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Disrupted functional connectome in antisocial personality disorder

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Abstract

Studies on antisocial personality disorder (ASPD) subjects focus on brain functional alterations in relation to antisocial behaviors. Neuroimaging research has identified a number of focal brain regions with abnormal structures or functions in ASPD. However, little is known about the connections among brain regions in terms of inter-regional whole-brain networks in ASPD patients, as well as possible alterations of brain functional topological organization. In this study, we employ resting-state functional magnetic resonance imaging (R-fMRI) to examine functional connectome of 32 ASPD patients and 35 normal controls by using a variety of network properties, including small-worldness, modularity, and connectivity. The small-world analysis reveals that ASPD patients have increased path length and decreased network efficiency, which implies a reduced ability of global integration of whole-brain functions. Modularity analysis suggests ASPD patients have decreased overall modularity, merged network modules, and reduced intra- and inter-module connectivities related to frontal regions. Also, network-based statistics show that an internal sub-network, composed of 16 nodes and 16 edges, is significantly affected in ASPD patients, where brain regions are mostly located in the fronto-parietal control network. These results suggest that ASPD is associated with both reduced brain integration and segregation in topological organization of functional brain networks, particularly in the fronto-parietal control network. These disruptions may contribute to disturbances in behavior and cognition in patients with ASPD. Our findings may provide insights into a deeper understanding of functional brain networks of ASPD.

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Acknowledgments

We thank all the volunteers for their participation in the study and the anonymous referees for their insightful comments and suggestions. The Funding Project of Education Ministry for the Development of Liberal Arts and Social Sciences (13YJCZH068), the China Postdoctoral Science Foundation (2015 M582879) and Key Laboratory of Basic Education Information Technology of Hunan Province (2015TP1017) helped support this work. Additionally, this study was partially supported by the National Natural Science Foundation of China (61420106001, 61375111, 81571298) and, in part, supported by NIH grants (AG041721, EB006733, EB008374, EB009634).

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Correspondence to Wei Wang or Dinggang Shen.

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All authors declare that they have no conflict of interest. All procedures followed are in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, and the applicable revisions at the time of the investigation. Written informed consent was obtained from all patients included in the study.

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Weixiong Jiang and Feng Shi are co-first authors.

Appendix

Appendix

Small-world Analysis

The small-world architectures of a network could be obtained by calculating clustering coefficient and characteristic path length (Watts and Strogatz 1998). For a weighted network N the clustering coefficient C w is the average of all nodal clustering coefficients, where nodal clustering coefficient \( {C}_i^w \) for a given node i is defined as (Onnela et al. 2005):

$$ {C}_i^w=\frac{1}{n}\sum_{i\in N}\frac{\sum_{j,h\in N}{\left({w}_{ij}{w}_{ih}{w}_{jh}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$3$}\right.}}{k_i\left({k}_i-1\right)} $$
(1)

Where n is the number of nodes, k i is the degree of node i, i.e., the number of non-zero connections, w ij is connection weights between node i and node j. The clustering coefficient quantifies the extent of local interconnectivity or cliquishness of a network. The characteristic path length L w of a weighted network N with n nodes is defined as:

$$ {L}^w=\frac{1}{n}\sum_{i\in N}\frac{\sum_{j\in N,j\ne i}{d}_{ij}^w}{n-1} $$
(2)

where \( {d}_{ij}^w \) is the weighted shortest path length between node i and node j and is computed as the smallest sum of the edge lengths throughout all of the possible paths in the network from node i and node j. The characteristic path length reflects the mean distance or routing efficiency between any given pair of nodes.

Their normalized versions (\( {\overset{\sim }{\mathrm{C}}}^{\mathrm{W}},{\overset{\sim }{\mathrm{L}}}^{\mathrm{W}} \)) were obtained using random networks, i.e., dividing the real values C wand LW by the corresponding mean derived from 100 random networks that preserved the same number of nodes, edges and degree distributions as the real brain networks (Maslov and Sneppen 2002; Sporns and Zwi 2004). During the random rewiring procedure, we specially retained the weight of each edge. A small-world network typically shows \( {\overset{\sim }{C}}^w>1 \) and \( {\overset{\sim }{L}}^w\approx 1 \) (Watts and Strogatz 1998).

Network Efficiency

Network efficiency metrics can be used to provide more biologically sensible properties for brain networks. The global efficiency (\( {E}_{glob}^w \)) and local efficiency (\( {E}_{loc}^w \)) quantify the extent of information transmission at the global network and the individual node levels, respectively (Latora and Marchiori 2001). For a network N with n nodes and k edges, the global efficiency of N can be computed as:

$$ {E}_{glob}^w=\frac{1}{n}\sum_{i\in N}\frac{\sum_{j\in N,j\ne i}{\left({d}_{ij}^w\right)}^{-1}}{n-1} $$
(3)

where \( {d}_{ij}^w \) is the shortest path length between node i and node j in N. Global efficiency measures the extent of parallel information transmission at the global network. The local efficiency of G is measured as:

$$ {E}_{loc}^w=\frac{1}{n}\sum_{i\in N}{E}_{glob}^w\left({N}_i\right) $$
(4)

where \( {E}_{glob}^w\left({N}_i\right) \) is the global efficiency of N i , the subgraph composed of the neighbors of node i. Local efficiency quantifies the fault tolerance of the network.

Modularity

Modularity is an important organizational principle for brain networks (Meunier et al. 2010). According to Newman’s algorithm (Newman 2004), the modularity index Qw of a weighted network is defined as

$$ {\mathrm{Q}}^w=\frac{1}{l^w}\sum_{i,j\in N}\left[{w}_{ij}-\frac{k_i^w{k}_j^w}{l^w}\right]{\delta}_{m_i,{m}_j} $$
(5)

Where 푙w is the sum of all weights in the network, w ij is connection weights between node i and node j, k i is the degree of node i, i.e., the number of non-zero connections, m i is the module containing node 푖, and \( {\delta}_{m_i,{m}_j}=1 \) if m i  = m j , and 0 otherwise. Modularity quantifies the extent of modular organization. The aim of the module identification process is to find a specific partition that yields the largest network modularity, \( {\overset{\sim }{\mathrm{Q}}}_{\max } \).

To assess the inter- and intra-modular connectivities, we calculated the participation coefficient (PC) and intra-module degree (MD) for each node to detect the inter- and intra-module connection density (Guimera and Amaral 2005). For a weighted network, participation coefficient is defined as:

$$ {y}_i^w=1-\sum_{m\in M}{\left(\frac{k_i^w(m)}{k_i^w}\right)}^2 $$
(6)

where 푀 is the set of modules and \( {k}_i^w(m) \) is the weight of links between i and all nodes in module m. For a weighted network, weighted within-module degree z-score is define

$$ {z}_i^w=\frac{k_i^w\left({m}_i\right)-{\overset{-}{k}}^w\left({m}_i\right)}{\sigma^{k^w\left({m}_i\right)}} $$
(7)

where m i is the module containing node i, \( {k}_i^w\left({m}_i\right) \)is the within-module degree of i (the number of links between i and all other nodes in m i ), and \( {\overset{-}{k}}^w\left({m}_i\right) \)and \( {\sigma}^{k^w\left({m}_i\right)} \)are the respective mean and standard deviation of the within-module m i degree distribution.

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Jiang, W., Shi, F., Liao, J. et al. Disrupted functional connectome in antisocial personality disorder. Brain Imaging and Behavior 11, 1071–1084 (2017). https://doi.org/10.1007/s11682-016-9572-z

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