Comparison of seeding methods for visualization of the corticospinal tracts using single tensor tractography.

OBJECTIVES: To compare five different seeding methods to delineate hand, foot, and lip components of the corticospinal tract (CST) using single tensor tractography. METHODS: We studied five healthy subjects and 10 brain tumor patients. For each subject, we used five different seeding methods, from (1) cerebral peduncle (CP), (2) posterior limb of the internal capsule (PLIC), (3) white matter subjacent to functional MRI activations (fMRI), (4) whole brain and then selecting the fibers that pass through both fMRI and CP (WBF-CP), and (5) whole brain and then selecting the fibers that pass through both fMRI and PLIC (WBF-PLIC). Two blinded neuroradiologists rated delineations as anatomically successful or unsuccessful tractography. The proportions of successful trials from different methods were compared by Fisher's exact test. RESULTS: To delineate hand motor tract, seeding through fMRI activation areas was more effective than through CP (p<0.01), but not significantly different from PLIC (p>0.1). WBF-CP delineated hand motor tracts in a larger proportion of trials than CP alone (p<0.05). Similarly, WBF-PLIC depicted hand motor tracts in a larger proportion of trials than PLIC alone (p<0.01). Foot motor tracts were delineated in all trials by either PLIC or whole brain seeding (WBF-CP and WBF-PLIC). Seeding from CP or fMRI activation resulted in foot motor tract visualization in 87% of the trials (95% confidence interval: 60-98%). The lip motor tracts were delineated only by WBF-PLIC and in 36% of trials (95% confidence interval: 11-69%). CONCLUSIONS: Whole brain seeding and then selecting the tracts that pass through two anatomically relevant ROIs can delineate more plausible hand and lip motor tracts than seeding from a single ROI. Foot motor tracts can be successfully delineated regardless of the seeding method used.

Impaired language pathways in tuberous sclerosis complex patients with autism spectrum disorders.

The purpose of this study was to examine the relationship between language pathways and autism spectrum disorders (ASDs) in patients with tuberous sclerosis complex (TSC). An advanced diffusion-weighted magnetic resonance imaging (MRI) was performed on 42 patients with TSC and 42 age-matched controls. Using a validated automatic method, white matter language pathways were identified and microstructural characteristics were extracted, including fractional anisotropy (FA) and mean diffusivity (MD). Among 42 patients with TSC, 12 had ASD (29%). After controlling for age, TSC patients without ASD had a lower FA than controls in the arcuate fasciculus (AF); TSC patients with ASD had even a smaller FA, lower than the FA for those without ASD. Similarly, TSC patients without ASD had a greater MD than controls in the AF; TSC patients with ASD had even a higher MD, greater than the MD in those without ASD. It remains unclear why some patients with TSC develop ASD, while others have better language and socio-behavioral outcomes. Our results suggest that language pathway microstructure may serve as a marker of the risk of ASD in TSC patients. Impaired microstructure in language pathways of TSC patients may indicate the development of ASD, although prospective studies of language pathway development and ASD diagnosis in TSC remain essential.

Comparison of blocked and event-related fMRI designs for pre-surgical language mapping.

Language functional magnetic resonance imaging (fMRI) is a promising non-invasive technique for pre-surgical planning in patients whose lesions are adjacent to or within critical language areas. Most language fMRI studies in patients use blocked experimental design. In this study, we compared a blocked design and a rapid event-related design with a jittered inter-stimulus-interval (ISI) (or stochastic design) for language fMRI in six healthy controls, and eight brain tumor patients, using a vocalized antonym generation task. Comparisons were based on visual inspection of fMRI activation maps and degree of language lateralization, both of which were assessed at a constant statistical threshold for each design. The results indicated a relatively high degree of discordance between the two task designs. In general, the event-related design provided maps with more robust activations in the putative language areas than the blocked design, especially for brain tumor patients. Our results suggest that the rapid event-related design has potential for providing comparable or even higher detection power over the blocked design for localizing language function in brain tumor patients, and therefore may be able to generate more sensitive language maps. More patient studies, and further investigation and optimization of language fMRI paradigms will be needed to determine the utility and validity of this approach for pre-surgical planning.

Group independent component analysis of language fMRI from word generation tasks.

Language fMRI has been used to study brain regions involved in language processing and has been applied to pre-surgical language mapping. However, in order to provide clinicians with optimal information, the sensitivity and specificity of language fMRI needs to be improved. Type II error of failing to reach statistical significance when the language activations are genuinely present may be particularly relevant to pre-surgical planning, by falsely indicating low surgical risk in areas where no activations are shown. Furthermore, since the execution of language paradigms involves cognitive processes other than language function per se, the conventional general linear model (GLM) method may identify non-language-specific activations. In this study, we assessed an exploratory approach, independent component analysis (ICA), as a potential complementary method to the inferential GLM method in language mapping applications. We specifically investigated whether this approach might reduce type II error as well as generate more language-specific maps. Fourteen right-handed healthy subjects were studied with fMRI during two word generation tasks. A similarity analysis across tasks was proposed to select components of interest. Union analysis was performed on the language-specific components to increase sensitivity, and conjunction analysis was performed to identify language areas more likely to be essential. Compared with GLM, ICA identified more activated voxels in the putative language areas, and signals from other sources were isolated into different components. Encouraging results from one brain tumor patient are also presented. ICA may be used as a complementary tool to GLM in improving pre-surgical language mapping.