Nonsuicidal self-injury (NSSI) involves the intentional destruction of one's own body tissues without suicidal intent. Prior research has shown that individuals with NSSI exhibit abnormal pain perception; however, the pain-processing neural circuits underlying NSSI remain poorly understood. This study leverages graph neural networks to predict NSSI risk and examine the learned connectivity of neural underpinnings using multimodal data. Resting-state functional MRI and diffusion tensor imaging were collected from 50 patients with NSSI, 79 healthy controls (HC), and 44 patients with mental disorder who did not engage in NSSI as disease controls (DC). We constructed pain-related brain networks for each participant. An interpretable graph attention networks (GAT) model was developed, considering demographic factors, to predict NSSI risk and highlight NSSI-specific connectivity using learned attention matrices. The proposed GAT model based on imaging data achieved an accuracy of 80%, and increased to 88% when self-reported pain scales were incorporated alongside imaging data in distinguishing patients with NSSI from HC. It highlighted amygdala-parahippocampus and inferior frontal gyrus (IFG)-insula connectivity as pivotal in NSSI-related pain processing. After incorporating imaging data of DC, the model's accuracy reached 74%, underscoring consistent neural connectivity patterns. The GAT model demonstrates high predictive accuracy for NSSI, enhanced by including self-reported pain scales. Our proposed GAT model underscores the significance in the functional integration of limbic regions, paralimbic regions and IFG in NSSI pain processing. Our findings suggest altered pain processing as a key mechanism in NSSI, providing insights for potential neural modulation intervention strategies.

Accurate preoperative diagnosis of parotid gland tumors (PGTs) is crucial for surgical planning since malignant tumors require more extensive excision. Though fine-needle aspiration biopsy is the diagnostic gold standard, its sensitivity in detecting malignancies is limited. While Deep learning (DL) models based on magnetic resonance imaging (MRI) are common in medicine, they are less studied for parotid gland tumors. This study used a 2.5D imaging approach (Incorporating Inter-Slice Information) to train a DL model to differentiate between benign and malignant PGTs. This retrospective study included 122 parotid tumor patients, using MRI and clinical features to build predictive models. In the traditional model, univariate analysis identified statistically significant features, which were then used in multivariate logistic regression to determine independent predictors. The model was built using four-fold cross-validation. The deep learning model was trained using 2D and 2.5D imaging approaches, with a transformer-based architecture employed for transfer learning. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUC) and confusion matrix metrics. In the traditional model, boundary and peritumoral invasion were identified as independent predictors for PGTs, and the model was constructed based on these features. The model achieved an AUC of 0.79 but demonstrated low sensitivity (0.54). In contrast, the DL model based on 2.5D T2 fat-suppressed images showed superior performance, with an AUC of 0.86 and a sensitivity of 0.78. The 2.5D imaging technique, when integrated with a transformer-based transfer learning model, demonstrates significant efficacy in differentiating between PGTs.

Breast cancer may persist within milk ducts (ductal carcinoma <i>in situ</i>, DCIS) or advance into surrounding breast tissue (invasive ductal carcinoma, IDC). Occasionally, invasiveness in cancer may be underestimated during biopsy, leading to adjustments in the treatment plan based on unexpected surgical findings. Artificial intelligence/computer-aided diagnosis (AI/CADx) techniques in medical imaging may have the potential to predict whether a lesion is purely DCIS or exhibits a mixture of IDC and DCIS components, serving as a valuable supplement to biopsy findings. To enhance the evaluation of AI/CADx performance, assessing variability on a lesion-by-lesion basis via likelihood assurance measures could add value. We evaluated the performance in the task of distinguishing between pure DCIS and mixed IDC/DCIS breast cancers using computer-extracted radiomic features from dynamic contrast-enhanced magnetic resonance imaging using 0.632+ bootstrapping methods (2000 folds) on 550 lesions (135 pure DCIS, 415 mixed IDC/DCIS). Lesion-based likelihood assurance was measured using a sureness metric based on the 95% confidence interval of the classifier output for each lesion. The median and 95% CI of the 0.632+-corrected area under the receiver operating characteristic curve for the task of classifying lesions as pure DCIS or mixed IDC/DCIS were 0.81 [0.75, 0.86]. The sureness metric varied across the dataset with a range of 0.0002 (low sureness) to 0.96 (high sureness), with combinations of high and low classifier output and high and low sureness for some lesions. Sureness metrics can provide additional insights into the ability of CADx algorithms to pre-operatively predict whether a lesion is invasive.

Cerebrovascular disease (CeVD) and cognitive impairment risk factors contribute to cognitive decline, but the role of brain age gap (BAG) in mediating this relationship remains unclear, especially in Southeast Asian populations. This study investigated the influence of cognitive impairment risk factors on cognition and examined how BAG mediates this relationship, particularly in individuals with varying CeVD burden. This cross-sectional study analyzed Singaporean community and memory clinic participants. Cognitive impairment risk factors were assessed using the Cognitive Impairment Scoring System (CISS), encompassing 11 sociodemographic and vascular factors. Cognition was assessed through a neuropsychological battery, evaluating global cognition and 6 cognitive domains: executive function, attention, memory, language, visuomotor speed, and visuoconstruction. Brain age was derived from structural MRI features using ensemble machine learning model. Propensity score matching balanced risk profiles between model training and the remaining sample. Structural equation modeling examined the mediation effect of BAG on CISS-cognition relationship, stratified by CeVD burden (high: CeVD+, low: CeVD-). The study included 1,437 individuals without dementia, with 646 in the matched sample (mean age 66.4 ± 6.0 years, 47% female, 60% with no cognitive impairment). Higher CISS was consistently associated with poorer cognitive performance across all domains, with the strongest negative associations in visuomotor speed (β = -2.70, <i>p</i> < 0.001) and visuoconstruction (β = -3.02, <i>p</i> < 0.001). Among the CeVD+ group, BAG significantly mediated the relationship between CISS and global cognition (proportion mediated: 19.95%, <i>p</i> = 0.01), with the strongest mediation effects in executive function (34.1%, <i>p</i> = 0.03) and language (26.6%, <i>p</i> = 0.008). BAG also mediated the relationship between CISS and memory (21.1%) and visuoconstruction (14.4%) in the CeVD+ group, but these effects diminished after statistical adjustments. Our findings suggest that BAG is a key intermediary linking cognitive impairment risk factors to cognitive function, particularly in individuals with high CeVD burden. This mediation effect is domain-specific, with executive function, language, and visuoconstruction being the most vulnerable to accelerated brain aging. Limitations of this study include the cross-sectional design, limiting causal inference, and the focus on Southeast Asian populations, limiting generalizability. Future longitudinal studies should verify these relationships and explore additional factors not captured in our model.

Accurate quantitative assessment using gadolinium-contrast magnetic resonance imaging (MRI) is crucial in therapy planning, surveillance and prognostic assessment of primary central nervous system lymphoma (PCNSL). The present study aimed to develop a multimodal artificial intelligence deep learning segmentation model to address the challenges associated with traditional 2D measurements and manual volume assessments in MRI. Data from 49 pathologically-confirmed patients with PCNSL from six Chinese medical centers were analyzed, and regions of interest were manually segmented on contrast-enhanced T1-weighted and T2-weighted MRI scans for each patient, followed by fully automated voxel-wise segmentation of tumor components using a 3-dimenstional convolutional deep neural network. Furthermore, the efficiency of the model was evaluated using practical indicators and its consistency and accuracy was compared with traditional methods. The performance of the models were assessed using the Dice similarity coefficient (DSC). The Mann-Whitney U test was used to compare continuous clinical variables and the χ² test was used for comparisons between categorical clinical variables. T1WI sequences exhibited the optimal performance (training dice: 0.923, testing dice: 0.830, outer validation dice: 0.801), while T2WI showed a relatively poor performance (training dice of 0.761, a testing dice of 0.647, and an outer validation dice of 0.643. In conclusion, the automatic multi-sequences MRI segmentation model for PCNSL in the present study displayed high spatial overlap ratio and similar tumor volume with routine manual segmentation, indicating its significant potential.

<b>Objective:</b>To development a deep learning（DL） model based on conventional MRI for automatic segmentation and differential diagnosis of nasopharyngeal carcinoma（NPC） and nasopharyngeal lymphoma（NPL）. <b>Methods:</b>The retrospective study included 142 patients with NPL and 292 patients with NPC who underwent conventional MRI at Renmin Hospital of Wuhan University from June 2012 to February 2023. MRI from 80 patients were manually segmented to train the segmentation model. The automatically segmented regions of interest（ROIs） formed four datasets: T1 weighted images（T1WI）, T2 weighted images（T2WI）, T1 weighted contrast-enhanced images（T1CE）, and a combination of T1WI and T2WI. The ImageNet-pretrained ResNet101 model was fine-tuned for the classification task. Statistical analysis was conducted using SPSS 22.0. The Dice coefficient loss was used to evaluate performance of segmentation task. Diagnostic performance was assessed using receiver operating characteristic（ROC） curves. Gradient-weighted class activation mapping（Grad-CAM） was imported to visualize the model's function. <b>Results:</b>The DICE score of the segmentation model reached 0.876 in the testing set. The AUC values of classification models in testing set were as follows: T1WI: 0.78（95%<i>CI</i> 0.67-0.81）, T2WI: 0.75（95%<i>CI</i> 0.72-0.86）, T1CE: 0.84（95%<i>CI</i> 0.76-0.87）, and T1WI+T2WI: 0.93（95%<i>CI</i> 0.85-0.94）. The AUC values for the two clinicians were 0.77（95%<i>CI</i> 0.72-0.82） for the junior, and 0.84（95%<i>CI</i> 0.80-0.89） for the senior. Grad-CAM analysis revealed that the central region of the tumor was highly correlated with the model's classification decisions, while the correlation was lower in the peripheral regions. <b>Conclusion:</b>The deep learning model performed well in differentiating NPC from NPL based on conventional MRI. The T1WI+T2WI combination model exhibited the best performance. The model can assist in the early diagnosis of NPC and NPL, facilitating timely and standardized treatment, which may improve patient prognosis.

Chronic back pain (CBP) affects over 80 million people in Europe, contributing to substantial healthcare costs and disability. Understanding modifiable risk factors, such as muscle composition, may aid in prevention and treatment. This study investigates the association between lean muscle mass (LMM) and intermuscular adipose tissue (InterMAT) with CBP using noninvasive whole-body magnetic resonance imaging (MRI). This cross-sectional analysis used whole-body MRI data from 30,868 participants in the German National Cohort (NAKO), collected between 1 May 2014 and 1 September 2019. CBP was defined as back pain persisting >3 months. LMM and InterMAT were quantified via MRI-based muscle segmentations using a validated deep learning model. Associations were analyzed using mixed logistic regression, adjusting for age, sex, diabetes, dyslipidemia, osteoporosis, osteoarthritis, physical activity, and study site. Among 27,518 participants (n = 12,193/44.3% female, n = 14,605/55.7% male; median age 49 years IQR 41; 57), 21.8% (n = 6003; n = 2999/50.0% female, n = 3004/50% male; median age 53 years IQR 46; 60) reported CBP, compared to 78.2% (n = 21,515; n = 9194/42.7% female, n = 12,321/57.3% male; median age 48 years IQR 39; 56) who did not. CBP prevalence was highest in those with low (<500 MET min/week) or high (>5000 MET min/week) self-reported physical activity levels (24.6% (n = 10,892) and 22.0% (n = 3800), respectively) compared to moderate (500-5000 MET min/week) levels (19.4% (n = 12,826); p < 0.0001). Adjusted analyses revealed that a higher InterMAT (OR 1.22 per 2-unit Z-score; 95% CI 1.13-1.30; p < 0.0001) was associated with an increased likelihood of chronic back pain (CBP), whereas higher lean muscle mass (LMM) (OR 0.87 per 2-unit Z-score; 95% CI 0.79-0.95; p = 0.003) was associated with a reduced likelihood of CBP. Stratified analyses confirmed these associations persisted in individuals with osteoarthritis (OA-CBP LMM: 22.9 cm³/kg/m; InterMAT: 7.53% vs OA-No CBP LMM: 24.3 cm³/kg/m; InterMAT: 6.96% both p < 0.0001) and osteoporosis (OP-CBP LMM: 20.9 cm³/kg/m; InterMAT: 8.43% vs OP-No CBP LMM: 21.3 cm³/kg/m; InterMAT: 7.9% p = 0.16 and p = 0.0019). Higher pain intensity (Pain Intensity Numerical Rating Scale ≥4) correlated with lower LMM (2-unit Z-score deviation = OR, 0.63; 95% CI, 0.57-0.70; p < 0.0001) and higher InterMAT (2-unit Z-score deviation = OR, 1.22; 95% CI, 1.13-1.30; p < 0.0001), independent of physical activity, osteoporosis and osteoarthritis. This large, population-based study highlights the associations of InterMAT and LMM with CBP. Given the limitations of the cross-sectional design, our findings can be seen as an impetus for further causal investigations within a broader, multidisciplinary framework to guide future research toward improved prevention and treatment. The NAKO is funded by the Federal Ministry of Education and Research (BMBF) [project funding reference numbers: 01ER1301A/B/C, 01ER1511D, 01ER1801A/B/C/D and 01ER2301A/B/C], federal states of Germany and the Helmholtz Association, the participating universities and the institutes of the Leibniz Association.

Machine learning identifies liver fat fraction (FF) measured by ¹H MR spectroscopy, insulinemia, and elastography as robust, non-invasive biomarkers for diagnosing steatohepatitis in liver transplant patients, validated through decision tree analysis. Compared to the general population (~5.8% prevalence), MASH is significantly more common in liver transplant recipients (~30%-50%). In patients with FF > 5.3%, the positive predictive value for MASH ranged up to 97%, more than twice the value observed in the general population.

Functional magnetic resonance imaging (fMRI) has opened new frontiers in neuroscience by instrumentally driving our understanding of brain function and development. Despite its substantial successes, fMRI studies persistently encounter obstacles stemming from inherent, unavoidable physiological confounds. The adverse effects of these confounds are especially noticeable with higher magnetic fields, which have been gaining momentum in fMRI experiments. This review focuses on the four major physiological confounds impacting fMRI studies: low-frequency fluctuations in both breathing depth and rate, low-frequency fluctuations in the heart rate, thoracic movements, and cardiac pulsatility. Over the past three decades, numerous correction techniques have emerged to address these challenges. Correction methods have effectively enhanced the detection of task-activated voxels and minimized the occurrence of false positives and false negatives in functional connectivity studies. While confound correction methods have merit, they also have certain limitations. For instance, model-based approaches require externally recorded physiological data that is often unavailable in fMRI studies. Methods reliant on independent component analysis, on the other hand, need prior knowledge about the number of components. Machine learning techniques, although showing potential, are still in the early stages of development and require additional validation. This article reviews the mechanics of physiological confound correction methods, scrutinizes their performance and limitations, and discusses their impact on fMRI studies.