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

Background and purpose Patients 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 methods Patients 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. Results: Thirty-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. Conclusions: Scalar 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

Failure to Identify the Left Arcuate Fasciculus at Diffusion Tractography Is a Specific Marker of Language Dysfunction in Pediatric Patients with Polymicrogyria

Background:. Polymicrogyric cortex demonstrates interindividual variation with regard to both extent of dyslamination and functional capacity. Given the relationship between laminar structure and white matter fibers, we sought to define the relationship between polymicrogyria (PMG), intrahemispheric association pathways, and network function. Methods:. Each arcuate fasciculus (AF) was categorized as present or absent. Language was characterized by a pediatric neurologist. The presence of dysplastic cortex in the expected anatomic locations of Broca's (BA) and Wernicke's areas (WA) was evaluated by two pediatric neuroradiologists blinded to DTI and language data. Results:. 16 PMG patients and 16 age/gender-matched controls were included. All normative controls had an identifiable left AF. 6/7 PMG patients with dysplastic cortex within BA and/or WA had no left AF; PMG patients without involvement of these regions had a lower frequency of absence of the left AF (p < 0.006). All patients without a left AF had some degree of language impairment. PMG patients without a left AF had a significantly greater frequency of language impairment compared to those PMG patients with a left AF (p < 0.003). Conclusion:. In patients with PMG (1) the presence of dysplastic cortex within WA and/or BA is associated with absence of the left AF and (2) absence of the left AF is associated with language impairment

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

Neurodevelopment continues through adolescence, with notable maturation of white matter tracts comprising regional fiber systems progressing at different rates. To identify factors that could contribute to regional differences in white matter microstructure development, large samples of youth spanning adolescence to young adulthood are essential to parse these factors. Recruitment of adequate samples generally relies on multi-site consortia but comes with the challenge of merging data acquired on different platforms. In the current study, diffusion tensor imaging (DTI) data were acquired on GE and Siemens systems through the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a multi-site study designed to track the trajectories of regional brain development during a time of high risk for initiating alcohol consumption. This cross-sectional analysis reports baseline Tract-Based Spatial Statistic (TBSS) of regional fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (L1), and radial diffusivity (LT) from the five consortium sites on 671 adolescents who met no/low alcohol or drug consumption criteria and 132 adolescents with a history of exceeding consumption criteria. Harmonization of DTI metrics across manufacturers entailed the use of human-phantom data, acquired multiple times on each of three non-NCANDA participants at each site’s MR system, to determine a manufacturer-specific correction factor. Application of the correction factor derived from human phantom data measured on MR systems from different manufacturers reduced the standard deviation of the DTI metrics for FA by almost a half, enabling harmonization of data that would have otherwise carried systematic error. Permutation testing supported the hypothesis of higher FA and lower diffusivity measures in older adolescents and indicated that, overall, the FA, MD, and L1 of the boys was higher than that of the girls, suggesting continued microstructural development notable in the boys. The contribution of demographic and clinical differences to DTI metrics was assessed with General Additive Models (GAM) testing for age, sex, and ethnicity differences in regional skeleton mean values. The results supported the primary study hypothesis that FA skeleton mean values in the no/low-drinking group were highest at different ages. When differences in intracranial volume were covaried, FA skeleton mean reached a maximum at younger ages in girls than boys and varied in magnitude with ethnicity. Our results, however, did not support the hypothesis that youth who exceeded exposure criteria would have lower FA or higher diffusivity measures than the no/low-drinking group; detecting the effects of excessive alcohol consumption during adolescence on DTI metrics may require longitudinal study

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

Background and objective: Epilepsy is associated with alterations in the structural framework of the cerebral network. The aim of this study was to measure the potential of global metrics of network architecture derived from resting state functional MRI to capture the impact of epilepsy on the developing brain. Methods: Pediatric patients were retrospectively identified with: 1. Focal epilepsy; 2. Brain MRI at 3 Tesla, including resting state functional MRI; 3. Full scale IQ measured by a pediatric neuropsychologist. The cerebral cortex was parcellated into approximately 700 gray matter network nodes. The strength of a connection between two nodes was defined as the correlation between their resting BOLD signal time series. The following global network metrics were then calculated: clustering coefficient, transitivity, modularity, path length, and global efficiency. Epilepsy duration was used as an index for the cumulative impact of epilepsy on the brain. Results: 45 patients met criteria (age: 4–19 years). After accounting for age of epilepsy onset, epilepsy duration was inversely related to IQ (p: 0.01). Epilepsy duration predicted by a machine learning algorithm on the basis of the five global network metrics was highly correlated with actual epilepsy duration (r: 0.95; p: 0.0001). Specifically, modularity and to a lesser extent path length and global efficiency were independently associated with epilepsy duration. Conclusions: We observed that a machine learning algorithm accurately predicted epilepsy duration based on global metrics of network architecture derived from resting state fMRI. These findings suggest that network metrics have the potential to form the basis for statistical models that translate quantitative imaging data into patient-level markers of cognitive deterioration

Normalization enhances brain network features that predict individual intelligence in children with epilepsy.

BACKGROUND AND PURPOSE:Architecture of the cerebral network has been shown to associate with IQ in children with epilepsy. However, subject-level prediction on this basis, a crucial step toward harnessing network analyses for the benefit of children with epilepsy, has yet to be achieved. We compared two network normalization strategies in terms of their ability to optimize subject-level inferences on the relationship between brain network architecture and brain function. MATERIALS AND METHODS:Patients with epilepsy and resting state fMRI were retrospectively identified. Brain network nodes were defined by anatomic parcellation, first in patient space (nodes defined for each patient) and again in template space (same nodes for all patients). Whole-brain weighted graphs were constructed according to pair-wise correlation of BOLD-signal time courses between nodes. The following metrics were then calculated: clustering coefficient, transitivity, modularity, path length, and global efficiency. Metrics computed on graphs in patient space were normalized to the same metric computed on a random network of identical size. A machine learning algorithm was used to predict patient IQ given access to only the network metrics. RESULTS:Twenty-seven patients (8-18 years) comprised the final study group. All brain networks demonstrated expected small world properties. Accounting for intrinsic population heterogeneity had a significant effect on prediction accuracy. Specifically, transformation of all patients into a common standard space as well as normalization of metrics to those computed on a random network both substantially outperformed the use of non-normalized metrics. CONCLUSION:Normalization contributed significantly to accurate subject-level prediction of cognitive function in children with epilepsy. These findings support the potential for quantitative network approaches to contribute clinically meaningful information in children with neurological disorders

Mutual Information Better Quantifies Brain Network Architecture in Children with Epilepsy

Purpose. Metrics of the brain network architecture derived from resting-state fMRI have been shown to provide physiologically meaningful markers of IQ in children with epilepsy. However, traditional measures of functional connectivity (FC), specifically the Pearson correlation, assume a dominant linear relationship between BOLD time courses; this assumption may not be valid. Mutual information is an alternative measure of FC which has shown promise in the study of complex networks due to its ability to flexibly capture association of diverse forms. We aimed to compare network metrics derived from mutual information-defined FC to those derived from traditional correlation in terms of their capacity to predict patient-level IQ. Materials and Methods. Patients were retrospectively identified with the following: (1) focal epilepsy; (2) resting-state fMRI; and (3) full-scale IQ by a neuropsychologist. Brain network nodes were defined by anatomic parcellation. Parcellation was performed at the size threshold of 350 mm2, resulting in networks containing 780 nodes. Whole-brain, weighted graphs were then constructed according to the pairwise connectivity between nodes. In the traditional condition, edges (connections) between each pair of nodes were defined as the absolute value of the Pearson correlation coefficient between their BOLD time courses. In the mutual information condition, edges were defined as the mutual information between time courses. The following metrics were then calculated for each weighted graph: clustering coefficient, modularity, characteristic path length, and global efficiency. A machine learning algorithm was used to predict the IQ of each individual based on their network metrics. Prediction accuracy was assessed as the fractional variation explained for each condition. Results. Twenty-four patients met the inclusion criteria (age: 8–18 years). All brain networks demonstrated expected small-world properties. Network metrics derived from mutual information-defined FC significantly outperformed the use of the Pearson correlation. Specifically, fractional variation explained was 49% (95% CI: 46%, 51%) for the mutual information method; the Pearson correlation demonstrated a variation of 17% (95% CI: 13%, 19%). Conclusion. Mutual information-defined functional connectivity captures physiologically relevant features of the brain network better than correlation. Clinical Relevance. Optimizing the capacity to predict cognitive phenotypes at the patient level is a necessary step toward the clinical utility of network-based biomarkers

Failure to Identify the Left Arcuate Fasciculus at Diffusion Tractography Is a Specific Marker of Language Dysfunction in Pediatric Patients with Polymicrogyria

Background. Polymicrogyric cortex demonstrates interindividual variation with regard to both extent of dyslamination and functional capacity. Given the relationship between laminar structure and white matter fibers, we sought to define the relationship between polymicrogyria (PMG), intrahemispheric association pathways, and network function. Methods. Each arcuate fasciculus (AF) was categorized as present or absent. Language was characterized by a pediatric neurologist. The presence of dysplastic cortex in the expected anatomic locations of Broca’s (BA) and Wernicke’s areas (WA) was evaluated by two pediatric neuroradiologists blinded to DTI and language data. Results. 16 PMG patients and 16 age/gender-matched controls were included. All normative controls had an identifiable left AF. 6/7 PMG patients with dysplastic cortex within BA and/or WA had no left AF; PMG patients without involvement of these regions had a lower frequency of absence of the left AF (p<0.006). All patients without a left AF had some degree of language impairment. PMG patients without a left AF had a significantly greater frequency of language impairment compared to those PMG patients with a left AF (p<0.003). Conclusion. In patients with PMG (1) the presence of dysplastic cortex within WA and/or BA is associated with absence of the left AF and (2) absence of the left AF is associated with language impairment