Purpose

To look at the relationship between brain network topology and the performance in a Judgment of Agency task in healthy subjects.

Methods

Sixteen naïve healthy subjects (age(mean±sd)=(38±16); education(mean±sd)=(15±4); males/females=6/10) participated in this study. The behavioural tasks were administered off-resonance after the MRI acquisition. Behavioural Task Participants were required to fixate a white cross at the centre of a black screen and to press with their left hand the space bar once the fixation cross had disappeared. The key press action triggered the appearance of a blue ball at the centre of the screen. Subjects were told that the ball appearance would be either caused by their own action or controlled by the computer. The delay between the key press and the ball appearance was systematically varied using two different staircase procedures: a descending one (70 trails with a starting delay of 1620 ms) and an ascending one (70 trails with a starting delay of 90 ms). In both procedures the delay was increased by 90 ms when subjects reported they caused the effect, and decreased by 180 ms otherwise. On each trial, participants were required to judge who was the agent that caused the ball appearance. MR data acquisition MRI data were collected using gradient-echo echo-planar imaging at 3T (Philips Achieva) using a (T2*)-weighted imaging sequence sensitive to BOLD (TR/TE=3000/30 ms, voxel size=2x2x3mm, flip angle=90°, 50 slices, 240 vol). A high-resolution T1-weighted whole-brain structural scan was also acquired (1mm isotropic). Subjects were instructed to lay in the scanner at rest with eyes open. Cardiac and respiratory cycles were recorded using the scanner’s built-in photoplethysmograph and a pneumatic chest belt, respectively. FMRI preprocessing Physiological noise correction consisted of removal of time-locked cardiac and respiratory artefacts (two cardiac harmonics and two respiratory harmonics plus four interaction terms)^1,2. Correction for head motion and slice-timing were performed using FSL. Head motion parameters were used to derive the frame-wise displacement (FD): time points with FD > 0.2 mm were replaced through a least-squares spectral decomposition³. Data were then demeaned, detrended and band-pass filtered (0.01-0.1 Hz), using Matlab (The Mathworks). For group analysis maps were transformed first linearly from functional space to structural space and then non-linearly to MNI standard space using Advanced Normalization Tools (ANTs). Finally data were spatially smoothed (5x5x5 mm FWHM). Behavioural Data analysis For each participant, the normalized number of trials across the sampled delays was fitted to a Gaussian function. The delay corresponding to the curve peak value was taken as the point of subjective equality (t_PSE). Network analysis For each subject, the square value of the BOLD correlation matrix was calculated, a threshold was applied to ensure that the Erdos–Renyi entropy S was equal two and the eigenvector centrality (EC) was calculated⁴. EC maps were entered in a multiple regression design (performed using SPM8), which included one regressor of interest (t_PSE) and three nuisance variables (gender, education and age). Results were considered statistically significant at p<0.001 voxel level uncorrected with a threshold on the cluster size k=400. The threshold chosen included two cluster level FWE corrections: p<0.1 (k=450) and p<0.05 (k=600).

Results

At the group level we found t_PSE(mean±sd)=(667±27)ms (Fig.1). Brain graphs were obtained from 40,000 voxels of the grey matter. Positive correlation between EC and t_PSE was found in the primary somatosensory cortex bilaterally (BA3, [-45,-22, 54] and [48, -19, 56], p<0.05 FWE), the premotor cortex bilaterally (BA6, [-52, -1, 50] and [42, -15, 63], p<0.05 FWE) and the right superior parietal lobule (BA7, [18, -57, 69], p<0.1 FWE) (Fig.2).

Discussion

We characterised the Sense of Agency (SoA) as a function of the delay between an action and its effect. We found that the functional network sustaining agency processing comprised the premotor and somatosensory cortices bilaterally, and the right superior parietal lobule. Such prefronto-parietal circuitry is densely connected and represents the pattern of a highly specialized network dedicated to the conscious experience of controlling external events. This findings support the comparator model account of SoA⁵, which suggests intensive crosstalk between motor and somatosensory areas, calling for a somatosensory-motor coding of sense of control⁶.

Conclusion

Here we demonstrated for the first time, that functional connectivity at rest in healthy subjects is related to self-agency attribution when action feedback is temporally distorted. Such findings are most informative for the debate regarding the relative contribution of physiological/bottom-up and top-down processes in self-agency attribution. They also provide a conceptual framework for interpreting disorders of SoA in neuropsychiatric illnesses characterized by both disrupted somatosensory systems and aberrant resting state activity.

Acknowledgements

No acknowledgement found.

References

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