Do patients suffering from Alzheimer's disease present an own-age bias in face recognition?

"What Did I Tell This Sad Person?": Memory for Emotional Destinations in Korsakoff's Syndrome

We investigated destination memory, defined as the ability to remember to whom a piece of information was previously transmitted, for emotional destinations (i.e., a happy or sad person) in Korsakoff's syndrome (KS). We asked patients with KS and control participants to tell facts to neutral, positive, or negative faces. On a subsequent recognition task, participants had to decide to whom they told each fact. Compared with control participants, patients with KS demonstrated lower recognition of neutral, emotionally positive, and emotionally negative destinations. Patients with KS demonstrated lower recognition of emotionally negative than for emotionally positive or neutral destinations, but there were no significant differences between recognition of neutral and emotionally positive destinations. Our study demonstrates a compromised ability to process negative destinations in KS. Our study highlights the relationship between memory decline and impaired emotional processing in KS.

Recognition of younger and older faces in Korsakoff's syndrome

The own-age bias refers to the observation that face recognition is typically superior for own-age faces compared with other-age faces. We investigated this bias in Korsakoff patients, as well as its relationship with social contact and episodic memory. Korsakoff patients and age-matched controls were exposed to older faces (own-age faces) and younger faces (other-age faces). In the recognition phase, they were invited to decide whether faces had been exposed in the encoding phase or not. Results revealed an own-age bias in control participants (i.e., high recognition of older than for younger faces), but not in Korsakoff patients (i.e., similar recognition of older and younger faces). Furthermore, both Korsakoff's syndrome and controls reported more social contact with old than with young individuals. Recognition of younger and older faces in Korsakoff patients was significantly correlated with episodic performance but not with social contact with younger and older people. We conclude that the lack of own-age bias in Korsakoff's syndrome is related rather to compromise of episodic memory than to diminished social contact with younger adults.

Dependency criterion based brain pathological age estimation of Alzheimer's disease patients with MR scans

Objectives

Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to the accelerated brain aging.

Methods

This paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to the prior knowledge. Secondly, use the support vector regression as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the correlation criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age.

Results

The experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer’s disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment—Alzheimer’s disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging.

Conclusion

In conclusion, this paper proposes a new kind of brain age—brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm.

Estimating the brain pathological age of Alzheimer's disease patients from MR image data based on the separability distance criterion

Traditional age estimation methods are based on the same idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to accelerated brain aging. This paper considers this deviation and searches for it by maximizing the separability distance value rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to prior knowledge. Secondly, use the support vector regression (SVR) as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the separability distance criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. The experimental results showed that the separability was apparently improved. For normal control-Alzheimer's disease (NC-AD), normal control-mild cognition impairment (NC-MCI), and MCI-AD, the average improvements were 0.178 (35.11%), 0.033 (14.47%), and 0.017 (39.53%), respectively. For NC-MCI-AD, the average improvement was 0.2287 (64.22%). The estimated brain pathological age could be not only more helpful to the classification of AD but also more precisely reflect accelerated brain aging. In conclusion, this paper offers a new method for brain age estimation that can distinguish different states of AD and can better reflect the extent of accelerated aging.

What is Your Risk of Contracting Alzheimer's Disease? A Telematics Tool Helps you to Predict it

Alzheimer's disease (AD) is the most common dementia in developed countries. Between the identified risk factors, one of the most important is the age. Its prevalence reaches 24 % in men and 33 % in women over 85 years. Increase in life expectancy, making it a serious public health problem. Prevention of Alzheimer's disease represents a major challenge to health. Given that Alzheimer's disease is largely dependent on the genetics of each person and uninterrupted progress of the age, which is try to make people aware that there are other factors that can alter your chance of developing the Alzheimer disease and although currently not reduce, help is not increased in the near or distant future.The aim of this paper is to develop and evaluate a Web-Mobile application (Alzhe Alert) used to calculate the risk of Alzheimer's from a short questionnaire using a computer or mobile device, so that any user, without requiring computer skills, can access the website to estimate their risk of developing the disease in the coming years depending on their habits and daily basis activities. The users who have realized the questionnaire can to observe in a graph the result, and they will know which is at risk for Alzheimer's at present and over the next 50 years if they continue with the same habits and lifestyle. The objective is that the users can be aware of the risk they have different habits of life about their health. Currently, 243 users (84 women and 159 men) of white race have completed the questionnaire. 76 % of the users have got a risk below the average.