Geometric growth of the normal human craniocervical junction from 0 to 18 years old.

The craniocervical junction (CCJ) forms the bridge between the skull and the spine, a highly mobile group of joints that allows the mobility of the head in every direction. The CCJ plays a major role in protecting the inferior brainstem (bulb) and spinal cord, therefore also requiring some stability. Children are subjected to multiple constitutive or acquired diseases involving the CCJ: primary bone diseases such as in FGFR-related craniosynostoses or acquired conditions such as congenital torticollis, cervical spine luxation, and neurological disorders. To design efficient treatment plans, it is crucial to understand the relationship between abnormalities of the craniofacial region and abnormalities of the CCJ. This can be approached by the study of control and abnormal growth patterns. Here we report a model of normal skull base growth by compiling a collection of geometric models in control children. Focused analyses highlighted specific developmental patterns for each CCJ bone, emphasizing rapid growth during infancy, followed by varying rates of growth and maturation during childhood and adolescence until reaching stability by 18 years of age. The focus was on the closure patterns of synchondroses and sutures in the occipital bone, revealing distinct closure trajectories for the anterior intra-occipital synchondroses and the occipitomastoid suture. The findings, although based on a limited dataset, showcased specific age-related changes in width and closure percentages, providing valuable insights into growth dynamics within the first 2 years of life. Integration analyses revealed intricate relationships between skull and neck structures, emphasizing coordinated growth at different stages. Specific bone covariation patterns, as found between the first and second cervical vertebrae (C1 and C2), indicated synchronized morphological changes. Our results provide initial data for designing inclusive CCJ geometric models to predict normal and abnormal growth dynamics.

The neural correlates of memory integration in value-based decision-making during human spatial navigation.

In daily life, we often make decisions based on relative value of the options, and we often derive these values from segmenting or integrating the outcomes of past episodes in memory. The neural correlates involved in value-based decision-making have been extensively studied in the literature, but few studies have investigated this topic in decisions that require segmenting or integrating episodic memory from related sources, and even fewer studies examine it in the context of spatial navigation. Building on the computational models from our previous studies, the current study investigates the neural substrates involved in decisions that require people either segment or integrate wayfinding outcomes involving different goals, across virtual spatial navigation tasks with differing demands. We find that when decisions require computation of spatial distances for navigation options, but also evaluation of one's prior spatial navigation ability with the task, the estimated value of navigational choices (EV) modulates neural activity in the dorsomedial prefrontal (dmPFC) cortex and ventrolateral prefrontal (vlFPC) cortex. However, superior parietal cortex tracked EV when decision-making tasks only require spatial distance memory but not evaluation of spatial navigation ability. Our findings reveal divergent neural substrates of memory integration in value-based decision-making under different spatial processing demands.

Episodic memory integration shapes value-based decision-making in spatial navigation.

Valued-based decision-making has been studied for decades in myriad topics such as consumer spending and gambling, but very rarely in spatial navigation despite the link between the two being highly relevant to survival. Furthermore, how people integrate episodic memories, and what factors are related to the extent of memory integration in value-based decision-making, remain largely unknown. In the current study, participants learned locations of various objects in a virtual environment and then decided whether to reach goal objects from familiar starting locations or unpredictable ones, with different penalties associated with each option. We developed computational models to test whether, when given an object to find, participants' starting location decisions reflected their past performance specific to that goal (Target-specific model) or integrated memory from performance with all goals in the environment (Target-common model). Because participants' wayfinding performance improved throughout the experiment, we were able to examine what factors related to the generalization of past experience. We found that most participants' decisions were better fit by the Target-common model, and for the people whose decisions were better fit by the Target-common model this integrative tendency may be tied to their concurrently greater performance variability with individual targets. Moreover, greater success on our task was predicted by an interaction between the ability to estimate probabilities relevant to decision-making and self-report general task ability. Collectively, our results show how related navigational episodic memories can be reflected in decision-making, and uncover individual differences contributing to such processes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A comparison of reinforcement learning models of human spatial navigation.

Reinforcement learning (RL) models have been influential in characterizing human learning and decision making, but few studies apply them to characterizing human spatial navigation and even fewer systematically compare RL models under different navigation requirements. Because RL can characterize one's learning strategies quantitatively and in a continuous manner, and one's consistency of using such strategies, it can provide a novel and important perspective for understanding the marked individual differences in human navigation and disentangle navigation strategies from navigation performance. One-hundred and fourteen participants completed wayfinding tasks in a virtual environment where different phases manipulated navigation requirements. We compared performance of five RL models (3 model-free, 1 model-based and 1 "hybrid") at fitting navigation behaviors in different phases. Supporting implications from prior literature, the hybrid model provided the best fit regardless of navigation requirements, suggesting the majority of participants rely on a blend of model-free (route-following) and model-based (cognitive mapping) learning in such navigation scenarios. Furthermore, consistent with a key prediction, there was a correlation in the hybrid model between the weight on model-based learning (i.e., navigation strategy) and the navigator's exploration vs. exploitation tendency (i.e., consistency of using such navigation strategy), which was modulated by navigation task requirements. Together, we not only show how computational findings from RL align with the spatial navigation literature, but also reveal how the relationship between navigation strategy and a person's consistency using such strategies changes as navigation requirements change.