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Reduced specialized processing in psychotic disorder: a graph theoretical analysis of cerebral functional connectivity

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ABSTRACT

Background: Previous research has shown that the human brain can be represented as a complex functional network that is characterized by specific topological properties, such as clustering coefficient, characteristic path length, and global/local efficiency. Patients with psychotic disorder may have alterations in these properties with respect to controls, indicating altered efficiency of network organization. This study examined graph theoretical changes in relation to differential genetic risk for the disorder and aimed to identify clinical correlates.

Methods: Anatomical and resting‐state MRI brain scans were obtained from 73 patients with psychotic disorder, 83 unaffected siblings, and 72 controls. Topological measures (i.e., clustering coefficient, characteristic path length, and small‐worldness) were used as dependent variables in a multilevel random regression analysis to investigate group differences. In addition, associations with (subclinical) psychotic/cognitive symptoms were examined.

Results: Patients had a significantly lower clustering coefficient compared to siblings and controls, with no difference between the latter groups. No group differences were observed for characteristic path length and small‐worldness. None of the topological properties were associated with (sub)clinical psychotic and cognitive symptoms.

Conclusions: The reduced ability for specialized processing (reflected by a lower clustering coefficient) within highly interconnected brain regions observed in the patient group may indicate state‐related network alterations. There was no evidence for an intermediate phenotype and no evidence for psychopathology‐related alterations.

No MeSH data available.


Related in: MedlinePlus

Topological measures of each group. Mean clustering coefficient (A), characteristic path length (B) and small‐worldness (C) for patients with psychotic disorder (red squares), siblings of patients with psychotic disorder (green triangles) and controls (blue diamonds) as a function of cost. Error bars correspond to standard error of the mean. The dotted lines represent the cost range (i.e., 0.30–0.50) that was used to calculate the mean of each topological measure.
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brb3508-fig-0001: Topological measures of each group. Mean clustering coefficient (A), characteristic path length (B) and small‐worldness (C) for patients with psychotic disorder (red squares), siblings of patients with psychotic disorder (green triangles) and controls (blue diamonds) as a function of cost. Error bars correspond to standard error of the mean. The dotted lines represent the cost range (i.e., 0.30–0.50) that was used to calculate the mean of each topological measure.

Mentions: The fMRI data were segmented into 90 regions (45 for each hemisphere) using the anatomically labeled template (AAL) reported by Tzourio‐Mazoyer et al. (2002). Regional mean time series over all voxels in each of the regions were computed and constituted the set of regional mean time series used for Pearson correlation analysis. Functional connectivity was then estimated by calculating the correlation between the mean time series of each pair of brain regions for each subject. A Fisher's r‐to‐z transformation was used on the Pearson correlation matrix in order to improve the normality of the Pearson correlation coefficients. Binary graphs were constructed by thresholding each subject's correlation matrix using a minimum spanning tree (MatLab BGL toolbox, http://dgleich.github.io/matlab-bgl/) followed by global thresholding (Alexander‐Bloch et al. 2010). In this sense, edges represented the correlations that were greater than the threshold, whereas no edges existed when the threshold was not surpassed. Graphs were constructed over a range of network costs, ranging from 0.1 to 0.9 at intervals of 0.05. The network cost refers to the number of edges in proportion to all possible edges included in the graph, such that at a cost equal to one there would be edges from each node to every other node (Alexander‐Bloch et al. 2013b). Group differences on topological properties were measured using a summary statistic following Alexander‐Bloch et al. (2010), that is, the mean of each topological measure was calculated over the range of costs from 0.3 to 0.5 (Fig. 1). Reasons to choose this range were: (1) previous work suggests that above a cost of 0.5 graphs become more random (Humphries et al. 2006), and less small‐world; and (2) topological measures are rather constant over this range (Alexander‐Bloch et al. 2010).


Reduced specialized processing in psychotic disorder: a graph theoretical analysis of cerebral functional connectivity
Topological measures of each group. Mean clustering coefficient (A), characteristic path length (B) and small‐worldness (C) for patients with psychotic disorder (red squares), siblings of patients with psychotic disorder (green triangles) and controls (blue diamonds) as a function of cost. Error bars correspond to standard error of the mean. The dotted lines represent the cost range (i.e., 0.30–0.50) that was used to calculate the mean of each topological measure.
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brb3508-fig-0001: Topological measures of each group. Mean clustering coefficient (A), characteristic path length (B) and small‐worldness (C) for patients with psychotic disorder (red squares), siblings of patients with psychotic disorder (green triangles) and controls (blue diamonds) as a function of cost. Error bars correspond to standard error of the mean. The dotted lines represent the cost range (i.e., 0.30–0.50) that was used to calculate the mean of each topological measure.
Mentions: The fMRI data were segmented into 90 regions (45 for each hemisphere) using the anatomically labeled template (AAL) reported by Tzourio‐Mazoyer et al. (2002). Regional mean time series over all voxels in each of the regions were computed and constituted the set of regional mean time series used for Pearson correlation analysis. Functional connectivity was then estimated by calculating the correlation between the mean time series of each pair of brain regions for each subject. A Fisher's r‐to‐z transformation was used on the Pearson correlation matrix in order to improve the normality of the Pearson correlation coefficients. Binary graphs were constructed by thresholding each subject's correlation matrix using a minimum spanning tree (MatLab BGL toolbox, http://dgleich.github.io/matlab-bgl/) followed by global thresholding (Alexander‐Bloch et al. 2010). In this sense, edges represented the correlations that were greater than the threshold, whereas no edges existed when the threshold was not surpassed. Graphs were constructed over a range of network costs, ranging from 0.1 to 0.9 at intervals of 0.05. The network cost refers to the number of edges in proportion to all possible edges included in the graph, such that at a cost equal to one there would be edges from each node to every other node (Alexander‐Bloch et al. 2013b). Group differences on topological properties were measured using a summary statistic following Alexander‐Bloch et al. (2010), that is, the mean of each topological measure was calculated over the range of costs from 0.3 to 0.5 (Fig. 1). Reasons to choose this range were: (1) previous work suggests that above a cost of 0.5 graphs become more random (Humphries et al. 2006), and less small‐world; and (2) topological measures are rather constant over this range (Alexander‐Bloch et al. 2010).

View Article: PubMed Central - PubMed

ABSTRACT

Background: Previous research has shown that the human brain can be represented as a complex functional network that is characterized by specific topological properties, such as clustering coefficient, characteristic path length, and global/local efficiency. Patients with psychotic disorder may have alterations in these properties with respect to controls, indicating altered efficiency of network organization. This study examined graph theoretical changes in relation to differential genetic risk for the disorder and aimed to identify clinical correlates.

Methods: Anatomical and resting‐state MRI brain scans were obtained from 73 patients with psychotic disorder, 83 unaffected siblings, and 72 controls. Topological measures (i.e., clustering coefficient, characteristic path length, and small‐worldness) were used as dependent variables in a multilevel random regression analysis to investigate group differences. In addition, associations with (subclinical) psychotic/cognitive symptoms were examined.

Results: Patients had a significantly lower clustering coefficient compared to siblings and controls, with no difference between the latter groups. No group differences were observed for characteristic path length and small‐worldness. None of the topological properties were associated with (sub)clinical psychotic and cognitive symptoms.

Conclusions: The reduced ability for specialized processing (reflected by a lower clustering coefficient) within highly interconnected brain regions observed in the patient group may indicate state‐related network alterations. There was no evidence for an intermediate phenotype and no evidence for psychopathology‐related alterations.

No MeSH data available.


Related in: MedlinePlus