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Record W4392200739 · doi:10.1101/2024.02.22.581581

Perturbations in Risk/Reward Decision Making and Frontal Cortical Catecholamine Regulation Induced by Mild Traumatic Brain Injury

2024· preprint· en· W4392200739 on OpenAlexaff
Christopher P. Knapp, Eleni Papadopoulos, Jessica A. Loweth, Ramesh Raghupathi, Stan Floresco, Barry D. Waterhouse, Rachel L. Navarra

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTraumatic brain injuryNeurosciencePrefrontal cortexCatecholamineAnterior cingulate cortexOrbitofrontal cortexPsychologyMedicineCognitionPsychiatry

Abstract

fetched live from OpenAlex

Abstract Mild traumatic brain injury (mTBI) can disrupt cognitive processes that influence risk taking behavior. Athletes, military personnel, and domestic violence victims often experience multiple mTBIs; however, little is known regarding the effects of repetitive injury (rmTBI) on risk/reward decision making or whether these outcomes are sex specific. Risk/reward decision making is mediated by the prefrontal cortex (PFC), which is composed of several sub-regions including the medial PFC (mPFC), anterior cingulate cortex (ACC), and orbitofrontal cortex (OFC). These regions are densely innervated by catecholaminergic fibers, which modulate PFC-mediated cognitive processes. Aberrant catecholamine activity within the PFC has been documented following TBI, which may underlie TBI-induced risky behavior. Tyrosine hydroxylase (TH) and norepinephrine transporter (NET) regulate catecholamine homeostasis within the PFC; however, it has not been determined how rmTBI affects these proteins. The present study aimed to characterize the effects of rmTBI on risk/reward decision making behavior and catecholamine transmitter regulatory proteins within the PFC. Risk/reward decision making was evaluated using a probabilistic discounting task (PDT) which required rats to choose between small/certain rewards delivered with 100% certainty and large/risky rewards delivered with decreasing probabilities over a session. Rats were first trained on the PDT and then exposed to sham, single (smTBI), or a series of three closed-head control cortical impact (CH-CCI) injuries over the course of one week, followed by four weeks of PDT testing. In week 1 post-final surgery, mTBI generally enhanced preference for the larger/riskier option with these effects seemingly more prominent in females. These effects resolved by week 2 post-final surgery indicating that the effects of mTBI on choice behavior are transient. By week 4, males, but not females, exhibited increased latencies to make riskier choices following rmTBI, demonstrating a delayed effect of injury on information processing speed. A separate group of rats was used to measure changes in levels of TH and NET within the mPFC, ACC, and OFC forty-eight hours after mTBI. No injury-induced differences were observed within the mPFC or ACC. In the OFC, females exhibited dramatic increases in TH levels following smTBI, but only small increases following rmTBI. Both males and females; however, experienced reduced levels of NET following rmTBI, which may function as a compensatory response to increased extracellular levels of catecholamines. Together, these results suggest that OFC is more susceptible to catecholamine instability after rmTBI, a finding indicating that not all areas of the PFC contribute equally to the observed TBI-induced catecholamine imbalances. Overall, combining the CH-CCI model of rmTBI with the PDT proved effective in revealing time-dependent and sex-specific changes in risk/reward decision making and catecholamine regulation following repetitive mild head injuries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.320
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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