Microstructural and neurochemical plasticity mechanisms interact to enhance human perceptual decision-making

Abstract

Experience and training are known to boost our skills and mold the brain's organization and function. Yet, structural plasticity and functional neurotransmission are typically studied at different scales (large-scale networks, local circuits), limiting our understanding of the adaptive interactions that support learning of complex cognitive skills in the adult brain. Here, we employ multimodal brain imaging to investigate the link between microstructural (myelination) and neurochemical (GABAergic) plasticity for decision-making. We test (in males, due to potential confounding menstrual cycle effects on GABA measurements in females) for changes in MRI-measured myelin, GABA, and functional connectivity before versus after training on a perceptual decision task that involves identifying targets in clutter. We demonstrate that training alters subcortical (pulvinar, hippocampus) myelination and its functional connectivity to visual cortex and relates to decreased visual cortex GABAergic inhibition. Modeling interactions between MRI measures of myelin, GABA, and functional connectivity indicates that pulvinar myelin plasticity interacts-through thalamocortical connectivity-with GABAergic inhibition in visual cortex to support learning. Our findings propose a dynamic interplay of adaptive microstructural and neurochemical plasticity in subcortico-cortical circuits that supports learning for optimized decision-making in the adult human brain.

Fig 1. Experimental design and stimuli.

(A) Radial and concentric Glass patterns are shown with inverted contrast for illustration purposes. Left: Prototype stimuli: 100% signal, spiral angle 0° for radial and 90° for concentric. Right: Stimuli used in the study: 25% signal, spiral angle 0° for radial and 90° for concentric. (B) Participants were trained on a signal-in-noise detection task with feedback for 3 consecutive training sessions (one per day). Participants completed the task without feedback in MRI test sessions before (baseline, pre-training) and after (post-training) training. (C) Percent behavioral improvement (mean performance per-session minus performance at baseline, divided by performance at baseline) across participants for test (green, in MRI scanner) and training (red, in laboratory) sessions (n = 20). Error bars indicate SEM across participants. Source data are provided at: https://doi.org/10.17863/CAM.93457.

Fig 2. Subcortical MT saturation increases after training and correlates with behavioral improvement.

(A) Th–HC cluster in MNI space (radiological convention, R-L; vPul x: 9.60 y: −25.60 z: 0.80; HC x: 21.60 y: −27.20 z: −11.20 (mm)) showing significantly higher MT after (post-training) compared to before (baseline, pre-training) training (p < 0.001) (S1 Table). (B) Mean MT (percentage change from baseline session) in the Th–HC cluster before vs. after training. (C) Parcellation of the Th–HC cluster (see Methods for details): vPul (no overlap with LGN), HC (S2 Table). Higher mean MT in (D) vPul and (E) HC after compared to before training. (n = 17). Error bars indicate SEM. Source data are provided at: https://doi.org/10.17863/CAM.93457. MT, magnetization transfer; HC, hippocampus; Th–HC, thalamic-hippocampal; vPul, ventral pulvinar.

Fig 3. Thalamocortical and visual-hippocampal networks.

(A) Visual-hippocampal network functional connectivity (V1, V2, V3, V4, HC) increased, while thalamocortical network functional connectivity (OCT, vPul, ACC) decreased after training. (B) Correlation matrix (units: Pearson’s r) showing the relationship between change (post-training–pre-training) in functional connectivity between cortical and subcortical regions and behavioral improvement. (C) Significant negative correlation between change in mean thalamocortical network functional connectivity and behavior improvement (r = −0.71, p = 0.001, CI [−0.88, −0.45]). (D) Significant positive correction between change in mean visual-hippocampal network functional connectivity and behavior improvement (r = 0.48, p = 0.050, CI [0.03, 0.78]). (n = 17). Source data are provided at: https://doi.org/10.17863/CAM.93457. ACC, anterior cingulate cortex; OCT, occipito-temporal cortex; vPul, ventral pulvinar.

Fig 4. Visual GABAergic plasticity.

(A) Group MRS voxel mask (cortical region common in 50% or more of participants) indicates OCT voxel placement displayed on the average MT scan across participants (MNI x: 47.2 y: −53.60 z: 8.80 (mm)), in MNI space (radiological convention, R-L). No significant differences in data quality measures were observed across sessions (S3 Table). (B) Significant negative correlation between OCT GABA+ change and behavioral improvement (n = 17; r = −0.60, p = 0.012, CI [−0.85, −0.07]). Source data are provided at: https://doi.org/10.17863/CAM.93457. MT, magnetization transfer; OCT, occipito-temporal cortex.

Fig 5. Linking microstructural, functional, and GABAergic plasticity to learning.

(A) SEM modeling (χ² = 0.058, p = 0.810) showed thalamocortical connectivity was a key predictor of behavioral improvement. (B) Setting this path to zero resulted in a significantly poorer fit to the data (χ² difference = 6.491, p = 0.011). (C) Setting to zero the path of OCT GABA+ to behavior or pulvinar MT to behavior resulted in a similar model fit (χ² difference = 0.388, p = 0.823) that did not differ significantly from the main model fit. Path lines and coefficients (completely standardized solution, β_STD) are shown in grey (n = 14). Source data are provided at: https://doi.org/10.17863/CAM.93457. MT, magnetization transfer; OCT, occipito-temporal cortex.