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MRI‐derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta‐Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis‐driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large‐scale meta‐ and mega‐analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large‐scale, collaborative studies of mental illness. This review discusses the major challenges facing neuroimaging research of bipolar disorder and highlights the major accomplishments, ongoing challenges and future goals of the ENIGMA Bipolar Disorder Working Group.

Purpose Congenital infection with human Cytomegalovirus (hCMV) is a common cause of severe neurodevelopmental disability, while postnatal infection of a term‐born infant will usually not lead to an adverse neurodevelopmental outcome. In preterm‐born infants, long‐term consequences of an early postnatal hCMV infection (usually via breast milk) are still controversial. This is highly relevant as preventative measures exist. Methods Data of 37 preterm‐born children (PT; ≤ 32 weeks of gestation and/or weighing ≤ 1500 g) was included. Of these, 14 acquired an early postnatal infection with hCMV (PT hCMV+), while 23 did not (PT hCMV−). Further, 38 healthy term‐born participants (FT) were included. Overall median age was 13.6 years (range 7.9–17.8 years). Global and local tissue volumes and brain surface parameters were analyzed. Consequences of prematurity were detected by comparing FT and PT, and sequelae of hCMV infection by comparing PT hCMV− and PT hCMV+. Findings Compared to FT, PT showed lower global gray matter (GM); interestingly, PT hCMV+ showed a trend toward higher global GM than PT hCMV−. Several clusters of local GM differed in volume between PT and FT, but none as a function of hCMV infection. Surface analyses between PT and FT identified predominantly right‐hemispheric regions of lower cortical thickness in PT. Unexpectedly, widespread clusters of higher cortical thickness were found bilaterally in predominantly frontal brain regions in PT hCMV+ compared to PT hCMV−, demonstrating a lasting effect of hCMV infection. Conclusion We found lower global and local GM volumes due to of prematurity. Additionally, we demonstrate long‐term effects of early postnatal hCMV infection on brain structure in PT, markedly different from those resulting from prematurity alone. This suggests distinct long‐term cerebral consequences of early postnatal hCMV infection in former preterm‐born children above and beyond those attributable to prematurity. Consequently, efforts to avoid HCMV infection in preterm‐born infants should be implemented. Cortical surface analyses of former preterm‐born children with (PT hCMV+) and without (PT hCMV−) early postnatal human Cytomegalovirus (hCMV) infection at ∼13 years of age. Note the strong effect of prematurity when compared to full‐term‐born children (FT, top), but the independent and even more widespread effect of HCMV infection (bottom).

Background Time-efficient and accurate whole volume thigh muscle segmentation is a major challenge in moving from qualitative assessment of thigh muscle MRI to more quantitative methods. This study developed an automated whole thigh muscle segmentation method using deep learning for reproducible fat fraction quantification on fat–water decomposition MRI. Results This study was performed using a public reference database (Dataset 1, 25 scans) and a local clinical dataset (Dataset 2, 21 scans). A U-net was trained using 23 scans (16 from Dataset 1, seven from Dataset 2) to automatically segment four functional muscle groups: quadriceps femoris, sartorius, gracilis and hamstring. The segmentation accuracy was evaluated on an independent testing set (3 × 3 repeated scans in Dataset 1 and four scans in Dataset 2). The average Dice coefficients between manual and automated segmentation were > 0.85. The average percent difference (absolute) in volume was 7.57%, and the average difference (absolute) in mean fat fraction (meanFF) was 0.17%. The reproducibility in meanFF was calculated using intraclass correlation coefficients (ICCs) for the repeated scans, and automated segmentation produced overall higher ICCs than manual segmentation (0.921 vs. 0.902). A preliminary quantitative analysis was performed using two-sample t test to detect possible differences in meanFF between 14 normal and 14 abnormal (with fat infiltration) thighs in Dataset 2 using automated segmentation, and significantly higher meanFF was detected in abnormal thighs. Conclusions This automated thigh muscle segmentation exhibits excellent accuracy and higher reproducibility in fat fraction estimation compared to manual segmentation, which can be further used for quantifying fat infiltration in thigh muscles.

The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults. Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1–90 days). Segmentation was performed using FreeSurfer v6.0. Reliability was assessed using volume reproducibility error (ε) and spatial overlapping coefficient (DICE) between test and retest session. Significant MRI site and vendor effects (p ​< ​.05) were found in a few subfields/nuclei for the ε, while extensive effects were found for the DICE score of most subfields/nuclei. Reliability was strongly influenced by volume, as ε correlated negatively and DICE correlated positively with volume size of structures (absolute value of Spearman’s r correlations >0.43, p ​< ​1.39E-36). In particular, volumes larger than 200 ​mm3 (for amygdalar nuclei) and 300 ​mm3 (for hippocampal subfields, except for molecular layer) had the best test-retest reproducibility (ε ​< ​5% and DICE ​> ​0.80). Our results support the use of volumetric measures of larger amygdalar nuclei and hippocampal subfields in multisite MRI studies. These measures could be useful for disease tracking and assessment of efficacy in drug trials. •Differences in MRI site/vendor had a limited effect on volume reproducibility.•Differences in MRI site/vendor had an extensive effect on spatial accuracy.•Reliability is good for larger amygdalar and hippocampal structures.•Automated volumetry is reliable in multicenter MRI studies.

Non-motor symptoms (NMS) in Parkinson's disease (PD) often precede motor manifestations and are challenging to detect with conventional MRI. This study investigates the use of the Multi-Flip-Angle and Multi-Echo Gradient Echo Sequence (MULTIPLEX) in MRI to detect previously undetectable microstructural changes in brain tissue associated with NMS in PD. A prospective study was conducted on 37 patients diagnosed with PD. Anxiety and depression levels were assessed using the Hamilton Anxiety Scale (HAMA) and Hamilton Depression Scale (HAMD), respectively. MRI techniques, including 3D T1-weighted imaging (3D T1WI) and MULTIPLEX - which encompasses T2*-mapping, T1-mapping, proton density-mapping, and quantitative susceptibility mapping (QSM)-were performed. Brain subregions were automatically segmented using deep learning, and their volume and quantitative parameters were correlated with NMS-related assessment scales using Spearman's rank correlation coefficient. Correlations were observed between QSM and T2* values of certain subregions within the left frontal and bilateral temporal lobes and both anxiety and depression (absolute -values ranging from 0.358 to 0.480, < 0.05). Additionally, volume measurements of regions within the bilateral frontal, temporal, and insular lobes exhibited negative correlations with anxiety and depression (absolute -values ranging from 0.354 to 0.658, < 0.05). In T1-mapping and proton density-mapping, no specific brain regions were found to be significantly associated with the NMS of PD under investigation. Quantitative parameters derived from MULTIPLEX MRI show significant associations with clinical evaluations of NMS in PD. Multiparametric MR neuroimaging may serve as a potential early diagnostic tool for PD.

Identifying brain tumors accurately through medical imaging is vital in supporting computer-aided diagnostic systems, playing an essential role in early disease identification and effective treatment planning. Manual analysis of medical scans, like MRI scans, often slow and susceptible to human error, emphasizing the growing demand for automated, efficient, and precise detection systems. In the proposed study, we present an enhanced approach to fine-tuning an object detection model for accurately identifying brain tumors, demonstrating the capabilities of deep learning techniques in medical imaging analysis. The proposed method leverages the YOLOv10 architecture, a state-of-the-art model recognized for its high detection speed and precision. Due to the limited availability of extensive labeled medical imaging datasets, a transfer learning approach is adopted by initializing the model with parameters trained on the COCO dataset. These parameters are then fine-tuned using a brain tumor-specific dataset to significantly enhance the model’s detection performance. The fine-tuned model gains a mean Average Precision (mAP) of 96.1% and a precision of 96.8%, surpassing the baseline performance of the original YOLOv10 model. These results highlight the efficacy of applying transfer learning techniques to medical imaging problems, particularly when dealing with scarce data resources. Furthermore, our approach demonstrates how modern object detection architectures can be efficiently adapted for specialized clinical tasks, offering promising pathways for future advancements in computer-aided diagnosis and healthcare applications.

To assess whether cerebral structural alterations in isolated rapid eye movement sleep behavior disorder (iRBD) are progressive and differ from those of normal aging and whether they are related to clinical symptoms. In a longitudinal study of 18 patients with iRBD (age, 66.1 ± 5.7 years; 13 males; follow-up, 1.6 ± 0.6 years) and 24 age-matched healthy controls (age, 67.0 ± 4.9 years; 12 males; follow-up, 2.0 ± 0.9 years), all participants underwent multiple extensive clinical examinations, neuropsychological tests, and magnetic resonance imaging at baseline and follow-up. Surface-based cortical reconstruction and automated subcortical structural segmentation were performed on T1-weighted images. We used mixed-effects models to examine the differences between the groups and the differences in anatomical changes over time. None of the patients with iRBD demonstrated phenoconversion during the follow-up. Patients with iRBD had thinner cortices in the frontal, occipital, and temporal regions, and more caudate atrophy, compared to that in controls. In similar regions, group-by-age interaction analysis revealed that patients with iRBD demonstrated significantly slower decreases in cortical thickness and caudate volume with aging than that observed in controls. Patients with iRBD had lower scores on the Korean version of the Mini-Mental Status Examination (  = 0.037) and frontal and executive functions (  = 0.049) at baseline than those in controls; however, no significant group-by-age interaction was identified. Patients with iRBD show brain atrophy in the regions that are overlapped with the areas that have been documented to be affected in early stages of Parkinson's disease. Such atrophy in iRBD may not be progressive but may be slower than that in normal aging. Cognitive impairment in iRBD is not progressive.

Brain age prediction based on anatomical MRI scans, as an essentially new measure in neuroimaging and aging research, provides a crucial marker for the early diagnosis of neurodegenerative diseases, cognitive health appraisal, and biological age prediction. Conventional machine learning models rely on handcrafted features, which can result in low accuracy and generalizability because they fail to capture the complex spatial, contextual, and structural information inherent in MRI images. While deep learning methods like CNNs and Transformers enhance feature extraction, they fail to adequately capture the brain’s structural connectivity patterns, leading to more significant prediction errors and lower reliability. To address these limitations, this study introduces NeuroAgeFusionNet: A hybrid deep learning framework leveraging CNNs, Transformers, and Graph Neural Networks (GNNs) to improve brain age estimation. The proposed framework uses a feature fusion mechanism with a hybrid modeling approach that optimizes spatial, contextual, and structural features for more comprehensive feature representation. Moreover, an uncertainty quantification module is built into the model to make predictions more robust by safeguarding them against unreliable estimates. On the UK Biobank dataset, our model achieves state-of-the-art performance with an MAE of 2.30, a Pearson correlation of 0.97, and an R 2 score of 0.96, significantly surpassing conventional approaches. A high-level abstract of the brain age estimation framework, which shows excellent potential for accuracy, low variance, and intelligible characteristics. Such advancements position NeuroAgeFusionNet as a valuable tool for clinical neuroscience applications that facilitate improved brain aging monitoring and early neurodegenerative disease detection.

The early detection of brain tumors is very important for treating them and improving the quality of life for patients. Through advanced imaging techniques, doctors can now make more informed decisions. This paper introduces a framework for a tumor detection system capable of grading gliomas. The system’s implementation begins with the acquisition and analysis of brain magnetic resonance images. Key features indicative of tumors and gliomas are extracted and classified as independent components. A deep learning model is then employed to categorize these gliomas. The proposed model classifies gliomas into three primary categories: meningioma, pituitary, and glioma. Performance evaluation demonstrates a high level of accuracy (99.21%), specificity (98.3%), and sensitivity (97.83%). Further research and validation are essential to refine the system and ensure its clinical applicability. The development of accurate and efficient tumor detection systems holds significant promise for enhancing patient care and improving survival rates.

PurposeThis study validated VParNet and nnU-Net for ventricular segmentation in pediatric hydrocephalus, a condition characterized by irregular and asymmetric ventricular shapes.MethodsManual segmentation of 139 MRI scans (ages range 2.6–20.3 years) was performed for the four ventricles and the aqueduct. A five-fold cross-validation was conducted for both models. VParNet was tested with its original weights and after retraining on pediatric data. nnU-Net was extended to also segment the aqueduct. Performance was evaluated using the Dice Similarity Coefficient (DSC), Intraclass Correlation Coefficient (ICC), and Minimal Detectable Change (MDC).ResultsVParNet preprocessing failed in 20.9% of cases, requiring subject exclusion. Both models showed good to excellent segmentation accuracy and reliability (DSC: 0.87–0.95; ICC: 0.81–1.0). Retraining VParNet improved DSC scores. MDC values (0.05–3.0) indicated high sensitivity for the lateral and third ventricles and acceptable sensitivity for the fourth ventricle. Aqueduct segmentation remained challenging (nnU-Net: DSC = 0.68; ICC = 0.81; MDC = 0.04).ConclusionAll tested models performed well in pediatric hydrocephalus segmentation, with no fundamental differences in overall performance. However, nnU-Net demonstrated key advantages due to its lack of preprocessing requirements, which allow the successful handling of even the most challenging subjects. These features make it easily implementable for clinical applications, providing fast and reliable ventricular segmentation and quantification.