Computational neuroanatomy of baby brains: A review

Benkarim

et al. 2019

Human Brain Mapping

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Defining anatomically and functionally meaningful parcellation maps on cortical surface atlases is of great importance in surface‐based neuroimaging analysis. The conventional cortical parcellation maps are typically defined based on anatomical cortical folding landmarks in adult surface atlases. However, they are not suitable for fetal brain studies, due to dramatic differences in brain size, shape, and properties between adults and fetuses. To address this issue, we propose a novel data‐driven method for parcellation of fetal cortical surface atlases into distinct regions based on the dynamic “growth patterns” of cortical properties (e.g., surface area) from a population of fetuses. Our motivation is that the growth patterns of cortical properties indicate the underlying rapid changes of microstructures, which determine the molecular and functional principles of the cortex. Thus, growth patterns are well suitable for defining distinct cortical regions in development, structure, and function. To comprehensively capture the similarities of cortical growth patterns among vertices, we construct two complementary similarity matrices. One is directly based on the growth trajectories of vertices, and the other is based on the correlation profiles of vertices' growth trajectories in relation to a set of reference points. Then, we nonlinearly fuse these two similarity matrices into a single one, which can better capture both their common and complementary information than by simply averaging them. Finally, based on this fused similarity matrix, we perform spectral clustering to divide the fetal cortical surface atlases into distinct regions. By applying our method on 25 normal fetuses from 26 to 29 gestational weeks, we construct age‐specific fetal cortical surface atlases equipped with biologically meaningful parcellation maps based on cortical growth patterns. Importantly, our generated parcellation maps reveal spatially contiguous, hierarchical and bilaterally relatively symmetric patterns of fetal cortical surface development.

Section: Introductionmentioning

confidence: 99%

Fetal cortical surface atlas parcellation based on growth patterns

Xia

Benkarim

et al. 2019

Human Brain Mapping

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“…The infant cortical surface atlases constructed in this work are different from the traditional volumetric infant atlases (Evans et al, ), since they respect and leverage the inherent 2D topology of the highly convoluted and geometrically complex cortical surfaces. Comparing to the analysis directly using the volumetric atlas, the cortical surface atlas is more suitable for the highly folded cerebral cortex with a sheet‐like structure (Fischl, Sereno, Tootell, et al, ; Glasser et al, ; Hill et al, ; Li, Wang, Yap, et al, ; Van Essen & Dierker, ), especially for cortical thickness, myelination, and functional connectivity.…”

Section: Discussionmentioning

confidence: 99%

“…Comparing to the analysis directly using the volumetric atlas, the cortical surface atlas is more suitable for the highly folded cerebral cortex with a sheet-like structure (Fischl, Sereno, Tootell, et al, 1999;Glasser et al, 2016;Hill et al, 2010;Li, Wang, Yap, et al, 2019;Van Essen & Dierker, 2007), especially for cortical thickness, myelination, and functional connectivity.…”

Section: Applicationsmentioning

confidence: 99%

Construction of 4D infant cortical surface atlases with sharp folding patterns via spherical patch‐based group‐wise sparse representation

Lin

et al. 2019

Human Brain Mapping

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4D (spatial + temporal) infant cortical surface atlases covering dense time points are highly needed for understanding dynamic early brain development. In this article, we construct a set of 4D infant cortical surface atlases with longitudinally consistent and sharp cortical attribute patterns at 11 time points in the first six postnatal years, that is, at 1, 3, 6, 9, 12, 18, 24, 36, 48, 60, and 72 months of age, which is targeted for better normalization of the dynamic changing early brain cortical surfaces. To ensure longitudinal consistency and unbiasedness, we adopt a two-stage group-wise surface registration. To preserve sharp cortical attribute patterns on the atlas, instead of simply averaging over the coregistered cortical surfaces, we leverage a spherical patch-based sparse representation using the augmented dictionary to overcome the potential registration errors. Our atlases provide not only geometric attributes of the cortical folding, but also cortical thickness and myelin content. Therefore, to address the consistency across different cortical attributes on the atlas, instead of sparsely representing each attribute independently, we jointly represent all cortical attributes with a group-wise sparsity constraint. In addition, to further facilitate region-based analysis using our atlases, we have also provided two widely used parcellations, that is, FreeSurfer parcellation and multimodal parcellation, on our 4D infant cortical surface atlases. Compared to cortical surface atlases constructed with other methods, our cortical surface atlases preserve sharper cortical folding attribute patterns, thus leading to better accuracy in registration of individual infant cortical surfaces to the atlas.

“…However, most of the autistic subjects involved in previous studies are 2+ years old. In fact, the first year of postnatal development represents the most drastic postnatal developing phase, with a rapid tissue growth and milestones of a wide range of cognitive and motor functions [7]. This early period is critical in neurodevelopmental disorders such as ASD [8].…”

Section: Introductionmentioning

confidence: 99%

Volume-Based Analysis of 6-Month-Old Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis

Lecture Notes in Computer Science

Shi

et al. 2018

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Autism spectrum disorder (ASD) is mainly diagnosed by the observation of core behavioral symptoms. Due to the absence of early biomarkers to detect infants either with or at-risk of ASD during the first postnatal year of life, diagnosis must rely on behavioral observations long after birth. As a result, the window of opportunity for effective intervention may have passed when the disorder is detected. Therefore, it is clinically urgent to identify imaging-based biomarkers for early diagnosis and intervention. In this paper, for the first time, we proposed a volume-based analysis of infant subjects with risk of ASD at very early age, i.e., as early as at 6 months of age. A critical part of volume-based analysis is to accurately segment 6-month-old infant brain MRI scans into different regions of interest, e.g., white matter, gray matter, and cerebrospinal fluid. This is actually very challenging since the tissue contrast at 6-month-old is extremely low, caused by inherent ongoing myelination and maturation. To address this challenge, we propose an anatomy-guided, densely-connected network for accurate tissue segmentation. Based on tissue segmentations, we further perform brain parcellation and statistical analysis to identify those significantly different regions between autistic and normal subjects. Experimental results on National Database for Autism Research (NDAR) show the advantages of our proposed method in terms of both segmentation accuracy and diagnosis accuracy over state-of-the-art results.