INS NYC 2024 Program

Poster	Poster Session 08 Program Schedule 02/16/2024 01:45 pm - 03:00 pm Room: Shubert Complex (Posters 1-60) Poster Session 08: Cognition \| Cognitive Reserve Variables Final Abstract #39 The Neurochemistry of Good Sleep: A Proton Magnetic Resonance Spectroscopy Study William Killgore, University of Arizona, Tucson, United States Marisa Silveri, Harvard Medical School, Belmont, United States Category: Sleep and Sleep Disorders Keyword 1: sleep Keyword 2: neurotransmitter systems Keyword 3: magnetic resonance spectroscopy Objective: Neurocognitive performance is directly affected by the quantity and quality of an individual’s sleep. Sleep is typically disrupted in the majority of neuropathological and psychiatric conditions, but little is known about the brain metabolites that play a role in healthy sleep. In this project, we examined the association between individual metabolites measured with proton magnetic resonance spectroscopy (¹H-MRS) and sleep metrics based on wrist actigraphy. It was hypothesized that objectively measured sleep metrics would be predicted by concentrations of brain metabolites normally associated with neuronal health (N-Acetylaspartate; NAA; choline; Cho) and neural inhibition (gamma-Aminobutyric acid; GABA), and reduced levels of excitatory neurotransmitters (glutamate+glutamine; Glx). Participants and Methods: 24 healthy adults (12 females, 12 males; 25.5 years, SD=5.2) wore an actigraph for seven consecutive days. Mean sleep metrics were extracted for the week, including: Time in Bed (TIB; number of minutes identified as rest), Total Sleep Time (TST; number of minutes scored as sleep), Sleep Efficiency (SE; the ratio of TST/TIB), Sleep Onset Latency (SOL; number of minutes to fall asleep), and Wake After Sleep Onset (WASO; number of minutes scored as awake following the first sleep bout). Participants completed ¹H-MRS neuroimaging at 3T. Metabolite data from the medial prefrontal cortex (mPFC), dorsolateral prefrontal cortex (dlPFC), and medial parietal-occipital cortex (P-OCC) were entered into a series of 5 multiple linear regression models to predict each actigraphic outcome. Results: Average SE over the 7-day period was 84.4% (SD=3.9). For SE, the regression analysis yielded a significant three predictor model, F(3,20)=11.92 (adjusted R²=.59), p=.0001, including mPFC Cho (𝛽=-.60), P-OCC NAA (𝛽=.56), and P-OCC Glx (𝛽=-.33). This suggests that better SE was highly associated with a combination of decreased Cho within the mPFC, as well as increased NAA and decreased Glx within the P-OCC. Average SOL over the 7-day period was 13.7 minutes (SD=6.7). For SOL, the regression analysis yielded a significant single predictor model, F(1,22)=12.11 (adjusted R²=.33), p=.002, based on mPFC Cho (𝛽=.60). This suggests that greater Cho within the mPFC was associated with a longer latency to fall asleep. Finally, average WASO over the 7-day period was 53.2 minutes (SD=15.8). For WASO, the regression analysis yielded a significant two predictor model, F(2,21) = 8.30 (adjusted R² = .39), p=.002, including mPFC Cho (𝛽=.56), P-OCC NAA (𝛽=-.41). This suggests that a combination of greater Cho within the mPFC and decreased NAA in the P-OCC was associated with more minutes of wake after sleep onset. Conclusions: Objectively measured sleep was predicted from brain metabolites within the medial default mode network (DMN), an interconnected system of cortical regions that is normally deactivated during effortful cognitive tasks. On the whole, objective sleep quality was predicted by a combined pattern of metabolites consistent with greater neuronal integrity, reduced cellular turnover, and lower excitatory neurotransmitters. Findings suggest potential metabolic and neuroanatomic targets for enhancing brain health to facilitate sleep quality and perhaps influence neuropathological processes and recovery of function.

Poster

02/16/2024
01:45 pm - 03:00 pm
Room: Shubert Complex (Posters 1-60)

Poster Session 08: Cognition | Cognitive Reserve Variables

Final Abstract #39

The Neurochemistry of Good Sleep: A Proton Magnetic Resonance Spectroscopy Study

William Killgore, University of Arizona, Tucson, United States
Marisa Silveri, Harvard Medical School, Belmont, United States

Category: Sleep and Sleep Disorders

Keyword 1: sleep
Keyword 2: neurotransmitter systems
Keyword 3: magnetic resonance spectroscopy

Objective:

Neurocognitive performance is directly affected by the quantity and quality of an individual’s sleep. Sleep is typically disrupted in the majority of neuropathological and psychiatric conditions, but little is known about the brain metabolites that play a role in healthy sleep. In this project, we examined the association between individual metabolites measured with proton magnetic resonance spectroscopy (¹H-MRS) and sleep metrics based on wrist actigraphy. It was hypothesized that objectively measured sleep metrics would be predicted by concentrations of brain metabolites normally associated with neuronal health (N-Acetylaspartate; NAA; choline; Cho) and neural inhibition (gamma-Aminobutyric acid; GABA), and reduced levels of excitatory neurotransmitters (glutamate+glutamine; Glx).

Participants and Methods:

24 healthy adults (12 females, 12 males; 25.5 years, SD=5.2) wore an actigraph for seven consecutive days. Mean sleep metrics were extracted for the week, including: Time in Bed (TIB; number of minutes identified as rest), Total Sleep Time (TST; number of minutes scored as sleep), Sleep Efficiency (SE; the ratio of TST/TIB), Sleep Onset Latency (SOL; number of minutes to fall asleep), and Wake After Sleep Onset (WASO; number of minutes scored as awake following the first sleep bout). Participants completed ¹H-MRS neuroimaging at 3T. Metabolite data from the medial prefrontal cortex (mPFC), dorsolateral prefrontal cortex (dlPFC), and medial parietal-occipital cortex (P-OCC) were entered into a series of 5 multiple linear regression models to predict each actigraphic outcome.

Results:

Average SE over the 7-day period was 84.4% (SD=3.9). For SE, the regression analysis yielded a significant three predictor model, F(3,20)=11.92 (adjusted R²=.59), p=.0001, including mPFC Cho (𝛽=-.60), P-OCC NAA (𝛽=.56), and P-OCC Glx (𝛽=-.33). This suggests that better SE was highly associated with a combination of decreased Cho within the mPFC, as well as increased NAA and decreased Glx within the P-OCC. Average SOL over the 7-day period was 13.7 minutes (SD=6.7). For SOL, the regression analysis yielded a significant single predictor model, F(1,22)=12.11 (adjusted R²=.33), p=.002, based on mPFC Cho (𝛽=.60). This suggests that greater Cho within the mPFC was associated with a longer latency to fall asleep. Finally, average WASO over the 7-day period was 53.2 minutes (SD=15.8). For WASO, the regression analysis yielded a significant two predictor model, F(2,21) = 8.30 (adjusted R² = .39), p=.002, including mPFC Cho (𝛽=.56), P-OCC NAA (𝛽=-.41). This suggests that a combination of greater Cho within the mPFC and decreased NAA in the P-OCC was associated with more minutes of wake after sleep onset.

Conclusions:

Objectively measured sleep was predicted from brain metabolites within the medial default mode network (DMN), an interconnected system of cortical regions that is normally deactivated during effortful cognitive tasks. On the whole, objective sleep quality was predicted by a combined pattern of metabolites consistent with greater neuronal integrity, reduced cellular turnover, and lower excitatory neurotransmitters. Findings suggest potential metabolic and neuroanatomic targets for enhancing brain health to facilitate sleep quality and perhaps influence neuropathological processes and recovery of function.