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Background: Treatment-resistant depression (TRD) refers to patients with major depressive disorder who do not remit after 2 or more antidepressant trials. TRD is common and highly debilitating, but its neurobiological basis remains poorly understood. Recent neuroimaging studies have revealed cortical connectivity gradients that dissociate primary sensorimotor areas from higher-order associative cortices. This fundamental topography determines cortical information flow and is affected by psychiatric disorders. We examined how TRD impacts gradient-based hierarchical cortical organization.
Methods: In this secondary study, we analyzed resting-state functional magnetic resonance imaging data from a mindfulness-based intervention enrolling 56 patients with TRD and 28 healthy control subjects. Using gradient extraction tools, baseline measures of cortical gradient dispersion within and between functional brain networks were derived, compared across groups, and associated with graph theoretical measures of network topology. In patients, correlation analyses were used to associate measures of cortical gradient dispersion with clinical measures of anxiety, depression, and mindfulness at baseline and following the intervention.
Results: Cortical gradient dispersion was reduced within major intrinsic brain networks in patients with TRD. Reduced cortical gradient dispersion correlated with increased network degree assessed through graph theory-based measures of network topology. Lower dispersion among default mode, control, and limbic network nodes related to baseline levels of trait anxiety, depression, and mindfulness. Patients' baseline limbic network dispersion predicted trait anxiety scores 24 weeks after the intervention.
Conclusions: Our findings provide preliminary support for widespread alterations in cortical gradient architecture in TRD, implicating a significant role for transmodal and limbic networks in mediating depression, anxiety, and lower mindfulness in patients with TRD.
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http://dx.doi.org/10.1016/j.bpsc.2022.10.009 | DOI Listing |
J Magn Reson Imaging
September 2025
School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
Background: The dynamic progression of gray matter (GM) microstructural alterations following radiotherapy (RT) in patients, and the relationship between these microstructural abnormalities and cortical morphometric changes remains unclear.
Purpose: To longitudinally characterize RT-related GM microstructural changes and assess their potential causal links with classic morphometric alterations in patients with nasopharyngeal carcinoma (NPC).
Study Type: Prospective, longitudinal.
Cereb Cortex
August 2025
Nencki Institute of Experimental Biology, PAS, 3 Pasteur Street, 02-093 Warsaw, Poland.
In the visual cortices, receptive fields (RFs) are arranged in a gradient from small sizes in the center of the visual field to the largest sizes at the periphery. Using functional magnetic resonance imaging (fMRI) mapping of population RFs, we investigated RF adaptation in V1, V2, and V3 in patients after long-term photoreceptor degeneration affecting the central (Stargardt disease [STGD]) and peripheral (Retinitis Pigmentosa [RP]) regions of the retina. In controls, we temporarily limited the visual field to the central 10° to model peripheral loss.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
Department of Mathematical and Computational Methods, National Laboratory for Scientific Computing, Petrópolis, Brazil.
Understanding cerebral circulation is crucial for early diagnosis and patient-oriented therapies for brain conditions. However, blood flow simulations at the organ scale have been limited. This work introduces a framework for modeling extensive vascular networks in the human cerebral cortex and conducting pulsatile blood flow simulations.
View Article and Find Full Text PDFBone Rep
September 2025
Department of Orthopaedic Surgery, Vanderbilt University Medical Center, 1215 21st Ave. S., Suite 4200, Nashville, TN 37232, USA.
This study applied Raman spectroscopy (RS) to ex vivo human cadaveric femoral mid-diaphysis cortical bone specimens ( = 118 donors; age range 21-101 years) to predict fracture toughness properties via machine learning (ML) models. Spectral features, together with demographic variables (age, sex) and structural parameters (cortical porosity, volumetric bone mineral density), were fed into support vector regression (SVR), extreme tree regression (ETR), extreme gradient boosting (XGB), and ensemble models to predict fracture-toughness metrics such as crack-initiation toughness (K) and energy-to-fracture (J-integral). Feature selection was based on Raman-derived mineral and organic matrix parameters, such as νPhosphate (PO)/CH-wag, νPO/Amide I, and others, to capture the complex composition of bone.
View Article and Find Full Text PDFJ Microbiol Biotechnol
September 2025
Environmental Diseases Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea.
Shiga toxin (Stx) is a virulence factor produced by serotype 1 and Stx-producing (STEC). It causes severe renal damage, leading to hemolytic uremic syndrome (HUS). The main target organ of Stx, the kidney, plays a role in maintaining water homeostasis in the body by increasing an osmotic gradient from the cortex to the medulla.
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