<b><i>Background:</i></b> Combining functional magnetic resonance imaging (fMRI) and machine learning (ML) can be used to identify therapeutic targets and evaluate the effect of repetitive transcranial magnetic stimulation (rTMS) in neural networks in tobacco use disorder. We investigated whether large-scale network connectivity can predict the rTMS effect on smoking cessation. <b><i>Methods:</i></b> Smoking cue exposure task-fMRI (T-fMRI) and resting-state fMRI (Rs-fMRI) scans were acquired before and after the 10 sessions of active or sham rTMS (10 Hz, 3000 pulses per session) over the left dorsal lateral prefrontal cortex in 42 treatment-seeking smokers. Five large-scale networks (default model network, central executive network, dorsal attention network, salience network [SN], and reward network) were compared before and after 10 sessions of rTMS, as well as between active and sham rTMS conditions. We performed neural network and regression analysis on the average connectivity of large-scale networks and the effectiveness of rTMS induced by rTMS. <b><i>Results:</i></b> Regression analyses indicated higher salience connectivity in T-fMRI and lower reward connectivity in Rs-fMRI, predicting a better outcome of TMS treatment for smoking cessation (<i>p</i> < 0.01, Bonferroni corrected). Neural Network analyses suggested that SN was the most important predictor of rTMS effectiveness in both T-fMRI (0.33 of feature importance) and Rs-fMRI (0.37 feature importance). <b><i>Conclusions:</i></b> Both T-fMRI and Rs-fMRI connectivity in SN predict a better outcome of TMS treatment for smoking cessation, but in opposite directions. The work shows that ML models can be used to target TMS treatment. Given the small sample size, all ML findings should be replicated in a larger cohort to ensure their validity.

Major depressive disorder (MDD) is a prevalent mental health condition that negatively impacts both individual well-being and global public health. Automated detection of MDD using structural magnetic resonance imaging (sMRI) and deep learning (DL) methods holds increasing promise for improving diagnostic accuracy and enabling early intervention. Most existing methods employ either voxel-level features or handcrafted regional representations built from predefined brain atlases, limiting their ability to capture complex brain patterns. This paper develops a unified pipeline that utilizes Vision Transformers (ViTs) for extracting 3D region embeddings from sMRI data and Graph Neural Network (GNN) for classification. We explore two strategies for defining regions: (1) an atlas-based approach using predefined structural and functional brain atlases, and (2) an cube-based method by which ViTs are trained directly to identify regions from uniformly extracted 3D patches. Further, cosine similarity graphs are generated to model interregional relationships, and guide GNN-based classification. Extensive experiments were conducted using the REST-meta-MDD dataset to demonstrate the effectiveness of our model. With stratified 10-fold cross-validation, the best model obtained 78.98% accuracy, 76.54% sensitivity, 81.58% specificity, 81.58% precision, and 78.98% F1-score. Further, atlas-based models consistently outperformed the cube-based approach, highlighting the importance of using domain-specific anatomical priors for MDD detection.

Deep learning (DL) based on MRI of medulloblastoma enables risk stratification, potentially aiding in therapeutic decisions. This study aims to develop DL models that identify four medulloblastoma molecular subgroups and prognostic-related genetic signatures. This retrospective study enrolled 325 patients for model development and an independent external validation cohort of 124 patients, totaling 449 MB patients from 2 medical institutes. Consecutive patients with newly diagnosed MB at MRI (T1-weighted, T2-weighted, and contrast-enhanced T1-weighted) at two medical institutes between January 2015 and June 2023 were identified. Two-stage sequential DL models were designed-MB-CNN that first identifies wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. Further, prognostic-related genetic signatures using DL models (MB-CNN_TP53/MYC/Chr11) were developed to predict TP53 mutation, MYC amplification, and chromosome 11 loss status. A hybrid model combining MB-CNN and conventional data (clinical information and MRI features) was compared to a logistic regression model constructed only with conventional data. Four-classification tasks were evaluated with confusion matrices (accuracy) and two-classification tasks with ROC curves (area under the curve (AUC)). The datasets comprised 449 patients (mean age ± SD at diagnosis, 13.55 years ± 2.33, 249 males). MB-CNN accurately classified MB subgroups in the external test dataset, achieving a median accuracy of 77.50% (range in 76.29% to 78.71%). MB-CNN_TP53/MYC/Chr11 models effectively predicted signatures (AUC of TP53 in SHH: 0.91, MYC amplification in Group 3: 0.87, chromosome 11 loss in Group 4: 0.89). The accuracy of the hybrid model outperformed the logistic regression model (82.20% vs. 59.14%, P = .009) and showed comparable performance to MB-CNN (82.20% vs. 77.50%, P = 0.105). MRI-based DL models allowed identification of the molecular medulloblastoma subgroups and prognostic-related genetic signatures.

Anomaly detection and classification in medical imaging are critical for early diagnosis but remain challenging due to limited annotated data, class imbalance, and the high cost of expert labeling. Emerging vision foundation models such as DINOv2, pretrained on extensive, unlabeled datasets, offer generalized representations that can potentially alleviate these limitations. In this study, we propose an attention-based global aggregation framework tailored specifically for 3D medical image anomaly classification. Leveraging the self-supervised DINOv2 model as a pretrained feature extractor, our method processes individual 2D axial slices of brain MRIs, assigning adaptive slice-level importance weights through a soft attention mechanism. To further address data scarcity, we employ a composite loss function combining supervised contrastive learning with class-variance regularization, enhancing inter-class separability and intra-class consistency. We validate our framework on the ADNI dataset and an institutional multi-class headache cohort, demonstrating strong anomaly classification performance despite limited data availability and significant class imbalance. Our results highlight the efficacy of utilizing pretrained 2D foundation models combined with attention-based slice aggregation for robust volumetric anomaly detection in medical imaging. Our implementation is publicly available at https://github.com/Rafsani/DinoAtten3D.git.

Time-series forecasting and causal discovery are central in neuroscience, as predicting brain activity and identifying causal relationships between neural populations and circuits can shed light on the mechanisms underlying cognition and disease. With the rise of foundation models, an open question is how they compare to traditional methods for brain signal forecasting and causality analysis, and whether they can be applied in a zero-shot setting. In this work, we evaluate a foundation model against classical methods for inferring directional interactions from spontaneous brain activity measured with functional magnetic resonance imaging (fMRI) in humans. Traditional approaches often rely on Wiener-Granger causality. We tested the forecasting ability of the foundation model in both zero-shot and fine-tuned settings, and assessed causality by comparing Granger-like estimates from the model with standard Granger causality. We validated the approach using synthetic time series generated from ground-truth causal models, including logistic map coupling and Ornstein-Uhlenbeck processes. The foundation model achieved competitive zero-shot forecasting fMRI time series (mean absolute percentage error of 0.55 in controls and 0.27 in patients). Although standard Granger causality did not show clear quantitative differences between models, the foundation model provided a more precise detection of causal interactions. Overall, these findings suggest that foundation models offer versatility, strong zero-shot performance, and potential utility for forecasting and causal discovery in time-series data.

Time-series forecasting and causal discovery are central in neuroscience, as predicting brain activity and identifying causal relationships between neural populations and circuits can shed light on the mechanisms underlying cognition and disease. With the rise of foundation models, an open question is how they compare to traditional methods for brain signal forecasting and causality analysis, and whether they can be applied in a zero-shot setting. In this work, we evaluate a foundation model against classical methods for inferring directional interactions from spontaneous brain activity measured with functional magnetic resonance imaging (fMRI) in humans. Traditional approaches often rely on Wiener-Granger causality. We tested the forecasting ability of the foundation model in both zero-shot and fine-tuned settings, and assessed causality by comparing Granger-like estimates from the model with standard Granger causality. We validated the approach using synthetic time series generated from ground-truth causal models, including logistic map coupling and Ornstein-Uhlenbeck processes. The foundation model achieved competitive zero-shot forecasting fMRI time series (mean absolute percentage error of 0.55 in controls and 0.27 in patients). Although standard Granger causality did not show clear quantitative differences between models, the foundation model provided a more precise detection of causal interactions. Overall, these findings suggest that foundation models offer versatility, strong zero-shot performance, and potential utility for forecasting and causal discovery in time-series data.

Medical imaging has undergone significant advancements with the integration of deep learning techniques, leading to enhanced accuracy in image analysis. These methods autonomously extract relevant features from medical images, thereby improving the detection and classification of various diseases. Among imaging modalities, Magnetic Resonance Imaging (MRI) is particularly valuable due to its high contrast resolution, which enables the differentiation of soft tissues, making it indispensable in the diagnosis of brain disorders. The accurate classification of brain tumors is crucial for diagnosing many neurological conditions. However, conventional classification techniques are often limited by high computational complexity and suboptimal accuracy. Motivated by these issues, an innovative model is proposed in this work for segmenting and classifying brain tumors. The research aims to develop a robust and efficient deep learning framework that can assist clinicians in making precise and early diagnoses, ultimately leading to more effective treatment planning. The proposed methodology begins with the acquisition of MRI images from standardized medical imaging databases. Subsequently, the abnormal regions from the images are segmented using the Multiscale Bilateral Awareness Network (MBANet), which incorporates multi-scale operations to enhance feature representation and image quality. A novel classificationarchitecture then processes the segmented images, termed Region Vision Transformer-based Adaptive EfficientNetB7 with Atrous Spatial Pyramid Pooling (RVAEB7-ASPP). To optimize the performance of the classification model, hyperparameters are fine-tuned using the Modified Random Parameter-based Hippopotamus Optimization Algorithm (MRP-HOA). The model's effectiveness is verified through a comprehensive experimental evaluation that utilizes various performance metrics and is compared to current state-of-the-art methods. The proposed MRP-HOA-RVAEB7-ASPP model achieves an impressive classification accuracy of 98.2%, significantly outperforming conventional approaches in brain tumor classification tasks. The MBANet effectively performs brain tumor segmentation, while the RVAEB7-ASPP model provides reliable classification. The integration of the MRP-HOA-RVAEB7-ASPP model optimizes feature extractions and parameter tuning, leading to improved accuracy and robustness. The integration of advanced segmentation, adaptive feature extraction, and optimal parameter tuning enhances the reliability and accuracy of the model. This framework provides a more effective and trustworthy solution for the early detection and clinical assessment of brain tumors, leading to improved patient outcomes through timely intervention.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that currently affects over 55 million individuals worldwide. Conventional diagnostic approaches often rely on subjective clinical assessments and isolated biomarkers, limiting their accuracy and early-stage effectiveness. With the rising global burden of AD, there is an urgent need for objective, automated tools that enhance diagnostic precision using neuroimaging data. This study proposes a novel diagnostic framework combining a fine-tuned VGG19 deep convolutional neural network with an eXtreme Gradient Boosting (XGBoost) classifier. The model was trained and validated on the OASIS MRI dataset (Dataset 2), which was manually balanced to ensure equitable class representation across the four AD stages. The VGG19 model was pre-trained on ImageNet and fine-tuned by unfreezing its last ten layers. Data augmentation strategies, including random rotation and zoom, were applied to improve generalization. Extracted features were classified using XGBoost, incorporating class weighting, early stopping, and adaptive learning. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. The proposed VGG19-XGBoost model achieved a test accuracy of 99.6%, with an average precision of 1.00, a recall of 0.99, and an F1-score of 0.99 on the balanced OASIS dataset. ROC curves indicated high separability across AD stages, confirming strong discriminatory power and robustness in classification. The integration of deep feature extraction with ensemble learning demonstrated substantial improvement over conventional single-model approaches. The hybrid model effectively mitigated issues of class imbalance and overfitting, offering stable performance across all dementia stages. These findings suggest the method's practical viability for clinical decision support in early AD diagnosis. This study presents a high-performing, automated diagnostic tool for Alzheimer's disease based on neuroimaging. The VGG19-XGBoost hybrid architecture demonstrates exceptional accuracy and robustness, underscoring its potential for real-world applications. Future work will focus on integrating multimodal data and validating the model on larger and more diverse populations to enhance clinical utility and generalizability.

BackgroundBrain atrophy is a hallmark of multiple sclerosis (MS). For clinical translatability and individual-level predictions, brain atrophy needs to be put into context of the broader population, using reference or normative models. MethodsReference models of MRI-derived brain volumes were established from a large healthy control (HC) multi-cohort dataset (N=63 115, 51% females). The reference models were applied to two independent MS cohorts (N=362, T1w-scans=953, follow-up time up to 12 years) to assess deviations from the reference, defined as Z-values. We assessed the overlap of deviation profiles and their stability over time using individual-level transitions towards or out of significant reference deviation states (|Z|>1{middle dot}96). A negative binomial model was used for case-control comparisons of the number of extreme deviations. Linear models were used to assess differences in Z-score deviations between MS and propensity-matched HCs, and associations with clinical scores at baseline and over time. The utilized normative BrainReference models, scripts and usage instructions are freely available. FindingsWe identified a temporally stable, brain morphometric phenotype of MS. The right and left thalami most consistently showed significantly lower-than-reference volumes in MS (25% and 26% overlap across the sample). The number of such extreme smaller-than-reference values was 2{middle dot}70 in MS compared to HC (4{middle dot}51 versus 1{middle dot}67). Additional deviations indicated stronger disability (Expanded Disability Status Scale: {beta}=0{middle dot}22, 95% CI 0{middle dot}12 to 0{middle dot}32), Paced Auditory Serial Addition Test score ({beta}=-0{middle dot}27, 95% CI -0{middle dot}52 to -0{middle dot}02), and Fatigue Severity Score ({beta}=0{middle dot}29, 95% CI 0{middle dot}05 to 0{middle dot}53) at baseline, and over time with EDSS ({beta}=0{middle dot}07, 95% CI 0{middle dot}02 to 0{middle dot}13). We additionally provide detailed maps of reference-deviations and their associations with clinical assessments. InterpretationWe present a heterogenous brain phenotype of MS which is associated with clinical manifestations, and particularly implicating the thalamus. The findings offer potential to aid diagnosis and prognosis of MS. FundingNorwegian MS-union, Research Council of Norway (#223273; #324252); the South-Eastern Norway Regional Health Authority (#2022080); and the European Unions Horizon2020 Research and Innovation Programme (#847776, #802998). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSReference values and normative models have yet to be widely applied to neuroimaging assessments of neurological disorders such as multiple sclerosis (MS). We conducted a literature search in PubMed and Embase (Jan 1, 2000-September 12, 2025) using the terms "MRI" AND "multiple sclerosis", with and without the keywords "normative model*" and "atrophy", without language restrictions. While normative models have been applied in psychiatric and developmental disorders, few studies have addressed their use in neurological conditions. Existing MS research has largely focused on global atrophy and has not provided regional reference charts or established links to clinical and cognitive outcomes. Added value of this studyWe provide regionally detailed brain morphometry maps derived from a heterogeneous MS cohort spanning wide ranges of age, sex, clinical phenotype, disease duration, disability, and scanner characteristics. By leveraging normative modelling, our approach enables individualised brain phenotyping of MS in relation to a population based normative sample. The analyses reveal clinically meaningful and spatially consistent patterns of smaller brain volumes, particularly in the thalamus and frontal cortical regions, which are linked to disability, cognitive impairment, and fatigue. Robustness across scanners, centres, and longitudinal follow-up supports the stability and generalisability of these findings to real-world MS populations. Implications of all the available evidenceNormative modelling offers an individualised, sensitive, and interpretable approach to quantifying brain structure in MS by providing individual-specific reference values, supporting earlier detection of neurodegeneration and improved patient stratification. A consistent pattern of thalamic and fronto-parietal deviations defines a distinct morphometric profile of MS, with potential utility for early and personalised diagnosis and disease monitoring in clinical practice and clinical trials.

Idiopathic normal pressure hydrocephalus (iNPH) is a common and debilitating condition whose diagnosis is made challenging due to the unspecific and common clinical presentation. The aim of our study was to determine if data driven patterns of cerebrospinal fluid (CSF) distribution can be used to predict iNPH diagnosis and response to treatment. We established a cohort of iNPH patients and age/sex-matched controls. We used Non-negative Matrix Factorization (NMF) on CSF probability maps from segmentation of T1-weighted MRI to obtain patterns or components of CSF distribution across participants and a load on each component in each participant. Visual assessment of morphologic phenotype was performed by a neuroradiologist, and clinical symptom improvement was assessed via retrospective chart review. We used the NMF component loads to predict diagnosis and clinical outcome after ventriculoperitoneal shunt placement for treatment of iNPH. Similar models were developed using manual Evan's index and callosal angle measurements. We included 98 iNPH patients and 98 controls split into test (20 %) and train (80 %) sets. The optimal NMF decomposition identified 7 patterns of CSF distribution in our cohort. Accuracy for predicting a clinical diagnosis of iNPH using the automated NMF model was 96 %/97 % in the train/test sets, which was similar to the performance of the manual measure models (92 %/97 %). Visualizing the voxels that contributed most to the NMF models revealed that the voxels most associated with a disproportionately enlarged subarachnoid space hydrocephalus (DESH) were the ones with higher probability of iNPH diagnosis. Neither NMF nor manual metrics performed well for prediction of qualitative clinical outcomes. NMF-generated patterns of CSF distribution showed high accuracy in discerning individuals with iNPH from controls. The patterns most relying on DESH features showed highest potential for independently predicting NPH diagnosis. The algorithm we proposed should not be perceived as a replacement for human expertise but rather as an additional tool to assist clinicians in achieving accurate diagnoses.