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Microstructural and neurochemical plasticity mechanisms interact to enhance human perceptual decision-making [1]

['Joseph J. Ziminski', 'Department Of Psychology', 'University Of Cambridge', 'Cambridge', 'United Kingdom', 'Polytimi Frangou', 'Vasilis M. Karlaftis', 'Uzay Emir', 'Purdue University School Of Health Sciences', 'West Lafayette']

Date: 2023-03

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.

Funding: This work was supported by grants to ZK from the Wellcome Trust [grant number 205067/Z/16/Z, 221633/Z/20/Z], the Biotechnology and Biological Sciences Research Council [grant numbers H012508, BB/P021255/1]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

We employ multimodal brain imaging to investigate the links between microstructural (i.e., myelination) and functional (i.e., neurochemical) mechanisms that regulate sensory processing and support perceptual decisions. We use (a) quantitative MRI to measure myelination markers (i.e., magnetization transfer (MT) saturation) reflecting myelin formation or remodeling; (b) magnetic resonance spectroscopy (MRS) to measure inhibition; and (c) resting-state fMRI (rs-fMRI) to measure functional connectivity. We test whether learning-dependent changes in these MRI-derived markers of brain plasticity predict learning, i.e., our ability to improve after training on a perceptual decision task (i.e., identifying targets embedded in cluttered scenes) [ 15 ]. Our results demonstrate a key role of thalamocortical structural (i.e., myelination) and neurochemical interactions for improved perceptual decisions in the adult human brain. In particular, we show that learning to identify targets in clutter alters subcortical (pulvinar, hippocampus) myelination and its functional connectivity to visual cortex and relates to decreased visual cortex GABAergic inhibition. Modeling interactions between these processes suggests that adaptive myelination in pulvinar supports learning for perceptual decisions through thalamocortical interactions with GABAergic plasticity in visual cortex. These adaptive thalamocortical interactions may facilitate selecting task-relevant features from noise early in the training, while visual-hippocampal interactions may refine feature processing for target identification later in the training. Our findings demonstrate a tight interplay between microstructural plasticity and functional neurotransmission mechanisms in subcortico-cortical circuits that interact to support optimized perceptual decisions.

Previous studies demonstrate that interactions between myelination, neurochemistry, and activity shape neuronal processing. In particular, myelination of GABAergic interneurons accounts for up to half of the myelin content in the neocortex and disruption of fast-spiking GABAergic interneuron myelination in sensory cortex results in profound deficits in interneuron function [ 8 , 9 ]. Further, both GABAergic interneurons and myelination are thought to promote network synchrony and regulate thalamocortical network oscillations [ 10 , 11 ]. In particular, driving excitatory signals (e.g., visual signals) to GABAergic interneurons promotes inhibitory transmission as a regulator of cortical connectivity [ 12 , 13 ]. Further, feedforward inhibition in sensory cortex has been shown to promote pyramidal output firing to synchronize with gamma-band oscillations associated with intraregional connectivity [ 12 , 14 ]. Yet, how these interactions between myelination, GABAergic inhibition, and functional connectivity shape learning in the human brain remains unknown. In light of previous work in animal models, we hypothesize that interactions between network myelination and visual cortex GABA may shape network connectivity to facilitate learning and plasticity in the human brain.

Learning from experience and adapting to changes in our environments is key for skillful actions. Experience and training are known to boost our skills by altering the brain’s structural organization and functional activity. Recent work has challenged the traditional view that structural plasticity is confined to development. In particular, training [ 1 ] and neural [ 2 ] or sensory [ 3 ] stimulation have been shown to promote myelination in the adult brain, the process of insulating neural axons to enhance neurotransmission (for reviews, see [ 4 – 6 ]). Further, training has been shown to alter neurochemical (i.e., GABAergic) signaling that is known to regulate neural activity (for review, see [ 7 ]). Yet, microstructural (i.e., myelination) and neurochemical plasticity have mostly been studied at different scales (large-scale networks, local circuits), limiting our understanding of the interactive mechanisms that underlie learning of complex cognitive skills.

(A) SEM modeling (χ 2 = 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 (χ 2 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 (χ 2 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.

We then employed structural equation modeling to test the role of this multimodal plasticity in learning for perceptual decisions. We demonstrate a key role of thalamocortical connectivity in linking thalamic myelin with visual GABAergic plasticity and predicting behavioral improvement. In particular, we tested a model with the following paths: (a) learning-dependent changes in pulvinar MT predict changes in thalamocortical connectivity; (b) learning-dependent changes in thalamocortical connectivity predict changes in OCT GABA+; and (c) learning-dependent changes in pulvinar MT, thalamocortical connectivity, and OCT GABA+ predict changes in behavior. Path directionality in our model—noting that structural equation models with reversed directionality are equivalent when bivariate relationships are modeled—was guided by (a) previous work on the role of pulvinar in regulating interactions between attentional and visual networks [ 40 , 41 ] and (b) our mediation analysis showing that functional connectivity mediated the effect of pulvinar myelination on OCT GABA+. This model ( Fig 5A ) showed a good fit to the data (df = 1.0, χ 2 = 0.058, p = 0.810, SRMR = 0.012) and the following significant interactions: (a) changes in pulvinar MT predicted changes in thalamocortical connectivity (β STD = 0.504, p = 0.029); (b) changes in thalamocortical connectivity predicted changes in OCT GABA+ (β STD = 0.72, p < 0.001); and (c) changes in thalamocortical connectivity predicted changes in behavior (β STD = −0.75, p = 0.005). Changes in OCT GABA+ (β STD = 0.05, p = 0.831) and pulvinar MT (β STD = −0.12, p = 0.546) did not predict significantly changes in behavior when accounting for thalamocortical connectivity. To further interrogate the role of thalamocortical connectivity as a key predictor of behavior, we constrained the path between thalamocortical connectivity and behavior to zero; this model resulted in a poor fit ( Fig 5B ; df = 2.0, χ 2 = 6.549, p = 0.038, SRMR = 0.101 χ 2 difference = 6.491, p = 0.011). In contrast, constraining to zero the paths of OCT GABA+ to behavior and pulvinar MT to behavior did not significantly affect the model fit ( Fig 5C ; df = 3.0, χ 2 = 0.446, p = 0.931, SRMR = 0.031, χ 2 difference = 0.388, p = 0.823). These results suggest a key role of thalamocortical interactions in predicting behavioral improvement due to training. Further, a model that tested the link between visual-hippocampal connectivity, hippocampal MT, and OCT GABAergic plasticity resulted in a poor fit (i.e., df = 1, χ 2 = 10.231, p = 0.001, SRMR = 0.219), suggesting that our results are specific to thalamocortical connectivity.

First, we conducted a mediation analysis to model interactions between microstructural (i.e., myelin), functional (i.e., functional connectivity), and neurochemical (GABAergic inhibition) plasticity. This analysis showed that pulvinar myelin plasticity influences GABAergic processing in visual cortex through thalamocortical connectivity (total effect c = 0.44, z = 2.07, p = 0.038, [0.024, 0.85]). In particular, the effect of learning-dependent changes in pulvinar MT (predictor) on OCT GABA+ (outcome) was mediated by changes in thalamocortical network connectivity (mediator): indirect effect: ab = 0.36, z = 1.99, p = 0.046, [0.01, 0.71]; no significant direct effect c’ = 0.08, z = 0.39, p = 0.696, [0.32, 0.47]. These results suggest that learning-dependent changes in pulvinar myelination drive changes in thalamocortical connectivity that gates sensory processing (i.e., gain control) through GABAergic inhibition in visual cortex.

Taken together, our results demonstrate a strong relationship between decrease in visual GABAergic inhibition and learning for improved perceptual decisions. Limitations in MRS brain coverage meant that our study focused on specific areas (i.e., OCT versus PPC), providing evidence for the specific role of visual GABAergic plasticity in learning rather than general task engagement. Future work is needed to investigate GABAergic plasticity across areas in the subcortico-cortical networks we showed to be involved in learning for perceptual decisions.

Additional analyses ( S3 Fig and S3 Table ) tested the specificity of our results. First, the relationship between GABAergic plasticity and improved perceptual decisions was specific to training; i.e., (a) the relationship between OCT GABA+ change and behavioral improvement remained significant following regression of baseline measures ( S3A Fig ; r = −0.65, p = 0.005, CI [−0.89, −0.25]; and (b) there was no significant correlation between differences in GABA+ and behavior for sessions before training (i.e., pre-training minus baseline (r = −0.03, p = 0.917, CI [−0.47, 0.39]). Second, this relationship remained significant when controlling for voxel tissue composition (alpha correction of grey matter concentration; r = −0.64, p = 0.006, CI [−0.87, −0.28]; regression of GABA+ concentration with CSF voxel concentration; r = −0.60, p = 0.011, CI [−0.85, −0.17]; 1 –fCSF division of GABA+ concentration; r = −0.60, p = 0.010, CI [−0.84, −0.17]) and reference metabolite (i.e., NAA rather than water; r = −0.60, p = 0.011, CI [−0.81, −0.33]). Third, the relationship between OCT GABA+ change and behavioral improvement was specific to (a) GABA+ rather than other metabolites (i.e., glutamate; r = 0.23, p = 0.375, CI [−0.42, 0.76]; Steiger’s Z comparison showed that the correlations of GABA+ change versus glutamate change with behavioral improvement were significantly different; z = −2.69, p = 0.007).

To control for the possibility that the learning-dependent changes we observed in OCT GABA+ were due to differences in task difficulty across sessions (i.e., the task was more difficult before than after training), we analyzed reaction times as a measure of task difficulty. Analysis of reaction times did not show any significant relationship between OCT GABA+ change and reaction times differences before versus after training (r = −0.19, p = 0.51, [−0.50, 0.13]). Further, we measured GABA+ in posterior parietal cortex (PPC) that is known to be involved in attentional processing [ 38 , 39 ]. We reasoned that any potential differences in attention due to task difficulty would result in differences in PPC GABA+ across sessions. However, we did not observe a significant correlation between PPC GABA+ change and behavioral improvement ( S3C and S3D Fig ; r = 0.36, p = 0.154, CI [−0.22, 0.72]; Steigers Z comparison showed that the correlations of GABA+ change in OCT versus PPC with behavioral improvement were significantly different z = −3.16, p = 0.002). This is consistent with our previous work [ 15 ] showing dissociable task-dependent GABAergic plasticity in OCT and PPC; i.e., GABA+ decrease due to training was specific to OCT for the SN task (compared to GABA+ increase for training on a Feature differences task). Finally, during scanning (pre, post-training session) participants did not receive feedback, suggesting that learning-dependent changes in GABA+ relate to performance that is sustained following training.

(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.

Previous studies provide evidence that MRS-assessed GABA relates to behavioral improvement due to training, consistent with the role of GABA, the primary inhibitory neurotransmitter, in brain plasticity [ 15 , 23 , 36 ]. Here, we tested whether GABAergic plasticity in OCT that is known to be involved in perceptual decisions and learning [ 15 , 23 ] relates to behavioral improvement following multisession training (OCT; Fig 4A ). In particular, we measured OCT GABA+ during performance on the SN task, before versus after training, to interrogate learning-dependent changes in GABAergic inhibition related to behavioral improvement. We observed a significant relationship between changes in OCT GABA+/water (post-training minus pre-training) and behavioral improvement in the SN task ( Fig 4B ; GABA+: r = −0.60, p = 0.012, CI [−0.85, −0.07]), consistent with the role of decreased GABAergic inhibition in learning-dependent plasticity [ 7 , 15 , 23 , 37 ].

Taken together, these results suggest that training alters both microstructural myelination processes and functional connectivity in distinct subcortico-cortical networks to support learning for perceptual decisions. Our results showing higher thalamocortical connectivity before training, but higher visual-hippocampal connectivity after training, suggest that thalamocortical and visual-hippocampal networks may contribute to early versus late learning for perceptual decisions, respectively.

Second, correlating learning-dependent changes (post-training minus pre-training) in functional connectivity in these networks with behavioral improvement showed positive correlations for the visual-hippocampal network, while negative correlations for the thalamocortical network ( Fig 3B–3D ); i.e., decreased thalamocortical network connectivity ( Fig 3C ; r = −0.71, p = 0.001, CI [−0.88, −0.45]) while increased visual-hippocampal connectivity after training ( Fig 3D ; r = 0.48, p = 0.050, CI [0.03, 0.78]) related to behavioral improvement. These correlations (i.e., thalamocortical versus visual-hippocampal FC correlation with behavioral improvement) were significantly different from each other (z = −3.94, p < 0.001) and remained significant when accounting for variability at baseline (i.e., regressing out functional connectivity at baseline; thalamocortical network connectivity: r = −0.72, p = 0.001, CI [−0.90, −0.47]; visual-hippocampal connectivity: r = 0.51, p = 0.035, CI [0.06, 0.79]). Further, no significant correlations were observed between connectivity differences before training (i.e., pre-training minus baseline) and behavioral improvement (thalamocortical: r = 0.04, p = 0.88, CI = [−0.41, 0.44]; visual-hippocampal: r = −0.27, p = 0.302, CI = [−0.67, 0.16]), confirming the relationship was specific to the training period. Finally, we did not observe any significant correlations between behavioral improvement and changes in functional connectivity between (a) IFG and OCT (r = −0.12, p = 0.647, CI [−0.58, 0.38]); IFG and V1 (r = 0001, p = 0.996, [−0.39, 0.51]) and (b) ITC and OCT (r = −0.40, p = 0.111, CI [−0.75, 0.05]); ITC and V1 (r = 0.21, p = 0.418, [−0.26, 0.63]), suggesting that the learning-dependent changes we observed were specific to subcortico-cortical connectivity.

( 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 = −0.72, 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.

We used rs-fMRI to investigate learning-dependent changes in functional connectivity in these subcortico-cortical networks. Previous work has demonstrated a strong link between functional resting-state networks and task-activated networks that have been shown to overlap [ 26 , 27 ]. Reactivation of task-activated networks has been shown to occur at rest following task performance [ 28 , 29 ] and may play a role in memory consolidation [ 29 , 30 ]. Further, previous studies have demonstrated changes in rs-fMRI network activity following training on perceptual or motor tasks [ 31 – 35 ]. First, we found that thalamocortical network connectivity was stronger before training, while visual-hippocampal network connectivity after training ( Fig 3A ). In particular, network functional connectivity (i.e., mean connectivity of all network node pairs) across sessions differed significantly between networks; i.e., thalamocortical connectivity decreased, while visual-hippocampal connectivity increased after training (two-way mixed ANOVA, significant network × session (early-training comprising baseline and pre-training versus post-training) interaction: F2,64 = 3.39, p = 0.040).

Next, we asked whether the learning-dependent changes we observed in myelination relate to changes in functional connectivity, given the role of myelination in enhancing neurotransmission [ 4 – 6 ]. Specifically, we tested whether training alters subcortico-cortical functional networks seeded from the subcortical regions (vPul, HC) that showed myelin plasticity; i.e., we focused on 2 networks: (a) a thalamocortical network including vPul, occipito-temporal cortex (OCT), and anterior cingulate cortex (ACC), which are known to be connected to the pulvinar [ 20 , 21 ] and involved in learning for perceptual decisions [ 22 , 23 ]; and (b) a visual–hippocampal network including HC and visual areas (V1, V2, V3, and V4) that are known to be connected to HC and have been implicated in learning for perceptual decisions [ 24 , 25 ]. This seed-to-target connectivity analysis—despite limitations in capturing whole-brain networks—allows us to target the link between myelin and functional plasticity.

To provide finer scale analysis of the subcortical cluster, we parcellated the Th–HC region into subregions, using detailed subcortical atlases ( Fig 2C–2E and S2 Table ). We found that MT change was significantly negatively correlated with behavioral improvement in the ventral pulvinar (vPul; r = −0.49, p = 0.045, CI [−0.82, −0.01]) and hippocampus (HC; r = −0.59, p = 0.012, CI [−0.90, −0.05]) ( S1A and S1B Fig ). To ensure that this relationship was not due to variability between participants before training (i.e., at baseline), we regressed out baseline measures and found that the relationships between MT and behavioral change (post-training minus pre-training) remained significant ( S1C and S1D Fig : vPul r = −0.58, p = 0.015, CI [−0.80, −0.22]; HC: r = −0.62, p = 0.008, CI [−0.89, −0.11]). Further analyses on white matter showed significant increase in MT in a white matter cluster adjacent to the grey matter regions ( S2A Fig ) that related to behavioral improvement ( S2B Fig ; r = −0.59, p = 0.013, CI [−0.81, −0.19]). Taken together, these analyses show that training results in behaviorally relevant changes in MRI markers of myelin (MT) in subcortical regions.

These changes in MT were specific to training; i.e., comparing MT in 2 sessions before training (i.e., whole-brain GLM: baseline versus pre-training) did not show any significant clusters. This lack of significant MT differences between baseline and pre-training sessions suggests the changes we observed in MT following training—within the same participants—were specific to training rather than potential confounds (e.g., pH of the targeted tissue) [ 19 ]. Further, these learning-dependent changes were specific to MT—rather than other MRI-derived myelination markers—which have been suggested to reflect changes in myelin formation or remodeling. Vasculature-related signals (i.e., T1 or T2 relaxation rates) may contribute to changes in MRI-derived markers of myelination (MT, R2*, R1). However, we did not find any clusters (whole-brain GLM: pre- versus post-training) that showed significant changes in transverse relaxation rate (R2*), nor any significant (all p > 0.05) differences in R2*, R1 across sessions within the clusters that showed MT changes, suggesting that the MT-specific changes we observed could not be simply due to vasculature-related signals.

( 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.

To investigate whether training in the SN task alters myelination processes, we tested changes before versus after training (whole-brain GLM: pre- versus post-training) in MT saturation, an MRI indicator of myelin content that has been shown to be measured reliably by multiparameter mapping (MPM) [ 16 – 18 ]. We found significant MT increase in grey matter after training in thalamic-hippocampal (Th–HC), inferior frontal gyrus (IFG), and inferior temporal cortex (ITC) regions (whole-brain repeated-measures GLM; S1 Table and Fig 2A and 2B ).

Training improved participant performance in the task ( Fig 1C ; one-way repeated measures ANOVA across sessions, Greenhouse–Geisser corrected; F 5, 95 = 19.74, p < 0.001). In particular, performance accuracy increased in the post-training compared to the pre-training session (Post hoc comparisons, Sidak corrected, p < 0.001). This behavioral improvement was specific to training; i.e., there was no significant differences in performance between baseline and pre-training sessions (p = 0.997).

(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 .

We trained participants on a perceptual decision task that involves identifying radial versus concentric dot patterns (Glass patterns) embedded in noise (signal-in-noise (SN) task; Fig 1A ). Participants completed 3 behavioral training sessions with feedback and 3 test sessions (during MRI scanning) without feedback (baseline, pre-training, post-training) ( Fig 1B ). For each participant, we tested for learning-dependent changes in task performance and MRI-derived markers of plasticity before (pre-training) versus after (post-training) training (3 sessions on consecutive days). To test learning specificity, we compared the pre-training session to a baseline session (3 days before the pre-training session) that served as a within-subject no-training control.

Discussion

Here, we provide evidence that structural myelin plasticity in subcortical regions interacts with functional mechanisms of cortical neurotransmission to support learning. In contrast to previous studies that have focused on cortical circuits (i.e., frontoparietal and sensory areas) of learning [42] and decision-making [43], we provide evidence that thalamocortical plasticity plays a key role in improving perceptual decisions through training. Using a multimodal quantitative imaging approach, we demonstrate that adaptive myelination supports perceptual decision-making through thalamocortical interactions with neurochemical plasticity mechanisms in visual cortex. Our findings advance our understanding of experience-dependent plasticity mechanisms that support perceptual decision-making in the following main respects.

First, we demonstrate that training increases thalamic myelination, as indicated by learning-dependent changes in grey matter MT measured by quantitative MRI (MPM). Despite the fact that MT imaging measures myelin indirectly by imaging water protons within and close to the myelin sheath, recent work shows that MT maps have high reliability and are strongly linked to histological measures of myelin content [16–18]. Further, quantitative MRI (MPM) allows us to measure not only white but also grey matter myelin and test directly the link between microstructure and neural processing in grey matter. While white matter myelination has been associated with increased transmission speed along long-range axons, grey matter myelination has been associated with microcircuit function related to learning [44] and may serve to improve local activity synchronization [4]. Previous human brain imaging studies, using standard structural MRI or diffusion tensor imaging, have focused on learning-dependent changes in white matter in a range of tasks, including motor [45,46] and perceptual [47,48] learning. In contrast, testing learning-dependent changes in grey matter MT, we provide evidence for a tight link between microstructural plasticity and functional neurotransmission for optimized processing within thalamocortical circuits.

Our results support an active role of myelin in adult learning [1,4,5,49], consistent with animal studies showing increased levels of oligodendrocyte precursor cells (OPCs) that promote axon myelination due to training [44,50,51]. Understanding the link between myelination processes and learning (i.e., behavioral improvement) depends on myelination dynamics. Insights in understanding this link come from animals studies showing rapid OPC proliferation and differentiation at initiation of learning or neuronal stimulation in vivo [2,4,44,52]. Further, previous work [46] suggests that prolonged early learning results in higher myelination but reduced behavioral improvement. Consistent with this work, we found a negative relationship between myelin plasticity and behavioral improvement. Additional analyses (S1 Fig) indicate that individuals who found the task more difficult (i.e., showed slower learning rate and weaker improvement) remained at the early phase of learning for longer, resulting in increased myelination. Future studies are needed to determine the precise kinetics of different myelination processes (i.e., OPC proliferation, myelin remodeling) and how they map onto learning across different time scales [6]. More frequent sampling of myelin content during training could capture myelin increase dynamics at the early phase of training.

Second, our results demonstrate that training reorganizes functional networks that are involved in perceptual decision-making during the course of learning and predict behavioral improvement. In particular, thalamocortical connectivity was stronger before training, while functional connectivity between hippocampal and early visual cortex regions was stronger after training. These results suggest that thalamocortical networks support performance at early stages of learning (initial exposure and engagement with the task), when task demands are higher. This is consistent with the role of pulvinar in regulating visual processing and the role of thalamocortical networks in coordinating visual attentional processing [21,41,53,54]. In contrast, refining feature representations for target identification involves hippocampal and early visual regions that engage later in training. These results are consistent with previous studies implicating the HC in improved task performance following training [55,56]. Further, the involvement of early compared to higher visual areas at later stages of learning [57] has been suggested to afford finer processing of visual information at higher spatial resolution for detecting targets in clutter [58]. This is supported by previous perceptual learning studies showing learning-induced changes in synaptic strength that are associated with LTP-like processes in early visual cortex [59].

Third, we provide evidence that thalamocortical connectivity links thalamic myelin plasticity to visual GABAergic plasticity. In particular, we show that decreased visual cortex GABA—as measured by MRS—due to multisession training in the SN task relates to behavioral improvement, extending our previous work measuring GABAergic plasticity within a single training session [15,23]. Reductions in cortical GABA have been shown to increase neuronal gain by reducing shunting inhibition through tonic GABA receptors [60]. Despite our currently limited knowledge on the neural origins of MRS-GABA [61], it is possible that GABAergic inhibition in visual cortex serves as a gain control mechanism that supports our ability to detect task-relevant features for target identification, while filtering out background noise [15,23].

Importantly, modeling thalamocortical connectivity, pulvinar MT, and visual GABA signals suggests that myelin plasticity in the pulvinar supports learning through thalamocortical interactions with GABAergic inhibition in visual cortex. It is likely that adaptive myelination in pulvinar results in learning-dependent changes in thalamocortical connectivity and GABAergic plasticity in visual cortex to support optimized perceptual decisions, consistent with the role of pulvinar in regulating visual processing [40,41]. In particular, connections from pulvinar to visual cortex have been suggested to regulate inhibitory processing by synapsing directly onto GABAergic interneurons, promoting oscillatory activity and thalamocortical connectivity [41]. Here, we provide evidence that these interactive plasticity mechanisms are key in supporting not only sensory processing but also learning of complex cognitive skills (i.e., identifying targets in cluttered scenes).

Finally, our study sample was limited to male participants due to potential confounding effects of menstrual cycle on GABA measurements. This has been the topic of extensive research [62–67] with several studies restricting MRS-GABA studies to males (e.g., [68–76]). Key hormones (estrogen, progesterone) exerting a suppressive or facilitatory effect on GABA transmission [77, 63–65] may confound within-subject GABA measurements over time. Developing precise methods for controlling for the effects of menstrual cycle on MRS GABA measurements is hampered by physiological complexity (i.e., phase and regional effects of menstrual cycle on GABA) and limited knowledge of the kinetics of menstrual cycle GABA changes in humans. As our study involves repeated MRS GABA measurements over time, it is not possible to satisfactorily control for menstrual cycle effects; i.e., not only the phase and duration of the menstrual cycle but also the kinetics of GABA changes across menstrual cycle days would likely differ substantially across participants. Ongoing large-scale multisite studies are expected to provide normative data that will advance our understanding of these cyclical effects, allow us to develop precise control methodologies, and conduct similar studies including female participants to enhance the generalizability of our findings.

In sum, our findings provide evidence for a tight interplay between microstructural plasticity and functional neurotransmission mechanisms in subcortico-cortical networks for perceptual decision-making. We propose that myelin plasticity in the pulvinar early in the training may regulate—through thalamocortical connectivity—GABAergic gain control mechanisms in visual cortex for selecting task-relevant features. In contrast, as performance becomes more automated later in the training, connectivity across hippocampal and early visual networks increases to facilitate finer processing of task-relevant features and support the identification of targets in clutter. Capturing the dynamics of microstructural (i.e., myelin) and functional (i.e., neurochemical) interactions that drive experience-dependent plasticity is key for understanding how experience molds the adult brain and supports our ability for adaptive behavior across the lifespan.

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