Affected Anatomical Rich Club and Structural–Functional Coupling in Young Offspring of Schizophrenia and Bipolar Disorder Patients

Collin, Guusje; Scholtens, Lianne H.; Kahn, René S.; Hillegers, Manon H. J.; Heuvel, Martijn P. van den

doi:10.1016/j.biopsych.2017.06.013

Cited by 128 publications

(100 citation statements)

References 71 publications

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“…To overcome the drawback concerning the asymmetrical network density between the anatomical and functional connectomes (Rubinov & Sporns, 2010; see Discussion for more details), the correlations between them were corrected with a mathematical strategy that constrained functional connectivity network with edges from nonzero structural connectivity network . SC-FC coupling has been proven to be one of the most valuable topological properties for characterizing one's overall organization (Collin et al, 2017;Rudie et al, 2013;Zhang, Liao, et al, 2011). Similarly, the functional connectivity was also selected to form the corresponding vectors.…”

Section: Node-based Graph Theoretical Metricsmentioning

confidence: 99%

“…In this vein, we postulate that the individuals with promising topological modularity would be prone to make a better choice in intertemporal decisionmaking resulting from the economic-efficient pattern of brain structural and functional connectomes. Brain organization with rich-club regime produces the weights for high-degree regions to be more intimately connected among themselves than regions with the comparatively lower degree, which is widely considered to integrate the parallel information among the diverse portions of whole-brain connectivity network (Bullmore & Sporns, 2012;Collin, Scholtens, Kahn, Hillegers, & van den Heuvel, 2017;Grayson et al, 2014;Van Den Heuvel & Sporns, 2011). Highly hierarchical organization of brain connectomes reflect a biologically plausible regime in favor of the processing and assignment of information, whereas the strong assortativity between hub regions can predominately elevate robustness of brain networks (Barthélemy, Barrat, Pastor-Satorras, & Vespignani, 2004;Bullmore & Sporns, 2009, 2012.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, a compelling body of studies has also demonstrated the high spatial resemblances between these multi-modalities within the whole-brain network (Hagmann et al, 2008;Marder & Goaillard, 2006). Previously, disrupted coupling of whole-brain structural and functional connectivity network is widely observed in psychiatric diseases and suboptimal behaviors, such as schizophrenia (Cocchi et al, 2014), bipolar disorder (Collin et al, 2017), cocaine abuse (Hu, Salmeron, Gu, Stein, & Yang, 2015), and internet addiction (Bi et al, 2015). Accordingly, we also harbor the idea that the communication of structural and functional connectomes can provide a more worthwhile predictor for human discounting behavior relative to either one.…”

Section: Introductionmentioning

confidence: 99%

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Altered structural and functional brain network overall organization predict human intertemporal decision‐making

Chen

et al. 2018

Human Brain Mapping

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Intertemporal decision‐making is naturally ubiquitous to us: individuals always make a decision with different consequences occurring at different moments. These choices are invariably involved in life‐changing outcomes regarding marriage, education, fertility, long‐term well‐being, and even public policy. Previous studies have clearly uncovered the neurobiological mechanism of the intertemporal decision in the schemes of regional location or sub‐network. However, it still remains unclear how to characterize intertemporal behavior with multimodal whole‐brain network metrics to date. Here, we combined diffusion tensor image and resting‐state functional connectivity MRI technology, in conjunction with graph‐theoretical analysis, to explore the link between topological properties of integrated structural and functional whole‐brain networks and intertemporal decision‐making. Graph‐theoretical analysis illustrated that the participants with steep discounting rates exhibited the decreased global topological organizations including small‐world and rich‐club regimes in both functional and structural connectivity networks, and reflected the dreadful local topological dynamics in the modularity of functional connectome. Furthermore, in the cross‐modalities configuration, the same relationship was predominantly observed for the coupling of structural–functional connectivity as well. Above topological metrics are commonly indicative of the communication pattern of simultaneous global and local parallel information processing, and it thus reshapes our accounts on intertemporal decision‐making from functional regional/sub‐network scheme to multimodal brain overall organization.

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Section: Node-based Graph Theoretical Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Altered structural and functional brain network overall organization predict human intertemporal decision‐making

Chen

et al. 2018

Human Brain Mapping

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“…No additional article was found in the manual search. Most of the studies were conducted in adolescents [23][24][25][26][27][28][29][30] with only two studies conducted in young adults [31,32]. The samples of subjects at-risk for BD were comprised of healthy offspring of bipolar patients [25,28,29], healthy and symptomatic offspring of bipolar patients [24,26,27,30], symptomatic offspring of bipolar patients [23] and healthy and symptomatic offspring or siblings of bipolar patients [31,32].…”

Section: Study Selectionmentioning

confidence: 99%

“…The samples of subjects at-risk for BD were comprised of healthy offspring of bipolar patients [25,28,29], healthy and symptomatic offspring of bipolar patients [24,26,27,30], symptomatic offspring of bipolar patients [23] and healthy and symptomatic offspring or siblings of bipolar patients [31,32]. Some studies were conducted by the same research groups, with partially overlapping samples [23,26,27,30] The studies focused in 4 functional imaging domains: 2 studies employed an emotion processing task [23,26]; 2 studies employed a cognitive-affective task [25,31]; 3 studies employed a reward processing task [27,28,30] and 3 were resting state fMRI studies [24,29,32]. Table 1 resume the main methodological characteristics and results of the selected studies.…”

Section: Study Selectionmentioning

confidence: 99%

Neural connectivity in youth at-risk for bipolar disorder: a review of functional magnetic resonance imaging studies

Santos¹,

Coroa²,

Caldeira³

et al. 2017

IJCNMH

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Background: Delayed diagnosis of bipolar disorder (BD) is common in adolescents and young adults and the search for biomarkers to help in early diagnosis in BD at-risk populations is an important goal of neuroimaging research. Functional connectivity studies in BD patients suggests that anomalous connectivity between prefrontal and limbic regions could be risk biomarkers for BD. The aim of this review is to provide an overview of the neuroimaging literature that employed functional connectivity techniques in adolescents and young adults at-risk for BD.

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Relations between structural and EEG‐based graph metrics in healthy controls and schizophrenia patients

Gómez‐Pilar

Luis‐García

Lubeiro

et al. 2018

Human Brain Mapping

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Our aim was to assess structural and functional networks in schizophrenia patients; and the possible prediction of the latter based on the former. The possible dependence of functional network properties on structural alterations has not been analyzed in schizophrenia. We applied averaged path-length (PL), clustering coefficient, and density (D) measurements to data from diffusion magnetic resonance and electroencephalography in 39 schizophrenia patients and 79 controls. Functional data were collected for the global and theta frequency bands during an odd-ball task, prior to stimulus delivery and at the corresponding processing window. Connectivity matrices were constructed from tractography and registered cortical segmentations (structural) and phase-locking values (functional). Both groups showed a significant electroencephalographic task-related modulation (change between prestimulus and response windows) in the global and theta bands. Patients showed larger structural PL and prestimulus density in the global and theta bands, and lower PL task-related modulation in the theta band. Structural network values predicted prestimulus global band values in controls and global band task-related modulation in patients. Abnormal functional values found in patients (prestimulus density in the global and theta bands and task-related modulation in the theta band) were not predicted by structural data in this group. Structural and functional network abnormalities respectively predicted cognitive performance and positive symptoms in patients. Taken together, the alterations in the structural and functional theta networks in the patients and the lack of significant relations between these alterations, suggest that these types of network abnormalities exist in different groups of schizophrenia patients.

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Affected Anatomical Rich Club and Structural–Functional Coupling in Young Offspring of Schizophrenia and Bipolar Disorder Patients

Cited by 128 publications

References 71 publications

Altered structural and functional brain network overall organization predict human intertemporal decision‐making

Altered structural and functional brain network overall organization predict human intertemporal decision‐making

Neural connectivity in youth at-risk for bipolar disorder: a review of functional magnetic resonance imaging studies

Relations between structural and EEG‐based graph metrics in healthy controls and schizophrenia patients

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