2 results
64 Comparison of Post-Concussion Symptom Network Structure at Baseline and Post-Concussion
- Christine Salva, Grace J Goodwin, Hana Kuwabara, Jessica Woodyatt, Julia E Maietta, Thomas Kinsora, Staci Ross, Daniel N Allen
-
- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 169-170
-
- Article
-
- You have access Access
- Export citation
-
Objective:
Recent conceptualizations of concussion symptoms have begun to shift from a latent perspective (which suggests a common cause; i.e., head injury), to a network perspective (where symptoms influence and interact with each other throughout injury and recovery). Recent research has examined the network structure of the Post-Concussion Symptom Scale (PCSS) cross-sectionally at pre-and post-concussion, with the most important symptoms including dizziness, sadness, and feeling more emotional. However, within-subject comparisons between network structures at pre-and post-concussion have yet to be made. These analyses can provide invaluable information on whether concussion alters symptom interactions. This study examined within-athlete changes in PCSS network connectivity and centrality (the importance of different symptoms within the networks) from baseline to post-concussion.
Participants and Methods:Participants were selected from a larger longitudinal database of high school athletes who completed the PCSS in English as part of their standard athletic training protocol (N=1,561). The PCSS is a 22-item self-report measure of common concussion symptoms (i.e., headache, vomiting, dizziness, etc.) in which individuals rate symptom severity on a 7-point Likert scale. Participants were excluded if they endorsed history of brain surgery, neurodevelopmental disorder, or treatment history for epilepsy, migraines, psychiatric disorders, or alcohol/substance use. Network analysis was conducted on PCSS ratings from a baseline and acute post-concussion (within 72-hours post-injury) assessment. In each network, the nodes represented individual symptoms, and the edges connecting them their partial correlations. Estimations of the regularized partial correlation networks were completed using the Gaussian graphical model, and the GLASSO algorithm was used for regularization. Each symptom’s expected influence (the sum of its partial correlations with other symptoms) was calculated to identify the most central symptoms in each network. Recommended techniques from Epskamp et al. (2018) were completed for assessing the accuracy of the estimated symptom importance and relationships. Network Comparison Tests were conducted to observe changes in network connectivity, structure, and node influence.
Results:Both baseline and acute post-concussion networks contained negative and positive relationships. The expected influence of symptoms was stable in both networks, with difficulty concentrating having the greatest expected influence in both. The strongest edges in the networks were between symptoms within similar domains of functioning (e.g., sleeping less was associated with trouble falling asleep). Network connectivity was not significantly different between networks (S=0.43), suggesting the overall degree to which symptoms are related was not different at acute post-concussion. Network structure significantly differed at acute post-concussion (M=0.305), suggesting specific relationships in the acute post-concussion network were different than they were at baseline. In the acute post concussion network, vomiting was less central and sensitivity to noise and mentally foggy more central.
Conclusions:PCSS network structure at acute post-concussion is altered, suggesting concussion may disrupt symptom networks and certain symptoms’ associations with the experience of others after sustaining a concussive injury. Future research should compare PCSS networks later in recovery to examine if similar structural changes remain or return to baseline structure, with the potential that observing PCSS network structure changes post-concussion could inform symptom resolution trajectories.
52 Differences in Neuropsychological Test Performance and Symptom Data in Schizophrenia with Co-Occurring Cannabis Use
- Jessica J Woodyatt, Grace J Goodwin, Bern G. Lee, Yuan Rairata, Gia Calip, Daniel N Allen
-
- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 924-925
-
- Article
-
- You have access Access
- Export citation
-
Objective:
Long-term exposure to the psychoactive ingredient in cannabis, delta-9-tetrahydrocanabinol (THC), has been consistently raised as a notable risk factor for schizophrenia. Additionally, cannabis is frequently used as a coping mechanism for individuals diagnosed with schizophrenia. Cannabis use in schizophrenia has been associated with greater severity of psychotic symptoms, non-compliance with medication, and increased relapse rates. Neuropsychological changes have also been implicated in long-term cannabis use and the course of illness of schizophrenia. However, the impact of co-occurring cannabis use in individuals with schizophrenia on cognitive functioning is less thoroughly explored. The purpose of this meta-analysis was to examine whether neuropsychological test performance and symptoms in schizophrenia differ as a function of THC use status. A second aim of this study was to examine whether symptom severity moderates the relationship between THC use and cognitive test performance among people with schizophrenia.
Participants and Methods:Peer-reviewed articles comparing schizophrenia with and without cannabis use disorder (SZ SUD+; SZ SUD-) were selected from three scholarly databases; Ovid, Google Scholar, and PubMed. The following search terms were applied to yield studies for inclusion: neuropsychology, cognition, cognitive, THC, cannabis, marijuana, and schizophrenia. 11 articles containing data on psychotic symptoms and neurocognition, with SZ SUD+ and SZ SUD- groups, were included in the final analyses. Six domains of neurocognition were identified across included articles (Processing Speed, Attention, Working Memory, Verbal Learning Memory, and Reasoning and Problem Solving). Positive and negative symptom data was derived from eligible studies consisting of the Positive and Negative Syndrome Scale (PANSS), the Scale for the Assessment of Positive Symptoms (SAPS), the Scale for the Assessment of Negative Symptoms (SANS), Self-Evaluation of Negative Symptoms (SNS), Brief Psychiatric Rating Scale (BPRS), and Structured Clinical Interview for DSM Disorders (SCID) scores. Meta analysis and meta-regression was conducted using R.
Results:No statistically significant differences were observed between SZ SUD+ and SZ SUD-across the cognitive domains of Processing Speed, Attention, Working Memory, Verbal Learning Memory, and Reasoning and Problem Solving. Positive symptom severity was found to moderate the relationship between THC use and processing speed, but not negative symptoms. Positive and negative symptom severity did not significantly moderate the relationship between THC use and the other cognitive domains.
Conclusions:Positive symptoms moderated the relationship between cannabis use and processing speed among people with schizophrenia. The reasons for this are unclear, and require further exploration. Additional investigation is warranted to better understand the impact of THC use on other tests of neuropsychological performance and symptoms in schizophrenia.