The hierarchical organization of the salience, default, and executive networks in social inferences
Poster No:
326
Submission Type:
Abstract Submission
Authors:
Yu Chen1, Winson Yang2, Myrthe Rijpma1, Jesse Brown1, Alex Lee1, Gianina Toller1, Howard Rosen3, Joel Kramer1, Bruce Miller4, Katherine Rankin5
Institutions:
1University of California San Francisco, San Francisco, CA, 2Harvard Medical School, Boston, MA, 3UCSF, San Francisco, CA, 4Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, UCSF, San Francisco, CA, 5Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA
First Author:
Co-Author(s):
Bruce Miller
Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, UCSF
San Francisco, CA
Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, UCSF
San Francisco, CA
Katherine Rankin
Memory and Aging Center, Department of Neurology, University of California San Francisco
San Francisco, CA
Memory and Aging Center, Department of Neurology, University of California San Francisco
San Francisco, CA
Introduction:
Dynamic interactions among salience (SN), default mode (DMN), and executive networks (EN) are implicated in the attentional capture of self-related events and the guidance of goal-directed social cognition (Menon, 2015). Socioemotional dysfunction manifests in the earliest stages of behavioral variant frontotemporal dementia (bvFTD) (Lanata et al., 2016; Piguet et al., 2011), though studies have not examined whether social cognition impairments correspond to altered directional communication between brain networks. This study aimed to determine if the resting effective connectivity among the SN, DMN, and EN networks predicts the ability to read others' intentions, in bvFTD patients and healthy adults.
Methods:
Seventeen patients with bvFTD and 23 age-, sex-, and education-matched healthy controls (HC) were included in this study. The Awareness of Social Inference Test (TASIT) – Social Inference-Enriched (SIE) (McDonald et al., 2003), employing conversational videos to assess the understanding of intentions during insincere communications, was used to evaluate accuracy of social inferencing. We defined 16 bilateral regions of interest based on the Brainnetome Atlas (Fan et al., 2016): the ventral anterior insular (vAI), cingulate gyrus (CG), dorsal lateral thalamus (dlTha) for the SN; the posterior cingulate (PCC), ventral medial prefrontal cortex (vmPFC), and hippocampus for the DMN; and the middle frontal gyrus (mFG) and inferior parietal lobe (IPL) for the EN. We applied spectral dynamic causal modeling to the task-free functional MRI scans of these participants to characterize the effective connectivity patterns within and between the 3 networks separately in patients and controls. We used parametric empirical Bayes (PEB) scheme to draw subjects out of local optima using the group mean as the empirical prior that furnishes a more efficient and robust estimation of effective connectivity parameters (Friston et al., 2015). We used the second level PEB framework to specify linear models representing each group's average effective connectivity. Bayesian models were applied to account for the estimated uncertainty about the connection strengths. Finally, we performed linear regression analyses controlling for age to examine the relationship of network effective connections with individual's ability to understand social cues using the TASIT SIE "Do" Total Score.
Results:
Overall, patterns of network effective connectivity for bvFTD patients differed from HCs. In HCs, 60 excitatory effective connections were identified within and between networks at a posterior probability (Pp) of 99%, with the strongest being reciprocal connections between the networks. In contrast, bvFTD patients exhibited 49 excitatory connections within and between networks, along with one inhibitory connection within the SN from the right vAI to the left dlTha. BvFTD patients performed significantly worse on the TASIT SIE than HCs, and the neural predictors of performance differed between groups. Among HCs, individuals with stronger outputs from the DMN and SN to the EN, particularly from the bilateral vmPFC to mFG and from the left CG to right PCC, were more likely to make accurate social inferences. Conversely, among bvFTDs, more accurate performance was predicted by information flow from the EN (bilateral mFG and IPL nodes) to other nodes in the EN network, with few SN or DMN networks initiating flow to other nodes. These findings suggest that directional outputs from the SN and DMN to the EN are a preferential foundation for healthy socioemotional reasoning.
·Figure 1. Matrices of mean effective connectivity of the healthy control and bvFTD group, accompanied with schematic overviews of the effective connectivity results
·Figure 2. Matrices and schematic illustration of effective node connections that predict higher TASIT-SIE Do scores
Conclusions:
This study clarifies for the first time how directional information flow among SN, DMN, and EN networks contributes to social inferential reasoning in healthy controls and bvFTD. SN and DMN outputs to the EN are crucial for optimal social reasoning, with information flow disruption being central to bvFTD patients' deficits in making inferences about others' intentions.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Emotion, Motivation and Social Neuroscience:
Social Cognition 2
Social Neuroscience Other
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling
Keywords:
Cognition
MRI
Neurological
Other - spectral dynamic causal modeling, effective connectivity, dementia, social networks, social inferences, social cognition
1|2Indicates the priority used for review
Provide references using author date format
Friston, K., Zeidman, P., & Litvak, V. (2015). Empirical Bayes for DCM: A Group Inversion Scheme. Front Syst Neurosci, 9, 164. doi:10.3389/fnsys.2015.00164
Lanata, S. C., & Miller, B. L. (2016). The behavioural variant frontotemporal dementia (bvFTD) syndrome in psychiatry. J Neurol Neurosurg Psychiatry, 87(5), 501-511. doi:10.1136/jnnp-2015-310697
McDonald, S., Flanagan, S., Rollins, J., & Kinch, J. (2003). TASIT: A new clinical tool for assessing social perception after traumatic brain injury. J Head Trauma Rehabil, 18(3), 219-238. doi:10.1097/00001199-200305000-00001
Menon, V. (2015). Large-Scale Functional Brain Organization. 449-459. doi:10.1016/b978-0-12-397025-1.00024-5
Piguet, O., Hornberger, M., Mioshi, E., & Hodges, J. R. (2011). Behavioural-variant frontotemporal dementia: diagnosis, clinical staging, and management. The Lancet Neurology, 10(2), 162-172. doi:10.1016/s1474-4422(10)70299-4