Characterizing the connectome in schizophrenia with diffusion spectrum imaging

Abstract

Schizophrenia is a complex psychiatric disorder characterized by disabling symptoms and cognitive deficit. Recent neuroimaging findings suggest that large parts of the brain are affected by the disease, and that the capacity of functional integration between brain areas is decreased. In this study we questioned (i) which brain areas underlie the loss of network integration properties observed in the pathology, (ii) what is the topological role of the affected regions within the overall brain network and how this topological status might be altered in patients, and (iii) how white matter properties of tracts connecting affected regions may be disrupted. We acquired diffusion spectrum imaging (a technique sensitive to fiber crossing and slow diffusion compartment) data from 16 schizophrenia patients and 15 healthy controls, and investigated their weighted brain networks. The global connectivity analysis confirmed that patients present disrupted integration and segregation properties. The nodal analysis allowed identifying a distributed set of brain nodes affected in the pathology, including hubs and peripheral areas. To characterize the topological role of this affected core, we investigated the brain network shortest paths layout, and quantified the network damage after targeted attack toward the affected core. The centrality of the affected core was compromised in patients. Moreover the connectivity strength within the affected core, quantified with generalized fractional anisotropy and apparent diffusion coefficient, was altered in patients. Taken together, these findings suggest that the structural alterations and topological decentralization of the affected core might be major mechanisms underlying the schizophrenia dysconnectivity disorder.

Functional hierarchy underlies preferential connectivity disturbances in schizophrenia

“…Also, it is important to consider that our simulations assumed no structural differences between groups and instead generated predictions based on functional parameters. Given possible interareal anatomical connectivity alteration in chronic SCZ patients (99), future work that uses whole-brain probabilistic tractography approaches (100) will be key to generate appropriate modeling constraints across groups to improve model fits.…”

Diffusion Imaging of White Matter in Schizophrenia: Progress and Future Directions

“…DSI is another related technique aimed at clearly mapping regions of crossing fibers, and which has been able to reveal entirely new features of the organization of the WM (125–127). DSI has been only recently applied in schizophrenia, in investigations of the mirror neuron network (128), language networks (129), connectome based analyses (130), and a sibling study (128). Calculation of a permeability diffusivity index (PDI) that can putatively model more complex aspects of cellular microstructure has been used in schizophrenia, and found that membrane permeability is decreased in patients (131) and may be an important part of the accelerated aging observed in schizophrenia (132).…”

Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data

“…The brain connectome provides unprecedented information about global and regional conformations of neuronal network architecture. This information is particularly relevant as it relates to neurological or psychiatric disorders such as epilepsy (Richardson, 2012; Engel et al, 2013; Taylor et al, 2014), schizophrenia (Rubinov and Bullmore, 2013; Crossley et al, 2014; Griffa et al, 2015), and Alzheimer's disease (Xie and He, 2011; Daianu et al, 2013; Zhu et al, 2014), which are believed to be directly associated with restructuring of complex neuronal networks.…”