Amnestic people with Alzheimer's disease who remembered the Kobe earthquake

1998 ◽  
Vol 172 (5) ◽  
pp. 425-428 ◽  
Author(s):  
Manabu Ikeda ◽  
Etsuro Mori ◽  
Nobutsugu Hirono ◽  
Toru Imamura ◽  
Tatsuo Shimomura ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Emotional Memory ◽  
Memory Retention ◽  
Resonance Imaging ◽  
Structured Interviews ◽  
Emotional Involvement ◽  
Kobe Earthquake ◽  
Factual Content ◽  
Magnetic Resonance Imaging Mri

BackgroundEmotional memory is a special category of memory for events arousing strong emotions. To investigate the effects of emotional involvement on memory retention in individuals with Alzheimer's disease we studied peoples' memories of distressing experiences during a devastating earthquake.MethodFifty-one subjects with probable Alzheimer's disease who experienced the Kobe earthquake at home in the greater Kobe area were studied. Memories of the earthquake were assessed 6 and 10 weeks after the disaster in semi-structured interviews, and were compared with memories of a magnetic resonance imaging (MRI) examination given after the earthquake.ResultsForty-four (86.3%) of the subjects remembered the earthquake and 16 (31.4%) of subjects remembered the MRI experience. Factual content of the earthquake was lost in most of the subjects.ConclusionsFear reinforces memory retention of an episode in subjects with Alzheimer's disease but does not enhance retention of its context, despite repeated exposure to the information.

scholarly journals A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

2020 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Automated Segmentation ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Segmentation Methods ◽  
Magnetic Resonance Imaging Mri ◽  
Underlying Mechanisms ◽  
The Brain

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disease that leads to anatomical atrophy, as evidenced by magnetic resonance imaging (MRI). Automated segmentation methods are developed to help with the segmentation of different brain areas. However, their reliability has yet to be fully investigated. To have a more comprehensive understanding of the distribution of changes in AD, as well as investigating the reliability of different segmentation methods, in this study we compared volumes of cortical and subcortical brain segments, using automated segmentation methods in more than 60 areas between AD and healthy controls (HC).MethodsA total of 44 MRI images (22 AD and 22 HC, 50% females) were taken from the minimal interval resonance imaging in Alzheimer’s disease (MIRIAD) dataset. HIPS, volBrain, CAT and BrainSuite segmentation methods were used for the subfields of hippocampus, and the rest of the brain.ResultsWhile HIPS, volBrain and CAT showed strong conformity with the past literature, BrainSuite misclassified several brain areas. Additionally, the volume of the brain areas that successfully discriminated between AD and HC showed a correlation with mini mental state examination (MMSE) scores. The two methods of volBrain and CAT showed a very strong correlation. These two methods, however, did not correlate with BrainSuite.ConclusionOur results showed that automated segmentation methods HIPS, volBrain and CAT can be used in the classification of AD and HC. This is an indication that such methods can be used to inform researchers and clinicians of underlying mechanisms and progression of AD.

scholarly journals Emotional Memory in Patients with Alzheimer’s Disease: A Report of Two Cases

2012 ◽  
Vol 2012 ◽  
pp. 1-3 ◽  
Author(s):  
Akira Okada ◽  
Junko Matsuo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Family Member ◽  
Long Memory ◽  
Daily Life ◽  
Emotional Memory ◽  
Memory Retention ◽  
Long Period ◽  
Emotional Events

Highly emotional events in daily life can be preserved in memory and such memory is generally referred to as emotional memory. Some reports have demonstrated that emotional memory is also found in patients with Alzheimer’s disease (AD). However, to our knowledge, there have been no reports about how long memory retention for emotional events can continue in patients with AD. In this paper, we present two patients with AD who lost an immediate family member during followup and retained the memory over a long period despite progression of the AD.

scholarly journals System for Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 28
Author(s):  
Alejandro Puente-Castro ◽  
Cristian Robert Munteanu ◽  
Enrique Fernandez-Blanco
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Reference Data ◽  
Disease Diagnosis ◽  
Resonance Imaging ◽  
Automatic Assessment ◽  
Learning Techniques ◽  
Magnetic Resonance Imaging Mri

Automatic detection of Alzheimer’s disease is a very active area of research. This is due to its usefulness in starting the protocol to stop the inevitable progression of this neurodegenerative disease. This paper proposes a system for the detection of the disease by means of Deep Learning techniques in magnetic resonance imaging (MRI). As a solution, a model of neuronal networks (ANN) and two sets of reference data for training are proposed. Finally, the goodness of this system is verified within the domain of the application.

scholarly journals Impact of emotion on memory

2000 ◽  
Vol 177 (4) ◽  
pp. 343-347 ◽  
Author(s):  
Hiroaki Kazui ◽  
Etsuro Mori ◽  
Mamoru Hashimoto ◽  
Nobutsugu Hirono ◽  
Toru Imamura ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Declarative Memory ◽  
Emotional Experience ◽  
Normal Subjects ◽  
Emotional Impact ◽  
Memory Retention ◽  
Kobe Earthquake ◽  
Control Factors ◽  
People With Dementia

BackgroundIn an earlier study we showed that a powerful emotional experience (the Kobe earthquake) reinforced memory retention in patients with Alzheimer's disease, but we could not control factors other than the emotional impact of the earthquake.AimsTo test our previous findings in a controlled experimental study.MethodRecall tests consisting of two short stories were administered to 34 patients with Alzheimer's disease and 10 normal subjects. The two stories were identical except for one passage in each story: one was emotionally charged (arousing story) and the other (neutral story) was not.ResultsIn both groups, the emotionally charged passage in the arousing story was remembered better than the counterpart in the neutral story. In addition, the extent of the memory improvement was similar in the subjects and in the controls.ConclusionsThe results provide further evidence that emotional arousal enhances declarative memory in patients with Alzheimer's disease, and give a clue to the management of people with dementia.

scholarly journals A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

2020 ◽  
Vol 9 (7) ◽  
pp. 2146
Author(s):  
Gopi Battineni ◽  
Nalini Chintalapudi ◽  
Francesco Amenta ◽  
Enea Traini
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Magnetic Resonance ◽  
Neurodegenerative Disorder ◽  
Support Vector ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.

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