Diffusion-tensor-imaging 1-year-old and 2-year-old infant brain atlases with comprehensive gray and white matter labels.

Human infancy is marked by fastest postnatal brain structural changes. It also coincides with the onset of many neurodevelopmental disorders. Atlas-based automated structure labeling has been widely used for analyzing various neuroimaging data. However, the relatively large and nonlinear neuroanatomical differences between infant and adult brains can lead to significant offsets of the labeled structures in infant brains when adult brain atlas is used. Age-specific 1- and 2-year-old brain atlases covering all major gray and white matter (GM and WM) structures with diffusion tensor imaging (DTI) and structural MRI are critical for precision medicine for infant population yet have not been established. In this study, high-quality DTI and structural MRI data were obtained from 50 healthy children to build up three-dimensional age-specific 1- and 2-year-old brain templates and atlases. Age-specific templates include a single-subject template as well as two population-averaged templates from linear and nonlinear transformation, respectively. Each age-specific atlas consists of 124 comprehensively labeled major GM and WM structures, including 52 cerebral cortical, 10 deep GM, 40 WM, and 22 brainstem and cerebellar structures. When combined with appropriate registration methods, the established atlases can be used for highly accurate automatic labeling of any given infant brain MRI. We demonstrated that one can automatically and effectively delineate deep WM microstructural development from 3 to 38 months by using these age-specific atlases. These established 1- and 2-year-old infant brain DTI atlases can advance our understanding of typical brain development and serve as clinical anatomical references for brain disorders during infancy.

Neural correlates of aftereffects induced by adaptations to single and average durations.

Duration perception can be heavily distorted owing to repetitive exposure to a relatively long or short sensory event, often causing a duration aftereffect. Here, we used a novel procedure to show that adaptations to both single and average durations produced the duration aftereffect. Participants completed a duration reproduction task (Experiment 1) or a duration category rating task (Experiment 2) after long-term adaptations to a stimulus of medium duration and to stimuli of averagely medium duration. We found that adaptations to both single and average durations resulted in duration aftereffects. The simultaneously recorded functional magnetic resonance imaging (fMRI) data revealed that the reduction in neural activity due to long-term adaptation to single duration was observed in the right supramarginal gyrus (SMG) of the parietal lobe, while adaptation to average duration resulted in fMRI adaptations in the left postcentral gyrus (PCG) and middle cingulate gyrus (MCG). At the individual level, the magnitude of the behavioral aftereffect was positively correlated with the magnitude of fMRI adaptation in the right SMG after adaptation to single duration, while there were no significantly positive correlations between the behavioral aftereffect and fMRI adaptations in the left PCG and MCG. These results suggest that there are different neural mechanisms for aftereffects caused by adaptations to single and average durations.

Infant brain regional cerebral blood flow increases supporting emergence of the default-mode network.

Human infancy is characterized by most rapid regional cerebral blood flow (rCBF) increases across lifespan and emergence of a fundamental brain system default-mode network (DMN). However, how infant rCBF changes spatiotemporally across the brain and how the rCBF increase supports emergence of functional networks such as DMN remains unknown. Here, by acquiring cutting-edge multi-modal MRI including pseudo-continuous arterial-spin-labeled perfusion MRI and resting-state functional MRI of 48 infants cross-sectionally, we elucidated unprecedented 4D spatiotemporal infant rCBF framework and region-specific physiology-function coupling across infancy. We found that faster rCBF increases in the DMN than visual and sensorimotor networks. We also found strongly coupled increases of rCBF and network strength specifically in the DMN, suggesting faster local blood flow increase to meet extraneuronal metabolic demands in the DMN maturation. These results offer insights into the physiological mechanism of brain functional network emergence and have important implications in altered network maturation in brain disorders.

Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks.

Brain network parcellation based on resting-state functional MRI (rs-fMRI) is affected by noise, resulting in spurious small patches and decreased functional homogeneity within each network. Obtaining robust and homogeneous parcellation of neonate brain is more difficult, because neonate rs-fMRI is associated with relatively higher level of noise and no prior knowledge from a functional neonate atlas is available as spatial constraints. To meet these challenges, we developed a novel data-driven Regularized Normalized-cut (RNcut) method. RNcut is formulated by adding two regularization terms, a smoothing term using Markov random fields and a small-patch removal term, to conventional normalized-cut (Ncut) method. The RNcut and competing methods were tested with simulated datasets with known ground truth and then applied to both adult and neonate rs-fMRI datasets. Based on the parcellated networks generated by RNcut, intra-network connectivity was quantified. The test results from simulated datasets demonstrated that the RNcut method is more robust (p < 0.01) to noise and can delineate parcellated functional networks with significantly better (p < 0.01) spatial contiguity and significantly higher (p < 0.01) functional homogeneity than competing methods. Application of RNcut to neonate and adult rs-fMRI dataset revealed distinctive functional brain organization of neonate brains from that of adult brains. Collectively, we developed a novel data-driven RNcut method by integrating conventional Ncut with two regularization terms, generating robust and homogeneous functional parcellation without imposing spatial constraints. A broad range of brain network applications and analyses, especially neonate and infant brain parcellation with noisy and large sample of datasets, can potentially benefit from this RNcut method.

Novel missense variant in TTN cosegregating with familial atrioventricular block.

BACKGROUND: Cardiovascular diseases are the most common cause of death globally. In which atrioventricular block (AVB) is a common disorder with genetic causes, but the responsible genes have not been fully identified yet. To determine the underlying causative genes involved in cardiac AVB, here we report a three-generation Chinese family with severe autosomal dominant cardiac AVB that has been ruled out as being caused by known genes mutations. METHODS: Whole-exome sequencing was performed in five affected family members across three generations, and co-segregation analysis was validated on other members of this family. RESULTS: Whole-exome sequencing and subsequent co-segregation validation identified a novel germline heterozygous point missense mutation, c.49287C > A (p.N16429K), in the titin (TTN, NM_001267550.2) gene in all 5 affected family members but not in the unaffected family members, neither in the large population according to the Genome Aggregation Database (https://gnomad.broadinstitute.org/). The point mutation is predicted to be functionally deleterious by in-silico software tools. Our finding was further supported by the conservative analysis across species. CONCLUSION: Based on this study, TTN was identified as a potential novel candidate gene for autosomal dominant AVB; this study expands the mutational spectrum of TTN gene and is the first to implicate TTN mutations as AVB disease causing in a Chinese pedigree.

Differential White Matter Maturation from Birth to 8 Years of Age.

Comprehensive delineation of white matter (WM) microstructural maturation from birth to childhood is critical for understanding spatiotemporally differential circuit formation. Without a relatively large sample of datasets and coverage of critical developmental periods of both infancy and early childhood, differential maturational charts across WM tracts cannot be delineated. With diffusion tensor imaging (DTI) of 118 typically developing (TD) children aged 0-8 years and 31 children with autistic spectrum disorder (ASD) aged 2-7 years, the microstructure of every major WM tract and tract group was measured with DTI metrics to delineate differential WM maturation. The exponential model of microstructural maturation of all WM was identified. The WM developmental curves were separated into fast, intermediate, and slow phases in 0-8 years with distinctive time period of each phase across the tracts. Shorter periods of the fast and intermediate phases in certain tracts, such as the commissural tracts, indicated faster earlier development. With TD WM maturational curves as the reference, higher residual variance of WM microstructure was found in children with ASD. The presented comprehensive and differential charts of TD WM microstructural maturation of all major tracts and tract groups in 0-8 years provide reference standards for biomarker detection of neuropsychiatric disorders.

Delineation of early brain development from fetuses to infants with diffusion MRI and beyond.

Dynamic macrostructural and microstructural changes take place from the mid-fetal stage to 2 years after birth. Delineating structural changes of the brain during early development provides new insights into the complicated processes of both typical development and the pathological mechanisms underlying various psychiatric and neurological disorders including autism, attention deficit hyperactivity disorder and schizophrenia. Decades of histological studies have identified strong spatial and functional maturation gradients in human brain gray and white matter. The recent improvements in magnetic resonance imaging (MRI) techniques, especially diffusion MRI (dMRI), relaxometry imaging, and magnetization transfer imaging (MTI) have provided unprecedented opportunities to non-invasively quantify and map the early developmental changes at whole brain and regional levels. Here, we review the recent advances in understanding early brain structural development during the second half of gestation and the first two postnatal years using modern MR techniques. Specifically, we review studies that delineate the emergence and microstructural maturation of white matter tracts, as well as dynamic mapping of inhomogeneous cortical microstructural organization unique to fetuses and infants. These imaging studies converge into maturational curves of MRI measurements that are distinctive across different white matter tracts and cortical regions. Furthermore, contemporary models offering biophysical interpretations of the dMRI-derived measurements are illustrated to infer the underlying microstructural changes. Collectively, this review summarizes findings that contribute to charting spatiotemporally heterogeneous gray and white matter structural development, offering MRI-based biomarkers of typical brain development and setting the stage for understanding aberrant brain development in neurodevelopmental disorders.

Brain white matter microstructural alterations in children of type I Gaucher disease characterized with diffusion tensor MR imaging.

OBJECTIVES: To investigate white matter (WM) microstructural alterations in type I Gaucher disease (type I GD) pediatric patients and explore the correlation between the disease duration and WM changes. METHODS: Twenty-two GD patients and twenty-two sex- and age-matched typical development (TD) children were recruited. Changes in WM were investigated using diffusion tensor imaging (DTI) and applying atlas-based tract analysis. For all DTI measurements, independent-samples t-test was applied to report significant differences between type I GD and TD. Partial correlation was applied to determine whether the disease duration was correlated with DTI measurements. RESULTS: Bidirectional fractional anisotropy (FA) changes were found in the bilateral superior cerebellar peduncle, right posterior limb of the internal capsule, right posterior corona radiata, and right posterior thalamic radiation (p < 0.05). Higher mean diffusivity (MD)was found in the right superior corona radiata, middle cerebellar peduncle, right posterior thalamic radiation and right superior longitudinal fasciculus (p < 0.05) in type I GD. And higher radial diffusivity (RD) was also found in the left superior cerebellar peduncle (p < 0.05) in type I GD. The disease duration of type I GD patients is positively correlated with axial diffusivity and MD in multiple major WM tracts. CONCLUSION: DTI findings supported the microstructural alterations of multiple WM tracts in type I GD patients.

Perceptual Learning of Contrast Detection in the Human Lateral Geniculate Nucleus.

The brain is continuously modified by perceptual experience throughout life. Perceptual learning, which refers to the long-term performance improvement resulting from practice, has been widely used as a paradigm to study experience-dependent brain plasticity in adults [1, 2]. In the visual system, adult plasticity is largely believed to be restricted to the cortex, with subcortical structures losing their capacity for change after a critical period of development [3, 4]. Although various cortical mechanisms have been shown to mediate visual perceptual learning [5-12], there has been no reported investigation of perceptual learning in subcortical nuclei. Here, human subjects were trained on a contrast detection task for 30 days, leading to a significant contrast sensitivity improvement that was specific to the trained eye and the trained visual hemifield. Training also resulted in an eye- and hemifield-specific fMRI signal increase to low-contrast patterns in the magnocellular layers of the lateral geniculate nucleus (LGN), even when subjects did not pay attention to the patterns. Such an increase was absent in the parvocellular layers of the LGN and visual cortical areas. Furthermore, the behavioral benefit significantly correlated with the neural enhancement. These findings suggest that LGN signals can be amplified by training to detect faint patterns. Neural plasticity induced by perceptual learning in human adults might not be confined to the cortical level but might occur as early as at the thalamic level.