The skull is highly durable and plays a significant role in sex determination as one of the most dimorphic bones. The facial canal (FC), a clinically significant canal within the temporal bone, houses the facial nerve. This study aims to estimate sex using morphometric measurements from the FC through machine learning (ML) and artificial neural networks (ANNs). The study utilized Computed Tomography (CT) images of 200 individuals (100 females, 100 males) aged 19-65 years. These images were retrospectively retrieved from the Picture Archiving and Communication Systems (PACS) at Düzce University Faculty of Medicine, Department of Radiology, covering 2021-2024. Bilateral measurements of nine temporal bone parameters were performed in axial, coronal, and sagittal planes. ML algorithms including Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), Decision Tree (DT), Extra Tree Classifier (ETC), Random Forest (RF), Logistic Regression (LR), Gaussian Naive Bayes (GaussianNB), and k-Nearest Neighbors (k-NN) were used, alongside a multilayer perceptron classifier (MLPC) from ANN algorithms. Except for QDA (Acc 0.93), all algorithms achieved an accuracy rate of 0.97. SHapley Additive exPlanations (SHAP) analysis revealed the five most impactful parameters: right SGAs, left SGAs, right TSWs, left TSWs and, the inner mouth width of the left FN, respectively. FN-centered morphometric measurements show high accuracy in sex determination and may aid in understanding FN positioning across sexes and populations. These findings may support rapid and reliable sex estimation in forensic investigations-especially in cases with fragmented craniofacial remains-and provide auxiliary diagnostic data for preoperative planning in otologic and skull base surgeries. They are thus relevant for surgeons, anthropologists, and forensic experts. Not applicable.

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that affects behavior, attention, and learning. Current diagnoses rely heavily on subjective assessments, underscoring the need for objective imaging-based methods. This study aims to explore whether structural connectivity networks derived from MRI can reveal alterations associated with ADHD and support data-driven understanding. We collected brain MRI data from 947 individuals (aged 7-26 years; 590 males, 356 females, 1 unspecified) across eight centers, sourced from the Neuro Bureau ADHD-200 preprocessed dataset. Transformer-based deep learning models were used to learn relationships between different brain regions and construct structural connectivity networks. To prepare input for the model, each region was transformed into a standardized data sequence using four different strategies. The strength of connectivity between brain regions was then measured to identify structural differences related to ADHD. Five-fold cross-validation and statistical analyses were used to evaluate model robustness and group differences, respectively. Here we show that the proposed method performs well in distinguishing ADHD individuals from healthy controls, with accuracy reaching 71.9 percent and an area under curve of 0.74. The structural networks also reveal significant differences in connectivity patterns (paired t-test: P = 0.81 × 10^-6), particularly involving regions responsible for motor and executive function. Notably, the importance rankings of several brain regions, including the thalamus and caudate, differ markedly between groups. This study shows that ADHD may be associated with connectivity alterations in multiple brain regions. Our findings suggest that brain structural connectivity networks built using Transformer-based methods offer a promising tool for both diagnosis and further research into brain structure.

BackgroundOptimisation of Deep Brain Stimulation (DBS) settings is a key aspect in achieving clinical efficacy in movement disorders, such as the Parkinsons disease. Modern techniques attempt to solve the problem through data-intensive statistical and machine learning approaches, adding significant overhead to the existing clinical workflows. Here, we present an optimisation approach for DBS electrode contact and current selection, grounded in routinely collected MRI data, well-established tools (Lead-DBS) and, optionally, clinical review records. MethodsThe pipeline, packaged in a cross-platform tool, uses lead reconstruction data and simulation of volume of tissue activated to estimate the contacts in optimal position relative to the target structure, and suggest optimal stimulation current. The tool then allows further interactive user optimisation of the current settings. Existing electrode contact evaluations can be optionally included in the calculation process for further fine-tuning and adverse effect avoidance. ResultsBased on a sample of 177 implanted electrode reconstructions from 89 Parkinsons disease patients, we demonstrate that DBS parameter setting by our algorithm is more effective in covering the target structure (Wilcoxon p<6e-12, Hedges g>0.34) and minimising electric field leakage to neighbouring regions (p<2e-15, g>0.84) compared to expert parameter settings. ConclusionThe proposed automated method, for optimisation of the DBS electrode contact and current selection shows promising results and is readily applicable to existing clinical workflows. We demonstrate that the algorithmically selected contacts perform better than manual selections according to electric field calculations, allowing for a comparable clinical outcome without the iterative optimisation procedure.

BackgroundSubjective cognitive decline (SCD) is recognized as an early phase in the progression of Alzheimer's disease (AD).ObjectiveTo explore the abnormal patterns of morphological and functional connectivity coupling (MC-FC coupling) and their potential diagnostic significance in SCD.MethodsThe data of 52 individuals with SCD and 51 age-gender-education matched healthy controls (HC) who underwent resting-state functional magnetic resonance imaging and high-resolution 3D T₁-weighted imaging were retrieved to build the MC and FC of gray matter. Support vector machine (SVM) methods were used for differentiating between SCD and HC.ResultsSCD individuals exhibited MC-FC decoupling in the frontoparietal network compared with HC (p = 0.002, 5000 permutations). Using these adjusted MC-FC coupling metrics, SVM analysis achieved 74.76% accuracy, 64.71% sensitivity, and 92.31% specificity (p < 0.001, 5000 permutations). Additionally, the stronger MC-FC coupling of the left inferior temporal gyrus (r = 0.294, p = 0.034) and right posterior cingulate gyrus (r = 0.372, p = 0.007) in SCD individuals was positively correlated with subjective memory complaint performance.ConclusionsThe findings of this study provide insight into the idiosyncratic feature of brain organization underlying SCD from the prospective of MC-FC coupling and highlight the potential of MC-FC coupling for the identification of the preclinical stage of AD.

Sparse-view computed tomography (CT) holds promise for reducing radiation exposure and enabling novel system designs. Traditional reconstruction algorithms, including Filtered Backprojection (FBP) and Model-Based Iterative Reconstruction (MBIR), often produce artifacts in sparse-view data. Deep Learning Reconstruction (DLR) offers potential improvements, but task-based evaluations of DLR in sparse-view CT remain limited. This study employs an Artificial Intelligence (AI) observer to evaluate the diagnostic accuracy of FBP, MBIR, and DLR for intracranial hemorrhage detection and classification, offering a cost-effective alternative to human radiologist studies. A public brain CT dataset with labeled intracranial hemorrhages was used to train an AI observer model. Sparse-view CT data were simulated, with reconstructions performed using FBP, MBIR, and DLR. Reconstruction quality was assessed using metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). Diagnostic utility was evaluated using Receiver Operating Characteristic (ROC) analysis and Area Under the Curve (AUC) values for One-vs-Rest and One-vs-One classification tasks. DLR outperformed FBP and MBIR in all quality metrics, demonstrating reduced noise, improved structural similarity, and fewer artifacts. The AI observer achieved the highest classification accuracy with DLR, while FBP surpassed MBIR in task-based accuracy despite inferior image quality metrics, emphasizing the value of task-based evaluations. DLR provides an effective balance of artifact reduction and anatomical detail in sparse-view CT brain imaging. This proof-of-concept study highlights AI observer models as a viable, cost-effective alternative for evaluating CT reconstruction techniques.

To develop a robust group-level brain parcellation method using deep learning based on resting-state functional magnetic resonance imaging (rs-fMRI), aiming to release the model assumptions made by previous approaches. We proposed Brain Deep Embedded Clustering (BDEC), a deep clustering model that employs a loss function designed to maximize inter-class separation and enhance intra-class similarity, thereby promoting the formation of functionally coherent brain regions. Compared to ten widely used brain parcellation methods, the BDEC model demonstrates significantly improved performance in various functional homogeneity metrics. It also showed favorable results in parcellation validity, downstream tasks, task inhomogeneity, and generalization capability. The BDEC model effectively captures intrinsic functional properties of the brain, supporting reliable and generalizable parcellation outcomes. BDEC provides a useful parcellation for brain network analysis and dimensionality reduction of rs-fMRI data, while also contributing to a deeper understanding of the brain's functional organization.

Glioma, the most common primary intracranial tumor, poses significant challenges to precision diagnosis and treatment due to its heterogeneity and invasiveness. With the introduction of the 2021 WHO classification standard based on molecular biomarkers, the role of imaging in non-invasive subtyping and therapeutic monitoring of gliomas has become increasingly crucial. While conventional MRI shows limitations in assessing metabolic status and differentiating tumor recurrence, positron emission tomography (PET) combined with radiomics and artificial intelligence technologies offers a novel paradigm for precise diagnosis and treatment monitoring through quantitative extraction of multimodal imaging features (e.g., intensity, texture, dynamic parameters). This review systematically summarizes the technical workflow of PET radiomics (including tracer selection, image segmentation, feature extraction, and model construction) and its applications in predicting molecular subtypes (such as IDH mutation and MGMT methylation), distinguishing recurrence from treatment-related changes, and prognostic stratification. Studies demonstrate that amino acid tracers (e.g., ¹⁸F-FET, ¹¹C-MET) combined with multimodal radiomics models significantly outperform traditional parametric analysis in diagnostic efficacy. Nevertheless, current research still faces challenges including data heterogeneity, insufficient model interpretability, and lack of clinical validation. Future advancements require multicenter standardized protocols, open-source algorithm frameworks, and multi-omics integration to facilitate the transformative clinical translation of PET radiomics from research to practice.

Deep learning has revolutionized neuroimage analysis by delivering unprecedented speed and accuracy. However, the narrow scope of many training datasets constrains model robustness and generalizability. This challenge is particularly acute in magnetic resonance imaging (MRI), where image appearance varies widely across pulse sequences and scanner hardware. A recent domain-randomization strategy addresses the generalization problem by training deep neural networks on synthetic images with randomized intensities and anatomical content. By generating diverse data from anatomical segmentation maps, the approach enables models to accurately process image types unseen during training, without retraining or fine-tuning. It has demonstrated effectiveness across modalities including MRI, computed tomography, positron emission tomography, and optical coherence tomography, as well as beyond neuroimaging in ultrasound, electron and fluorescence microscopy, and X-ray microtomography. This tutorial paper reviews the principles, implementation, and potential of the synthesis-driven training paradigm. It highlights key benefits, such as improved generalization and resistance to overfitting, while discussing trade-offs such as increased computational demands. Finally, the article explores practical considerations for adopting the technique, aiming to accelerate the development of generalizable tools that make deep learning more accessible to domain experts without extensive computational resources or machine learning knowledge.

The relationship between mild neurovascular conflict (NVC) and trigeminal neuralgia (TN) remains ill-defined, especially as mild NVC is often seen in asymptomatic population without any facial pain. We aim to analyze the trigeminal nerve microstructure using artificial intelligence (AI) to distinguish symptomatic and asymptomatic nerves between idiopathic TN (iTN) and the asymptomatic control group with incidental grade‑1 NVC. Seventy-eight symptomatic trigeminal nerves with grade-1 NVC in iTN patients, and an asymptomatic control group consisting of Bell's palsy patients free from facial pain (91 grade-1 NVC and 91 grade-0 NVC), were included in the study. Three hundred seventy-eight radiomic features were extracted from the original MRI images and processed with Laplacian-of-Gaussian filters. The dataset was split into 80% training/validation and 20% testing. Nested cross-validation was employed on the training/validation set for feature selection and model optimization. Furthermore, using the same pipeline approach, two customized deep learning models, Dense Atrous Spatial Pyramid Pooling (ASPP) -201 and MobileASPPV2, were classified using the same pipeline approach, incorporating ASPP blocks. Performance was assessed over ten and five runs for radiomics-based and deep learning-based models. Subspace Discriminant Ensemble Learning (SDEL) attained an accuracy of 78.8%±7.13%, Support Vector Machines (SVM) reached 74.8%±9.2%, and K-nearest neighbors (KNN) achieved 79%±6.55%. Meanwhile, DenseASPP-201 recorded an accuracy of 82.0 ± 8.4%, and MobileASPPV2 achieved 73.2 ± 5.59%. The AI effectively distinguished symptomatic and asymptomatic nerves with grade‑1 NVC. Further studies are required to fully elucidate the impact of vascular and nonvascular etiologies that may lead to iTN.

Degenerative cerebellar ataxia, a group of progressive neurodegenerative disorders, is characterised by cerebellar atrophy and impaired motor learning. Using CerebNet, a deep learning algorithm for cerebellar segmentation, this study investigated the relationship between cerebellar subregion volumes and motor learning ability. We analysed data from 37 patients with degenerative cerebellar ataxia and 18 healthy controls. Using CerebNet, we segmented four cerebellar subregions: the anterior lobe, superior posterior lobe, inferior posterior lobe, and vermis. Regression analyses examined the associations between cerebellar volumes and motor learning performance (adaptation index [AI]) and ataxia severity (Scale for Assessment and Rating of Ataxia [SARA]). The inferior posterior lobe volume showed a significant positive association with AI in both single (B = 0.09; 95% CI: [0.03, 0.16]) and multiple linear regression analyses (B = 0.11; 95% CI: [0.008, 0.20]), an association that was particularly evident in the pure cerebellar ataxia subgroup. SARA scores correlated with anterior lobe, superior posterior lobe, and vermis volumes in single linear regression analyses, but these associations were not maintained in multiple linear regression analyses. This selective association suggests a specialised role for the inferior posterior lobe in motor learning processes. This study reveals the inferior posterior lobe's distinct role in motor learning in patients with degenerative cerebellar ataxia, advancing our understanding of cerebellar function and potentially informing targeted rehabilitation approaches. Our findings highlight the value of advanced imaging technologies in understanding structure-function relationships in cerebellar disorders.