Category: 7. Science

The advanced air mobility industry is currently working to produce novel aircraft ranging from air taxis to autonomous cargo drones, and all of those designs will require extensive testing – which is why NASA is working to give them a head-start by studying a special kind of model wing. The wing is a scale model of a design used in a type of aircraft called a “tiltwing,” which can swing its wing and rotors from vertical to horizontal. This allows the aircraft to take off, hover, and land like a helicopter, or fly like a fixed-wing airplane. This design enables versatility in a range of operating environments.

Several companies are working on tiltwings, but NASA’s research into the scale wing will also impact nearly all types of advanced air mobility aircraft designs.

“NASA research supporting advanced air mobility demonstrates the agency’s commitment to supporting this rapidly growing industry,” said Brandon Litherland, principal investigator for the test at NASA’s Langley Research Center in Hampton, Virginia. “Tool improvements in these areas will greatly improve our ability to accurately predict the performance of new advanced air mobility aircraft, which supports the adoption of promising designs. Gaining confidence through testing ensures we can identify safe operating conditions for these new aircraft.”

In May and June, NASA tested a 7-foot wing model with multiple propellers in the 14-by-22-Foot Subsonic Wind Tunnel at Langley. The model is a “semispan,” or the right half of a complete wing. Understanding how multiple propellers and the wing interact under various speeds and conditions provides valuable insight for the advanced air mobility industry. This information supports improved aircraft designs and enhances the analysis tools used to assess the safety of future designs.

This work is managed by the Revolutionary Vertical Lift Technology project under NASA’s Advanced Air Vehicles Program in support of NASA’s Advanced Air Mobility mission, which seeks to deliver data to guide the industry’s development of electric air taxis and drones.

“This tiltwing test provides a unique database to validate the next generation of design tools for use by the broader advanced air mobility community,” said Norm Schaeffler, the test director, based at Langley. “Having design tools validated for a broad range of aircraft will accelerate future design cycles and enable informed decisions about aerodynamic and acoustic performance.”

The wing is outfitted with over 700 sensors designed to measure pressure distribution, along with several other types of tools to help researchers collect data from the wing and propeller interactions. The wing is mounted on special sensors to measure the forces applied to the model. Sensors in each motor-propeller hub to measure the forces acting on the components independently.

The model was mounted on a turntable inside the wind tunnel, so the team could collect data at different wing tilt angles, flap positions, and rotation rates. The team also varied the tunnel wind speed and adjusted the relative positions of the propellers.  

Researchers collected data relevant to cruise, hover, and transition conditions for advanced air mobility aircraft. Once they analyze this data, the information will be released to industry on NASA’s website.

The present study reveals the utility of going beyond model-driven (i.e., GLM or multiple regression) contrast-based analyses of task fMRI data. Voxel-wise GLM analysis applies the same a priori specified model of the brain’s temporal response at every voxel, with resulting contrast maps (e.g., faces > shapes) reflecting an aggregate map comprised of any voxel significantly activated by the task, according to the a priori model. As shown in previous work⁷, this map vastly underestimates the extent of task-relevant neuronal activity, in part because brain regions and networks with temporal responses to the task that differ from the a priori model time courses will not be identified, and in part because the common subtraction paradigm to generate a contrast map removes brain activation common to both conditions. This neglects the dynamic relationships between brain areas and networks. Further, even if a subtraction map reflects a more spatially distributed pattern of brain activation, a GLM-based contrast map is an aggregate brain activation map that can reflect activity of an undifferentiated mix of temporally distinct networks. Here, by using tICA to resolve dynamic and temporally distinct task-related activity and networks, we uncover an appreciably larger-than-previously-considered engagement of the brain by the EFMT: 74% of cortex and circumscribed subcortical regions, including the amygdala and cerebellum.

We found that EFMT-recruited brain networks with greater activity during face matching relative to shapes (i.e., Networks 1, 2, 3, 4, 6, 7, and 10) have diverse temporal activation patterns, likely reflecting distinct aspects of task performance. For instance, Network 10 activity ramped up during face blocks, while Network 2 activity remained more constant during face blocks. Several networks also displayed changes in their activation patterns across the course of the task run. For instance, Network 3 displayed strong activation during the first shape block, but little to no activation during subsequent shape blocks. Such differences between early and later blocks were observed for multiple networks, despite our focus on the second task run, suggesting ongoing impacts of learning and novelty on brain network activation within runs, even after a full run of practice. These within-block and within-run network activation changes (Fig. 1) identified using the tICA data-driven approach are not captured by standard voxel-wise GLM analyses, which model each task condition as a block of constant activity convolved with a hemodynamic response function and may reflect overlooked (or difficult to model) effects of practice, habituation, or fatigue. Overall, tICA’s use of fine-grained temporal information identified a richer-than-expected tapestry of concurrent neurobiological processes recruited by the EFMT, distinguishing brain networks with diverse activation patterns and with distinct relations to task performance and general cognition.

The 10 EFMT-recruited networks identified by tICA involve the interaction of visual association cortex with different sets of non-visual brain regions in distinct temporal fashions. These diverse interactions are supported by the multifaceted anatomical connections between the visual cortex and the rest of the brain. Kravitz et al.³⁶ detail the ventral visual network’s projections to at least six distinct areas that support different cognitive functions, including an occipitotemporal-amygdala pathway involved in detecting and processing emotionally salient stimuli and an occipitotemporal-VLPFC pathway supporting object working memory. Networks 2 and 6 show extensive recruitment of both the occipitotemporal-VLPFC pathway and the occipitotemporal-amygdala pathway. However, their time courses suggest distinct roles in task performance. Network 2 remains active throughout face blocks, peaking mid-block, suggesting its engagement of working memory processes that support maintenance of the task rule set despite the presence of salient emotional stimuli, supported by DAN-mediated spatial executive attention. On the other hand, Network 6 activity increases over the course of each face block and is distinguished from Network 2 by the suppression of medial visual networks engaged during low-level processing of visual stimuli; as such, Network 6 may support the focus of visual attention as the interference load created by the emotional faces accrues across the block.

Notably, Network 2 also had the highest feature importance for predicting emotion interference, showing high subject loadings on Network 2 predicted high levels of emotion interference across samples. Network 10 was also significantly and reproducibly associated with individual differences in emotion interference during the EFMT as well as with general cognition across both groups. Both networks are preferentially activated during face blocks, but with distinct time courses (Network 2 activity peaking in the middle of face blocks and Network 10 activity peaking at the end of blocks), suggesting distinct roles of each network in the emotion regulation process. Unlike Network 2, Network 10 showed increased activation over the course of the faces blocks, activating most strongly within the second half of each of these blocks. While also containing lateral visual areas, Network 10 primarily resembled a right-lateralized FPN component, suggesting a role for this network in top-down executive control, which may become increasingly recruited over the course of the more challenging task blocks. This interpretation is bolstered by the observation that working memory task performance was a top predictive feature of Network 10 loadings. Network 5 (DMN + contextual association network) was also significantly associated with emotion interference, but in contrast to Networks 2 and 10, it was preferentially activated during inter-block intervals and suppressed during face blocks, which aligns with the DMN’s role as a task-negative network. This suggests that the capacity to suppress internally-focused thoughts during active periods of the task, and/or the ability to prepare for upcoming blocks during rest periods between task conditions, can also impact key aspects of task performance.

Even though individual differences in the recruitment of the remaining seven networks were not robustly associated with variability in emotion interference, they almost certainly play roles in supporting other cognitive aspects of task engagement and completion, as supported by the significant relations between several of these networks and general cognition (Fig. 7). The emotional face-matching condition and shape-matching (control) condition differ in several aspects besides emotion content that require engagement of multiple cognitive processes. First, the face stimuli are considerably more complex than the ovals used in the shape-matching condition, requiring modulation of focus and cognitive effort. Further, visual processing of human faces by other humans employs specialized brain mechanisms that enable rapid holistic assessment of facial identity and emotion³⁷, requiring recruitment of distinct visual processing streams by the two conditions. Finally, the emotional face condition requires early attentional screening and later downregulation of salient yet extraneous emotional information to focus on the task-relevant aspects of the stimulus (i.e., matching the face identity). Each of these cognitive aspects of the task likely follows distinct time courses and is differentially impacted by practice, fatigue, repetition priming, and habituation.

For example, of these networks, 1 and 4 show increased engagement of the ventral visual stream, amygdala, and VLPFC in conjunction with deactivation of DAN and the premotor network. The negative spatial component of Network 4 also resembled the action mode network³⁸, posited to activate during goal-directed and externally focused tasks. As such, each network is situated to process emotionally salient stimuli and modulate attention. These networks may, in part, act as complements to Network 2 to support spatially-focused attention within the matching task, providing concurrent suppression of attention to non-relevant areas of the visual field.

The positive component of Network 9 showed significant overlap with both DMN and FPN, although in contrast to Network 10 (which was associated with emotion interference and cognition), the FPN regions of Network 9 were left-lateralized. Network 9 also dramatically differed from Network 10 in its time course, showing suppression early in faces blocks as well as early in the first shapes block, which decreased over the course of these blocks. Activation of Network 9 occurred primarily within the inter-block interval, suggesting this executive network might play a role in task switching. The time course of Network 5 closely resembled that of Network 9 despite limited spatial overlap. Network 5 primarily overlapped with the DMN, which is activated during unconstrained resting state periods in the absence of goal-directed activity. As such, suppression of Network 5 during the first shapes task block and the faces blocks likely reflects the need for increased task focus during these periods and a consequent reduction in internally-focused or self-reflective cognitive processes. The increased activation of this network during the inter-block intervals may likewise result from a temporary increase in such internal reflections.

For Networks 3 and 7, which were rapidly activated or suppressed during the between-block transitions, activation may reflect the brief rest period, the presentation of the written instruction card, and preparation for a change in task requirements. The overlap with the language network in both cases suggests that the written instructions do play a role in the recruitment of these networks. The recruitment of DMN nodes and suppression of premotor and somatomotor cortex within Network 7, along with deactivation of visual networks and DAN during the intertrial interval in both networks, may reflect the temporary reduction in task demands. The recruitment of the parietal memory network during these intervals in Network 3 may be due to the need to recall general task instructions. In addition to the intertrial period, these networks were also differentially engaged during the faces and shapes conditions, though the engagement of Network 3 during the first shape block resembled that observed during subsequent faces blocks, and the suppression of Network 7 during shapes blocks decreased over the course of the run. These patterns converge to suggest roles for these two networks in the general recruitment and redistribution of attentional resources during the task.

Perhaps most surprisingly, despite the EFMT’s clear recruitment of multiple brain networks and regions implicated in emotion processing and internal affective state (e.g., amygdala), none of the EFMT networks reflected individual differences in internalizing/negative affect or well-being/positive affect. This coincided with the absence of the subgenual cingulate, a brain region centrally linked to negative affect and depression^26,27, as a node in any of the EFMT-recruited networks. This was particularly striking given the involvement of nearly 75% of the cortex in one or more of these networks. One possible reason for the absence of subgenual cingulate in these networks is the known fMRI signal dropout in this brain region due to its proximity to tissue-air boundaries³⁹. However, other brain regions known to experience similar signal dropout were detected here (e.g., fusiform gyrus)³⁹. Moreover, other studies of related in-scanner tasks that engage emotion interference have also failed to reliably detect subgenual cingulate activity^40,41. Together with convergent findings from the systematic review of the EFMT literature¹³ and other recent studies^42,43, our findings suggest that while the EFMT may engage emotion-related processes, it may not be salient, challenging, or evocative enough to be a truly incisive tool for assessing individual differences in the NVS constructs. Notably, although several large-scale neuroimaging studies include this task as an RDoC NVS probe, the task is actually not listed in the RDoC matrix as a standard probe for the NVS.

There are some limitations to the present study. First, while tICA is a purely data-driven technique, its use involves some subjective decision points as described in the Methods, including the choice of model order and the classification of noise and signal components. Second, while the tICA identified 10 task-related networks and their temporal profiles, the task performance data that are available are somewhat limited for interpreting the task components that may be engaging each network. Probing the roles of each of these networks by linking to more detailed performance measures will be an exciting new direction for research. A second limitation of our study is that the participants in the HCP study were generally healthy young adults, with no or only sub-clinical levels of anxiety, depression, and drug use. This may limit our ability to link brain networks to individual variability in negative affect. However, our findings are consistent with the review by Savage et al.¹³, which also found no consistent relationship between brain activation in primarily emotion-processing brain areas during EFMT and mental health disorder diagnoses, including major and bipolar depression, anxiety-related disorders, and obsessive compulsive disorder, among others. But given the wealth of insights into brain dynamics from tensor ICA, exploration of this task in clinical populations using the tensor ICA approach may reveal relationships between brain network dynamics and clinical measures.

In summary, we used tICA to identify and characterize spatiotemporal features of brain network processes recruited by the EFMT, revealing a rich dynamic landscape of brain activity that has not been observed using conventional contrast-based analyses of this important task. Our findings call into question the EFMT’s suitability to evoke brain activity that distinguishes individual differences in NVS function in healthy young adults, although more work is needed to determine whether brain dynamics during EFMT is of utility in clinical populations.

Archaeologists have uncovered primitive sharp-edged stone tools on the Indonesian island of Sulawesi, adding another piece to an evolutionary puzzle involving mysterious ancient humans who lived in a region known as Wallacea.

Located beyond mainland Southeast Asia, Wallacea includes a group of islands between Asia and Australia, among which Sulawesi is the largest. Previously, researchers have found evidence that an unusual, small-bodied human species dubbed Homo floresiensis — also called “hobbits” due to comparisons with the diminutive characters in fantasy author J.R.R. Tolkien’s books — lived on the nearby island of Flores from 700,000 years ago until about 50,000 years ago.

The newly discovered flaked stone tools, which date back between 1.04 million to 1.48 million years ago, represent the oldest evidence for human habitation of Sulawesi and suggest the island might have been inhabited by early human ancestors, or hominins, at the same time — or possibly earlier — than Flores. Researchers reported the findings in a study published Wednesday in the journal Nature.

Researchers are still trying to answer key questions about these Wallacea island hominins — namely when and how they arrived on the islands, which would have required an ocean crossing.

Flaked stone tools were earlier uncovered on Flores and dated to about 1.02 million years ago. The latest find suggests there might have been a link between the populations on Flores and Sulawesi — and that perhaps Sulawesi was a stepping stone for the hobbits on Flores, according to the authors of the new research, who have studied sites on Flores.

“We have long suspected that the Homo floresiensis lineage of Flores, which probably represents a dwarfed variant of early Asian Homo erectus, came originally from Sulawesi to the north, so the discovery of this very old stone technology on Sulawesi adds further weight to this possibility,” said co-lead study author Dr. Adam Brumm, professor of archaeology at Griffith University’s Australian Research Centre for Human Evolution.

Excavations conducted by co-lead study author Budianto Hakim, senior archaeologist at the National Research and Innovation Agency of Indonesia, began on Sulawesi in 2019 after a stone artifact was spotted protruding from a sandstone outcrop in an area known as the Calio site in a modern cornfield.

The site — in the vicinity of a river channel — would have been where hominins made their tools and hunted 1 million years ago, according to the archaeologists, who also found animal fossils in the area. Among the finds was a jawbone of the now-extinct Celebochoerus, a type of pig with unusually large upper tusks.

At the conclusion of excavations in 2022, the team uncovered seven stone tools. Dating of the sandstone and fossils resulted in an age estimate for the tools of at least 1.04 million years old to potentially 1.48 million years old. Hominin-related artifacts previously found on Sulawesi had been dated to 194,000 years ago.

The small, sharp stone fragments used as tools were likely fashioned from larger pebbles in nearby riverbeds, and they were probably used for cutting or scraping, Brumm said. The tools are similar to early human stone technology discoveries made before on Sulawesi and other Indonesian islands as well as early hominin sites in Africa, he added.

“They reflect a so-called ‘least-effort’ approach to reducing stones into useful, sharp-edged tools; these are uncomplicated implements, but it requires a certain level of skill and experience to make these tools — they result from precise and controlled flaking of stone, not randomly bashing rocks together,” Brumm said.

But who was responsible for making these tools in the first place?

“It’s a significant piece of the puzzle, but the Calio site has yet to yield any hominin fossils,” Brumm said. “So while we now know there were tool-makers on Sulawesi a million years ago, their identity remains a mystery.”