Deficits in face perception in the amnestic form of mild cognitive impairment

Eye movement patterns during viewing face images with neutral expressions in patients with early-stage Alzheimer's disease and amnestic mild cognitive impairment

BACKGROUND

Alzheimer's disease (AD) neuropathology affects the brain regions responsible for visuospatial skills. Accumulating evidence points to visual difficulties involving face processing in AD and amnestic mild cognitive impairment (aMCI). No study has so far examined eye movement patterns when viewing faces with neutral expressions in patients with AD.

AIM

The objective of this study aimed to examine the eye movements of patients with early-stage AD, aMCI, and healthy controls (HC) during viewing face images.

MATERIALS&METHODS

Thirty-one AD, 37 aMCI, and 33 HC were included in the study. Eye movements in facial stimuli were recorded with the EyeLink 1000 Plus eye-tracker.

RESULTS

Our findings showed that AD patients looked less at the eye area of interest than the nose and mouth areas of interest compared to aMCI and HC. Regardless of the group, all participants looked at the eye and nose areas of interest more and longer in the mouth area of interest. In addition, the first fixation duration to the eye area of interest of all participants was shorter than that of the nose and mouth.

DISCUSSION

Consistent with our study, studies in healthy adults revealed eye movement patterns that focused more on the eyes and nose. AD patients are unable to pay attention to the salient parts of faces, tending to focus instead on the non-informative parts.

CONCLUSION

Our study is the first to reveal eye movement differences in face processing in AD.

Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease

Exploring functional interactions among various brain regions is helpful for understanding the pathological underpinnings of neurological disorders. Brain networks provide an important representation of those functional interactions, and thus are widely applied in the diagnosis and classification of neurodegenerative diseases. Many mental disorders involve a sharp decline in cognitive ability as a major symptom, which can be caused by abnormal connectivity patterns among several brain regions. However, conventional functional connectivity networks are usually constructed based on pairwise correlations among different brain regions. This approach ignores higher-order relationships, and cannot effectively characterize the high-order interactions of many brain regions working together. Recent neuroscience research suggests that higher-order relationships between brain regions are important for brain network analysis. Hyper-networks have been proposed that can effectively represent the interactions among brain regions. However, this method extracts the local properties of brain regions as features, but ignores the global topology information, which affects the evaluation of network topology and reduces the performance of the classifier. This problem can be compensated by a subgraph feature-based method, but it is not sensitive to change in a single brain region. Considering that both of these feature extraction methods result in the loss of information, we propose a novel machine learning classification method that combines multiple features of a hyper-network based on functional magnetic resonance imaging in Alzheimer's disease. The method combines the brain region features and subgraph features, and then uses a multi-kernel SVM for classification. This retains not only the global topological information, but also the sensitivity to change in a single brain region. To certify the proposed method, 28 normal control subjects and 38 Alzheimer's disease patients were selected to participate in an experiment. The proposed method achieved satisfactory classification accuracy, with an average of 91.60%. The abnormal brain regions included the bilateral precuneus, right parahippocampal gyrus\hippocampus, right posterior cingulate gyrus, and other regions that are known to be important in Alzheimer's disease. Machine learning classification combining multiple features of a hyper-network of functional magnetic resonance imaging data in Alzheimer's disease obtains better classification performance.

Face-specific memory deficits and changes in eye scanning patterns among patients with amnestic mild cognitive impairment

Amnestic mild cognitive impairment (aMCI) is a prodromal stage of Alzheimer's disease (AD). Previous studies have shown functional and structural degradation of the fusiform face area, which is a core region for face processing, in addition to medial temporal lobe degradation. We predicted that patients with aMCI exhibit a loss of face processing and/or face memory, accompanied by abnormal eye scanning patterns, since patients who have deficits in face perception (i.e. prosopagnosia) exhibit such tendencies. Eighteen patients with aMCI and age-matched healthy controls were tested for perception and short-term memory of visually presented faces and houses while their gaze was recorded. Patients with aMCI showed a decline in memory, compared with control observers, for faces, but not for houses. Patients looked more at the mouth of faces, compared with control observers. We demonstrate here the loss of short-term face memory in aMCI with abnormal scanning patterns that might reflect the cerebral abnormality found in patients with aMCI.

Famous landmark identification in amnestic mild cognitive impairment and Alzheimer's disease

BACKGROUND

Identification of famous landmarks (FLI), famous faces (FFI) and recognition of facial emotions (FER) is affected early in the course of Alzheimer's disease (AD). FFI, FER and FLI may represent domain specific tasks relying on activation of distinct regions of the medial temporal lobe, which are affected successively during the course of AD. However, the data on FFI and FER in MCI are controversial and FLI domain remains almost unexplored.

OBJECTIVES

To determine whether and how are these three specific domains impaired in head to head comparison of patients with amnestic MCI (aMCI) single domain (SD-aMCI) and multiple domain (MD-aMCI). We propose that FLI might be most reliable in differentiating SD-aMCI, which is considered to be an earlier stage of AD pathology spread out, from the controls.

PATIENTS AND METHODS

A total of 114 patients, 13 with single domain (SD-aMCI) and 30 with multiple domains (MD-aMCI), 29 with mild AD and 42 controls underwent standard neurological and neuropsychological evaluations as well as tests of FLI, FER and FFI.

RESULTS

Compared to the control group, AD subjects performed worse on FFI (p = 0.020), FER (p<0.001) and FLI (p<0.001), MD-aMCI group had significantly worse scores only on FLI (p = 0.002) and approached statistical significance on FER (0.053). SD-aMCI group performed significantly worse only on FLI (p = 0.028) compared to controls.

CONCLUSIONS

Patients with SD-aMCI had an isolated impairment restricted to FLI, while patients with MD-aMCI showed impairment in FLI as well as in FER. Patients with mild dementia due to AD have more extensive impairment of higher visual perception. The results suggest that FLI testing may contribute to identification of patients at risk of AD. We hypothesize that clinical examination of all three domains might reflect the spread of the disease from transentorhinal cortex, over amygdala to fusiform gyrus.

Specific and disease stage-dependent episodic memory-related brain activation patterns in Alzheimer's disease: a coordinate-based meta-analysis

Episodic memory is typically affected during the course of Alzheimer's disease (AD). Due to the pronounced heterogeneity of functional neuroimaging studies on episodic memory impairments in mild cognitive impairment (MCI) and AD regarding their methodology and findings, we aimed to delineate consistent episodic memory-related brain activation patterns. We performed a systematic, quantitative, coordinate-based whole-brain activation likelihood estimation meta-analysis of 28 functional magnetic resonance imaging (fMRI) studies comprising 292 MCI and 102 AD patients contrasted to 409 age-matched control subjects. We included episodic encoding and/or retrieval phases, investigated the effects of group, verbal or image stimuli and correlated mean Mini-Mental-Status-Examination (MMSE) scores with the modelled activation estimates. MCI patients presented increased right hippocampal activation during memory encoding, decreased activation in the left hippocampus and fusiform gyrus during retrieval tasks, as well as attenuated activation in the right anterior insula/inferior frontal gyrus during verbal retrieval. In AD patients, however, stronger activation within the precuneus during encoding tasks was accompanied by attenuated right hippocampal activation during retrieval tasks. Low cognitive performance (MMSE scores) was associated with stronger activation of the precuneus and reduced activation of the right (para)hippocampus and anterior insula/inferior frontal gyrus. This meta-analysis provides evidence for a specific and probably disease stage-dependent brain activation pattern related to the pathognomonic AD characteristic of episodic memory loss.

Recognition of facial emotional expression in amnestic mild cognitive impairment

We examined whether recognition of facial emotional expression would be affected in amnestic mild cognitive impairment (aMCI). A total of 50 elderly persons met the initial inclusion criteria; 10 were subsequently excluded (Geriatric Depression Score > 5). 22 subjects were classified with aMCI based on published criteria (single domain aMCI [SD-aMCI], n = 10; multiple domain aMCI [MD-aMCI], n = 12); 18 subjects were cognitively normal. All underwent standard neurological and neuropsychological evaluations as well as tests of facial emotion recognition (FER) and famous faces identification (FFI). Among normal controls, FFI was negatively correlated with Mini-Mental Status Examination scores and positively correlated with executive function. Among patients with aMCI, FER was correlated with attention/speed of processing. No other correlations were significant. In a multinomial logistic regression model adjusted for age, gender, and education, a poorer score on FER, but not on FFI, was associated with greater odds of being classified as MD-aMCI (odds ratio [OR], 3.82; 95% confidence interval [CI], 1.05-13.91; p = 0.042). This association was not explained by memory or global cognitive score. There was no association between FER or FFI and SD-aMCI (OR, 1.13; 95% CI, 0.36-3.57; p = 0.836). Therefore, FER, but not FFI, may be impaired in MD-aMCI. This implies that in MD-aMCI, the tasks of FER and FFI may involve segregated neurocognitive networks.