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This study presents a novel fractional order model of Alzheimer's disease (mental disorder) using the Caputo derivative to accurately capture long term memory and hereditary effects in neurodegeneration. The mathematical model incorporates key pathological constituents including neurons, amyloid beta (Aβ), tau proteins and microglial responses, allowing detailed simulation of their dynamic interactions. Fundamental properties of the model, including positivity, boundedness, invariant regions and equilibrium points, are rigorously analyzed to ensure biological feasibility. Sensitivity analysis identifies amyloid toxicity as the most influential driver of neuronal loss underscoring its central role in AD progression. Furthermore, a Physics Informed Neural Network (PINN) is developed to approximate system dynamics from noisy observations while ensuring compliance with biological and physical constraints. Compared to standard neural networks the PINN exhibits superior accuracy and robustness especially under data scarcity. By integrating fractional calculus, optimal control and machine learning, this work advances computational modeling of Alzheimer's disease and offers insights into therapeutic optimization.

The field of neuroimaging has undergone profound transformation in recent years, driven primarily by rapid 3 advances in machine learning (ML), and especially deep learning (DL), techniques. These computational 4 innovations have been amplified by ongoing improvements in brain imaging modalities, such as structural 5 MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and positron emission tomography 6 (PET), which now deliver increasingly high-resolution, multimodal views of brain structure, function, 7 connectivity, and metabolism.The intersection of neuroimaging and machine learning marks an inflection point in neuroscience; 9 a confluence where vast, high-dimensional brain data meet computational power capable of turning 10 complexity into clinically actionable insights. This convergence has led to the rapidly expanding domain of 11 machine learning for neuroimaging, inviting a new generation of studies that leverage ML to transcend the 12 limitations of traditional neuroimaging analysis.Traditional statistical approaches and model-based techniques often struggle with the defining features 14 of modern neuroimaging datasets: extreme high dimensionality, substantial heterogeneity across 15 subjects/scanners/protocols, nonlinear patterns, and large-scale "big data" from consortia and multimodal 16 fusion. In contrast, ML/DL paradigms provide powerful approaches to address the aforementioned 17 challenges by virtue of their ability to learn complex, non-linear relationships in the underlying data through Several challenges remain, such as overfitting, interpretability, explainability, computational demands, 23 and generalization across diverse datasets. Nevertheless, this integration holds transformative potential for 24 more accurate, individualized, and clinically translatable neuroscience insights.This Research Topic was conceived to provide a platform for researchers involved in a variety of related 26 domains such as neuroscience, medical imaging, and artificial intelligence to come together, and report 27 their state-of-the-art findings in the application of ML to neuroimaging. Our objective was to address both 28 the methodological and translational challenges involved, whilst also critically examining their clinical 29 relevance, robustness, interpretability, explainability and ethical implications in this rapidly evolving field.The Research Topic comprises 13 peer-reviewed articles, including original research papers and 31 methodological contributions, that collectively reflect the diversity and dynamism of current machine 32 learning research in brain imaging. This collection aims not only to push the methodological boundaries, but 33 to showcase the contributions made to translate the ML-driven neurimaging into real-world neurodiagnostic 34 and therapeutic applications. The collection also includes studies emphasizing multimodal data integration, where complementary

IntroductionThe accumulation of genomic and brain data opens new opportunities for resource friendly, data driven brain exploration. A key challenge is to develop versatile and accessible strategies that integrate and mine multimodal datasets for novel neuroscientific insights. Here, we optimized an integrated workflow for mapping multigenic evolutionary traits in the human brain across cognitive, cellular, and molecular levels.MethodsAt the input stage, the workflow fuses an evolutionary genetic dataset with searchable synthetic functional magnetic resonance imaging (fMRI) databases that are pre clustered into concise psychological domains for improved interpretability. At its core, a Genetic Algorithm for Generalized Biclustering (GABi) mines gene sets under evolutionary selection that also show high expression correlation with fMRI networks.ResultsApplying this workflow, we identified evolutionary patterns spanning cognitive traits, brain cell types, and molecular mechanisms. Focusing on socio affective traits, the algorithm highlighted peaks in adaptive selection in networks for social interaction (language) and social concepts (theory of mind) across hominid, early hominin, and anatomically modern human (AMH) ancestry. These traits emerge from a broad spectrum of excitatory (glutamatergic) and inhibitory (GABAergic) neuronal, as well as non neuronal, cell types. The associated Gene Ontology (GO) terms were enriched for cell signaling, synaptic organization, and neuronal morphology.DiscussionTogether, these findings demonstrate an integrated workflow for molecular to systems level exploration of the brain and provide new perspectives on the evolutionary history of human socio affective functions. This approach can be adapted to screen for functional traits in the context of mental disorders or applied to the brains of other phylogenies in a similar manner.

BackgroundSecondary quantitative analysis of brain magnetic resonance imaging (MRI) can provide valuable information for many neurological diseases, including multiple sclerosis (MS), but it demands complete datasets that are often unavailable clinically. We investigated how image synthesis via deep learning using cycle-consistent generative adversarial networks (CycleGANs) compared with Pix2Pix as a related method, based on T1-weighted and T2-weighted brain MRI in MS, following verification on two streamlined datasets. The synthesized images were also evaluated against the source data.MethodsThe streamlined datasets involved 1,113 healthy participants from the Human Connectome Project (HCP) and 318 participants from the Parkinson’s Progression Markers Initiative (PPMI). The MS cohort in this study included 105 participants scanned with different protocols. Image synthesis was bidirectional between T1- and T2-weighted MRI using CycleGAN with and without spectral normalization, as well as Pix2Pix. Utility testing focused on T1-weighted MRI that was most often unavailable in MS, and that involved lesion detection, brain volumetry, and lesion texture analysis.ResultsAll CycleGAN models performed competitively, while Pix2Pix performed better, mostly with streamlined datasets (p < 0.001). The average peak signal-to-noise ratio ranged from 24.860–28.570 versus 28.520–31.100, and the structural similarity index ranged from 0.838–0.901 versus 0.924–0.943. With spectral normalization, CycleGAN improved in PPMI but not in HCP and generally not in MS (p < 0.001). Furthermore, the synthesized images showed high similarity to the source data in utility tests, although Pix2Pix T1 images appeared more heterogeneous in lesion texture than source T1 images.ConclusionCycleGAN without spectral normalization appeared feasible for synthesizing common clinical brain MRI, including T1-weighted images usable for subsequent quantitative analysis in MS.

Dyslexia is a prevalent neurodevelopmental disorder that impairs a children's ability to reading, writing, and language processing despite normal cognitive skills. Early identification is vital for timely support and interventions in children with dyslexia. This study aimed to develop an efficient EEG-based pipeline for dyslexia detection using deep learning techniques, while providing a consistent evaluation protocol for fair comparison across models and prior approaches. EEG recordings were acquired from 51 participants (26: dyslexic and 25: non-dyslexic), aged 5-10 years, during cognitive task performance. These signals were processed, segmented, and decomposed into standard frequency bands (alpha, beta, delta, and theta) using the discrete wavelet transform to capture discriminative neural patterns. Filter-based feature selection techniques were applied before classification to optimize performance and reduce redundancy to identify the most informative features. These ranked and individual band-wise features were systematically evaluated with classical machine learning baselines (Decision Trees, SVM, k-NN, and ensemble learners) alongside the proposed deep neural networks. In addition, we benchmarked end-to-end raw-EEG deep learning baselines (1D-CNN, LSTM, and EEGNet) and re-implemented representative existing pipelines, all evaluated on our dataset using the same evaluation protocol. The proposed compact deep neural network with four hidden layers achieved the best performance, reaching classification accuracy of 98.85%, outperforming all baseline models, raw-EEG deep learning baselines, and re-implemented approaches. These findings support the feasibility of DWT-driven EEG analysis combined with deep learning for more accurate and early dyslexia detection. The proposed approach holds promise as a non-invasive screening tool to support improved educational outcomes through early diagnosis and targeted intervention.

Brain tumor diagnosis from magnetic resonance imaging (MRI) remains a challenging task due to the high variability in tumor appearance and the limitations of manual interpretation. To address these challenges, this paper proposes NeuroFusionNet, a deep learning framework for automated brain tumor classification from MRI. The framework integrates GAN-based synthetic image generation with transfer learning using a fine-tuned VGG16 backbone. Real and GAN-generated MRI images are passed through VGG16 to extract discriminative feature representations, which are then used for final classification. To adapt the model to domain-specific MRI characteristics while preserving pretrained knowledge, the last ten layers of VGG16 are fine-tuned and the remaining layers are kept frozen. The effectiveness of NeuroFusionNet is validated on two publicly available brain MRI datasets. Experimental results demonstrate that the proposed learning framework achieves classification accuracies of 99.05 and 98.75% on the Brain Tumor MRI Dataset and the MRI with Bounding Boxes Dataset, respectively, consistently outperforming several state-of-the-art neural architectures, including VGG16, VGG19, MobileNetV2, DenseNet121, and NASNetLarge. The results suggest that NeuroFusionNet is effective for the evaluated public MRI datasets; additional external validation is required.

BackgroundSpinal cord atrophy is a key biomarker for tracking disease progression in neurological disorders, including multiple sclerosis, amyotrophic lateral sclerosis, and spinal cord injury. Recent MRI advancements have improved atrophy detection, particularly in the cervical region, facilitating longitudinal studies. However, validating atrophy quantification algorithms remains challenging due to limited ground truth data.ObjectiveThis study introduces SynSpine, a workflow for generating synthetic spinal cord MRI data (i.e., digital phantoms) with controlled levels of artificial atrophy. These phantoms support the development and preliminary validation of spinal cord imaging pipelines designed to measure degeneration over time.MethodsThe workflow consists of two phases: (1) generating synthetic MR images by isolating, extracting and scaling the spinal cord, simulating atrophy on the PAM50 template; (2) performing non-rigid registration to align the synthetic images with the subject’s native space, ensuring accurate anatomical correspondence. A proof-of-concept application utilizing the Active Surface and Reg methods implemented in Jim demonstrated its effectiveness in detecting atrophy across various levels of simulated atrophy and noise.ResultsSynSpine successfully generates synthetic spinal cord images with varying atrophy levels. Non-rigid registration did not significantly affect atrophy measurements. Atrophy estimation errors, estimated using Active Surface and Reg methods, varied with both simulated atrophy magnitude and noise level, exhibiting region-dependent differences. Increased noise led to higher measurement errors.ConclusionThis work presents a novel and modular framework for simulating spinal cord atrophy data using digital phantoms, offering a controlled setting for testing spinal cord analysis pipelines. As the simulated atrophy may over-simplify in vivo conditions, future research will focus on enhancing the realism of the synthetic dataset by simulating additional pathologies, thus improving its application for evaluating spinal cord atrophy in clinical and research contexts.

Dementia poses a major challenge to individuals and public health systems. Detecting cognitive decline through spontaneous speech offers a promising, non-invasive avenue for diagnosis of mild cognitive impairment (MCI) and dementia, enabling timely intervention and improved outcomes. This study describes our submission to the PROCESS Signal Processing Grand Challenge (ICASSP 2025), which tasked participants with predicting cognitive decline from speech samples. Our method combines eGeMAPS features from openSMILE, HuBERT (a self-supervised speech representation model), and GPT-4o, OpenAI's state-of-the-art large language model. These are integrated with the custom LSTM and ResMLP neural networks, and supported by Scikit-learn regressors/classifiers for both cognitive score regression and dementia classification. Our regression model based on LightGBM achieved an RMSE of 2.7775, placing us 10th out of 80 teams globally and surpassing the RoBERTa baseline by 7.5%. For the three-class classification task (Dementia/MCI/Control), our LSTM model obtained an F1-score of 0.5521, ranking 20th of 106 and marginally outperforming the best baseline. We trained models on speech data from 157 study participants, with independent evaluation performed on a separate test set of 40 individuals. We discoved that integrating large language models with self-supervised speech representations enhances the detection of cognitive decline. The proposed approach offers a scalable, data-driven method for early cognitive screening and may support emerging applications in neuropsychological informatics.