Brain cerebral cortical microstructure underlies neuronal circuit formation and function emergence during brain maturation. Regionally distinctive cortical microstructural architecture profiles around birth result from immensely complicated and spatiotemporally heterogeneous underlying cellular and molecular processes (Silbereis et al., 2016), including neurogenesis, synapse formation, dendritic arborization, axonal growth, pruning, and myelination. Disturbance of such precisely regulated maturational events is associated with mental disorders (Innocenti and Price, 2005). Diffusion magnetic resonance imaging (dMRI) has been widely used for quantifying microstructural changes in white matter (WM) maturation (e.g. Dubois et al., 2008; Mukherjee et al., 2001). Because of its sensitivity to organized cortical tissue (e.g. radial glial scaffold; Rakic, 1995; Sidman and Rakic, 1973) unique in the fetal and infant brain, dMRI also offers insights into maturation of cortical cytoarchitecture. Cortical fractional anisotropy (FA), a dMRI-derived measurement, of infant and fetal brain can effectively quantify local cortical microstructural architecture related to dendritic arborization and synaptic formation. Thus, cortical FA can potentially be used to infer specific brain circuit formation. In early cortical development, most of cortical neurons are generated in the ventricular and subventricular zone. These neurons migrate toward the cortical surface along a radially arranged scaffolding of glial cells where relatively high FA values are usually observed (Huang et al., 2013; McKinstry et al., 2002). During emergence of brain circuits, increasing dendritic arborization (Bystron et al., 2008; Sidman and Rakic, 1973), synaptic formation (Huttenlocher and Dabholkar, 1997), and myelination of intracortical axons (Yakovlev and Lecours, 1967) disrupt the highly organized radial glia in the immature cortex and result in cortical FA decreases. Such reproducible cortical FA change patterns were documented in many studies of perinatal human brain development (Ball et al., 2013; Huang et al., 2006; Huang et al., 2009; Huang et al., 2013; Kroenke et al., 2007; McKinstry et al., 2002; Neil et al., 1998; Ouyang et al., 2019a; Ouyang et al., 2019b; Yu et al., 2016), suggesting sensitivity of cortical FA measures to maturational processes of cortical microstructure. Diffusion-MRI-based regional cortical microstructure at birth, encoding rich ‘footage’ of regional cellular and molecular processes, may provide novel information regarding typical cortical development and biomarkers for neuropsychiatric disorders.

The first 2 years of life is a critical period for behavioral development, with brain development in this period most rapid across the lifespan. In parallel to rapid maturation of cortical architecture and establishment of complex neuronal connections (Hüppi et al., 1998; Ouyang et al., 2019a; Pfefferbaum et al., 1994), babies learn to walk, talk, and build the core capacities for lifetime. Infant behaviors including cognition, language, and motor emerge during this time and become measurable at around 2 years of age. Reliable diagnosis for many neuropsychiatric disorders, such as autism spectrum disorder (ASD), can be made only around 2 years of age or later (Marín, 2016), as diagnoses rely on observing behavioral problems that are difficult to recognize in early infancy (Arpi and Ferrari, 2013; Ozonoff et al., 2010). On the other hand, early intervention for ASD, especially before 2 years of age, has demonstrated potential on improving outcomes (Rogers et al., 2014). Given that infants cannot communicate with language or writing in early infancy, there may be no better way to assess their brain development other than neuroimaging. Prediction of future cognition and behavior at 2 years of age or later based on brain features around birth creates an invaluable time window for individualized biomarker detection and early tailored intervention leading to better outcomes.

Individual differences in brain WM microstructural architectures (Scholz et al., 2009; Yu et al., 2020), behavior, and functions (Braga and Buckner, 2017; Xu et al., 2019) have been well recognized. Individual variability in brain structures and associated individual variability in future behaviors can be harnessed for robust prediction at the single-subject level (Kanai and Rees, 2011; Rosenberg et al., 2018), a step further than group classification. A few studies have been conducted previously to investigate within-sample imaging-outcome correlations (Ball et al., 2015; Counsell et al., 2014; Deoni et al., 2016; Hintz et al., 2015; Keunen et al., 2017; Peyton et al., 2020; Wee et al., 2017; Woodward et al., 2006), while such correlation approaches made it impossible to be applied to new and incoming subjects. Machine learning approaches that can adopt new subjects and yield continuous prediction values have been explored only recently based on WM structural networks (Girault et al., 2019; Kawahara et al., 2017). Compared to association of WM microstructure with cortical regions through WM fiber end point connectivity, association of cortical microstructure with cortical regions is more direct. Thus, FA of a specific cortical region can be used to directly reflect certain functions of the same cortical region. Our previous study Ouyang et al., 2019b demonstrated that cortical FA predicted neonate age with high accuracy. Regionally distinctive cortical microstructure around birth encodes the information that may predict distinctive functions manifested by future behavior and potentially identify the most sensitive regions as imaging markers to detect early behavioral abnormality. However, dMRI-based cortical microstructure has not been evaluated for predicting either discrete or continuous future behavioral measurement so far. And dMRI-based cortical microstructure has not been incorporated into a machine-learning-based prediction model for predicting future behavior, either.

In this study, we leveraged individual variability of cortical microstructure profiles of neonate brains for predicting future behavior. A novel machine-learning-based model using regional cortical microstructure markers from dMRI was developed to predict continuous outcome values. This model is also capable of incorporating new subjects in contrast to within-sample imaging-outcome correlations. We hypothesized that dMRI-based cortical microstructure at birth only (without inclusion of any WM microstructure information) could robustly predict the future neurodevelopmental outcomes. Out of 107 recruited neonates, high-resolution (0.656 × 0.656 × 1.6 mm³) dMRI data were acquired from 87 neonates, of which 46 underwent a follow-up study at their 2 years of age for neurobehavioral assessments of cognitive, language, and motor abilities. Cortical microstructural architectures at birth were quantified by cortical FA on the cortical skeleton to alleviate partial volume effects (Ouyang et al., 2019b; Yu et al., 2016). Regional cortical FA measures were then used to form feature vectors to predict neurodevelopmental outcomes at 2 years of age. We further quantified the contribution of each cortical region in predicting different outcomes, as distinctive behaviors are likely encoded in uniquely distributed pattern across the cerebral cortex.

We leveraged individual variability in the cortical microstructural architecture at birth for a robust prediction of future behavioral outcomes in continuous values. Cortical microstructure at birth, encoding rich ‘footage’ of regional cellular and molecular processes in early human brain development, was evaluated as the baseline measurements for prediction. Our previous studies (Huang et al., 2009; Ouyang et al., 2019b; Yu et al., 2016) found that individual neonate cortical microstructure profile characterized by different levels of dendritic arborization could be reliably quantified with dMRI-based cortical FA that reveals cortical maturation signature. In this study, we further demonstrated that individual variability of cortical microstructure profile around birth can be used to robustly predict the cognitive and language outcomes of individual infant at 2 years of age. By harnessing different encoding patterns of cognitive and language functions across the entire cortex, we quantified distinguishable contributions of each cortical region and highlighted the most sensitive regions for predicting different outcomes. Cortical regions contributing heavily to the prediction models exhibited distinctive functional selectivity for cognition and language. To our knowledge, this is the first study evaluating regional cortical microstructure for predicting future behavior, laying the foundation for future works using cortical microstructure profile as ‘neuromarkers’ to predict the risk of an individual developing health-related behavioral abnormalities (e.g. ASD). The prediction model is also capable of incorporating new and incoming subjects, a step further than previous within-sample imaging-outcome correlation studies (Ball et al., 2015; Counsell et al., 2014; Deoni et al., 2016; Hintz et al., 2015; Keunen et al., 2017; Peyton et al., 2020; Wee et al., 2017; Woodward et al., 2006). Before the presented prediction can be used to identify individual infant at risk of neurodevelopmental disorders (e.g. ASD) at a time when the infant is pre-symptomatic in behavioral assessments, the prediction model needs to be further refined with a larger sample size.

Cerebral cortex plays a central role in human cognition and behaviors. High performance achieved in prediction of cognition and language based on cortical FA measures (Figure 2) is probably due to sensitivity of cortical microstructural changes to maturational processes including synaptic formation, dendritic arborization, and axonal growth. Distinctive maturation processes manifested by differentiated cortical FA changes across cortical regions in fetal and infant brains were reproducibly reported development (Ball et al., 2013; Huang et al., 2006; Huang et al., 2009; Huang et al., 2013; Kroenke et al., 2007; McKinstry et al., 2002; Neil et al., 1998; Ouyang et al., 2019a; Ouyang et al., 2019b; Yu et al., 2016). Although cortical thickness, volume, or surface area from structural MRI scans (i.e. T1- or T2-weighted images) were conventionally primary structural measurements to characterize infant cerebral cortex development (Hazlett et al., 2017; Hill et al., 2010; Lyall et al., 2015), they cannot characterize the complex microstructural processes that take place inside the cortical mantle. Compared to macrostructural changes quantified by these conventional measurements, the underlying microstructural processes quantified by cortical FA may be more sensitive to infants with pathology such as those with risk of ASD. Because WM microstructure (e.g. FA) measurement is more widely used in dMRI studies than cortical microstructure measurement, we also evaluated WM FA at birth for predicting cognitive and language outcome at 2 years of age (Figure 4). Despite the fact that microstructural measures from solely cerebral cortex or WM at birth have similar sensitivity in predicting neurodevelopment outcomes at 2 years old (Figure 4), cortical microstructure is more directly associated with specific cortical regions and thus certain cortical functions, compared to association of WM with the cortical regions through end point connectivity. On the other hand, combined cortical and WM microstructural measures at birth offered more baseline information and resulted in improved prediction (Figure 4b and c).

Human brain development in the first 2 years is most rapid across the lifespan. During first 2 years after birth, overall size of an infant brain increases dramatically, reaching close to 90% of an adult brain volume by 2 years of age (Pfefferbaum et al., 1994). Despite rapid development, infancy period (0–2 years) of human is probably the longest among all mammals with cognitive and language functions unique in human emerging during this critical period. For instance, infants start to learn their mother tongue from babbling to full sentences, during the age of 6 months to 2–3 years (Kuhl, 2004). This lengthy yet extremely dynamic brain development processes make the prediction of 2-year neurodevelopmental outcome with brain information at birth ultimately invaluable. DMRI-based cortical microstructure at birth, before any behavioral tests could be performed, can well predict both cognition and language outcomes at 2 years of age (Figure 2). A regionally heterogeneous distribution pattern across cerebral cortex was displayed (Figure 2). Furthermore, consistent with their functions documented in the literature, the cortical regions contributing heavily to the prediction models exhibited distinguishable functional selectivity for cognition and language (Figure 3 and Figure 3—figure supplement 1). The cognitive scale in Bayley-III estimates cognitive functions including object relatedness, memory, problem solving, and manipulation on the basis of nonverbal activities (Bayley, 2006). Cortical regions with high weight in the prediction model are tightly associated with cognitive functions. Right rectus, precuneus, parahippocampal, and left fusiform gyri were among the top 10 cortical regions (painted red in Figure 3b) where microstructural measures contributed uniquely to the cognition prediction, but not to the language prediction. Parahippocampal gyrus provides poly-sensory input to the hippocampus (Witter et al., 2000) and holds an essential position for mediating memory function (Young et al., 1997). Precuneus is a pivotal hub essential in brain’s default-mode network (Buckner et al., 2008) and involved in various higher-order cognitive functions (Cavanna and Trimble, 2006). Rectus and fusiform gyri are associated with higher-level social cognition processes (Viskontas et al., 2007). For regions contributing heavily to both cognition and language prediction and painted yellow in Figure 3b, bilateral entorhinal gyri likely serve as a pivot junctional region mediating the processes of different types of sensory information during the cortex-hippocampus interplay (Witter et al., 2000). Middle and lateral fronto-orbital gyrus are involved in cognitive processes including learning, memory, and decision-making (Wikenheiser and Schoenbaum, 2016). Left postcentral gyrus with high feature contribution weight in both prediction models is associated with the general motor demands of performing tasks. Besides higher-order cognitive functions, language functions are also unique in human beings. The first 2 years of life is a critical and sensitive period for the speech-perception and speech-production development (Kuhl, 2004; Werker and Hensch, 2015). The language scale from Bayley-III includes two subdomains: receptive communication and expressive communication. Here the term ‘communication’ refers to any way that a child uses to interact with others, and includes communication in prelinguistic stage (e.g. eye gaze, gesture, facial expression, vocalizations, and words), social-emotional skills, and communication in more advanced stage of language emergence (Bayley, 2006). The distinctive regions with high feature contribution weights only in language prediction model included left inferior frontal gyrus (IFG), cingular gyrus, insular cortex, and right angular gyrus, painted green in Figure 3b. These regions are related to receptive and expressive communication. It is striking that left IFG, known as ‘Broca’s area’, was identified by this data-driven prediction model because it is well known that Broca’s area plays a pivotal role in producing language (Poeppel, 2014). Angular gyrus, another crucial language region in the parietal lobe, supports the integration of semantic information into context and transfers visually perceived words to Wernicke’s area. Left insula, a part of the articulatory network in the dual-stream model of speech processing, is involved in translating acoustic speech signals into articulatory representations in the frontal lobe (Hickok and Poeppel, 2007). Since the Bayley language scale also includes a number of items reflecting social-emotional skills, such as how a child responds to his/her name or reacts when interrupted in play, the high feature contribution weight of insular cortex may be due to its important role in social emotions (Lamm and Singer, 2010). The high feature contribution weight of cingulate cortex might be related to its key involvement in emotion and social behavior (Bush et al., 2000). Taken together, identifying these regions with highest feature contribution weights sheds light on understanding local brain structural basis underlying emergence of distinctive functions manifested by daily behavior, enhancing our knowledge of brain-behavior relationships.

Motor scores from Bayley-III were not predicted reliably in this study possibly due to low variability and low signal-to-noise ratio (SNR) of cortical FA measurement in primary sensorimotor cortical regions associated with motor function. Cortical FA measurements at primary sensorimotor cortex are relatively low compared to those at other cortices (Ball et al., 2013; Ouyang et al., 2019b) and are barely above the noise floor, as primary sensorimotor cortex develops earlier compared to cortical regions associated with higher-order brain functions. Individual variability of cortical microstructure at primary sensorimotor cortex cannot be well captured with relatively low SNR for the cortical FA measurements at these regions. High individual variability enables reliable prediction. Low functional variability at primary sensorimotor cortex was found in a largely overlapped cohort in a separate study (Xu et al., 2019) from our group, and was reproducibly found in another cohort (http://developingconnectome.org). With strict exclusion criteria of participating cohort around birth, higher average and lower variance of motor scores than those of cognition or language scores play an important role in poor prediction of motor scores. Larger group variability in motor scores and larger sample size can offset the limitations elaborated above and enhance the prediction of motor scores.

Technical considerations, limitations, and future directions are discussed below. The capacity of cortical microstructural profile at birth to predict later behavior is substantial (Figure 2). We trained models to classify the low and normal outcomes. The ROC curves and accuracy measurements in Figure 2—figure supplement 2 demonstrated high accuracy of the classification of low- and normal-outcome groups. Robustness of the prediction model was further tested against various factors. These factors included different cortical parcellation schemes (random and finer parcellations versus parcellation based on an atlas) for measuring feature vectors and individual age adjustment (Figure 2—figure supplement 1). Importantly, high performance of the prediction models is reproducible after taking above-mentioned factors into consideration. We used MAE as a metric to measure prediction errors as MAE is more robust to extreme or outlier values compared to other prediction errors such as mean square error or root mean squared error (Willmott and Matsuura, 2005). Despite relatively high dMRI resolution (0.656 × 0.656 × 1.6 mm³) being used, partial volume effects (Jeon et al., 2012) cannot be ignored for measuring cortical FA. The partial volume effects are different across brain with thinner cortical regions more severely affected. To maximally alleviate the partial volume effects and enhance the measurement accuracy, we adopted a ‘cortical skeleton’ approach (Ouyang et al., 2019b; Yu et al., 2016), demonstrated in the Figure 1—figure supplement 4, to measure cortical microstructure at the center or ‘core’ of the cortical plate. Although both preterm and term-born infants are included, none of them was clinically referred. All infants had been recruited solely for brain research and rigorously screened by a neonatologist and a pediatric neuroradiologist to exclude any infants with signs of brain injury (see Materials and methods for more details). To limit the effects of exposure to the extrauterine environment, this study was designed to make the interval between birth and scan age as short as possible. As a result, we did not find any significant correlation between birth age or MRI scan age and neurodevelopmental outcomes (see Supplementary file 2). The literature (Bonifacio et al., 2010) also indicated that the effects of premature birth on brain development are considered to be relatively trivial compared with the effects of brain injury and co-morbid condition which was not presented in any recruited infant due to strict exclusion criteria. However, including preterm infants is still considered a limitation as more pronounced neurodevelopmental deviation of children with preterm birth yet without any brain injury is possible after 2 years of age. Despite this limitation, this later deviation may not significantly affect conclusion of this study focused on 0–2 years of age. The established prediction here incorporating MRI of preterm and term born neonates scanned across 32–42 PMW could benefit future outcome prediction studies based on in utero MRI of the fetuses in the third trimester with recent advances in utero MRI techniques (e.g. Khan et al., 2019; Thomason et al., 2013; Vasung et al., 2020). With potential long-term effects on neurodevelopment in preterm neonates, in utero MRI may be a better choice for future studies on normal brain development in the third trimester than preterm neonates. Although we have taken many precautions to extract cortical FA measures and tested internal validity of our prediction analysis, several limitations will need to be addressed in future research. Despite the fact that relatively high performance of behavioral prediction at a group level was achieved with current cohort of infants, the prediction model will benefit from validation (e.g. k-fold cross-validation) and replication with an independent infant cohort of a larger sample size for generalization. Thus, prediction model with individual variability representing a general population from a much larger cohort is warranted in future research before this approach is effective for meaningfully predicting the outcome for a single individual. As indicated in Figure 4b and c, more baseline information resulted in improved prediction. Future research will also benefit from incorporating distinctive and complementary measurements from multimodal neuroimaging (e.g. Kwon et al., 2014; Smyser et al., 2016), including structural (i.e. T1- or T2-weighted), functional, and diffusion MRI. Genetic factors could also be incorporated. With these multimodal measurements, more advanced machine learning algorithms such as multi-kernel and deep learning need to be adopted and developed to further improve prediction. This study included a healthy cohort of infants for evaluating cortical microstructure for predicting future behavior. Such evaluation in the setting of pathology needs to be further validated. The observed neurodevelopmental outcomes were also contributed by unmeasured factors such as maternal age and tertiary educational level as well as other home environment variable following discharge from the hospital, all of which should be taken into consideration in the future prediction model.

In conclusion, whole-brain cortical FA at birth, encoding rich information of dendritic arborization and synaptic formation, could be reliably used for predicting neurodevelopmental outcomes of 2-year-old infants by leveraging individual variability of these measures. Feature contribution weight in cognitive or language prediction is heterogeneous across brain regions. The cortical regions contributing heavily to the prediction models exhibited distinguishable functional selectivity for cognition and language. Identifying regions with highest feature contribution weights offers preliminary findings on understanding local brain microstructural basis underlying emergence of future behavior, enhancing our knowledge of brain-behavior relationships. These findings also suggest that cortical microstructural information at birth may be potentially used for prediction of behavioral abnormality in infants with high risk for brain disorders early at a time when infants are pre-symptomatic in behavioral assessments and intervention may be most effective.

Neonate MRI datasets are publicly available and can be freely downloaded from brainmrimap.org (a public website maintained by Huang lab). Behavioral datasets are available in the supplemental information of this publication. Source codes used for prediction are available from first author’s github repository (https://github.com/MHouyang/Prediction-of-neurodevelopmental-outcome ; copy archived at https://archive.softwareheritage.org/swh:1:rev:e3506bfbfa03db27651b1803e5cb662b623f5360/).

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Author details

1. Minhui Ouyang

   Radiology Research, Children’s Hospital of Philadelphia, Philadelphia, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

2. Qinmu Peng

   1. Radiology Research, Children’s Hospital of Philadelphia, Philadelphia, United States
   2. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Software, Validation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Tina Jeon

   Radiology Research, Children’s Hospital of Philadelphia, Philadelphia, United States

   Contribution

   Data curation, Formal analysis, Investigation, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

4. Roy Heyne

   Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, United States

   Contribution

   Resources, Data curation, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

5. Lina Chalak

   Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, United States

   Contribution

   Resources, Data curation, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

6. Hao Huang

   1. Radiology Research, Children’s Hospital of Philadelphia, Philadelphia, United States
   2. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   huangh6@email.chop.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9103-4382

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