Monica Perkins

Bio: Monica Perkins is an academic researcher from Monash University. The author has contributed to research in topics: Burn injury & Medicine. The author has an hindex of 3, co-authored 6 publications receiving 43 citations.

A pain in the bud? Implications of cross-modal sensitivity for pain experience

Abstract: There is growing evidence that enhanced sensitivity to painful clinical procedures and chronic pain are related to greater sensitivity to other sensory inputs, such as bitter taste. We examined cross-modal sensitivities in two studies. Study 1 assessed associations between bitter taste sensitivity, pain tolerance, and fear of pain in 48 healthy young adults. Participants were classified as non-tasters, tasters and super-tasters using a bitter taste test (6-n-propythiouracil; PROP). The latter group had significantly higher fear of pain (Fear of Pain Questionnaire) than tasters (p=.036, effect size r = .48). There was only a trend for an association between bitter taste intensity ratings and intensity of pain at the point of pain tolerance in a cold pressor test (p=.04). In Study 2, 40 healthy young adults completed the Adolescent/Adult Sensory Profile before rating intensity and unpleasantness of innocuous (33 °C), moderate (41 °C), and high intensity (44 °C) thermal pain stimulations. The sensory-sensitivity subscale was positively correlated with both intensity and unpleasantness ratings. Canonical correlation showed that only sensitivity to audition and touch (not taste/smell) were associated with intensity of moderate and high (not innocuous) thermal stimuli. Together these findings suggest that there are cross-modal associations predominantly between sensitivity to exteroceptive inputs (i.e., taste, touch, sound) and the affective dimensions of pain, including noxious heat and intolerable cold pain, in healthy adults. These cross-modal sensitivities may arise due to greater psychological aversion to salient sensations, or from shared neural circuitry for processing disparate sensory modalities.

Association between gender and outcomes of acute burns patients

Abstract: BACKGROUND Burn injuries are a complex and serious public health concern. Where the total body surface area of the burn exceeds 50%, mortality rates as high as 48% have been reported. While the association between gender and burn injury outcomes has been explored, findings are inconsistent. METHODS Adult patients (>15 years) admitted between 1 July 2009 and 30 June 2018 to intensive care units of burn centres that provide specialist burn care in Australia and New Zealand were included. Raw mortality rates were examined and a multivariable Cox proportional hazards regression was used to investigate the association between gender and time to in-hospital death. RESULTS There were 2227 eligible burn injury admissions. Men comprised the majority (77.6%). The proportion of women who died in hospital was greater than men and the adjusted odds of in-hospital mortality were 34% lower in men (odds ratio 0.66; 95% confidence interval (CI) 0.45-0.98). The unadjusted rate of in-hospital mortality for men was 44% lower than women (hazard ratio 0.56; 95% CI 0.41-0.76). After adjusting for confounders, there was no association between gender and survival time (hazard ratio 0.76; 95% CI 0.54-1.06). CONCLUSION After adjustment for key differences in case-mix between men and women, there was an association between gender and in-hospital mortality and no association between gender and time to death. Our findings indicate that the worse outcomes observed for women are associated with different age and patterns of injury, and provide further information to direct and inform targeted prevention measures for vulnerable populations.

Data-driven strategies for optimal bicycle network growth.

Abstract: Urban transportation networks, from pavements and bicycle paths to streets and railways, provide the backbone for movement and socioeconomic life in cities. To make urban transport sustainable, cities are increasingly investing to develop their bicycle networks. However, it is yet unclear how to extend them comprehensively and effectively given a limited budget. Here we investigate the structure of bicycle networks in cities around the world, and find that they consist of hundreds of disconnected patches, even in cycling-friendly cities like Copenhagen. To connect these patches, we develop and apply data-driven, algorithmic network growth strategies, showing that small but focused investments allow to significantly increase the connectedness and directness of urban bicycle networks. We introduce two greedy algorithms to add the most critical missing links in the bicycle network focusing on connectedness, and show that they outmatch both a random approach and a baseline minimum investment strategy. Our computational approach outlines novel pathways from car-centric towards sustainable cities by taking advantage of urban data available on a city-wide scale. It is a first step towards a quantitative consolidation of bicycle infrastructure development that can become valuable for urban planners and stakeholders.

Data-driven strategies for optimal bicycle network growth

Abstract: Urban transportation networks, from sidewalks and bicycle paths to streets and rail lines, provide the backbone for movement and socioeconomic life in cities. These networks can be understood as layers of a larger multiplex transport network. Because most cities are car-centric, the most developed layer is typically the street layer, while other layers can be highly disconnected. To make urban transport sustainable, cities are increasingly investing to develop their bicycle networks. However, given the usually patchy nature of the bicycle network layer, it is yet unclear how to extend it comprehensively and effectively given a limited budget. Here we develop data-driven, algorithmic network growth strategies and apply them to cities around the world, showing that small but focused investments allow to significantly increase the connectedness and directness of urban bicycle networks. We motivate the development of our algorithms with a network component analysis and with multimodal urban fingerprints that reveal different classes of cities depending on the connectedness between different network layers. We introduce two greedy algorithms to add the most critical missing links in the bicycle layer: The first algorithm connects the two largest connected components, the second algorithm connects the largest with the closest component. We show that these algorithms outmatch both a random approach and a baseline minimum investment strategy that connects the closest components ignoring size. Our computational approach outlines novel pathways from car-centric towards sustainable cities by taking advantage of urban data available on a city-wide scale. It is a first step towards a quantitative consolidation of bicycle infrastructure development that can become valuable for urban planners and stakeholders.

Aberrant modulations of static functional connectivity and dynamic functional network connectivity in chronic migraine.

Abstract: Background Chronic migraine (CM) is a common and disabling neurological disorder that affects 1-2% of the global population. The aim of the present study was to identify the functional characteristics of the CM brain using static functional connectivity (s-FC), static functional network connectivity (s-FNC), and dynamic functional network connectivity (d-FNC) analyses. Methods In the present study, 17 CM patients and 20 sex- and age-matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. We utilized independent component (IC) analysis to identify 13 ICs. These 13 ICs were then classified into the following 6 resting-state networks (RSNs): the default mode network (DMN), executive control network (ECN), dorsal attention network, auditory network (AN), visual network (VN), and cerebellum network. Subsequently, s-FC, s-FNC, and d-FNC analyses of 13 ICs were employed for between-group comparisons. Three temporal metrics (fraction of time spent, mean dwell time, and number of transitions), which were derived from the state-transition vector, were calculated for group comparisons. In addition, correlation analyses were performed between these dynamic metrics and clinical characteristics [mean visual analog scale (VAS) scores, days with headache per month, days with migraine pain feature per month, and disease duration]. Results In the comparison of s-FC of 13 ICs within RSNs between the CM and HC groups, increased connectivity was observed in the left angular gyrus (Angular_L) of the ECN (IC 2) and the right superior parietal gyrus (Parietal_Sup_R) of the AN (IC 5), and reduced connectivity was found in the left superior frontal gyrus (Frontal_Sup_2_L) of the AN (IC 5) and DMN (IC 19), the right calcarine sulcus (Calcarine_R) of the VN (IC 7), and the left precuneus (Precuneus_L) of the DMN (IC 17) in CM patients. In the comparison of the d-FNC of 13 IC pairs within RSNs between the two groups, the CM group exhibited significantly decreased connections between the DMN (IC 11) and AN (IC 5), and increased connections between the ECN (IC 2, IC 4) and DMN (IC 19), ECN (IC 4) and AN (IC 5), and ECN (IC 4) and VN (IC 13) in state 1. However, no significant differences in s-FNC were observed between the two groups during the s-FNC analysis. Between-group comparisons of three dynamic metrics between the CM and HC groups showed a longer fraction of time spent and mean dwell time in state 2 for CM patients. Furthermore, from the correlation analyses between these metrics and clinical characteristics, we observed a significant positive correlation between the number of transitions and mean VAS scores. Conclusions Our findings suggest that functional features of the CM brain may fluctuate over time instead of remaining static, and provide further evidence that migraine chronification may be related to abnormal pattern connectivity between sensory and cognitive brain networks.