Dataset Information

Combined DTI Tractography and Functional MRI Study of the Language Connectome in Healthy Volunteers: Extensive Mapping of White Matter Fascicles and Cortical Activations.

ABSTRACT: Despite a better understanding of brain language organization into large-scale cortical networks, the underlying white matter (WM) connectivity is still not mastered. Here we combined diffusion tensor imaging (DTI) fiber tracking (FT) and language functional magnetic resonance imaging (fMRI) in twenty healthy subjects to gain new insights into the macroscopic structural connectivity of language. Eight putative WM fascicles for language were probed using a deterministic DTI-FT technique: the arcuate fascicle (AF), superior longitudinal fascicle (SLF), uncinate fascicle (UF), temporo-occipital fascicle, inferior fronto-occipital fascicle (IFOF), middle longitudinal fascicle (MdLF), frontal aslant fascicle and operculopremotor fascicle. Specific measurements (i.e. volume, length, fractional anisotropy) and precise cortical terminations were derived for each WM fascicle within both hemispheres. Connections between these WM fascicles and fMRI activations were studied to determine which WM fascicles are related to language. WM fascicle volumes showed asymmetries: leftward for the AF, temporoparietal segment of SLF and UF, and rightward for the frontoparietal segment of the SLF. The lateralization of the AF, IFOF and MdLF extended to differences in patterns of anatomical connections, which may relate to specific hemispheric abilities. The leftward asymmetry of the AF was correlated to the leftward asymmetry of fMRI activations, suggesting that the lateralization of the AF is a structural substrate of hemispheric language dominance. We found consistent connections between fMRI activations and terminations of the eight WM fascicles, providing a detailed description of the language connectome. WM fascicle terminations were also observed beyond fMRI-confirmed language areas and reached numerous cortical areas involved in different functional brain networks. These findings suggest that the reported WM fascicles are not exclusively involved in language and might be related to other cognitive functions such as visual recognition, spatial attention, executive functions, memory, and processing of emotional and behavioral aspects.

SUBMITTER: Vassal F

PROVIDER: S-EPMC4814138 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Combined DTI Tractography and Functional MRI Study of the Language Connectome in Healthy Volunteers: Extensive Mapping of White Matter Fascicles and Cortical Activations.

Vassal François F Schneider Fabien F Boutet Claire C Jean Betty B Sontheimer Anna A Lemaire Jean-Jacques JJ

PloS one 20160330 3

Despite a better understanding of brain language organization into large-scale cortical networks, the underlying white matter (WM) connectivity is still not mastered. Here we combined diffusion tensor imaging (DTI) fiber tracking (FT) and language functional magnetic resonance imaging (fMRI) in twenty healthy subjects to gain new insights into the macroscopic structural connectivity of language. Eight putative WM fascicles for language were probed using a deterministic DTI-FT technique: the arcu ...[more]

PMID: 27029050

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Project description:ObjectiveTo evaluate brai structural connectivity in children with traumatic injury (TI) following a motor vehicle accident using graph theory analysis of DTI tractography data.MethodsDTI scans were acquired on a 3 T Philips scanner from children aged 8-15 years approximately 2 months post-injury. The TI group consisted of children with traumatic brain injury (TBI; n = 44) or extracranial injury (EI; n = 23). Healthy control children (n = 36) were included as an age-matched comparison group. A graph theory approach was applied to DTI tractography data to investigate injury-related differences in connectivity network characteristics. Group differences in structural connectivity evidenced by graph metrics including efficiency, strength, and modularity were assessed using the multi-threshold permutation correction (MTPC) and network-based statistic (NBS) methods.ResultsAt the global network level, global efficiency and mean network strength were lower, and modularity was higher, in the TBI than in the control group. Similarly, strength was lower and modularity higher when comparing the EI to the control group. At the vertex level, nodal efficiency, vertex strength, and average shortest path length were different between all pairwise comparisons of the three groups. Both nodal efficiency and vertex strength were higher in the control than in the EI group, which in turn were higher than in the TBI group. The opposite between-group relationships were seen with path length. These between-group differences were distributed throughout the brain, in both hemispheres. NBS analysis resulted in a cluster of 22 regions and 21 edges with significantly lower connectivity in the TBI group compared to controls. This cluster predominantly involves the frontal lobe and subcortical gray matter structures in both hemispheres.ConclusionsGraph theory analysis of DTI tractography showed diffuse differences in structural brain network connectivity in children 2 months post-TI. Network differences were consistent with lower network integration and higher segregation in the injured groups compared to healthy controls. Findings suggest that inclusion of trauma-exposed comparison groups in studies of TBI outcome is warranted to better characterize the indirect effect of stress on brain networks.

Project description:ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance.

Dataset Information

Combined DTI Tractography and Functional MRI Study of the Language Connectome in Healthy Volunteers: Extensive Mapping of White Matter Fascicles and Cortical Activations.

Publications

Combined DTI Tractography and Functional MRI Study of the Language Connectome in Healthy Volunteers: Extensive Mapping of White Matter Fascicles and Cortical Activations.

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