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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

Consensus functional connections demonstrated in the left and top view.Regions are color-coded by category and size-coded by weight as in Fig 3. Red lines represent increased functional connections, and blue lines represent decreased functional connections.
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pone.0124153.g004: Consensus functional connections demonstrated in the left and top view.Regions are color-coded by category and size-coded by weight as in Fig 3. Red lines represent increased functional connections, and blue lines represent decreased functional connections.

Mentions: To present the findings in a clear and concise manner, region weights and consensus functional connections are displayed in a circle graph (Fig 3) using a MATLAB tool developed by ourselves, and projected to a surface rendering of a human brain (Fig 4) using the software BrainNet Viewer [28]. Regions are color-coded by category and size-coded by weight. For the patients, the line colors representing the changed directions of the relative consensus functional connections are red for decreases and blue for increases. In this investigation, 105 consensus functional connections were obtained from each fold of the LOOCV, and 58.10% of these connections decreased in the patients compared with the healthy controls. The brain regions related to the consensus functional connectivity were primarily located within the following areas: (i) the default mode network (DMN). mainly containing the bilateral superior frontal gyrus (medial), bilateral superior frontal gyrus (medial orbital), right middle temporal gyrus and right inferior temporal gyrus; (ii) the control network, which can be divided into two independent control networks, the cingulo-opercular network (CON) and the frontal-parietal network [29, 30]. comprising the bilateral thalamus, bilateral insula, bilateral anterior cingulate, bilateral superior frontal gyrus (dorsolateral) and bilateral inferior frontal gyrus (orbital part); and (iii) the cerebellum. In addition, several brain regions also exhibited greater weights than others, ie. the bilateral superior temporal gyrus, right temporal pole: superior temporal gyrus, bilateral supramarginal gyrus, left rolandic operculum, right paracentral lobule and left median cingulate.


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)

Consensus functional connections demonstrated in the left and top view.Regions are color-coded by category and size-coded by weight as in Fig 3. Red lines represent increased functional connections, and blue lines represent decreased functional connections.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0124153.g004: Consensus functional connections demonstrated in the left and top view.Regions are color-coded by category and size-coded by weight as in Fig 3. Red lines represent increased functional connections, and blue lines represent decreased functional connections.
Mentions: To present the findings in a clear and concise manner, region weights and consensus functional connections are displayed in a circle graph (Fig 3) using a MATLAB tool developed by ourselves, and projected to a surface rendering of a human brain (Fig 4) using the software BrainNet Viewer [28]. Regions are color-coded by category and size-coded by weight. For the patients, the line colors representing the changed directions of the relative consensus functional connections are red for decreases and blue for increases. In this investigation, 105 consensus functional connections were obtained from each fold of the LOOCV, and 58.10% of these connections decreased in the patients compared with the healthy controls. The brain regions related to the consensus functional connectivity were primarily located within the following areas: (i) the default mode network (DMN). mainly containing the bilateral superior frontal gyrus (medial), bilateral superior frontal gyrus (medial orbital), right middle temporal gyrus and right inferior temporal gyrus; (ii) the control network, which can be divided into two independent control networks, the cingulo-opercular network (CON) and the frontal-parietal network [29, 30]. comprising the bilateral thalamus, bilateral insula, bilateral anterior cingulate, bilateral superior frontal gyrus (dorsolateral) and bilateral inferior frontal gyrus (orbital part); and (iii) the cerebellum. In addition, several brain regions also exhibited greater weights than others, ie. the bilateral superior temporal gyrus, right temporal pole: superior temporal gyrus, bilateral supramarginal gyrus, left rolandic operculum, right paracentral lobule and left median cingulate.

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