point out the need to expand the knowledge gained from descriptive studies with studies targeting specific motor control processes. Teasing apart deteriorated versus compensating processes represents precious knowledge that could significantly improve the prevention and rehabilitation of age-related loss of mobility.

[Use of new technologies in the diagnosis of neurodegenerative pathologies]

These last ten years, new technologies are more and more used in therapeutic and rehabilitation programms for patients with dementia, and used for the diagnosis of theses diseases, from the signal treatment. A review of litterature shows this growing interest among the scientific communauty for these new technologies.

Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer's Disease and Mild Cognitive Impairment

BACKGROUND

Gait, speech, and drawing behaviors have been shown to be sensitive to the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, previous studies focused on only analyzing individual behavioral modalities, although these studies suggested that each of these modalities may capture different profiles of cognitive impairments associated with AD.

OBJECTIVE

We aimed to investigate if combining behavioral data of gait, speech, and drawing can improve classification performance compared with the use of individual modality and if each of these behavioral data can be associated with different cognitive and clinical measures for the diagnosis of AD and MCI.

METHODS

Behavioral data of gait, speech, and drawing were acquired from 118 AD, MCI, and cognitively normal (CN) participants.

RESULTS

Combining all three behavioral modalities achieved 93.0% accuracy for classifying AD, MCI, and CN, and only 81.9% when using the best individual behavioral modality. Each of these behavioral modalities was statistically significantly associated with different cognitive and clinical measures for diagnosing AD and MCI.

CONCLUSION

Our findings indicate that these behaviors provide different and complementary information about cognitive impairments such that classification of AD and MCI is superior to using either in isolation.

Does a Live Performance Impact Synchronization to Musical Rhythm in Cognitively Impaired Elderly?

BACKGROUND

Music-based interventions appear to be efficient approaches to improve emotional, social, and cognitive functioning of patients with neurodegenerative diseases.

OBJECTIVE

Because benefits seem to increase with patient's motor involvement, we studied sensorimotor synchronization (SMS) abilities of patients with cognitive impairments (Alzheimer's disease, vascular and mixed dementia) and of patients with no evidence of cognitive impairments. More specifically, we compared the impact of a live performance by a musician to a video recording on SMS.

METHODS

SMS to a metronomic or a musical stimulus was assessed while patients watched a live musician or his pre-recorded video.

RESULTS

SMS to a metronome was better than to music but this effect was modulated by the social context. While SMS to a metronome was better when facing a video than a live performance, there was no impact of social context on SMS to music. No group differences of SMS were found.

CONCLUSION

The decrease in SMS to a metronome in a live performance may be due to social pressure. Such a pressure might be removed in pleasant social activities, like moving with music in a group, explaining the lack of effect on SMS to music. We found no performance differences in groups, suggesting relatively spared SMS in cognitively impaired patients. By showing that it is possible to encourage patients to synchronize with others, even when facing a video, our results indicate that SMS can be used as a relevant predictor in clinical trials and open up promising therapeutic options for isolated patients.

Alzheimer's Disease Classification With a Cascade Neural Network

Classification of Alzheimer's Disease (AD) has been becoming a hot issue along with the rapidly increasing number of patients. This task remains tremendously challenging due to the limited data and the difficulties in detecting mild cognitive impairment (MCI). Existing methods use gait [or EEG (electroencephalogram)] data only to tackle this task. Although the gait data acquisition procedure is cheap and simple, the methods relying on gait data often fail to detect the slight difference between MCI and AD. The methods that use EEG data can detect the difference more precisely, but collecting EEG data from both HC (health controls) and patients is very time-consuming. More critically, these methods often convert EEG records into the frequency domain and thus inevitably lose the spatial and temporal information, which is essential to capture the connectivity and synchronization among different brain regions. This paper proposes a cascade neural network with two steps to achieve a faster and more accurate AD classification by exploiting gait and EEG data simultaneously. In the first step, we propose attention-based spatial temporal graph convolutional networks to extract the features from the skeleton sequences (i.e., gait) captured by Kinect (a commonly used sensor) to distinguish between HC and patients. In the second step, we propose spatial temporal convolutional networks to fully exploit the spatial and temporal information of EEG data and classify the patients into MCI or AD eventually. We collect gait and EEG data from 35 cognitively health controls, 35 MCI, and 17 AD patients to evaluate our proposed method. Experimental results show that our method significantly outperforms other AD diagnosis methods (91.07 vs. 68.18%) in the three-way AD classification task (HC, MCI, and AD). Moreover, we empirically found that the lower body and right upper limb are more important for the early diagnosis of AD than other body parts. We believe this interesting finding can be helpful for clinical researches.

Supervised Digital Neuropsychological Tests for Cognitive Decline in Older Adults: Usability and Clinical Validity Study

Background

Dementia is a major and growing health problem, and early diagnosis is key to its management.

Objective

With the ultimate goal of providing a monitoring tool that could be used to support the screening for cognitive decline, this study aims to develop a supervised, digitized version of 2 neuropsychological tests: Trail Making Test and Bells Test. The system consists of a web app that implements a tablet-based version of the tests and consists of an innovative vocal assistant that acts as the virtual supervisor for the execution of the test. A replay functionality is added to allow inspection of the user’s performance after test completion.

Methods

To deploy the system in a nonsupervised environment, extensive functional testing of the platform was conducted, together with a validation of the tablet-based tests. Such validation had the two-fold aim of evaluating system usability and acceptance and investigating the concurrent validity of computerized assessment compared with the corresponding paper-and-pencil counterparts.

Results

The results obtained from 83 older adults showed high system acceptance, despite the patients’ low familiarity with technology. The system software was successfully validated. A concurrent validation of the system reported good ability of the digitized tests to retain the same predictive power of the corresponding paper-based tests.

Conclusions

Altogether, the positive results pave the way for the deployment of the system to a nonsupervised environment, thus representing a potential efficacious and ecological solution to support clinicians in the identification of early signs of cognitive decline.

Electrophysiological Processes on Motor Imagery Mediate the Association Between Increased Gray Matter Volume and Cognition in Amnestic Mild Cognitive Impairment

Motor imagery is considered as an ideal window to observe neural processes of action representations. Behavioral evidence has indicated an alteration of motor imagery in amnestic mild cognitive impairment (aMCI). However, it still remains unclear on the altered neurophysiological processing mechanism of motor imagery and whether this mechanism links the abnormal biological basis of motor imagery with impaired cognition in aMCI. This study was to investigate the altered neurophysiological processing mechanism of motor imagery and to examine the relationships between this knowledge and the altered structural basis of motor imagery with impaired cognition in aMCI. A hand mental rotation paradigm was used to manipulate the processing of motor imagery while event-related brain potentials (ERPs) were recorded and gray matter (GM) voxel-based morphometry was performed in 20 aMCI and 29 healthy controls. Compared with controls, aMCI exhibited lower ERP amplitudes in parietal cortex and higher ERP amplitudes in frontal cortex during motor imagery. In addition, aMCI showed reduced GM volumes in cerebellum posterior lobe, insula and hippocampus/parahippocampal gyrus, and increased GM volumes in middle cingulate gyrus and superior frontal gyrus. Most importantly, increased ERP amplitude significantly mediated the association between increased GM and cognition. This study provided a novel evidence for the relationships between the electrophysiological processing mechanism and structural basis of motor imagery with impaired cognition in aMCI. It suggests that improving neural activity by stimulating the frontal lobe can potentially contribute to acquire motor imagery skills for neurological rehabilitation in aMCI subjects.

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in cognitive functioning; thus, analyzing handwriting in elderly people may facilitate the diagnosis and monitoring of these impairments. A large body of knowledge has been collected in the last thirty years thanks to the advent of new technologies which allow researchers to investigate not only the static characteristics of handwriting but also especially the dynamic aspects of the handwriting process. The present paper aims at providing an overview of the most relevant literature investigating the application of dynamic handwriting analysis in neurodegenerative disease assessment. The focus, in particular, is on Parkinon’s disease (PD) and Alzheimer’s disease (AD), as the two most widespread neurodegenerative disorders. More specifically, the studies taken into account are grouped in accordance with three main research questions: disease insight, disease monitoring, and disease diagnosis. The net result is that dynamic handwriting analysis is a powerful, noninvasive, and low-cost tool for real-time diagnosis and follow-up of PD and AD. In conclusion of the paper, open issues still demanding further research are highlighted.

Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study

We aimed to explore the online signature modality for characterizing early-stage Alzheimer’s disease (AD). A few studies have explored this modality, whereas many on online handwriting have been published. We focused on the analysis of raw temporal functions acquired by the digitizer on signatures produced during a simulated check-filling task. Sample entropy was exploited to measure the information content in raw time sequences. We show that signatures of early-stage AD patients have lower information content than those of healthy persons, especially in the time sequences of pen pressure and pen altitude angle with respect to the tablet. The combination of entropy values on two signatures for each person was classified with two linear classifiers often used in the literature: support vector machine and linear discriminant analysis. The improvements in sensitivity and specificity were significant with respect to the a priori group probabilities in our population of AD patients and healthy subjects. We show that altitude angle, when combined with pen pressure, conveys crucial information on the wrist-hand-finger system during signature production for pathology detection.

Signature dynamics in Alzheimer's disease

Forensic document examiners are often called upon to opine on the authenticity of handwritten signatures by individuals with diminished mental capacity. Legal arguments surrounding the decisional capacity of an individual with dementia can be found in many cases involving wills, deeds, trusts, and contracts. The purpose of this study was to provide estimates of feature variability derived from dynamic analyses of signatures written by individuals with dementia of the Alzheimer type (AD) compared with age-comparable healthy individuals. Dynamic features of digitally captured signatures were analyzed to test the hypothesis that AD signature features will show greater variability compared with signatures from age-comparable healthy subjects. The study enrolled 69 AD and 74 age comparable healthy subjects. Results revealed four main findings from AD signatures: (1) that the temporal, spatial and fluency characteristics of signature formation did not differ from signatures of healthy writers; (2) variability in dynamic features over a series of repetitive signatures fell within 10% of the natural variation of healthy subjects; (3) there was a significant association between increased dynamic signature feature variability and increased dementia severity for stylized and mixed signatures only; and (4) despite significant decline in cognitive status over a 1-year period, dynamic signature features remained stable. Overall, these results suggest that signature writing is preserved in AD. The association between dementia severity and dynamic feature variability among AD subjects with stylized or mixed signatures warrants further research.

Managing Paratonia in Persons With Dementia: Short-term Effects of Supporting Cushions and Harmonic Techniques

OBJECTIVES

Paratonia, a form of hypertonia typically seen in dementia, is often associated with difficulties in positioning and daily care. No evidence-based therapy or clinical guideline for management is available. In this study, the short-term effect of harmonic techniques (HT) and supporting cushions (SC) on paratonia was explored.

DESIGN

This was a multicenter interventional clinical trial with AB/BA crossover design. Each intervention (SC or HT) was subsequently implemented over 1 week in each of the participants.

SETTING AND PARTICIPANTS

The study included 22 participants with moderate to severe paratonia from 9 different nursing homes in Flanders, Belgium.

METHODS

Measurements of biceps brachii and rectus femoris muscle tone (MyotonPRO), maximal elbow and knee extension (goniometer), and pain (Pain Assessment Checklist for Seniors With Limited Ability to Communicate) were performed on 3 different days within 1 week. The effect of HT on nursing care was evaluated with the Pain Assessment Checklist for Seniors With Limited Ability to Communicate and visual analog scale ratings of discomfort items.

RESULTS

After 30 minutes of positioning with SC, participants had lower biceps brachii muscle tone (P = .041) and higher maximal elbow extension (P = .006) than without SC. After a 30-minute session of HT, a significant increase in biceps brachii muscle tone (P = .032) and maximal extension of elbow (P < .001) and knee (P = .028) was found. Pain (P = .003) and discomfort (P = .001 to P = .019) during morning care were significantly lower when care was preceded by 30 minutes of HT.

CONCLUSIONS/IMPLICATIONS

This explorative study revealed beneficial short-term effects on range of motion for both SC and HT and a positive effect of SC on upper limb muscle tone. Beneficial effects of HT were found on resident's pain and caregiver's discomfort during care. The results of the present study are encouraging and can contribute to the development of evidence-based interventions for paratonia.

Reduced graphomotor procedural learning in children and adolescents with ADHD

PURPOSE

The present study sought to determine if children and adolescents with ADHD demonstrate reduced procedural learning of a graphomotor program.

METHOD

Thirty-two children and adolescents between age 9 and 15 with (n = 16) and without ADHD (n = 16) participated in the study. Each group of participants practiced a novel grapheme on a digitizing tablet 30 times. Participants with ADHD were off stimulant medication or were medication naïve.

RESULTS

Control participants demonstrated significant improvement in graphomotor fluency from the beginning to the end of practice, T = 2, z = -2.534, p = .009, whereas participants with ADHD did not, T = 4, z = -1.810, p = .074.

CONCLUSIONS

Consistent with findings in adults with ADHD, results indicate that graphomotor procedural learning in children and adolescents with ADHD is attenuated. Findings have implications for future research that may inform remediation of handwriting difficulties, academic accommodations, and using digitizing technology for neuropsychological assessment.