Research data supporting "Neurochemical and functional interactions for improved perceptual decisions through training"
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Jia, K., Frangou, P., Karlaftis, V., Ziminski, J., Giorgio, J., Rideaux, R., Zamboni, E., et al. (2022). Research data supporting "Neurochemical and functional interactions for improved perceptual decisions through training" [Dataset]. https://doi.org/10.17863/CAM.82236
For the main study, participants took part in one behavioural session in the lab and two brain imaging scans (before behavioural training) comprising rs-fMRI and MRS. For the tDCS study, participants took part in one behavioural session with stimulation (Anodal) or without stimulation (Sham) in the lab. Participants in both studies were trained on an orientaion identification task and completed a pre-training test block and five training blocks. 1. Behavioural data: First, we calculated performance accuracy per participant and block. We then fitted the behavioural data per block with a drift diffusion model to compute: (a) drift rate (DR), (b) decision threshold (TH), and (c) non-decision time. For statistical analysis, we computed change as the difference between the pre-training block and the max-training block (i.e. we selected the block with the higher accuracy between the last two training blocks per participant to account for potential fatigue effects towards the end of the training). DR change and TH change were further used for multiple regression analysis. 2. MRS data: MRS data were collected from two voxels on a 3T scanner: an Early Visual (EV) and a dorsolateral pre-frontal cortex (DLPFC) voxel. MRS data were analysed with the LC-Model to quantify metabolite concentrations: GABA+, Glutamate, Glutamine and NAA. GABA+/NAA, Glu/NAA and MRS quality indices (SNR, CRLB, linewidth, tissue composition) were used for control analyses. 3. rs-fMRI data: Resting-state fMRI data were collected on a 3T scanner (2mm isotropic resolution). Data were pre-processed following the Human Connectome Project pipeline for multi-band data: motion correction, EPI-to-EPI coregistration, EPI-to-T1 coregistration, MNI normalisation, spatial smoothing, wavelet despiking, ICA denoising. rs-fMRI data were then used to test for functional connectivity correlations with behaviour and GABA+. 4. Brain maps: The zip folder contains brain masks for: (a) EV voxel (50% overlap across participants), (b) DLPFC voxel (50% overlap across participants), and (c) M1 mask (control region). The masks are in MNI space and are provided in .nii format (i.e. Nifti). For more information, please see the Jia_Frangou_Karlaftis_Ziminski_data_description.doc file.
Matlab 2019a, SPM 12.3 (https://www.fil.ion.ucl.ac.uk/spm/), GIFT v3.0b (http://mialab.mrn.org/software/gift/), BrainWavelet 2.0 (http://www.brainwavelet.org), MRspa v1.5c (www.cmrr.umn.edu/downloads/mrspa), Diffusion Model Analysis Toolbox (https://ppw.kuleuven.be/okp/software/dmat/)
perceptual decisions, learning, drift diffusion model, MR Spectroscopy, Glutamate, functional connectivity, resting-state, transcranial direct current stimulation, early visual, dorsolateral PFC
Publication Reference: https://doi.org/10.1152/jn.00308.2021https://www.repository.cam.ac.uk/handle/1810/334572
This record's DOI: https://doi.org/10.17863/CAM.82236
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/