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Neurochemical Alterations in Bilateral DLPFC after Structure Learning Training in Healthy Adults

Poster No:

1104 

Submission Type:

Abstract Submission 

Authors:

Deepika Shukla1,2, Chia‑Lun Liu1,2, Xiaoqin Cheng1,2,3, Chie Takahashi4, SH Annabel Chen1,2,5,6,7, John Suckling8,2, Zoe Kourtzi4,2, Balazs Gulyas1,5,2, Eleanor Koo1,2, Wei Ler Koo1,2, Jia Yuan Janet Tan1,2, Marisha Ubrani1,2, Boon Linn Choo1,2, Min Hong1,2, CLIC Consortium2

Institutions:

1Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, Singapore, Singapore, 2Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES), Singapore, Singapore, 3Department of Psychology, University of Innsbruck, Innsbruck, Austria, 4Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom, 5Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, 6School of Social Sciences, Nanyang Technological University, Singapore, Singapore, 7National Institute of Education, Nanyang Technological University, Singapore, Singapore, 8Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, United Kingdom

First Author:

Deepika Shukla  
Centre for Research and Development in Learning (CRADLE), Nanyang Technological University|Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)
Singapore, Singapore|Singapore, Singapore

Co-Author(s):

Chia‑Lun Liu  
Centre for Research and Development in Learning (CRADLE), Nanyang Technological University|Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)
Singapore, Singapore|Singapore, Singapore
Xiaoqin Cheng  
Centre for Research and Development in Learning (CRADLE), Nanyang Technological University|Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)|Department of Psychology, University of Innsbruck
Singapore, Singapore|Singapore, Singapore|Innsbruck, Austria
Chie Takahashi, Dr  
Department of Psychology, University of Cambridge
Cambridge, CB2 3EB, United Kingdom
SH Annabel Chen, PhD  
Centre for Research and Development in Learning (CRADLE), Nanyang Technological University|Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)|Lee Kong Chian School of Medicine, Nanyang Technological University|School of Social Sciences, Nanyang Technological University|National Institute of Education, Nanyang Technological University
Singapore, Singapore|Singapore, Singapore|Singapore, Singapore|Singapore, Singapore|Singapore, Singapore
John Suckling  
Department of Psychiatry, University of Cambridge|Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)
Cambridge, CB2 0SZ, United Kingdom|Singapore, Singapore
Zoe Kourtzi  
Department of Psychology, University of Cambridge|Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)
Cambridge, CB2 3EB, United Kingdom|Singapore, Singapore
Balazs Gulyas  
Centre for Research and Development in Learning (CRADLE), Nanyang Technological University|Lee Kong Chian School of Medicine, Nanyang Technological University|Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)
Singapore, Singapore|Singapore, Singapore|Singapore, Singapore
Eleanor Koo  
Centre for Research and Development in Learning (CRADLE), Nanyang Technological University|Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)
Singapore, Singapore|Singapore, Singapore
Wei Ler Koo  
Centre for Research and Development in Learning (CRADLE), Nanyang Technological University|Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)
Singapore, Singapore|Singapore, Singapore
Jia Yuan Janet Tan  
Centre for Research and Development in Learning (CRADLE), Nanyang Technological University|Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)
Singapore, Singapore|Singapore, Singapore
Marisha Ubrani  
Centre for Research and Development in Learning (CRADLE), Nanyang Technological University|Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)
Singapore, Singapore|Singapore, Singapore
Boon Linn Choo  
Centre for Research and Development in Learning (CRADLE), Nanyang Technological University|Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)
Singapore, Singapore|Singapore, Singapore
Min Hong  
Centre for Research and Development in Learning (CRADLE), Nanyang Technological University|Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)
Singapore, Singapore|Singapore, Singapore
CLIC Consortium  
Centre for Lifelong Learning and Individualised Cognition (CLIC), Cambridge Centre for Advanced Research and Education in Singapore(CARES)
Singapore, Singapore
E-Poster

Introduction:

Structural learning (SL) integrates "Learning to learn" approach of individual's abilities to extract underlying pattern and develop rules to adapt new changes through cognitive flexibility (CF). Homeostatic plasticity in neuronal circuits is crucial for critical learning and depends upon coordinated modulation of synaptic excitation and inhibition through Glutamate and GABA interactions [1]. Disruption in this coordinated neurotransmitter's interplay triggers cognitive deficits [2], while adaptive modulation contributes to relearning capacity [3-4]. Studies reported associative interaction with learning and cognitive skills development with neurotransmitters [5], but the underlying neuro-cognitive model of these interactions is illusive. Using controlled SL training intervention, we aim to investigate the effect of learning in both the neuronal and behavioral levels to assess its transferability to other cognitive abilities.

Methods:

113 healthy volunteers aged between 18-55 years were pseudo-randomized to control (C) (55) and training (T) (58) groups matching with age (mean±SD: 28.21±7.89), gender: (65-F, 48-M) and intelligence (IQ) (109±16.30). T-group underwent 2-week SL training [6]. Out of the 113, 106 (C:53, T:53) participants completed with post-session magnetic resonance (MR) imaging, of which 7 (C:2, T:5) withdrew/dropped out of the study. All MR scans were performed in 3T Siemens MAGNETOM Prisma MRI scanner with a 64-channel head coil. All participants consented to Cognitive testing and MRI sessions with ethics approval from NTU-IRB.
MR spectroscopy (MRS) for GABA quantitation in bilateral (left (L)- and right(R)-dorsolateral prefrontal cortex (DLPFC) were performed at two different time points of pre- and post- SL training sessions along with cognitive assessments. Each MR session included 3D T1-MPRAGE (TR=2000ms; TE=22.6ms; TI=800ms; flip-angle=8°; FOV=256×256; slices=176; voxel-size=1×1×1mm3) and 1H-MEGA-PRESS MRS (voi: 30x15x30 mm3, TR=2000ms, TE=68ms, ON=1.98ppm, OFF=7.5ppm, Navg:128) with one unsuppressed water spectra of Navg=4. Voxels were placed close to middle frontal gyrus maximizing gray matter. Manual shimming resulted linewidth < 16 Hz. MRS data in BIDS structure was applied for pre-processing and Osprey [7] was used for quantitation of GABA+ (GABA + macromolecule) and Glx (Glutamate+ glutamine). Quality check for MRS data included visual artefacts, head movements, broad Creatine (Cr) linewidth in the OFF-spectra, and poor fitting.

Results:

Tissue corrected GABA+ and Glx levels in both L- & R-DLPFC of study groups did not differ at pre-training stage. After training, the T-group showed significant reduction in R-DLPFC Glx (p = 0.007, mean-diff: -2.479) compared to C-group (Fig.1a). Paired comparison between sessions showed significant decrease in post-training R-DLPFC GABA+ in T-group (p = 0.03, mean-diff: 0.656) but not in C-group (p= 0.12) (Fig. 1d). No significant difference was observed for L-DLPFC GABA+, Glx and GABA+/Glx ratio across groups and sessions. MRS measures did not relate to SL test-scores. However, R-DLPFC Glx in the T-group correlated positively with switch-cost reaction time (r = 0.3247, p = 0.0409) between shift-repeat trials of color-shape task, indicating reduced Glx levels in the R-DLPFC relates to short reaction time in the T-group (Fig. 2b). GABA+/Glx ratio in T-group showed significant positive relation (r = 0.317, p < 0.05) with probability shift measure levels in contrast to negative relation (r = -0.093) observed in C-group. A strategy shifting ability in the T-group is observed in CF, and other cognitive domains (i.e. working memory, inhibition, and non-verbal intelligence) in contrast to C-group (Fig. 2a).
Supporting Image: Figure_1.png
Supporting Image: Figure_2.png
 

Conclusions:

SL training evidenced significant modulation in neurochemicals after training and associates to response time and accuracy measure in CF. We also observed potential transfer of SL training to selective cognitive domains of working memory and Inhibition and fluid intelligence.

Higher Cognitive Functions:

Executive Function, Cognitive Control and Decision Making
Imagery

Learning and Memory:

Skill Learning 1

Lifespan Development:

Lifespan Development Other

Novel Imaging Acquisition Methods:

MR Spectroscopy 2

Keywords:

ADULTS
Cognition
GABA
Glutamate
Learning
Magnetic Resonance Spectroscopy (MRS)
Other - Mega-PRESS, Cognitive flexibility, Structure Learning, Dorso-lateral Prefrontal cortex (DLPFC)

1|2Indicates the priority used for review

Provide references using author date format

1. Wen, Y., Dong, Z., Liu, J., Axerio-Cilies, P., Du, Y., Li, J., Chen, L., Zhang, L., Liu, L., Lu, J., Zhou, N., Chuan Wu, D., & Wang, Y. T. (2022). Glutamate and GABAA receptor crosstalk mediates homeostatic regulation of neuronal excitation in the mammalian brain. Signal transduction and targeted therapy, 7(1), 340.
2. Li, H., Heise, K. F., Chalavi, S., Puts, N. A. J., Edden, R. A. E., & Swinnen, S. P. (2022). The role of MRS-assessed GABA in human behavioral performance. Progress in neurobiology, 212, 102247.
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5. Zacharopoulos, G., Sella, F., Cohen Kadosh, K., Hartwright, C., Emir, U., & Cohen Kadosh, R. (2021). Predicting learning and achievement using GABA and glutamate concentrations in human development. PLoS biology, 19(7), e3001325.
6. Liu, C. L., Cheng, X., Choo, B. L., Hong, M., Teo, J. L., Koo, W. L., Tan, J. Y. J., Ubrani, M. B., Suckling, J., Gulyás, B., Leong, V., Kourtzi, Z., Sahakian, B., Robbins, T., & Chen, A. S. (2023). Potential cognitive and neural benefits of a computerised cognitive training programme based on Structure Learning in healthy adults: study protocol for a randomised controlled trial. Trials, 24(1), 517.
7. Oeltzschner, G., Zöllner, H. J., Hui, S. C. N., Mikkelsen, M., Saleh, M. G., Tapper, S., & Edden, R. A. E. (2020). Osprey: Open-source processing, reconstruction & estimation of magnetic resonance spectroscopy data. Journal of neuroscience methods, 343, 108827.