Publications

Submitted
Warton FL, Taylor PA, Warton CMR, Molteno CD, Wintermark P, Zöllei L, van AJ, der Kouwe, Jacobson JL, Jacobson SW, et al. Reduced fractional anisotropy in projection, association and commissural fibre networks in neonates with prenatal methamphetamine exposure. Developmental Neurobiology. Submitted;n/a (n/a). Publisher's VersionAbstract
Abstract Prenatal exposure to methamphetamine is associated with neurostructural changes, including alterations in white matter microstructure. This study investigated the effects of methamphetamine exposure on microstructure of global white matter networks in neonates. Pregnant women were interviewed beginning in mid-pregnancy regarding their methamphetamine use. Diffusion weighted imaging sets were acquired for 23 non-sedated neonates. White matter bundles associated with pairs of target regions within five networks (commissural fibres, left and right projection fibres, and left and right association fibres) were estimated using probabilistic tractography, and fractional anisotropy (FA) and diffusion measures determined within each connection. Multiple regression analyses showed that increasing methamphetamine exposure was significantly associated with reduced FA in all five networks, after control for potential confounders. Increased exposure was associated with lower axial diffusivity in the right association fibre network and with increased radial diffusivity in the right projection and left and right association fibre networks. Within the projection and association networks a subset of individual connections showed a negative correlation between FA and methamphetamine exposure. These findings are consistent with previous reports in older children and demonstrate that microstructural changes associated with methamphetamine exposure are already detectable in neonates.
2021
Jeong H, Ntolkeras G, Alhilani M, Atefi SR, Zöllei L, Fujimoto K, Pourvaziri A, Lev MH, Grant EP, Bonmassar G. Development, validation, and pilot MRI safety study of a high-resolution, open source, whole body pediatric numerical simulation model. PLoS One. 2021;16 (1) :e0241682.Abstract
Numerical body models of children are used for designing medical devices, including but not limited to optical imaging, ultrasound, CT, EEG/MEG, and MRI. These models are used in many clinical and neuroscience research applications, such as radiation safety dosimetric studies and source localization. Although several such adult models have been reported, there are few reports of full-body pediatric models, and those described have several limitations. Some, for example, are either morphed from older children or do not have detailed segmentations. Here, we introduce a 29-month-old male whole-body native numerical model, "MARTIN", that includes 28 head and 86 body tissue compartments, segmented directly from the high spatial resolution MRI and CT images. An advanced auto-segmentation tool was used for the deep-brain structures, whereas 3D Slicer was used to segment the non-brain structures and to refine the segmentation for all of the tissue compartments. Our MARTIN model was developed and validated using three separate approaches, through an iterative process, as follows. First, the calculated volumes, weights, and dimensions of selected structures were adjusted and confirmed to be within 6% of the literature values for the 2-3-year-old age-range. Second, all structural segmentations were adjusted and confirmed by two experienced, sub-specialty certified neuro-radiologists, also through an interactive process. Third, an additional validation was performed with a Bloch simulator to create synthetic MR image from our MARTIN model and compare the image contrast of the resulting synthetic image with that of the original MRI data; this resulted in a "structural resemblance" index of 0.97. Finally, we used our model to perform pilot MRI safety simulations of an Active Implantable Medical Device (AIMD) using a commercially available software platform (Sim4Life), incorporating the latest International Standards Organization guidelines. This model will be made available on the Athinoula A. Martinos Center for Biomedical Imaging website.
Ahtam B, Turesky TK, Zöllei L, Standish J, Grant EP, Gaab N, Im K. Intergenerational Transmission of Cortical Sulcal Patterns from Mothers to their Children. Cereb Cortex. 2021;31 (4) :1888-1897.Abstract
Intergenerational effects are described as the genetic, epigenetic, as well as pre- and postnatal environmental influence parents have on their offspring's behavior, cognition, and brain. During fetal brain development, the primary cortical sulci emerge with a distinctive folding pattern that are under strong genetic influence and show little change of this pattern throughout postnatal brain development. We examined intergenerational transmission of cortical sulcal patterns by comparing primary sulcal patterns between children (N = 16, age 5.5 ± 0.81 years, 8 males) and their biological mothers (N = 15, age 39.72 ± 4.68 years) as well as between children and unrelated adult females. Our graph-based sulcal pattern comparison method detected stronger sulcal pattern similarity for child-mother pairs than child-unrelated pairs, where higher similarity between child-mother pairs was observed mostly for the right lobar regions. Our results also show that child-mother versus child-unrelated pairs differ for daughters and sons with a trend toward significance, particularly for the left hemisphere lobar regions. This is the first study to reveal significant intergenerational transmission of cortical sulcal patterns, and our results have important implications for the study of the heritability of complex behaviors, brain-based disorders, the identification of biomarkers, and targets for interventions.
Terrier L-M, Hadjikhani N, Velut S, Magnain C, Amelot A, Bernard F, Zöllei L, Destrieux C. The trigeminal system: The meningovascular complex- A review. J Anat. 2021;239 (1) :1-11.Abstract
Supratentorial sensory perception, including pain, is subserved by the trigeminal nerve, in particular, by the branches of its ophthalmic division, which provide an extensive innervation of the dura mater and of the major brain blood vessels. In addition, contrary to previous assumptions, studies on awake patients during surgery have demonstrated that the mechanical stimulation of the pia mater and small cerebral vessels can also produce pain. The trigeminovascular system, located at the interface between the nervous and vascular systems, is therefore perfectly positioned to detect sensory inputs and influence blood flow regulation. Despite the fact that it remains only partially understood, the trigeminovascular system is most probably involved in several pathologies, including very frequent ones such as migraine, or other severe conditions, such as subarachnoid haemorrhage. The incomplete knowledge about the exact roles of the trigeminal system in headache, blood flow regulation, blood barrier permeability and trigemino-cardiac reflex warrants for an increased investigation of the anatomy and physiology of the trigeminal system. This translational review aims at presenting comprehensive information about the dural and brain afferents of the trigeminovascular system, in order to improve the understanding of trigeminal cranial sensory perception and to spark a new field of exploration for headache and other brain diseases.
2020
Cheng J, Dalca AV, Fischl B, Zöllei L. Cortical surface registration using unsupervised learning. Neuroimage. 2020;221 :117161.Abstract
Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface properties and perform registration by aligning cortical folding patterns in that space. This strategy produces accurate spatial alignment, but often requires high computational cost. Recently, convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration. However, due to distortions introduced by projecting a sphere to a 2D plane, a direct application of recent learning-based methods to surfaces yields poor results. In this study, we present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks that addresses these issues. SphereMorph uses a UNet-style network associated with a spherical kernel to learn the displacement field and warps the sphere using a modified spatial transformer layer. We propose a resampling weight in computing the data fitting loss to account for distortions introduced by polar projection, and demonstrate the performance of our proposed method on two tasks, including cortical parcellation and group-wise functional area alignment. The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency. The source code of SphereMorph will be released to the public upon acceptance of this manuscript at https://github.com/voxelmorph/spheremorph.
Warton FL, Taylor PA, Warton CMR, Molteno CD, Wintermark P, Zöllei L, van der Kouwe AJ, Jacobson JL, Jacobson SW, Meintjes EM. Reduced fractional anisotropy in projection, association, and commissural fiber networks in neonates with prenatal methamphetamine exposure. Dev Neurobiol. 2020;80 (11-12) :381-398.Abstract
Prenatal exposure to methamphetamine is associated with neurostructural changes, including alterations in white matter microstructure. This study investigated the effects of methamphetamine exposure on microstructure of global white matter networks in neonates. Pregnant women were interviewed beginning in mid-pregnancy regarding their methamphetamine use. Diffusion weighted imaging sets were acquired for 23 non-sedated neonates. White matter bundles associated with pairs of target regions within five networks (commissural fibers, left and right projection fibers, and left and right association fibers) were estimated using probabilistic tractography, and fractional anisotropy (FA) and diffusion measures determined within each connection. Multiple regression analyses showed that increasing methamphetamine exposure was significantly associated with reduced FA in all five networks, after control for potential confounders. Increased exposure was associated with lower axial diffusivity in the right association fiber network and with increased radial diffusivity in the right projection and left and right association fiber networks. Within the projection and association networks a subset of individual connections showed a negative correlation between FA and methamphetamine exposure. These findings are consistent with previous reports in older children and demonstrate that microstructural changes associated with methamphetamine exposure are already detectable in neonates.
Cheng J, Dalca AV, Fischl B, Zöllei L. Cortical surface registration using unsupervised learning. NeuroImage. 2020;221 :117161. Publisher's VersionAbstract
Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface properties and perform registration by aligning cortical folding patterns in that space. This strategy produces accurate spatial alignment, but often requires high computational cost. Recently, convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration. However, due to distortions introduced by projecting a sphere to a 2D plane, a direct application of recent learning-based methods to surfaces yields poor results. In this study, we present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks that addresses these issues. SphereMorph uses a UNet-style network associated with a spherical kernel to learn the displacement field and warps the sphere using a modified spatial transformer layer. We propose a resampling weight in computing the data fitting loss to account for distortions introduced by polar projection, and demonstrate the performance of our proposed method on two tasks, including cortical parcellation and group-wise functional area alignment. The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency. The source code of SphereMorph will be released to the public upon acceptance of this manuscript at https://github.com/voxelmorph/spheremorph.
Zöllei L, Iglesias JE, Ou Y, Grant EP, Fischl B. Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years. Neuroimage. 2020;218 :116946.Abstract
The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package.
Turesky T, Xie W, Kumar S, Sliva DD, Gagoski B, Vaughn J, Zöllei L, Haque R, Hafiz Kakon S, Islam N, et al. Relating anthropometric indicators to brain structure in 2-month-old Bangladeshi infants growing up in poverty: A pilot study. Neuroimage. 2020;210 :116540.Abstract
Anthropometric indicators, including stunting, underweight, and wasting, have previously been associated with poor neurocognitive outcomes. This link may exist because malnutrition and infection, which are known to affect height and weight, also impact brain structure according to animal models. However, a relationship between anthropometric indicators and brain structural measures has not been tested yet, perhaps because stunting, underweight, and wasting are uncommon in higher-resource settings. Further, with diminished anthropometric growth prevalent in low-resource settings, where biological and psychosocial hazards are most severe, one might expect additional links between measures of poverty, anthropometry, and brain structure. To begin to examine these relationships, we conducted an MRI study in 2-3-month-old infants growing up in the extremely impoverished urban setting of Dhaka, Bangladesh. The sample size was relatively small because the challenges of investigating infant brain structure in a low-resource setting needed to be realized and resolved before introducing a larger cohort. Initially, fifty-four infants underwent T1 sequences using 3T MRI, and resulting structural images were segmented into gray and white matter maps, which were carefully evaluated for accurate tissue labeling by a pediatric neuroradiologist. Gray and white matter volumes from 29 infants (79 ​± ​10 days-of-age; F/M ​= ​12/17), whose segmentations were of relatively high quality, were submitted to semi-partial correlation analyses with stunting, underweight, and wasting, which were measured using height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ) scores. Positive semi-partial correlations (after adjusting for chronological age and sex and correcting for multiple comparisons) were observed between white matter volume and HAZ and WAZ; however, WHZ was not correlated with any measure of brain volume. No associations were observed between income-to-needs or maternal education and brain volumetric measures, suggesting that measures of poverty were not associated with total brain tissue volume in this sample. Overall, these results provide the first link between diminished anthropometric growth and white matter volume in infancy. Challenges of conducting a developmental neuroimaging study in a low-resource country are also described.
2019
Turesky TK, Jensen SKG, Yu X, Kumar S, Wang Y, Sliva DD, Gagoski B, Sanfilippo J, Zöllei L, Boyd E, et al. The relationship between biological and psychosocial risk factors and resting-state functional connectivity in 2-month-old Bangladeshi infants: A feasibility and pilot study. Dev Sci. 2019;22 (5) :e12841.Abstract
Childhood poverty has been associated with structural and functional alterations in the developing brain. However, poverty does not alter brain development directly, but acts through associated biological or psychosocial risk factors (e.g. malnutrition, family conflict). Yet few studies have investigated risk factors in the context of infant neurodevelopment, and none have done so in low-resource settings such as Bangladesh, where children are exposed to multiple, severe biological and psychosocial hazards. In this feasibility and pilot study, usable resting-state fMRI data were acquired in infants from extremely poor (n = 16) and (relatively) more affluent (n = 16) families in Dhaka, Bangladesh. Whole-brain intrinsic functional connectivity (iFC) was estimated using bilateral seeds in the amygdala, where iFC has shown susceptibility to early life stress, and in sensory areas, which have exhibited less susceptibility to early life hazards. Biological and psychosocial risk factors were examined for associations with iFC. Three resting-state networks were identified in within-group brain maps: medial temporal/striatal, visual, and auditory networks. Infants from extremely poor families compared with those from more affluent families exhibited greater (i.e. less negative) iFC in precuneus for amygdala seeds; however, no group differences in iFC were observed for sensory area seeds. Height-for-age, a proxy for malnutrition/infection, was not associated with amygdala/precuneus iFC, whereas prenatal family conflict was positively correlated. Findings suggest that it is feasible to conduct infant fMRI studies in low-resource settings. Challenges and practical steps for successful implementations are discussed.
Cohen MA, Dilks DD, Koldewyn K, Weigelt S, Feather J, Kell AJ, Keil B, Fischl B, Zöllei L, Wald L, et al. Representational similarity precedes category selectivity in the developing ventral visual pathway. Neuroimage. 2019;197 :565-574.Abstract
Many studies have investigated the development of face-, scene-, and body-selective regions in the ventral visual pathway. This work has primarily focused on comparing the size and univariate selectivity of these neural regions in children versus adults. In contrast, very few studies have investigated the developmental trajectory of more distributed activation patterns within and across neural regions. Here, we scanned both children (ages 5-7) and adults to test the hypothesis that distributed representational patterns arise before category selectivity (for faces, bodies, or scenes) in the ventral pathway. Consistent with this hypothesis, we found mature representational patterns in several ventral pathway regions (e.g., FFA, PPA, etc.), even in children who showed no hint of univariate selectivity. These results suggest that representational patterns emerge first in each region, perhaps forming a scaffold upon which univariate category selectivity can subsequently develop. More generally, our findings demonstrate an important dissociation between category selectivity and distributed response patterns, and raise questions about the relative roles of each in development and adult cognition.
Ferradal SL, Gagoski B, Jaimes C, Yi F, Carruthers C, Vu C, Litt JS, Larsen R, Sutton B, Grant EP, et al. System-Specific Patterns of Thalamocortical Connectivity in Early Brain Development as Revealed by Structural and Functional MRI. Cereb Cortex. 2019;29 (3) :1218-1229.Abstract
The normal development of thalamocortical connections plays a critical role in shaping brain connectivity in the prenatal and postnatal periods. Recent studies using advanced magnetic resonance imaging (MRI) techniques in neonates and infants have shown that abnormal thalamocortical connectivity is associated with adverse neurodevelopmental outcomes. However, all these studies have focused on a single neuroimaging modality, overlooking the dynamic relationship between structure and function at this early stage. Here, we study the relationship between structural and functional thalamocortical connectivity patterns derived from healthy full-term infants scanned with diffusion-weighted MRI and resting-state functional MRI within the first weeks of life (mean gestational age = 39.3 ± 1.2 weeks; age at scan = 24.2 ± 7.9 days). Our results show that while there is, in general, good spatial agreement between both MRI modalities, there are regional variations that are system-specific: regions involving primary-sensory cortices exhibit greater structural/functional overlap, whereas higher-order association areas such as temporal and posterior parietal cortices show divergence in spatial patterns of each modality. This variability illustrates the complementarity of both modalities and highlights the importance of multimodal approaches.
Zöllei L, Jaimes C, Saliba E, Grant EP, Yendiki A. TRActs constrained by UnderLying INfant anatomy (TRACULInA): An automated probabilistic tractography tool with anatomical priors for use in the newborn brain. Neuroimage. 2019;199 :1-17.Abstract
The ongoing myelination of white-matter fiber bundles plays a significant role in brain development. However, reliable and consistent identification of these bundles from infant brain MRIs is often challenging due to inherently low diffusion anisotropy, as well as motion and other artifacts. In this paper we introduce a new tool for automated probabilistic tractography specifically designed for newborn infants. Our tool incorporates prior information about the anatomical neighborhood of white-matter pathways from a training data set. In our experiments, we evaluate this tool on data from both full-term and prematurely born infants and demonstrate that it can reconstruct known white-matter tracts in both groups robustly, even in the presence of differences between the training set and study subjects. Additionally, we evaluate it on a publicly available large data set of healthy term infants (UNC Early Brain Development Program). This paves the way for performing a host of sophisticated analyses in newborns that we have previously implemented for the adult brain, such as pointwise analysis along tracts and longitudinal analysis, in both health and disease.
2018
Harms MP, Somerville LH, Ances BM, Andersson J, Barch DM, Bastiani M, Bookheimer SY, Brown TB, Buckner RL, Burgess GC, et al. Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects. Neuroimage. 2018;183 :972-984.Abstract
The Human Connectome Projects in Development (HCP-D) and Aging (HCP-A) are two large-scale brain imaging studies that will extend the recently completed HCP Young-Adult (HCP-YA) project to nearly the full lifespan, collecting structural, resting-state fMRI, task-fMRI, diffusion, and perfusion MRI in participants from 5 to 100+ years of age. HCP-D is enrolling 1300+ healthy children, adolescents, and young adults (ages 5-21), and HCP-A is enrolling 1200+ healthy adults (ages 36-100+), with each study collecting longitudinal data in a subset of individuals at particular age ranges. The imaging protocols of the HCP-D and HCP-A studies are very similar, differing primarily in the selection of different task-fMRI paradigms. We strove to harmonize the imaging protocol to the greatest extent feasible with the completed HCP-YA (1200+ participants, aged 22-35), but some imaging-related changes were motivated or necessitated by hardware changes, the need to reduce the total amount of scanning per participant, and/or the additional challenges of working with young and elderly populations. Here, we provide an overview of the common HCP-D/A imaging protocol including data and rationales for protocol decisions and changes relative to HCP-YA. The result will be a large, rich, multi-modal, and freely available set of consistently acquired data for use by the scientific community to investigate and define normative developmental and aging related changes in the healthy human brain.
Ou Y, Zöllei L, Da X, Retzepi K, Murphy SN, Gerstner ER, Rosen BR, Grant EP, Kalpathy-Cramer J, Gollub RL. Field of View Normalization in Multi-Site Brain MRI. Neuroinformatics. 2018;16 (3-4) :431-444.Abstract
Multi-site brain MRI analysis is needed in big data neuroimaging studies, but challenging. The challenges lie in almost every analysis step including skull stripping. The diversities in multi-site brain MR images make it difficult to tune parameters specific to subjects or imaging protocols. Alternatively, using constant parameter settings often leads to inaccurate, inconsistent and even failed skull stripping results. One reason is that images scanned at different sites, under different scanners or protocols, and/or by different technicians often have very different fields of view (FOVs). Normalizing FOV is currently done manually or using ad hoc pre-processing steps, which do not always generalize well to multi-site diverse images. In this paper, we show that (a) a generic FOV normalization approach is possible in multi-site diverse images; we show experiments on images acquired from Philips, GE, Siemens scanners, from 1.0T, 1.5T, 3.0T field of strengths, and from subjects 0-90 years of ages; and (b) generic FOV normalization improves skull stripping accuracy and consistency for multiple skull stripping algorithms; we show this effect for 5 skull stripping algorithms including FSL's BET, AFNI's 3dSkullStrip, FreeSurfer's HWA, BrainSuite's BSE, and MASS. We have released our FOV normalization software at http://www.nitrc.org/projects/normalizefov .
Edlow BL, Keene DC, Perl DP, Iacono D, Folkerth RD, Stewart W, MacDonald CL, Augustinack J, Diaz-Arrastia R, Estrada C, et al. Multimodal Characterization of the Late Effects of Traumatic Brain Injury: A Methodological Overview of the Late Effects of Traumatic Brain Injury Project. J Neurotrauma. 2018;35 (14) :1604-1619.Abstract
Epidemiological studies suggest that a single moderate-to-severe traumatic brain injury (TBI) is associated with an increased risk of neurodegenerative disease, including Alzheimer's disease (AD) and Parkinson's disease (PD). Histopathological studies describe complex neurodegenerative pathologies in individuals exposed to single moderate-to-severe TBI or repetitive mild TBI, including chronic traumatic encephalopathy (CTE). However, the clinicopathological links between TBI and post-traumatic neurodegenerative diseases such as AD, PD, and CTE remain poorly understood. Here, we describe the methodology of the Late Effects of TBI (LETBI) study, whose goals are to characterize chronic post-traumatic neuropathology and to identify in vivo biomarkers of post-traumatic neurodegeneration. LETBI participants undergo extensive clinical evaluation using National Institutes of Health TBI Common Data Elements, proteomic and genomic analysis, structural and functional magnetic resonance imaging (MRI), and prospective consent for brain donation. Selected brain specimens undergo ultra-high resolution ex vivo MRI and histopathological evaluation including whole-mount analysis. Co-registration of ex vivo and in vivo MRI data enables identification of ex vivo lesions that were present during life. In vivo signatures of postmortem pathology are then correlated with cognitive and behavioral data to characterize the clinical phenotype(s) associated with pathological brain lesions. We illustrate the study methods and demonstrate proof of concept for this approach by reporting results from the first LETBI participant, who despite the presence of multiple in vivo and ex vivo pathoanatomic lesions had normal cognition and was functionally independent until her mid-80s. The LETBI project represents a multidisciplinary effort to characterize post-traumatic neuropathology and identify in vivo signatures of postmortem pathology in a prospective study.
Warton FL, Taylor PA, Warton CMR, Molteno CD, Wintermark P, Lindinger NM, Zöllei L, van der Kouwe A, Jacobson JL, Jacobson SW, et al. Prenatal methamphetamine exposure is associated with corticostriatal white matter changes in neonates. Metab Brain Dis. 2018;33 (2) :507-522.Abstract
Diffusion tensor imaging (DTI) studies have shown that prenatal exposure to methamphetamine is associated with alterations in white matter microstructure, but to date no tractography studies have been performed in neonates. The striato-thalamo-orbitofrontal circuit and its associated limbic-striatal areas, the primary circuit responsible for reinforcement, has been postulated to be dysfunctional in drug addiction. This study investigated potential white matter changes in the striatal-orbitofrontal circuit in neonates with prenatal methamphetamine exposure. Mothers were recruited antenatally and interviewed regarding methamphetamine use during pregnancy, and DTI sequences were acquired in the first postnatal month. Target regions of interest were manually delineated, white matter bundles connecting pairs of targets were determined using probabilistic tractography in AFNI-FATCAT, and fractional anisotropy (FA) and diffusion measures were determined in white matter connections. Regression analysis showed that increasing methamphetamine exposure was associated with reduced FA in several connections between the striatum and midbrain, orbitofrontal cortex, and associated limbic structures, following adjustment for potential confounding variables. Our results are consistent with previous findings in older children and extend them to show that these changes are already evident in neonates. The observed alterations are likely to play a role in the deficits in attention and inhibitory control frequently seen in children with prenatal methamphetamine exposure.
Warton FL, Meintjes EM, Warton CMR, Molteno CD, Lindinger NM, Carter CR, Zöllei L, Wintermark P, Jacobson JL, van der Kouwe A, et al. Prenatal methamphetamine exposure is associated with reduced subcortical volumes in neonates. Neurotoxicol Teratol. 2018;65 :51-59.Abstract
OBJECTIVES: Prenatal exposure to methamphetamine is associated with a range of neuropsychological, behavioural and cognitive deficits. A small number of imaging studies suggests that these may be mediated by neurostructural changes, including reduced volumes of specific brain regions. This study investigated potential volumetric changes in the brains of neonates with prenatal methamphetamine exposure. To our knowledge no previous studies have examined methamphetamine effects on regional brain volumes at this age. STUDY DESIGN: Mothers were recruited antenatally and interviewed regarding methamphetamine use during pregnancy. Mothers in the exposure group reported using methamphetamine≥twice/month during pregnancy; control infants had no exposure to methamphetamine or other drugs and minimal exposure to alcohol. MRI scans were performed in the first postnatal month, following which anatomical images were processed using FreeSurfer. Subcortical and cerebellar regions were manually segmented and their volumes determined using FreeView. Pearson correlations were used to analyse potential associations between methamphetamine exposure and regional volumes. The associations between methamphetamine exposure and regional volumes were then examined adjusting for potential confounding variables. RESULTS: Methamphetamine exposure was associated with reduced left and right caudate and thalamus volumes. The association in the right caudate remained significant following adjustment for potential confounding variables. CONCLUSIONS: Our findings showing reduced caudate and thalamus volumes in neonates with prenatal methamphetamine exposure are consistent with previous findings in older exposed children, and demonstrate that these changes are already detectable in neonates. Continuing research is warranted to examine whether reduced subcortical volumes are predictive of cognitive, behavioural and affective impairment in older children.
Jaimes C, Cheng HH, Soul J, Ferradal S, Rathi Y, Gagoski B, Newburger JW, Grant EP, Zöllei L. Probabilistic tractography-based thalamic parcellation in healthy newborns and newborns with congenital heart disease. J Magn Reson Imaging. 2018;47 (6) :1626-1637.Abstract
BACKGROUND: Given the central role of the thalamus in motor, sensory, and cognitive development, methods to study emerging thalamocortical connectivity in early infancy are of great interest. PURPOSE: To determine the feasibility of performing probabilistic tractography-based thalamic parcellation (PTbTP) in typically developing (TD) neonates and to compare the results with a pilot sample of neonates with congenital heart disease (CHD). STUDY TYPE: Institutional Review Board (IRB)-approved cross-sectional study. MODEL: We prospectively recruited 20 TD neonates and five CHD neonates (imaged preoperatively). FIELD STRENGTH/SEQUENCE: MRI was performed at 3.0T including diffusion-weighted imaging (DWI) and 3D magnetization prepared rapid gradient-echo (MPRAGE). ASSESSMENT: A radiologist and trained research assistants segmented the thalamus and seven cortical targets for each hemisphere. Using the thalami as seeds and the cortical labels as targets, FSL library tools were used to generate probabilistic tracts. A Hierarchical Dirichlet Process algorithm was then used for clustering analysis. A radiologist qualitatively assessed the results of clustering. Quantitative analyses were also performed. STATISTICAL TESTS: We summarized the demographic data and results of clustering with descriptive statistics. Linear regressions covarying for gestational age were used to compare groups. RESULTS: In 17 of 20 TD neonates, we identified five connectivity-determined clusters, which correlate with known thalamic nuclei and subnuclei. In four neonates with CHD we observed a spectrum of abnormalities including fewer and disorganized clusters or small supernumerary clusters (up to seven per thalamus). After covarying for differences in corrected gestational age (cGA), the fractional anisotropy (FA), volume, and normalized thalamic volume were significantly lower in CHD neonates (P < 0.01). DATA CONCLUSIONS: Using PTbTP clusters, correlating well with the location and connectivity of known thalamic nuclei, were identified in TD neonates. Differences in thalamic clustering outputs were identified in four neonates with CHD, raising concern for disordered thalamic connectivity. PTbTP is feasible in TD and CHD neonates. Preliminary findings suggest the prenatal origins of altered connectivity in CHD. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2018;47:1626-1637.
Papadelis C, Butler EE, Rubenstein M, Sun L, Zollei L, Nimec D, Snyder B, Grant PE. Reorganization of the somatosensory cortex in hemiplegic cerebral palsy associated with impaired sensory tracts. Neuroimage Clin. 2018;17 :198-212.Abstract
Functional neuroimaging studies argue that sensory deficits in hemiplegic cerebral palsy (HCP) are related to deviant somatosensory processing in the ipsilesional primary somatosensory cortex (S1). A separate body of structural neuroimaging literature argues that these deficits are due to structural damage of the ascending sensory tracts (AST). The relationship between the functional and structural integrity of the somatosensory system and the sensory performance is largely unknown in HCP. To address this relationship, we combined findings from magnetoencephalography (MEG) and probabilistic diffusion tractography (PDT) in 10 children with HCP and 13 typically developing (TD) children. With MEG, we mapped the functionally active regions in the contralateral S1 during tactile stimulation of the thumb, middle, and little fingers of both hands. Using these MEG-defined functional active regions as regions of interest for PDT, we estimated the diffusion parameters of the AST. Somatosensory function was assessed via two-point discrimination tests. Our MEG data showed: (i) an abnormal somatotopic organization in all children with HCP in either one or both of their hemispheres; (ii) longer Euclidean distances between the digit maps in the S1 of children with HCP compared to TD children; (iii) suppressed gamma responses at early latencies for both hemispheres of children with HCP; and (iv) a positive correlation between the Euclidean distances and the sensory tests for the more affected hemisphere of children with HCP. Our MEG-guided PDT data showed: (i) higher mean and radian diffusivity of the AST in children with HCP; (ii) a positive correlation between the axial diffusivity of the AST with the sensory tests for the more affected hemisphere; and (iii) a negative correlation between the gamma power change and the AD of the AST for the MA hemisphere. Our findings associate for the first time bilateral cortical functional reorganization in the S1 of HCP children with abnormalities in the structural integrity of the AST, and correlate these abnormalities with behaviorally-assessed sensory deficits.

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