Independent contribution of individual white matter pathways to language function in pediatric epilepsy patients

Abstract

Background and purposePatients with epilepsy and malformations of cortical development (MCDs) are at high risk for language and other cognitive impairment. Specific impairments, however, are not well correlated with the extent and locale of dysplastic cortex; such findings highlight the relevance of aberrant cortico-cortical interactions, or connectivity, to the clinical phenotype. The goal of this study was to determine the independent contribution of well-described white matter pathways to language function in a cohort of pediatric patients with epilepsy.Materials and methodsPatients were retrospectively identified from an existing database of pediatric epilepsy patients with the following inclusion criteria: 1. diagnosis of MCDs, 2. DTI performed at 3 T, and 3. language characterized by a pediatric neurologist. Diffusion Toolkit and Trackvis (http://www.trackvis.org) were used for segmentation and analysis of the following tracts: corpus callosum, corticospinal tracts, inferior longitudinal fasciculi (ILFs), inferior fronto-occipital fasciculi (IFOFs), uncinate fasciculi (UFs), and arcuate fasciculi (AFs). Mean diffusivity (MD) and fractional anisotropy (FA) were calculated for each tract. Wilcoxon rank sum test (corrected for multiple comparisons) was used to assess potential differences in tract parameters between language-impaired and language-intact patients. In a separate analysis, a machine learning algorithm (random forest approach) was applied to measure the independent contribution of the measured diffusion parameters for each tract to the clinical phenotype (language impairment). In other words, the importance of each tract parameter was measured after adjusting for the contribution of all other tracts.ResultsThirty-three MCD patients were included (age range: 3–18 years). Twenty-one patients had intact language, twelve had language impairment. All tracts were identified bilaterally in all patients except for the AF, which was not identified on the right in 10 subjects and not identified on the left in 11 subjects. MD and/or FA within the left AF, UF, ILF, and IFOF differed between language-intact and language-impaired groups. However, only parameters related to the left uncinate, inferior fronto-occipital, and arcuate fasciculi were independently associated with the clinical phenotype.ConclusionsScalar metrics derived from the left uncinate, inferior fronto-occipital, and arcuate fasciculi were independently associated with language function. These results support the importance of these pathways in human language function in patients with MCDs.

The Arcuate Fasciculus and Language Development in a Cohort of Pediatric Patients with Malformations of Cortical Development

“…More recently, Saporta et al 24 suggested the functional relevance of such a finding after failing to identify the arcuate in 3 patients with congenital perisylvian syndrome and severe language dysfunction. Our results are also in line with work by Paldino et al, 20 who used a machine-learning approach to predict language phenotype on the basis of wholebrain tractography data in a cohort of patients with epilepsy and MCDs. Specifically, they found that the left arcuate fasciculus was an important contributor to the language phenotype.…”

“…[14][15][16][17][18][19] Recent work has demonstrated the potential for machine learning to translate quantitative data from whole-brain tractography into phenotypic information regarding language function in an individual patient. 20 Although highly accurate, this technique is time-consuming and requires substantial expertise in image processing and mathematics/statistics. These issues constitute a barrier to widespread adoption of such approaches into clinical practice, especially outside academic centers.…”

Harmonizing DTI measurements across scanners to examine the development of white matter microstructure in 803 adolescents of the NCANDA study

“…No model detected a maximum for the uncinate fasciculus, which is reported to develop with reading and language skills (Paldino et al, 2014). These late-developing fiber systems included the superior longitudinal fasciculus, which has been identified in meta-analyses as the tract most consistently reported to be associated with continued development (Peters et al, 2012) and integral to selective functions of verbal fluency, reading (Hoeft et al, 2011; Zhang et al, 2014), vocabulary (Tamnes et al, 2010b), and working memory (Ostby et al, 2011; Vestergaard et al, 2011).…”

Metrics of brain network architecture capture the impact of disease in children with epilepsy

“…In other words, the ability of the model to predict epilepsy duration in each individual was tested in a previously unseen subset of patients. Machine learning approaches, therefore, represent an effective method by which metrics derived from quantitative imaging can be assessed with respect to their potential translation into clinically meaningful information at the level of a single patient (Paldino et al, 2014). Details regarding this particular technique have been previously described (Breiman, 2001).…”