| Part1Cognitive is related to functional connectivitynetwork in healthy adults: An resting-statemagnetoencephalography studyObjective It is not clear about cognitive function mechanism. We studythe brain resting-state functional connectivity network to reveal thebrain mechanism for cognitive function.Methods Resting-state MEG data were collected from32healthyadults. The Phase Lag Index(PLI) was computed to assess functionalconnectivity in Delta band (1-4Hz), Theta band (4-8Hz), Alpha band(8-12Hz), Beta band (12-30Hz), low Gamma band (30-45Hz) and highGamma-band (55-70Hz) after2LOGN threshold of functionalconnectivity network of sparse, the network characteristics such ascluster coefficient C and path length L was measured, and the smallworld index was calculated.20healthy adults underwentneuropsychological evaluation, including attention test, verbal fluencytest, memory test, motor skill test, visual-spatial organization andintelligence tests. Intelligence tests includes arithmetic test, digitsymbol test, completion of drawing test and similar test. We analyzedthe correlation between Z-score and small world index, age and smallworld index, gender and small world index in healthy adults.Results Functional connectivity network of healthy subjects showedthe “small-world” network characteristics in Delta band, Theta band,Alpha band, Beta band, low Gamma band and high Gamma-band.Lower Gamma band small world index of resting-state functional connectivity network showed a significant positive correlation withattention test (P=0.049), visual-spatial organization test (P=0.006),arithmetic test (P=0.04) and similar test(P=0.000). Upper Gamma bandsmall world index of resting-state functional connectivity networkshowed a significant positive correlation with visual-spatialorganization test (P=0.04), arithmetic test (P=0.041) and completion ofdrawing test (P=0.023). Alpha band small world index of resting-statefunctional connectivity network showed a significant positivecorrelation with similar test (P=0.041). Age and Lower Gamma bandsmall world index of resting-state functional connectivity networkshowed a significant negatively correlation. Delta band small worldindex of resting-state functional connectivity network showssignificant difference in different gender of subjects.Conclusion The healthy adult brain resting-state functional connectivitynetwork has the characteristics of the "small world network". Gammaband small world index of resting-state functional connectivitynetwork and cognitive function in a significant correlation. Age andgender factors have some influence on the brain resting-statefunctional connectivity network. Part2Detection of functional connectivity network ofglioma using resting-state magnetoencephalographyObjective Our study aims to assess the effects of functionalconnectivity of Glioma by resting-state MEG, to reveal the variation ofresting-state functional connectivity network of glioma, to explore themechanism of cognitive dysfunction of glioma patients, and to find thebiomarker of resting-state functional connectivity network of glioma.Methods Resting-state MEG data were collected from31gliomapatients and32healthy adults. The Phase Lag Index(PLI) wascomputed to assess functional connectivity in Delta band (1-4Hz),Theta band (4-8Hz), Alpha band (8-12Hz), Beta band (12-30Hz), lowGamma band (30-45Hz) and high Gamma-band (55-70Hz) after2LOGNthreshold of functional connectivity network of sparse, the networkcharacteristics such as cluster coefficient C and path length L wasmeasured, and the small world index was calculated.20gliomapatients and20healthy adults underwent neuropsychologicalevaluation, including attention test, verbal fluency test, memory test,motor skill test, visual-spatial organization and intelligence tests.Intelligence tests includes arithmetic test, digit symbol test,completion of drawing test and similar test. We calculated thedifference of Z-score between glioma patients and healthy adults, andanalyzed the correlation between Z-score and small world index, ageand small world index, gender and small world index in gliomapatients. Results Functional connectivity network of glioma showed the“small-world” network characteristics were presented in Delta band,Theta band, Alpha band, Beta band, and the “small-world” networkcharacteristics disappeared in low Gamma band and high Gamma-band.Compared to healthy adults, glioma patients displayed lower clusteringcoefficient, lower “small-world” index in the lower Gamma（P=0.021,P=0.021） band and upper Gamma band (P=0.043, P=0.037). Gliomawithout symptomatic epilepsy displayed lower clustering coefficient(P=0.040) and lower “small-world” index (P=0.042) in the lowerGamma band. Glioma associated with symptomatic epilepsy displayedlower clustering coefficient (P=0.008),longer path length (P=0.033)and lower “small-world” index (P=0.006) in the upper Gamma band,whereas a lower clustering coefficient (P=0.042) in the Theta band.There was not significant difference of clustering coefficient, pathlength and “small-world” index between glioma associated withsymptomatic epilepsy patients and without symptomatic epilepsypatients. Compared to low-grade glioma, high-grade glioma displayedhigher clustering coefficient, shorter path length and higher“small-world” index. There was not significant difference of clusteringcoefficient, path length and “small-world” index between left and righthemisphere glioma patients. Compared to healthy controls, patientsperformed poorer on attention, verbal fluency, arithmetic, digit symboland completion of drawing. Upper Gamma band small world index ofresting-state functional connectivity network showed a significantpositive correlation with verbal fluency test (P=0.026) in gliomapatients. Alpha band small world index of resting-state functional connectivity network showed a significant positive correlation witharithmetic （P=0.019）and completion of drawing test (P=0.018).Therewas not significant correlation between age and resting-statefunctional connectivity network. Alpha band （P=0.041） small worldindex of resting-state functional connectivity network showssignificant difference in different gender of subjects.Conclusion Glioma can disrupt the brain’s "small-world" networkfeatures, and resulting in a lack of cognitive function. Gamma bandand Alpha band resting state functional connectivity network can beused as a biomarker for early diagnosis of glioma patients, lowerGamma band resting state functional connectivity network may be apotential biomarker of the diagnosis of glioma, upper Gamma bandresting-state functional connectivity networks may be a potentialbiomarker of glioma associated with symptomatic epilepsy, Delta bandresting state functional connectivity networks may be a potentialbiomarker to distinguish the different grade of gliomas. Gender factorshave some influence on the brain resting-state functional connectivitynetwork. |
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