Heightened risk-taking tendencies during adolescence have been hypothesized to be attributable to physiological differences of maturation in key brain regions. The socioemotional system (e.g., nucleus accumbens), which is instrumental in reward response, shows a relatively earlier development trajectory than the cognitive control system (e.g., medial prefrontal cortex), which regulates impulse response. This developmental imbalance between heightened reward seeking and immature cognitive control potentially makes adolescents more susceptible to engaging in risky activities. Here, we assess brain structure in the socioemotional and cognitive control systems through viscoelastic stiffness measured with magnetic resonance elastography (MRE) and volumetry, as well as risk-taking tendencies measured using two experimental tasks in 40 adolescents (mean age ​= ​13.4 years old). MRE measures of regional brain stiffness reflect brain health and development via myelin content and glial matrix makeup, and have been shown to be highly sensitive to cognitive processes as compared to measures of regional brain volume and diffusion weighted imaging metrics. We find here that the viscoelastic and volumetric differences between the nucleus accumbens and the prefrontal cortex are correlated with increased risk-taking behavior in adolescents. These differences in development between the two brain systems can be used as an indicator of those adolescents who are more prone to real world risky activities and a useful measure for characterizing response to intervention.

This paper examines the question what kind of ethics prevail in contemporary urban transport planning and what potential does an ethics of care hold for practice? Researchers have given ample attention to the need for better governance and coordination, and despite acknowledging the need to reduce reliance on private cars, little has been said by them about what ethics can or should guide planning to bring about such frameworks for caring. This area of research merits urgent work given our collective need to address the socio‐spatial, climate, and health impacts of car dependence. Taking as our focus transport planning in Victoria, Australia, we consider how an ethics of care could open new ways to redress how transport planning has perpetuated injustices in metropolitan Melbourne. We draw on secondary research to consider both the conditions that cultivated the current transport planning landscape and pathways for possible change that lie ahead. The research highlights opportunities to consider care as an ethical framework for transport planning that could amplify justice and equity claims in urban transport planning for Australian cities and that has salience for other cities elsewhere.

Cerebrovascular reactivity (CVR), the ability of cerebral blood vessels to dilate or constrict in response to a vasoactive stimulus, is a clinically meaningful measure of cerebrovascular health. Head motion and other noise sources substantially impact CVR quality, particularly in clinical populations. In this study, we evaluated multi-echo fMRI techniques, including optimal combination of echoes (ME-OC) and multi-echo independent component analysis (ME-ICA), for improving CVR quality relative to single-echo fMRI in participants with stroke. In a breath-hold fMRI dataset, ME-OC significantly improved CVR quality metrics and reduced the percentage of negative CVR values in normal-appearing gray and white matter ( p <0.05). ME-ICA reduced the dependence of BOLD signals on head motion but did not improve CVR quality metrics. In a separate resting-state dataset, ME-OC effects were largely consistent with the breath-hold dataset, but ME-ICA also significantly improved CVR quality metrics and reduced negative CVR values in normal-appearing gray and white matter relative to ME-OC ( p <0.05). These findings demonstrate that multi-echo fMRI can improve CVR estimation in clinical populations, particularly in low signal-to-noise datasets, enhancing the feasibility of CVR analyses in stroke studies and allowing for better visualization of stroke-related CVR deficits.

Cerebrovascular reactivity (CVR), the ability of cerebral blood vessels to dilate or constrict in response to a vasoactive stimulus, is a clinically useful measure of cerebrovascular health. CVR is often measured using a breath-hold task to modulate blood CO2 levels during an fMRI scan. Measuring end-tidal CO2 (PETCO2) with a nasal cannula during the task allows CVR amplitude to be calculated in standard units (vascular response per unit change in CO2, or %BOLD/mmHg) and CVR delay to be calculated in seconds. The use of standard units allows for normative CVR ranges to be established and for CVR comparisons to be made across subjects and scan sessions. Although breath holding can be successfully performed by diverse patient populations, obtaining accurate PETCO2 measurements requires additional task compliance; specifically, participants must breathe exclusively through their nose and exhale immediately before and after each breath hold. Meeting these requirements is challenging, even in healthy participants, and this has limited the translational potential of breath-hold fMRI for CVR mapping. Previous work has focused on using alternative regressors such as respiration volume per time (RVT), derived from respiration-belt measurements, to map CVR. Because measuring RVT does not require additional task compliance from participants, it is a more feasible measure than PETCO2. However, using RVT does not produce CVR amplitude in standard units. In this work, we explored how to achieve CVR amplitude maps, in standard units, and CVR delay maps, when breath-hold task PETCO2 data quality is low. First, we evaluated whether RVT could be scaled to units of mmHg using a subset of PETCO2 data of sufficiently high quality. Second, we explored whether a PETCO2 timeseries predicted from RVT using deep learning allows for more accurate CVR measurements. Using a dense-mapping breath-hold fMRI dataset, we showed that both rescaled RVT and rescaled, predicted PETCO2 can be used to produce maps of CVR amplitude in standard units and CVR delay with strong absolute agreement to ground-truth maps. The rescaled, predicted PETCO2 regressor resulted in superior accuracy for both CVR amplitude and delay. In an individual with regions of increased CVR delay due to Moyamoya disease, the predicted PETCO2 regressor also provided greater sensitivity to pathology than RVT. Ultimately, this work will increase the clinical applicability of CVR in populations exhibiting decreased task compliance.

Multi-echo independent component analysis (ME-ICA) has been demonstrated to improve sensitivity and reliability of task functional magnetic resonance imaging (fMRI) data and, in particular, motor-task data with inherent task-correlated head motion. However, previous work has shown that an overly aggressive ME-ICA denoising approach may unintentionally remove task-related signal, while a more conservative approach may not effectively mitigate noise. While the effects of varied implementations of ME-ICA on signal and noise characteristics have been tested thoroughly in breath-hold data, the effects of similar modeling decisions have not been studied in motor-task data, which present with a more localized neural response. Here, we tested and compared the impacts of three analysis methods using rejected ME-ICA components as regressors in subject-level modeling: Aggressive (simple inclusion of ME-ICA regressors), Moderate (excluding task-correlated ME-ICA regressors from the model), and Conservative (orthogonalization of ME-ICA regressors to the core model and accepted ME-ICA components). We applied these methods to data from healthy and multiple sclerosis populations that included performance of hand-grasp, shoulder-abduction, and ankle-flexion tasks. We found that when the amount of head motion and its correlation with the task was high and the expected task-evoked signal was relatively low, the Conservative method led to significantly higher activation, t-statistics, and test-retest similarity in motor regions compared to the Aggressive method. Future motor-task studies may wish to implement similar models to prevent loss of motor signal, while still mitigating the effects of task-correlated head motion.

Cerebrovascular reactivity (CVR), the ability of cerebral blood vessels to dilate or constrict in response to a vasoactive stimulus, is a clinically useful measure of cerebrovascular health. CVR is often measured using a breath-hold task to modulate blood CO ₂ levels during an fMRI scan. Measuring end-tidal CO ₂ (P _ET CO ₂ ) with a nasal cannula during the task allows CVR amplitude to be calculated in standard units (vascular response per unit change in CO ₂ , or %BOLD/mmHg) and CVR delay to be calculated in seconds. The use of standard units allows for normative CVR ranges to be established and for CVR comparisons to be made across subjects and scan sessions. Although breath holding can be successfully performed by diverse patient populations, obtaining accurate P _ET CO ₂ measurements requires additional task compliance; specifically, participants must breathe exclusively through their nose and exhale immediately before and after each breath hold. Meeting these requirements is challenging, even in healthy participants, and this has limited the translational potential of breath-hold fMRI for CVR mapping. Previous work has focused on using alternative regressors such as respiration volume per time (RVT), derived from respiration-belt measurements, to map CVR. Because measuring RVT does not require additional task compliance from participants, it is a more feasible measure than P _ET CO ₂ . However, using RVT does not produce CVR amplitude in standard units. In this work, we explored how to achieve CVR amplitude maps, in standard units, and CVR delay maps, when breath-hold task P _ET CO ₂ data quality is low. First, we evaluated whether RVT could be scaled to units of mmHg using a subset of P _ET CO ₂ data of sufficiently high quality. Second, we explored whether a P _ET CO ₂ timeseries predicted from RVT using deep learning allows for more accurate CVR measurements. Using a dense-mapping breath-hold fMRI dataset, we showed that both rescaled RVT and rescaled, predicted P _ET CO ₂ can be used to produce maps of CVR amplitude in standard units and CVR delay with strong absolute agreement to ground-truth maps. The rescaled, predicted P _ET CO ₂ regressor resulted in superior accuracy for both CVR amplitude and delay. In an individual with regions of increased CVR delay due to Moyamoya disease, the predicted P _ET CO ₂ regressor also provided greater sensitivity to pathology than RVT. Ultimately, this work will increase the clinical applicability of CVR in populations exhibiting decreased task compliance.