White matter development and language abilities during infancy in autism spectrum disorder.

White matter (WM) fiber tract differences are present in autism spectrum disorder (ASD) and could be important markers of behavior. One of the earliest phenotypic differences in ASD are language atypicalities. Although language has been linked to WM in typical development, no work has evaluated this association in early ASD. Participants came from the Infant Brain Imaging Study and included 321 infant siblings of children with ASD at high likelihood (HL) for developing ASD; 70 HL infants were later diagnosed with ASD (HL-ASD), and 251 HL infants were not diagnosed with ASD (HL-Neg). A control sample of 140 low likelihood infants not diagnosed with ASD (LL-Neg) were also included. Infants contributed expressive language, receptive language, and diffusion tensor imaging data at 6-, 12-, and 24 months. Mixed effects regression models were conducted to evaluate associations between WM and language trajectories. Trajectories of microstructural changes in the right arcuate fasciculus were associated with expressive language development. HL-ASD infants demonstrated a different developmental pattern compared to the HL-Neg and LL-Neg groups, wherein the HL-ASD group exhibited a positive association between WM fractional anisotropy and language whereas HL-Neg and LL-Neg groups showed weak or no association. No other fiber tracts demonstrated significant associations with language. In conclusion, results indicated arcuate fasciculus WM is linked to language in early toddlerhood for autistic toddlers, with the strongest associations emerging around 24 months. To our knowledge, this is the first study to evaluate associations between language and WM development during the pre-symptomatic period in ASD.

Early brain development in infants at high risk for autism spectrum disorder.

Brain enlargement has been observed in children with autism spectrum disorder (ASD), but the timing of this phenomenon, and the relationship between ASD and the appearance of behavioural symptoms, are unknown. Retrospective head circumference and longitudinal brain volume studies of two-year olds followed up at four years of age have provided evidence that increased brain volume may emerge early in development. Studies of infants at high familial risk of autism can provide insight into the early development of autism and have shown that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life. These observations suggest that prospective brain-imaging studies of infants at high familial risk of ASD might identify early postnatal changes in brain volume that occur before an ASD diagnosis. In this prospective neuroimaging study of 106 infants at high familial risk of ASD and 42 low-risk infants, we show that hyperexpansion of the cortical surface area between 6 and 12 months of age precedes brain volume overgrowth observed between 12 and 24 months in 15 high-risk infants who were diagnosed with autism at 24 months. Brain volume overgrowth was linked to the emergence and severity of autistic social deficits. A deep-learning algorithm that primarily uses surface area information from magnetic resonance imaging of the brain of 6-12-month-old individuals predicted the diagnosis of autism in individual high-risk children at 24 months (with a positive predictive value of 81% and a sensitivity of 88%). These findings demonstrate that early brain changes occur during the period in which autistic behaviours are first emerging.

Sex differences associated with corpus callosum development in human infants: A longitudinal multimodal imaging study.

The corpus callosum (CC) is the largest connective pathway in the human brain, linking cerebral hemispheres. There is longstanding debate in the scientific literature whether sex differences are evident in this structure, with many studies indicating the structure is larger in females. However, there are few data pertaining to this issue in infancy, during which time the most rapid developmental changes to the CC occur. In this study, we examined longitudinal brain imaging data collected from 104 infants at ages 6, 12, and 24 months. We identified sex differences in brain-size adjusted CC area and thickness characterized by a steeper rate of growth in males versus females from ages 6-24 months. In contrast to studies of older children and adults, CC size was larger for male compared to female infants. Based on diffusion tensor imaging data, we found that CC thickness is significantly associated with underlying microstructural organization. However, we observed no sex differences in the association between microstructure and thickness, suggesting that the role of factors such as axon density and/or myelination in determining CC size is generally equivalent between sexes. Finally, we found that CC length was negatively associated with nonverbal ability among females.

Rapid Radial T1 and T2 Mapping of the Hip Articular Cartilage With Magnetic Resonance Fingerprinting.

BACKGROUND: Quantitative MRI can detect early changes in cartilage biochemical components, but its routine clinical implementation is challenging. PURPOSE: To introduce a novel technique to measure T1 and T2 along radial sections of the hip for accurate and reproducible multiparametric quantitative cartilage assessment in a clinically feasible scan time. STUDY TYPE: Reproducibility, technical validation. SUBJECTS/PHANTOM: A seven-compartment phantom and three healthy volunteers. FIELD STRENGTH/SEQUENCE: A novel MR pulse sequence that simultaneously measures proton density (PD), T1 , and T2 at 3 T was developed. Automatic positioning and semiautomatic cartilage segmentation were implemented to improve consistency and simplify workflow. ASSESSMENT: Intra- and interscanner variability of our technique was assessed over multiple scans on three different MR scanners. STATISTICAL TESTS: For each scan, the median of cartilage T1 and T2 over six radial slices was calculated. Restricted maximum likelihood estimation of variance components was used to estimate intrasubject variances reflecting variation between results from the two scans using the same scanner (intrascanner variance) and variation among results from the three scanners (interscanner variance). RESULTS: The estimation error for T1 and T2 with respect to reference standard measurements was less than 3% on average for the phantom. The average interscanner coefficient of variation was 1.5% (1.2-1.9%) and 0.9% (0.0-3.7%) for T1 and T2 , respectively, in the seven compartments of the phantom. Total scan time in vivo was 7:13 minutes to obtain PD, T1 , and T2 maps along six radial hip sections at 0.6 × 0.6 × 4.0 mm3 voxel resolution. Interscanner variability for the in vivo study was 1.99% and 5.46% for T1 and T2 , respectively. in vivo intrascanner variability was 1.15% for T1 and 3.24% for T2 . DATA CONCLUSION: Our method, which includes slice positioning, model-based parameter estimation, and cartilage segmentation, is highly reproducible. It could enable employing quantitative hip cartilage evaluation for longitudinal and multicenter studies. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:810-815.

Walking, Gross Motor Development, and Brain Functional Connectivity in Infants and Toddlers.

Infant gross motor development is vital to adaptive function and predictive of both cognitive outcomes and neurodevelopmental disorders. However, little is known about neural systems underlying the emergence of walking and general gross motor abilities. Using resting state fcMRI, we identified functional brain networks associated with walking and gross motor scores in a mixed cross-sectional and longitudinal cohort of infants at high and low risk for autism spectrum disorder, who represent a dimensionally distributed range of motor function. At age 12 months, functional connectivity of motor and default mode networks was correlated with walking, whereas dorsal attention and posterior cingulo-opercular networks were implicated at age 24 months. Analyses of general gross motor function also revealed involvement of motor and default mode networks at 12 and 24 months, with dorsal attention, cingulo-opercular, frontoparietal, and subcortical networks additionally implicated at 24 months. These findings suggest that changes in network-level brain-behavior relationships underlie the emergence and consolidation of walking and gross motor abilities in the toddler period. This initial description of network substrates of early gross motor development may inform hypotheses regarding neural systems contributing to typical and atypical motor outcomes, as well as neurodevelopmental disorders associated with motor dysfunction.

Joint Attention and Brain Functional Connectivity in Infants and Toddlers.

Initiating joint attention (IJA), the behavioral instigation of coordinated focus of 2 people on an object, emerges over the first 2 years of life and supports social-communicative functioning related to the healthy development of aspects of language, empathy, and theory of mind. Deficits in IJA provide strong early indicators for autism spectrum disorder, and therapies targeting joint attention have shown tremendous promise. However, the brain systems underlying IJA in early childhood are poorly understood, due in part to significant methodological challenges in imaging localized brain function that supports social behaviors during the first 2 years of life. Herein, we show that the functional organization of the brain is intimately related to the emergence of IJA using functional connectivity magnetic resonance imaging and dimensional behavioral assessments in a large semilongitudinal cohort of infants and toddlers. In particular, though functional connections spanning the brain are involved in IJA, the strongest brain-behavior associations cluster within connections between a small subset of functional brain networks; namely between the visual network and dorsal attention network and between the visual network and posterior cingulate aspects of the default mode network. These observations mark the earliest known description of how functional brain systems underlie a burgeoning fundamental social behavior, may help improve the design of targeted therapies for neurodevelopmental disorders, and, more generally, elucidate physiological mechanisms essential to healthy social behavior development.

Twin-singleton developmental study of brain white matter anatomy.

Twin studies provide valuable insights into the analysis of genetic and environmental factors influencing human brain development. However, these findings may not generalize to singletons due to differences in pre- and postnatal environments. One would expect the effect of these differences to be greater during the early years of life. To address this concern, we compare longitudinal diffusion data of white matter regions for 26 singletons and 76 twins (monozygotic and dizygotic) from birth to 2 years of age. We use nonlinear mixed effect modeling where the temporal changes in the diffusion parameters are described by the Gompertz function. The Gompertz function describes growth trajectory in terms of intuitive parameters: asymptote, delay, and speed. We analyzed fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) for 21 regions of interest (ROIs). These ROIs included areas in the association, projection, and commissural fiber tracts. We did not find any differences in the diffusion parameters between monozygotic and dizygotic twins. In addition, FA and RD showed no developmental differences between singletons and twins for the regions analyzed. However, the delay parameter of the Gompertz function of AD for the anterior limb of the internal capsule and anterior corona radiata was significantly different between singletons and twins. Further analysis indicated that the differences are small, and twins "catch up" by the first few months of life. These results suggest that the effects of differences of pre- and postnatal environments between twins and singletons are minimal on white matter development and disappear early in life. Hum Brain Mapp 38:1009-1024, 2017. © 2016 Wiley Periodicals, Inc.

Splenium development and early spoken language in human infants.

The association between developmental trajectories of language-related white matter fiber pathways from 6 to 24 months of age and individual differences in language production at 24 months of age was investigated. The splenium of the corpus callosum, a fiber pathway projecting through the posterior hub of the default mode network to occipital visual areas, was examined as well as pathways implicated in language function in the mature brain, including the arcuate fasciculi, uncinate fasciculi, and inferior longitudinal fasciculi. The hypothesis that the development of neural circuitry supporting domain-general orienting skills would relate to later language performance was tested in a large sample of typically developing infants. The present study included 77 infants with diffusion weighted MRI scans at 6, 12 and 24 months and language assessment at 24 months. The rate of change in splenium development varied significantly as a function of language production, such that children with greater change in fractional anisotropy (FA) from 6 to 24 months produced more words at 24 months. Contrary to findings from older children and adults, significant associations between language production and FA in the arcuate, uncinate, or left inferior longitudinal fasciculi were not observed. The current study highlights the importance of tracing brain development trajectories from infancy to fully elucidate emerging brain-behavior associations while also emphasizing the role of the splenium as a key node in the structural network that supports the acquisition of spoken language.

Prenatal drug exposure affects neonatal brain functional connectivity.

Prenatal drug exposure, particularly prenatal cocaine exposure (PCE), incurs great public and scientific interest because of its associated neurodevelopmental consequences. However, the neural underpinnings of PCE remain essentially uncharted, and existing studies in school-aged children and adolescents are confounded greatly by postnatal environmental factors. In this study, leveraging a large neonate sample (N = 152) and non-invasive resting-state functional magnetic resonance imaging, we compared human infants with PCE comorbid with other drugs (such as nicotine, alcohol, marijuana, and antidepressant) with infants with similar non-cocaine poly drug exposure and drug-free controls. We aimed to characterize the neural correlates of PCE based on functional connectivity measurements of the amygdala and insula at the earliest stage of development. Our results revealed common drug exposure-related connectivity disruptions within the amygdala-frontal, insula-frontal, and insula-sensorimotor circuits. Moreover, a cocaine-specific effect was detected within a subregion of the amygdala-frontal network. This pathway is thought to play an important role in arousal regulation, which has been shown to be irregular in PCE infants and adolescents. These novel results provide the earliest human-based functional delineations of the neural-developmental consequences of prenatal drug exposure and thus open a new window for the advancement of effective strategies aimed at early risk identification and intervention.

Development of cortical shape in the human brain from 6 to 24months of age via a novel measure of shape complexity.

The quantification of local surface morphology in the human cortex is important for examining population differences as well as developmental changes in neurodegenerative or neurodevelopmental disorders. We propose a novel cortical shape measure, referred to as the 'shape complexity index' (SCI), that represents localized shape complexity as the difference between the observed distributions of local surface topology, as quantified by the shape index (SI) measure, to its best fitting simple topological model within a given neighborhood. We apply a relatively small, adaptive geodesic kernel to calculate the SCI. Due to the small size of the kernel, the proposed SCI measure captures fine differences of cortical shape. With this novel cortical feature, we aim to capture comparatively small local surface changes that capture a) the widening versus deepening of sulcal and gyral regions, as well as b) the emergence and development of secondary and tertiary sulci. Current cortical shape measures, such as the gyrification index (GI) or intrinsic curvature measures, investigate the cortical surface at a different scale and are less well suited to capture these particular cortical surface changes. In our experiments, the proposed SCI demonstrates higher complexity in the gyral/sulcal wall regions, lower complexity in wider gyral ridges and lowest complexity in wider sulcal fundus regions. In early postnatal brain development, our experiments show that SCI reveals a pattern of increased cortical shape complexity with age, as well as sexual dimorphisms in the insula, middle cingulate, parieto-occipital sulcal and Broca's regions. Overall, sex differences were greatest at 6months of age and were reduced at 24months, with the difference pattern switching from higher complexity in males at 6months to higher complexity in females at 24months. This is the first study of longitudinal, cortical complexity maturation and sex differences, in the early postnatal period from 6 to 24months of age with fine scale, cortical shape measures. These results provide information that complement previous studies of gyrification index in early brain development.

Performance of an efficient image-registration algorithm in processing MR renography data.

PURPOSE: To evaluate the performance of an edge-based registration technique in correcting for respiratory motion artifacts in magnetic resonance renographic (MRR) data and to examine the efficiency of a semiautomatic software package in processing renographic data from a cohort of clinical patients. MATERIALS AND METHODS: The developed software incorporates an image-registration algorithm based on the generalized Hough transform of edge maps. It was used to estimate glomerular filtration rate (GFR), renal plasma flow (RPF), and mean transit time (MTT) from 36 patients who underwent free-breathing MRR at 3T using saturation-recovery turbo-FLASH. The processing time required for each patient was recorded. Renal parameter estimates and model-fitting residues from the software were compared to those from a previously reported technique. Interreader variability in the software was quantified by the standard deviation of parameter estimates among three readers. GFR estimates from our software were also compared to a reference standard from nuclear medicine. RESULTS: The time taken to process one patient's data with the software averaged 12 ± 4 minutes. The applied image registration effectively reduced motion artifacts in dynamic images by providing renal tracer-retention curves with significantly smaller fitting residues (P < 0.01) than unregistered data or data registered by the previously reported technique. Interreader variability was less than 10% for all parameters. GFR estimates from the proposed method showed greater concordance with reference values (P < 0.05). CONCLUSION: These results suggest that the proposed software can process MRR data efficiently and accurately. Its incorporated registration technique based on the generalized Hough transform effectively reduces respiratory motion artifacts in free-breathing renographic acquisitions.

Altered corpus callosum morphology associated with autism over the first 2 years of life.

Numerous brain imaging studies indicate that the corpus callosum is smaller in older children and adults with autism spectrum disorder. However, there are no published studies examining the morphological development of this connective pathway in infants at-risk for the disorder. Magnetic resonance imaging data were collected from 270 infants at high familial risk for autism spectrum disorder and 108 low-risk controls at 6, 12 and 24 months of age, with 83% of infants contributing two or more data points. Fifty-seven children met criteria for ASD based on clinical-best estimate diagnosis at age 2 years. Corpora callosa were measured for area, length and thickness by automated segmentation. We found significantly increased corpus callosum area and thickness in children with autism spectrum disorder starting at 6 months of age. These differences were particularly robust in the anterior corpus callosum at the 6 and 12 month time points. Regression analysis indicated that radial diffusivity in this region, measured by diffusion tensor imaging, inversely predicted thickness. Measures of area and thickness in the first year of life were correlated with repetitive behaviours at age 2 years. In contrast to work from older children and adults, our findings suggest that the corpus callosum may be larger in infants who go on to develop autism spectrum disorder. This result was apparent with or without adjustment for total brain volume. Although we did not see a significant interaction between group and age, cross-sectional data indicated that area and thickness differences diminish by age 2 years. Regression data incorporating diffusion tensor imaging suggest that microstructural properties of callosal white matter, which includes myelination and axon composition, may explain group differences in morphology.

HYPERSPECTRAL AUTOFLUORESCENCE IMAGING OF DRUSEN AND RETINAL PIGMENT EPITHELIUM IN DONOR EYES WITH AGE-RELATED MACULAR DEGENERATION.

PURPOSE: To elucidate the molecular pathogenesis of age-related macular degeneration (AMD) and interpretation of fundus autofluorescence imaging, the authors identified spectral autofluorescence characteristics of drusen and retinal pigment epithelium (RPE) in donor eyes with AMD. METHODS: Macular RPE/Bruch membrane flat mounts were prepared from 5 donor eyes with AMD. In 12 locations (1-3 per eye), hyperspectral autofluorescence images in 10-nm-wavelength steps were acquired at 2 excitation wavelengths (λex 436, 480 nm). A nonnegative tensor factorization algorithm was used to recover 5 abundant emission spectra and their corresponding spatial localizations. RESULTS: At λex 436 nm, the authors consistently localized a novel spectrum (SDr) with a peak emission near 510 nm in drusen and sub-RPE deposits. Abundant emission spectra seen previously (S0 in Bruch membrane and S1, S2, and S3 in RPE lipofuscin/melanolipofuscin, respectively) also appeared in AMD eyes, with the same shapes and peak wavelengths as in normal tissue. Lipofuscin/melanolipofuscin spectra localizations in AMD eyes varied widely in their overlap with drusen, ranging from none to complete. CONCLUSION: An emission spectrum peaking at ∼510 nm (λex 436 nm) appears to be sensitive and specific for drusen and sub-RPE deposits. One or more abundant spectra from RPE organelles exhibit characteristic relationships with drusen.

Investigating Maternal Brain Structure and its Relationship to Substance Use and Motivational Systems.

Substance use during pregnancy and the postpartum period may have significant implications for both mother and the developing child. However, the neurobiological basis of the impact of substance use on parenting is less well understood. Here, we examined the impact of maternal substance use on cortical gray matter (GM) and white matter (WM) volumes and whether this was associated with individual differences in motivational systems of behavioral activation and inhibition. Mothers were included in the substance-using group if any addictive substance was used during pregnancy and/or in the immediate postpartum period (within 3 months of delivery). GM volume was reduced in substance-using mothers compared to non-substance-using mothers, particularly in frontal brain regions. In substance-using mothers, we also found that frontal GM was negatively correlated with levels of behavioral activation (i.e., the motivation to approach rewarding stimuli). This effect was absent in non-substance-using mothers. Taken together, these findings indicate a reduction in GM volume is associated with substance use and that frontal GM volumetric differences may be related to approach motivation in substance-using mothers.

Morphometry of anatomical shape complexes with dense deformations and sparse parameters.

We propose a generic method for the statistical analysis of collections of anatomical shape complexes, namely sets of surfaces that were previously segmented and labeled in a group of subjects. The method estimates an anatomical model, the template complex, that is representative of the population under study. Its shape reflects anatomical invariants within the dataset. In addition, the method automatically places control points near the most variable parts of the template complex. Vectors attached to these points are parameters of deformations of the ambient 3D space. These deformations warp the template to each subject's complex in a way that preserves the organization of the anatomical structures. Multivariate statistical analysis is applied to these deformation parameters to test for group differences. Results of the statistical analysis are then expressed in terms of deformation patterns of the template complex, and can be visualized and interpreted. The user needs only to specify the topology of the template complex and the number of control points. The method then automatically estimates the shape of the template complex, the optimal position of control points and deformation parameters. The proposed approach is completely generic with respect to any type of application and well adapted to efficient use in clinical studies, in that it does not require point correspondence across surfaces and is robust to mesh imperfections such as holes, spikes, inconsistent orientation or irregular meshing. The approach is illustrated with a neuroimaging study of Down syndrome (DS). The results demonstrate that the complex of deep brain structures shows a statistically significant shape difference between control and DS subjects. The deformation-based modelingis able to classify subjects with very high specificity and sensitivity, thus showing important generalization capability even given a low sample size. We show that the results remain significant even if the number of control points, and hence the dimension of variables in the statistical model, are drastically reduced. The analysis may even suggest that parsimonious models have an increased statistical performance. The method has been implemented in the software Deformetrica, which is publicly available at www.deformetrica.org.

Prenatal cocaine effects on brain structure in early infancy.

Prenatal cocaine exposure (PCE) is related to subtle deficits in cognitive and behavioral function in infancy, childhood and adolescence. Very little is known about the effects of in utero PCE on early brain development that may contribute to these impairments. The purpose of this study was to examine brain structural differences in infants with and without PCE. We conducted MRI scans of newborns (mean age = 5 weeks) to determine cocaine's impact on early brain structural development. Subjects were three groups of infants: 33 with PCE co-morbid with other drugs, 46 drug-free controls and 40 with prenatal exposure to other drugs (nicotine, alcohol, marijuana, opiates, SSRIs) but without cocaine. Infants with PCE exhibited lesser total gray matter (GM) volume and greater total cerebral spinal fluid (CSF) volume compared with controls and infants with non-cocaine drug exposure. Analysis of regional volumes revealed that whole brain GM differences were driven primarily by lesser GM in prefrontal and frontal brain regions in infants with PCE, while more posterior regions (parietal, occipital) did not differ across groups. Greater CSF volumes in PCE infants were present in prefrontal, frontal and parietal but not occipital regions. Greatest differences (GM reduction, CSF enlargement) in PCE infants were observed in dorsal prefrontal cortex. Results suggest that PCE is associated with structural deficits in neonatal cortical gray matter, specifically in prefrontal and frontal regions involved in executive function and inhibitory control. Longitudinal study is required to determine whether these early differences persist and contribute to deficits in cognitive functions and enhanced risk for drug abuse seen at school age and in later life.

E(3) × SO(3)-Equivariant Networks for Spherical Deconvolution in Diffusion MRI.

We present Roto-Translation Equivariant Spherical Deconvolution (RT-ESD), an E(3)×SO(3) equivariant framework for sparse deconvolution of volumes where each voxel contains a spherical signal. Such 6D data naturally arises in diffusion MRI (dMRI), a medical imaging modality widely used to measure microstructure and structural connectivity. As each dMRI voxel is typically a mixture of various overlapping structures, there is a need for blind deconvolution to recover crossing anatomical structures such as white matter tracts. Existing dMRI work takes either an iterative or deep learning approach to sparse spherical deconvolution, yet it typically does not account for relationships between neighboring measurements. This work constructs equivariant deep learning layers which respect to symmetries of spatial rotations, reflections, and translations, alongside the symmetries of voxelwise spherical rotations. As a result, RT-ESD improves on previous work across several tasks including fiber recovery on the DiSCo dataset, deconvolution-derived partial volume estimation on real-world in vivo human brain dMRI, and improved downstream reconstruction of fiber tractograms on the Tractometer dataset. Our implementation is available at https://github.com/AxelElaldi/e3so3_conv.

Regional characterization of longitudinal DT-MRI to study white matter maturation of the early developing brain.

The human brain undergoes rapid and dynamic development early in life. Assessment of brain growth patterns relevant to neurological disorders and disease requires a normative population model of growth and variability in order to evaluate deviation from typical development. In this paper, we focus on maturation of brain white matter as shown in diffusion tensor MRI (DT-MRI), measured by fractional anisotropy (FA), mean diffusivity (MD), as well as axial and radial diffusivities (AD, RD). We present a novel methodology to model temporal changes of white matter diffusion from longitudinal DT-MRI data taken at discrete time points. Our proposed framework combines nonlinear modeling of trajectories of individual subjects, population analysis, and testing for regional differences in growth pattern. We first perform deformable mapping of longitudinal DT-MRI of healthy infants imaged at birth, 1 year, and 2 years of age, into a common unbiased atlas. An existing template of labeled white matter regions is registered to this atlas to define anatomical regions of interest. Diffusivity properties of these regions, presented over time, serve as input to the longitudinal characterization of changes. We use non-linear mixed effect (NLME) modeling where temporal change is described by the Gompertz function. The Gompertz growth function uses intuitive parameters related to delay, rate of change, and expected asymptotic value; all descriptive measures which can answer clinical questions related to quantitative analysis of growth patterns. Results suggest that our proposed framework provides descriptive and quantitative information on growth trajectories that can be interpreted by clinicians using natural language terms that describe growth. Statistical analysis of regional differences between anatomical regions which are known to mature differently demonstrates the potential of the proposed method for quantitative assessment of brain growth and differences thereof. This will eventually lead to a prediction of white matter diffusion properties and associated cognitive development at later stages given imaging data at early stages.

Associations between white matter microstructure and infants' working memory.

Working memory emerges in infancy and plays a privileged role in subsequent adaptive cognitive development. The neural networks important for the development of working memory during infancy remain unknown. We used diffusion tensor imaging (DTI) and deterministic fiber tracking to characterize the microstructure of white matter fiber bundles hypothesized to support working memory in 12-month-old infants (n=73). Here we show robust associations between infants' visuospatial working memory performance and microstructural characteristics of widespread white matter. Significant associations were found for white matter tracts that connect brain regions known to support working memory in older children and adults (genu, anterior and superior thalamic radiations, anterior cingulum, arcuate fasciculus, and the temporal-parietal segment). Better working memory scores were associated with higher FA and lower RD values in these selected white matter tracts. These tract-specific brain-behavior relationships accounted for a significant amount of individual variation above and beyond infants' gestational age and developmental level, as measured with the Mullen Scales of Early Learning. Working memory was not associated with global measures of brain volume, as expected, and few associations were found between working memory and control white matter tracts. To our knowledge, this study is among the first demonstrations of brain-behavior associations in infants using quantitative tractography. The ability to characterize subtle individual differences in infant brain development associated with complex cognitive functions holds promise for improving our understanding of normative development, biomarkers of risk, experience-dependent learning and neuro-cognitive periods of developmental plasticity.