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

Hillebrandt H, Friston KJ, Blakemore SJ - Sci Rep (2014)

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: 1] Institute of Cognitive Neuroscience, University College London, London, WC1N 3AR, United Kingdom [2] Moral Cognition Laboratory, Department of Psychology, Harvard University, Cambridge, MA, 02138, United States.

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.

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(A) The winning model: The full model was the winning model, with the highest evidence; in this model all connections were modulated by the Animate – Inanimate motion modulator. The driving input, the ‘All motion' contrast, entered into V5 and the pSTS. Wider lines represent stronger modulation or input relative to its comparison: V5 received more input (Mean parameter estimate = 0.96) than the pSTS (Mean parameter estimate = −0.06) and the Animate – Inanimate motion contrast modulated the forward connection from V5 to the pSTS significantly more strongly (Mean parameter estimate = 1.22) than the backward connection (Mean parameter estimate = 0.16) and the (inhibitory) self-connection of the pSTS (Mean parameter estimate = −0.19) less strongly than the self-connection of V5 (Mean parameter estimate = −0.03). This means that the (inhibitory) self-connection in the pSTS decreased more than the (inhibitory) self-connection in V5. In other words, since the (inhibitory) self-connection was decreased more towards zero, the pSTS activation is modulated by animacy. (B) VOIs used in the DCM analyses based on the mean of all participants' VOI centre coordinates and illustration of the modulatory connectivity between them. The first VOI, based on the peaks of the All Motion contrast, was in the MT+/V5 (44 −64 4; circled in blue). The other VOI was activated by the conjunction of the All motion contrast and the Animate – Inanimate motion contrast [All Motion & Animate – Inanimate motion] and was located in the pSTS (54 −50 16, circled in green). The colour of the line represents the source of the strongest bidirectional modulatory connection.
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f2: (A) The winning model: The full model was the winning model, with the highest evidence; in this model all connections were modulated by the Animate – Inanimate motion modulator. The driving input, the ‘All motion' contrast, entered into V5 and the pSTS. Wider lines represent stronger modulation or input relative to its comparison: V5 received more input (Mean parameter estimate = 0.96) than the pSTS (Mean parameter estimate = −0.06) and the Animate – Inanimate motion contrast modulated the forward connection from V5 to the pSTS significantly more strongly (Mean parameter estimate = 1.22) than the backward connection (Mean parameter estimate = 0.16) and the (inhibitory) self-connection of the pSTS (Mean parameter estimate = −0.19) less strongly than the self-connection of V5 (Mean parameter estimate = −0.03). This means that the (inhibitory) self-connection in the pSTS decreased more than the (inhibitory) self-connection in V5. In other words, since the (inhibitory) self-connection was decreased more towards zero, the pSTS activation is modulated by animacy. (B) VOIs used in the DCM analyses based on the mean of all participants' VOI centre coordinates and illustration of the modulatory connectivity between them. The first VOI, based on the peaks of the All Motion contrast, was in the MT+/V5 (44 −64 4; circled in blue). The other VOI was activated by the conjunction of the All motion contrast and the Animate – Inanimate motion contrast [All Motion & Animate – Inanimate motion] and was located in the pSTS (54 −50 16, circled in green). The colour of the line represents the source of the strongest bidirectional modulatory connection.

Mentions: We created and estimated DCMs24 with DCM12 (version 5370) as implemented in SPM12b. The DCMs were based on the VOIs (volumes of interest) described above (V5 and the pSTS) and used the main effect of Animate – Inanimate motion to modulate the connections between these two regions (see Figure 2A). All DCMs were deterministic (as opposed to stochastic for DCMs without experimental input, see25), bilinear (as opposed to nonlinear DCMs, where activity between two regions is modulated by a third region, see26), two-state models27, with mean-centred inputs. Two-state DCMs differ from one-state models in that the activity in one brain region is modelled with both excitatory and inhibitory neuronal populations. This allows one to use positivity constraints that enforce extrinsic (between region) connectivity to be excitatory, while self or recurrent (intrinsic) connections are treated as inhibitory27. It is important to note that the hemodynamics in the current DCM are a function of excitatory states only – and the contributions to the BOLD signal from the inhibitory states are expressed indirectly, through interactions, with excitatory populations, at the neuronal level27. Note that the fixed and modulatory parameters were always scale parameters (exponentiated) to ensure positivity as per convention for two-state DCMs, so that the extrinsic connections were always excitatory27. Scale parameters of two-state DCMs are thus higher than parameter estimates from one-state DCMs. Our unexponentiated modulatory parameter estimates ranged from -2.7 to 3.9 Hz, similar to one-state DCM parameter estimates reported in other studies1128. While the two state-DCMs use exponentiated scale parameters that introduce positivity constraints and are more plausible to interpret, these values are likely not normally distributed and heteroscedastic, because the exponential function is the inverse function of the natural logarithm (which is commonly used to transform data to meet the assumption of a normal distribution, see2930). Thus, we used the original unexponentiated non-scale parameter estimates for all statistics, but the exponentiated parameter estimates for plots and interpretation.


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

Hillebrandt H, Friston KJ, Blakemore SJ - Sci Rep (2014)

(A) The winning model: The full model was the winning model, with the highest evidence; in this model all connections were modulated by the Animate – Inanimate motion modulator. The driving input, the ‘All motion' contrast, entered into V5 and the pSTS. Wider lines represent stronger modulation or input relative to its comparison: V5 received more input (Mean parameter estimate = 0.96) than the pSTS (Mean parameter estimate = −0.06) and the Animate – Inanimate motion contrast modulated the forward connection from V5 to the pSTS significantly more strongly (Mean parameter estimate = 1.22) than the backward connection (Mean parameter estimate = 0.16) and the (inhibitory) self-connection of the pSTS (Mean parameter estimate = −0.19) less strongly than the self-connection of V5 (Mean parameter estimate = −0.03). This means that the (inhibitory) self-connection in the pSTS decreased more than the (inhibitory) self-connection in V5. In other words, since the (inhibitory) self-connection was decreased more towards zero, the pSTS activation is modulated by animacy. (B) VOIs used in the DCM analyses based on the mean of all participants' VOI centre coordinates and illustration of the modulatory connectivity between them. The first VOI, based on the peaks of the All Motion contrast, was in the MT+/V5 (44 −64 4; circled in blue). The other VOI was activated by the conjunction of the All motion contrast and the Animate – Inanimate motion contrast [All Motion & Animate – Inanimate motion] and was located in the pSTS (54 −50 16, circled in green). The colour of the line represents the source of the strongest bidirectional modulatory connection.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150124&req=5

f2: (A) The winning model: The full model was the winning model, with the highest evidence; in this model all connections were modulated by the Animate – Inanimate motion modulator. The driving input, the ‘All motion' contrast, entered into V5 and the pSTS. Wider lines represent stronger modulation or input relative to its comparison: V5 received more input (Mean parameter estimate = 0.96) than the pSTS (Mean parameter estimate = −0.06) and the Animate – Inanimate motion contrast modulated the forward connection from V5 to the pSTS significantly more strongly (Mean parameter estimate = 1.22) than the backward connection (Mean parameter estimate = 0.16) and the (inhibitory) self-connection of the pSTS (Mean parameter estimate = −0.19) less strongly than the self-connection of V5 (Mean parameter estimate = −0.03). This means that the (inhibitory) self-connection in the pSTS decreased more than the (inhibitory) self-connection in V5. In other words, since the (inhibitory) self-connection was decreased more towards zero, the pSTS activation is modulated by animacy. (B) VOIs used in the DCM analyses based on the mean of all participants' VOI centre coordinates and illustration of the modulatory connectivity between them. The first VOI, based on the peaks of the All Motion contrast, was in the MT+/V5 (44 −64 4; circled in blue). The other VOI was activated by the conjunction of the All motion contrast and the Animate – Inanimate motion contrast [All Motion & Animate – Inanimate motion] and was located in the pSTS (54 −50 16, circled in green). The colour of the line represents the source of the strongest bidirectional modulatory connection.
Mentions: We created and estimated DCMs24 with DCM12 (version 5370) as implemented in SPM12b. The DCMs were based on the VOIs (volumes of interest) described above (V5 and the pSTS) and used the main effect of Animate – Inanimate motion to modulate the connections between these two regions (see Figure 2A). All DCMs were deterministic (as opposed to stochastic for DCMs without experimental input, see25), bilinear (as opposed to nonlinear DCMs, where activity between two regions is modulated by a third region, see26), two-state models27, with mean-centred inputs. Two-state DCMs differ from one-state models in that the activity in one brain region is modelled with both excitatory and inhibitory neuronal populations. This allows one to use positivity constraints that enforce extrinsic (between region) connectivity to be excitatory, while self or recurrent (intrinsic) connections are treated as inhibitory27. It is important to note that the hemodynamics in the current DCM are a function of excitatory states only – and the contributions to the BOLD signal from the inhibitory states are expressed indirectly, through interactions, with excitatory populations, at the neuronal level27. Note that the fixed and modulatory parameters were always scale parameters (exponentiated) to ensure positivity as per convention for two-state DCMs, so that the extrinsic connections were always excitatory27. Scale parameters of two-state DCMs are thus higher than parameter estimates from one-state DCMs. Our unexponentiated modulatory parameter estimates ranged from -2.7 to 3.9 Hz, similar to one-state DCM parameter estimates reported in other studies1128. While the two state-DCMs use exponentiated scale parameters that introduce positivity constraints and are more plausible to interpret, these values are likely not normally distributed and heteroscedastic, because the exponential function is the inverse function of the natural logarithm (which is commonly used to transform data to meet the assumption of a normal distribution, see2930). Thus, we used the original unexponentiated non-scale parameter estimates for all statistics, but the exponentiated parameter estimates for plots and interpretation.

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: 1] Institute of Cognitive Neuroscience, University College London, London, WC1N 3AR, United Kingdom [2] Moral Cognition Laboratory, Department of Psychology, Harvard University, Cambridge, MA, 02138, United States.

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.

Show MeSH