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Background: Cortical morphological alterations are evident in major depressive disorder (MDD), yet the underlying neurobiological processes that contribute to their characteristic spatial pattern remain unclear.
Methods: Large-scale, multi-site structural MRI data from a homogeneous Chinese cohort of 1,442 MDD patients and 1,277 healthy controls were used to calculate cortical morphological measures, which were compared between groups to determine cortical morphological alterations in MDD. A connectome constraint model was then used to examine whether structural connectome shapes MDD-related cortical morphological alterations, followed by performance of a network diffusion model to identify the epicenters.
Results: Group comparisons demonstrated a broadly distributed cortical thickness (CT) reduction in MDD, with the prefrontal cortex affected more prominently. Based on the normative structural connectome, we derived the estimated CT alteration of each brain node according to its connected neighbors, and found a strong spatial correlation between the empirical and estimated CT alterations, indicating structural connectome constraint on cortical atrophy in MDD. Concurrently, we identified the left lateral prefrontal cortex as the putative epicenters of cortical atrophy. Moreover, analyses across first-episode, early-stage, and chronic MDD subgroups revealed reduced connectome constraint with increasing illness duration. Additionally, our results were robust against several methodological variations and were largely reproducible in the cross-ethnic ENIGMA cohort of 1,902 MDD patients and 7,658 controls.
Conclusions: These findings represent a substantial advance in our understanding of the network-based spread of cortical atrophy in MDD and highlight the prospect of the left prefrontal cortex as a key target for early interventions.
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http://dx.doi.org/10.1016/j.biopsych.2025.06.030 | DOI Listing |
J Neurosci Methods
September 2025
European Laboratory for Non-linear Spectroscopy, via Nello Carrara 1, 50019 Sesto Fiorentino, Italy; National Institute of Optics -National Research Council (CNR-INO), 50125 Sesto Fiorentino, Italy. Electronic address:
Background: Tissue clearing techniques combined with light-sheet fluorescence microscopy (LSFM) enable high-resolution 3D imaging of biological structures without physical sectioning. While widely used in neuroscience to determine brain architecture and connectomics, their application for spinal cord mapping remains more limited, posing challenges for studying demyelinating diseases like multiple sclerosis. Myelin visualization in cleared tissues is particularly difficult due to the lipid-removal nature of most clearing protocols, and alternative immunolabeling approaches failed to reach satisfying results.
View Article and Find Full Text PDFNeuroscience
September 2025
Department of Psychology & Health Studies, University of Saskatchewan, Saskatoon, Canada. Electronic address:
Attentional processes are crucial to ensure successful reading, and theories of dyslexia propose that dysfunctional attention networks may contribute to the observed reading deficits. The goals of this study were to localize a region of the frontal-eye-field (FEF) involved in both reading and attention and examine its connectivity with regions in the reading and attention networks, given the known role of the FEF in attentional processes and theorized role in reading. In Experiment 1, we revisited the results of our previous hybrid reading and attention study.
View Article and Find Full Text PDFComput Med Imaging Graph
August 2025
Institute of Advanced Technology, Zhejiang University of Technology, Hangzhou, China. Electronic address:
The segmentation of cranial nerves (CNs) tract provides a valuable quantitative tool for the analysis of the morphology and trajectory of individual CNs. Multimodal CN segmentation networks, e.g.
View Article and Find Full Text PDFNutrient state shapes not only what animals eat, but how they eat it. In , protein deprivation prolongs protein-specific feeding bursts, yet the motor mechanism underlying this change remains unknown. Using EM connectomics, we identified a feed-forward pathway from protein-sensitive gustatory receptor neurons to swallowing motor neurons.
View Article and Find Full Text PDFNeural Netw
August 2025
Department of Mathematics - University of Padua, Padova, Italy.
Functional Magnetic Resonance Imaging (fMRI) provides spatio-temporal maps of brain activity; however, extracting the rich information they contain is challenging. Traditional approaches use only summary statistics, losing details that might be hidden in the complex temporal dynamics. Deep neural networks are emerging as an apt solution in this context, given their ability to handle vast amounts of structured data.
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