Neurochemical (GABA and Glu), Cognitive Dynamics and Myelination in Cognitive Flexibility
Presented During:
Thursday, June 26, 2025: 12:18 PM - 12:30 PM
Brisbane Convention & Exhibition Centre
Room:
M3 (Mezzanine Level)
Poster No:
639
Submission Type:
Abstract Submission
Authors:
Deepika Shukla1, Eleanor Koo1, Wei Ler Koo1, Boon Linn Choo1, Chie Takahashi2, Balázs Gulyás1, John Suckling2, Zoe Kourtzi2, SH Annabel Chen1
Institutions:
1Nanyang Technological University, Singapore, 2University of Cambridge, United Kingdom
First Author:
Co-Author(s):
Introduction:
Cognitive flexibility (CF) is critical for adapting learned behaviours, with the Right dorsolateral prefrontal cortex (rDLPFC) playing a pivotal role in non-verbal CF, and managing the exploration-exploitation decision-making under uncertainty[1,2]. This flexibility is supported by a dynamic neuronal excitation-inhibition (E-I) balance, mainly mediated by Glu and GABA, respectively. Research indicates that higher GABA in DLPFC correlated with enhanced task performance[3], while elevated GABA/Glu associated with relevant selection[4]. An optimal GABA/Glu ratio supports CF, ensuring effective encoding of new information, while suppressing irrelevant inputs[5]. From structural perspective, myelination and E-I balance contribute to improved structural and functional coupling[6], facilitating synchronized neural activity crucial for complex cognitive tasks and coordination[7].
This study investigates effects of structure learning (SL) training on neuronal and microstructural brain changes and their association with cognitive measures. We hypothesize positive modulation of E-I balance in rDLPFC, associated with enhanced myelination. Furthermore, we anticipate that these modulations will correlate with improvements in cognitive performance.
This study investigates effects of structure learning (SL) training on neuronal and microstructural brain changes and their association with cognitive measures. We hypothesize positive modulation of E-I balance in rDLPFC, associated with enhanced myelination. Furthermore, we anticipate that these modulations will correlate with improvements in cognitive performance.
Methods:
106 healthy adults (C:53, T:53), aged 18-55 yr completed the study protocol[8]. Only T-group underwent 2-week SL intervention. Both groups completed CF assessments, including Color-Shape Task (CST) (pre & post), and Intra-/Extra-Dimensional Set Shifting (IED) Task. The study received NTU-IRB approval, and All participants provided informed consent before undergoing scanning with a 3T Siemens system equipped with a 64-channel head coil.
Pre- and post-session MRI data acquisition included 3D T1-MPRAGE (TR=2000ms;TE=22.6ms; TI=800ms;flip-angle=8°;FOV=256×256; slices=176;voxel=1×1×1mm3) and 1H-MEGA-PRESS MRS focused on bilateral DLPFC (voi:30x15x30mm3,TR=2000ms,TE=68ms,ON=1.98ppm, OFF=7.5ppm, Navg:128) with one unsuppressed water spectra (Navg=4). Multi-Parameter Mapping (MPM) were generated using multi-echo FLASH imaging (TE:2.46-19.68ms) for T1w, MTw, and PDw (voxel =1mm3;slices=176;FOV=256;matrix=256×256;GRAPPA =2) along with RF sensitivity maps.
MRS data were processed using Osprey to extract GABA, Glu, and GABA/Glu ratio from rDLPFC (Fig1.1). MPM data was processed using hMRI toolbox, generating normalized MT maps for gray (GM) and white matter (WM). MT values from the rDLPFC mask were extracted (Fig1.1). Only complete datasets (C:44, T:45) were included in the analysis. Pre-to-post training changes (Δ) were analysed using Jamovi toolbox.
Pre- and post-session MRI data acquisition included 3D T1-MPRAGE (TR=2000ms;TE=22.6ms; TI=800ms;flip-angle=8°;FOV=256×256; slices=176;voxel=1×1×1mm3) and 1H-MEGA-PRESS MRS focused on bilateral DLPFC (voi:30x15x30mm3,TR=2000ms,TE=68ms,ON=1.98ppm, OFF=7.5ppm, Navg:128) with one unsuppressed water spectra (Navg=4). Multi-Parameter Mapping (MPM) were generated using multi-echo FLASH imaging (TE:2.46-19.68ms) for T1w, MTw, and PDw (voxel =1mm3;slices=176;FOV=256;matrix=256×256;GRAPPA =2) along with RF sensitivity maps.
MRS data were processed using Osprey to extract GABA, Glu, and GABA/Glu ratio from rDLPFC (Fig1.1). MPM data was processed using hMRI toolbox, generating normalized MT maps for gray (GM) and white matter (WM). MT values from the rDLPFC mask were extracted (Fig1.1). Only complete datasets (C:44, T:45) were included in the analysis. Pre-to-post training changes (Δ) were analysed using Jamovi toolbox.
Results:
At baseline, CST_RT(p=0.03) differ significantly between groups. ANCOVA, controlling age, revealed a significant main effect of group for ΔGlu (F(2,86)=12.142, p<0.001), with post-hoc analysis indicating a decrease (Mean-diff=1.86, cohen's d=0.74) in T-group (Fig1.2c).
Whole brain GLM analysis of MPM ΔMT maps showed no significant clusters in bilateral DLPFC regions. However, ΔMT in both GM and WM of rDLPFC correlated weakly in the C-group (r=0.29,p=0.03) and, more strongly in the T-group(r=0.52,p<0.001). Additionally, ΔGABA correlated positively with ΔMT in both GM(r=0.25,p=0.049) and WM(r=0.29, p=0.03) (Fig2.1).
In T-group, ΔGlu correlated positively with ΔCST_RT(r=0.20,p=0.03). ΔGABA/Glu showed a significant positive correlation with ΔCST_Acc(r=0.35,p=0.01) and a moderate negative correlation with IED beta(r=-0.31,p=0.02). ΔMT in WM also negatively correlation with the IED beta scores(r= -0.270, p=0.038) (Fig2.2).
Furthermore, ΔGABA/Glu (r=-0.40,p=0.004), and ΔGABA(r=-0.27,p=0.04) showed negative correlations with the mean ICD score of SL measure (Fig2.3).
Whole brain GLM analysis of MPM ΔMT maps showed no significant clusters in bilateral DLPFC regions. However, ΔMT in both GM and WM of rDLPFC correlated weakly in the C-group (r=0.29,p=0.03) and, more strongly in the T-group(r=0.52,p<0.001). Additionally, ΔGABA correlated positively with ΔMT in both GM(r=0.25,p=0.049) and WM(r=0.29, p=0.03) (Fig2.1).
In T-group, ΔGlu correlated positively with ΔCST_RT(r=0.20,p=0.03). ΔGABA/Glu showed a significant positive correlation with ΔCST_Acc(r=0.35,p=0.01) and a moderate negative correlation with IED beta(r=-0.31,p=0.02). ΔMT in WM also negatively correlation with the IED beta scores(r= -0.270, p=0.038) (Fig2.2).
Furthermore, ΔGABA/Glu (r=-0.40,p=0.004), and ΔGABA(r=-0.27,p=0.04) showed negative correlations with the mean ICD score of SL measure (Fig2.3).
Conclusions:
Training-induced neuronal inhibition in the rDLPFC enhances tissue integrity by improving regional gray matter and white matter myelination. Balanced rDLPFC E-I modulation in the rDLPFC governs the SL strategies, demonstrating associative transfer to CF, though not directly influencing myelination.
Emotion, Motivation and Social Neuroscience:
Social Cognition 1
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 2
Learning and Memory:
Neural Plasticity and Recovery of Function
Novel Imaging Acquisition Methods:
MR Spectroscopy
Multi-Modal Imaging
Keywords:
ADULTS
GABA
Glutamate
Magnetic Resonance Spectroscopy (MRS)
MRI
Myelin
Neurotransmitter
NORMAL HUMAN
Plasticity
1|2Indicates the priority used for review
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