Effective connectivity during animacy perception – dynamic causal modelling of Human Connectome Project data

Abstract

Biological agents are the most complex systems humans have to model and predict. In predictive coding, high-level cortical areas inform sensory cortex about incoming sensory signals, a comparison between the predicted and actual sensory feedback is made, and information about unpredicted sensory information is passed forward to higher-level areas. Predictions about animate motion – relative to inanimate motion – should result in prediction error and increase signal passing from lower level sensory area MT+/V5, which is responsive to all motion, to higher-order posterior superior temporal sulcus (pSTS), which is selectively activated by animate motion. We tested this hypothesis by investigating effective connectivity in a large-scale fMRI dataset from the Human Connectome Project. 132 participants viewed animations of triangles that were designed to move in a way that appeared animate (moving intentionally), or inanimate (moving in a mechanical way). We found that forward connectivity from V5 to the pSTS increased, and inhibitory self-connection in the pSTS decreased, when viewing intentional motion versus inanimate motion. These prediction errors associated with animate motion may be the cause for increased attention to animate stimuli found in previous studies.

Mentalizing in schizophrenia: A multivariate functional MRI study

“…As the Triangles Task is part of the social cognition battery used in the Human Connectome Project (Barch, et al, 2013;Hillebrandt, Friston, & Blakemore, 2014) it is likely to receive considerable attention. Therefore understanding the deficits of clinical groups such as those with schizophrenia and the underlying neurophysiological differences is of paramount importance.…”

An afferent white matter pathway from the pulvinar to the amygdala facilitates fear recognition

“…The HCP intensity normalised the data and spatially transformed it to MNI152 space using FSL (see Glasser et al 2013 for full details on preprocessing pipeline). We further increased the signal-to-noise ratio of the fMRI data in SPM12 (SPM12, www.fil.ion.ucl.ac.uk/spm) by applying spatial smoothing using a 4mm Gaussian kernel 49 .…”

“…We closely modelled this first-level general linear model (GLM) analysis on the work by Hillebrandt et al, (2014), such that we did not slice time correct the multiband data due to the fast TR. We partitioned the GLM into sessions (left-to-right and right-to-left encoding) and we included head motion as a regressor.…”

“…We then placed spheres with a radius of 12mm around these four coordinates to search for the participant-specific local maxima within each participant's session-specific SPM for the faces minus shapes contrast (adjusted for the t effects of interest, p < .05 uncorrected). Note that for the purposes of extracting the fMRI data for the DCM nodes, one does not need corrected p-values 49 . Next, we defined the ROIs by a 6mm radius sphere around the participant-and session-specific local maxima.…”

“…http://dx.doi.org/10.1101/277368 doi: bioRxiv preprint first posted online Mar. 7, 2018; We used a "two-state" DCM model, which accounts for both excitatory and inhibitory neural populations 49,57 . Our model space was dictated by our specific, theory-driven hypotheses about subcortical and cortical visual pathways to the amygdala, as well as by the significant regions of the BOLD signal observed at the group level in our GLM analysis.…”