Cortical Surface Reconstruction Research Articles

Children born very preterm experience altered cortical expansion over the first decade of life.

The brain develops rapidly from the final trimester of gestation through childhood, with cortical surface area expanding greatly in the first decade of life. However, it is unclear exactly where and how cortical surface area changes after birth, or how prematurity affects these developmental trajectories. Fifty-two very preterm (gestational age at birth = 26 ± 1.6 weeks) and 41 full-term (gestational age at birth = 39 ± 1.2 weeks) infants were scanned using structural magnetic resonance imaging at term-equivalent age and again at 9/10 years of age. Individual cortical surface reconstructions were extracted for each scan. Infant and 9/10 cortical surfaces were aligned using anatomically constrained Multimodal Surface Matching (aMSM), a technique that allows calculation of local expansion gradients across the cortical surface for each individual subject. At the neonatal time point, very preterm infants had significantly smaller surface area than their full-term peers (P < 0.001), but at the age 9/10-year time point, very preterm and full-term children had comparable surface area (P > 0.05). Across all subjects, cortical expansion by age 9/10 years was most pronounced in frontal, temporal, and supramarginal/inferior parietal junction areas, which are key association cortices (P Spin < 0.001). Very preterm children showed greater cortical surface area expansion between term-equivalent age and age 9/10 compared to their full-term peers in the medial and lateral frontal areas, precuneus, and middle temporal/banks of the superior sulcus junction (P < 0.05). Furthermore, within the very preterm group, expansion was highly variable within the orbitofrontal cortex and posterior regions of the brain. By mapping these patterns across the cortex, we identify differences in association cortices that are known to be important for executive functioning, emotion processing, and social cognition. Additional longitudinal work will be needed to understand if increased expansion in very preterm children is adaptive, or if differences persist into adulthood.

Intraoperative cortical stimulation mapping with laryngeal electromyography for the localization of human laryngeal motor cortex.

The objectives of this study were to describe the authors' clinical methodology and outcomes for mapping the laryngeal motor cortex (LMC) and define localization of the LMC in a cohort of neurosurgical patients undergoing intraoperative brain mapping. Because of mapping variability across patients, the authors aimed to define the probabilistic distribution of cortical sites that evoke laryngeal movement, as well as adjacent cortical somatotopic representations for the face (mouth), tongue, and hand. Thirty-six patients underwent left (n = 18) or right (n = 18) craniotomy with asleep motor mapping. For each patient, electromyography (EMG) electrodes were placed in the face, tongue, and hand; a nerve integrity monitor (NIM) endotracheal tube with surface electrodes detected EMG activity from the bilateral vocal folds. After dense cortical stimulation was delivered throughout the sensorimotor cortex, motor responses were then mapped onto a three-dimensional reconstruction of the patient's cortical surfaces for location characterization of the evoked responses. Finally, stimulation sites were transformed into a two-dimensional coordinate system for probabilistic mapping of the stimulation site relative to the central sulcus and sylvian fissure. The authors found that the LMC was predominantly localized to a mid precentral gyrus region, dorsal to face representation and surrounding a transverse sulcus ventral to the hand knob. In 14 of 36 patients, the authors identified additional laryngeal responses located ventral to all orofacial representations, providing evidence for dual LMC representations. The authors determined the probabilistic distribution of the LMC. Cortical stimulation mapping with an NIM endotracheal tube is an easy and effective method for mapping the LMC and is simply integrated into the current neuromonitoring methods for brain mapping.

Atrophy of posteromedial sulci in aging and Alzheimer’s disease differs by sulcal type and location

AbstractBackgroundRecent work has shown that the morphology of tertiary sulci — small, evolutionarily‐newer sulci that emerge last in gestation — is associated with individual differences in cognitive development and symptoms of neuropsychiatric disorders, but these sulci have not been investigated in aging. Here we extend these findings on tertiary sulci to the aging brain.MethodWe manually defined sulci on cortical surface reconstructions of T1 MRI scans in FreeSurfer and extracted the cortical thickness and mean anterior coordinate of each sulcus. We defined sulci in posteromedial cortex (PMC) in 72 younger adults (YA; age 18‐35) from the Human Connectome Project, 72 cognitively normal older adults (OA; age 65‐90) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and 72 age‐matched amyloid positive older adults with AD from ADNI: 4,341 sulci total. We used a mixed‐effects ANOVA to examine the effects of group (YA, OA, AD) as a between‐subject variable, and sulcal type (tertiary or non‐tertiary) and hemisphere (left/right) as within‐subject variables on sulcal cortical thickness. We used LASSO regression with nested cross‐validation for feature selection to determine which sulcal measures are most related to ADNI Memory Composite (MEM) scores, and LOOCV linear regression to predict MEM scores from the best model. Because not all sulci are found in all participants, only the most consistent were included in the LASSO models.ResultIn PMC, tertiary sulci show more age‐ and AD‐related thinning compared to non‐tertiary (group x sulcal type interaction p&lt;.0001; Fig. 1). Additionally, atrophy in PMC shows a more anterior bias in aging that is absent in AD (Fig. 2). The best model for predicting ADNI‐MEM scores relies on a subset of mostly posterior sulci, suggesting a posterior shift in atrophy may be more related to cognitive decline (Fig. 3).ConclusionThis is the first study to investigate tertiary sulci in aging and AD. Results show tertiary sulci in PMC are especially vulnerable to atrophy in aging and that a subset of PMC sulci are particularly associated with memory. The results provide insight into the anatomical specificity of age‐ and AD‐related atrophy in PMC and its association with cognitive decline.

Atrophy of posteromedial sulci in aging and Alzheimer’s disease differs by sulcal type and location

AbstractBackgroundRecent work has shown that the morphology of tertiary sulci — small, evolutionarily‐newer sulci that emerge last in gestation — is associated with individual differences in cognitive development and symptoms of neuropsychiatric disorders, but these sulci have not been investigated in aging. Here we extend these findings on tertiary sulci to the aging brain.MethodWe manually defined sulci on cortical surface reconstructions of T1 MRI scans in FreeSurfer and extracted the cortical thickness and mean anterior coordinate of each sulcus. We defined sulci in posteromedial cortex (PMC) in 72 younger adults (YA; age 18‐35) from the Human Connectome Project, 72 cognitively normal older adults (OA; age 65‐90) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and 72 age‐matched amyloid positive older adults with AD from ADNI: 4,341 sulci total. We used a mixed‐effects ANOVA to examine the effects of group (YA, OA, AD) as a between‐subject variable, and sulcal type (tertiary or non‐tertiary) and hemisphere (left/right) as within‐subject variables on sulcal cortical thickness. We used LASSO regression with nested cross‐validation for feature selection to determine which sulcal measures are most related to ADNI Memory Composite (MEM) scores, and LOOCV linear regression to predict MEM scores from the best model. Because not all sulci are found in all participants, only the most consistent were included in the LASSO models.ResultIn PMC, tertiary sulci show more age‐ and AD‐related thinning compared to non‐tertiary (group x sulcal type interaction p&lt;.0001; Fig. 1). Additionally, atrophy in PMC shows a more anterior bias in aging that is absent in AD (Fig. 2). The best model for predicting ADNI‐MEM scores relies on a subset of mostly posterior sulci, suggesting a posterior shift in atrophy may be more related to cognitive decline (Fig. 3).ConclusionThis is the first study to investigate tertiary sulci in aging and AD. Results show tertiary sulci in PMC are especially vulnerable to atrophy in aging and that a subset of PMC sulci are particularly associated with memory. The results provide insight into the anatomical specificity of age‐ and AD‐related atrophy in PMC and its association with cognitive decline.

Accelerated preprocessing of large numbers of brain images by parallel computing on supercomputers

“Preprocessing” is the first step required in brain image analysis that improves the overall quality and reliability of the results. However, it is computationally demanding and time-consuming, particularly to handle and parcellate complicatedly folded cortical ribbons of the human brain. In this study, we aimed to shorten the analysis time for data preprocessing of 1410 brain images simultaneously on one of the world's highest-performing supercomputers, “Fugaku.” The FreeSurfer was used as a benchmark preprocessing software for cortical surface reconstruction. All the brain images were processed simultaneously and successfully analyzed in a calculation time of 17.33 h. This result indicates that using a supercomputer for brain image preprocessing allows big data analysis to be completed shortly and flexibly, thus suggesting the possibility of supercomputers being used for expanding large data analysis and parameter optimization of preprocessing in the future.

Cortical growth from infancy to adolescence in preterm and term-born children.

Early life experiences can exert a significant influence on cortical and cognitive development. Very preterm birth exposes infants to several adverse environmental factors during hospital admission, which affect cortical architecture. However, the subsequent consequence of very preterm birth on cortical growth from infancy to adolescence has never been defined; despite knowledge of critical periods during childhood for establishment of cortical networks. Our aims were to: chart typical longitudinal cortical development and sex differences in cortical development from birth to adolescence in healthy term-born children; estimate differences in cortical development between children born at term and very preterm; and estimate differences in cortical development between children with normal and impaired cognition in adolescence. This longitudinal cohort study included children born at term (≥37 weeks' gestation) and very preterm (<30 weeks' gestation) with MRI scans at ages 0, 7 and 13 years (n = 66 term-born participants comprising 34 with one scan, 18 with two scans and 14 with three scans; n = 201 very preterm participants comprising 56 with one scan, 88 with two scans and 57 with three scans). Cognitive assessments were performed at age 13 years. Cortical surface reconstruction and parcellation were performed with state-of-the-art, equivalent MRI analysis pipelines for all time points, resulting in longitudinal cortical volume, surface area and thickness measurements for 62 cortical regions. Developmental trajectories for each region were modelled in term-born children, contrasted between children born at term and very preterm, and contrasted between all children with normal and impaired cognition. In typically developing term-born children, we documented anticipated patterns of rapidly increasing cortical volume, area and thickness in early childhood, followed by more subtle changes in later childhood, with smaller cortical size in females than males. In contrast, children born very preterm exhibited increasingly reduced cortical volumes, relative to term-born children, particularly during ages 0-7 years in temporal cortical regions. This reduction in cortical volume in children born very preterm was largely driven by increasingly reduced cortical thickness rather than area. This resulted in amplified cortical volume and thickness reductions by age 13 years in individuals born very preterm. Alterations in cortical thickness development were found in children with impaired language and memory. This study shows that the neurobiological impact of very preterm birth on cortical growth is amplified from infancy to adolescence. These data further inform the long-lasting impact on cortical development from very preterm birth, providing broader insights into neurodevelopmental consequences of early life experiences.

Associations between Age and Cortical Thickness in Brain Regions Supporting Speech Motor Control

AbstractBackgroundPrior research has demonstrated consistent associations between age and cortical thickness, as well as longitudinal reductions in cortical thickness in older adults (e.g., Salat et al., 2004; Storsve et al., 2014). To date, the aging of regions supporting speech production has not been directly investigated. Such information is, however, needed to differentiate age‐related cortical atrophy in speech regions from that occurring due to underlying neurodegenerative diseases. We examined cross‐sectional associations between age and cortical thickness of brain regions supporting speech motor control. These areas include subregions of the premotor cortex, primary motor area, supplementary motor area, pars opercularis, posterior inferior frontal sulcus, and insula as defined by the Directions Into Velocities of Articulators (DIVA) model ‐ a model for neural representation and mechanisms of speech motor control (Tourville &amp; Guenther, 2011).MethodThe data were obtained from the Alzheimer’s Disease Neuroimaging Initiative GO/2 (ADNI). Participants included 163 (77 M, 86 F) cognitively intact adults aged 56‐89 years (Mean = 73.45, SD = 6.23). All participants were right‐handed and had at least 12 years of education (Mean = 16.53, SD = 2.55). Whole‐brain T1‐weighted MRI images were acquired by a 3‐Tesla Siemens scanner. FreeSurfer software was used for cortical surface reconstruction and to extract the cortical thickness of speech regions as identified by the SpeechLabel Parcellation System (Cai et al., 2014) developed based on the DIVA model.ResultSignificant associations between age and cortical thickness was observed in subregions of left premotor cortex, primary motor area, supplementary motor area, and presupplementary motor area with more limited effects in the right hemisphere. Cortical thickness of pars opercularis, posterior inferior frontal sulcus, and insula did not show significant changes over the aging process. Cross‐sectional interactions were found with regions in the left hemisphere being thicker at younger ages and having a larger negative age slope compared to their corresponding regions in the right hemisphere.ConclusionThe findings of the study suggest that certain speech‐related regions show atrophy with aging, however, these changes are not uniform. In addition, laterality in cortical thickness of speech regions at younger ages was observed to diminish over the normal aging process.

SurfNN: Joint Reconstruction of Multiple Cortical Surfaces from Magnetic Resonance Images.

To achieve fast, robust, and accurate reconstruction of the human cortical surfaces from 3D magnetic resonance images (MRIs), we develop a novel deep learning-based framework, referred to as SurfNN, to reconstruct simultaneously both inner (between white matter and gray matter) and outer (pial) surfaces from MRIs. Different from existing deep learning-based cortical surface reconstruction methods that either reconstruct the cortical surfaces separately or neglect the interdependence between the inner and outer surfaces, SurfNN reconstructs both the inner and outer cortical surfaces jointly by training a single network to predict a midthickness surface that lies at the center of the inner and outer cortical surfaces. The input of SurfNN consists of a 3D MRI and an initialization of the midthickness surface that is represented both implicitly as a 3D distance map and explicitly as a triangular mesh with spherical topology, and its output includes both the inner and outer cortical surfaces, as well as the midthickness surface. The method has been evaluated on a large-scale MRI dataset and demonstrated competitive cortical surface reconstruction performance.

ResAttn-recon: Residual self-attention based cortical surface reconstruction

Introduction: The accurate cerebral cortex surface reconstruction is crucial for the study of neurodegenerative diseases. Existing voxelwise segmentation-based approaches like FreeSurfer and FastSurfer are limited by the partial volume effect, meaning that reconstruction details highly rely on the resolution of the input volume. In the computer version area, the signed distance function has become an efficient method for 3D shape representation, the inherent continuous nature makes it easy to capture the fine details of the target object at an arbitrary resolution. Additionally, as one of the most valuable breakthroughs in deep learning research, attention is a powerful mechanism developed to enhance the performance of the encoder-decoder architecture.Methods: To further improve the reconstruction accuracy of the cortical surface, we proposed ResAttn-Recon, a residual self-attention based encoder-decoder framework. In this framework, we also developed a lightweight decoder network with skip connections. Furthermore, a truncated and weighted L1 loss function are proposed to accelerate network convergence, compared to simply applying the L1 loss function.Results: The intersection over union curve in the training process achieved a steeper slope and a higher peak (0.948 vs. 0.920) with a truncated L1 loss. Thus, the average symmetric surface distance (AD) for the inner and outer surfaces is 0.253 ± 0.051 and the average Hausdorff distance (HD) is 0.629 ± 0.186, which is lower than that of DeepCSR, whose absolute distance equals 0.283 ± 0.059 and Hausdorff distance equals 0.746 ± 0.245.Discussion: In conclusion, the proposed residual self-attention-based framework can be a promising approach for improving the cortical surface reconstruction performance.

IBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction.

The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from multiple imaging sites with different scanners and imaging protocols for the investigation of normal and abnormal early brain development. However, it is extremely challenging to precisely process and quantify infant brain development with these multisite imaging data because infant brain MRI scans exhibit (a) extremely low and dynamic tissue contrast caused by ongoing myelination and maturation and (b) inter-site data heterogeneity resulting from the use of diverse imaging protocols/scanners. Consequently, existing computational tools and pipelines typically perform poorly on infant MRI data. To address these challenges, we propose a robust, multisite-applicable, infant-tailored computational pipeline that leverages powerful deep learning techniques. The main functionality of the proposed pipeline includes preprocessing, brain skull stripping, tissue segmentation, topology correction, cortical surface reconstruction and measurement. Our pipeline can handle both T1w and T2w structural infant brain MR images well in a wide age range (from birth to 6 years of age) and is effective for different imaging protocols/scanners, despite being trained only on the data from the Baby Connectome Project. Extensive comparisons with existing methods on multisite, multimodal and multi-age datasets demonstrate superior effectiveness, accuracy and robustness of our pipeline. We have maintained a website, iBEAT Cloud, for users to process their images with our pipeline ( http://www.ibeat.cloud ), which has successfully processed over 16,000 infant MRI scans from more than 100 institutions with various imaging protocols/scanners.

CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs.

We present CortexODE, a deep learning framework for cortical surface reconstruction. CortexODE leverages neural ordinary differential equations (ODEs) to deform an input surface into a target shape by learning a diffeomorphic flow. The trajectories of the points on the surface are modeled as ODEs, where the derivatives of their coordinates are parameterized via a learnable Lipschitz-continuous deformation network. This provides theoretical guarantees for the prevention of self-intersections. CortexODE can be integrated to an automatic learning-based pipeline, which reconstructs cortical surfaces efficiently in less than 5 seconds. The pipeline utilizes a 3D U-Net to predict a white matter segmentation from brain Magnetic Resonance Imaging (MRI) scans, and further generates a signed distance function that represents an initial surface. Fast topology correction is introduced to guarantee homeomorphism to a sphere. Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively. The proposed pipeline is evaluated on large-scale neuroimage datasets in various age groups including neonates (25-45 weeks), young adults (22-36 years) and elderly subjects (55-90 years). Our experiments demonstrate that the CortexODE-based pipeline can achieve less than 0.2mm average geometric error while being orders of magnitude faster compared to conventional processing pipelines.

SurfFlow: A Flow-Based Approach for Rapid and Accurate Cortical Surface Reconstruction from Infant Brain MRI.

The infant brain undergoes rapid changes in volume, shape, and structural organization during the first postnatal year. Accurate cortical surface reconstruction (CSR) is essential for understanding rapid changes in cortical morphometry during early brain development. However, existing CSR methods, designed for adult brain MRI, fall short in reconstructing cortical surfaces from infant MRI, owing to the poor tissue contrasts, partial volume effects, and rapid changes in cortical folding patterns. Here, we introduce an infant-centric CSR method in light of these challenges. Our method, SurfFlow, utilizes three seamlessly connected deformation blocks to sequentially deform an initial template mesh to target cortical surfaces. Remarkably, our method can rapidly reconstruct a high-resolution cortical surface mesh with 360k vertices in approximately one second. Performance evaluation based on an MRI dataset of infants 0 to 12 months of age indicates that SurfFlow significantly reduces geometric errors and substantially improves mesh regularity compared with state-of-the-art deep learning approaches.

Toward evaluation of multiresolution cortical thickness estimation with FreeSurfer, MaCRUISE, and BrainSuite.

Advances in Magnetic Resonance Imaging hardware and methodologies allow for promoting the cortical morphometry with submillimeter spatial resolution. In this paper, we generated 3D self-enhanced high-resolution (HR) MRI imaging, by adapting 1 deep learning architecture, and 3 standard pipelines, FreeSurfer, MaCRUISE, and BrainSuite, have been collectively employed to evaluate the cortical thickness. We systematically investigated the differences in cortical thickness estimation for MRI sequences at multiresolution homologously originated from the native image. It has been revealed that there systematically exhibited the preferences in determining both inner and outer cortical surfaces at higher resolution, yielding most deeper cortical surface placements toward GM/WM or GM/CSF boundaries, which directs a consistent reduction tendency of mean cortical thickness estimation; on the contrary, the lower resolution data will most probably provide a more coarse and rough evaluation in cortical surface reconstruction, resulting in a relatively thicker estimation. Although the differences of cortical thickness estimation at the diverse spatial resolution varied with one another, almost all led to roughly one-sixth to one-fifth significant reduction across the entire brain at the HR, independent to the pipelines we applied, which emphasizes on generally coherent improved accuracy in a data-independent manner and endeavors to cost-efficiency with quantitative opportunities.

A structural MRI study of global developmental delay in infants (

To use structural magnetic resonance imaging (3D-MRI) to evaluate the abnormal development of the cerebral cortex in infants with global developmental delay (GDD). The GDD group includes 67 infants aged between 112 and 699 days with global developmental delay and who underwent T1-weighted MRI scans in Shanxi Children's Hospital from December 2019 to March 2022. The healthy control (HC) group includes 135 normal developing infants aged between 88 and 725 days in Shanxi Children's Hospital from September 2020 to August 2021. Whole-brain T1-weighted MRI scans were carried out with a 3.0-T magnetic resonance scanner, which was later processed using InfantSurfer to perform MR image processing and cortical surface reconstruction. Two morphological features of the cortical surface of the 68 brain regions were computed, i.e., the cortical thickness (CT) and cortical surface area (SA), and compared between the GDD and HC groups. With regard to the CT, the HC group showed a rapid decrease at first and then a slow increase after birth, and the CT of the GDD group decreased slowly and then became relatively stable. The GDD group showed bilaterally higher hemispherical average CT than those in the HC group. In detail, for the left hemisphere, except in the entorhinal and temporal poles in which the average CT values of the two brain regions were lower than those of the HC group, the CT of the 26 brain regions in the GDD group was higher than those of the HC group (p < 0.05). For the right hemisphere, the CT of the entorhinal in the GDD group was lower than that in the HC group. Otherwise, the CT of the remaining 28 brain regions was higher than those in the HC group (p < 0.05). With regard to the SA, both groups showed a rapid increase after birth till 23 months and remained quite stable afterward. The GDD group shows lower SA bilaterally than that in the HC group. In detail, SA in the GDD group was lower in most cortical regions of both hemispheres than in the HC group (p < 0.05), except for the right temporal pole and entorhinal. When testing for brain asymmetry, we found that the HC group showed obvious asymmetry of CT and SA, while only a few cortical regions in the GDD group showed asymmetry.

Local Extraction of Extra-Axial CSF from structural MRI.

The quantification of cerebrospinal fluid (CSF), specifically the extra-axial cerebrospinal fluid (EA-CSF), which is the CSF in the subarachnoid space surrounding the cortical surface of the brain, has recently been shown to play an important role in the neuropathology of autism spectrum disorder (ASD) in infants. While prior work addressed measuring the global volume of EA-CSF, there was no available tool that quantifies the local, anatomical distribution of the EA-CSF. A localized EA-CSF quantification would provide more accurate and interpretable measurements. In our recent work, we proposed such a local EA-CSF extraction by using a pipeline that combines probabilistic brain tissue segmentation, cortical surface reconstruction and streamline-based local EA-CSF quantification. Yet, that system had several shortcomings, in particular a lack of available software tools, as well as a quantification where EA-CSF portions are counted multiple times. The purpose of this article is to present a novel, graphical user interface based, publicly available software tool, called LocalEACSF, which allows the user to easily run an adapted version of this pipeline and provide a set of straightforward quality control visualizations to assess the quality of the EA-CSF quantification. This tool further adds improvements and optimizations to the prior assessment. The LocalEACSF tool allows neuroimaging labs to compute a local extraction of extra-axial CSF in their neuroimaging studies in order to investigate its role in normal and atypical brain development, without the need for extensive technical knowledge.

Fast cortical surface reconstruction from MRI using deep learning

Reconstructing cortical surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based functional and anatomical image analyses. Conventional algorithms for cortical surface reconstruction are computationally inefficient and typically take several hours for each subject, causing a bottleneck in applications when a fast turnaround time is needed. To address this challenge, we propose a fast cortical surface reconstruction (FastCSR) pipeline by leveraging deep machine learning. We trained our model to learn an implicit representation of the cortical surface in volumetric space, termed the “level set representation”. A fast volumetric topology correction method and a topology-preserving surface mesh extraction procedure were employed to reconstruct the cortical surface based on the level set representation. Using 1-mm isotropic T1-weighted images, the FastCSR pipeline was able to reconstruct a subject’s cortical surfaces within 5 min with comparable surface quality, which is approximately 47 times faster than the traditional FreeSurfer pipeline. The advantage of FastCSR becomes even more apparent when processing high-resolution images. Importantly, the model demonstrated good generalizability in previously unseen data and showed high test–retest reliability in cortical morphometrics and anatomical parcellations. Finally, FastCSR was robust to images with compromised quality or with distortions caused by lesions. This fast and robust pipeline for cortical surface reconstruction may facilitate large-scale neuroimaging studies and has potential in clinical applications wherein brain images may be compromised.

Maternal Obesity during Pregnancy is Associated with Lower Cortical Thickness in the Neonate Brain.

Recent studies have suggested that maternal obesity during pregnancy is associated with differences in neurodevelopmental outcomes in children. In this study, we aimed to investigate the relationships between maternal obesity during pregnancy and neonatal brain cortical development. Forty-four healthy women (28 normal-weight, 16 obese) were prospectively recruited at <10 weeks' gestation, and their healthy full-term neonates (23 boys, 21 girls) underwent brain MR imaging. All pregnant women had their body composition (fat mass percentage) measured at ∼12 weeks of pregnancy. All neonates were scanned at ∼2 weeks of age during natural sleep without sedation, and their 3D T1-weighted images were postprocessed by the new iBEAT2.0 software. Brain MR imaging segmentation and cortical surface reconstruction and parcellation were completed using age-appropriate templates. Mean cortical thickness for 34 regions in each brain hemisphere defined by the UNC Neonatal Cortical Surface Atlas was measured, compared between groups, and correlated with maternal body fat mass percentage, controlled for neonate sex and race, postmenstrual age at MR imaging, maternal age at pregnancy, and the maternal intelligence quotient and education. Neonates born to obese mothers showed significantly lower (P ≤ .05, false discovery rate-corrected) cortical thickness in the left pars opercularis gyrus, left pars triangularis gyrus, and left rostral middle frontal gyrus. Mean cortical thickness in these frontal lobe regions negatively correlated (R = -0.34, P = .04; R = -0.50, P = .001; and R = -0.42, P = .01; respectively) with the maternal body fat mass percentage measured at early pregnancy. Maternal obesity during pregnancy is associated with lower neonate brain cortical thickness in several frontal lobe regions important for language and executive functions.

36.6 Brain Structural Trajectories in Youth at Familial Risk for Psychosis According to Development of Psychotic Spectrum Symptoms

The objective of this presentation is to evaluate child and adolescent offspring of patients with schizophrenia (SzO) or bipolar disorder (BpO) using clinical, cognitive, and structural MRI measures, in order to help understand changes taking place in the brain in individuals at heightened risk for disease during a key developmental period. A total of 128 individuals (33 SzO and 46 BpO, considered jointly as Familial High Risk [FHR], and 49 controls; aged 6-17 years) underwent clinical, cognitive, and neuroimaging assessment at baseline, 2-year follow-up, and 4-year follow-up. Twenty FHR participants (11 SzO and 9 BpO) developed psychotic spectrum symptoms during follow-up, while 59 FHR participants did not. MRI was performed using a 3Tesla scanner; cortical surface reconstruction was applied to measure cortical thickness, surface area, and grey matter volume. FHR participants who developed psychotic spectrum symptoms over time showed greater time-related mean cortical thinning than those who did not and than controls. By subgroups, this effect was present in both BpO and SzO in the occipital cortex. At baseline, FHR participants who developed psychotic spectrum symptoms over time had smaller total surface area and grey matter volume than those who did not and than controls. Over time, all FHR participants showed less longitudinal decrease in surface area than controls. In those who developed psychotic spectrum symptoms over time, this effect was driven by BpO, while in those who did not, this was due to SzO, who also showed less grey matter volume reduction. The emergence of psychotic spectrum symptoms in FHR was indexed by smaller cross-sectional surface area and progressive cortical thinning. Relative preservation of the surface area over time may signal different processes according to familial risk. These findings lay the foundation for future studies aimed at stratification of FHR youth.

Tractography patterns of pedunculopontine nucleus deep brain stimulation

Deep brain stimulation of the pedunculopontine nucleus is a promising surgical procedure for the treatment of Parkinsonian gait and balance dysfunction. It has, however, produced mixed clinical results that are poorly understood. We used tractography with the aim to rationalise this heterogeneity. A cohort of eight patients with postural instability and gait disturbance (Parkinson’s disease subtype) underwent pre-operative structural and diffusion MRI, then progressed to deep brain stimulation targeting the pedunculopontine nucleus. Pre-operative and follow-up assessments were carried out using the Gait and Falls Questionnaire, and Freezing of Gait Questionnaire. Probabilistic diffusion tensor tractography was carried out between the stimulating electrodes and both cortical and cerebellar regions of a priori interest. Cortical surface reconstructions were carried out to measure cortical thickness in relevant areas. Structural connectivity between stimulating electrode and precentral gyrus (r = 0.81, p = 0.01), Brodmann areas 1 (r = 0.78, p = 0.02) and 2 (r = 0.76, p = 0.03) were correlated with clinical improvement. A negative correlation was also observed for the superior cerebellar peduncle (r = −0.76, p = 0.03). Lower cortical thickness of the left parietal lobe and bilateral premotor cortices were associated with greater pre-operative severity of symptoms. Both motor and sensory structural connectivity of the stimulated surgical target characterises the clinical benefit, or lack thereof, from surgery. In what is a challenging region of brainstem to effectively target, these results provide insights into how this can be better achieved. The mechanisms of action are likely to have both motor and sensory components, commensurate with the probable nature of the underlying dysfunction.

Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising

Automatic cerebral cortical surface reconstruction is a useful tool for cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T1-weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-mm isotropic resolution for improved accuracy of cortical surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of cortical surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the cortical surface reconstruction resulting from denoised single-repetition sub-millimeter T1-weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0.6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ~0.016, high whole-brain averaged peak signal-to-noise ratio of ~33.5 dB and structural similarity index of ~0.92, and minimal gray matter–white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray matter–white matter surface placement, gray matter–cerebrospinal fluid surface placement and cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm—sufficiently accurate for most applications. These discrepancies were approximately one third to half of those from 1-mm isotropic resolution data. The denoising performance was equivalent to averaging ~2.5 repetitions of the data in terms of image similarity, and 1.6–2.2 repetitions in terms of the cortical surface placement accuracy. The scan-rescan variability of the cortical surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved cortical surface reconstruction at sub-millimeter resolution.