MR-Based Neuroimaging

1. Introduction

In recent years, the application of magnetic resonance imaging (MRI) to neuroscience has undergone significant development, thanks to the introduction of advanced acquisition techniques and quantitative analysis [1,2,3,4]. Brain MRI has progressively evolved from a primarily morphological tool to an integrated platform capable of exploring macroscopic structural characteristics and measures reflecting tissue integrity, functional activity, and adequate vascularization of different brain structures. Within this evolving framework, the application of artificial intelligence to MRI data has significantly advanced the ability to derive complex features beyond conventional imaging analysis [5,6,7]. Indeed, MRI-derived parameters, across all modalities, may be clinically relevant and can serve as biomarkers amenable to processing with artificial intelligence techniques.

From structural to functional MRI (fMRI) and diffusion tensor imaging (DTI) to emerging multimodal approaches, MR-based neuroimaging has become a cornerstone in our understanding of human brain function and underlying disease manifestations.

This Special Issue, titled “MR-based Neuroimaging”, brings together a range of studies that reflect the field’s expanding potential to address new and pressing questions in neuroscience, with a particular focus on how imaging tools can better characterize neurological and psychiatric disorders. New technological advancements now allow for greater resolution, more sophisticated analyses, and the integration of machine learning and artificial intelligence tools. These advancements provide new opportunities to develop biomarkers for early disease detection and improve treatment monitoring. Creating personalized therapeutic strategies is another major potential opportunity. The articles in this collection also address the remaining shortcomings that are evident in this field, such as the integration of multimodal data, the need for standardization of imaging protocols, and the translation of neuroimaging results into actionable clinical interventions.

This Special Issue includes a broad, multidisciplinary set of contributions that demonstrate the profound and accelerated evolution of MRI as a central tool for understanding the nervous system, including brain microstructure, cognitive function, and rare or frequent diseases, and addressing technological, computational, and even sustainability aspects.

2. An Overview of Published Articles

The studies presented in this Special Issue contribute to ongoing efforts to use MR-derived information to improve our knowledge of neurological diseases. Taken together, the published works outline three main directions: (i) refinement of imaging techniques, (ii) integration with clinical and cognitive biomarkers, and (iii) translation towards predictive, personalized, and sustainable applications.

The first research direction concerns improving the structural and microstructural characterization of nervous tissue. Studies based on DWI and DTI demonstrate how advanced metrics, including histogram and texture analyses, can reveal early white matter alterations in heterogeneous clinical contexts, from aging and the use of anticoagulants to post-radiotherapy cognitive decline. Advanced DTI also offers insights into spinal cord pathology and pain in degenerative cervical myelopathy, revealing how microstructural disruption relates to clinical symptoms. In the pediatric and genetic fields, the proposal for a new MRI classification of cortical tubers, emphasizing findings derived from susceptibility-weighted imaging in patients with tuberous sclerosis who present intralesional calcifications, represents a concrete example of how advanced imaging can redefine diagnostic criteria and improve genotype–phenotype correlation. Moreover, reviews of GRIN-related epilepsies and degenerative cervical myelopathy highlight the growing role of advanced MRI in linking molecular alterations, structural changes, and clinical manifestations, thereby providing a neuroanatomical substrate for clinical signs that are otherwise difficult to interpret.

The second thematic axis emerges from the focus on brain function and cognitive correlates. Contributions on idiopathic normal-pressure hydrocephalus and the estimation of the “c” factor in the Philadelphia neurodevelopmental cohort highlight the importance of integrating neuropsychological and neuroimaging data to understand the complexity of cognitive functioning and psychopathology. In this regard, fractional diffusion-based fMRI offers a significant opportunity to broaden the scope of functional imaging, as the proposed approach provides complementary information to traditional BOLD fMRI and, interestingly, may provide a more specific contrast.

The third, increasingly relevant area concerns technological and computational innovation. The application of deep learning to the segmentation of TOF-MRA angiography images and the development of new models for the automatic identification of small hyperintense white matter lesions demonstrate the central role that artificial intelligence is taking within the neuroradiological workflow, as AI protocols are becoming an integral part of the neuroradiological diagnostic process. Furthermore, innovative MRI techniques, such as simultaneous multi-slice imaging with radial projection for cerebrovascular pulse wave velocity measurement, are creating interesting possibilities for noninvasive assessment of vascular physiology.

Finally, this Special Issue also addresses the sustainability of MR neuroimaging, a current but often overlooked topic that needs to be taken into consideration. Indeed, the examination of energy use and optimization methods in high-field MRI systems is a necessary perspective, ensuring that technological progress walks hand in hand with environmental and economic responsibility.

3. Conclusions

The contributions collected in this Special Issue highlight that MR-based neuroimaging is undergoing a profound transformation, moving from a predominantly descriptive technique to an integrated, quantitative tool to analyze brain function. Ensuring a balance between the implementation of novel sequences and advanced image analysis protocols, mainly via artificial intelligence, and the caution needed in real-world clinical applications is still an ongoing challenge in driving neuroimaging toward increasingly precise and personalized practice.

The studies in this collection underscore the increasing importance of integrating structural and functional MRI-derived information with cognitive, genetic, and clinical biomarkers acting on multiple levels in this delicate transformation process. Computational approaches, such as machine learning and deep learning, are crucial for facilitating this integration, enabling the efficient translation of complex imaging data into the clinical realm to support all phases of clinical management, including diagnosis, prognosis, and treatment strategies.

Finally, equally important is that this Special Issue underscores the need to align technological innovation with sustainability, ensuring that MRI advancements are both clinically impactful and environmentally responsible.

In conclusion, taken together, the articles presented in the collection capture the current state of the art and provide clear indications for future directions, encouraging further research on the continued evolution of MRI neuroimaging toward a more personalized and impactful practice.