Alleviated negative rather than positive attentional bias in patients with depression in remission: an eye-tracking study

Li, Mi; Lu, Shengfu; Wang, Gang; Feng, Lei; Fu, Bingbing; Zhong, Ning

doi:10.1177/0300060516662134

Cited by 27 publications

(21 citation statements)

References 52 publications

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“…Symptom severity has been found to correlate both with an attentional bias for sad and happy faces (Duque and Vazquez, 2015). Some studies report a persisting attentional bias to sad faces in remitted MD (Soltani et al, 2015), while others found no significant difference as compared to controls concerning sad faces, but a decreased attentional bias toward happy faces (Li et al, 2016a). …”

Section: Discussionmentioning

confidence: 99%

The Effect of Emotion and Reward Contingencies on Relational Memory in Major Depression: An Eye-Movement Study with Follow-Up

et al. 2016

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Background: Episodic memory disturbances were found to constitute a potential trait marker for major depression (MD). The recall of positive or rewarding information in a relational context is specifically impaired. Eye-movement recording constitutes a novel, direct approach to examine implicit memory performance. Here we aimed to assess the effect of emotional context and implicit virtual monetary reward or loss on viewing patterns in association with relational memory in a 6-months follow-up study in MD.Materials and Methods: Twenty-eight patients with MD and 30 healthy participants were trained to associate a face (happy/sad/neutral) with a background scene. After each pair a virtual monetary reward or loss appeared briefly. During testing, scenes were presented as a cue and then overlaid with three previously studied faces. Participants were asked to recall the matching face if present (Match trials), with eye-movements and subsequent forced-choice recognition being recorded.Results: Explicit recognition of the matching face was impaired in the MD group as compared to controls. In correlation with this, viewing of the matching face was significantly reduced in the MD group. We found a significant interaction of group (MD vs HC) with the relational memory condition (Match and Non-match), facial emotion and monetary reward and loss. MD patients attended longer to previously rewarded stimuli, but significantly less to sad faces in the Match condition. The relational memory impairment persisted at follow-up and correlated with symptom severity both at baseline and follow-up. Viewing patterns associated with previous virtual reward were associated with clinical symptoms at follow-up.Conclusion: Our current results provide novel evidence for a specific relational memory impairment in MD as supported by abnormal eye-movement behavior and a deficit in explicit recognition. MD patients showed an attentional bias to rewarded stimuli and decreased viewing of sad faces when relational memory information was present.

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Section: Discussionmentioning

confidence: 99%

The Effect of Emotion and Reward Contingencies on Relational Memory in Major Depression: An Eye-Movement Study with Follow-Up

et al. 2016

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“…Siegle et al [71] reported faster pupillary responses in non-depressed individuals to positive rather than negative stimuli. In contrast, depressed persons displayed slower pupil dilation responses to positive stimuli in conditions associated with reduced cognitive load (see also [72], [73], [74], [75], [76], [77]). More recently Price et al [78] investigated attentional bias, including pupil bias and diameter, to predict depression symptoms over a two year follow up period in a sample of adolescents displaying high ratings of anxiety.…”

Section: Nonverbal Signs For Depression Assessmentmentioning

confidence: 99%

Automatic Assessment of Depression Based on Visual Cues: A Systematic Review

Pampouchidou

Simos

Marias

et al. 2019

IEEE Trans. Affective Comput.

139

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Automatic depression assessment based on visual cues is a rapidly growing research domain. The present exhaustive review of existing approaches as reported in over sixty publications during the last ten years focuses on image processing and machine learning algorithms. Visual manifestations of depression, various procedures used for data collection, and existing datasets are summarized. The review outlines methods and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification and regression approaches, as well as different fusion strategies. A quantitative meta-analysis of reported results, relying on performance metrics robust to chance, is included, identifying general trends and key unresolved issues to be considered in future studies of automatic depression assessment utilizing visual cues alone or in combination with vocal or verbal cues.

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“…They captured their pupils' adaptation modes in continuous stages of darkness and light and explored the quantitative measuring method of autism. Typical applications of eye movement information in the diagnosis of depression were described in paper [11][12][13][14]. Shengfu Lu et al had patients with major depressive disorder (MDD) and nondepressed controls complete eye-tracking tasks and analyzed their attention preference to positive, negative, and neutral expressions [11].…”

Section: Introductionmentioning

confidence: 99%

Disease Classification Based on Synthesis of Multiple Long Short-Term Memory Classifiers Corresponding to Eye Movement Features

Mao

Liu

et al. 2020

IEEE Access

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Medical research confirms that eye movement abnormalities are related to a variety of psychological activities, mental disorders and physical diseases. However, as the specific manifestations of various diseases in terms of eye movement disorders remain unclear, the accurate diagnosis of diseases according to eye movement is difficult. In this paper, a deep neural network (DNN) method is employed to establish a disease discrimination model according to eye movement. First, multiple eye-tracking experiments are designed to obtain eye images. Second, pupil characteristics, including position and size, are extracted, and the feature vectors of eye movement are obtained from the normalized pupil information. Based on a long short-term memory (LSTM) network, a classifier that corresponds to each feature, which is referred to as a weak classifier, is built. The experimental samples are preclassified, and the classification ability of each weak classifier for different diseases is also calculated. Last, a strong classifier is achieved for disease discrimination by synthesizing all the weak classifiers and their classification abilities. By classification testing for three categories of healthy controls, brain injury patients and vertigo patients, the experimental results demonstrated the efficiency of this method. With the deep learning method, more medical information can be excavated from eye movement to improve the values in disease diagnosis.

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Alleviated negative rather than positive attentional bias in patients with depression in remission: an eye-tracking study

Cited by 27 publications

References 52 publications

The Effect of Emotion and Reward Contingencies on Relational Memory in Major Depression: An Eye-Movement Study with Follow-Up

The Effect of Emotion and Reward Contingencies on Relational Memory in Major Depression: An Eye-Movement Study with Follow-Up

Automatic Assessment of Depression Based on Visual Cues: A Systematic Review

Disease Classification Based on Synthesis of Multiple Long Short-Term Memory Classifiers Corresponding to Eye Movement Features

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