Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture

Abstract

∀These authors contributed equally to this work. AbstractBackground. There is growing recognition that connectome architecture shapes cortical and subcortical grey matter atrophy across a spectrum of neurological and psychiatric diseases. Whether connectivity contributes to tissue volume loss in schizophrenia in the same manner remains unknown. Methods. Here we relate tissue volume loss in patients with schizophrenia to patterns of structural and functional connectivity. Grey matter deformation was estimated in a sample of N = 133 individuals with chronic schizophrenia (48 female, 34.7 ± 12.9 years) and N = 113 controls (64 female, 23.5 ± 8.4 years). Deformation-based morphometry (DBM) was used to estimate cortical and subcortical grey matter deformation from T1-weighted MR images. Structural and functional connectivity patterns were derived from an independent sample of N = 70 healthy participants using diffusion spectrum imaging and resting-state functional MRI. Results. We find that regional deformation is correlated with the deformation of structurally-and functionally-connected neighbours. Distributed deformation patterns are circumscribed by specific functional systems (the ventral attention network) and cytoarchitectonic classes (limbic class), with an epicenter in the anterior cingulate cortex. Conclusions. Altogether, the present study demonstrates that brain tissue volume loss in schizophrenia is conditioned by structural and functional connectivity, accounting for 25-35% of regional variance in deformation. Keywords. connectome | schizophrenia | intrinsic networks | disease epicenter | anterior cingulate | ventral attention network Running title. Network-driven tissue volume loss in schizophrenia

A meta-analytic approach to mapping co-occurrent grey matter volume increases and decreases in psychiatric disorders

“…2). Given prior reports that strongly connected areas tend to share GM changes in the same direction (Cauda et al, 2018b;Shafiei et al, 2019), our findings suggest that such regions may have a limited capacity for compensation, possibly due to diaschisis and other maladaptive responses (Carrera and Tononi, 2014;Fornito et al, 2015). Therefore, a region which is not strongly connected to a damaged area but has a related function may be the best candidate to take on a compensatory role.…”

Topographic gradients of intrinsic dynamics across neocortex

“…More generally, the present findings are part of a larger trend in the field to understand structure-function relationships by considering molecular [4,28,77,107], cellular [3,70,81,83] and physiological [25,82] attributes of network nodes, thereby conceptually linking local and global brain organization [55,89]. In such "annotated networks", macroscale network architecture is thought to reflect similarity in local properties, and vice versa, such that areas with similar properties are more likely to be anatomically connected and to functionally interact with one another [11,42,46,104].…”

“…The tract weights were then optimized by estimating an appropriate cross-section multiplier for each streamline following the procedure proposed by Smith and colleagues [86] and a connectivity matrix was built for each participant using the same parcellation as described above. Finally, we used a consensus approach to construct a binary group-level structural connectivity matrix, preserving the edge length distribution in individual participants [10,67,68,83].…”

Network-based atrophy modelling in the common epilepsies: a worldwide ENIGMA study

“…To elucidate how grey matter atrophy targets hub regions in TLE and IGE, we tested the hypothesis that the underlying connectivity of specific regions-or epicenters-constrains syndrome-specific patterns of atrophy. Other neuroimaging studies of psychiatric and neurodegenerative diseases have employed epicenter mapping techniques to track and predict the spread of atrophy in individual patients (Brown et al, 2019) and disease cohorts (Raj et al, 2012;Shafiei et al, 2019;Zhou et al, 2012). Critically, each epilepsy syndrome harbored distinct epicenters and reflected its own pathophysiology: temporo-limbic cortical regions, ipsilateral to the seizure focus, emerged as epicenters of atrophy in TLE, while fronto-central cortices were identified as disease epicenters in IGE.…”

“…Complementing the nodal stress hypothesis, in which patterns of cortical atrophy and hub regions appear spatially concomitant, disease epicenter mapping can identify one or more specific regions-or epicenters-whose connectivity profile may play a central role in the whole-brain manifestation of focal and generalized epilepsies (Filippi et al, 2020;Raj et al, 2012;Shafiei et al, 2019;Zeighami et al, 2015;Zhou et al, 2012). Among common epilepsies, application of these models to TLE and IGE is justified as both syndromes have been associated with pathophysiological anomalies in mesiotemporal and subcortico-cortical networks and represent conceptual extremes of a focal to generalized continuum of epilepsy subtypes (Bernhardt et al, 2013;Bernhardt et al, 2009a;Keller et al, 2014;O'Muircheartaigh et al, 2012;Weng et al, 2020).…”