Biology subjects

Mill, R.

Publications and source records attributed to Mill, R..

2 recordsLinked to original sources

bioRxiv2020

Brain Network Mechanisms of Visual Shape Completion

Visual shape completion represents object shape, size, and number from spatially segregated edges. Despite being extensively investigated, the processs underlying brain regions, networks, and functional connections are still not well understood. To shed light on the topic, we scanned (fMRI) healthy adults during rest and during a task in which they discriminated pac-man configurations that formed or failed to form visually completed shapes (illusory and fragmented condition, respectively). Task activation differences (illusory-fragmented), resting-state functional connectivity, and multivariate pattern differences were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping (ActFlow) was used to evaluate the likely involvement of resting-state connections for shape completion. We identified 34 differentially-active parcels including a posterior temporal region, PH, whose activity was consistent across all 20 observers. Significant task regions primarily occupied the secondary visual network but also incorporated the frontoparietal, dorsal attention, default mode, and cingulo-opercular networks. Each parcels task activation difference could be modeled via its resting-state connections with the remaining parcels (r=.62, p<10-9), suggesting that such connections undergird shape completion. Functional connections from the dorsal attention network were key in modeling activation differences in the secondary visual network and across all remaining networks. Taken together, these results suggest that shape completion relies upon a distributed but densely interconnected network coalition that is centered in the secondary visual network, coordinated by the dorsal attention network, and inclusive of at least three other networks. HighlightsO_LIShape completion differentially activates regions distributed across five networks C_LIO_LIThe secondary visual network plays the clearest role in shape completion C_LIO_LIDorsal attention functional connections likely coordinate activity across networks C_LIO_LIPosterior temporal region, PH, played a highly consistent role in completion C_LI

neuroscience↗

bioRxiv2018

Task activations produce spurious but systematic inflation of task functional connectivity estimates

Most neuroscientific studies have focused on task-evoked activations (activity amplitudes at specific brain locations), providing limited insight into the functional relationships between separate brain locations. Task-state functional connectivity (FC) - statistical association between brain activity time series during task performance moves beyond task-evoked activations by quantifying functional interactions during tasks. However, many task-state FC studies do not remove the first-order effect of taskevoked activations prior to estimating task-state FC. It has been argued that this results in the ambiguous inference \"likely active or interacting during the task\", rather than the intended inference \"likely interacting during the task\". Utilizing a neural mass computational model, we verified that task-evoked activations substantially and inappropriately inflate task-state FC estimates, especially in functional MRI (fMRI) data. Various methods attempting to address this problem have been developed, yet the efficacies of these approaches have not been systematically assessed. We found that most standard approaches for fitting and removing mean task-evoked activations were unable to correct these inflated correlations. In contrast, methods that flexibly fit mean task-evoked response shapes effectively corrected the inflated correlations without reducing effects of interest. Results with empirical fMRI data confirmed the models predictions, revealing activation-induced task-state FC inflation for both Pearson correlation and psychophysiological interaction (PPI) approaches. These results demonstrate that removal of mean task-evoked activations using an approach that flexibly models task-evoked response shape is an important preprocessing step for valid estimation of task-state FC.\n\nHighlightsO_LIComputational model shows task inflation of functional connectivity estimates\nC_LIO_LIHemodynamic responses cause task activations to further inflate estimates\nC_LIO_LIStandard approaches to remove task activations leave many false positives\nC_LIO_LIMethods that flexibly fit hemodynamic response shape effectively correct inflation\nC_LIO_LICorrection of functional connectivity inflation verified with empirical fMRI data\nC_LI

neuroscience↗