Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training

The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow (tertiary) sulcal regions are largely overlooked in the literature due to the scarcity of manual/well-defined annotations and their large neuroanatomical variability. In this paper, we present an automated framework for regional labeling of both primary/secondary and tertiary sulci of the dorsal portion of lateral prefrontal cortex (LPFC) using spherical convolutional neural networks. We propose two core components that enhance the inference of sulcal labels to overcome such large neuroanatomical variability: (1) surface data augmentation and (2) context-aware training. (1) To take into account neuroanatomical variability, we synthesize training data from the proposed feature space that embeds intermediate deformation trajectories of spherical data in a rigid to non-rigid fashion, which bridges an augmentation gap in conventional rotation data augmentation. (2) Moreover, we design a two-stage training process to improve labeling accuracy of tertiary sulci by informing the biological associations in neuroanatomy: inference of primary/secondary sulci and then their spatial likelihood to guide the definition of tertiary sulci. In the experiments, we evaluate our method on 13 deep and shallow sulci of human LPFC in two independent data sets with different age ranges: pediatric (N=60) and adult (N=36) cohorts. We compare the proposed method with a conventional multi-atlas approach and spherical convolutional neural networks without/with rotation data augmentation. In both cohorts, the proposed data augmentation improves labeling accuracy of deep and shallow sulci over the baselines, and the proposed context-aware training offers further improvement in the labeling of shallow sulci over the proposed data augmentation. We share our tools with the field and discuss applications of our results for understanding neuroanatomical-functional organization of LPFC and the rest of cortex (https://github.com/ilwoolyu/SphericalLabeling).

Neural Responses to a Putative Set-shifting Task in Children with Autism Spectrum Disorder

While much progress has been made toward understanding the neurobiology of social and communication deficits associated with autism spectrum disorder (ASD), less is known regarding the neurobiological basis of restricted and repetitive behaviors (RRBs) central to the ASD diagnosis. Symptom severity for RRBs in ASD is associated with cognitive inflexibility. Thus, understanding the neural mechanisms underlying cognitive inflexibility in ASD is critical for tailoring therapies to treat this understudied yet pervasive symptom. Here we used a set-shifting paradigm adopted from the developmental cognitive neuroscience literature involving flexible switching between stimulus categories to examine task performance and neural responses in children with ASD. Behaviorally, we found little evidence for group differences in performance on the set-shifting task. Compared with typically developing children, children with ASD exhibited greater activation of the parahippocampal gyrus during performance on trials requiring switching. These findings suggest that children with ASD may need to recruit memory-based neural systems to a greater degree when learning to flexibly associate stimuli with responses. LAY SUMMARY: Children with autism often struggle to behave in a flexible way when faced with unexpected challenges. We examined brain responses during a task thought to involve flexible thinking and found that compared with typically developing children, those with autism relied more on brain areas involved in learning and memory to complete the task. This study helps us to understand what types of cognitive tasks are best suited for exploring the neural basis of cognitive flexibility in children with autism. Autism Res 2020, 13: 1501-1515. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.

AUTOMATIC LABELING OF CORTICAL SULCI USING SPHERICAL CONVOLUTIONAL NEURAL NETWORKS IN A DEVELOPMENTAL COHORT

In this paper, we present the automatic labeling framework for sulci in the human lateral prefrontal cortex (PFC). We adapt an existing spherical U-Net architecture with our recent surface data augmentation technique to improve the sulcal labeling accuracy in a developmental cohort. Specifically, our framework consists of the following key components: (1) augmented geometrical features being generated during cortical surface registration, (2) spherical U-Net architecture to efficiently fit the augmented features, and (3) postrefinement of sulcal labeling by optimizing spatial coherence via a graph cut technique. We validate our method on 30 healthy subjects with manual labeling of sulcal regions within PFC. In the experiments, we demonstrate significantly improved labeling performance (0.7749) in mean Dice overlap compared to that of multi-atlas (0.6410) and standard spherical U-Net (0.7011) approaches, respectively (p < 0.05). Additionally, the proposed method achieves a full set of sulcal labels in 20 seconds in this developmental cohort.

Measuring Cognitive Flexibility with the Flexible Item Selection Task: From fMRI Adaptation to Individual Connectome Mapping

Cognitive flexibility, the ability to appropriately adjust behavior in a changing environment, has been challenging to operationalize and validate in cognitive neuroscience studies. Here, we investigate neural activation and directed functional connectivity underlying cognitive flexibility using an fMRI-adapted version of the Flexible Item Selection Task (FIST) in adults (n = 32, ages 19-46 years). The fMRI-adapted FIST was reliable, showed comparable performance to the computer-based version of the task, and produced robust activation in frontoparietal, anterior cingulate, insular, and subcortical regions. During flexibility trials, participants directly engaged the left inferior frontal junction, which influenced activity in other cortical and subcortical regions. The strength of intrinsic functional connectivity between select brain regions was related to individual differences in performance on the FIST, but there was also significant individual variability in functional network topography supporting cognitive flexibility. Taken together, these results suggest that the FIST is a valid measure of cognitive flexibility, which relies on computations within a broad corticosubcortical network driven by inferior frontal junction engagement.

Aberrant functional connectivity of inhibitory control networks in children with autism spectrum disorder

Development of inhibitory control is a core component of executive function processes and a key aspect of healthy development. Children with autism spectrum disorder (ASD) show impairments in performance on inhibitory control tasks. Nevertheless, the research on the neural correlates of these impairments is inconclusive. Here, we explore the integrity of inhibitory control networks in children with ASD and typically developing (TD) children using resting state functional Magnetic Resonance Imagaing (MRI). In a large multisite sample, we find evidence for significantly greater functional connectivity (FC) of the right inferior frontal junction (rIFJ) with the posterior cingulate gyrus, and left and right frontal poles in children with ASD compared with TD children. Additionally, TD children show greater FC of rIFJ with the superior parietal lobule (SPL) compared with children with ASD. Furthermore, although higher rIFJ-SPL and rIFJ-IPL FC was related to better inhibitory control behaviors in both ASD and TD children, rIFJ-dACC FC was only associated with inhibitory control behaviors in TD children. These results provide preliminary evidence of differences in intrinsic functional networks supporting inhibitory control in children with ASD, and provide a basis for further exploration of the development of inhibitory control in children with the disorder. Autism Research 2018, 11: 1468-1478. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Inhibitory control is an important process in healthy cognitive development. Behavioral studies suggest that inhibitory control is impaired in autism spectrum disorder (ASD). However, research examining the neural correlates underlying inhibitory control differences in children with ASD is inconclusive. This study reveals differences in functional connectivity of brain networks important for inhibitory control in children with ASD compared with typically developing children. Furthermore, it relates brain network differences to parent-reported inhibitory control behaviors in children with ASD. These findings provide support for the hypothesis that differences in brain connectivity may underlie observable behavioral deficits in inhibitory control in children with the disorder.

Resting-State Brain Signal Variability in Prefrontal Cortex Is Associated With ADHD Symptom Severity in Children

Atypical brain function in attention-deficit/hyperactivity disorder (ADHD) has been identified using both task-activation and functional connectivity fMRI approaches. Recent work highlights the potential for another measure derived from functional neuroimaging data, brain signal variability, to reveal insights into clinical conditions. Higher brain signal variability has previously been linked with optimal behavioral performance. At present, little is known regarding the relationship between resting-state brain signal variability and ADHD symptom severity. The current study examined the relationship between a measure of moment-to-moment brain signal variability called mean-square successive difference (MSSD) and ADHD symptomatology in a group of children (7–12 years old) with (n = 40) and without (n = 30) a formal diagnosis of ADHD. A categorical analysis comparing subjects with and without a clinical diagnosis of ADHD showed no differences in MSSD between groups. A dimensional analysis revealed a positive relationship between MSSD and overall ADHD symptom severity and inattention across children with and without an ADHD diagnosis. Specifically, this positive relationship was found in medial prefrontal areas comprising the default mode network. These results demonstrate a link between intrinsic brain signal variability and ADHD symptom severity that cuts across diagnostic categories, and point to a locus of dysfunction consistent with previous neuroimaging literature.

Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder

Children with neurodevelopmental disorders benefit most from early interventions and treatments. The development and validation of brain-based biomarkers to aid in objective diagnosis can facilitate this important clinical aim. The objective of this review is to provide an overview of current progress in the use of neuroimaging to identify brain-based biomarkers for autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), two prevalent neurodevelopmental disorders. We summarize empirical work that has laid the foundation for using neuroimaging to objectively quantify brain structure and function in ways that are beginning to be used in biomarker development, noting limitations of the data currently available. The most successful machine learning methods that have been developed and applied to date are discussed. Overall, there is increasing evidence that specific features (for example, functional connectivity, gray matter volume) of brain regions comprising the salience and default mode networks can be used to discriminate ASD from typical development. Brain regions contributing to successful discrimination of ADHD from typical development appear to be more widespread, however there is initial evidence that features derived from frontal and cerebellar regions are most informative for classification. The identification of brain-based biomarkers for ASD and ADHD could potentially assist in objective diagnosis, monitoring of treatment response and prediction of outcomes for children with these neurodevelopmental disorders. At present, however, the field has yet to identify reliable and reproducible biomarkers for these disorders, and must address issues related to clinical heterogeneity, methodological standardization and cross-site validation before further progress can be achieved.