Concurrent neuroimaging and neurostimulation reveals a causal role for dlPFC in coding of task-relevant information

Dorsolateral prefrontal cortex (dlPFC) is proposed to drive brain-wide focus by biasing processing in favour of task-relevant information. A longstanding debate concerns whether this is achieved through enhancing processing of relevant information and/or by inhibiting irrelevant information. To address this, we applied transcranial magnetic stimulation (TMS) during fMRI, and tested for causal changes in information coding. Participants attended to one feature, whilst ignoring another feature, of a visual object. If dlPFC is necessary for facilitation, disruptive TMS should decrease coding of attended features. Conversely, if dlPFC is crucial for inhibition, TMS should increase coding of ignored features. Here, we show that TMS decreases coding of relevant information across frontoparietal cortex, and the impact is significantly stronger than any effect on irrelevant information, which is not statistically detectable. This provides causal evidence for a specific role of dlPFC in enhancing task-relevant representations and demonstrates the cognitive-neural insights possible with concurrent TMS-fMRI-MVPA.


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Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection. Jade B. Jackson Mar 12, 2021 Data were collected using the Psychophysics Toolbox version-3 software in MATLAB, as described in the methods Univariate analyses were carried out using SPM5, multivariate decoding analyses using The Decoding Toolbox, and statistical analyses using SPSS and JASP, as described in the methods The ethical approval for this study does not allow us to share raw data openly. Source data for Figures 3, 5 and 6, template regions of interest, and code used to analyse this data are publicly available on Open Science Framework (https://osf.io/r3g7c/).

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October 2018

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Magnetic resonance imaging Experimental design
Design type

Design specifications
Behavioral performance measures A sample size of 20 participants was chosen based on previous literature using similar paradigms, and was the maximum possible given the available funding, the requirement to scan each participant twice, and the technical challenges involved in acquiring fMRI data concurrent with TMS. A post hoc power analysis estimated that for our main analysis of interest we achieved 62% power to detect a TMS*Relevancy interaction at alpha = 0.05 for the reported effect size of partial eta-squared = 0.2.
Thirty-one healthy volunteers signed up for the experiment. However, four participants did not pass the TMS screening requirements, and seven participants did not complete the second scanning session so the data from these 11 participants are not included.
These findings have not yet been replicated. TMS-fMRI is still unusual and technically and practically challenging to carry out.
There was no randomization as this is a repeated measures (within-subjects) design Blinding was not relevant to this study as it was a repeated measures (within-subjects) design The final group consisted of twenty participants (15 female, 5 male; mean age = 21.6 years, SD= 3.36). All participants were righthanded with normal or corrected-to-normal vision and no history of neurological or psychiatric disorder.
Participants were recruited through the University of Reading's School of Psychology Research Panel (online advertisement). Accordingly the sample is biased in that it consisted solely of undergraduate or postgraduate students at the University of Reading.
The experiment was approved by the University of Reading Research Ethics Committee.

Event-related
In the experimental session (session 2) participants completed 8 runs of trials, each of which consisted of two blocks, one of the color task and one of the form task, for a total of 1536 trials, as specified in the Methods.
We recorded button presses which we analysed for reaction time and accuracy. As specified in the methods, we compared behavioural data for stimuli in which colour and form mapped onto the same button-press response (congruent) to those where the two stimulus dimensions indicated different button-press responses (incongruent), using three-way repeated measures ANOVAs, with factors TMS (Control, Active), Feature (Colour, Form), and Congruency (Congruent, Incongruent), with post-hoc t-tests. We report means, confidence intervals, effect size and probability. We used a sequential ascending T2*-weighted EPI acquisition sequence with the following parameters: acquisition time 2450ms; echo time 30 ms; 35 oblique axial slices with a slice thickness of 3.0 mm and a 0.70 mm inter-slice gap; in plane resolution 3.0×3.0 mm; matrix 64×64; field of view 256 mm; flip angle 90°; 50% phase oversampling in the phase encoding direction to shift any Nyquist ghost artefact, due to the presence of the TMS coil, to outside the volume of interest.
Whole brain MRI data were preprocessed using SPM 5 (Wellcome Department of Imaging Neuroscience, www. fil.ion.ucl.ac.uk/spm) in MatLab 2013b. Functional MRI data were converted from DICOM to NIFTI format, spatially realigned to the first functional scan and slice timing corrected, and structural images were co-registered to the mean EPI. EPIs were smoothed slightly (4 mm FWHM Gaussian kernel) to improve signal-to-noise ratio for multivariate analyses, and were smoothed separately with a larger smoothing kernel for univariate analyses (8 mm FWHM Gaussian kernel). In all cases the data were high pass filtered (128s).
EPI data for multivariate analysis were not normalised and were analysed in native space. Structural scans were normalised, using the linear segment and normalise routine of SPM5 (Wellcome Department of Imaging Neuroscience, London, UK; www.fil.ion.ucl.ac.uk) to derive the individual participant normalisation parameters needed for transformation of ROIs into native space, TMS target definition, and to normalise the searchlight accuracy maps derived in native space.

T1 template of SPM5
We removed artefacts associated with the TMS pulse only. For this, we first identified slices with a signal magnitude of > 1.5 SD from the run mean and visually inspected them visually for presence of the TMS artefact. These slices were replaced by the mean of the same slices from the preceding and proceeding volumes (following Feredoes et al., 2011). Next, we manually removed and interpolated over any remaining slices that were acquired during TMS pulse delivery, identifying them based on timing and visual inspection. This was necessary because, depending on the affected slice, the Control TMS condition did not always produce deviations > 1.5SD from the mean. We ensured that the same number of slices were removed and interpolated over in the Active and Control TMS conditions.
We did not censor any volumes We carried out both mass univariate and multivariate tests as detailed in the Methods. For the main analysis, we specified a first level design in which we estimated the activity associated with the two colours and two forms of the objects, using correct trials only. Each trial contributed to the estimation of two beta values: the relevant feature (green or blue in the colour task, and cuby or smoothy in the form task) and the irrelevant feature (cuby or smoothy in the colour task, and green or blue in the form task), for the Control and Active trials separately (8 regressors per block). To account for trial by trial variation in reaction time (Todd, Nystrom, & Cohen, 2013), trials were modelled as events lasting from stimulus onset until response (Henson, 2007;Grinband, Wager, Lindquist, Ferrera, & Hirsch, 2008;Woolgar, Golland, & Bode, 2014) convolved with the hemodynamic response of SPM5. Second level analyses are random effects (across subjects).
For the main analysis, we entered classification scores for the MD regions into a four factor ANOVA with factors TMS ( We derived labels manually using the Brodmann and AAL templates of MRICroN and the Harvard-Cortical and subcortical structural atlases of Fsl Our main inferences are made on an ROI basis. Additional inference at whole brain level comes from an exploratory searchligh-based whole brain analysis of classification, which was assessed for inference at the cluster level. Since we ran this exploratory analysis to check for the specificity of the results (i.e. to rule out that it was a very general effect across many brain regions) we used a lenient voxelwise threshold of p < 0.0001 and corrected for multiple comparisons at the cluster-level using FWE.