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79 Continuous Theta Burst Stimulation (cTBS) over the Inferior Parietal Cortex Decreases Default Mode Connectivity and Improves Overnight Sleep in People with Insomnia
- William D. S. Killgore, Samantha Jankowski, Kymberly Henderson-Arredondo, Christopher Trapani, Heidi Elledge, Daniel Lucas, Andrew Le, Emmett Suckow, Lindsey Hildebrand, Michelle Persich, Brianna Zahorecz, Cohelly Salazar, Tyler Watson, Camryn Wellman, Deva Reign, Yu-Chin Chen, Ying-Hui Chou, Natalie S. Dailey
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 587-588
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Objective:
Chronic insomnia is a highly prevalent disorder affecting approximately one-in-three Americans. Insomnia is associated with increased cognitive and brain arousal. Compared to healthy individuals, those with insomnia tend to show greater activation/connectivity within the default mode network (DMN) of the brain, consistent with the hyperarousal theory. We investigated whether it would be possible to suppress activation of the DMN to improve sleep using a type of repetitive transcranial magnetic stimulation (rTMS) known as continuous theta burst stimulation (cTBS).
Participants and Methods:Participants (n=9, 6 female; age=25.4, SD=5.9 years) meeting criteria for insomnia/sleep disorder on standardized scales completed a counterbalanced sham-controlled crossover design in which they served as their own controls on two separate nights of laboratory monitored sleep on separate weeks. Each session included two resting state functional magnetic resonance imaging (fMRI) sessions separated by a brief rTMS session. Stimulation involved a 40 second cTBS stimulation train applied over an easily accessible cortical surface node of the DMN located at the left inferior parietal lobe. After scanning/stimulation, the participant was escorted to an isolated sleep laboratory bedroom, fitted with polysomnography (PSG) electrodes, and allowed an 8-hour sleep opportunity from 2300 to 0700. PSG was monitored continuously and scored for standard outcomes, including total sleep time (TST), percentage of time various sleep stages, and number of arousals.
Results:Consistent with our hypothesis, a single session of active cTBS produced a significant reduction of functional connectivity (p < .05, FDR corrected) within the DMN. In contrast, the sham condition produced no changes in functional connectivity from pre- to post-treatment. Furthermore, after controlling for age, we also found that the active treatment was associated with meaningful trends toward greater overnight improvements in sleep compared to the sham condition. First, the active cTBS condition was associated with significantly greater TST compared to sham (F(1,7)=14.19, p=.007, partial eta-squared=.67). Overall, individuals obtained 26.5 minutes more sleep on the nights that they received the active cTBS compared to the sham condition. Moreover, the active cTBS condition was associated with a significant increase in the percentage of time in rapid eye movement (REM%) sleep compared to the sham condition (F(1,7)=7.05, p=.033, partial eta-squared=.50), which was significant after controlling for age. Overall, active treatment was associated with an increase of 6.76% more of total sleep time in REM compared to sham treatment. Finally, active cTBS was associated with fewer arousals from sleep (t(8) = -1.84, p = .051, d = .61), with an average of 15.1 fewer arousals throughout the night than sham.
Conclusions:Overall, these findings suggest that this simple and brief cTBS approach can alter DMN brain functioning in the expected direction and was associated with trends toward improved objectively measured sleep, including increased TST and REM% and fewer arousals during the night following stimulation. These findings emerged after only a single 40-second treatment, and it remains to be seen whether multiple treatments over several days or weeks can sustain or even improve upon these outcomes.
58 Preliminary Development of a Virtual Reality Neuropsychological Assessment System
- William D. Killgore, Kymberly Henderson-Arredondo, Natalie S. Dailey, Jason Zhang, Samantha Jankowski, Ao Li, Huayu Li, Deva Reign, Emmett Suckow, Lindsey Hildebrand, Camryn Wellman, Jerzy Rozenblit, Janet Roveda
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 735-736
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Objective:
While there exist numerous validated neuropsychological tests and batteries to measure cognitive and behavioral capacities, the vast majority of these are time intensive and difficult to administer and score outside of the clinic. Moreover, many existing assessments may have limited ecological validity in some contexts (e.g., military operations). Therefore, we have been developing a novel approach to administering neuropsychological assessment using a virtual reality (VR) “game” that will collect simultaneously acquired multidimensional data that is synthesized by machine learning algorithms to identify neurocognitive strengths and weaknesses in a fraction of the time of typical assessment approaches. For our initial pilot project, we developed a preliminary VR task that involved a brief game-like military “shoot/no-shoot” task that collected data on hits, false alarms, discriminability, and response times under a context-dependent rule set. This prototype task will eventually be expanded to include a significantly more complex set of tasks with greater cognitive demands, sensor feeds, and response variables that could be modified to fit many other contexts. The objective of this project was to construct a rudimentary pilot version and demonstrate whether it could predict outcomes on standard neuropsychological assessments.
Participants and Methods:To demonstrate proof-of-concept, we collected data from 20 healthy participants from the general population (11 male; age=24.8, SD=7.8) with high average intelligence (IQ = 112, SD=10.7). All participants completed the Wechsler Abbreviated Scale of Intelligence-II (WASI-II), and several neuropsychological tests including the ImPACT, the Attention and Executive Function modules of the Neuropsychological Assessment Battery (NAB), and the VR task. Initially, we used a prior dataset from 359 participants (n=191 mild traumatic brain injury; n=120healthy control; n=48 sleep deprived) to serve as a training sample for machine learning models. Based on these outcomes, we applied machine learning, as well as standard multiple regression approaches to predict neuropsychological outcomes in the 20 test participants.
Results:In this limited study, the machine learning approach did not converge on a meaningful prediction due to the instability of the small sample. However, standard multiple linear regression using stepwise entry/deletion of the VR task variables significantly predicted neuropsychological performance. The VR task predicted WASI-II vocabulary (R=.457, p=.043), NAB Attention Index (R=.787, p=.001), and NAB Executive Function Index (R=.715, p=.002). Interestingly, these performances were generally as good or better than the predictions resulting from the ImPACT, a commercially available neuropsychological test battery, which correlated with WASI-II vocabulary (R=.557, p=.011), NAB Attention Index (R=.574, p=.008), and NAB Executive Function Index (R=.619, p=.004).
Conclusions:Our pilot VR task was able to predict performances on standard neuropsychological assessment measures at a level comparable to that of a commercially available computerized assessment battery, providing preliminary evidence of concurrent validity. Ongoing work is expanding this rudimentary task into one involving greater complexity and nuance. As multivariate data integration models are incorporated into the tasks and extraction features, future work will collect data on much larger samples of individuals to develop and refine the machine learning models. With additional work this approach may provide an important advance in neuropsychological assessment methods.
47 Predicting Impulsivity Across Multiple Stages of Time-Since-Injury in mild TBI using Resting State Functional Connectivity
- Deva Reign, Samantha Jankowski, Natalie S. Dailey, William D.S. Killgore
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 153-154
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Objective:
Mild traumatic brain injury (mTBI) remains one of the most silent recurrent head injuries reported in the United States. mTBI accounts for nearly 75 percent of all traumatic brain injuries in the American population. Brain injury is often associated with impulsivity, but the association between resting state functional connectivity (rsFC) and impulsivity at multiple stages since time-since-injury (TSI) is unclear. We hypothesized that rsFC within the default mode network (DMN) would predict impulsivity across multiples stages of recovery in mild TBI.
Participants and Methods:Participants healthy controls (HC: n=35 total [15 male, 20 female], age M=24.40, SD=5.95; mTBI: n=121 total [43 male; 78 female], age M=24.76, SD=7.48). Participants completed a cross-sectional study design at various post-injury time points ranging from (2W, 1M,3M,6M,12M). Participants a neuroimaging session and behavioral tasks including a psychomotor vigilance task. Impulsivity was assessed as a combination of false starts and impulsive responses on behavioral tasks. The neuroimaging session included a rsFC scan. To predict impulsivity from brain connectivity, we conducted a series of stepwise linear regression analyses with the 11 functional brain connections (extracted as Fisher’s z-transformed correlations between regions) as predictors and each of the 13 neurocognitive factor scores separately. We focus here on the outcomes for the impulsivity factor.
Results:Results showed greater positive connectivity between the and Right Frontal Pole and the anterior cingulate cortex (ACC; seed) (ß = .158, t = 1.98, p = .049) which was associated with greater impulsivity. Individuals in the 2W group demonstrated one significant predictor (R = .632, R2 = .399, F = 5.32, p = .050). Largely, there was greater positive connectivity between the Right Frontal Pole and the ACC (seed) and (ß = .632, t = 2.31, p = .050) which was associated with higher impulsivity at the 2W time-since-injury. No predictors emerged for the 1M, 3M, or 6M conditions. However, individuals in the 12M group demonstrated two significant predictor connections (R = .497, R2 = .247, F = 5.73, p = .007). Overall, a linear combination of greater negative (anticorrelated) connectivity between the Right Frontal Pole and the mPFC (seed) (/? = -.576, t = -3.53, p = .002) and greater positive connectivity between the Paracingulate Cortex (seed) and the Left Lateral Prefrontal Cortex (ß = .368, t = 2.14, p = .039) was also associated with greater impulsivity in individuals with mTBI at 12M.
Conclusions:These findings suggest functional connectivity between the anterior node of the DMN and prefrontal cortex regions involved in behavioral control was predictive of higher impulsivity in individuals with mTBI at 2W and 12M post injury, but not at other time frames. Interestingly, these connections differed at the two time points. Acutely, greater impulsivity was associated with greater connectivity among regions involved in error detection, exploration, and emotion. At one year, the connections involve regions associated with error monitoring and inhibitory processes. This may reflect compensatory strategy development during recovery.
46 Resting State FC in the Default Mode Networks: A Prediction Model for Elevated Aggression from Connectivity Metrics and Neurocognitive Performance Across Multiple Stages of Recovery in mild TBI
- Deva Reign, Samantha Jankowski, Natalie S. Dailey, William D.S. Killgore
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 152-153
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Objective:
Mild traumatic brain injury (mTBI) remains one of the most prevalent brain injuries, affecting approximately one-in-sixty Americans. Previous studies have shown an association between white matter integrity and aggression at chronic stages (either 6-months or 12 months post-mTBI) however, the association between white matter axonal damage, neuropsychological outcomes, and elevated aggression in multiple stages since time-since-injury (TSI) is unclear. We hypothesized that functional connectivity between the default mode network (DMN), a key brain network involved in cognitive, self-reflective, and emotional processes, and other cortical regions would predict elevated aggression and emotional disturbances across multiples stages of recovery in mild TBI.
Participants and Methods:Participants healthy controls (HC: n=35 total [15 male, 20 female], age M=24.40, SD=5.95; mTBI: n=121 total [43 male; 78 female], age M = 24.76, SD=7.48). Participants completed a cross-sectional study design at specific post-injury time points ranging from (2W, 1M,3M,6M,12M). Participants completed a comprehensive neuropsychological battery and a neuroimaging session, including resting state functional connectivity (FC). Here, we focus on the FC outcomes for the DMN. During the neuropsychological assessment, participants completed tests that measured learning and memory, speed of information processing, executive function, and attention. To predict neuropsychological performance from brain connectivity, we conducted a series of stepwise linear regression analyses with the 11 functional brain connections (extracted as Fisher’s z-transformed correlations between regions) as predictors and each of the 13 neurocognitive factor scores separately.
Results:Consistent with our hypothesis, one predictor materialized as significant (R = .187, R2 = .035, F = 5.55, p = .020) for the Total Sample. Largely, positive connectivity between Right Inferior Frontal Gyrus and the PCC (seed) was associated with increased aggression in the Total Sample of all participants (ß = .187, t = 2.36, p = .020). One predictor materialized as significant in Individuals the 2W group, (R = .719, R2 = .518, F = 8.58, p = .019). In general, greater negative (anticorrelated) connectivity between the Left Lateral Occipital Cortex (ß = -.719, t = -2.93, p = .019) and the PCC (seed) and was associated with greater aggression at 2W, but no predictors emerged at 1M or 3M. Individuals in the 6M group showed one significant predictor (R = .675, R2 = .455, F = 16.71, p = .001). Specifically, greater positive connectivity between the Right Lateral Occipital Cortex (/? = .675, t = 4.09, p = .001) and PCC (seed) was associated with greater aggression at 6M. No associations were evident at 12M.
Conclusions:Overall, these findings suggest functional connectivity between the posterior hub of the DMN and cortical regions within the occipital cortex was predictive of higher aggression in individuals with mTBI. However, the direction of this connectivity differed at 2W versus 6M, suggesting a complex process of recovery that may contribute differentially to aggression in patients with mTBI. As these regions are involved in self-consciousness and visual perception, this may point toward future avenues for aiding in functional recovery of emotional dysregulation in patients with persistent post-concussion syndrome.