Brain regions with abnormal network properties in severe epilepsy of Lennox-Gastaut phenotype: Multivariate analysis of task-free fMRI

被引:16
作者
Pedersen, Mangor [1 ,2 ]
Curwood, Evan K. [2 ]
Archer, John S. [3 ,4 ]
Abbott, David F. [1 ,2 ]
Jackson, Graeme D. [1 ,2 ,3 ,4 ]
机构
[1] Florey Inst Neurosci & Mental Hlth, Melbourne, Vic, Australia
[2] Univ Melbourne, Dept Neurosci & Mental Hlth, Melbourne, Vic, Australia
[3] Univ Melbourne, Dept Med, Melbourne, Vic, Australia
[4] Austin Hlth, Dept Neurol, Melbourne, Vic, Australia
基金
澳大利亚国家健康与医学研究理事会; 英国医学研究理事会;
关键词
Secondary generalized epilepsy; Lennox-Gastaut syndrome; fMRI; Functional connectivity; Multivariate pattern analysis; MULTI-VOXEL; CONNECTIVITY; CENTRALITY; SUBJECT; MACHINE; IMPACT;
D O I
10.1111/epi.13135
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
ObjectiveLennox-Gastaut syndrome, and the similar but less tightly defined Lennox-Gastaut phenotype, describe patients with severe epilepsy, generalized epileptic discharges, and variable intellectual disability. Our previous functional neuroimaging studies suggest that abnormal diffuse association network activity underlies the epileptic discharges of this clinical phenotype. Herein we use a data-driven multivariate approach to determine the spatial changes in local and global networks of patients with severe epilepsy of the Lennox-Gastaut phenotype. MethodsWe studied 9 adult patients and 14 controls. In 20min of task-free blood oxygen level-dependent functional magnetic resonance imaging data, two metrics of functional connectivity were studied: Regional homogeneity or local connectivity, a measure of concordance between each voxel to a focal cluster of adjacent voxels; and eigenvector centrality, a global connectivity estimate designed to detect important neural hubs. Multivariate pattern analysis of these data in a machine-learning framework was used to identify spatial features that classified disease subjects. ResultsMultivariate pattern analysis was 95.7% accurate in classifying subjects for both local and global connectivity measures (22/23 subjects correctly classified). Maximal discriminating features were the following: increased local connectivity in frontoinsular and intraparietal areas; increased global connectivity in posterior association areas; decreased local connectivity in sensory (visual and auditory) and medial frontal cortices; and decreased global connectivity in the cingulate cortex, striatum, hippocampus, and pons. SignificanceUsing a data-driven analysis method in task-free functional magnetic resonance imaging, we show increased connectivity in critical areas of association cortex and decreased connectivity in primary cortex. This supports previous findings of a critical role for these association cortical regions as a final common pathway in generating the Lennox-Gastaut phenotype. Abnormal function of these areas is likely to be important in explaining the intellectual problems characteristic of this disorder.
引用
收藏
页码:1767 / 1773
页数:7
相关论文
共 34 条
[1]  
[Anonymous], 2004, KERNEL METHODS PATTE
[2]   Conceptualizing Lennox-Gastaut syndrome as a secondary network epilepsy [J].
Archer, John S. ;
Warren, Aaron E. L. ;
Jackson, Graeme D. ;
Abbott, David F. .
FRONTIERS IN NEUROLOGY, 2014, 5
[3]   Lennox-Gastaut syndrome and phenotype: Secondary network epilepsies [J].
Archer, John S. ;
Warren, Aaron E. L. ;
Stagnitti, Monique R. ;
Masterton, Richard A. J. ;
Abbott, David F. ;
Jackson, Graeme D. .
EPILEPSIA, 2014, 55 (08) :1245-1254
[4]   Lennox-Gastaut syndrome: a consensus approach on diagnosis, assessment, management, and trial methodology [J].
Arzimanoglou, Alexis ;
French, Jacqueline ;
Blume, Warren T. ;
Cross, J. Helen ;
Ernst, Jan-Peter ;
Feucht, Martha ;
Genton, Pierre ;
Guerrini, Renzo ;
Kluger, Gerhard ;
Pellock, John M. ;
Perucca, Emilio ;
Wheless, James W. .
LANCET NEUROLOGY, 2009, 8 (01) :82-93
[5]   The brain's default network - Anatomy, function, and relevance to disease [J].
Buckner, Randy L. ;
Andrews-Hanna, Jessica R. ;
Schacter, Daniel L. .
YEAR IN COGNITIVE NEUROSCIENCE 2008, 2008, 1124 :1-38
[6]   Complex brain networks: graph theoretical analysis of structural and functional systems [J].
Bullmore, Edward T. ;
Sporns, Olaf .
NATURE REVIEWS NEUROSCIENCE, 2009, 10 (03) :186-198
[7]   Advances and pitfalls in the analysis and interpretation of resting-state FMRI data [J].
Cole, David M. ;
Smith, Stephen M. ;
Beckmann, Christian F. .
FRONTIERS IN SYSTEMS NEUROSCIENCE, 2010, 4
[8]   What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis [J].
Davis, Tyler ;
LaRocque, Karen F. ;
Mumford, Jeanette A. ;
Norman, Kenneth A. ;
Wagner, Anthony D. ;
Poldrack, Russell A. .
NEUROIMAGE, 2014, 97 :271-283
[9]  
Deen Ben, 2012, Nature, V491, pS20
[10]   Epileptic encephalopathy [J].
Dulac, O .
EPILEPSIA, 2001, 42 :23-26