|Functional Connectivity & Brain Networks|
Young Investigator Award Finalist: Differential Interictal Activity of the Precuneus/Posterior Cingulate Cortex Revealed by Resting State FMRI at 3T in Generalized Versus Partial Seizure
Su Lui1, Luo Ouyang2, Qin Chen1, Xiaoqi Huang1, Hehan Tang1, Huafu Chen2, Dong Zhou1, Graham J Kemp3, Qiyong Gong1
1West China Hospital of Sichuan University, Chendu, People's Republic of China; 2University of Electronic Science and Technology of China, Chendu, People's Republic of China; 3University of Liverpool, Liverpool, UK
Resting state functional MRI was used to characterize the pattern of active brain regions in patients with epilepsy. 9 patients with partial seizure, 19 patients with generalized seizure SGC and 34 normal controls were recruited. 200 volumes of EPI images collected on a 3T were processed using the method of Fransson, which reveals information on regional low-frequency BOLD signal oscillations in the resting state without any a priori hypothesis. In GS, the lack of activation in precuneus/PCC may partly account for their more severe interictal deficits, compared to PS, in cognitive functions such as concentration and memory
Disrupted Functional Connectivity Networks in Patients with
Localization-Related Cryptogenic Epilepsy
Jacobus F.A. Jansen1, Koen H.P. Stakenborg1, Marielle C.G. Vlooswijk1, H J.M. Majoie1, Paul A.M. Hofman1, Marc C.T.F.M. de Krom1, Albert P. Aldenkamp1, Walter H. Backes1
1Maastricht University Hospital, Maastricht, Netherlands
Decline of cognitive function is the most frequent co-morbid disorder in epilepsy. It is unclear what neuronal mechanisms underlie the cognitive and behavioral changes. Healthy controls and patients with chronic epilepsy underwent fMRI examination at 3.0 T, to assess possible changes in fMRI silent word generation activation patterns, lateralization indices and functional connectivities. No differences in word generation activation maps between healthy controls and patients with epilepsy were found, whereas functional connectivity analysis proved to be able to observe subtle differences in activation patterns. The functional connectivity properties seem to be indicative of the quality of performance, which is strengthened by the high correlation of functional connectivity values and cognitive scores.
A Longitudinal MR Functional Connectivity Study in Pediatric Subjects
from 2wks to 2yrs Old Using Low-Frequency BOLD Synchronization
Quan Zhu1, Chung-Yi Yang2, John H. Gilmore3, Weili Lin3
1Duke University, Durham, North Carolina, USA; 2National Taiwan University Hospital, Taiwan; 3University of North Carolina at Chapel Hill, Chapel Hill, USA
In this work, we report results on a longitudinal study where low-frequency BOLD synchronization was employed to explore the development of cortical connectivity in the Broca’s and Wernicke’s areas and Anterior Cingulate cortex (ACC) in pediatric subjects from 2wks to 2 yrs old, and demonstrate that it is feasible to investigate brain functional connectivity using BOLD synchronization in pediatric subjects without sedation, even in very young ages. Our results suggest that the application of this technique could potentially improve our understanding of brain functional development.
Age-Related Connectivity Changes in FMRI Data From Children
Performing a Covert Verb Generation Task
Prasanna Rasika Karunanayaka1, Scott Kerry Holland1, Vincent Jerome Schmithorst1, Elena Plante2
1Cincinnati Children's Hospital Research Foundation, Cincinnati, USA; 2University of Arizona, Tuscon, USA
Group ICA is a powerful data-driven technique capable of revealing the functional networks of the human brain based on fMRI data .To investigate the neuroanatomical bases of covert verb generation in 336 children, we used the fMRI paradigm of silent verb generation. Structural Equation Modeling (SEM) and group independent component analysis was combined to investigate the age-related connectivity changes among brain regions associated with covert verb generation. The results show the advantage of investigating covert verb generation in terms of cognitive modules and the associated developmental trends in connectivity.
Brain Connectivity During the Processing of Nouns and Verbs: A
Dynamic Bayesian Network Analysis
Deqiang Qiu1, Shing-Chung Ngan1, Li-Hai Tan1, Alice HD Chan1, Henry KF Mak1, Pek-Lan Khong1
1The University of Hong Kong, Hong Kong, People's Republic of China
Dynamic Bayesian network was used to study the connections among the brain regions activated during processing of nouns and verbs. Under simplifying assumptions, we arrived at a dynamic Bayesian network learning algorithm with reduced time complexity, which allowed us to test all possible connectivity models exhaustively and choose the best model based on the Bayesian information criterion (BIC) score. We found a posterior to anterior flow of processing of both nouns and verbs. The left medial frontal gyrus was found to play an important role in the network. For verb processing, strong involvements of motor cortex and cerebellum were found.
Connectivity of Complex Networks: A Monte Carlo-Based Approach for
Dynamic Causal Modeling
Dardo Tomasi1, Gene-Jack Wang1, Ruiliang Wang1, Nora D. Volkow2
1Brookhaven National Laboratory, Upton, USA; 2National Institutes of Health, Bethesda, USA
We here propose a data-driven dynamic causal modeling method that does not require a priori hypotheses to study the connectivity of complex brain networks. We show that stochastic methods commonly used in physics to solve complex many-body problems, and parallel computers can be used to find the optimal connectivity models of activated networks without a priori assumptions. Our approach demonstrate that the connectivity of the working memory is identical for the left and right brain hemispheres and the importance of the parietal-prefrontal loop in working memory.
The Functional Anatomy of SMA at Rest: Clustering and Connectivity
Independently Measured with DTI and RS-FMRI
Hubert M.J. Fonteijn1, 2, Evelinda Baerends2, 3, David Gordon Norris2
1Helmholtz Institute, Utrecht, Netherlands; 2F.C. Donders Institute for Cognitive Neuroimaging, Nijmegen, Netherlands; 3Leiden Institute for Brain and Cognition (LIBC), Leiden, Netherlands
There is an increasing interest in the analysis of resting-state fMRI data. It is however unclear at what level resting-state networks are organized. To address this issue, we have investigated resting state activation patterns from the SMA region, based on a parcellation scheme using anatomical connectivity from DTI. We have investigated whether the activation from the subregions are correlating exclusively with voxels within these subregions. We have also been able to establish similar clusters based on resting-state data alone, using discrete wavelets and fuzzy clustering and we have investigated resting state connectivity patterns with the rest of the brain.
Higher-Order Contrast Functions Improve Performance of Independent
Component Analysis of Functional MRI Data with Low Signal-To-Noise Ratio
Vincent Jerome Schmithorst1, Scott Kerry Holland1
1Children's Hospital Medical Center, Cincinnati, Ohio, USA
Independent Component Analysis (ICA) is increasingly being used as a data-driven methodology for the analysis of functional MRI (fMRI) data. The use of higher-order contrast functions (such as kurtosis or skewness), as opposed to lower-order functions (such as ln cosh) typically used for superGaussian sources, has been proposed due to the specific characteristics of fMRI signals. We present preliminary results from simulations and resting-state fMRI data comparing kurtosis to the ln cosh function in the presence of low SNR. Results suggest that optimal ICA performance in the presence of low SNR may also warrant the use of higher-order contrast functions.
Partitioning Functional Connectivity Networks Using
“Community Structure” Algorithms
Adam Schwarz1, Alessandro Gozzi1, Angelo Bifone1
1GlaxosmithKline Medicine Research Centre, Verona, Italy
We have investigated the structure of brain functional connectivity networks using an approach developed to detect communities in complex networks of social interactions. We apply this algorithm to pharmacological MRI data, and demonstrate anatomically meaningful and functionally compelling subdivisions that correspond to specific neurotransmitter systems targeted by the drug. The biological interpretation of these “communities” as a signature of functional segregation within the integrated network of brain activity is discussed, together with the relative merits of this approach with respect to existing methods to investigate the structure of functional connectivity networks.