Most recent paper

Brain Imaging Behav. 2025 Sep 27. doi: 10.1007/s11682-025-01050-5. Online ahead of print.

ABSTRACT

Prosocial decisions in daily life are often influenced by cognitive constraints, such as time pressure and cognitive load, which can impact how we process information and make decisions that benefit others. Understanding how these constraints interact with our brain's intrinsic connectivity patterns and contribute to individual differences is crucial. This study investigates the neural mechanisms underlying the effects of cognitive constraints on prosocial decision-making. We developed a resting-state functional connectivity (rsFC) network model using machine learning regression to predict how cognitive constraints influence prosocial choices, while accounting for individual variability through intersubject representational similarity analysis (IS-RSA). Our findings reveal that the rsFC network-including regions involved in affective processing (insula, INS; amygdala, AMYG), empathy (temporo-parietal junction, TPJ; medial cingulate gyrus, MCG), and valuation (ventral striatum, VS; ventral prefrontal cortex, vmPFC)-predicts the impact of cognitive constraints on decision-making. Notably, rsFC between MCG and TPJ and bilateral TPJ connectivity showed intersubject variability that aligned with behavioral responses. These findings elucidate how cognitive constraints shape prosocial decision-making at the neural level, uncovering individual variability that advances theoretical understanding and offers practical implications for fostering prosociality in cognitively demanding contexts.

PMID:41014463 | DOI:10.1007/s11682-025-01050-5

Hum Brain Mapp. 2025 Oct 1;46(14):e70364. doi: 10.1002/hbm.70364.

ABSTRACT

Modeling dynamic neuronal activity within brain networks enables the precise tracking of rapid temporal fluctuations across different brain regions. However, current approaches in computational neuroscience fall short of capturing and representing the spatiotemporal dynamics within each brain network. We developed a novel weakly supervised spatiotemporal dense prediction model capable of generating personalized 4D dynamic brain networks from fMRI data, providing a more granular representation of brain activity over time. We developed a model that leverages the vision transformer (ViT) as its backbone, jointly encoding spatial and temporal information from fMRI inputs using two different configurations: space-time and sequential encoders. The model generates 4D brain network maps that evolve over time, capturing dynamic changes in both spatial and temporal dimensions. In the absence of ground-truth data, we used spatially constrained windowed independent component analysis (ICA) components derived from fMRI data as weak supervision to guide the training process. The model was evaluated using large-scale resting-state fMRI datasets, and statistical analyses were conducted to assess the effectiveness of the generated dynamic maps using various metrics. Our model effectively produced 4D brain maps that captured both inter-subject and temporal variations, offering a dynamic representation of evolving brain networks. Notably, the model demonstrated the ability to produce smooth maps from noisy priors, effectively denoising the resulting brain dynamics. Additionally, statistically significant differences were observed in the temporally averaged brain maps, as well as in the summation of absolute temporal gradient maps, between patients with schizophrenia and healthy controls. For example, within the Default Mode Network (DMN), significant differences emerged in the temporally averaged space-time configurations, particularly in the thalamus, where healthy controls exhibited higher activity levels compared to subjects with schizophrenia. These findings highlight the model's potential for differentiating between clinical populations. The proposed spatiotemporal dense prediction model offers an effective approach for generating dynamic brain maps by capturing significant spatiotemporal variations in brain activity. Leveraging weak supervision through ICA components enables the model to learn dynamic patterns without direct ground-truth data, making it a robust and efficient tool for brain mapping. Significance: This work presents an important new approach for dynamic brain mapping, potentially opening up new opportunities for studying brain dynamics within specific networks. By framing the problem as a spatiotemporal dense prediction task in computer vision, we leverage the spatiotemporal ViT architecture combined with weakly supervised learning techniques to efficiently and effectively estimate these maps.

PMID:41014302 | PMC:PMC12476115 | DOI:10.1002/hbm.70364

Arthritis Res Ther. 2025 Sep 26;27(1):181. doi: 10.1186/s13075-025-03646-0.

ABSTRACT

OBJECTIVES: Knee osteoarthritis (KOA), a degenerative joint disease marked by chronic pain, is associated with systemic inflammation that may extend to neurocognitive dysfunction. While chronic low-grade inflammation in KOA has been implicated in mild cognitive impairment (MCI), a prodromal stage of dementia, the mediating role of inflammation in brain functional reorganization remains unclear.

METHODS: This study integrated neuroimaging, inflammatory biomarkers, and machine learning to investigate inflammation-mediated brain functional alterations in 63 KOA patients with/without MCI. Serum levels of pro-inflammatory cytokines (IL-6, TNF-α) and resting-state fMRI data were analyzed using voxel-wise Regional Homogeneity (ReHo) and Amplitude of Low-Frequency Fluctuation (ALFF).

RESULTS: Comparisons across healthy controls, KOA-MCI, and KOA-non-MCI groups identified MCI-linked functional alterations in the medial prefrontal cortex (mPFC), precuneus, and superior temporal gyrus. Mediation analysis revealed that mPFC ReHo significantly mediated the relationship between elevated IL-6 and cognitive decline. Machine learning models incorporating ReHo features from mPFC demonstrated robust classification of MCI status (AUC: 0.87), validated in an external dataset.

CONCLUSION: Our findings suggest that IL-6-driven mPFC dysfunction is a potential pathway linking KOA-related inflammation to MCI, while highlighting the combined utility of ReHo/ALFF metrics in mPFC, precuneus, and temporal regions as potential neuroimaging biomarkers. This multimodal approach advances understanding of neuroinflammatory mechanisms in osteoarthritis and provides a framework for early detection of cognitive vulnerability in KOA populations.

PMID:41013635 | PMC:PMC12465908 | DOI:10.1186/s13075-025-03646-0

BMC Psychiatry. 2025 Sep 26;25(1):877. doi: 10.1186/s12888-025-07379-x.

ABSTRACT

BACKGROUND: Sleep disorders are common among adolescents with depression, yet lack reliable neuroimaging diagnostic techniques. This study aimed to predict sleep disorders in depressed adolescents using brain network features, including betweenness centrality (BC) and functional connectivity (FC).

METHODS: 117 adolescents diagnosed with depression underwent resting-state fMRI. Whole-brain FC (reflecting inter-regional relationships) and BC (quantifying a node's importance for network information flow) were analyzed. Differences in FC and BC between depressed adolescents with sleep disorders and depressed adolescents without sleep disorders were compared using two-sample t-tests in a discovery dataset (n = 86). A support vector machine (SVM) classifier was trained to differentiate these groups. Validation employed leave-one-out cross-validation (LOOCV) internally and an independent dataset (n = 31).

RESULTS: Depressed adolescents with sleep disorders showed elevated BC in the right middle temporal gyrus (MTG.R) and decreased BC in the left median cingulate and paracingulate gyri (DCG.L) and left caudate nucleus (CAU.L), indicating altered information flow hubs. Alterations in FC were observed across several regions, with the most pronounced changes occurring between the left middle occipital gyrus and MTG.R (MOG.L-MTG.R). The SVM model, using combined whole-brain BC and FC features, achieved 81.40% accuracy during LOOCV and identified discriminative features. Predictive performance was validated externally, yielding 74.19% accuracy.

CONCLUSIONS: Significant functional brain network alterations occur in depressed adolescents with sleep disorders. Integrating brain network analysis(BC and FC analysis) with machine learning techniques offers a promising approach to identifying neuroimaging markers for diagnosing sleep disorders in depressed adolescents.

PMID:41013389 | PMC:PMC12465739 | DOI:10.1186/s12888-025-07379-x

Life (Basel). 2025 Aug 25;15(9):1342. doi: 10.3390/life15091342.

ABSTRACT

Long-term physical training is known to induce brain plasticity, yet how these neural adaptations evolve across different stages of training remains underexplored. This two-year longitudinal study investigated the stage-dependent effects of endurance running on brain structure and resting-state function in healthy college students. Thirty participants were recruited into three groups based on their endurance training level: high-level runners, moderate-level runners, and sedentary controls. All participants underwent baseline and two-year follow-up MRI scans, including T1-weighted structural imaging and resting-state fMRI. The results revealed that the high-level runners exhibited a significant increase in degree centrality (DC) in the left dorsolateral prefrontal cortex (DLPFC). In the moderate-level group, more widespread changes were observed, including increased gray matter volume (GMV) in bilateral prefrontal cortices, medial frontal regions, the right insula, the right putamen, and the right temporo-parieto-occipital junction, along with decreased GMV in the posterior cerebellum. Additionally, DC decreased in the left thalamus and increased in the right temporal lobe and bilateral DLPFC; the fractional amplitude of low-frequency fluctuations (fALFF) in the right precentral gyrus was also elevated. These brain regions are involved in executive control, sensorimotor integration, and motor coordination, which may suggest potential functional implications for cognitive and motor performance; however, such interpretations should be viewed cautiously given the modest sample size and study duration. No significant changes were found in the control group. These findings demonstrate that long-term endurance training induces distinct patterns of brain plasticity at different training stages, with more prominent and widespread changes occurring during earlier phases of training.

PMID:41010285 | PMC:PMC12471654 | DOI:10.3390/life15091342

Brain Sci. 2025 Sep 4;15(9):964. doi: 10.3390/brainsci15090964.

ABSTRACT

Background/Objectives: Neural compensation, in which healthy brain regions take over functions lost due to lesions, is a potential biomarker for functional recovery after stroke. However, previous neuroimaging studies often speculated on neural compensation simply based on greater measures in patients (compared to healthy controls) without demonstrating a more direct link between these measures and the functional recovery. Because taking over the function of a lesion region means taking on a similar role as that lesion region in its functional network, the present study attempted to explore neural compensation based on the similarity of functional connectivity (FC) patterns between a healthy regions and lesion regions. Methods: Seventeen stroke patients (13M4F, 63.2 ± 9.1 y.o.) underwent three resting-state functional MRI (rs-fMRI) sessions during rehabilitation. FC patterns of their lesion regions were derived by lesion network analysis; and these patterns were correlated with healthy FC patterns derived from each brain voxel of 51 healthy subjects (32M19F, 61.0 ± 14.3 y.o.) for the assessment of pattern similarity. Results: We identified five healthy regions showing decreasing FC similarity (29-54%, all corrected p < 0.05, effect size η2: 0.10-0.20) to the lesion network over time. These decreasing similarities were associated with increasing behavioral scores on activities of daily living (ADL, p < 0.001, η2 = 0.90), suggesting greater neural compensation at early-stage post-stroke and reduced compensation toward the end of effective rehabilitation. Conclusions: Besides direct FC measures, the present results propose an alternative biomarker of neural compensation in functional recovery from stroke. For sensorimotor recoveries like ADL, this biomarker could be more sensitive than direct measures of lesion connectivity in the motor network.

PMID:41008324 | PMC:PMC12468017 | DOI:10.3390/brainsci15090964