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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

Neuroimage. 2025 Oct 17:121533. doi: 10.1016/j.neuroimage.2025.121533. Online ahead of print.

ABSTRACT

BACKGROUND: Transcranial magnetic stimulation (TMS) activates neural circuits in targeted and connected areas, yet it remains unclear how "TMS-dose" relates to local or network responses, particularly outside the primary motor hand area.

METHODS: Eighteen healthy volunteers received TMS pulse-pairs (pp-TMS) of the left dorsal premotor cortex (PMd) during functional magnetic resonance imaging (fMRI) at 3 Tesla, using an inter-pulse interval of 33 ms. We mapped stimulus-response profiles across the premotor-motor network, applying pp-TMS as slow-frequency trains of pulse-pairs (i.e., blocked design) or single pulse-pairs (i.e., event-related design). Pulse-pairs were delivered at intensities of 65, 80, 95, or 110% of resting motor threshold (RMT).

RESULTS: Pooling the effects of pp-TMS across intensities revealed that both, single pulse-pairs and pp-TMS trains produced a bilateral reduction in regional activity within the targeted dorsal premotor-motor network. Dose-dependent analysis revealed a non-linear dose-response pattern, with maximal suppression of left and right PMd activity at 80% of RMT. The event-related and blocked fMRI design yielded similar results with net suppressive effects being more consistent during trains and dose-response dependencies being more consistent in response to single pulse-pairs. Auditory co-stimulation triggered bilateral increases in the auditory cortices.

CONCLUSION: Focal ppTMS may exert a net suppressive effect on neural activity within the targeted network. Network effects may follow a non-linear response pattern and may peak at relatively low stimulation intensities. These findings highlight the value of interleaved TMS-fMRI in elucidating the impact of stimulation parameters on regional and network-level brain dynamics.

PMID:41110651 | DOI:10.1016/j.neuroimage.2025.121533

Neuroimage. 2025 Oct 17:121544. doi: 10.1016/j.neuroimage.2025.121544. Online ahead of print.

ABSTRACT

AIM: Type 2 diabetes mellitus (T2DM) is a metabolic disorder associated with cognitive decline. This study investigated the interplay between altered effective connectivity (EC) in the Papez circuit, regional atrophy, and choroid plexus volume (CPV) in relation to T2DM-related cognitive deficits.

METHODS: Eighty T2DM patients and 75 healthy controls underwent multimodal MRI. Resting-state fMRI data were analyzed using spectral dynamic causal modeling (spDCM) to quantify EC between Papez circuit regions. Structural 3D-T1 images were processed to measure regional volumes and the CPV, which might serve as a proxy for glymphatic activity. Participants underwent standardized cognitive assessments to evaluate neurocognitive function.

RESULTS: T2DM patients exhibited significant EC reductions in three Papez circuit pathways: from the hippocampus (HPC) to the mammillary body (MB) (p = 0.001), from the anterior thalamic nucleus (AT) to the anterior cingulate cortex (ACC) (p = 0.005), and from the entorhinal cortex (ERC) to the HPC (p < 0.001). Structural atrophy was observed in the left hippocampus (p = 0.001) and left thalamic (p = 0.006), accompanied by CPV enlargement (p = 0.027). Notably, the HPC to MB connectivity was partially mediated by the CPV, with a significant indirect effect of 0.1456 (95% CI: 0.0335, 0.2665). The Complex Figure Test delay (CFT-delay) score was positively correlated with the reduced EC in Papez circuit pathways; however, there was a negative correlation between the CFT-delay score and the CPV (r = -0.344, p = 0.002). Furthermore, reduced EC in the Papez circuit exhibited a negative correlation with the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR).

CONCLUSION: Our study highlights the complex interplay between Papez circuit functional disruptions, structural atrophy within the circuit, as well as the CPV alterations in T2DM-related cognitive decline. These findings offer potential biomarkers for early detection of cognitive impairments and novel therapeutic targets for intervention in T2DM.

PMID:41110647 | DOI:10.1016/j.neuroimage.2025.121544

Psychiatry Res Neuroimaging. 2025 Oct 10;354:112077. doi: 10.1016/j.pscychresns.2025.112077. Online ahead of print.

ABSTRACT

BACKGROUND: Previous studies have demonstrated the efficacy of transcranial magnetic stimulation (TMS) on treating nicotine addiction. However, the underlying mechanisms remain unclear. This study aims to investigate the effects of repetitive TMS (rTMS) on nicotine addiction using resting-state functional magnetic resonance imaging (rs-fMRI).

MATERIALS AND METHODS: In this study, adult male participants with nicotine addiction were prospectively recruited, and assigned to either the rTMS group (n = 19) or Sham group (n = 12). Two groups underwent ten sessions of either real or sham rTMS treatment over a two-week period. Clinical assessments related to smoking craving and rs-fMRI scans were conducted before and after treatment. The regional homogeneity (ReHo) was utilized to explore differences in local neural synchronization between the rTMS and Sham groups.

RESULTS: After treatment, the smoking cravings were significantly reduced in two groups. Significant group-by-time interaction effects were observed in the left orbital part of the inferior frontal gyrus, left middle frontal gyrus (MFG), and right angular gyrus (AG). Post-hoc analyses revealed that, compared with pre-treatment, the rTMS group exhibited increased ReHo values after treatment in these areas, while the Sham group exhibited decreased values. Furthermore, within the rTMS group, post-treatment ReHo values in the left MFG were negatively correlated with post-treatment short Tobacco Craving Questionnaire (sTCQ)-Impulse scores. Similarly, the changes in ReHo values of the left MFG from pre- to post-treatment within the rTMS group were negatively correlated with changes in sTCQ-Impulse scores.

CONCLUSION: Our findings demonstrated that rTMS treatment may improve nicotine-related dependence by modulating local neural synchronization in the prefrontal cortex (PFC) and AG. Furthermore, ReHo values in the left MFG may serve as a promising neuroimaging biomarker for predicting nicotine addiction cessation.

PMID:41110183 | DOI:10.1016/j.pscychresns.2025.112077

Psychiatry Res Neuroimaging. 2025 Oct 16;354:112078. doi: 10.1016/j.pscychresns.2025.112078. Online ahead of print.

ABSTRACT

BACKGROUND: We tested the hypothesis that Schizophrenia (SCZ) involves a systematic breakdown in brain network organization across different levels of graph-theoretical hierarchy.

METHODS: Using resting-state fMRI from 43 SCZ patients and 63 matched healthy controls, we implemented an analytical multi-level framework. This integrated: global graph theory metrics to assess overall network topology; macronetwork metrics to measure functional specialization of large-scale systems; network-based statistics (NBS) to identify specific, altered pathways at the local level; a multigraph model to visualize hub reorganization between networks.

RESULTS: We revealed a coherent pattern of multi-level dysfunction. Globally, SCZ networks showed increased local clustering and connection density, indicating a shift toward a less efficient, overly segregated architecture. At the macroscale, sensory and salience networks displayed elevated local connectivity, while higher-order cognitive networks (e.g., DMN, DAN) showed reduced specialization and increased cross-talk. Locally, NBS identified a core subnetwork of weakened connectivity within temporal-orbitofrontal-cingulate circuits. The multigraph model synthesized these findings, showing a widespread reduction in the integrative role of key cognitive hubs.

CONCLUSIONS: Our findings establish a model of SCZ as a disorder of disintegrated brain network hierarchy, where disruptions at the level of local circuits and functional specializations collectively lead to global topological inefficiency.

PMID:41110182 | DOI:10.1016/j.pscychresns.2025.112078