scholarly journals Classification of Alzheimer Disease, Mild Cognitive Impairment, and Normal Cognitive Status with Large-Scale Network Analysis Based on Resting-State Functional MR Imaging

Radiology ◽  
2011 ◽  
Vol 259 (1) ◽  
pp. 213-221 ◽  
Author(s):  
Gang Chen ◽  
B. Douglas Ward ◽  
Chunming Xie ◽  
Wenjun Li ◽  
Zhilin Wu ◽  
...  
Author(s):  
Vahid Rashedi ◽  
Mahshid Foroughan ◽  
Negin Chehrehnegar

Introduction: The Montreal Cognitive Assessment (MoCA) is a cognitive screening test widely used in clinical practice and suited for the detection of Mild Cognitive Impairment (MCI). The aims were to evaluate the psychometric properties of the Persian MoCA as a screening test for mild cognitive dysfunction in Iranian older adults and to assess its accuracy as a screening test for MCI and mild Alzheimer disease (AD). Method: One hundred twenty elderly with a mean age of 73.52 ± 7.46 years participated in this study. Twenty-one subjects had mild AD (MMSE score ≤21), 40 had MCI, and 59 were cognitively healthy controls. All the participants were administered the Mini-Mental State Examination (MMSE) to evaluate their general cognitive status. Also, a battery of comprehensive neuropsychological assessments was administered. Results: The mean score on the Persian version of the MoCA and the MMSE were 19.32 and 25.62 for MCI and 13.71 and 22.14 for AD patients, respectively. Using an optimal cutoff score of 22 the MoCA test detected 86% of MCI subjects, whereas the MMSE with a cutoff score of 26 detected 72% of MCI subjects. In AD patients with a cutoff score of 20, the MoCA had a sensitivity of 94% whereas the MMSE detected 61%. The specificity of the MoCA was 70% and 90% for MCI and AD, respectively. Discussion: The results of this study show that the Persian version of the MoCA is a reliable screening tool for detection of MCI and early stage AD. The MoCA is more sensitive than the MMSE in screening for cognitive impairment, proving it to be superior to MMSE in detecting MCI and mild AD.


2021 ◽  
Vol 13 ◽  
Author(s):  
Martina Vettore ◽  
Matteo De Marco ◽  
Claudia Pallucca ◽  
Matteo Bendini ◽  
Maurizio Gallucci ◽  
...  

“Mild cognitive impairment” (MCI) is a diagnosis characterised by deficits in episodic memory (aMCI) or in other non-memory domains (naMCI). Although the definition of subtypes is helpful in clinical classification, it provides little insight on the variability of neurofunctional mechanisms (i.e., resting-state brain networks) at the basis of symptoms. In particular, it is unknown whether the presence of a high load of white-matter hyperintensities (WMHs) has a comparable effect on these functional networks in aMCI and naMCI patients. This question was addressed in a cohort of 123 MCI patients who had completed an MRI protocol inclusive of T1-weighted, fluid-attenuated inversion recovery (FLAIR) and resting-state fMRI sequences. T1-weighted and FLAIR images were processed with the Lesion Segmentation Toolbox to quantify whole-brain WMH volumes. The CONN toolbox was used to preprocess all fMRI images and to run an independent component analysis for the identification of four large-scale haemodynamic networks of cognitive relevance (i.e., default-mode, salience, left frontoparietal, and right frontoparietal networks) and one control network (i.e., visual network). Patients were classified based on MCI subtype (i.e., aMCI vs. naMCI) and WMH burden (i.e., low vs. high). Maps of large-scale networks were then modelled as a function of the MCI subtype-by-WMH burden interaction. Beyond the main effects of MCI subtype and WMH burden, a significant interaction was found in the salience and left frontoparietal networks. Having a low WMH burden was significantly more associated with stronger salience-network connectivity in aMCI (than in naMCI) in the right insula, and with stronger left frontoparietal-network connectivity in the right frontoinsular cortex. Vice versa, having a low WMH burden was significantly more associated with left-frontoparietal network connectivity in naMCI (than in aMCI) in the left mediotemporal lobe. The association between WMH burden and strength of connectivity of resting-state functional networks differs between aMCI and naMCI patients. Although exploratory in nature, these findings indicate that clinical profiles reflect mechanistic interactions that may play a central role in the definition of diagnostic and prognostic statuses.


2010 ◽  
Vol 6 ◽  
pp. S11-S11
Author(s):  
Gang Chen ◽  
B. Douglas Ward ◽  
Chunming Xie ◽  
Zhilin Wu ◽  
Jennifer Jones ◽  
...  

2018 ◽  
Author(s):  
Kelly Shen ◽  
Gleb Bezgin ◽  
Michael Schirner ◽  
Petra Ritter ◽  
Stefan Everling ◽  
...  

AbstractModels of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.


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