Evaluating the human ongoing visual search performance by eye tracking application and sequencing tests

Eye tracking assessment of Parkinson's disease: a clinical retrospective analysis

Parkinson's disease (PD) presents a significant clinical challenge due to its profound motor and cognitive impacts. Early diagnosis is crucial for implementing effective, stage-based treatment strategies. Recently, eye-tracking technology has emerged as a promising tool for the non-invasive diagnosis and monitoring of various neurological disorders, including PD. This retrospective study analyzed eye-tracking parameters, specifically visually-guided saccades (VGS), in PD patients within a clinical setting. We reviewed eye-tracking data from 62 PD patients, focusing on eye movement performance in horizontal and vertical VGS tasks. Our findings revealed significant correlations between demographic profiles, Mini-Mental State Examination (MMSE) scores, pattern recognition, and spatial working memory tests with saccadic performance in PD patients. Despite the retrospective nature of the study, our results support the potential of eye-tracking technology as a valuable diagnostic tool in PD assessment and monitoring. Future research should prioritize longitudinal studies and more comprehensive assessments to further understand and enhance the clinical application of eye-tracking in PD.

Different saccadic profile in bulbar versus spinal-onset amyotrophic lateral sclerosis

Two clinical phenotypes characterize the onset of amyotrophic lateral sclerosis (ALS): the spinal variant, with symptoms beginning in the limbs, and the bulbar variant, affecting firstly speech and swallowing. The two variants show some distinct features in the histopathology, localization and prognosis, but to which extent they really differ clinically and pathologically remains to be clarified. Recent neuropathological and neuroimaging studies have suggested a broader spreading of the neurodegenerative process in ALS, extending beyond the motor areas, toward other cortical and deep grey matter regions, many of which are involved in visual processing and saccadic control. Indeed, a wide range of eye movement deficits have been reported in ALS, but they have never been used to distinguish the two ALS variants. Since quantifying eye movements is a very sensitive and specific method for the study of brain networks, we compared different saccadic and visual search behaviours across spinal ALS patients (n = 12), bulbar ALS patients (n = 6) and healthy control subjects (n = 13), along with cognitive and MRI measures, with the aim to define more accurately the two patients subgroups and possibly clarify a different underlying neural impairment. We found separate profiles of visually-guided saccades between spinal (short saccades) and bulbar (slow saccades) ALS, which could result from the pathologic involvement of different pathways. We suggest an early involvement of the parieto-collicular-cerebellar network in spinal ALS and the fronto-brainstem circuit in bulbar ALS. Overall, our data confirm the diagnostic value of the eye movements analysis in ALS and add new insight on the involved neural networks.

Towards tacit knowledge mining within context: Visual cognitive graph model and eye movement image interpretation

Visual attention is one of the most important brain cognitive functions, which filters the rich information of the outside world to ensure the efficient operation of limited cognitive resources. The underlying knowledge, i.e., tacit knowledge, hidden in the human attention allocation performances, is context-related and is hard to be expressed by experts, but it is essential for novice operator training and interaction system design. Traditional models of visual attention allocation and corresponding analysis methods seldomly involve task contextual information or present the tacit knowledge in an explicit and quantified way. Thus, it is challenging to pass on the expert's tacit knowledge to the novice or utilize it to construct an interaction system by employing traditional methods. Therefore, this paper first proposes a new model called the visual cognitive graph model based on graph theory to model the visual attention allocation associated with the task context. Then, based on this graph model, utilize the data mining method to reveal attention patterns within context to quantitatively analyze the operator's tacit knowledge during operation tasks. We introduced three physical quantities derived from graph theory to describe the tacit knowledge, which can be used directly to construct an interaction system or operator training. For example, discover the essential information within the task context, the relevant information affecting critical information, and the bridge information revealing the decision-making process. We tested the proposed method in the example of flight operation, the comparison results with the traditional eye movement graph model demonstrate that the proposed visual cognitive model can compromise the task context. The comparison results with the statistical analysis method demonstrate that our tacit knowledge mining method can reveal the underlying knowledge hidden in the visual information. Finally, we give practical applications in the examples of operator training guidance and adaptive interaction system. Our proposed method can explore more in-depth knowledge of visual information, such as the correlations of different obtained information and the way operator obtains information, most of which are even not noticed by operators themselves.

Eye movements in Parkinson's disease during visual search

Visual spatial dysfunction is not uncommon in Parkinson's disease. We hypothesized that visual search behavior is impaired in Parkinson's disease and the deficits correlate with changes in the amplitudes and frequency of fixational and non-fixational rapid eye movements. We measured eye movements, the horizontal and vertical angular position vectors of the right and left eye using high-resolution video oculography, in the Parkinsonian cohort who viewed a blank scene and pictures with real-life scene. Latter was associated with a task of searching an object hidden in a clutter, either at an expected or an unexpected location. Parkinsonian cohort took longer initial time to reach the region of interest. The ultimate response time was comparable in both Parkinson's disease and their healthy peers. The fixation duration was comparable in two cohorts but there was a trend wise decline for the ones located at unexpected locations. Parkinson's disease participants made more fixational saccades with significantly larger amplitude and less non-fixational saccades with significantly smaller amplitude during blank scene viewing. However, overall scanned area of the blank scene was not affected in Parkinson's disease. The Parkinson's disease participants made less non-fixational saccades with amplitudes comparable to healthy control during the visual search of a target object. Fixational saccades during visual search were larger in Parkinson's disease particularly when target was placed at an unexpected location, but the frequency was unchanged.

A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data

Eye-tracking can offer a novel clinical practice and a non-invasive tool to detect neuropathological syndromes. In this paper, we show some analysis on data obtained from the visual sequential search test. Indeed, such a test can be used to evaluate the capacity of looking at objects in a specific order, and its successful execution requires the optimization of the perceptual resources of foveal and extrafoveal vision. The main objective of this work is to detect if some patterns can be found within the data, to discern among people with chronic pain, extrapyramidal patients and healthy controls. We employed statistical tests to evaluate differences among groups, considering three novel indicators: blinking rate, average blinking duration and maximum pupil size variation. Additionally, to divide the three patient groups based on scan-path images—which appear very noisy and all similar to each other—we applied deep learning techniques to embed them into a larger transformed space. We then applied a clustering approach to correctly detect and classify the three cohorts. Preliminary experiments show promising results.

Visual Sequential Search Test Analysis: An Algorithmic Approach

In this work we present an algorithmic approach to the analysis of the Visual Sequential Search Test (VSST) based on the episode matching method. The data set included two groups of patients, one with Parkinson’s disease, and another with chronic pain syndrome, along with a control group. The VSST is an eye-tracking modified version of the Trail Making Test (TMT) which evaluates high order cognitive functions. The episode matching method is traditionally used in bioinformatics applications. Here it is used in a different context which helps us to assign a score to a set of patients, under a specific VSST task to perform. Experimental results provide statistical evidence of the different behaviour among different classes of patients, according to different pathologies.

Spatial ranking strategy and enhanced peripheral vision discrimination optimize performance and efficiency of visual sequential search

Visual sequential search might use a peripheral spatial ranking of the scene to put the next target of the sequence in the correct order. This strategy, indeed, might enhance the discriminative capacity of the human peripheral vision and spare neural resources associated with foveation. However, it is not known how exactly the peripheral vision sustains sequential search and whether the sparing of neural resources has a cost in terms of performance. To elucidate these issues, we compared strategy and performance during an alpha-numeric sequential task where peripheral vision was modulated in three different conditions: normal, blurred, or obscured. If spatial ranking is applied to increase the peripheral discrimination, its use as a strategy in visual sequencing should differ according to the degree of discriminative information that can be obtained from the periphery. Moreover, if this strategy spares neural resources without impairing the performance, its use should be associated with better performance. We found that spatial ranking was applied when peripheral vision was fully available, reducing the number and time of explorative fixations. When the periphery was obscured, explorative fixations were numerous and sparse; when the periphery was blurred, explorative fixations were longer and often located close to the items. Performance was significantly improved by this strategy. Our results demonstrated that spatial ranking is an efficient strategy adopted by the brain in visual sequencing to highlight peripheral detection and discrimination; it reduces the neural cost by avoiding unnecessary foveations, and promotes sequential search by facilitating the onset of a new saccade.

Evaluating the influence of motor control on selective attention through a stochastic model: the paradigm of motor control dysfunction in cerebellar patient

Attention allows us to selectively process the vast amount of information with which we are confronted, prioritizing some aspects of information and ignoring others by focusing on a certain location or aspect of the visual scene. Selective attention is guided by two cognitive mechanisms: saliency of the image (bottom up) and endogenous mechanisms (top down). These two mechanisms interact to direct attention and plan eye movements; then, the movement profile is sent to the motor system, which must constantly update the command needed to produce the desired eye movement. A new approach is described here to study how the eye motor control could influence this selection mechanism in clinical behavior: two groups of patients (SCA2 and late onset cerebellar ataxia LOCA) with well-known problems of motor control were studied; patients performed a cognitively demanding task; the results were compared to a stochastic model based on Monte Carlo simulations and a group of healthy subjects. The analytical procedure evaluated some energy functions for understanding the process. The implemented model suggested that patients performed an optimal visual search, reducing intrinsic noise sources. Our findings theorize a strict correlation between the "optimal motor system" and the "optimal stimulus encoders."