Climate Trauma from Wildfire Exposure Impacts Cognitive Decision-Making

Climate Trauma from Wildfire Exposure Impacts Cognitive Decision-Making | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Climate Trauma from Wildfire Exposure Impacts Cognitive Decision-Making Jason Nan, Satish Jaiswal, Dhakshin Ramanathan, Mathew Withers, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4385857/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate trauma refers to the mental health sequalae of climate disaster events. We have previously shown evidence for climate trauma with high prevalence of trauma, anxiety and depression symptoms after California’s 2018 Camp Fire wildfire. Here, we investigate whether this climate trauma impacts cognitive decision-making and its neural correlates. One year after the wildfire, we recruited three groups - those directly exposed (n = 27), indirectly exposed (who community members who witnessed the wildfire n = 21), versus non-exposed controls from a nearby region (n = 27). Participants performed a decision-making task that led to immediate and cumulative point rewards on each trial with simultaneous electroencephalography (EEG) recordings. We evaluated Win-Stay behavior in choosing to stay with the greater expected value option. Directly-exposed individuals showed significantly reduced Win-Stay behavior relative to the other groups. EEG analyses showed significantly greater parietal alpha activity for the selected choice and ensuing rewards in the directly-exposed individuals, with underlying cortical source in posterior cingulate cortex. Overall, these findings suggest that climate trauma may significantly impact neuro-cognitive processing in the context of value-based decision-making, and may serve as a useful biomarker target for future mental health interventions in climate change impacted communities. Biological sciences/Neuroscience/Cognitive neuroscience/Decision Biological sciences/Neuroscience/Reward decision-making reward EEG alpha posterior cingulate cortex Figures Figure 1 Figure 2 Figure 3 Introduction Climate change is a global crisis that has drawn the attention of health scientists worldwide to understand and address the impacts of extreme weather events and climate disasters 1,2 . In the western United States, warming of 1.5°C over the last 30 years has paralleled a ~ 1000% increase in annual forest-fire area 3 . As climate change accelerated disasters such as the wildfires in the western US become frequent, significant impacts are being observed not just on human physical health but also mental health 4–6 . Our recent studies in communities impacted by California’s deadliest wildfire to-date, the Camp Fire of 2018, have demonstrated mental health sequelae as well as neurocognitive impacts of wildfire exposure 7,8 . In a sample of 725 California residents, Silveira et al 8 found that indiviudals directly exposed to the wildfire disaster, showed significantly pronounced symptoms of post-traumatic stress disorder (PTSD), depression and anxiety even a year after the wildfire event. From here on out, we use the term climate trauma to refer to the complex mental health impacts of a climate disaster event 9 . There are a very few study that have investigated the cognitive and neurobiological impacts of climate disasters 10 . In a recent empirical study, we examined a range of core cognitve abilities in individuals affected by the 2018 California wildfire disaster 7 . These cognitive assessments were conducted one year after the widlfire event and included tasks of selective attention, response inhibition, interference processing, working memory and emotion bias. This study found a significant and selective deficit in interference processing, i.e., the ability to deal with distractions in individuals who had suffered from climate trauma from the wildfire disaster relative to those who did not. Additionally, electroencephalographic (EEG) recordings conducted simultaneous to the cognitive tasks showed significantly greater activity in frontal cortex in individuals who were directly fire-exposed relative to others, and specifically for the impacted interference processing task. Notably, this state of frontal hyperarousal observed under climate trauma also dovetails with evidence for frontal cortex hyperexcitability observed in PTSD 11,12 , and may reflect the greater cognitive effort needed to process irrelevant distractions 13,14 . One of the critical cognitive functions affected by trauma, in general, is decision-making 15,16 , particularly in the form of deficits in reward functioning 17 . Studies show that individuals with PTSD exhibit higher approach-aversion conflict when making decisions 18 and show changes in reward processing often characterized as anhedonia 17 . However, findings are still mixed regarding whether and how decision-making and associated rewards are affected in PTSD 17,19 .Yet, recovering from trauma of any kind, including climate trauma, requires optimal reward processing in order to build stress resilience 20 . Thus, understanding of cognitive and neural mechanisms underlying decision-making and associated reward processing can offer insights into novel diagnostic methods and personalized treatment strategies. To the best of our knowldege, the current research is the first to study cognitive and neural processes during decision-making and ensuing reward processing in the context of climate trauma. For this study, individuals who had been exposed to the 2018 California wildfire disaster as well as non-exposed controls took part in a two-choice decision-making task; we refer to this task as Lucky Door as the stimuli contain two doors linked to probabalistic gain or loss of coin rewards 21 . One unique feature of the Lucky Door task is that it can distinguish decision-making bias towards frequency of immediate gains from often more advantageous long-term expected value-based decision-making; it does so by assigning greater gain frequency vs. greater expected value (EV) to distinct choice doors. On the Lucky Door task we focus our evaluation on the ability to stay with choices that deliver greater long-term EV as this is a useful marker of foresight or long-term decision-making. For this, we use the Win-Stay metric that quantifies participants staying with the same choice after receiving a win, i.e., positive coin reward on the greater EV door; the Win-Stay metric has been employed in several recent studies 21–24 . In a study of decision-making across the lifespan, Balasubramani et al 21 showed that the Win-Stay metric is more sensitive to distinguishing behavior on choices that differ in EV than comparing the overall proportion of times one choice was made vs. another. Hence, here we hypothesize that long-term decision-making ability indexed by Win-Stay behavior on the greater EV choice may be impacted by climate trauma in the context of a widlfire disaster. We further hypothesize that effects of climate trauma on decision-making ability may be linked to underlying neural dynamics, especially within fronto-parietal brain regions that dictate decision-making and reward learning. There is convergent evidence from healthy participants as well as lesion studies demonstrating the critical role of the fronto-parietal regions in decision-making 25–28 . Relevant to the current research, Paulus et al 27 have further shown that response inconsistencies with regard to Win–Stay (or Lose–Shift) behavior in a two-choice prediction task are linked with activation of parietal cortex in a functional neuroimaging study. Given, the lack of neural evidence for the implications of climate trauma, other neurophysiological studies of non-climate trauma may serve as a reference guide for understanding 20,29–31 . Overall, this study hypothesizes that reward-related decision-making dynamics indexed by the Win-Stay measure may differ for individuals who have experienced climate trauma and further associated with neural dynamics in fronto-parietal brain regions. Methods Participants. This study included 75 participants (mean age: 24.57 ± 6.20 years, range: 18–47 years, 63 females), who took part in the cognitive and neural decision-making evaluation and were a subset of participants sampled in our previous wildfire study 8 . All participants were sampled at 12 months after the 2018 Camp Fire in Northern California, i.e. all study data was collected prior to the COVID-19 pandemic period. This sample included three groups of participants: directly exposed to the wildfire (n = 27), indirectly exposed to the wildfire (n = 21), and non-exposed controls who were age and gender-matched to the directly exposed group (n = 27). The groups were classified based on self-reports on the Life Events Checklist 5 8 , i.e., in the context of the fire, the three groups responded as ‘happened to me personally’ for the directly exposed group; ‘witnessed it happen to someone else’ for the indirectly exposed group; and ‘learned about it or not applicable’ for non-exposed controls, respectively. All participants provided written informed consent for the study approved by the local university Institutional Review Board (IRB). Specifically, the directly and indirectly exposed participants were located at California State University (CSU) at Chico, within 10–15 miles of the Camp Fire, and were approved by the CSU Chico IRB#22838, while non-exposed controls were located in the San Diego region, 600 miles away from the Camp Fire, and were approved by the University of California, San Diego IRB#180140. The majority of participants (95%) were right-handed. All participants had normal/corrected-to-normal vision and hearing and no participant reported color blindness. All participants had at least a high-school education. Sample size and power. Our sample size was adequately powered to detect medium effect size group differences (Cohen’s d > 0.5) at beta of 0.8 and alpha significance level of 0.05 as calculated using the G*Power software 32 . Demographics. All participants provided demographic information by self-report including age, gender, and ethnicity. Socio-economic status was measured on the Family Affluence Scale 33 ; this scale measures individual wealth based on ownership of objects of value (e.g., car/computer) and produces a composite score ranging from 0 (low affluence) to 9 (high affluence). Mental health. All participants self-reported whether they had experienced recent trauma as per the standard PTSD checklist screen (“were you recently bothered by a past experience that caused you to believe you would be injured or killed?” 1: Not bothered at all, 2: Bothered a little, 3: Bothered a lot) (Blevins et al., 2015). Participants rated anxiety symptoms on the Generalized Anxiety Disorder: GAD7 scale 34 and depression symptoms on the Patient Health Questionnaire: PHQ9 scale 35 . All participant demographics and mental health characteristics have been tabulated and discussed in our previous study in which the same sample underwent other neuro-cognitive assessments 7 , and also shown in Supplementary Table 1 . Experimental Task. We investigated a two-choice decision-making task 21 that we refer to as Lucky Door in which participants were given the below instruction: “You will see two doors. Tap left or right to choose a door. You will gain or lose coins at each door. Choose the lucky door.” Thus, participants chose between one of two doors, either a rare gain door (RareG, probability for gains P = 0.3, for losses P = 0.7) or a rare loss door (RareL, probability for losses P = 0.3, for gains P = 0.7). Participants used the left and right arrow keys on the keyboard to make their door choice. Door choice was monitored throughout the task. The task choice decisions on each trial were response-constrained, not time-constrained, i.e. participants could take their time to select their choice. The task consisted of two blocks, an experimental block and a baseline block that were counterbalanced across participants. In the experimental block, expected value (EV) was greater for the RareG door ( P = 0.3 for + 60 coins, P = 0.7 for − 20 coins, EV = + 40) than for the RareL door ( P = 0.3 for − 60 coins, P = 0.7 for + 20 coins; EV = − 40). Manipulation of EV, with greater expected value tied to the RareG door, allowed for investigating individual propensities to prioritize long-term (or cumulative) versus short-term (or immediate) rewards. The RareG door was assigned greater EV because selecting this door suggests EV magnitude-based decision processing in subjects as opposed to simply choosing based on frequency of gains, in which case the RareL choice should be preferred. In the baseline block, EV was the same for both RareG ( P = 0.3 for + 70 coins, P = 0.7 for − 30 coins, EV = 0) and for the RareL door ( P = 0.3 for − 70 coins, P = 0.7 for + 30 coins; EV = 0), and allowed investigation of gain frequency bias towards the RareL door without EV differences. Forty trials were presented per block approximating similar trial numbers as previous human reward task studies 36,37 . Figure 1 A shows a schematic of the task stimulus sequence. On each task trial, a fixation cue was followed by two door choices that remained on the screen until a choice was made. After choice selection, central fixation was presented for 500-ms duration followed by selected choice presentation for 500-ms duration, then immediate reward presentation for 500-ms duration corresponding to the reward for the selected door on that trial, and then cumulative reward presentation for 500-ms duration corresponding to total reward earned until that trial during the block. The Lucky Door task was deployed in Unity as part of the assessment suite on the BrainE (short for Brain Engagement) platform 38 . The Lab Streaming Layer (LSL, 39 ) protocol was used to time-stamp each stimulus/response event during the task. Study participants engaged with the assessment on a Windows 10 laptop sitting at a comfortable viewing distance. Behavior Analysis. Behavioral data were obtained from 74 of 75 participants, except for missing data from 1 participant in the control group. The main behavior metric was Win-Stay, i.e., participant’s willingness to stay with the RareG door that had greater expected value (but lesser immediate gains) after they encountered a winning trial in the experimental block. Win-Stay was calculated as the ratio of times a participant stayed with the RareG choice after a win compared to total number of trials after a win. On the baseline block that had no EV differences, we also calculated Win-Stay for RareG choices as a control to confirm the hypothesis that Win-Stay behavior selectively shows group differences on the experimental block 21 . To analyze group differences while accounting for all covariates of age, gender, ethnicity, socioeconomic score, and mental health scores of anxiety and depression, we modeled the behavior metrics across all three groups with a linear model using the fitlm function in MATLAB with robust regression option applied to reduce outlier influence 40 . EEG Processing. EEG simultaenous to the decision-making task was acquired in most participants (n = 57) with missing EEG due to technical issues in 3 participants in the control group, 7 participants in the indirectly exposed group, and 8 participants in the directly exposed group. RareG trials were analyzed coinciding with the behavioral analyses on these trials. Since we are analyzing neural correlates related to decision making and ensuing reward processing, we segmented the trial structure into three distinct time period associated with choice defined as 0-500 ms after the chosen door is presented, immediate reward defined as 500–1000 ms after the chosen door appears, and cumulative reward defined as 1000–1500 ms after the chosen door appears. These three timings are also shown in Fig. 1 A. Neural data analyses were conducted using a uniform two-step processing pipeline published in several of our studies 7,21,38,41–48 . Step 1) EEG channel data processing was conducted using the EEGLAB toolbox v2020 in MATLAB v2022b. EEG data was resampled at 250 Hz and filtered in the 1–45 Hz range to exclude ultraslow DC drifts at 45Hz. There were no missing channels in the EEG data across subjects. Epoched data were cleaned using the autorej function in EEGLAB to remove noisy trials, i.e. >5SD outliers rejected over max 8 iterations, followed by further cleaning of electrooculographic, electromyographic or non-brain source artifacts using the Sparse Bayesian learning (SBL) algorithm ( https://github.com/aojeda/PEB ) 46 . In addition to the automatic rejection, we also implemented an amplitude criteria where any trial exceeding 100 uV was considered noisy and removed. The cleaned data were then band filtered in the physiologically relevant theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) frequency bands. Epoched events were then extracted and averaged across trials to remove single trial noise. Step 2) We used the block-Sparse Bayesian learning (BSBL-2S) algorithm to localize frequency band filtered EEG data and partitioned the signals into cortical regions of interest (ROIs) and artifact sources 46,49 . For the source space activations, ROIs were based on the standard 68 brain region Desikan-Killiany atlas 50 using the Colin-27 head model 51 . BSBL-2S is a two-step algorithm in which the first-step is equivalent to low-resolution electromagnetic tomography (LORETA 52 ). LORETA estimates sources subject to smoothness constraints, i.e. nearby sources tend to be co-activated, which may produce source estimates with a high number of false positives that are not biologically plausible. To guard against this, BSBL-2S applies sparsity constraints in the second step wherein blocks of irrelevant sources are pruned. Notably, this data-driven sparsity constraint reduces the effective number of sources considered at any given time as a solution. The sparsity is imposed at the level of cortical ROIs, thereby projecting the data onto this space of few ROIs, and reducing the uncertainty of the inverse solution. Thus, it is not that only higher channel density data can yield source solutions, the ill-posed inverse problem can also be solved by imposing more aggressive constraints on the solution to converge on the source model at lower channel densities, as also supported by prior research 53,54 . Of note, the BSBL-2S two-stage algorithm has been benchmarked to produce evidence-optimized inverse source models at 0.95AUC relative to the ground truth, while without the second stage < 0.9AUC is obtained, verified using both data and simulations 46,49 . We have also shown that cortical source mapping with this method has high test-retest reliability (Cronbach’s alpha = 0.77, p < 0.0001) obtained with recordings conducted one-week apart 38 . Neural Data Analysis. Here, we applied a standardized pipeline with modifiable parameters to streamline both scalp and source space neural analyses. A github with the source code can be found in https://github.com/jasonnan2/Automated-Analysis-of-EEG/ This standardized pipeline included - Outlier rejection on the final trial-averaged scalp and source data, which sets any datapoint > 5SD across all subjects to NaN. Baseline correction was done on both scalp and source activity relative to the − 250 msec to -50 msec fixation time window prior to choice presentation in each scalp electrode/ source ROI within each subject. Differential scalp topography maps comparing groups were plotted for each of the three frequency bands (theta, alpha, beta) and three trial periods (choice, immediate reward, cumulative reward) for a total of 9 scalp maps. Patterns of significant electrodes were validated with permutation clustering across 10,000 iterations, and false discovery rate (FDR) corrections were applied for 9 topographic map comparisons 55 . Relevant average alpha band event-related activity observed in step 3 above, was defined in a posterior electrode cluster (Pz, P3, P4, and POz) for significance testing across groups. To find relationships between behavior and neurophysiology, we fit linear models to test for group x neural interaction predicting behavior data, controlling for significant demographic covariates. Interactions were tested for average alpha activity in the posterior electrode cluster as well as in the individual component electrodes with FDR corrections applied for multiple comparisosn. Withing-group Spearman’s correlations were used to follow-up on any significant neuro-behavioral group interactions obtained in step 5 above. Cortical source activity was analyzed corresponding to the relevant alpha band posterior scalp activity found in steps 3–4 above. Results Behavioral Performance. The two-choice decision-making task design and corresponding Win-Stay behavior performance on high EV (rare gain or RareG) choices, which resulted in greater long-term cumulative reward, are shown in Fig. 1 . A robust linear regression of Win-Stay behavior with participant group as predictor showed a significant effect only for the directly-exposed group (β=-0.22±0.1, tstat=-2.2, p = 0.03) but not for the indirectly-exposed or non-exposed group (p > 0.08). Thus, only the directly-exposed group showed lower Win-Stay choices relative to the other two groups. The regression model also included covariates of age, gender, ethnicity, socioeconomic scores, anxiety and depression; the model was overall significant (adjusted R 2 = 0.12, Fstat = 2.38, p = 0.03) and only age was a significant covariate in the model (β=-0.02±0.01, tstat=-2.8, p = 0.007). We further modeled Win-Stay behavior on RareG trials on the baseline block that had no EV differences between choices. This model was overall not significant (adjusted R 2 = 0.1, Fstat = 1.96, p = 0.07), had age as a covariate (β=-0.02±0.01, tstat=-3.4, p = 0.001) but showed no effect of group (p > 0.1), confirming our hypothesis that Win-Stay behavior only differs when there are EV differences between choices. Neural Processing. As there were no significant behavioral differences between the indirectly exposed group and non-exposed groups, for neural analyses we combined these into one group ( Other ) to compare against the directly exposed group. Figure 2 A shows EEG scalp topographies contrasting group neural activity in the directly exposed vs. other group in theta, alpha and beta frequency bands within the 500 ms time period after choice, immediate reward and cumulative reward presentations. Electrodes showing significant group differences (i.e., directly exposed vs. other) after permutation clustering are marked with + (p < 0.0001), and notably appeared only in the alpha band. Given the known posterior parieto-occipital origins of alpha band activity 56–63 and its typical topography appearing in our scalp maps, we further quantified parietal cluster alpha (at Pz, P3, P4, and POz electrodes) in grouped bar graphs in Fig. 2 B. Parietal alpha differences were consistently found during the choice (t( 54 )=-2.6; p < 0.05), immediate reward (t( 54 )=-2.8; p < 0.01) and cumulative reward periods (t( 54 )=-2.7; p < 0.01) as compared in between-group t-tests. We also checked that parietal alpha activity did not significantly differ between the indirectly exposed and non-exposed groups that were combined in the other group (p > 0.43). To investigate whether alpha activity is a neural correlate of behavior, we implemented robust regression models that predicted Win-Stay behavior on the high EV choice; predictors included group (directly-exposed vs. other), parietal alpha activity and the interaction between group and alpha activity. Since age was a significant predictor of Win-Stay behavior, it was entered as a covariate in all models. No models using average alpha activity in the parietal cluster showed a significant neural effect on behavior. Hence, we explored models for individual electrodes in the cluster, correcting for multiple comparisons across electrodes (P3, Pz, P4, POz) and time windows (choice, immediate reward and cumulative reward). In this case, only the model for Pz alpha activity during the choice period showed a significant neural activity by group interaction (β = 4.2 ± 2.1, tstat = 2.0, p = 0.05) as well as a significant effect of group (β = 0.18 ±0.09, tstat = 2.1, p = 0.04) and age (β= -0.02 ± 0.007, tstat=-3.0, p = 0.004) but no effect of neural activity alone (p > 0.4); no significant neural effects were observed in the immediate/cumulative reward periods. Figure 3 A shows the adjusted linear fit neurobehavior model for Pz alpha activity in the choice period. Figure 3 B illustrates the group specific alpha activity response as it relates to Win-Stay behavior; a significant Spearman’s correlation was observed only in the other group (rho = 0.34 p = 0.04) but not in the directly exposed group (p = 0.5). Figure 3 C shows the cortical source localization of the parietal alpha activity during the choice period masked by significant difference between activity in the directly exposed vs. other group; the source region as highlighted in the figure was observed to be right posterior cingulate cortex with greater activity in the directly exposed than the other group (t( 54 )=-2.33; p < 0.05) as seen in Fig. 3 D. Discussion In the current study, our main objective was to investigate how climate mental health trauma from a major wildfire disaster may affect cognitive decision-making in community participants. In this context we specifically investigated the ability to engage and stay with high expected value choices marked by the Win-Stay behavior metric obtained on high EV trials, and further probed the neural dynamics of such behavioral modulation as regulated by climate trauma. We observed that individuals directly exposed/affected by the climate trauma event showed significantly lower ability to stay with the high EV choice after winning compared to indirectly exposed community participants (who witnessed the wildifire in their community but were not directly impacted) as well as non-exposed control participants. Additionally, there were no behavioral differences in Win-Stay behavior between the indirectly exposed and non-exposed participants. At the neural level, across three pertinent processing time windows of selected choice presentation, immediate reward and cumulative reward presentation, we observed significantly greater alpha band EEG activity especially over parieto-occipital brain regions in the directly exposed group vs. all other participants. Finally, a robust regression model investigating neurobehavioral relationships showed that alpha activity at the midline parietal electrode (Pz) during choice presentation could predict Win-Stay behavior with a significant group interaction wherein only non-directly exposed participants (i.e., indirectly exposed and non-exposed group individuals) positively modulated their alpha activity with Win-Stay behavior, while no such modulation was observed in the directly exposed group. The observation of impacted cognitive decision-making due to climate trauma, as indexed by the Win-Stay metric was consistent with our primary hypothesis. We hypothesized this impact based on prior studies of decision making in general (i.e., non-climate) PTSD and depression 15–17,64,65 . For instance, Sailer et al 16 examined reward processing in clinically diagnosed PTSD patients using a decision-making task (adapted from 66,67 ), and observed that individuals with PTSD showed lower accuracy in late phase reward learning relative to control subjects, suggesting lower efficiency of reward learning in PTSD. Similarly in the decision-making reward task we deploy here, high EV choices also need to be implictly learned and differentiated from low EV choices, and failure of such learning would result in low Win-Stay behavior on high EV trials. In the EEG neural recordings, we observed significantly greater parietal alpha on high EV trials in the directly-exposed group vs. all other participants. This observation is in line with previous reports in PTSD, showing involvement of fronto-parietal regions in decision-reward processes 68,69 . For example in a functional neuroimaging study on combat veterans, Howlett et al 69 observed an exaggerated neural response, specifically in the parietal region (left pre-cuneus/inferior parietal lobule and right inferior parietal lobule) to suprising errors while the participants were performing a probabilistic learning task. Interrogating neurobehavioral correlations, we found that parietal alpha, specifically at the Pz electrode during choice presentation showed group-specific modulations in the context of Win-Stay behavior. In the non-directly exposed i.e. other group participants, greater Pz alpha was associated with greater Win-Stay performance. In contrast, in the directly-exposed group, Pz alpha was generally of greater magnitude in the group as a whole but did not show modulation with Win-Stay behavior. Flexible alpha modulation during decision-making behavior has been associated with greater task-related cognitive effort in healthy participants 70,71 , which may explain our findings in the other group i.e., greater Win-Stay behavior is achieved with greater cognitive effort. Studies also suggest that reward-related learning during decision-making harnesses working memory processes 72,73 , and relatedly, prior work has shown that parietal alpha indexes working memory performance 74–76 . Thus, parietal alpha modulation in the other group participants may also suggest that they successfully recruit working memory processes for learning the high EV choices and thereby, generate greater Win-Stay performance. Overall higher parietal alpha magnitudes in the directly-exposed group suggest overall greater cognitive effort in the group, but an inability to translate this to superior behavior performance. The difference in parietal alpha activity in scalp EEG localized to a significant cortical source difference observed in posterior cingulate cortex (PCC), with greater activity observed in the directly exposed vs. other group. Several studies have reported the role of the PCC, a key node of the posterior default mode network, in modulation of ruminative behavior 77–80 . Rumination is also one of the primary ways in which emotion regulation is impacted in affective disorders 81,82 , and further predicts PTSD 83 . Thus, it may be plausible that directly exposed individuals under the duress of climate trauma, engage in distracted rumination behavior indexed by PCC source activitiy, which may affect their decision-making strategy and hence reduce Win-Stay performance. The study's limitations encompass the potential for observed group differences to be inherent traits predating the traumatic wildfire event. This constraint is common to disaster research, as investigations typically occur post-event. As climate disasters become more frequent, it would be important to extend this neuro-cognitive research longitudinally to understand pre vs. post-disaster effects. It has also been well-documented that individuals in lower socioeconomic strata are more vulnerable to suffering from climate related disasters 84 . However, our cohort did not have significant group differences in socioeconomic scores. An additional constraint is our utilization of a moderate channel density EEG system for neural recordings, and future validation could be achieved through the use of a high-density EEG or alternative neuroimaging techniques such as functional magnetic resonance imaging. Yet, notably, it is important to highlight that the choice of the moderate channel density EEG was motivated by its cost-effectiveness and adaptability to community settings 7 . In such community studies, there is a crucial need to strike a balance between scalable feasibility, cost considerations, and data resolution 85 . Future community research should also focus on procuring larger sample sizes of the neuro-cognitive data. Overall, the current research is the first to examine the effect of climate trauma on decision making. We observed that directly fire-exposed individuals showed impacted decision-reward strategies indexed by reduction in Win-Stay performance on high expected value choices alongside higher alpha activity in posterior parietal regions compared to other, indirectly exposed or non-exposed study participants. Cortical source localization revealed significantly greater PCC activity in the directly exposed group suggesting that distracted rumination that often originates from PCC may be a potential contributor to impacted decision-making in this group. Future neuro-cognitively targeted trauma interventions in this context may thus aim to reduce PCC related default mode network activity. Our related intervention work with a scalable digital mindfulness and compassion training has shown significant default mode network suppression alongside enhancement of mindfulness and compassion behaviors 43 . Thus, such scalable strategies may also be tailored as potential interventions for climate-related trauma. This is especially pertinent since our prior observational studies point to mindfulness as a protective trait in this traumatic setting 8,86 . With the planet experiencing escalating temperatures, an increasing number of individuals confront extreme climate events, underscoring the urgency to explore novel resiliency tools from diverse disciplines. In this regard, we unveil objective neuro-cognitive markers of reward-related decision-making that can be potentially used to guide interventions, and map the success of such intervention within climate vulnerable communities. Declarations Conflicts of Interest The authors declare no conflict of interest. Author Contributions Conceptualization: JM; methodology: JN and JM; formal analysis: JN; investigation: JN, MCW and JM; resources: JM; data curation: JN, MCW; writing—original draft preparation: JN and SJ; writing—review and editing: JN, SJ, DR, MCW and JM; visualization: JN and JM; supervision: JM; project administration: JM; funding acquisition: JM. Acknowledgements This work was supported by seed grants from the Tang Prize Foundation (JM), the Hope for Depression Research Foundation (JM) and the CA CARES (Climate Action, Resilience, and Environmental Sustainability) Proof of Concept Funds (JM). The BrainE software is copyrighted for commercial use (Regents of the University of California Copyright #SD2018-816) and free for research and educational purposes. Data Availability De-identified and processed study data are available upon request from the corresponding author. References Intergovernmental Panel on Climate Change (IPCC). Global Warming of 1.5°C: IPCC Special Report on Impacts of Global Warming of 1.5°C above Pre-Industrial Levels in Context of Strengthening Response to Climate Change, Sustainable Development, and Efforts to Eradicate Poverty . (Cambridge University Press, 2022). doi: 10.1017/9781009157940 . Romanello, M. et al. The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels. The Lancet 400, 1619–1654 (2022). Mora, C. et al. Broad threat to humanity from cumulative climate hazards intensified by greenhouse gas emissions. Nat. Clim. Change 8, 1062–1071 (2018). Burrows, K. et al. 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Low delta and high alpha power are associated with better conflict control and working memory in high mindfulness, low anxiety individuals. Soc. Cogn. Affect. Neurosci. 14, 645–655 (2019). Jensen, O., Gelfand, J., Kounios, J. & Lisman, J. E. Oscillations in the alpha band (9–12 Hz) increase with memory load during retention in a short-term memory task. Cereb. Cortex N. Y. N 1991 12, 877–882 (2002). Tuladhar, A. M. et al. Parieto-occipital sources account for the increase in alpha activity with working memory load. Hum. Brain Mapp. 28, 785–792 (2007). Kokonyei, G. et al. Anticipation and violated expectation of pain are influenced by trait rumination: An fMRI study. Cogn. Affect. Behav. Neurosci. 19, 56–72 (2019). Leech, R. & Sharp, D. J. The role of the posterior cingulate cortex in cognition and disease. Brain 137, 12–32 (2014). Raichle, M. E. et al. A default mode of brain function. Proc. Natl. Acad. Sci. 98, 676–682 (2001). Ramanathan, D. et al. Posterior default mode network activity underlies distracted interoceptive attention and provides an objective basis for low mindfulness & depression. Preprint at https://doi.org/ 10.21203/rs.3.rs-951928/v3 (2024). Desrosiers, A., Vine, V., Klemanski, D. H. & Nolen-Hoeksema, S. MINDFULNESS AND EMOTION REGULATION IN DEPRESSION AND ANXIETY: COMMON AND DISTINCT MECHANISMS OF ACTION: Research Article: Mindfulness and Emotion Regulation. Depress. Anxiety 30, 654–661 (2013). Jaiswal, S., Muggleton, N. G., Juan, C.-H. & Liang, W.-K. Indices of association between anxiety and mindfulness: a guide for future mindfulness studies. Personal. Neurosci. 2, e9 (2019). Michael, T., Halligan, S. L., Clark, D. M. & Ehlers, A. Rumination in posttraumatic stress disorder. Depress. Anxiety 24, 307–317 (2007). EPA. Climate Change and the Health of Socially Vulnerable People. https://www.epa.gov/climateimpacts/climate-change-and-health-socially-vulnerable-people (2003). Mishra, J. Transforming knowledge to help others in a global setting. (2019) doi: 10.5281/ZENODO.2619955 . Kornbluh, M. et al. Exploring civic behaviors amongst college students in a year of national unrest. J. Community Psychol. 50, 2950–2972 (2022). Additional Declarations There is NO Competing Interest. Supplementary Files ClimateLDsupplementary.docx nrreportingsummary.pdf Reporting Summary Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4385857","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":305864063,"identity":"ea9ba0b7-c5d0-40af-8648-5ae51532a22b","order_by":0,"name":"Jason Nan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBACxgYQWQEiEhgYeIjXcoYULRB9baRoYW4/e/Bz4bzD8vLtCYwP3rYRY0FPXrL0zG2HDTececBsOJcoLQ05BtK82w4nGEgksEnzEqWl/43xb945hxPkZySw/yZOy4wcM2nehsMJDDcS2JiJ1PLGzJrnWDrQLw+bJeecI0KLYX+O8W2eGmtgiCUf/PCmjBgtDQgLG3CqQgHyxCkbBaNgFIyCEQ0Adok0mWni6EcAAAAASUVORK5CYII=","orcid":"","institution":"University of California San Diego","correspondingAuthor":true,"prefix":"","firstName":"Jason","middleName":"","lastName":"Nan","suffix":""},{"id":305864064,"identity":"e253cee7-a1cf-4ed0-b20c-709b81892235","order_by":1,"name":"Satish Jaiswal","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Satish","middleName":"","lastName":"Jaiswal","suffix":""},{"id":305864065,"identity":"d1617134-df3b-48e7-9c01-095ea126c0a5","order_by":2,"name":"Dhakshin Ramanathan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Dhakshin","middleName":"","lastName":"Ramanathan","suffix":""},{"id":305864066,"identity":"e12e3cb2-b598-4d79-8460-b1fc005fb33a","order_by":3,"name":"Mathew Withers","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mathew","middleName":"","lastName":"Withers","suffix":""},{"id":305864067,"identity":"46fc8b38-3195-49f7-915e-24c9f2196b84","order_by":4,"name":"Jyoti Mishra","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jyoti","middleName":"","lastName":"Mishra","suffix":""}],"badges":[],"createdAt":"2024-05-08 02:00:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4385857/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4385857/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57943968,"identity":"3a8a5a27-534f-4843-bfc3-72c6c751655b","added_by":"auto","created_at":"2024-06-07 19:08:38","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":193055,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTask Design and Performance. A. \u003c/strong\u003eFlow of a \u003cem\u003eLucky Door \u003c/em\u003etask trial. On each trial, participants are initially presented with a choice of two doors. After they make a choice, they are shown the chosen door (for a duration for 500 ms), then presented with the amount of coins they received for that choice (immediate reward, shown for a duration of 500 ms) followed by presentation of their total coin tally (cumulative reward, also shown for 500 ms). \u003cstrong\u003eB.\u003c/strong\u003e Box plot of the Win-Stay behavioral metric for high expected value (EV) choices, showing group based probability of participants staying with the high EV door after winning coins. Individuals directly exposed to fires show significantly lower Win-Stay behavior relative to other, i.e., indirectly exposed and non-fire-exposed study participants.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4385857/v1/810806a3d60d824c50298232.jpeg"},{"id":57943973,"identity":"9fcc6a29-3147-4b5c-bf26-0c6c722e2829","added_by":"auto","created_at":"2024-06-07 19:08:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":369402,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNeural activity contrasted in directly exposed vs. other (i.e., indirectly exposed and non-exposed combined) groups. A. \u003c/strong\u003eBaseline corrected scalp topography plots are shown for the 500 ms choice period, immediate reward period and cumulative reward period in theta, alpha and beta frequency bands contrasting activity in the directly exposed vs. other group participants. ‘+’ points represent permutation corrected significant electrode clusters at p\u0026lt;0.0001. \u003cstrong\u003eB. \u003c/strong\u003eBar graphs showing activity in the parietal alpha cluster (P3, Pz, P4, POz) observed to be significantly greater in the directly exposed vs. other group. Activity values are in mV.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4385857/v1/8fc69db1ba9c027eea5f5dd8.png"},{"id":57943972,"identity":"e27df2aa-b777-46e0-9f7d-475ecf5db103","added_by":"auto","created_at":"2024-06-07 19:08:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNeurobehavioral model relating posterior alpha activity to Win-Stay behavior A. \u003c/strong\u003eRobust regression model of alpha activity at Pz interacting with the between-subjects group factor as it predicts Win-Stay behavior in the choice period (F=4.43, p\u0026lt;0.04). \u003cstrong\u003eB. \u003c/strong\u003ePz alpha activity during the choice period showed a differential relationship with Win-Stay behavior within each group. Within-group Spearman’s correlations found a positive neurobehavioral correlation in the other group (r=0.34 p=0.04), but not in the directly exposed goup (p=0.5). \u003cstrong\u003eC. \u003c/strong\u003eAlpha activity during the choice period localized to a cortical source in the posterior cingulate cortex (PCC) region. \u003cstrong\u003eD.\u003c/strong\u003e Right PCC alpha activity was greater in the directly exposed vs. other group (p\u0026lt;0.05, arbitrary source units).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4385857/v1/56109d8ef4e2e408a302f2c7.png"},{"id":61936964,"identity":"93ca07ac-efc1-4454-a0bb-fec0cb4cf230","added_by":"auto","created_at":"2024-08-07 09:20:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1216546,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4385857/v1/1417b554-0b39-4faa-983f-e7ff9d25659a.pdf"},{"id":57943974,"identity":"2f622384-fa59-41a1-b8c3-36ccb0f151b6","added_by":"auto","created_at":"2024-06-07 19:08:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15672,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"ClimateLDsupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4385857/v1/88001c2c3a4537d3d38ecb4d.docx"},{"id":57944136,"identity":"b9fd3659-5589-4b54-9138-df6866a30f7f","added_by":"auto","created_at":"2024-06-07 19:08:52","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1030686,"visible":true,"origin":"","legend":"\u003cp\u003eReporting Summary\u003c/p\u003e","description":"","filename":"nrreportingsummary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4385857/v1/a7516bcb0ae43afdd3f1e356.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Climate Trauma from Wildfire Exposure Impacts Cognitive Decision-Making","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eClimate change is a global crisis that has drawn the attention of health scientists worldwide to understand and address the impacts of extreme weather events and climate disasters \u003csup\u003e1,2\u003c/sup\u003e. In the western United States, warming of 1.5\u0026deg;C over the last 30 years has paralleled a\u0026thinsp;~\u0026thinsp;1000% increase in annual forest-fire area \u003csup\u003e3\u003c/sup\u003e. As climate change accelerated disasters such as the wildfires in the western US become frequent, significant impacts are being observed not just on human physical health but also mental health \u003csup\u003e4\u0026ndash;6\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur recent studies in communities impacted by California\u0026rsquo;s deadliest wildfire to-date, the Camp Fire of 2018, have demonstrated mental health sequelae as well as neurocognitive impacts of wildfire exposure \u003csup\u003e7,8\u003c/sup\u003e. In a sample of 725 California residents, Silveira et al\u003csup\u003e8\u003c/sup\u003e found that indiviudals directly exposed to the wildfire disaster, showed significantly pronounced symptoms of post-traumatic stress disorder (PTSD), depression and anxiety even a year after the wildfire event. From here on out, we use the term \u003cem\u003eclimate trauma\u003c/em\u003e to refer to the complex mental health impacts of a climate disaster event \u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere are a very few study that have investigated the cognitive and neurobiological impacts of climate disasters \u003csup\u003e10\u003c/sup\u003e. In a recent empirical study, we examined a range of core cognitve abilities in individuals affected by the 2018 California wildfire disaster \u003csup\u003e7\u003c/sup\u003e. These cognitive assessments were conducted one year after the widlfire event and included tasks of selective attention, response inhibition, interference processing, working memory and emotion bias. This study found a significant and selective deficit in interference processing, i.e., the ability to deal with distractions in individuals who had suffered from climate trauma from the wildfire disaster relative to those who did not. Additionally, electroencephalographic (EEG) recordings conducted simultaneous to the cognitive tasks showed significantly greater activity in frontal cortex in individuals who were directly fire-exposed relative to others, and specifically for the impacted interference processing task. Notably, this state of frontal hyperarousal observed under climate trauma also dovetails with evidence for frontal cortex hyperexcitability observed in PTSD \u003csup\u003e11,12\u003c/sup\u003e, and may reflect the greater cognitive effort needed to process irrelevant distractions \u003csup\u003e13,14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne of the critical cognitive functions affected by trauma, in general, is decision-making \u003csup\u003e15,16\u003c/sup\u003e, particularly in the form of deficits in reward functioning \u003csup\u003e17\u003c/sup\u003e. Studies show that individuals with PTSD exhibit higher approach-aversion conflict when making decisions \u003csup\u003e18\u003c/sup\u003e and show changes in reward processing often characterized as anhedonia \u003csup\u003e17\u003c/sup\u003e. However, findings are still mixed regarding whether and how decision-making and associated rewards are affected in PTSD \u003csup\u003e17,19\u003c/sup\u003e.Yet, recovering from trauma of any kind, including climate trauma, requires optimal reward processing in order to build stress resilience \u003csup\u003e20\u003c/sup\u003e. Thus, understanding of cognitive and neural mechanisms underlying decision-making and associated reward processing can offer insights into novel diagnostic methods and personalized treatment strategies.\u003c/p\u003e \u003cp\u003eTo the best of our knowldege, the current research is the first to study cognitive and neural processes during decision-making and ensuing reward processing in the context of climate trauma. For this study, individuals who had been exposed to the 2018 California wildfire disaster as well as non-exposed controls took part in a two-choice decision-making task; we refer to this task as \u003cem\u003eLucky Door\u003c/em\u003e as the stimuli contain two doors linked to probabalistic gain or loss of coin rewards \u003csup\u003e21\u003c/sup\u003e. One unique feature of the \u003cem\u003eLucky Door\u003c/em\u003e task is that it can distinguish decision-making bias towards frequency of immediate gains from often more advantageous long-term expected value-based decision-making; it does so by assigning greater gain frequency vs. greater expected value (EV) to distinct choice doors. On the \u003cem\u003eLucky Door\u003c/em\u003e task we focus our evaluation on the ability to stay with choices that deliver greater long-term EV as this is a useful marker of foresight or long-term decision-making. For this, we use the Win-Stay metric that quantifies participants staying with the same choice after receiving a win, i.e., positive coin reward on the greater EV door; the Win-Stay metric has been employed in several recent studies \u003csup\u003e21\u0026ndash;24\u003c/sup\u003e. In a study of decision-making across the lifespan, Balasubramani et al\u003csup\u003e21\u003c/sup\u003e showed that the Win-Stay metric is more sensitive to distinguishing behavior on choices that differ in EV than comparing the overall proportion of times one choice was made vs. another. Hence, here we hypothesize that long-term decision-making ability indexed by Win-Stay behavior on the greater EV choice may be impacted by climate trauma in the context of a widlfire disaster.\u003c/p\u003e \u003cp\u003eWe further hypothesize that effects of climate trauma on decision-making ability may be linked to underlying neural dynamics, especially within fronto-parietal brain regions that dictate decision-making and reward learning. There is convergent evidence from healthy participants as well as lesion studies demonstrating the critical role of the fronto-parietal regions in decision-making \u003csup\u003e25\u0026ndash;28\u003c/sup\u003e. Relevant to the current research, Paulus et al\u003csup\u003e27\u003c/sup\u003e have further shown that response inconsistencies with regard to Win\u0026ndash;Stay (or Lose\u0026ndash;Shift) behavior in a two-choice prediction task are linked with activation of parietal cortex in a functional neuroimaging study. Given, the lack of neural evidence for the implications of climate trauma, other neurophysiological studies of non-climate trauma may serve as a reference guide for understanding \u003csup\u003e20,29\u0026ndash;31\u003c/sup\u003e. Overall, this study hypothesizes that reward-related decision-making dynamics indexed by the Win-Stay measure may differ for individuals who have experienced climate trauma and further associated with neural dynamics in fronto-parietal brain regions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eParticipants.\u003c/b\u003e This study included 75 participants (mean age: 24.57\u0026thinsp;\u0026plusmn;\u0026thinsp;6.20 years, range: 18\u0026ndash;47 years, 63 females), who took part in the cognitive and neural decision-making evaluation and were a subset of participants sampled in our previous wildfire study \u003csup\u003e8\u003c/sup\u003e. All participants were sampled at 12 months after the 2018 Camp Fire in Northern California, i.e. all study data was collected prior to the COVID-19 pandemic period. This sample included three groups of participants: directly exposed to the wildfire (n\u0026thinsp;=\u0026thinsp;27), indirectly exposed to the wildfire (n\u0026thinsp;=\u0026thinsp;21), and non-exposed controls who were age and gender-matched to the directly exposed group (n\u0026thinsp;=\u0026thinsp;27). The groups were classified based on self-reports on the Life Events Checklist 5 \u003csup\u003e8\u003c/sup\u003e, i.e., in the context of the fire, the three groups responded as \u0026lsquo;happened to me personally\u0026rsquo; for the directly exposed group; \u0026lsquo;witnessed it happen to someone else\u0026rsquo; for the indirectly exposed group; and \u0026lsquo;learned about it or not applicable\u0026rsquo; for non-exposed controls, respectively.\u003c/p\u003e\u003cp\u003eAll participants provided written informed consent for the study approved by the local university Institutional Review Board (IRB). Specifically, the directly and indirectly exposed participants were located at California State University (CSU) at Chico, within 10\u0026ndash;15 miles of the Camp Fire, and were approved by the CSU Chico IRB#22838, while non-exposed controls were located in the San Diego region, 600 miles away from the Camp Fire, and were approved by the University of California, San Diego IRB#180140. The majority of participants (95%) were right-handed. All participants had normal/corrected-to-normal vision and hearing and no participant reported color blindness. All participants had at least a high-school education.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample size and power.\u003c/b\u003e Our sample size was adequately powered to detect medium effect size group differences (Cohen\u0026rsquo;s d\u0026thinsp;\u0026gt;\u0026thinsp;0.5) at beta of 0.8 and alpha significance level of 0.05 as calculated using the G*Power software \u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDemographics.\u003c/b\u003e All participants provided demographic information by self-report including age, gender, and ethnicity. Socio-economic status was measured on the Family Affluence Scale \u003csup\u003e33\u003c/sup\u003e; this scale measures individual wealth based on ownership of objects of value (e.g., car/computer) and produces a composite score ranging from 0 (low affluence) to 9 (high affluence).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMental health.\u003c/b\u003e All participants self-reported whether they had experienced recent trauma as per the standard PTSD checklist screen (\u0026ldquo;were you recently bothered by a past experience that caused you to believe you would be injured or killed?\u0026rdquo; 1: Not bothered at all, 2: Bothered a little, 3: Bothered a lot) (Blevins et al., 2015). Participants rated anxiety symptoms on the Generalized Anxiety Disorder: GAD7 scale\u003csup\u003e34\u003c/sup\u003e and depression symptoms on the Patient Health Questionnaire: PHQ9 scale \u003csup\u003e35\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAll participant demographics and mental health characteristics have been tabulated and discussed in our previous study in which the same sample underwent other neuro-cognitive assessments \u003csup\u003e7\u003c/sup\u003e, and also shown in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e \u003cb\u003eExperimental Task.\u003c/b\u003e We investigated a two-choice decision-making task \u003csup\u003e21\u003c/sup\u003e that we refer to as \u003cem\u003eLucky Door\u003c/em\u003e in which participants were given the below instruction:\u003c/p\u003e \u003cp\u003e\u0026ldquo;You will see two doors.\u003c/p\u003e \u003cp\u003eTap left or right to choose a door.\u003c/p\u003e \u003cp\u003eYou will gain or lose coins at each door. Choose the lucky door.\u0026rdquo;\u003c/p\u003e \u003cp\u003eThus, participants chose between one of two doors, either a rare gain door (RareG, probability for gains \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3, for losses \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7) or a rare loss door (RareL, probability for losses \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3, for gains \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7). Participants used the left and right arrow keys on the keyboard to make their door choice. Door choice was monitored throughout the task. The task choice decisions on each trial were response-constrained, not time-constrained, i.e. participants could take their time to select their choice.\u003c/p\u003e \u003cp\u003eThe task consisted of two blocks, an experimental block and a baseline block that were counterbalanced across participants. In the experimental block, expected value (EV) was greater for the RareG door (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3 for +\u0026thinsp;60 coins, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7 for \u0026minus;\u0026thinsp;20 coins, EV\u0026thinsp;=\u0026thinsp;+\u0026thinsp;40) than for the RareL door (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3 for \u0026minus;\u0026thinsp;60 coins, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7 for +\u0026thinsp;20 coins; EV\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;40). Manipulation of EV, with greater expected value tied to the RareG door, allowed for investigating individual propensities to prioritize long-term (or cumulative) versus short-term (or immediate) rewards. The RareG door was assigned greater EV because selecting this door suggests EV magnitude-based decision processing in subjects as opposed to simply choosing based on frequency of gains, in which case the RareL choice should be preferred.\u003c/p\u003e \u003cp\u003eIn the baseline block, EV was the same for both RareG (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3 for +\u0026thinsp;70 coins, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7 for \u0026minus;\u0026thinsp;30 coins, EV\u0026thinsp;=\u0026thinsp;0) and for the RareL door (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3 for \u0026minus;\u0026thinsp;70 coins, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7 for +\u0026thinsp;30 coins; EV\u0026thinsp;=\u0026thinsp;0), and allowed investigation of gain frequency bias towards the RareL door without EV differences.\u003c/p\u003e \u003cp\u003eForty trials were presented per block approximating similar trial numbers as previous human reward task studies \u003csup\u003e36,37\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA shows a schematic of the task stimulus sequence. On each task trial, a fixation cue was followed by two door choices that remained on the screen until a choice was made. After choice selection, central fixation was presented for 500-ms duration followed by selected choice presentation for 500-ms duration, then immediate reward presentation for 500-ms duration corresponding to the reward for the selected door on that trial, and then cumulative reward presentation for 500-ms duration corresponding to total reward earned until that trial during the block.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eLucky Door\u003c/em\u003e task was deployed in Unity as part of the assessment suite on the \u003cem\u003eBrainE\u003c/em\u003e (short for Brain Engagement) platform \u003csup\u003e38\u003c/sup\u003e. The Lab Streaming Layer (LSL,\u003csup\u003e39\u003c/sup\u003e) protocol was used to time-stamp each stimulus/response event during the task. Study participants engaged with the assessment on a Windows 10 laptop sitting at a comfortable viewing distance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBehavior Analysis.\u003c/b\u003e Behavioral data were obtained from 74 of 75 participants, except for missing data from 1 participant in the control group. The main behavior metric was Win-Stay, i.e., participant\u0026rsquo;s willingness to stay with the RareG door that had greater expected value (but lesser immediate gains) after they encountered a winning trial in the experimental block. Win-Stay was calculated as the ratio of times a participant stayed with the RareG choice after a win compared to total number of trials after a win. On the baseline block that had no EV differences, we also calculated Win-Stay for RareG choices as a control to confirm the hypothesis that Win-Stay behavior selectively shows group differences on the experimental block \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo analyze group differences while accounting for all covariates of age, gender, ethnicity, socioeconomic score, and mental health scores of anxiety and depression, we modeled the behavior metrics across all three groups with a linear model using the fitlm function in MATLAB with robust regression option applied to reduce outlier influence \u003csup\u003e40\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEEG Processing.\u003c/b\u003e EEG simultaenous to the decision-making task was acquired in most participants (n\u0026thinsp;=\u0026thinsp;57) with missing EEG due to technical issues in 3 participants in the control group, 7 participants in the indirectly exposed group, and 8 participants in the directly exposed group. RareG trials were analyzed coinciding with the behavioral analyses on these trials. Since we are analyzing neural correlates related to decision making and ensuing reward processing, we segmented the trial structure into three distinct time period associated with choice defined as 0-500 ms after the chosen door is presented, immediate reward defined as 500\u0026ndash;1000 ms after the chosen door appears, and cumulative reward defined as 1000\u0026ndash;1500 ms after the chosen door appears. These three timings are also shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA.\u003c/p\u003e \u003cp\u003eNeural data analyses were conducted using a uniform two-step processing pipeline published in several of our studies \u003csup\u003e7,21,38,41\u0026ndash;48\u003c/sup\u003e. Step 1) EEG channel data processing was conducted using the EEGLAB toolbox v2020 in MATLAB v2022b. EEG data was resampled at 250 Hz and filtered in the 1\u0026ndash;45 Hz range to exclude ultraslow DC drifts at \u0026lt;\u0026thinsp;1Hz and high-frequency noise produced by muscle movements and external electrical sources at \u0026gt;\u0026thinsp;45Hz.\u003c/p\u003e \u003cp\u003eThere were no missing channels in the EEG data across subjects. Epoched data were cleaned using the autorej function in EEGLAB to remove noisy trials, i.e. \u0026gt;5SD outliers rejected over max 8 iterations, followed by further cleaning of electrooculographic, electromyographic or non-brain source artifacts using the Sparse Bayesian learning (SBL) algorithm (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/aojeda/PEB\u003c/span\u003e\u003cspan address=\"https://github.com/aojeda/PEB\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e46\u003c/sup\u003e. In addition to the automatic rejection, we also implemented an amplitude criteria where any trial exceeding 100 uV was considered noisy and removed. The cleaned data were then band filtered in the physiologically relevant theta (4\u0026ndash;8 Hz), alpha (8\u0026ndash;13 Hz), and beta (13\u0026ndash;30 Hz) frequency bands. Epoched events were then extracted and averaged across trials to remove single trial noise.\u003c/p\u003e \u003cp\u003eStep 2) We used the block-Sparse Bayesian learning (BSBL-2S) algorithm to localize frequency band filtered EEG data and partitioned the signals into cortical regions of interest (ROIs) and artifact sources \u003csup\u003e46,49\u003c/sup\u003e. For the source space activations, ROIs were based on the standard 68 brain region Desikan-Killiany atlas \u003csup\u003e50\u003c/sup\u003e using the Colin-27 head model \u003csup\u003e51\u003c/sup\u003e. BSBL-2S is a two-step algorithm in which the first-step is equivalent to low-resolution electromagnetic tomography (LORETA \u003csup\u003e52\u003c/sup\u003e). LORETA estimates sources subject to smoothness constraints, i.e. nearby sources tend to be co-activated, which may produce source estimates with a high number of false positives that are not biologically plausible. To guard against this, BSBL-2S applies sparsity constraints in the second step wherein blocks of irrelevant sources are pruned. Notably, this data-driven sparsity constraint reduces the effective number of sources considered at any given time as a solution. The sparsity is imposed at the level of cortical ROIs, thereby projecting the data onto this space of few ROIs, and reducing the uncertainty of the inverse solution. Thus, it is not that only higher channel density data can yield source solutions, the ill-posed inverse problem can also be solved by imposing more aggressive constraints on the solution to converge on the source model at lower channel densities, as also supported by prior research \u003csup\u003e53,54\u003c/sup\u003e. Of note, the BSBL-2S two-stage algorithm has been benchmarked to produce evidence-optimized inverse source models at 0.95AUC relative to the ground truth, while without the second stage\u0026thinsp;\u0026lt;\u0026thinsp;0.9AUC is obtained, verified using both data and simulations \u003csup\u003e46,49\u003c/sup\u003e. We have also shown that cortical source mapping with this method has high test-retest reliability (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.77, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) obtained with recordings conducted one-week apart \u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eNeural Data Analysis.\u003c/b\u003e Here, we applied a standardized pipeline with modifiable parameters to streamline both scalp and source space neural analyses. A github with the source code can be found in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jasonnan2/Automated-Analysis-of-EEG/\u003c/span\u003e\u003cspan address=\"https://github.com/jasonnan2/Automated-Analysis-of-EEG/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThis standardized pipeline included -\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eOutlier rejection on the final trial-averaged scalp and source data, which sets any datapoint\u0026thinsp;\u0026gt;\u0026thinsp;5SD across all subjects to NaN.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBaseline correction was done on both scalp and source activity relative to the \u0026minus;\u0026thinsp;250 msec to -50 msec fixation time window prior to choice presentation in each scalp electrode/ source ROI within each subject.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDifferential scalp topography maps comparing groups were plotted for each of the three frequency bands (theta, alpha, beta) and three trial periods (choice, immediate reward, cumulative reward) for a total of 9 scalp maps. Patterns of significant electrodes were validated with permutation clustering across 10,000 iterations, and false discovery rate (FDR) corrections were applied for 9 topographic map comparisons \u003csup\u003e55\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRelevant average alpha band event-related activity observed in step 3 above, was defined in a posterior electrode cluster (Pz, P3, P4, and POz) for significance testing across groups.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo find relationships between behavior and neurophysiology, we fit linear models to test for group x neural interaction predicting behavior data, controlling for significant demographic covariates. Interactions were tested for average alpha activity in the posterior electrode cluster as well as in the individual component electrodes with FDR corrections applied for multiple comparisosn.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWithing-group Spearman\u0026rsquo;s correlations were used to follow-up on any significant neuro-behavioral group interactions obtained in step 5 above.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCortical source activity was analyzed corresponding to the relevant alpha band posterior scalp activity found in steps 3\u0026ndash;4 above.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eBehavioral Performance.\u003c/b\u003e The two-choice decision-making task design and corresponding Win-Stay behavior performance on high EV (rare gain or RareG) choices, which resulted in greater long-term cumulative reward, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A robust linear regression of Win-Stay behavior with participant group as predictor showed a significant effect only for the directly-exposed group (β=-0.22\u0026plusmn;0.1, tstat=-2.2, p\u0026thinsp;=\u0026thinsp;0.03) but not for the indirectly-exposed or non-exposed group (p\u0026thinsp;\u0026gt;\u0026thinsp;0.08). Thus, only the directly-exposed group showed lower Win-Stay choices relative to the other two groups. The regression model also included covariates of age, gender, ethnicity, socioeconomic scores, anxiety and depression; the model was overall significant (adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.12, Fstat\u0026thinsp;=\u0026thinsp;2.38, p\u0026thinsp;=\u0026thinsp;0.03) and only age was a significant covariate in the model (β=-0.02\u0026plusmn;0.01, tstat=-2.8, p\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e \u003cp\u003eWe further modeled Win-Stay behavior on RareG trials on the baseline block that had no EV differences between choices. This model was overall not significant (adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.1, Fstat\u0026thinsp;=\u0026thinsp;1.96, p\u0026thinsp;=\u0026thinsp;0.07), had age as a covariate (β=-0.02\u0026plusmn;0.01, tstat=-3.4, p\u0026thinsp;=\u0026thinsp;0.001) but showed no effect of group (p\u0026thinsp;\u0026gt;\u0026thinsp;0.1), confirming our hypothesis that Win-Stay behavior only differs when there are EV differences between choices.\u003c/p\u003e\u003cp\u003e \u003cb\u003eNeural Processing.\u003c/b\u003e As there were no significant behavioral differences between the indirectly exposed group and non-exposed groups, for neural analyses we combined these into one group (\u003cem\u003eOther\u003c/em\u003e) to compare against the directly exposed group. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA shows EEG scalp topographies contrasting group neural activity in the directly exposed vs. other group in theta, alpha and beta frequency bands within the 500 ms time period after choice, immediate reward and cumulative reward presentations. Electrodes showing significant group differences (i.e., directly exposed vs. other) after permutation clustering are marked with + (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and notably appeared only in the alpha band. Given the known posterior parieto-occipital origins of alpha band activity \u003csup\u003e56\u0026ndash;63\u003c/sup\u003e and its typical topography appearing in our scalp maps, we further quantified parietal cluster alpha (at Pz, P3, P4, and POz electrodes) in grouped bar graphs in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. Parietal alpha differences were consistently found during the choice (t(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e)=-2.6; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), immediate reward (t(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e)=-2.8; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and cumulative reward periods (t(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e)=-2.7; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) as compared in between-group t-tests. We also checked that parietal alpha activity did not significantly differ between the indirectly exposed and non-exposed groups that were combined in the other group (p\u0026thinsp;\u0026gt;\u0026thinsp;0.43).\u003c/p\u003e \u003cp\u003eTo investigate whether alpha activity is a neural correlate of behavior, we implemented robust regression models that predicted Win-Stay behavior on the high EV choice; predictors included group (directly-exposed vs. other), parietal alpha activity and the interaction between group and alpha activity. Since age was a significant predictor of Win-Stay behavior, it was entered as a covariate in all models. No models using average alpha activity in the parietal cluster showed a significant neural effect on behavior. Hence, we explored models for individual electrodes in the cluster, correcting for multiple comparisons across electrodes (P3, Pz, P4, POz) and time windows (choice, immediate reward and cumulative reward). In this case, only the model for Pz alpha activity during the choice period showed a significant neural activity by group interaction (β\u0026thinsp;=\u0026thinsp;4.2 \u0026plusmn; 2.1, tstat\u0026thinsp;=\u0026thinsp;2.0, p\u0026thinsp;=\u0026thinsp;0.05) as well as a significant effect of group (β\u0026thinsp;=\u0026thinsp;0.18 \u0026plusmn;0.09, tstat\u0026thinsp;=\u0026thinsp;2.1, p\u0026thinsp;=\u0026thinsp;0.04) and age (β= -0.02 \u0026plusmn; 0.007, tstat=-3.0, p\u0026thinsp;=\u0026thinsp;0.004) but no effect of neural activity alone (p\u0026thinsp;\u0026gt;\u0026thinsp;0.4); no significant neural effects were observed in the immediate/cumulative reward periods. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA shows the adjusted linear fit neurobehavior model for Pz alpha activity in the choice period. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB illustrates the group specific alpha activity response as it relates to Win-Stay behavior; a significant Spearman\u0026rsquo;s correlation was observed only in the other group (rho\u0026thinsp;=\u0026thinsp;0.34 p\u0026thinsp;=\u0026thinsp;0.04) but not in the directly exposed group (p\u0026thinsp;=\u0026thinsp;0.5). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC shows the cortical source localization of the parietal alpha activity during the choice period masked by significant difference between activity in the directly exposed vs. other group; the source region as highlighted in the figure was observed to be right posterior cingulate cortex with greater activity in the directly exposed than the other group (t(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e)=-2.33; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the current study, our main objective was to investigate how climate mental health trauma from a major wildfire disaster may affect cognitive decision-making in community participants. In this context we specifically investigated the ability to engage and stay with high expected value choices marked by the Win-Stay behavior metric obtained on high EV trials, and further probed the neural dynamics of such behavioral modulation as regulated by climate trauma. We observed that individuals directly exposed/affected by the climate trauma event showed significantly lower ability to stay with the high EV choice after winning compared to indirectly exposed community participants (who witnessed the wildifire in their community but were not directly impacted) as well as non-exposed control participants. Additionally, there were no behavioral differences in Win-Stay behavior between the indirectly exposed and non-exposed participants. At the neural level, across three pertinent processing time windows of selected choice presentation, immediate reward and cumulative reward presentation, we observed significantly greater alpha band EEG activity especially over parieto-occipital brain regions in the directly exposed group vs. all other participants. Finally, a robust regression model investigating neurobehavioral relationships showed that alpha activity at the midline parietal electrode (Pz) during choice presentation could predict Win-Stay behavior with a significant group interaction wherein only non-directly exposed participants (i.e., indirectly exposed and non-exposed group individuals) positively modulated their alpha activity with Win-Stay behavior, while no such modulation was observed in the directly exposed group.\u003c/p\u003e \u003cp\u003eThe observation of impacted cognitive decision-making due to climate trauma, as indexed by the Win-Stay metric was consistent with our primary hypothesis. We hypothesized this impact based on prior studies of decision making in general (i.e., non-climate) PTSD and depression \u003csup\u003e15\u0026ndash;17,64,65\u003c/sup\u003e. For instance, Sailer et al\u003csup\u003e16\u003c/sup\u003e examined reward processing in clinically diagnosed PTSD patients using a decision-making task (adapted from \u003csup\u003e66,67\u003c/sup\u003e), and observed that individuals with PTSD showed lower accuracy in late phase reward learning relative to control subjects, suggesting lower efficiency of reward learning in PTSD. Similarly in the decision-making reward task we deploy here, high EV choices also need to be implictly learned and differentiated from low EV choices, and failure of such learning would result in low Win-Stay behavior on high EV trials.\u003c/p\u003e \u003cp\u003e In the EEG neural recordings, we observed significantly greater parietal alpha on high EV trials in the directly-exposed group vs. all other participants. This observation is in line with previous reports in PTSD, showing involvement of fronto-parietal regions in decision-reward processes \u003csup\u003e68,69\u003c/sup\u003e. For example in a functional neuroimaging study on combat veterans, Howlett et al\u003csup\u003e69\u003c/sup\u003e observed an exaggerated neural response, specifically in the parietal region (left pre-cuneus/inferior parietal lobule and right inferior parietal lobule) to suprising errors while the participants were performing a probabilistic learning task. Interrogating neurobehavioral correlations, we found that parietal alpha, specifically at the Pz electrode during choice presentation showed group-specific modulations in the context of Win-Stay behavior. In the non-directly exposed i.e. other group participants, greater Pz alpha was associated with greater Win-Stay performance. In contrast, in the directly-exposed group, Pz alpha was generally of greater magnitude in the group as a whole but did not show modulation with Win-Stay behavior. Flexible alpha modulation during decision-making behavior has been associated with greater task-related cognitive effort in healthy participants \u003csup\u003e70,71\u003c/sup\u003e, which may explain our findings in the other group i.e., greater Win-Stay behavior is achieved with greater cognitive effort. Studies also suggest that reward-related learning during decision-making harnesses working memory processes \u003csup\u003e72,73\u003c/sup\u003e, and relatedly, prior work has shown that parietal alpha indexes working memory performance \u003csup\u003e74\u0026ndash;76\u003c/sup\u003e. Thus, parietal alpha modulation in the other group participants may also suggest that they successfully recruit working memory processes for learning the high EV choices and thereby, generate greater Win-Stay performance. Overall higher parietal alpha magnitudes in the directly-exposed group suggest overall greater cognitive effort in the group, but an inability to translate this to superior behavior performance.\u003c/p\u003e \u003cp\u003eThe difference in parietal alpha activity in scalp EEG localized to a significant cortical source difference observed in posterior cingulate cortex (PCC), with greater activity observed in the directly exposed vs. other group. Several studies have reported the role of the PCC, a key node of the posterior default mode network, in modulation of ruminative behavior \u003csup\u003e77\u0026ndash;80\u003c/sup\u003e. Rumination is also one of the primary ways in which emotion regulation is impacted in affective disorders \u003csup\u003e81,82\u003c/sup\u003e, and further predicts PTSD \u003csup\u003e83\u003c/sup\u003e. Thus, it may be plausible that directly exposed individuals under the duress of climate trauma, engage in distracted rumination behavior indexed by PCC source activitiy, which may affect their decision-making strategy and hence reduce Win-Stay performance.\u003c/p\u003e \u003cp\u003eThe study's limitations encompass the potential for observed group differences to be inherent traits predating the traumatic wildfire event. This constraint is common to disaster research, as investigations typically occur post-event. As climate disasters become more frequent, it would be important to extend this neuro-cognitive research longitudinally to understand pre vs. post-disaster effects. It has also been well-documented that individuals in lower socioeconomic strata are more vulnerable to suffering from climate related disasters \u003csup\u003e84\u003c/sup\u003e. However, our cohort did not have significant group differences in socioeconomic scores. An additional constraint is our utilization of a moderate channel density EEG system for neural recordings, and future validation could be achieved through the use of a high-density EEG or alternative neuroimaging techniques such as functional magnetic resonance imaging. Yet, notably, it is important to highlight that the choice of the moderate channel density EEG was motivated by its cost-effectiveness and adaptability to community settings \u003csup\u003e7\u003c/sup\u003e. In such community studies, there is a crucial need to strike a balance between scalable feasibility, cost considerations, and data resolution \u003csup\u003e85\u003c/sup\u003e. Future community research should also focus on procuring larger sample sizes of the neuro-cognitive data.\u003c/p\u003e \u003cp\u003eOverall, the current research is the first to examine the effect of climate trauma on decision making. We observed that directly fire-exposed individuals showed impacted decision-reward strategies indexed by reduction in Win-Stay performance on high expected value choices alongside higher alpha activity in posterior parietal regions compared to other, indirectly exposed or non-exposed study participants. Cortical source localization revealed significantly greater PCC activity in the directly exposed group suggesting that distracted rumination that often originates from PCC may be a potential contributor to impacted decision-making in this group. Future neuro-cognitively targeted trauma interventions in this context may thus aim to reduce PCC related default mode network activity. Our related intervention work with a scalable digital mindfulness and compassion training has shown significant default mode network suppression alongside enhancement of mindfulness and compassion behaviors \u003csup\u003e43\u003c/sup\u003e. Thus, such scalable strategies may also be tailored as potential interventions for climate-related trauma. This is especially pertinent since our prior observational studies point to mindfulness as a protective trait in this traumatic setting \u003csup\u003e8,86\u003c/sup\u003e. With the planet experiencing escalating temperatures, an increasing number of individuals confront extreme climate events, underscoring the urgency to explore novel resiliency tools from diverse disciplines. In this regard, we unveil objective neuro-cognitive markers of reward-related decision-making that can be potentially used to guide interventions, and map the success of such intervention within climate vulnerable communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eConceptualization: JM; methodology: JN and JM; formal analysis: JN; investigation: JN, MCW and JM; resources: JM; data curation: JN, MCW; writing\u0026mdash;original draft preparation: JN and SJ; writing\u0026mdash;review and editing: JN, SJ, DR, MCW and JM; visualization: JN and JM; supervision: JM; project administration: JM; funding acquisition: JM.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003e This work was supported by seed grants from the Tang Prize Foundation (JM), the Hope for Depression Research Foundation (JM) and the CA CARES (Climate Action, Resilience, and Environmental Sustainability) Proof of Concept Funds (JM). The BrainE software is copyrighted for commercial use (Regents of the University of California Copyright #SD2018-816) and free for research and educational purposes.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eDe-identified and processed study data are available upon request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIntergovernmental Panel on Climate Change (IPCC). \u003cem\u003eGlobal Warming of 1.5\u0026deg;C: IPCC Special Report on Impacts of Global Warming of 1.5\u0026deg;C above Pre-Industrial Levels in Context of Strengthening Response to Climate Change, Sustainable Development, and Efforts to Eradicate Poverty\u003c/em\u003e. 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Community Psychol. 50, 2950\u0026ndash;2972 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"decision-making, reward, EEG, alpha, posterior cingulate cortex","lastPublishedDoi":"10.21203/rs.3.rs-4385857/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4385857/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate trauma refers to the mental health sequalae of climate disaster events. We have previously shown evidence for climate trauma with high prevalence of trauma, anxiety and depression symptoms after California\u0026rsquo;s 2018 Camp Fire wildfire. Here, we investigate whether this climate trauma impacts cognitive decision-making and its neural correlates. One year after the wildfire, we recruited three groups - those directly exposed (n\u0026thinsp;=\u0026thinsp;27), indirectly exposed (who community members who witnessed the wildfire n\u0026thinsp;=\u0026thinsp;21), versus non-exposed controls from a nearby region (n\u0026thinsp;=\u0026thinsp;27). Participants performed a decision-making task that led to immediate and cumulative point rewards on each trial with simultaneous electroencephalography (EEG) recordings. We evaluated Win-Stay behavior in choosing to stay with the greater expected value option. Directly-exposed individuals showed significantly reduced Win-Stay behavior relative to the other groups. EEG analyses showed significantly greater parietal alpha activity for the selected choice and ensuing rewards in the directly-exposed individuals, with underlying cortical source in posterior cingulate cortex. Overall, these findings suggest that climate trauma may significantly impact neuro-cognitive processing in the context of value-based decision-making, and may serve as a useful biomarker target for future mental health interventions in climate change impacted communities.\u003c/p\u003e","manuscriptTitle":"Climate Trauma from Wildfire Exposure Impacts Cognitive Decision-Making","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 19:08:17","doi":"10.21203/rs.3.rs-4385857/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2530f90b-438e-4f98-ad1f-c17b42e98668","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32301392,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Decision"},{"id":32301393,"name":"Biological sciences/Neuroscience/Reward"}],"tags":[],"updatedAt":"2024-09-05T04:39:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 19:08:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4385857","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4385857","identity":"rs-4385857","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}