scholarly journals Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based Neuromarkers

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6210
Author(s):  
Su Yang ◽  
Jose Miguel Sanchez Bornot ◽  
Ricardo Bruña Fernandez ◽  
Farzin Deravi ◽  
Sanaul Hoque ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cross Validation ◽  
Recognition Rate ◽  
Support Vector ◽  
Average Recognition Rate ◽  
Significant Performance ◽  
Healthy Control ◽  
Cortical Regions ◽  
Leave One Out

Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence as a spatial indicator for effective regions of interest (ROIs) localization, subsequently discriminating the participants with mild cognitive impairment (MCI) from a group of age-matched healthy control (HC) elderly participants. We found that the cortical regions could be divided into two distinctive groups based on their coherence indices. Compared to HC, some ROIs showed increased connectivity (hyper-connected ROIs) for MCI participants, whereas the remaining ROIs demonstrated reduced connectivity (hypo-connected ROIs). Based on these findings, a series of wavelet-based source-level neuromarkers for MCI detection are proposed and explored, with respect to the two distinctive ROI groups. It was found that the neuromarkers extracted from the hyper-connected ROIs performed significantly better for MCI detection than those from the hypo-connected ROIs. The neuromarkers were classified using support vector machine (SVM) and k-NN classifiers and evaluated through Monte Carlo cross-validation. An average recognition rate of 93.83% was obtained using source-reconstructed signals from the hyper-connected ROI group. To better conform to clinical practice settings, a leave-one-out cross-validation (LOOCV) approach was also employed to ensure that the data for testing was from a participant that the classifier has never seen. Using LOOCV, we found the best average classification accuracy was reduced to 83.80% using the same set of neuromarkers obtained from the ROI group with functional hyper-connections. This performance surpassed the results reported using wavelet-based features by approximately 15%. Overall, our work suggests that (1) certain ROIs are particularly effective for MCI detection, especially when multi-resolution wavelet biomarkers are employed for such diagnosis; (2) there exists a significant performance difference in system evaluation between research-based experimental design and clinically accepted evaluation standards.

scholarly journals Functional signature of conversion in Mild Cognitive Impairment patients

2018 ◽  
Author(s):  
Stefano Delli Pizzi ◽  
Miriam Punzi ◽  
Stefano L Sensi ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Brain Atrophy ◽  
Cortical Atrophy ◽  
Study Cohort ◽  
Dramatic Decline ◽  
Healthy Control ◽  
Cortical Regions

AbstractThe entorhinal-hippocampal circuit is a strategic hub for memory but also the first site to be affected in the Alzheimer’s Disease (AD)-related pathology. We investigated MRI patterns of brain atrophy and functional connectivity in a study cohort obtained from the Alzheimer’s Disease Neuroimaging Initiative database including healthy control (HC), Mild Cognitive Impairment (MCI), and AD subjects. MCI individuals were clinically evaluated 24 months after the MRI scan, and the group further divided into a subset of subjects who either did (c-MCI) or did not (nc-MCI) convert to AD. Compared to HC subjects, AD patients exhibited a collapse of long-range connectivity from the hippocampus and entorhinal cortex, pronounced cortical/sub-cortical atrophy, and a dramatic decline in cognitive performances. c-MCI patients showed entorhinal and hippocampal hypo-connectivity, no signs of cortical thinning but evidence of right hippocampus atrophy. On the contrary, nc-MCI patients showed lack of brain atrophy, largely preserved cognitive functions, hippocampal and entorhinal hyper-connectivity with selected neocortical/sub-cortical regions mainly involved in memory processing and brain meta-stability. This hyper-connectivity can represent an early compensatory strategy to overcome the progression of cognitive impairment. This functional signature can also be employed for the diagnosis of c-MCI subjects.

Resting State EEG Multiscale Entropy Dynamics in Mild Cognitive Impairment and Early Alzheimer’s Disease

2014 ◽  
Author(s):  
Joseph McBride ◽  
Xiaopeng Zhao ◽  
Nancy Munro ◽  
Gregory Jicha ◽  
Charles Smith ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Multiscale Entropy ◽  
Support Vector ◽  
P Value ◽  
Early Alzheimer’S Disease ◽  
Leave One Out ◽  
Normal Controls

Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia such as Alzheimer’s disease (AD). This study explores non-event-related multiscale entropy (MSE) measures as features for effectively discriminating between normal aging, MCI, and AD participants. Resting EEG records from 48 age-matched participants (mean age 75.7 years) — 15 normal controls (NC), 16 MCI, and 17 early AD — are examined. Multiscale entropy curves are computed for short EEG segments and averaged over the segments. Binary discriminations among the three groups are conducted using support vector machine models. Leave-one-out cross-validation accuracies of 80.7% (p-value <0.0018) for MCI vs. NC, 87.5% (p-value <1.322E−4) for AD vs. NC, and 90.9% (p-value <2.788E−5) for MCI vs. AD are achieved. Results demonstrate influence of cognitive deficits on multiscale entropy dynamics of non-event-related EEG.

Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment

2021 ◽  
pp. 1-15
Author(s):  
Sung Hoon Kang ◽  
Bo Kyoung Cheon ◽  
Ji-Sun Kim ◽  
Hyemin Jang ◽  
Hee Jin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Apolipoprotein E ◽  
Cross Validation ◽  
Good Accuracy ◽  
External Validation ◽  
Amnestic Mild Cognitive Impairment ◽  
Temporal Region ◽  
Apolipoprotein E Genotype

Background: Amyloid (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer’s disease. However, Aβ evaluation through amyloid positron emission tomography (PET) is limited due to high cost and safety issues. Objective: We therefore aimed to develop and validate prediction models of Aβ positivity for aMCI using optimal interpretable machine learning (ML) approaches utilizing multimodal markers. Methods: We recruited 529 aMCI patients from multiple centers who underwent Aβ PET. We trained ML algorithms using a training cohort (324 aMCI from Samsung medical center) with two-phase modelling: model 1 included age, gender, education, diabetes, hypertension, apolipoprotein E genotype, and neuropsychological test scores; model 2 included the same variables as model 1 with additional MRI features. We used four-fold cross-validation during the modelling and evaluated the models on an external validation cohort (187 aMCI from the other centers). Results: Model 1 showed good accuracy (area under the receiver operating characteristic curve [AUROC] 0.837) in cross-validation, and fair accuracy (AUROC 0.765) in external validation. Model 2 led to improvement in the prediction performance with good accuracy (AUROC 0.892) in cross validation compared to model 1. Apolipoprotein E genotype, delayed recall task scores, and interaction between cortical thickness in the temporal region and hippocampal volume were the most important predictors of Aβ positivity. Conclusion: Our results suggest that ML models are effective in predicting Aβ positivity at the individual level and could help the biomarker-guided diagnosis of prodromal AD.

Differences in and correlations between cognitive abilities and brain volumes in healthy control, mild cognitive impairment, and Alzheimer disease groups

2016 ◽  
Vol 29 (4) ◽  
pp. 473-480 ◽  
Author(s):  
Soon-Cheol Chung ◽  
Mi-Hyun Choi ◽  
Hyung-Sik Kim ◽  
Jung-Chul Lee ◽  
Sung-Jun Park ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Alzheimer Disease ◽  
Cognitive Abilities ◽  
Brain Volumes ◽  
Healthy Control

scholarly journals Reliable Measurements of theβ-Amyloid Pool in Blood Could Help in the Early Diagnosis of AD

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Pedro Pesini ◽  
Virginia Pérez-Grijalba ◽  
Inmaculada Monleón ◽  
Mercè Boada ◽  
Lluís Tárraga ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Diagnosis ◽  
Small Proportion ◽  
Blood Cells ◽  
Plasma Proteins ◽  
Blood Samples ◽  
Striking Result ◽  
Healthy Control ◽  
Amnesic Mild Cognitive Impairment

The present study was aimed at assessing the capability of Aβ1-40 and Aβ1-42 levels in undiluted plasma (UP), diluted plasma (DP), and cell bound (CB) to distinguish between early stages of Alzheimer's disease (AD), amnesic mild cognitive impairment (MCI), and healthy control (HC). Four blood samples from each participant were collected during one month and the levels of Aβ1-40 and Aβ1-42 were determined by a blinded proprietary ELISA sandwich (Araclon Biotech. Zaragoza, Spain). First striking result was that the amount of Aβ1-40 and Aβ1-42 in UP represented only a small proportion (~15%) of the total beta-amyloid pool in blood (βAPB) described here as the sum of Aβ1-40 and Aβ1-42 in blood where they are free in plasma, bound to plasma proteins, and bound to blood cells. Furthermore, we found that levels of Aβ1-40 and Aβ1-42 in UP, DP, and CB were significantly higher in MCI when compared to HC. On average, the totalβAPB was 1.8 times higher in MCI than in HC (P=0.03) and allowed to discriminate between MCI and HC with a sensitivity and specificity over 80%. Thus, quantification of several markers of theβAPB could be useful and reliable in the discrimination between MCI and HC.

scholarly journals To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Justine Staal ◽  
Francesco Mattace-Raso ◽  
Hennie A. M. Daniels ◽  
Johannes van der Steen ◽  
Johan J. M. Pel
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Predictive Power ◽  
Area Under The Curve ◽  
Classification Performance ◽  
Support Vector ◽  
Potential Biomarker

BackgroundResearch into Alzheimer’s disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer’s disease.MethodHere, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal stage, mild cognitive impairment with underlying the Alzheimer’s disease pathology. The functionality of this network was tested in terms of timing, accuracy, and speed with goal-directed eye-hand tasks. The predictive power was determined by comparing the classification performance of a zero-rule algorithm (baseline), a decision tree, a support vector machine, and a neural network using functional parameters to classify controls without cognitive disorders, mild cognitive impaired patients, and Alzheimer’s disease patients.ResultsFair to good classification was achieved between controls and patients, controls and mild cognitive impaired patients, and between controls and Alzheimer’s disease patients with the support vector machine (77–82% accuracy, 57–93% sensitivity, 63–90% specificity, 0.74–0.78 area under the curve). Classification between mild cognitive impaired patients and Alzheimer’s disease patients was poor, as no algorithm outperformed the baseline (63% accuracy, 0% sensitivity, 100% specificity, 0.50 area under the curve).Comparison with Existing Method(s)The classification performance found in the present study is comparable to that of the existing CSF and MRI biomarkers.ConclusionThe data suggest that visuomotor network dysfunctions have potential in biomarker research and the proposed eye-hand tasks could add to existing tests to form a clear definition of the preclinical phenotype of AD.

scholarly journals LINGUISTIC MEASURES OF SPOKEN UTTERANCES FOR DETECTING MILD COGNITIVE IMPAIRMENT

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S224-S225
Author(s):  
Meysam Asgari ◽  
Jeffrey Kaye ◽  
Hiroko Dodge
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Stimulation ◽  
Support Vector ◽  
Svm Classifier ◽  
Healthy Controls ◽  
Document Frequency ◽  
Trained Interviewer ◽  
Validation Procedure ◽  
Characteristic Area

Abstract Studies have shown that speech characteristics can aid in early-identification of those with mild cognitive impairment (MCI). We performed a linguistic analysis on spoken utterances of 41 participants (15 MCI, 26 healthy controls) from conversations with a trained interviewer using the Term Frequency-Inverse Document Frequency (TF-IDF) method. Data came from a randomized controlled behavioral clinical trial (ClinicalTrials.gov: NCT01571427) to examine effects of conversation-based cognitive stimulation on cognitive functions among older adults with normal cognition or MCI, which served as a pilot study for I-CONECT. From the collected spoken utterances we first constructed a fixed-dimensional feature vector using TF-IDF. Next, to distinguish between MCI and healthy controls, we trained a support vector machine (SVM) classifier on per-subject feature vectors according to 5-fold cross-validation procedure. Our results verify the effectiveness of TF-IDF features in this classification task with Receiver Operating Characteristic Area Under Curve of 81%, well above chance at 65%.

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