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Ashok Panigrahy

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7 papers

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YNIMG Journal 2024 Journal Article

Identifying novel data-driven subgroups in congenital heart disease using multi-modal measures of brain structure

Marlee M. Vandewouw
Ami Norris-Brilliant
Anum Rahman
Stephania Assimopoulos
Sarah U. Morton
Azadeh Kushki
Sean Cunningham
Eileen King

Individuals with congenital heart disease (CHD) have an increased risk of neurodevelopmental impairments. Given the hypothesized complexity linking genomics, atypical brain structure, cardiac diagnoses and their management, and neurodevelopmental outcomes, unsupervised methods may provide unique insight into neurodevelopmental variability in CHD. Using data from the Pediatric Cardiac Genomics Consortium Brain and Genes study, we identified data-driven subgroups of individuals with CHD from measures of brain structure. Using structural magnetic resonance imaging (MRI; N = 93; cortical thickness, cortical volume, and subcortical volume), we identified subgroups that differed primarily on cardiac anatomic lesion and language ability. In contrast, using diffusion MRI (N = 88; white matter connectivity strength), we identified subgroups that were characterized by differences in associations with rare genetic variants and visual-motor function. This work provides insight into the differential impacts of cardiac lesions and genomic variation on brain growth and architecture in patients with CHD, with potentially distinct effects on neurodevelopmental outcomes.

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YNIMG Journal 2018 Journal Article

A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks

Rafael Ceschin
Alexandria Zahner
William Reynolds
Jenna Gaesser
Giulio Zuccoli
Cecilia W. Lo
Vanathi Gopalakrishnan
Ashok Panigrahy

Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuroimaging datasets requires adequate sample size for training and well-defined signal intensity based structural differentiation. There is a lack of effective automated diagnostic tools for the reliable detection of brain dysmaturation in the neonatal period, related to small sample size and complex undifferentiated brain structures, despite both translational research and clinical importance. Volumetric information alone is insufficient for diagnosis. In this study, we developed a computational framework for the automated classification of brain dysmaturation from neonatal MRI, by combining a specific deep neural network implementation with neonatal structural brain segmentation as a method for both clinical pattern recognition and data-driven inference into the underlying structural morphology. We implemented three-dimensional convolution neural networks (3D-CNNs) to specifically classify dysplastic cerebelli, a subset of surface-based subcortical brain dysmaturation, in term infants born with congenital heart disease. We obtained a 0. 985 ± 0. 0241-classification accuracy of subtle cerebellar dysplasia in CHD using 10-fold cross-validation. Furthermore, the hidden layer activations and class activation maps depicted regional vulnerability of the superior surface of the cerebellum, (composed of mostly the posterior lobe and the midline vermis), in regards to differentiating the dysplastic process from normal tissue. The posterior lobe and the midline vermis provide regional differentiation that is relevant to not only to the clinical diagnosis of cerebellar dysplasia, but also genetic mechanisms and neurodevelopmental outcome correlates. These findings not only contribute to the detection and classification of a subset of neonatal brain dysmaturation, but also provide insight to the pathogenesis of cerebellar dysplasia in CHD. In addition, this is one of the first examples of the application of deep learning to a neuroimaging dataset, in which the hidden layer activation revealed diagnostically and biologically relevant features about the clinical pathogenesis. The code developed for this project is open source, published under the BSD License, and designed to be generalizable to applications both within and beyond neonatal brain imaging.

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YNICL Journal 2017 Journal Article

Tractography in the clinics: Implementing a pipeline to characterize early brain development

Fernando Yepes-Calderon
Yi Lao
Pierre Fillard
Marvin D. Nelson
Ashok Panigrahy
Natasha Lepore

In imaging studies of neonates, particularly in the clinical setting, diffusion tensor imaging-based tractography is typically unreliable due to the use of fast acquisition protocols that yield low resolution and signal-to-noise ratio (SNR). These image acquisition protocols are implemented with the aim of reducing motion artifacts that may be produced by the movement of the neonate's head during the scanning session. Furthermore, axons are not yet fully myelinated in these subjects. As a result, the water molecules' movements are not as constrained as in older brains, making it even harder to define structure using diffusion profiles. Here, we introduce a post-processing method that overcomes the difficulties described above, allowing the determination of reliable tracts in newborns. We tested our method using neonatal data and successfully extracted some of the limbic, association and commissural fibers, all of which are typically difficult to obtain by direct tractography. Geometrical and diffusion based features of the tracts are then utilized to compare premature babies to term babies. Our results quantify the maturation of white matter fiber tracts in neonates.

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YNICL Journal 2015 Journal Article

Developmental synergy between thalamic structure and interhemispheric connectivity in the visual system of preterm infants

Rafael Ceschin
Jessica L. Wisnowski
Lisa B. Paquette
Marvin D. Nelson
Stefan Blüml
Ashok Panigrahy

Thalamic structural co-variation with cortical regions has been demonstrated in preterm infants, but its relationship to cortical function and severity of non-cystic white matter injury (non-cystic WMI) is unclear. The relationship between thalamic morphology and both cortical network synchronization and cortical structural connectivity has not been established. We tested the hypothesis that in preterm neonates, thalamic volume would correlate with primary cortical visual function and microstructural integrity of cortico-cortical visual association pathways. A total of 80 term-equivalent preterm and 44 term-born infants underwent high-resolution structural imaging coupled with visual functional magnetic resonance imaging or diffusion tensor imaging. There was a strong correlation between thalamic volume and primary visual cortical activation in preterms with non-cystic WMI (r = 0.81, p-value = 0.001). Thalamic volume also correlated strongly with interhemispheric cortico-cortical connectivity (splenium) in preterm neonates with a relatively higher severity of non-cystic WMI (p-value < 0.001). In contrast, there was lower correlation between thalamic volume and intrahemispheric cortico-cortical connectivity, including the inferior longitudinal fasciculus and inferior frontal orbital fasciculus. This study shows distinct temporal overlap in the disruption of thalamo-cortical and interhemispheric cortico-cortical connectivity in preterm infants suggesting developmental synergy between thalamic morphology and the emergence of cortical networks in the last trimester.

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YNICL Journal 2015 Journal Article

Regional vulnerability of longitudinal cortical association connectivity

Rafael Ceschin
Vince K. Lee
Vince Schmithorst
Ashok Panigrahy

Preterm born children with spastic diplegia type of cerebral palsy and white matter injury or periventricular leukomalacia (PVL), are known to have motor, visual and cognitive impairments. Most diffusion tensor imaging (DTI) studies performed in this group have demonstrated widespread abnormalities using averaged deterministic tractography and voxel-based DTI measurements. Little is known about structural network correlates of white matter topography and reorganization in preterm cerebral palsy, despite the availability of new therapies and the need for brain imaging biomarkers. Here, we combined novel post-processing methodology of probabilistic tractography data in this preterm cohort to improve spatial and regional delineation of longitudinal cortical association tract abnormalities using an along-tract approach, and compared these data to structural DTI cortical network topology analysis. DTI images were acquired on 16 preterm children with cerebral palsy (mean age 5.6 ± 4) and 75 healthy controls (mean age 5.7 ± 3.4). Despite mean tract analysis, Tract-Based Spatial Statistics (TBSS) and voxel-based morphometry (VBM) demonstrating diffusely reduced fractional anisotropy (FA) reduction in all white matter tracts, the along-tract analysis improved the detection of regional tract vulnerability. The along-tract map-structural network topology correlates revealed two associations: (1) reduced regional posterior-anterior gradient in FA of the longitudinal visual cortical association tracts (inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, optic radiation, posterior thalamic radiation) correlated with reduced posterior-anterior gradient of intra-regional (nodal efficiency) metrics with relative sparing of frontal and temporal regions; and (2) reduced regional FA within frontal-thalamic-striatal white matter pathways (anterior limb/anterior thalamic radiation, superior longitudinal fasciculus and cortical spinal tract) correlated with alteration in eigenvector centrality, clustering coefficient (inter-regional) and participation co-efficient (inter-modular) alterations of frontal-striatal and fronto-limbic nodes suggesting re-organization of these pathways. Both along tract and structural topology network measurements correlated strongly with motor and visual clinical outcome scores. This study shows the value of combining along-tract analysis and structural network topology in depicting not only selective parietal occipital regional vulnerability but also reorganization of frontal-striatal and frontal-limbic pathways in preterm children with cerebral palsy. These finding also support the concept that widespread, but selective posterior-anterior neural network connectivity alterations in preterm children with cerebral palsy likely contribute to the pathogenesis of neurosensory and cognitive impairment in this group.

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YNICL Journal 2015 Journal Article

Relationship of white matter network topology and cognitive outcome in adolescents with d-transposition of the great arteries

Ashok Panigrahy
Vincent J. Schmithorst
Jessica L. Wisnowski
Christopher G. Watson
David C. Bellinger
Jane W. Newburger
Michael J. Rivkin

Patients with congenital heart disease (CHD) are at risk for neurocognitive impairments. Little is known about the impact of CHD on the organization of large-scale brain networks. We applied graph analysis techniques to diffusion tensor imaging (DTI) data obtained from 49 adolescents with dextro-transposition of the great arteries (d-TGA) repaired with the arterial switch operation in early infancy and 29 healthy referent adolescents. We examined whether differences in neurocognitive functioning were related to white matter network topology. We developed mediation models revealing the respective contributions of peri-operative variables and network topology on cognitive outcome. Adolescents with d-TGA had reduced global efficiency at a trend level (p = 0.061), increased modularity (p = 0.012), and increased small-worldness (p = 0.026) as compared to controls. Moreover, these network properties mediated neurocognitive differences between the d-TGA and referent adolescents across every domain assessed. Finally, structural network topology mediated the neuroprotective effect of longer duration of core cooling during reparative neonatal cardiac surgery, as well as the detrimental effects of prolonged hospitalization. Taken together, worse neurocognitive function in adolescents with d-TGA is mediated by global differences in white matter network topology, suggesting that disruption of this configuration of large-scale networks drives neurocognitive dysfunction. These data provide new insights into the interplay between perioperative factors, brain organization, and cognition in patients with complex CHD.

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YNIMG Journal 2006 Journal Article

Somatosensory lateralization in the newborn brain

Stephan G. Erberich
Ashok Panigrahy
Philippe Friedlich
Istvan Seri
Marvin D. Nelson
Floyd Gilles

Since the onset and early postnatal development of hemispheric lateralization in the human brain are unknown, we studied cortical activation induced by passive extension and flexion of the hand in neonates using functional magnetic resonance imaging (fMRI). In contrast to that seen in older age groups, somatosensory areas in the pre- and postcentral gyri of the neonate showed no significant hemispheric lateralization at term. Instead, our findings from independent left- and right-hand experiments suggest the presence of an emerging trend of contralateral lateralization of the somatosensory system at around term.

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