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Background: In vivo neuroimaging modalities such as magnetic resonance imaging (MRI), functional MRI (fMRI), magnetoencephalography (MEG), magnetic resonance spectroscopy (MRS), and quantitative susceptibility mapping (QSM) are useful techniques to understand brain anatomical structure, functional activity, source localization, neurochemical profiles, and tissue susceptibility respectively. Integrating unique and distinct information from these neuroimaging modalities will further help to enhance the understanding of complex neurological diseases.
Objective: To develop a processing scheme for multimodal data integration in a seamless manner on healthy young population, thus establishing a generalized framework for various clinical conditions (e.g., Alzheimer's disease).
Methods: A multimodal data integration scheme has been developed to integrate the outcomes from multiple neuroimaging data (fMRI, MEG, MRS, and QSM) spatially. Furthermore, the entire scheme has been incorporated into a user-friendly toolbox- "PRATEEK".
Results: The proposed methodology and toolbox has been tested for viability among fourteen healthy young participants. The data-integration scheme was tested for bilateral occipital cortices as the regions of interest and can also be extended to other anatomical regions. Overlap percentage from each combination of two modalities (fMRI-MRS, MEG-MRS, fMRI-QSM, and fMRI-MEG) has been computed and also been qualitatively assessed for combinations of the three (MEG-MRS-QSM) and four (fMRI-MEG-MRS-QSM) modalities.
Conclusion: This user-friendly toolbox minimizes the need of an expertise in handling different neuroimaging tools for processing and analyzing multimodal data. The proposed scheme will be beneficial for clinical studies where geometric information plays a crucial role for advance brain research.
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http://dx.doi.org/10.3233/JAD-210440 | DOI Listing |
Cereb Cortex
August 2025
Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.
Over three decades, statistical parametric mapping has transformed neuroimaging from descriptive mapping to causal inference, placing generative models at the core of causal explanations for brain function. It inspired to a large degree The Virtual Brain, which builds subject-specific digital twins from multimodal data, enabling brain simulations and exploration. Both frameworks converge at parameter estimation, where model and data meet, providing the mathematical manifestation of cause-effect in pathophysiology.
View Article and Find Full Text PDFInt J Surg
September 2025
Department of Human Structure and Repair, Ghent University Faculty of Medicine, Belgium.
Background: Staging laparoscopy (SL) is an essential procedure for peritoneal metastasis (PM) detection. Although surgeons are expected to differentiate between benign and malignant lesions intraoperatively, this task remains difficult and error-prone. The aim of this study was to develop a novel multimodal machine learning (MML) model to differentiate PM from benign lesions by integrating morphologic characteristics with intraoperative SL images.
View Article and Find Full Text PDFCancer Med
September 2025
Department of Computer Engineering, Social and Biological Network Analysis Laboratory, University of Kurdistan, Sanandaj, Iran.
Background: Ovarian cancer (OC) remains the most lethal gynecological malignancy, largely due to its late-stage diagnosis and nonspecific early symptoms. Advances in biomarker identification and machine learning offer promising avenues for improving early detection and prognosis. This review evaluates the role of biomarker-driven ML models in enhancing the early detection, risk stratification, and treatment planning of OC.
View Article and Find Full Text PDFHum Brain Mapp
September 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Investigating neuroimaging data to identify brain-based markers of mental illnesses has gained significant attention. Nevertheless, these endeavors encounter challenges arising from a reliance on symptoms and self-report assessments in making an initial diagnosis. The absence of biological data to delineate nosological categories hinders the provision of additional neurobiological insights into these disorders.
View Article and Find Full Text PDFBiochem Biophys Rep
December 2025
Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany.
Brillouin microscopy allows mechanical investigations of biological materials at the subcellular level and can be integrated with Raman spectroscopy for simultaneous chemical mapping, thus enabling a more comprehensive interpretation of biomechanics. The present study investigates different in vitro glioblastoma models using a combination of Brillouin and Raman microspectroscopy. Spheroids of the U87-MG cell line and two patient-derived cell lines as well as patient-derived organoids were used.
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