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Discriminative analysis of Parkinson's disease based on whole-brain functional connectivity.

Chen Y, Yang W, Long J, Zhang Y, Feng J, Li Y, Huang B - PLoS ONE (2015)

Bottom Line: Recently, there has been an increasing emphasis on applications of pattern recognition and neuroimaging techniques in the effective and accurate diagnosis of psychiatric or neurological disorders.These disease-related resting-state network alterations might play important roles in the pathophysiology of this disease.Our results suggest that analyses of whole-brain resting-state functional connectivity patterns have the potential to improve the clinical diagnosis and treatment evaluation of PD.

View Article: PubMed Central - PubMed

Affiliation: Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.

ABSTRACT
Recently, there has been an increasing emphasis on applications of pattern recognition and neuroimaging techniques in the effective and accurate diagnosis of psychiatric or neurological disorders. In the present study, we investigated the whole-brain resting-state functional connectivity patterns of Parkinson's disease (PD), which are expected to provide additional information for the clinical diagnosis and treatment of this disease. First, we computed the functional connectivity between each pair of 116 regions of interest derived from a prior atlas. The most discriminative features based on Kendall tau correlation coefficient were then selected. A support vector machine classifier was employed to classify 21 PD patients with 26 demographically matched healthy controls. This method achieved a classification accuracy of 93.62% using leave-one-out cross-validation, with a sensitivity of 90.47% and a specificity of 96.15%. The majority of the most discriminative functional connections were located within or across the default mode, cingulo-opercular and frontal-parietal networks and the cerebellum. These disease-related resting-state network alterations might play important roles in the pathophysiology of this disease. Our results suggest that analyses of whole-brain resting-state functional connectivity patterns have the potential to improve the clinical diagnosis and treatment evaluation of PD.

No MeSH data available.


Related in: MedlinePlus

The permutation distribution of the generation rates (1,000 repetitions) when selecting the 150 most discriminating features: the x- and y-labels represent the generalization rate and occurrence number, respectively.GR0 is the generation rate obtained using the real class labels.
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pone.0124153.g002: The permutation distribution of the generation rates (1,000 repetitions) when selecting the 150 most discriminating features: the x- and y-labels represent the generalization rate and occurrence number, respectively.GR0 is the generation rate obtained using the real class labels.

Mentions: Identifying the number of selected features k using the parameter search method, the classification accuracy for discriminating the individuals as either controls or patients was 95.74% (95.24% sensitivity; 96.15% specificity). The values of the parameter k involved in the classification in each cross-validation fold fell around 145 (Fig 1), and the mean of these values was 149.15. Thus, we simply fixed the parameter k at 150 in each fold, and the classifier achieved an accuracy of 93.62% (permutation test, p < 0.001; 90.47% sensitivity; 96.15% specificity). From now on, all the results presented below are based on the selection of a constant parameter k of 150. The permutation distribution of the generation rates is shown in Fig 2. Using the generalization rate as the statistic, the results shown in Fig 2 demonstrate that the classifier learned the relationship between the data and the labels with a probability of being wrong of less than 0.001.


Discriminative analysis of Parkinson's disease based on whole-brain functional connectivity.

Chen Y, Yang W, Long J, Zhang Y, Feng J, Li Y, Huang B - PLoS ONE (2015)

The permutation distribution of the generation rates (1,000 repetitions) when selecting the 150 most discriminating features: the x- and y-labels represent the generalization rate and occurrence number, respectively.GR0 is the generation rate obtained using the real class labels.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401568&req=5

pone.0124153.g002: The permutation distribution of the generation rates (1,000 repetitions) when selecting the 150 most discriminating features: the x- and y-labels represent the generalization rate and occurrence number, respectively.GR0 is the generation rate obtained using the real class labels.
Mentions: Identifying the number of selected features k using the parameter search method, the classification accuracy for discriminating the individuals as either controls or patients was 95.74% (95.24% sensitivity; 96.15% specificity). The values of the parameter k involved in the classification in each cross-validation fold fell around 145 (Fig 1), and the mean of these values was 149.15. Thus, we simply fixed the parameter k at 150 in each fold, and the classifier achieved an accuracy of 93.62% (permutation test, p < 0.001; 90.47% sensitivity; 96.15% specificity). From now on, all the results presented below are based on the selection of a constant parameter k of 150. The permutation distribution of the generation rates is shown in Fig 2. Using the generalization rate as the statistic, the results shown in Fig 2 demonstrate that the classifier learned the relationship between the data and the labels with a probability of being wrong of less than 0.001.

Bottom Line: Recently, there has been an increasing emphasis on applications of pattern recognition and neuroimaging techniques in the effective and accurate diagnosis of psychiatric or neurological disorders.These disease-related resting-state network alterations might play important roles in the pathophysiology of this disease.Our results suggest that analyses of whole-brain resting-state functional connectivity patterns have the potential to improve the clinical diagnosis and treatment evaluation of PD.

View Article: PubMed Central - PubMed

Affiliation: Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.

ABSTRACT
Recently, there has been an increasing emphasis on applications of pattern recognition and neuroimaging techniques in the effective and accurate diagnosis of psychiatric or neurological disorders. In the present study, we investigated the whole-brain resting-state functional connectivity patterns of Parkinson's disease (PD), which are expected to provide additional information for the clinical diagnosis and treatment of this disease. First, we computed the functional connectivity between each pair of 116 regions of interest derived from a prior atlas. The most discriminative features based on Kendall tau correlation coefficient were then selected. A support vector machine classifier was employed to classify 21 PD patients with 26 demographically matched healthy controls. This method achieved a classification accuracy of 93.62% using leave-one-out cross-validation, with a sensitivity of 90.47% and a specificity of 96.15%. The majority of the most discriminative functional connections were located within or across the default mode, cingulo-opercular and frontal-parietal networks and the cerebellum. These disease-related resting-state network alterations might play important roles in the pathophysiology of this disease. Our results suggest that analyses of whole-brain resting-state functional connectivity patterns have the potential to improve the clinical diagnosis and treatment evaluation of PD.

No MeSH data available.


Related in: MedlinePlus