Multiscale neural signatures of major depressive, anxiety, and stress-related disorders

Abstract

Significance Major depressive, anxiety, and stress-related disorders are highly comorbid and may affect similar neurocircuitry and cognitive processes. However, the neurocircuitry underlying shared dimensions of cognitive impairment is unclear and holds the promise of reimagining psychiatric nosology. Here we leverage population imaging data ( n = 27,132) to show that while major depressive and anxiety disorders share functional and structural neural signatures, stress-related disorders are distinct from these two conditions. We report that better cognitive function is associated with lower connectivity of specific nodes of the default mode and frontoparietal networks. These findings provide population benchmarks for brain–cognition associations in healthy participants and those with lifetime major depressive and anxiety disorders, advancing our understanding of intrinsic brain networks underlying cognitive dysfunction.

Connectivity between default mode and frontoparietal networks mediates the association between global amyloid‐β and episodic memory

“…We used FSLNets ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLNets ) to generate connectivity matrices using ridge‐regularized partial connectivity that were subsequently analyzed. We previously mapped these 21 ICs onto the Yeo 7‐network parcellation (Yeo et al, 2011 ) in MNI152 space (Zhukovsky, Wainberg, et al, 2022 ), to contextualize the spatial locations of the ICs within the brain's largest networks (Visual, Somatomotor, Dorsal/Ventral Attention, Limbic, Frontoparietal, and DMNs). We favored a data‐driven parcellation as it captures large‐scale aspects of brain networks and allows us to directly compare our results to findings from a large‐scale UK Biobank data set.…”

“…As a result, the predictor matrix X included 210 partial pairwise connectivities (364 × 210), while the outcome matrix Y included delayed memory recall scores (number of logical units remembered from a story; 364 × 1). Following previous PLS applications (Morgan et al, 2019 ; Zhukovsky, Wainberg, et al, 2022 ), we used permutation testing ( n = 5000) to assess whether PLS latent variables explained a significant amount of variance in memory. During each permutation, memory scores were shuffled randomly and the PLS was repeated, whereby we recorded the amount of variance in the permuted outcome explained by the PLS model.…”

Unravelling the relationship between amyloid accumulation and brain network function in normal aging and very mild cognitive decline: a longitudinal analysis

“…We used previously published mapping between the IC maps and seven Yeo networks for added interpretation value. 31 , 32 …”

Identifying tripartite relationship among cortical thickness, neuroticism, and mood and anxiety disorders

“…Gray matter (GM) measures show good test–retest reliability 18 – 21 , are critical structural underpinnings of neural activity, and, as such, are promising neural markers of neuroticism and predictors of future mood and anxiety disorders, as measured by subsyndromal-syndromal mood and anxiety symptom severity. Previous studies in adults have provided mixed results, however, with numerous studies demonstrating widespread decreases in cortical thicknesses, particularly in the prefrontal, cingulate, temporal, and parietal cortices in individuals with 22 – 39 and at risk of 40 – 44 mood and anxiety disorders, while some studies indicate cortical thickening, related to these disorders 28 , 34 , 45 – 48 . The mixed nature of these findings suggests that the relationship between cortical thickness and mood and anxiety might be driven by other factors such as personality traits, specifically by neuroticism, but these relationships remain to be examined.…”