Structural Changes of the Corpus Callosum in Mild Cognitive Impairment and Alzheimer’s Disease

2006 ◽  
Vol 21 (4) ◽  
pp. 215-220 ◽  
Author(s):  
Philipp A. Thomann ◽  
Torsten Wüstenberg ◽  
Johannes Pantel ◽  
Marco Essig ◽  
Johannes Schröder
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Corpus Callosum ◽  
Structural Changes

Deformation-based Statistical Shape Analysis of the Corpus Callosum in Mild Cognitive Impairment and Alzheimer’s Disease

2018 ◽  
Vol 15 (12) ◽  
pp. 1151-1160 ◽  
Author(s):  
Zihan Jiang ◽  
Huilin Yang ◽  
Xiaoying Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Corpus Callosum ◽  
Shape Analysis ◽  
Disease Assessment ◽  
Statistical Shape Analysis ◽  
Superior Part ◽  
Statistical Shape

Objective: In this study, we investigated the influence that the pathology of Alzheimer’s disease (AD) exerts upon the corpus callosum (CC) using a total of 325 mild cognitive impairment (MCI) subjects, 155 AD subjects, and 185 healthy control (HC) subjects. Method: Regionally-specific morphological CC abnormalities, as induced by AD, were quantified using a large deformation diffeomorphic metric curve mapping based statistical shape analysis pipeline. We also quantified the association between the CC shape phenotype and two cognitive measures; the Mini Mental State Examination (MMSE) and the Alzheimer’s Disease Assessment Scale-Cognitive Behavior Section (ADAS-cog). To identify AD-relevant areas, CC was sub-divided into three subregions; the genu, body, and splenium (gCC, bCC, and sCC). Results: We observed significant shape compressions in AD relative to that in HC, mainly concentrated on the superior part of CC, across all three sub-regions. The HC-vs-MCI shape abnormalities were also concentrated on the superior part, but mainly occurred on bCC and sCC. The significant MCI-vs-AD shape differences, however, were only detected in part of sCC. In the shape-cognition association, significant negative correlations to ADAS-cog were detected for shape deformations at regions belonging to gCC and sCC and significant positive correlations to MMSE at regions mainly belonging to sCC. Conclusion: Our results suggest that the callosal shape deformation patterns, especially those of sCC, linked tightly to the cognitive decline in AD, and are potentially a powerful biomarker for monitoring the progression of AD.

P2-181 Regionally specific atrophy of the corpus callosum in Alzheimer's disease and mild cognitive impairment

2004 ◽  
Vol 25 ◽  
pp. S278
Author(s):  
Paul Wang ◽  
Andrew J. Saykin ◽  
Laura A. Flashman ◽  
Heather A. Wishart ◽  
Laura A. Rabin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Corpus Callosum

scholarly journals Eigenvector alignment: assessing functional network changes in amnestic mild cognitive impairment and Alzheimer’s disease

2020 ◽  
Author(s):  
Ruaridh Clark ◽  
Niia Nikolova ◽  
William J. McGeown ◽  
Malcolm Macdonald
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Structural Changes ◽  
Amnestic Mild Cognitive Impairment ◽  
Functional Networks ◽  
Limited Information ◽  
The Impact

AbstractEigenvector alignment, introduced herein to investigate human brain functional networks, is adapted from methods developed to detect influential nodes and communities in networked systems. It is used to identify differences in the brain networks of subjects with Alzheimer’s disease (AD), amnestic Mild Cognitive Impairment (aMCI) and healthy controls (HC). Well-established methods exist for analysing connectivity networks composed of brain regions, including the widespread use of centrality metrics such as eigenvector centrality. However, these metrics provide only limited information on the relationship between regions, with this understanding often sought by comparing the strength of pairwise functional connectivity. Our holistic approach, eigenvector alignment, considers the impact of all functional connectivity changes before assessing the strength of the functional relationship, i.e. alignment, between any two regions. This is achieved by comparing the placement of regions in a Euclidean space defined by the network’s dominant eigenvectors. Eigenvector alignment recognises the strength of bilateral connectivity in cortical areas of healthy control subjects, but also reveals degradation of this commissural system in those with AD. Surprisingly little structural change is detected for key regions in the Default Mode Network, despite significant declines in the functional connectivity of these regions. In contrast, regions in the auditory cortex display significant alignment changes that begin in aMCI and are the most prominent structural changes for those with AD. Alignment differences between aMCI and AD subjects are detected, including notable changes to the hippocampal regions. These findings suggest eigenvector alignment can play a complementary role, alongside established network analytic approaches, to capture how the brain’s functional networks develop and adapt when challenged by disease processes such as AD.

P1-341: Changes of bending angles of the corpus callosum in mild cognitive impairment and Alzheimer's disease patients

2008 ◽  
Vol 4 ◽  
pp. T319-T319
Author(s):  
Chan-Seung Chung ◽  
Jae-Hong Lee ◽  
Duk-Lyul Na ◽  
Doh Kwan Kim ◽  
Sang Yun Kim ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Corpus Callosum ◽  
Bending Angles

scholarly journals Neuroimage Biomarker Identification of the Conversion of Mild Cognitive Impairment to Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Te-Han Kung ◽  
Tzu-Cheng Chao ◽  
Yi-Ru Xie ◽  
Ming-Chyi Pai ◽  
Yu-Min Kuo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Structural Changes ◽  
Feature Selection Method ◽  
Cognitive Capacity ◽  
Principal Curvatures ◽  
Hippocampal Subfields

An efficient method to identify whether mild cognitive impairment (MCI) has progressed to Alzheimer’s disease (AD) will be beneficial to patient care. Previous studies have shown that magnetic resonance imaging (MRI) has enabled the assessment of AD progression based on imaging findings. The present work aimed to establish an algorithm based on three features, namely, volume, surface area, and surface curvature within the hippocampal subfields, to model variations, including atrophy and structural changes to the cortical surface. In this study, a new biomarker, the ratio of principal curvatures (RPC), was proposed to characterize the folding patterns of the cortical gyrus and sulcus. Along with volumes and surface areas, these morphological features associated with the hippocampal subfields were assessed in terms of their sensitivity to the changes in cognitive capacity by two different feature selection methods. Either the extracted features were statistically significantly different, or the features were selected through a random forest model. The identified subfields and their structural indices that are sensitive to the changes characteristic of the progression from MCI to AD were further assessed with a multilayer perceptron classifier to help facilitate the diagnosis. The accuracy of the classification based on the proposed method to distinguish whether a MCI patient enters the AD stage amounted to 79.95%, solely using the information from the features selected by a logical feature selection method.

scholarly journals Corpus Callosum Atrophy Rate in Mild Cognitive Impairment and Prodromal Alzheimer's Disease

2015 ◽  
Vol 45 (3) ◽  
pp. 921-931 ◽  
Author(s):  
Sahar Elahi ◽  
Alvin H. Bachman ◽  
Sang Han Lee ◽  
John J. Sidtis ◽  
Babak A. Ardekani ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Corpus Callosum ◽  
Atrophy Rate ◽  
Prodromal Alzheimer’S Disease

scholarly journals A multipredictor model to predict the conversion of mild cognitive impairment to Alzheimer’s disease by using a predictive nomogram

2019 ◽  
Vol 45 (2) ◽  
pp. 358-366 ◽  
Author(s):  
Kexin Huang ◽  
◽  
Yanyan Lin ◽  
Lifeng Yang ◽  
Yubo Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebral Cortex ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Structural Changes ◽  
Expression Patterns ◽  
Image Features ◽  
Predictive Tool ◽  
Clinical Factor

Abstract Predicting the probability of converting from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is still a challenging task. This study aims at providing a personalized MCI-to-AD conversion estimation by using a multipredictor nomogram that integrates neuroimaging features, cerebrospinal fluid (CSF) biomarker, and clinical assessments. To do so, 290 MCI patients were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), of whom 76 has converted to AD and 214 remained with MCI. All subjects were randomly divided into a primary and validation cohort. Radiomics signature (Rad-sig) was obtained based on 17 cerebral cortex features selected by using Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Clinical factors and amyloid-beta peptide (Aβ) concentration were selected by using Spearman correlation between the converted and not-converted patients. Then, a nomogram that combines image features, clinical factor, and Aβ concentration was constructed and validated. Furthermore, we explored the associations between various predictors from the macro- to the microperspective by assessing gene expression patterns. Our results showed that the multipredictor nomogram (C-index 0.978 and 0.956 in both cohorts, respectively) outperformed the nomogram using either Rad-sig or Aβ concentration as individual predictors. Significant associations were found between neuropsychological scores, cerebral cortex features, Aβ levels, and underlying gene pathways. Our study may have a clinical impact as a powerful predictive tool for predicting the conversion probability of MCI and providing associations between cognitive impairment, structural changes, Aβ levels, and underlying biological patterns from the macro- to the microperspective.

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