. Significance: Time-domain functional near-infrared spectroscopy (TD-fNIRS) has been considered as the gold standard of noninvasive optical brain imaging devices. However, due to the high cost, complexity, and large form factor, it has not been as widely adopted as continuous wave NIRS systems. Aim: Kernel Flow is a TD-fNIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-fNIRS system while integrating all of the components into a small modular device. Approach: The Kernel Flow modules are built around miniaturized laser drivers, custom integrated circuits, and specialized detectors. The modules can be assembled into a system with dense channel coverage over the entire head. Results: We show performance similar to benchtop systems with our miniaturized device as characterized by standardized tissue and optical phantom protocols for TD-fNIRS and human neuroscience results. Conclusions: The miniaturized design of the Kernel Flow system allows for broader applications of TD-fNIRS.

Improvements in vehicle safety require understanding of the neural systems that support the complex, dynamic task of real-world driving. We used functional near infrared spectroscopy (fNIRS) and pupilometry to quantify cortical and physiological responses during a realistic, simulated driving task in which vehicle dynamics were manipulated. Our results elucidate compensatory changes in driver behavior in response to changes in vehicle handling. We also describe associated neural and physiological responses under different levels of mental workload. The increased cortical activation we observed during the late phase of the experiment may indicate motor learning in prefrontal-parietal networks. Finally, relationships among cortical activation, steering control, and individual personality traits suggest that individual brain states and traits may be useful in predicting a driver's response to changes in vehicle dynamics. Results such as these will be useful for informing the design of automated safety systems that facilitate safe and supportive driver-car communication.

Demands on visuospatial working memory are a ubiquitous part of everyday life. As such, significant efforts have been made to understand how the brain responds to these demands in real-world environments. Multiple brain imaging studies have highlighted a fronto-parietal cortical network that underlies visuospatial working memory, is modulated by cognitive load, and that appears to respond uniquely to encoding versus retrieval components. Furthermore, multiple studies have identified functional connectivity in regions of the fronto-parietal network during working memory tasks. Together, these findings have helped outline important aspects of the neural architecture that underlies visuospatial working memory. Here, we provide results from the first fNIRS-based investigation of fronto-parietal signatures of cortical activation and functional connectivity during a computer-based visuospatial working memory task. Our results indicate that the local maxima of cortical activation and functional coherence do not necessarily overlap spatially, and that cortical activation is significantly more susceptible to task-specific demands compared to functional connectivity. These results highlight important and novel information regarding neurotypical signatures of cortical activation and functional connectivity during visuospatial working memory. Our findings also demonstrate the utility of fNIRS for interrogating these cognitive processes.

Visuomotor ability is quite crucial for everyday functioning, particularly in driving and sports. While there is accumulating evidence regarding neural correlates of visuomotor transformation, less is known about the brain regions that accommodate visuomotor mapping under different cognitive demands. We concurrently measured cortical activity and pupillary response, using functional near infrared spectroscopy (fNIRS) and eye-tracking glasses, to examine the neural systems linked to pupil dilation under varying cognitive demands. Twenty-three healthy adults performed two sessions of a navigation task, in which the cognitive load was manipulated by either reversing the visuomotor mapping or increasing the speed of the moving object. We identified a region in the right superior parietal lobule that responded to both types of visuomotor load and its activity was associated with larger pupillary response and better performance in the task. Our multimodal analyses suggest that activity in this region arises from the need for increased attentional effort and alertness for visuomotor control and is an ideal candidate for objective measurement of visuomotor cognitive load. Our data extend previous findings connecting changes in pupil diameter to neural activity under varying cognitive demand and have important implications for examining brain-behavior associations in real-world tasks such as driving and sports.

Individuals with Autism Spectrum Disorder (ASD) and their relatives process faces differently from typically developed (TD) individuals. In an fMRI face-viewing task, TD and undiagnosed sibling (SIB) children (5–18 years) showed face specialization in the right amygdala and ventromedial prefrontal cortex (vmPFC), with left fusiform and right amygdala face specialization increasing with age in TD subjects. SIBs showed extensive antero-medial temporal lobe activation for faces that was not present in any other group, suggesting a potential compensatory mechanism. In ASD, face specialization was minimal but increased with age in the right fusiform and decreased with age in the left amygdala, suggesting atypical development of a frontal-amygdala-fusiform system which is strongly linked to detecting salience and processing facial information.