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Imaging Neurosci (Camb). 2025 Oct 15;3:IMAG.a.936. doi: 10.1162/IMAG.a.936. eCollection 2025.

ABSTRACT

Ketamine produces fast-acting antidepressant effects in treatment-resistant depression (TRD). Prior studies have shown altered functional dynamics between brain networks in major depression. We thus sought to determine whether functional brain network dynamics are modulated by ketamine therapy in TRD. Participants with TRD (n = 58, mean age = 40.7 years, female = 48.3%) completed resting-state fMRI scans and clinical assessments (mood and rumination) at baseline and 24 h after receiving 4 ketamine infusions (0.5 mg/kg) over 2 weeks. Healthy controls (HC) (n = 56, mean age = 32.8 years, female = 57.1%) received the same assessments at baseline and after 2 weeks in a subsample without treatment. A co-activation pattern (CAP) analysis identified recurring patterns of brain activity across all subjects using k-means clustering. Statistical analyses compared CAP metrics including the fraction of time (FT) spent in a brain state, and the transition probability (TP) from one state to another over time and associations with clinical improvement. Follow-up analyses compared HC and TRD at baseline. Six brain state clusters were identified, including patterns resembling the salience (SN), central executive (CEN), visual (VN), default mode (DMN), and somatomotor (SMN) networks. Following ketamine treatment, TRD patients showed decreased FT for the VN (p = 7.4E-04) and increased FT for the CEN state (p = 1.9E-03). For TP metrics, SN-CEN increased (p = 5.8E-04) and SN-VN decreased (p = 3.6E-03). Decreased FT for the SN associated with improved rumination (p = 1.9E-03). At baseline, lower FT for CEN (p = 5.70E-04) and TP for SN-CEN (p = 0.016) and higher TP for SN-VN (p = 2.60E-03) distinguished TRD from HCs. CAP metrics remained stable over time in a subsample of HCs (n = 18). These findings suggest ketamine modulates brain network dynamics between SN, CEN, and VN in TRD, which may normalize dynamic patterns seen in TRD at baseline toward patterns seen in controls. Changes in SN state dynamics may correspond to improvements in ruminative symptoms following ketamine therapy.

PMID:41113939 | PMC:PMC12529346 | DOI:10.1162/IMAG.a.936

Nat Sci Sleep. 2025 Oct 14;17:2681-2698. doi: 10.2147/NSS.S550127. eCollection 2025.

ABSTRACT

PURPOSE: Poor sleep quality is prevalent across the population and may significantly impact both physical and mental health. However, our understanding of the complex mechanisms underlying poor sleep quality is still incomplete, particularly regarding the various contributing factors. To address this, we utilized a data-driven causal discovery analysis (CDA) approach to explore causal pathways of sleep quality.

PATIENTS AND METHODS: We relied on a large sample of healthy young adults from the Human Connectome Project (HCP; n = 1206 [54% female, 56% unmarried/non-cohabiting]) to explore causal pathways of sleep quality. We first used exploratory factor analysis to cluster 122 broad phenotypic variables into 21 factors and computed the functional connectivity of 13 resting-state brain networks. Then, using Greedy Fast Causal Inference (GFCI), we simultaneously integrated the obtained phenotypic factors, brain network connectivity, and sleep quality into the causal discovery analysis and ultimately constructed a causal model.

RESULTS: The model proposes a hierarchical structure with causal effects propagating through complex interactions across multiple domains, ultimately linked to changes in sleep quality. Our causal model identified three phenotypic factors (negative affect, somaticism, and delay discounting) as directly linked to sleep quality. In addition, we examined causal models of sleep quality across gender (male and female) and relationship status (unmarried/non-cohabiting and married/cohabiting) and found some demographic-specific pathways.

CONCLUSION: Our data-driven model reveals complex mechanisms by which factors from different domains influence sleep quality and highlights several key factors that influence sleep quality, which may have important implications for the development of sleep theories and the improvement of sleep quality.

PMID:41113905 | PMC:PMC12535245 | DOI:10.2147/NSS.S550127

World J Psychiatry. 2025 Oct 19;15(10):108861. doi: 10.5498/wjp.v15.i10.108861. eCollection 2025 Oct 19.

ABSTRACT

BACKGROUND: Sepsis is a life-threatening condition defined by organ dysfunction, triggered by a dysregulated host response to infection. there is limited published literature combining cognitive impairment with topological property alterations in brain networks in sepsis survivors. Therefore, we employed graph theory and Granger causality analysis (GCA) methods to analyze resting-state functional magnetic resonance imaging (rs-fMRI) data, aiming to explore the topological alterations in the brain networks of intensive care unit (ICU) sepsis survivors. Using correlation analysis, the interplay between topological property alterations and cognitive impairment was also investigated.

AIM: To explore the topological alterations of the brain networks of sepsis survivors and their correlation with cognitive impairment.

METHODS: Sixteen sepsis survivors and nineteen healthy controls from the community were recruited. Within one month after discharge, neurocognitive tests were administered to assess cognitive performance. Rs-fMRI was acquired and the topological properties of brain networks were measured based on graph theory approaches. GCA was conducted to quantify effective connectivity (EC) between brain regions showing positive topological alterations and other regions in the brain. The correlations between topological properties and cognitive were analyzed.

RESULTS: Sepsis survivors exhibited significant cognitive impairment. At the global level, sepsis survivors showed lower normalized clustering coefficient (γ) and small-worldness (σ) than healthy controls. At the local level, degree centrality (DC) and nodal efficiency (NE) decreased in the right orbital part of inferior frontal gyrus (ORBinf.R), NE decreased in the left temporal pole of superior temporal gyrus (TPOsup.L) whereas DC and NE increased in the right cerebellum Crus 2 (CRBLCrus2.R). Regarding directional connection alterations, EC from left cerebellum 6 (CRBL6.L) to ORBinf.R and EC from TPOsup.L to right cerebellum 1 (CRBLCrus1.R) decreased, whereas EC from right lingual gyrus (LING.R) to TPOsup.L increased. The implementation of correlation analysis revealed a negative correlation between DC in CRBLCrus2.R and both Mini-mental state examination (r = -0.572, P = 0.041) and Montreal cognitive assessment (MoCA) scores (r = -0.629, P = 0.021) at the local level. In the CRBLCrus2.R cohort, a negative correlation was identified between NE and MoCA scores, with a statistically significant result of r = -0.633 and P = 0.020.

CONCLUSION: Frontal, temporal and cerebellar topological property alterations are possibly associated with cognitive impairment of ICU sepsis survivors and may serve as biomarkers for early diagnosis.

PMID:41112595 | PMC:PMC12531965 | DOI:10.5498/wjp.v15.i10.108861