The lateral prefrontal cortex (LPFC) is disproportionately expanded in humans compared to non-human primates, although the relationship between LPFC brain structures and uniquely human cognitive skills is largely unknown. Here, we test the relationship between variability in LPFC tertiary sulcal morphology and reasoning scores in a cohort of children and adolescents. Using a data-driven approach in independent discovery and replication samples, we show that the depth of specific LPFC tertiary sulci is associated with individual differences in reasoning scores beyond age. To expedite discoveries in future neuroanatomical-behavioral studies, we share tertiary sulcal definitions with the field. These findings support a classic but largely untested theory linking the protracted development of tertiary sulci to late-developing cognitive processes.

The relationship between structural variability in late-developing association cortices like the lateral prefrontal cortex (LPFC) and the development of higher-order cognitive skills is not well understood. Recent findings show that the morphology of LPFC sulci predicts reasoning performance; this work led to the observation of substantial individual variability in the morphology of one of these sulci, the para-intermediate frontal sulcus (pimfs). Here, we sought to characterize this variability and assess its behavioral significance. To this end, we identified the pimfs in a developmental cohort of 72 participants, ages 6-18. Subsequent analyses revealed that the presence or absence of the ventral component of the pimfs was associated with reasoning, even when controlling for age. This finding shows that the cortex lining the banks of sulci can support the development of complex cognitive abilities and highlights the importance of considering individual differences in local morphology when exploring the neurodevelopmental basis of cognition.

The neuroanatomical changes that underpin cognitive development are of major interest in neuroscience. Of the many aspects of neuroanatomy to consider, tertiary sulci are particularly attractive as they emerge last in gestation, show a protracted development after birth, and are either human- or hominoid-specific. Thus, they are ideal targets for exploring morphological-cognitive relationships with cognitive skills that also show protracted development such as working memory (WM). Yet, the relationship between sulcal morphology and WM is unknown-either in development or more generally. To fill this gap, we adopted a data-driven approach with cross-validation to examine the relationship between sulcal depth in lateral prefrontal cortex (LPFC) and verbal WM in 60 children and adolescents between ages 6 and 18. These analyses identified 9 left, and no right, LPFC sulci (of which 7 were tertiary) whose depth predicted verbal WM performance above and beyond the effect of age. Most of these sulci are located within and around contours of previously proposed functional parcellations of LPFC. This sulcal depth model outperformed models with age or cortical thickness. Together, these findings build empirical support for a classic theory that tertiary sulci serve as landmarks in association cortices that contribute to late-maturing human cognitive abilities.

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).

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.