Eyetracking metrics reveal impaired spatial anticipation in behavioural variant frontotemporal dementia

Effectiveness of the Hayling and Brixton Tests for Detecting Dementia, Progressive Cognitive Decline, and Mild Cognitive Impairment in Middle to Older Aged Adults: A Systematic Review and Meta-analysis

The aging population is increasing the prevalence of dementia, neurodegenerative disorders, and mild cognitive impairment, which are associated with cognitive declines in executive functioning. In people with these disorders, accurate tests can aid in the early detection of executive functioning decline and facilitate access to interventions. The Hayling and Brixton tests (HBTs) are popular executive functioning tests that assess inhibitory control. The HBTs may be especially effective for detecting people with disorders that are associated with disinhibition, such as behavioral-variant frontotemporal dementia (bvFTD). However, the effectiveness of the HBTs for detecting cognitive decline in dementia, neurodegenerative disorders, and mild cognitive impairment has yet to be collated. A comprehensive search of five databases identified 50 studies that compared the HBTs performances of adults aged 40 years and over with a dementia, neurodegenerative disorder, or mild cognitive impairment (e.g., Parkinson's disease, Alzheimer's dementia, bvFTD) and cognitively-healthy controls. Hedges' g effect sizes compared groups on the five HBTs scores (Inhibition Errors, Inhibition Reaction Time (RT), Automatic RT, Inhibition minus Automatic RT, and Brixton Errors). The disorders (combined) showed negative effects on all HBTs scores (g - 0.37 to - 1.13), with dementia (combined) performing the worst (g - 0.54 to - 1.56). Automatic RT and Inhibition Errors were the most effective scores for detecting cognitive decline in dementia (g - 1.55; g - 1.34). The dementia types performed similar after outliers were removed and only studies with low risk-of-bias were analyzed. Overall, the HBTs are effective for detecting cognitive decline in middle to older aged adults, especially those with dementia. However, no score type can be recommended for differentiating the dementia types, such as AD and bvFTD.

Detecting Abnormal Eye Movements in Patients with Neurodegenerative Diseases - Current Insights

This review delineates the ocular motor disturbances across a spectrum of neurodegenerative disorders, including Alzheimer's Disease (AD) and related disorders (ADRD), Parkinson's Disease (PD), atypical parkinsonism, and others, leveraging advancements in eye-tracking technology for enhanced diagnostic precision. We delve into the different classes of eye movements, their clinical assessment, and specific abnormalities manifesting in these diseases, highlighting the nuanced differences and shared patterns. For instance, AD and ADRD are characterized by increased saccadic latencies and instability in fixation, while PD features saccadic hypometria and mild smooth pursuit impairments. Atypical parkinsonism, notably Progressive Supranuclear Palsy (PSP) and Corticobasal Syndrome (CBS), presents with distinct ocular motor signatures such as vertical supranuclear gaze palsy and saccadic apraxia, respectively. Our review underscores the diagnostic value of eye movement analysis in differentiating between these disorders and also posits the existence of underlying common pathological mechanisms. We discuss how eye movements have potential as biomarkers for neurodegenerative diseases but also some of the existing limitations.

Poststroke Executive Function in Relation to White Matter Damage on Clinically Acquired CT Brain Imaging

BACKGROUND

Executive function (EF) impairments are prevalent post stroke and are associated with white matter (WM) damage on MRI. However, less is known about the relationship between poststroke EF and WM damage on CT imaging.

OBJECTIVE

To investigate the relationship between poststroke EF and WM damage associated with stroke lesions and WM hypointensities (WMHs) on clinically acquired CT imaging.

METHOD

This study analyzed data from the Oxford Cognitive Screening Program, which recruited individuals aged ≥18 years with a confirmed stroke from an acute stroke unit. The individuals completed a follow-up assessment 6 months post stroke. We included individuals with a CT scan showing a visible stroke who completed follow-up EF assessment using the Oxford Cognitive Screen-Plus rule-finding task. We manually delineated stroke lesions and quantified then dichotomized WM damage caused by the stroke using the HCP-842 atlas. We visually rated then dichotomized WMHs using the Age-Related White Matter Changes Scale.

RESULTS

Among 87 stroke survivors (M age = 73.60 ± 11.75; 41 female; 61 ischemic stroke), multivariable linear regression showed that stroke damage to the medial lemniscus ( B = -8.86, P < 0.001) and the presence of WMHs ( B = -5.42, P = 0.005) were associated with poorer EF 6 months post stroke after adjusting for covariates including age and education.

CONCLUSION

Poorer EF was associated with WM damage caused by stroke lesions and WMHs on CT. These results confirm the importance of WM integrity for EF post stroke and demonstrate the prognostic utility of CT-derived imaging markers for poststroke cognitive outcomes.

Updating the study protocol: Insight 46 - a longitudinal neuroscience sub-study of the MRC National Survey of Health and Development - phases 2 and 3

BACKGROUND

Although age is the biggest known risk factor for dementia, there remains uncertainty about other factors over the life course that contribute to a person's risk for cognitive decline later in life. Furthermore, the pathological processes leading to dementia are not fully understood. The main goals of Insight 46-a multi-phase longitudinal observational study-are to collect detailed cognitive, neurological, physical, cardiovascular, and sensory data; to combine those data with genetic and life-course information collected from the MRC National Survey of Health and Development (NSHD; 1946 British birth cohort); and thereby contribute to a better understanding of healthy ageing and dementia.

METHODS/DESIGN

Phase 1 of Insight 46 (2015-2018) involved the recruitment of 502 members of the NSHD (median age = 70.7 years; 49% female) and has been described in detail by Lane and Parker et al. 2017. The present paper describes phase 2 (2018-2021) and phase 3 (2021-ongoing). Of the 502 phase 1 study members who were invited to a phase 2 research visit, 413 were willing to return for a clinic visit in London and 29 participated in a remote research assessment due to COVID-19 restrictions. Phase 3 aims to recruit 250 study members who previously participated in both phases 1 and 2 of Insight 46 (providing a third data time point) and 500 additional members of the NSHD who have not previously participated in Insight 46.

DISCUSSION

The NSHD is the oldest and longest continuously running British birth cohort. Members of the NSHD are now at a critical point in their lives for us to investigate successful ageing and key age-related brain morbidities. Data collected from Insight 46 have the potential to greatly contribute to and impact the field of healthy ageing and dementia by combining unique life course data with longitudinal multiparametric clinical, imaging, and biomarker measurements. Further protocol enhancements are planned, including in-home sleep measurements and the engagement of participants through remote online cognitive testing. Data collected are and will continue to be made available to the scientific community.

Characterizing phonemic fluency by transfer learning with deep language models

Though phonemic fluency tasks are traditionally indexed by the number of correct responses, the underlying disorder may shape the specific choice of words-both correct and erroneous. We report the first comprehensive qualitative analysis of incorrect and correct words generated on the phonemic ('S') fluency test, in a large sample of patients (n = 239) with focal, unilateral frontal or posterior lesions and healthy controls (n = 136). We conducted detailed qualitative analyses of the single words generated in the phonemic fluency task using categorical descriptions for different types of errors, low-frequency words and clustering/switching. We further analysed patients' and healthy controls' entire sequences of words by employing stochastic block modelling of Generative Pretrained Transformer 3-based deep language representations. We conducted predictive modelling to investigate whether deep language representations of word sequences improved the accuracy of detecting the presence of frontal lesions using the phonemic fluency test. Our qualitative analyses of the single words generated revealed several novel findings. For the different types of errors analysed, we found a non-lateralized frontal effect for profanities, left frontal effects for proper nouns and permutations and a left posterior effect for perseverations. For correct words, we found a left frontal effect for low-frequency words. Our novel large language model-based approach found five distinct communities whose varied word selection patterns reflected characteristic demographic and clinical features. Predictive modelling showed that a model based on Generative Pretrained Transformer 3-derived word sequence representations predicted the presence of frontal lesions with greater fidelity than models of native features. Our study reveals a characteristic pattern of phonemic fluency responses produced by patients with frontal lesions. These findings demonstrate the significant inferential and diagnostic value of characterizing qualitative features of phonemic fluency performance with large language models and stochastic block modelling.

Eyes on dementia: an overview of the interplay between eye movements and cognitive decline

The economic and disease burden of dementia is forecasted to continue increasing. Considering its cognitive effects, timely diagnosis is important in developing a stage-based treatment plan and gathering data to support advocacy efforts and plan healthcare and social services. Eye-tracking technology has emerged as an efficient diagnostic tool in clinical practice and experimental studies. This review aimed to comprehensively analyze various aspects of eye-tracking technology, including pupillometry parameters, eye movements, eye-tracking devices, and neuropsychological tools. We conducted a systematic review retrieving articles published in the last ten years from six databases. Our results provide a complex overview for each included form of dementia/cognitive decline in terms of patient characteristics (age, sex-disaggregated by included pathologies), inclusion and exclusion criteria, devices, and neuropsychological tools. We also summarized findings on fixation stability tasks, saccadic evaluation, pupillometry, scene perception, object recognition, spatial memory, eye-tracking video tasks, and visual search. The eye-tracking method has become more common in cognitive assessments.

Novel instructionless eye tracking tasks identify emotion recognition deficits in frontotemporal dementia

Background

Current tasks measuring social cognition are usually ‘pen and paper’ tasks, have ceiling effects and include complicated test instructions that may be difficult to understand for those with cognitive impairment. We therefore aimed to develop a set of simple, instructionless, quantitative, tasks of emotion recognition using the methodology of eye tracking, with the subsequent aim of assessing their utility in individuals with behavioural variant frontotemporal dementia (bvFTD).

Methods

Using the Eyelink 1000 Plus eye tracker, 18 bvFTD and 22 controls completed tasks of simple and complex emotion recognition that involved viewing four images (one target face (simple) or pair of eyes (complex) and the others non-target) followed by a target emotion word and lastly the original four images alongside the emotion word. A dwell time change score was then calculated as the main outcome measure by subtracting the percentage dwell time for the target image before the emotion word appeared away from the percentage dwell time for the target image after the emotion word appeared. All participants also underwent a standard cognitive battery and volumetric T1-weighted magnetic resonance imaging.

Results

Analysis using a mixed effects model showed that the average (standard deviation) mean dwell time change score in the target interest area was 35 (27)% for the control group compared with only 4 (18)% for the bvFTD group (p < 0.05) for the simple emotion recognition task, and 15 (26)% for the control group compared with only 2 (18)% for the bvFTD group (p < 0.05) for the complex emotion recognition task. Worse performance in the bvFTD group correlated with atrophy in the right ventromedial prefrontal and orbitofrontal cortices, brain regions previously implicated in social cognition.

Conclusions

In summary, eye tracking is a viable tool for assessing social cognition in individuals with bvFTD, being well-tolerated and able to overcome some of the problems associated with standard psychometric tasks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00775-x.

Analyzing large Alzheimer's disease cognitive datasets: Considerations and challenges

Recent data-sharing initiatives of clinical and preclinical Alzheimer's disease (AD) have led to a growing number of non-clinical researchers analyzing these datasets using modern data-driven computational methods. Cognitive tests are key components of such datasets, representing the principal clinical tool to establish phenotypes and monitor symptomatic progression. Despite the potential of computational analyses in complementing the clinical understanding of AD, the characteristics and multifactorial nature of cognitive tests are often unfamiliar to computational researchers and other non-specialist audiences. This perspective paper outlines core features, idiosyncrasies, and applications of cognitive test data. We report tests commonly featured in data-sharing initiatives, highlight key considerations in their selection and analysis, and provide suggestions to avoid risks of misinterpretation. Ultimately, the greater transparency of cognitive measures will maximize insights offered in AD, particularly regarding understanding the extent and basis of AD phenotypic heterogeneity.

Augmenting Dementia Cognitive Assessment With Instruction-Less Eye-Tracking Tests

Eye-tracking technology is an innovative tool that holds promise for enhancing dementia screening. In this work, we introduce a novel way of extracting salient features directly from the raw eye-tracking data of a mixed sample of dementia patients during a novel instruction-less cognitive test. Our approach is based on self-supervised representation learning where, by training initially a deep neural network to solve a pretext task using well-defined available labels (e.g. recognising distinct cognitive activities in healthy individuals), the network encodes high-level semantic information which is useful for solving other problems of interest (e.g. dementia classification). Inspired by previous work in explainable AI, we use the Layer-wise Relevance Propagation (LRP) technique to describe our network's decisions in differentiating between the distinct cognitive activities. The extent to which eye-tracking features of dementia patients deviate from healthy behaviour is then explored, followed by a comparison between self-supervised and handcrafted representations on discriminating between participants with and without dementia. Our findings not only reveal novel self-supervised learning features that are more sensitive than handcrafted features in detecting performance differences between participants with and without dementia across a variety of tasks, but also validate that instruction-less eye-tracking tests can detect oculomotor biomarkers of dementia-related cognitive dysfunction. This work highlights the contribution of self-supervised representation learning techniques in biomedical applications where the small number of patients, the non-homogenous presentations of the disease and the complexity of the setting can be a challenge using state-of-the-art feature extraction methods.

Distinct performance profiles on the Brixton test in frontotemporal dementia

The Brixton Spatial Anticipation Test is a well-established test of executive function that evaluates the capacity to abstract, follow, and switch rules. There has been remarkably little systematic analysis of Brixton test performance in the prototypical neurodegenerative disorder of the frontal lobes: behavioural variant frontotemporal dementia (bvFTD) or evaluation of the test's ability to distinguish frontal from temporal lobe degenerative disease. We carried out a quantitative and qualitative analysis of Brixton performance in 76 patients with bvFTD and 34 with semantic dementia (SD) associated with temporal lobe degeneration. The groups were matched for demographic variables and illness duration. The bvFTD group performed significantly more poorly (U = 348, p < .0001, r = .58), 53% of patients scoring in the poor-impaired range compared with 6% of SD patients. Whereas bvFTD patients showed problems in rule acquisition and switching, SD patients did not, despite their impaired conceptual knowledge. Error analysis revealed more frequent perseverative errors in bvFTD, particularly responses unconnected to the stimulus, as well as random responses. Stimulus-bound errors were rare. Within the bvFTD group, there was variation in performance profile, which could not be explained by demographic, neurological, or genetic factors. The findings demonstrate sensitivity and specificity of the Brixton test in identifying frontal lobe degenerative disease and highlight the clinical value of qualitative analysis of test performance. From a theoretical perspective, the findings provide evidence that semantic knowledge and the capacity to acquire rules are dissociable. Moreover, they exemplify the separable functional contributions to executive performance.

Is the Brixton Spatial Anticipation Test sensitive to frontal dysfunction? Evidence from patients with frontal and posterior lesions

INTRODUCTION

The Brixton Spatial Anticipation Test is a widely used neuropsychological test, thought to assess executive functions and to be sensitive to frontal lobe lesions. Our aim was to investigate Brixton performance in patients with focal frontal or posterior lesions and healthy controls.

METHOD

We compared performance on the Brixton in a sample of 24 frontal patients, 18 posterior patients and 22 healthy controls. Both overall performance (total number of errors) and error types were analyzed.

RESULTS

We found no significant differences between frontal and posterior patients and healthy controls in overall Brixton performance. Moreover, our error analysis showed no difference between frontal patients, posterior patients and healthy controls. The only exception was that posterior patients had a greater tendency to guess and make more errors when following specific rules than healthy controls but this was no longer significant once fluid intelligence was controlled for. We also found no significant difference between the performance of patients with left lateral (n = 11), right lateral (n = 10) or superior medial (n = 18) frontal lesions and healthy controls.

CONCLUSIONS

The Brixton test is not sensitive to frontal lobe dysfunction. It is likely that the test draws on a range of cognitive abilities not specific to frontal lobe lesions. Hence, caution should be taken when drawing conclusions about its neural substrates.

Eye tracking - The overlooked method to measure cognition in neurodegeneration?

Eye tracking (ET) studies are becoming increasingly popular due to rapid methodological and technological advances as well as the development of cost efficient and portable eye trackers. Although historically ET has been mostly employed in psychophysics or developmental cognition studies, there is also promising scope to use ET for movement disorders and measuring cognitive processes in neurodegeneration. Particularly, ET can be a powerful tool for cognitive and neuropsychological assessments of patients with pathologies affecting motor and verbal abilities, as tasks can be adapted without requiring motor (except eye movements) or verbal responses. In this review, we will examine the existing evidence of ET methods in neurodegenerative conditions and its potential clinical impact for cognitive assessment. We highlight that current evidence for ET is mostly focused on diagnostics of cognitive impairments in neurodegenerative disorders, where it is debatable whether it has any more sensitivity or specificity than existing cognitive assessments. By contrast, there is currently a lack of ET studies in more advanced disease stages, when patients' motor and verbal functions can be significantly affected, and standard cognitive assessments are challenging or often not possible. We conclude that ET is a promising method not only for cognitive diagnostics but more importantly, for potential cognitive disease tracking in progressive neurodegenerative conditions.

Eye Movements in Neuropsychological Tasks

This chapter reviews how recording and analysis of eye movements have been applied to understanding cognitive functioning in patients with neurological disease. Measures derived from the performance of instructed eye movement tests such as the anti-saccade and memory-guided saccade tasks have been shown to be associated with cognitive test performance and the early stages of neurodegenerative disorders including Alzheimer's and Parkinson's disease. Other researchers have taken an ecological approach and recorded the uninstructed pattern of saccades made by patients during performance of established neuropsychological tasks. Studies that have analysed the eye movement strategies used in a number of widely used tests are reviewed, including the Corsi blocks, Tower of London, 'CANTAB' Spatial Working Memory and Brixton Spatial Anticipation test. The findings illustrate that eye movements are not purely in the service of vision, but support visuospatial working memory and forward action planning. Eye movement tests and measures also have potential for application in the assessment and diagnosis of neurological disease and cognitive impairment. Establishing large-scale normative data sets in healthy older adults and use of machine learning multivariate classifier algorithms may be key to further developing eye tracking applications in neuropsychological assessment.