Paul W. Burgess

Evidence indicates females may be more susceptible to sports-related concussion with worse and prolonged symptom severity according to menstrual cycle phase. We investigated whether menstrual cycle phases influence non-concussive heading kinematics in elite female footballers, and whether these impacts affect an athlete’s cognition. Five eumenorrheic elite female footballers (M _age = 23 ± 4 years) participated in a 16-week proof-of-concept study wherein menstrual cycle phases were tracked, and cognition was monitored. Participants performed a weekly controlled heading drill by heading a ball thrown from 5 m away. Head accelerations were measured using custom-moulded PROTECHT instrumented mouthguards. 256 headers revealed no significant differences in head acceleration across the various phases of the menstrual cycle. However, change in cognitive performance was related to heading completion and menstrual phase. Hormonal fluctuations during the menstrual cycle may influence cognition independent of head impact biomechanics among elite female athletes, under controlled conditions. We note that our study also demonstrated the safety and efficacy of the mouthguard equipment employed here, as well as the ease with which the protocol was received by the athletes. These outcomes should be considered when implementing future research with larger cohorts and the inclusion of match-related heading.

Orca, wolves, chimpanzees and humans share a similarly impressive capacity for group hunting, where individuals coordinate behaviour together to capture prey. Studying hunting behaviours has important implications for understanding how behaviour in group contexts may be indicative of cognitive decline. Despite growing interest in brain circuits for prey capture, the brain regions involved in tracking prey during a hunt and the behaviours in group hunt linked to success remain unclear. Here we combined functional near infrared spectroscopy (fNIRS) and a virtual minecraft world to examine behaviour, brain dynamics and brain synchrony involved in group hunting behaviour. We focused on the prefrontal cortex (PFC) due to its known role in planning and social coordination and recorded from pairs of individuals as they either cooperated to hunt another person (prey) or simply followed another person. Hunters were more successful if they managed to keep a smaller distance to the prey and moved at speeds that were more synchronised with their co-predator. At high-range frequencies for fNIRS (0.1-0.2Hz), we found greater brain-to-brain synchrony in lateral and medial (frontopolar) PFC regions during hunting compared with chance levels. Together, these findings provide insights into what behaviours and brain dynamics associated with successful group hunting.

Significance: There is a longstanding recommendation within the field of fNIRS to use oxygenated (HbO2) and deoxygenated (HHb) hemoglobin when analyzing and interpreting results. Despite this, many fNIRS studies do focus on HbO2 only. Previous work has shown that HbO2 on its own is susceptible to systemic interference and results may mostly reflect that rather than functional activation. Studies using both HbO2 and HHb to draw their conclusions do so with varying methods and can lead to discrepancies between studies. The combination of HbO2 and HHb has been recommended as a method to utilize both signals in analysis. Aim: We present the development of the hemodynamic phase correlation (HPC) signal to combine HbO2 and HHb as recommended to utilize both signals in the analysis. We use synthetic and experimental data to evaluate how the HPC and current signals used for fNIRS analysis compare. Approach: About 18 synthetic datasets were formed using resting-state fNIRS data acquired from 16 channels over the frontal lobe. To simulate fNIRS data for a block-design task, we superimposed a synthetic task-related hemodynamic response to the resting state data. This data was used to develop an HPC-general linear model (GLM) framework. Experiments were conducted to investigate the performance of each signal at different SNR and to investigate the effect of false positives on the data. Performance was based on each signal’s mean T-value across channels. Experimental data recorded from 128 participants across 134 channels during a finger-tapping task were used to investigate the performance of multiple signals [HbO2, HHb, HbT, HbD, correlation-based signal improvement (CBSI), and HPC] on real data. Signal performance was evaluated on its ability to localize activation to a specific region of interest. Results: Results from varying the SNR show that the HPC signal has the highest performance for high SNRs. The CBSI performed the best for medium-low SNR. The next analysis evaluated how false positives affect the signals. The analyses evaluating the effect of false positives showed that the HPC and CBSI signals reflect the effect of false positives on HbO2 and HHb. The analysis of real experimental data revealed that the HPC and HHb signals provide localization to the primary motor cortex with the highest accuracy. Conclusions: We developed a new hemodynamic signal (HPC) with the potential to overcome the current limitations of using HbO2 and HHb separately. Our results suggest that the HPC signal provides comparable accuracy to HHb to localize functional activation while at the same time being more robust against false positives.

Some people with autism spectrum disorders have been observed to experience difficulties with making correct inferences in conversations in social situations. However, the nature and origin of their problem is rarely investigated. This study used manipulations of video stimuli to investigate two questions. The first question was whether it is the number of people involved in social situations, that is, the source of problems in following conversations, or whether it is the increased mentalising demands required to comprehend interactions between several people. The second question asked was whether the nature and pattern of the errors that autism spectrum disorder participants show are the same as typically developing people make when they make an error. In total, 43 typically developed adults and 30 adults diagnosed with autism spectrum disorder were studied. We found that it was the amount of mentalising required, rather than the number of people involved, which caused problems for people with autism spectrum disorder in following conversations. Furthermore, the autism spectrum disorder participants showed a more heterogeneous pattern of errors, showing less agreement among themselves than the typically developed group as to which test items were hardest. So, fully understanding the observed behaviour consequent upon weakness in mentalising ability in people with autism spectrum disorders requires consideration of factors other than mentalising.

Previous research has shown that short-term fasting in healthy individuals (HIs) is associated with changes in risky decision-making. The current experiment was designed to examine the influence of short-term fasting in HIs on four types of impulsivity: reflection impulsivity, risky decision-making, delay aversion, and action inhibition. HIs were tested twice, once when fasted for 20 hours, and once when satiated. Participants demonstrated impaired action inhibition when fasted; committing significantly more errors of commission during a food-related Affective Shifting Task. Participants also displayed decreased reflection impulsivity when fasted, opening significantly more boxes during the Information Sampling Task (IST). There were no significant differences in performance between fasted and satiated sessions for risky decision-making or delay aversion. These findings may have implications for understanding eating disorders such as Bulimia Nervosa (BN). Although BN has been characterised as a disorder of poor impulse control, inconsistent findings when comparing individuals with BN and HIs on behavioural measures of impulsivity question this characterisation. Since individuals with BN undergo periods of short-term fasting, the inconsistent findings could be due to differences in the levels of satiation of participants. The current results indicate that fasting can selectively influence performance on the IST, a measure of impulsivity previously studied in BN. However, the results from the IST were contrary to the original hypothesis and should be replicated before specific conclusions can be made.

Functional Near InfraRed Spectroscopy (fNIRS) connectivity analysis is often performed using the measured oxy-haemoglobin (HbO₂) signal, while the deoxy-haemoglobin (HHb) is largely ignored. The in-common information of the connectivity networks of both HbO₂ and HHb is not regularly reported, or worse, assumed to be similar. Here we describe a methodology that allows the estimation of the symmetry between the functional connectivity (FC) networks of HbO₂ and HHb and propose a differential symmetry index (DSI) indicative of the in-common physiological information. Our hypothesis is that the symmetry between FC networks associated with HbO₂ and HHb is above what should be expected from random networks. FC analysis was done in fNIRS data collected from six freely-moving healthy volunteers over 16 locations on the prefrontal cortex during a real-world task in an out-of-the-lab environment. In addition, systemic data including breathing rate (BR) and heart rate (HR) were also synchronously collected and used within the FC analysis. FC networks for HbO₂ and HHb were established independently using a Bayesian networks analysis. The DSI between both haemoglobin (Hb) networks with and without systemic influence was calculated. The relationship between the symmetry of HbO₂ and HHb networks, including the segregational and integrational characteristics of the networks (modularity and global efficiency respectively) were further described. Consideration of systemic information increases the path lengths of the connectivity networks by 3%. Sparse networks exhibited higher asymmetry than dense networks. Importantly, our experimental connectivity networks symmetry between HbO₂ and HHb departs from random (t-test: t(509) = 26.39, p < 0.0001). The DSI distribution suggests a threshold of 0.2 to decide whether both HbO₂ and HHb FC networks ought to be studied. For sparse FC networks, analysis of both haemoglobin species is strongly recommended. Our DSI can provide a quantifiable guideline for deciding whether to proceed with single or both Hb networks in FC analysis.