Rudolph Pienaar

White matter maturation reshapes structural connectivity in the late developing human brain

From toddler to late teenager, the macroscopic pattern of axonal projections in the human brain remains largely unchanged while undergoing dramatic functional modifications that lead to network refinement. These functional modifications are mediated by increasing myelination and changes in axonal diameter and synaptic density, as well as changes in neurochemical mediators. Here we explore the contribution of white matter maturation to the development of connectivity between ages 2 and 18 y using high b-value diffusion MRI tractography and connectivity analysis. We measured changes in connection efficacy as the inverse of the average diffusivity along a fiber tract. We observed significant refinement in specific metrics of network topology, including a significant increase in node strength and efficiency along with a decrease in clustering. Major structural modules and hubs were in place by 2 y of age, and they continued to strengthen their profile during subsequent development. Recording resting-state functional MRI from a subset of subjects, we confirmed a positive correlation between structural and functional connectivity, and in addition observed that this relationship strengthened with age. Continuously increasing integration and decreasing segregation of structural connectivity with age suggests that network refinement mediated by white matter maturation promotes increased global efficiency. In addition, the strengthening of the correlation between structural and functional connectivity with age suggests that white matter connectivity in combination with other factors, such as differential modulation of axonal diameter and myelin thickness, that are partially captured by inverse average diffusivity, play an increasingly important role in creating brain-wide coherence and synchrony.

Cortical Surface Shape Analysis Based on Spherical Wavelets

In vivo quantification of neuroanatomical shape variations is possible due to recent advances in medical imaging and has proven useful in the study of neuropathology and neurodevelopment. In this paper, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRIs) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, which allows us to characterize the order of development of large-scale and finer folding patterns independently. Given a limited amount of training data, we use a regularization framework to estimate the parameters of the Gompertz model to improve the prediction performance on new data. We develop an efficient method to estimate this regularized Gompertz model based on the Broyden-Fletcher-Goldfarb-Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomic information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurologic deficits in newborns

Tract-based analysis of callosal, projection, and association pathways in pediatric patients with multiple sclerosis: a preliminary study.

BACKGROUND AND PURPOSE: Region-of-interest (ROI) and tract-based diffusion tensor imaging (DTI) analyses have detected increased apparent diffusion coefficients (ADCs) and decreased fractional anisotropy (FA) in callosal and projection systems of adult patients with multiple sclerosis (MS). We explored whether similar changes occur in pediatric patients with MS, assessing 3 major white matter pathways (interhemispheric, projection, and intrahemispheric) in both visibly involved and normal-appearing white matter (NAWM). MATERIALS AND METHODS: DTI datasets from 10 patients with established pediatric MS and 10 age- sex-, and imaging technique−matched controls were analyzed. Tracts were reconstructed by using a fiber assignment by continuous tracking algorithm with a diffusion-weighted imaging mask and a 35° angular threshold. Tracts were selected by using standard ROI placements on color FA maps cross-referenced to b = 0 T2-weighted images for studying white matter pathways. Ten identical ROIs were placed in NAWM on b = 0 T2-weighted images to ensure that both ROIs and resulting tracts passed through NAWM. RESULTS: In pediatric MS, all tracts had higher mean ADC values (P = .002 to P < .04) and lower mean FA (P = .009 to P < .02) than those in healthy controls. Even when the tracts were confined to NAWM, the mean ADC was higher (P < .004 to P < .05) and the mean FA was lower (P = .002 to P < .02). T2 lesion burden correlated with tract-based mean ADC. ROI mean ADC increased, and both tract and ROI mean FA decreased with increasing T2 lesion burden, however with a statistically nonsignificant correlation. CONCLUSIONS: Increased mean ADC and decreased mean FA occur in all 3 major white matter pathways, both in visibly involved white matter and NAWM in pediatric MS.

Assessment of the frequency-domain multi-distance method to evaluate the brain optical properties: Monte Carlo simulations from neonate to adult

The near infrared spectroscopy (NIRS) frequency-domain multi-distance (FD-MD) method allows for the estimation of optical properties in biological tissue using the phase and intensity of radiofrequency modulated light at different source-detector separations. In this study, we evaluated the accuracy of this method to retrieve the absorption coefficient of the brain at different ages. Synthetic measurements were generated with Monte Carlo simulations in magnetic resonance imaging (MRI)-based heterogeneous head models for four ages: newborn, 6 and 12 month old infants, and adult. For each age, we determined the optimal set of source-detector separations and estimated the corresponding errors. Errors arise from different origins: methodological (FD-MD) and anatomical (curvature, head size and contamination by extra-cerebral tissues). We found that the brain optical absorption could be retrieved with an error between 8–24% in neonates and infants, while the error increased to 19–44% in adults over all source-detector distances. The dominant contribution to the error was found to be the head curvature in neonates and infants, and the extra-cerebral tissues in adults.

INTRINSIC CURVATURE: A MARKER OF MILLIMETER-SCALE TANGENTIAL CORTICO-CORTICAL CONNECTIVITY?

In this paper, we draw a link between cortical intrinsic curvature and the distributions of tangential connection lengths. We suggest that differential rates of surface expansion not only lead to intrinsic curvature of the cortical sheet, but also to differential inter-neuronal spacing. We propose that there follows a consequential change in the profile of neuronal connections: specifically an enhancement of the tendency towards proportionately more short connections. Thus, the degree of cortical intrinsic curvature may have implications for short-range connectivity.

Quantitative comparison and analysis of sulcal patterns using sulcal graph matching: A twin study

The global pattern of cortical sulci provides important information on brain development and functional compartmentalization. Sulcal patterns are routinely used to determine fetal brain health and detect cerebral malformations. We present a quantitative method for automatically comparing and analyzing the sulcal pattern between individuals using a graph matching approach. White matter surfaces were reconstructed from volumetric T1 MRI data and sulcal pits, the deepest points in local sulci, were identified on this surface. The sulcal pattern was then represented as a graph structure with sulcal pits as nodes. The similarity between graphs was computed with a spectral-based matching algorithm by using the geometric features of nodes (3D position, depth and area) and their relationship. In particular, we exploited the feature of graph topology (the number of edges and the paths between nodes) to highlight the interrelated arrangement and patterning of sulcal folds. We applied this methodology to 48 monozygotic twins and showed that the similarity of the sulcal graphs in twin pairs was significantly higher than in unrelated pairs for all hemispheres and lobar regions, consistent with a genetic influence on sulcal patterning. This novel approach has the potential to provide a quantitative and reliable means to compare sulcal patterns.

Quantification and Discrimination of Abnormal Sulcal Patterns in Polymicrogyria

Polymicrogyria (PMG) is a malformation of cortical development characterized by an irregular gyral pattern and its diagnosis and severity have been qualitatively judged by visual inspection of imaging features. We aimed to provide a quantitative description of abnormal sulcal patterns for individual PMG brains using our sulcal graph-based analysis and examined the association with language impairment. The sulcal graphs were constructed from magnetic resonance images in 26 typical developing and 18 PMG subjects and the similarity between sulcal graphs was computed by using their geometric and topological features. The similarities between typical and PMG groups were significantly lower than the similarities measured within the typical group. Furthermore, more lobar regions were determined to be abnormal in most patients when compared with the visual diagnosis of PMG involvement, suggesting that PMG may have more global effects on cortical folding than previously expected. Among the PMG, the group with intact language development showed sulcal patterns more closely matched with the typical than the impaired group in the left parietal lobe. Our approach shows the potential to provide a quantitative means for detecting the severity and extent of involvement of cortical malformation and a greater understanding of genotype-phenotype and clinical-imaging features correlations.

Diffusion Tensor Analysis of Pediatric Multiple Sclerosis and Clinically Isolated Syndromes

BACKGROUND AND PURPOSE: DTI has shown focal and diffuse white matter abnormalities in adults and children with MS. Here we explore whether DTI abnormalities are present at the time of a first attack or CIS in children and whether early DTI features can predict the development of MS. MATERIALS AND METHODS: We assessed region-of-interest and tract-based mean ADC and mean FA values for 3 major white matter pathways and NAWM in 20 children with MS, 27 children with forms of CIS, and controls. Tracts were selected by using standard region-of-interest placements on color FA maps. Identical ROIs were placed in the NAWM on b = 0 T2-weighted images to ensure that both ROIs and resulting tracts passed through NAWM. Conventional MR imaging characteristics were assessed by visual inspection. Statistical analysis compared FA and ADC values between groups by a t test. Logistic regression assessed the predictive value of DTI measures and published conventional MR imaging measures for conversion from CIS to MS. RESULTS: In pediatric patients with MS, all white matter pathways and analysis confined to the NAWM demonstrated higher mean ADC values and lower mean FA than in controls. In contrast, there were no significant differences in mean ADC and mean FA of white matter pathways in all CIS cohorts compared with controls. In the CIS cohort, none of the DTI measures in white matter pathways or in NAWM were significantly associated with conversion to RRMS in univariate or multivariate models (P > .05 in all models). CONCLUSIONS: There are significant anisotropic abnormalities in the NAWM of major tracts in children with MS. In contrast, there were no significant changes in pediatric patients with CIS compared with controls at baseline. DTI measures did not predict conversion to MS. The period between CIS and conversion to pediatric MS may represent a window of opportunity for the prevention of diffuse damage in the CNS and potentially progressive disability.

A quantitative method for correlating observations of decreased apparent diffusion coefficient with elevated cerebral blood perfusion in newborns presenting cerebral ischemic insults.

In patients presenting with cerebral ischemic injury, the outcome of injured brain tissue quantified as decreased apparent diffusion coefficient (ADC) may depend on associated alterations in cerebral blood perfusion (CBP). This study proposes a non-biased method to quantify associations between ADC and CBP in newborns with global or focal cerebral ischemia. The study population consisted of nine neonates (age: 0 to 3 days) presenting with clinical and imaging evidence of ischemia (seven with global hypoxic ischemia, and two with focal arterial ischemic stroke) with decreased ADC. Six newborns without diffusion abnormalities on magnetic resonance (MR) imaging served as a comparative cohort (age: 0 days to 4 weeks). All patients underwent MR imaging including diffusion weighted imaging (DWI) to determine ADC and axial arterial spin labeling (ASL) to determine CBP. An algorithm was developed that uses the B0 volume from the DWI raw data as a reference, co-registers the ADC and ASL-CBP data to the B0, generates mask filters, and finally performs a statistical analysis to automatically select regions of interest (ROIs) with ADC or ASL-CBP values that deviate significantly from the rest of the brain. If ROIs are identified in this analysis, the algorithm then evaluates correlation based on ROI location and volume. A significant correlation was found between decreased ADC and elevated ASL-CBP with regions of elevated ASL-CBP typically larger than the corresponding ADC abnormality. The association between decreased diffusivity and increased ASL-CBP suggests that, for this cohort, cerebral ischemia is associated with hyperperfusion.

Cortical Surface Shape Analysis Based on Spherical Wavelet Transformation

Shape analysis of neuroanatomical structures has proven useful in the study of neuropathology and neurodevelopment. Advances in medical imaging have made it possible to study this shape variation in vivo. In this paper, we propose the use of a spherical wavelet transformation to extract cortical surface shape features, as wavelets can characterize the underlying functions in a local fashion in both space and frequency. Our results demonstrate the utility of the wavelet approach in both detecting the spatial scale and pattern of shape variation in synthetic data, and for quantifying and visualizing shape variations of cortical surface models in subject populations.