Poster No:
799
Submission Type:
Abstract Submission
Authors:
Mikhail Votinov1, Irina Knyazeva2, Lena Hofhansel3, Ute Habel3
Institutions:
1Institute of Neuroscience and Medicine (INM-10), Research Centre Jülich, Jülich, Germany, 2N.P. Bechtereva Institute of Human Brain, Saint Petersburg, Russian Federation, 3Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University Hospital, Aachen, Germany
First Author:
Mikhail Votinov
Institute of Neuroscience and Medicine (INM-10), Research Centre Jülich
Jülich, Germany
Co-Author(s):
Irina Knyazeva
N.P. Bechtereva Institute of Human Brain
Saint Petersburg, Russian Federation
Lena Hofhansel
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University Hospital
Aachen, Germany
Ute Habel
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University Hospital
Aachen, Germany
Introduction:
Evidence suggests that individuals engaged in criminal behavior may exhibit deficits in social cognition, manifesting as challenges in recognizing social cues and potential deficiencies in empathy and theory of mind (Mariano, M et al., 2017; Newbury-Helps, J., 2017). Our recent fMRI study revealed that offenders, being more sensitive to personal insults, exhibited heightened implicit aggression and decreased connectivity in cognitive control networks, including the dorsomedial prefrontal cortex, precuneus, and middle/superior temporal regions (Hofhansel, L et al., 2022). To corroborate these findings and enhance our understanding of disparities in cortical communication between brain regions involved in social processes among criminal offenders and the noncriminal population, we conducted resting-state MRI measurements combined with topological brain network analysis. Utilizing the social brain atlas, we anticipated observing differences primarily in limbic and high-level clusters.
Methods:
In our resting-state (rsMRI) study, we included 27 male violent offenders (OFF), convicted of at least one violent crime, and a matched noncriminal control group (HC). The study utilized a 3T scanner, and rsMRI data were acquired using EPI with parameters: 240 volumes; TR = 2300 ms; TE = 29 ms, lasting for 9 minutes. The fMRI data underwent preprocessing with the fmriprep tool (Esteban et al., 2019). Postprocessing for network analysis involved smoothing with a 6 mm kernel, regression of head motion parameters, mean WM and CFS signal, and high-pass filtering. Using the social brain atlas (Alcalá-López et al., 2018), representing 36 brain regions highly involved in social processes, we computed a connectivity matrix for each subject. A t-test contrasting the two groups for each pairwise connection was performed, with any edge having a t-value exceeding 2.9 considered as a set of suprathreshold links for the network-based statistic (NBS) approach (Zalesky, 2010). NBS simulations were conducted with the Brain Connectivity Toolbox for Python. To better understand group differences identified by NBS, we employed a graph analysis approach. Computed measures included the shortest path length (SPL), centrality, clustering coefficient, and node efficiency (Liu, Jin, et al., 2017). Binary graphs from correlation matrices were generated with thresholds spaced from 0 to 0.6, using NetworkX in Python.
Results:
The NBS revealed a subnetwork with 9 regions connected by 16 links and a separate subnetwork with a single link, significantly different for the OFF group. In the first cluster, all edges except one involved the left supramarginal gyrus (lSMG) region. The second cluster had a single link between the anterior midcingulate cortex and precuneus. Prominent group differences emerged in node-based network measures for lSMG and precuneus regions. Specifically, the shortest path length (SPL), representing the average path length between a predefined node and all others, showed higher values for the OFF group compared to HC for both lSMG and precuneus regions across all applied thresholds. The shortest path is crucial for efficient information transmission in a brain network, enabling faster information transfer and reducing overall brain consumption. Simultaneously, the Betweenness Centrality, quantifying the number of times a node acts as a bridge along the shortest path between two other nodes, was lower for both regions in the OFF group.
Conclusions:
Our results revealed distinctions in the topological network organization of temporal and parietal regions for the OFF group compared to the HC. These regions are associated with cognitive processes such as self-referential thinking, mentalizing, and perspective-taking. This aligns with our earlier task-based findings and could elucidate a deficiency in social cognition among criminal offenders.
Emotion, Motivation and Social Neuroscience:
Social Cognition 1
Social Interaction
Social Neuroscience Other
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
FUNCTIONAL MRI
Social Interactions
Other - resting-state fMRI, graph analysis, criminal offenders, social cognition, network analysis
1|2Indicates the priority used for review
Provide references using author date format
Mariano, M., Pino, M. C., Peretti, S., Valenti, M., & Mazza, M. (2017). Understanding criminal behavior: Empathic impairment in criminal offenders. Social Neuroscience, 12(4), 379-385.
Newbury-Helps, J., Feigenbaum, J., & Fonagy, P. (2017). Offenders with antisocial personality disorder display more impairments in mentalizing. Journal of personality disorders, 31(2), 232-255.
Hofhansel, L., Weidler, C., Clemens, B., Habel, U., & Votinov, M. (2022). Personal insult disrupts regulatory brain networks in violent offenders. Cerebral Cortex.
Alcalá-López, D., Smallwood, J., Jefferies, E., Van Overwalle, F., Vogeley, K., Mars, R. B., ... & Bzdok, D. (2018). Computing the social brain connectome across systems and states. Cerebral cortex, 28(7), 2207-2232.
Parkes, L., Fulcher, B., Yücel, M., & Fornito, A. (2018). An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. Neuroimage, 171, 415-436.
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., ... & Gorgolewski, K. J. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature methods, 16(1), 111-116.
Zalesky, Andrew, Alex Fornito, and Edward T. Bullmore. "Network-based statistic: identifying differences in brain networks." Neuroimage 53.4 (2010): 1197-1207.
Liu, Jin, et al. "Complex brain network analysis and its applications to brain disorders: a survey." Complexity 2017 (2017).
Liu, J., Li, M., Pan, Y., Lan, W., Zheng, R., Wu, F. X., & Wang, J. (2017). Complex brain network analysis and its applications to brain disorders: a survey. Complexity, 2017.