The fault lies on

Many psychiatric disorders are associated with abnormal resting-state functional connectivity between pairs of brain regions, although it remains unclear whether the fault resides within the pair of regions themselves or other regions connected to them. Identifying the source of dysfunction is crucial for understanding the etiology of different disorders. Using pathwayand network-based techniques to analyze resting-state functional magnetic imaging data from a large population of patients with attention-deficit-hyperactivity-disorder (239 patients, 251 controls), major depression (69 patients, 67 controls) and schizophrenia (169 patients, 162 controls), we show for the first time that only network-based cross-correlation identifies significant functional-connectivity changes in all three disorders which survive correction. This demonstrates that the primary source of dysfunction resides not in the regional pairs themselves but in their external connections. Combining pathway and network-based functional-connectivity analysis we established that in all three disorders, the counterparts of pairs of regions in the opposite hemisphere contribute 60-76% to altered functional-connectivity, compared with only 17-21% from the regions themselves. Thus 2 a transdiagnostic feature is of abnormal functional connectivity between brain regions produced via their contralateral counterparts. Our results demonstrate an important role for contralateral counterpart regions in contributing to altered regional connectivity in psychiatric disorders. Introduction An increasing number of functional connectivity studies using resting-state or task-related fMRI data from the brain have identified changes in the strength of coupling between pairs of connected regions associated with many mental disorders, including Alzheimer's disease (Wang K et al. 2007; Sheline YI and ME Raichle 2013), depression (Fitzgerald PB et al. 2008; Lui S et al. 2011; Tao H et al. 2013), anxiety (Sylvester C et al. 2012), schizophrenia (Liu Y et al. 2008; Camchong J et al. 2011; Guo S et al. 2014), autism (Müller R-A et al. 2011) and attention deficit hyperactivity disorder (ADHD) (Mazaheri A et al. 2010; Castellanos FX and E Proal 2012). There is also growing interest in establishing transdiagnostic approaches aiming to identify common molecular, neural and behavioral phenotypes in mental disorders (Buckholtz JW and A Meyer-Lindenberg 2012; Robbins TW et al. 2012; Consortium C-DGotPG 2013). Functional connectivity is primarily measured by temporal correlation of activities in pairs of brain regions and analyzed using either cross-correlation (Pearson) (Biswal B et al. 1995; Friston K et al. 1996) or partial-correlation (Marrelec G et al. 2006; Marrelec G et al. 2009; Tao H et al. 2013) techniques. Despite the extensive research carried out on functional connectivity analysis in mental disorders, it is still unclear whether the cause of the reported changes resides within the region pairs themselves, or in other external regions connected to them in the brain network. If we are going to be able to establish the optimal targets for both diagnostic and therapeutic advances the primary sources responsible for observed functional connectivity changes need to be identified. Cross-correlation analysis has the advantage that it takes into account contributions from all the different regions in a distributed brain network to the correlation observed between a specific pair of regions. However, while it may be a more accurate reflection of the brain as an interconnected network, cross-correlation techniques may be biased by detecting contributions from third party regions which are not actually structurally connected 3 (Damoiseaux JS and MD Greicius 2009; Honey C et al. 2009). This has led to the alternative use of partial-correlation techniques designed to consider functional links between pairs of regions in isolation, thereby removing contributions from all other third-party regions in the brain network (Buckholtz JW and A Meyer-Lindenberg 2012; Robbins TW et al. 2012; Consortium C-DGotPG 2013). This approach is more accurate in identifying functional connections between region pairs which are also structurally connected because third-party influences are excluded (Zhang D et al. 2010). However, the disadvantage is that it may not identify changes occurring at a more complex network level. In the current study we have therefore use a combination of cross(Pearson-) and partial-correlation analysis of resting-state fMRI data in individuals with three different psychiatric disorders (ADHD, Schizophrenia and Major Depression) compared to matched healthy controls. Our hypothesis was that if functional connectivity alterations of patients in pairs of regions are primarily contributed to by changes within these regions, then partial-correlation, which removes third-party influences and reflects direct interaction between a pair of regions, should be the most sensitive in detecting the functional connectivity alterations. If on the other hand such changes are primarily contributed to by third party regions, then the Pearson-correlation technique should be more sensitive. If the latter proved to be the case we could also combine these two approaches to identify which third party regions contribute most to observed functional connectivity changes. Here we adopted a triplets-ROI based partial-correlation approach especially suited to identify the influences from each individual third-party mediator. Our work therefore is expected to provide new insights into the mechanisms of how large-scale organization of functional networks changes in the disordered brain and therapeutic strategies.