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.

Development of neonatal connectome dynamics and its prediction for cognitive and language outcomes at age 2.

The functional brain connectome is highly dynamic over time. However, how brain connectome dynamics evolves during the third trimester of pregnancy and is associated with later cognitive growth remains unknown. Here, we use resting-state functional Magnetic Resonance Imaging (MRI) data from 39 newborns aged 32 to 42 postmenstrual weeks to investigate the maturation process of connectome dynamics and its role in predicting neurocognitive outcomes at 2 years of age. Neonatal brain dynamics is assessed using a multilayer network model. Network dynamics decreases globally but increases in both modularity and diversity with development. Regionally, module switching decreases with development primarily in the lateral precentral gyrus, medial temporal lobe, and subcortical areas, with a higher growth rate in primary regions than in association regions. Support vector regression reveals that neonatal connectome dynamics is predictive of individual cognitive and language abilities at 2  years of age. Our findings highlight network-level neural substrates underlying early cognitive development.

Spatiotemporal cerebral blood flow dynamics underlies emergence of the limbic-sensorimotor-association cortical gradient in human infancy.

Infant cerebral blood flow (CBF) delivers nutrients and oxygen to fulfill brain energy consumption requirements for the fastest period of postnatal brain development across lifespan. However, organizing principle of whole-brain CBF dynamics during infancy remains obscure. Leveraging a unique cohort of 100+ infants with high-resolution arterial spin labeled MRI, we found the emergence of the cortical hierarchy revealed by highest-resolution infant CBF maps available to date. Infant CBF across cortical regions increased in a biphasic pattern with initial rapid and sequentially slower rate, with break-point ages increasing along the limbic-sensorimotor-association cortical gradient. Increases in CBF in sensorimotor cortices were associated with enhanced language and motor skills, and frontoparietal association cortices for cognitive skills. The study discovered emergence of the hierarchical limbic-sensorimotor-association cortical gradient in infancy, and offers standardized reference of infant brain CBF and insight into the physiological basis of cortical specialization and real-world infant developmental functioning.

Development of sensorimotor-visual connectome gradient at birth predicts neurocognitive outcomes at 2 years of age.

Functional connectome gradients represent fundamental organizing principles of the brain. Here, we report the development of the connectome gradients in preterm and term babies aged 31-42 postmenstrual weeks using task-free functional MRI and its association with postnatal cognitive growth. We show that the principal sensorimotor-to-visual gradient is present during the late preterm period and continuously evolves toward a term-like pattern. The global measurements of this gradient, characterized by explanation ratio, gradient range, and gradient variation, increased with age (p < 0.05, corrected). Focal gradient development mainly occurs in the sensorimotor, lateral, and medial parietal regions, and visual regions (p < 0.05, corrected). The connectome gradient at birth predicts cognitive and language outcomes at 2-year follow-up (p < 0.005). These results are replicated using an independent dataset from the Developing Human Connectome Project. Our findings highlight early emergent rules of the brain connectome gradient and their implications for later cognitive growth.

Association between ASL MRI-derived cerebral blood flow and outcomes after pediatric cardiac arrest.

AIM: Cerebral blood flow (CBF) is dysregulated after cardiac arrest. It is unknown if post-arrest CBF is associated with outcome. We aimed to determine the association of CBF derived from arterial spin labelling (ASL) MRI with outcome after pediatric cardiac arrest. METHODS: Retrospective observational study of patients ≤18 years who had a clinically obtained brain MRI within 7 days of cardiac arrest between June 2005 and December 2019. Primary outcome was unfavorable neurologic status: change in Pediatric Cerebral Performance Category (PCPC) ≥1 from pre-arrest that resulted in hospital discharge PCPC 3-6. We measured CBF in whole brain and regions of interest (ROIs) including frontal, parietal, and temporal cortex, caudate, putamen, thalamus, and brainstem using pulsed ASL. We compared CBF between outcome groups using Wilcoxon Rank-Sum and performed logistic regression to associate each region's CBF with outcome, accounting for age, sex, and time between arrest and MRI. RESULTS: Forty-eight patients were analyzed (median age 2.8 [IQR 0.95, 8.8] years, 65% male). Sixty-nine percent had unfavorable outcome. Time from arrest to MRI was 4 [3,5] days and similar between outcome groups (p = 0.39). Whole brain median CBF was greater for unfavorable compared to favorable groups (28.3 [20.9,33.0] vs. 19.6 [15.3,23.1] ml/100 g/min, p = 0.007), as was CBF in individual ROIs. Greater CBF in the whole brain and individual ROIs was associated with higher odds of unfavorable outcome after controlling for age, sex, and days from arrest to MRI (aOR for whole brain 19.08 [95% CI 1.94, 187.41]). CONCLUSION: CBF measured 3-5 days after pediatric cardiac arrest by ASL MRI was independently associated with unfavorable outcome.

Automating Angle Measurements on Foot Radiographs in Young Children: Feasibility and Performance of a Convolutional Neural Network Model.

Measurement of angles on foot radiographs is an important step in the evaluation of malalignment. The objective is to develop a CNN model to measure angles on radiographs, using radiologists' measurements as the reference standard. This IRB-approved retrospective study included 450 radiographs from 216 patients (< 3 years of age). Angles were automatically measured by means of image segmentation followed by angle calculation, according to Simon's approach for measuring pediatric foot angles. A multiclass U-Net model with a ResNet-34 backbone was used for segmentation. Two pediatric radiologists independently measured anteroposterior and lateral talocalcaneal and talo-1st metatarsal angles using the test dataset and recorded the time used for each study. Intraclass correlation coefficients (ICC) were used to compare angle and paired Wilcoxon signed-rank test to compare time between radiologists and the CNN model. There was high spatial overlap between manual and CNN-based automatic segmentations with dice coefficients ranging between 0.81 (lateral 1st metatarsal) and 0.94 (lateral calcaneus). Agreement was higher for angles on the lateral view when compared to the AP view, between radiologists (ICC: 0.93-0.95, 0.85-0.92, respectively) and between radiologists' mean and CNN calculated (ICC: 0.71-0.73, 0.41-0.52, respectively). Automated angle calculation was significantly faster when compared to radiologists' manual measurements (3 ± 2 vs 114 ± 24 s, respectively; P < 0.001). A CNN model can selectively segment immature ossification centers and automatically calculate angles with a high spatial overlap and moderate to substantial agreement when compared to manual methods, and 39 times faster.

A Patch-Based Deep Learning Approach for Detecting Rib Fractures on Frontal Radiographs in Young Children.

Chest radiography is the modality of choice for the identification of rib fractures in young children and there is value for the development of computer-aided rib fracture detection in this age group. However, the automated identification of rib fractures on chest radiographs can be challenging due to the need for high spatial resolution in deep learning frameworks. A patch-based deep learning algorithm was developed to automatically detect rib fractures on frontal chest radiographs in children under 2 years old. A total of 845 chest radiographs of children 0-2 years old (median: 4 months old) were manually segmented for rib fractures by radiologists and served as the ground-truth labels. Image analysis utilized a patch-based sliding-window technique, to meet the high-resolution requirements for fracture detection. Standard transfer learning techniques used ResNet-50 and ResNet-18 architectures. Area-under-curve for precision-recall (AUC-PR) and receiver-operating-characteristic (AUC-ROC), along with patch and whole-image classification metrics, were reported. On the test patches, the ResNet-50 model showed AUC-PR and AUC-ROC of 0.25 and 0.77, respectively, and the ResNet-18 showed an AUC-PR of 0.32 and AUC-ROC of 0.76. On the whole-radiograph level, the ResNet-50 had an AUC-ROC of 0.74 with 88% sensitivity and 43% specificity in identifying rib fractures, and the ResNet-18 had an AUC-ROC of 0.75 with 75% sensitivity and 60% specificity in identifying rib fractures. This work demonstrates the utility of patch-based analysis for detection of rib fractures in children under 2 years old. Future work with large cohorts of multi-institutional data will improve the generalizability of these findings to patients with suspicion of child abuse.

Deep learning-based prediction of rib fracture presence in frontal radiographs of children under two years of age: a proof-of-concept study.

OBJECTIVE: In this proof-of-concept study, we aimed to develop deep-learning-based classifiers to identify rib fractures on frontal chest radiographs in children under 2 years of age. METHODS: This retrospective study included 1311 frontal chest radiographs (radiographs with rib fractures, n = 653) from 1231 unique patients (median age: 4 m). Patients with more than one radiograph were included only in the training set. A binary classification was performed to identify the presence or absence of rib fractures using transfer learning and Resnet-50 and DenseNet-121 architectures. The area under the receiver operating characteristic curve (AUC-ROC) was reported. Gradient-weighted class activation mapping was used to highlight the region most relevant to the deep learning models' predictions. RESULTS: On the validation set, the ResNet-50 and DenseNet-121 models obtained an AUC-ROC of 0.89 and 0.88, respectively. On the test set, the ResNet-50 model demonstrated an AUC-ROC of 0.84 with a sensitivity of 81% and specificity of 70%. The DenseNet-50 model obtained an AUC of 0.82 with 72% sensitivity and 79% specificity. CONCLUSION: In this proof-of-concept study, a deep learning-based approach enabled the automatic detection of rib fractures in chest radiographs of young children with performances comparable to pediatric radiologists. Further evaluation of this approach on large multi-institutional data sets is needed to assess the generalizability of our results. ADVANCES IN KNOWLEDGE: In this proof-of-concept study, a deep learning-based approach performed well in identifying chest radiographs with rib fractures. These findings provide further impetus to develop deep learning algorithms for identifying rib fractures in children, especially those with suspected physical abuse or non-accidental trauma.

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.

Disrupted white matter microstructure correlates with impulsivity in children and adolescents with bipolar disorder.

Altered white matter (WM) microstructure likely occurs in children with bipolar disorder (BD) with impulsivity representing one of the core features. However, altered WM microstructures and their age-related trendlines in children with BD and those at high-risk of developing BD, as well as correlations of WM microstructures with impulsivity, have been poorly investigated. In this study, diffusion MRI, cognitive, and impulsivity assessments were obtained from children/adolescents diagnosed with BD, offspring of individuals with BD (high-risk BD) and age-matched healthy controls. A novel atlas-based WM skeleton measurement approach was used to quantify WM microstructural integrity with all diffusion-tensor-imaging (DTI) metrics including fractional anisotropy, axial, mean and radial diffusivity to survey entire WM tracts and ameliorate partial volume effects. Among all DTI-derived metric measures, radial diffusivity quantifying WM myelination was found significantly higher primarily in corpus callosum and in the corona radiata in children with BD compared to controls. Distinguished from age-related progressively decreasing diffusivities and increasing fractional anisotropy in healthy controls, flattened age-related trendlines were found in BD group, and intermediate developmental rates were observed in high-risk group. Larger radial diffusivity in the corpus callosum and corona radiata significantly correlated with shorter response times to affective words that indicate higher impulsivity in the BD group, whereas no such correlation was found in the healthy control group. This work corroborates the progressive nature of pediatric BD and suggests that WM microstructural disruption involved in affective regulation and sensitive to impulsivity may serve as a biomarker of pediatric BD progression.

Association Between Quantitative Diffusion Weighted Magnetic Resonance Neuroimaging and Outcome After Pediatric Cardiac Arrest.

BACKGROUND AND OBJECTIVES: Diffusion magnetic resonance imaging (MRI) can quantify extent of hypoxic-ischemic brain injury after cardiac arrest. Our objective was to determine the association between adult-derived threshold of apparent diffusion coefficient (ADC) <650x10-6mm2/s in >10% of brain tissue and unfavorable outcome after pediatric cardiac arrest. Since ADC decreases exponentially as a function of increasing age, we determined association 1) having >10% of brain tissue below a novel age-dependent ADC threshold, and 2) age-normalized whole brain mean ADC and unfavorable outcome. METHODS: Retrospective study of patients ≤18 years old who had cardiac arrest and a clinically obtained brain MRI within 7 days. Primary outcome was unfavorable neurologic status at hospital discharge based on Pediatric Cerebral Performance Category (PCPC) score. ADC images were extracted from three-direction diffusion imaging. We determined whether each patient had >10% of voxels with ADC below prespecified thresholds. We computed whole brain mean ADC for each patient. RESULTS: One-hundred-thirty-four patients were analyzed. Patients with ADC <650x10-6mm2/s in >10% of voxels had 15 times higher odds (95%CI 5, 65) of unfavorable outcome compared to patients with ADC <650x10-6mm2/s (AUROC 0.72 [95%CI 0.63, 0.80]). This ADC criteria had a sensitivity and specificity of 0.49 and 0.94, and positive and negative predictive values of 0.93 and 0.52 for unfavorable outcome. The age-dependent ADC threshold that yielded optimal sensitivity and specificity for unfavorable outcome was <300x10-6mm2/s below each patient's predicted whole brain mean ADC. The sensitivity, specificity, positive and negative predictive values for this ADC threshold were 0.53, 0.96, 0.96, and 0.54, respectively (OR: 26.4 [95%CI 7.5, 168.3]; AUROC 0.74 [95%CI 0.66, 0.83]). Lower age-normalized whole brain mean ADC was also associated with unfavorable outcome (OR 0.42 [0.24, 0.64], AUROC 0.76 [95%CI 0.66, 0.82]). DISCUSSION: Quantitative diffusion thresholds on MRI within 7 days after cardiac arrest were associated with unfavorable outcome in children. Age-independent ADC threshold was highly specific for predicting unfavorable outcome. However, specificity and sensitivity increased when using age-dependent ADC thresholds. Age-dependent ADC thresholds may improve prognostic accuracy and require further investigation in larger cohorts. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that quantitative diffusion-weighted imaging (DWI) within 7 days post-arrest can predict an unfavorable clinical outcome in children.

How segmentation methods affect hippocampal radiomic feature accuracy in Alzheimer's disease analysis?

OBJECTIVES: Hippocampal radiomic features (HRFs) can serve as biomarkers in Alzheimer's disease (AD). However, how different hippocampal segmentation methods affect HRFs in AD is still unknown. The aim of the study was to investigate how different segmentation methods affect HRF accuracy in AD analysis. METHODS: A total of 1650 subjects were identified from the Alzheimer's Disease Neuroimaging Initiative database (ADNI). The mini-mental state examination (MMSE) and Alzheimer's disease assessment scale (ADAS-cog13) were also adopted. After calculating the HRFs of intensity, shape, and textural features from each side of the hippocampus in structural magnetic resonance imaging (sMRI), the consistency of HRFs calculated from 7 different hippocampal segmentation methods was validated, and the performance of machine learning-based classification of AD vs. normal control (NC) adopting the different HRFs was also examined. Additional 571 subjects from the European DTI Study on Dementia database (EDSD) were to validate the consistency of results. RESULTS: Between different segmentations, HRFs showed a high measurement consistency (R > 0.7), a high significant consistency between NC, mild cognitive impairment (MCI), and AD (T-value plot, R > 0.8), and consistent significant correlations between HRFs and MMSE/ADAS-cog13 (p < 0.05). The best NC vs. AD classification was obtained when the hippocampus was sufficiently segmented by primitive majority voting (threshold = 0.2). High consistent results were reproduced from independent EDSD cohort. CONCLUSIONS: HRFs exhibited high consistency across different hippocampal segmentation methods, and the best performance in AD classification was obtained when HRFs were extracted by the naïve majority voting method with a more sufficient segmentation and relatively low hippocampus segmentation accuracy. KEY POINTS: • The hippocampal radiomic features exhibited high measurement/statistical/clinical consistency across different hippocampal segmentation methods. • The best performance in AD classification was obtained when hippocampal radiomics were extracted by the naïve majority voting method with a more sufficient segmentation and relatively low hippocampus segmentation accuracy.

Deficits in Seizure Threshold and Other Behaviors in Adult Mice without Gross Neuroanatomic Injury after Late Gestation Transient Prenatal Hypoxia.

Intrauterine hypoxia is a common cause of brain injury in children resulting in a broad spectrum of long-term neurodevelopmental sequela, including life-long disabilities that can occur even in the absence of severe neuroanatomic damage. Postnatal hypoxia-ischemia rodent models are commonly used to understand the effects of ischemia and transient hypoxia on the developing brain. Postnatal models, however, have some limitations. First, they do not test the impact of placental pathologies on outcomes from hypoxia. Second, they primarily recapitulate severe injury because they provoke substantial cell death, which is not seen in children with mild hypoxic injury. Lastly, they do not model preterm hypoxic injury. Prenatal models of hypoxia in mice may allow us to address some of these limitations to expand our understanding of developmental brain injury. The published rodent models of prenatal hypoxia employ multiple days of hypoxic exposure or complicated surgical procedures, making these models challenging to perform consistently in mice. Furthermore, large animal models suggest that transient prenatal hypoxia without ischemia is sufficient to lead to significant functional impairment to the developing brain. However, these large animal studies are resource-intensive and not readily amenable to mechanistic molecular studies. Therefore, here we characterized the effect of late gestation (embryonic day 17.5) transient prenatal hypoxia (5% inspired oxygen) on long-term anatomical and neurodevelopmental outcomes in mice. Late gestation transient prenatal hypoxia increased hypoxia-inducible factor 1 alpha protein levels (a marker of hypoxic exposure) in the fetal brain. Hypoxia exposure predisposed animals to decreased weight at postnatal day 2, which normalized by day 8. However, hypoxia did not affect gestational age at birth, litter size at birth, or pup survival. No differences in fetal brain cell death or long-term gray or white matter changes resulted from hypoxia. Animals exposed to prenatal hypoxia did have several long-term functional consequences, including sex-dichotomous changes. Hypoxia exposure was associated with a decreased seizure threshold and abnormalities in hindlimb strength and repetitive behaviors in males and females. Males exposed to hypoxia had increased anxiety-related deficits, whereas females had deficits in social interaction. Neither sex developed any motor or visual learning deficits. This study demonstrates that late gestation transient prenatal hypoxia in mice is a simple, clinically relevant paradigm for studying putative environmental and genetic modulators of the long-term effects of hypoxia on the developing brain.

Flattened Structural Network Changes and Association of Hyperconnectivity With Symptom Severity in 2-7-Year-Old Children With Autism.

Understanding the brain differences present at the earliest possible diagnostic age for autism spectrum disorder (ASD) is crucial for delineating the underlying neuropathology of the disorder. However, knowledge of brain structural network changes in the early important developmental period between 2 and 7 years of age is limited in children with ASD. In this study, we aimed to fill the knowledge gap by characterizing age-related brain structural network changes in ASD from 2 to 7 years of age, and identify sensitive network-based imaging biomarkers that are significantly correlated with the symptom severity. Diffusion MRI was acquired in 30 children with ASD and 21 typically developmental (TD) children. With diffusion MRI and quantified clinical assessment, we conducted network-based analysis and correlation between graph-theory-based measurements and symptom severity. Significant age-by-group interaction was found in global network measures and nodal efficiencies during the developmental period of 2-7 years old. Compared with significant age-related growth of the structural network in TD, relatively flattened maturational trends were observed in ASD. Hyper-connectivity in the structural network with higher global efficiency, global network strength, and nodal efficiency were observed in children with ASD. Network edge strength in ASD also demonstrated hyper-connectivity in widespread anatomical connections, including those in default-mode, frontoparietal, and sensorimotor networks. Importantly, identified higher nodal efficiencies and higher network edge strengths were significantly correlated with symptom severity in ASD. Collectively, structural networks in ASD during this early developmental period of 2-7 years of age are characterized by hyper-connectivity and slower maturation, with aberrant hyper-connectivity significantly correlated with symptom severity. These aberrant network measures may serve as imaging biomarkers for ASD from 2 to 7 years of age.

Diffusion-MRI-based regional cortical microstructure at birth for predicting neurodevelopmental outcomes of 2-year-olds.

Cerebral cortical architecture at birth encodes regionally differential dendritic arborization and synaptic formation. It underlies behavioral emergence of 2-year-olds. Brain changes in 0-2 years are most dynamic across the lifespan. Effective prediction of future behavior with brain microstructure at birth will reveal structural basis of behavioral emergence in typical development and identify biomarkers for early detection and tailored intervention in atypical development. Here we aimed to evaluate the neonate whole-brain cortical microstructure quantified by diffusion MRI for predicting future behavior. We found that individual cognitive and language functions assessed at the age of 2 years were robustly predicted by neonate cortical microstructure using support vector regression. Remarkably, cortical regions contributing heavily to the prediction models exhibited distinctive functional selectivity for cognition and language. These findings highlight regional cortical microstructure at birth as a potential sensitive biomarker in predicting future neurodevelopmental outcomes and identifying individual risks of brain disorders.

Cerebral Pulsed Arterial Spin Labeling Perfusion Weighted Imaging Predicts Language and Motor Outcomes in Neonatal Hypoxic-Ischemic Encephalopathy.

Rationale and Objectives: To compare cerebral pulsed arterial spin labeling (PASL) perfusion among controls, hypoxic ischemic encephalopathy (HIE) neonates with normal conventional MRI(HIE/MRI⊕), and HIE neonates with abnormal conventional MRI(HIE/MRI⊖). To create a predictive machine learning model of neurodevelopmental outcomes using cerebral PASL perfusion. Materials and Methods: A total of 73 full-term neonates were evaluated. The cerebral perfusion values were compared by permutation test to identify brain regions with significant perfusion changes among 18 controls, 40 HIE/MRI⊖ patients, and 15 HIE/MRI⊕ patients. A machine learning model was developed to predict neurodevelopmental outcomes using the averaged perfusion in those identified brain regions. Results: Significantly decreased PASL perfusion in HIE/MRI⊖ group, when compared with controls, were found in the anterior corona radiata, caudate, superior frontal gyrus, precentral gyrus. Both significantly increased and decreased cerebral perfusion changes were detected in HIE/MRI⊕ group, when compared with HIE/MRI⊖ group. There were no significant perfusion differences in the cerebellum, brainstem and deep structures of thalamus, putamen, and globus pallidus among the three groups. The machine learning model demonstrated significant correlation (p < 0.05) in predicting language(r = 0.48) and motor(r = 0.57) outcomes in HIE/MRI⊖ patients, and predicting language(r = 0.76), and motor(r = 0.53) outcomes in an additional group combining HIE/MRI⊖ and HIE/MRI⊕. Conclusion: Perfusion MRI can play an essential role in detecting HIE regardless of findings on conventional MRI and predicting language and motor outcomes in HIE survivors. The perfusion changes may also reveal important insights into the reperfusion response and intrinsic autoregulatory mechanisms. Our results suggest that perfusion imaging may be a useful adjunct to conventional MRI in the evaluation of HIE in clinical practice.

Altered structural cerebral cortex in children with Tourette syndrome.

PURPOSE: In this study, we used magnetic resonance imaging (MRI) to investigate the anatomical alterations of cerebral cortex in children with Tourette syndrome (TS) and explore whether such deficits were related with their clinical symptoms. METHODS: All subjects were scanned in a 3.0T MRI scanner with three-dimensional T1-weighted images (3DT1WI). Then, some surface-based features were extracted by using the FreeSurfer software. After that, the between-group differences of those features were assessed. RESULTS: Sixty TS patients and 52 age- and gender-matched healthy control were included in this study. Surface-based analyses revealed altered cortical thickness, cortical sulcus, cortical curvature and local gyrification index (LGI) in TS group compared with healthy controls. The brain regions with significant-group differences in cortical thickness included postcentral gyrus, superiorparietal gyrus, rostral anterior cingulate cortex in the left hemisphere and frontal pole, lateral occipital gyrus, inferior temporal gyrus in the right hemisphere. In addition, the superior temporal gyrus, medial orbitofrontal gyrus, supramarginal gyrus, medial orbitofrontal gyrus, superiorparietal gyrus and lateral occipital gyrus showed significant between-group differences for cortical sulcus. Moreover, the brain regions with significant between-group differences in cortical curvature were located in caudal anterior cingulate cortex, supramarginal gyrus, inferior parietal gyrus and lateral occipital gyrus. The alteration of LGI were most prominent in the inferior temporal gyrus and insula. Additionally, there was no statistical difference in brain surface area for TS children compared with controls. CONCLUSION: The results of this study revealed that cortical thickness, sulcus, cortical curvature and LGI were changed in multiple brain regions for children with TS.

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.

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.

Characterization of MRI techniques to assess neonatal brain oxygenation and blood flow.

There is increasing interest in applying physiological MRI in neonates, based on the premise that physiological parameters may provide an early biomarker of neonatal brain health and injury. Two commonly used techniques are oxygen extraction fraction (OEF) measurement using T2 -relaxation-under-spin-tagging (TRUST) MRI and cerebral blood flow measurement using phase-contrast (PC) quantitative flow MRI, which collectively provide an assessment of the brain's oxygen consumption. However, prior research has only demonstrated proof of principle of these methods in neonates, without characterization or benchmarking of the techniques. This is because available time is limited in neonatal subjects, especially when scans are performed as add-ons to clinical scans (typically less than 5 min). The work presented aims to examine the TRUST and PC MRI sequences systematically in normal neonates, through research-dedicated scan sessions. A series of characterization and optimization studies were conducted in a total of 26 radiographically normal neonates on 3 T systems. Our results show that TRUST MRI at the superior sagittal sinus (SSS) provides an OEF measurement equivalent to that at the internal jugular vein (r = 0.80, n = 10), yet with shorter scan time. Lower resolution provided better precision in the TRUST measurement (p = 0.001, n = 9). Therefore, the preferred OEF measurement is to apply TRUST MRI at the SSS using a spatial resolution of 2.5 mm. For PC MRI, our results showed that non-gated PC MRI yielded blood flow measurements comparable to those from the more time-consuming gated approach in neonates (r = 0.89, n = 7). It was also found that blood flow could be overestimated by 18% when imaging resolution is larger than 0.3 mm (n = 7). Therefore, non-gated PC MRI with a spatial resolution of 0.3 mm is recommended for neonatal applications. In conclusion, this study verifies consistency of neonatal brain oxygenation and flow measurements across acquisition schemes and points to optimal strategies in parameter selection when using these sequences.