scholarly journals Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification

2021 ◽  
Vol 9 ◽  
Author(s):  
Weikai Li ◽  
Xiaowen Xu ◽  
Zhengxia Wang ◽  
Liling Peng ◽  
Peijun Wang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Effective Connectivity ◽  
Single Mode ◽  
Disease Diagnosis ◽  
Key Indicator ◽  
Connection Pattern ◽  
Single Pattern ◽  
Brain Connection ◽  
Normal Controls

Mild cognitive impairment (MCI) is generally considered to be a key indicator for predicting the early progression of Alzheimer’s disease (AD). Currently, the brain connection (BC) estimated by fMRI data has been validated to be an effective diagnostic biomarker for MCI. Existing studies mainly focused on the single connection pattern for the neuro-disease diagnosis. Thus, such approaches are commonly insufficient to reveal the underlying changes between groups of MCI patients and normal controls (NCs), thereby limiting their performance. In this context, the information associated with multiple patterns (e.g., functional connectivity or effective connectivity) from single-mode data are considered for the MCI diagnosis. In this paper, we provide a novel multiple connection pattern combination (MCPC) approach to combine different patterns based on the kernel combination trick to identify MCI from NCs. In particular, sixty-three MCI cases and sixty-four NC cases from the ADNI dataset are conducted for the validation of the proposed MCPC method. The proposed method achieves 87.40% classification accuracy and significantly outperforms methods that use a single pattern.

Diagnosis of Alzheimer's disease based on Deeply-Fused Nets

2020 ◽  
Vol 23 ◽  
Author(s):  
ChangZu Chen ◽  
Qi Wu ◽  
ZuoYong Li ◽  
Lei Xiao ◽  
Zhong Yi Hu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Disease Diagnosis ◽  
Automatic Diagnosis ◽  
Single Base ◽  
Base Network ◽  
Normal Controls

Aim and Objective: Fast and accurate diagnosis of Alzheimer's disease is very important for the care and further treatment of patients. Along with the development of deep learning, impressive progress has also been made in the automatic diagnosis of AD. Most existing studies on automatic diagnosis are concerned with a single base network, whose accuracy for disease diagnosis still needs to be improved. This study was undertaken to propose a method to improve the accuracy of automatic diagnosis of AD. Materials and Methods: MRI image data from the Alzheimer’s Disease Neuroimaging Initiative were used to train a deep learning model to achieve computer-aided diagnosis of Alzheimer's disease. The data consisted of 138 with AD, 280 with mild cognitive impairment, and 138 normal controls. Here, a new deeply-fused net is proposed, which combines several deep convolutional neural networks, thereby avoiding the error of a single base network and increasing the classification accuracy and generalization capacity. Results: Experiments show that when differentiating between patients with AD, mild cognitive impairment, and normal controls on a subset of the ADNI database without data leakage, the new architecture improves the accuracy by about 4 percentage points as compared to a single standard base network. Conclusion: This new approach exhibits better performance, but there is still much to be done before its clinical application. In the future, greater research effort will be devoted to improving the performance of the deeply-fused net.

Subjective Spatial Navigation Complaints - A Frequent Symptom Reported by Patients with Subjective Cognitive Decline, Mild Cognitive Impairment and Alzheimer's Disease

2018 ◽  
Vol 15 (3) ◽  
pp. 219-228 ◽  
Author(s):  
Jiri Cerman ◽  
Ross Andel ◽  
Jan Laczo ◽  
Martin Vyhnalek ◽  
Zuzana Nedelska ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Spatial Navigation ◽  
Subjective Cognitive Decline ◽  
Great Effort ◽  
Frequent Symptom ◽  
Normal Controls

Background: Great effort has been put into developing simple and feasible tools capable to detect Alzheimer's disease (AD) in its early clinical stage. Spatial navigation impairment occurs very early in AD and is detectable even in the stage of mild cognitive impairment (MCI). Objective: The aim was to describe the frequency of self-reported spatial navigation complaints in patients with subjective cognitive decline (SCD), amnestic and non-amnestic MCI (aMCI, naMCI) and AD dementia and to assess whether a simple questionnaire based on these complaints may be used to detect early AD. Method: In total 184 subjects: patients with aMCI (n=61), naMCI (n=27), SCD (n=63), dementia due to AD (n=20) and normal controls (n=13) were recruited. The subjects underwent neuropsychological examination and were administered a questionnaire addressing spatial navigation complaints. Responses to the 15 items questionnaire were scaled into four categories (no, minor, moderate and major complaints). Results: 55% of patients with aMCI, 64% with naMCI, 68% with SCD and 72% with AD complained about their spatial navigation. 38-61% of these complaints were moderate or major. Only 33% normal controls expressed complaints and none was ranked as moderate or major. The SCD, aMCI and AD dementia patients were more likely to express complaints than normal controls (p's<0.050) after adjusting for age, education, sex, depressive symptoms (OR for SCD=4.00, aMCI=3.90, AD dementia=7.02) or anxiety (OR for SCD=3.59, aMCI=3.64, AD dementia=6.41). Conclusion: Spatial navigation complaints are a frequent symptom not only in AD, but also in SCD and aMCI and can potentially be detected by a simple and inexpensive questionnaire.

scholarly journals Structural Neuroimaging in the Detection and Prognosis of Pre-Clinical and Early AD

2009 ◽  
Vol 21 (1-2) ◽  
pp. 3-12 ◽  
Author(s):  
Christine Fennema-Notestine ◽  
Linda K. McEvoy ◽  
Donald J. Hagler ◽  
Mark W. Jacobson ◽  
Anders M. Dale ◽  
...  
Keyword(s):  
At Risk ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Structural Change ◽  
Structural Mri ◽  
Cross Sectional ◽  
Structural Neuroimaging ◽  
Clinical Assessments ◽  
Structural Loss ◽  
Normal Controls

Current research supports the strong potential of structural MRI profiles, even within cross-sectional designs, as a promising method for the discrimination of Alzheimer's Disease (AD) from normal controls and for the prediction of Mild Cognitive Impairment (MCI) progression and conversion to AD. Findings suggest that measures of structural change in mesial and lateral temporal, cingulate, parietal and midfrontal areas may facilitate the assessment of a treatment's ability to halt the progressive structural loss that accompanies clinical decline in MCI. The performance of prediction is likely to continue to improve with the incorporation of measures from other neuroimaging modalities, clinical assessments, and neuromedical biomarkers, as the regional profile of individuals at risk for progression is refined.

IC-P-031: EFFECTIVE CONNECTIVITY WITHIN THE DEFAULT MODE NETWORK IN MILD COGNITIVE IMPAIRMENT AND ALZHEIMER'S DISEASE

2006 ◽  
Vol 14 (7S_Part_1) ◽  
pp. P35-P36
Author(s):  
Cole John Cook ◽  
Gyujoon Hwang ◽  
Veena A. Nair ◽  
Andrew L. Alexander ◽  
Piero G. Antuono ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Default Mode Network ◽  
Effective Connectivity ◽  
Default Mode

The heterogeneity of mild cognitive impairment: A neuropsychological analysis

2009 ◽  
Vol 16 (1) ◽  
pp. 84-93 ◽  
Author(s):  
DAVID J. LIBON ◽  
SHARON X. XIE ◽  
JOEL EPPIG ◽  
GRAHAM WICAS ◽  
MELISSA LAMAR ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Executive Control ◽  
Declarative Memory ◽  
Community Dwelling ◽  
Lexical Retrieval ◽  
Memory Clinic ◽  
Delayed Recognition ◽  
Single Versus ◽  
Normal Controls

AbstractA group of 94 nondemented patients self-referred to an outpatient memory clinic for memory difficulties were studied to determine the incidence of single versus multi-domain mild cognitive impairment (MCI) using Petersen criteria. Fifty-five community dwelling normal controls (NC) participants without memory complaints also were recruited. Tests assessing executive control, naming/lexical retrieval, and declarative memory were administered. Thirty-four patients exhibited single-domain MCI, 43 patients presented with multi-domain MCI. When the entire MCI sample (n = 77) was subjected to a cluster analysis, 14 patients were classified with amnesic MCI, 21 patients with dysexecutive MCI, and 42 patients were classified into a mixed/multi-domain MCI group involving low scores on tests of letter fluency, “animal” fluency, and delayed recognition discriminability. Analyses comparing the three cluster-derived MCI groups versus a NC group confirmed the presence of memory and dysexecutive impairment for the amnesic and dysexecutive MCI groups. The mixed MCI group produced lower scores on tests of letter fluency compared with the amnesic MCI and NC groups and lower scores on tests of naming and memory compared with the NC group. In summary, multi-domain MCI is quite common. These data suggest that MCI is a highly nuanced and complex clinical entity. (JINS, 2010, 16, 84–93.)

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