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MR-Based Neuroimaging, 2nd Edition
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MR-Based Neuroimaging, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Neuroscience and Neural Engineering".

Deadline for manuscript submissions: 30 October 2026 | Viewed by 2328

Special Issue Editors

Dr. Valeria Sacca

E-Mail Website
Guest Editor
Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
Interests: signal processing; machine learning; feature felection; EEG; fMRI; resting state fMRI; fMRI analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Fabiana Novellino

E-Mail Website
Guest Editor
Neuroimaging Unit, Institute of Bioimaging and Molecular Physiology, National Research Council (IBFM-CNR), Viale Europa, Catanzaro, Italy
Interests: neurodegenerative diseases; movement disorders; dementia; MRI; molecular imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue "MR-Based Neuroimaging".

This Special Issue will be focused on advancements in MRI techniques and quantitative MRI analysis, which are crucial in neuroimaging research. Contemporary and innovative analytical perspectives are now essential for uncovering MR-based biomarkers and understanding their role in the early stages of brain diseases.

This Special Issue explores a comprehensive range of MRI sequences, including functional and structural MRI, as well as diffusion tensor imaging. It will address both traditional methods and novel approaches, such as the application of machine learning and deep learning techniques.

Furthermore, this Special Issue is driven by the growing interest in understanding structural and functional connectivity through MR imaging, as well as the use of MR imaging to customize treatments for neurological disorders.

Additionally, this Special Issue addresses the challenges associated with integrating various MRI technologies as essential biomarkers for clinical use. It also outlines potential future directions, offering a roadmap for ongoing innovation.

Dr. Valeria Sacca
Dr. Fabiana Novellino
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • MRI
  • functional MRI
  • structural MRI
  • DTI
  • machine learning
  • deep learning
  • brain biomarkers
  • functional connectivity

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Related Special Issue

Published Papers (5 papers)

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Research

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15 pages, 3122 KB  
Article
AttentionMS-Net: An Attention-Enhanced Multi-Scale Framework for Alzheimer’s Disease Classification with Subject-Level Validation
by Osman Yildiz and Abdulhamit Subasi
Appl. Sci. 2026, 16(9), 4338; https://doi.org/10.3390/app16094338 - 29 Apr 2026
Viewed by 63
Abstract
Many MRI-based Alzheimer’s disease (AD) classification studies report near-perfect accuracy; however, these results are often inflated by data leakage caused by slice-level splitting, where correlated slices of the same subject appear in both the training and test sets. In this study, we introduce [...] Read more.
Many MRI-based Alzheimer’s disease (AD) classification studies report near-perfect accuracy; however, these results are often inflated by data leakage caused by slice-level splitting, where correlated slices of the same subject appear in both the training and test sets. In this study, we introduce AttentionMS-Net, an attention-enhanced multi-scale deep learning architecture that combines channel-spatial attention using the Convolutional Block Attention Module (CBAM) with multi-scale feature aggregation from intermediate EfficientNet-B3 layers for binary AD classification (Non-Demented vs. Demented). Using strict subject-level 10-fold cross-validation on the OASIS dataset (347 subjects, 86,437 slices), our experiments clearly show the impact of data leakage: image-level 10-fold CV achieves about 99.9% accuracy, whereas subject-level 10-fold CV with the same model results in 80.8% accuracy—a reduction of roughly 19 percentage points. Aggregating predictions at the subject level further improves accuracy to 82.4% (AUC: 0.889), suggesting that prediction errors are mainly slice-specific rather than subject-specific. Systematic ablation reveals a complementary interaction between attention and multi-scale components, neither of which performs as well alone. Post-hoc Grad-CAM++ visualization and SHAP analysis suggest that AttentionMS-Net’s attention patterns are focused on ventricular regions—visual biomarkers indicative of overall brain atrophy rather than early hippocampal degeneration. These findings highlight the unreliability of current benchmarks and establish methodologically rigorous baselines for future AD classification research. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging, 2nd Edition)
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20 pages, 13821 KB  
Article
Improved MRSI in a Murine Glioma Model Using semiLASER: Refining the Metabolomics Data Obtained from Murine Models
by Zoona Javed, Gary V. Martinez, Marta Mulero-Acevedo, Ana Paula Candiota, Carles Arus, Miquel E. Cabañas and Silvia Lope-Piedrafita
Appl. Sci. 2026, 16(8), 3788; https://doi.org/10.3390/app16083788 - 13 Apr 2026
Viewed by 299
Abstract
Background: Magnetic resonance spectroscopic imaging (MRSI) offers valuable metabolic information for assessing brain tumor progression and therapeutic response, but its performance in rodent models is often hindered by the low signal-to-noise ratio (SNR) and spatially heterogeneous spectral quality, particularly in peripheral voxels. These [...] Read more.
Background: Magnetic resonance spectroscopic imaging (MRSI) offers valuable metabolic information for assessing brain tumor progression and therapeutic response, but its performance in rodent models is often hindered by the low signal-to-noise ratio (SNR) and spatially heterogeneous spectral quality, particularly in peripheral voxels. These issues reduce the number of usable spectra available for quantitative and classifier-based analyses. To address this, we implemented a multi-voxel MRSI-semiLASER sequence—widely recommended in clinical practice—on a 7T Bruker Biospec system running ParaVision 5.1 to improve spectral homogeneity in mouse brain tumor studies. Results: Compared with the vendor CSI-PRESS sequence, MRSI-semiLASER produced more uniform spectra across the grid and achieved up to a 1.2-fold SNR increase in murine glioma, enabling a 20% reduction in slice thickness without compromising spectral quality. Importantly, the sequence produced a substantial gain in the proportion of spectra amenable to analysis, particularly in outer grid voxels that frequently fail with CSI-PRESS. The implemented MRSI-semiLASER sequence and instructions are openly available to the community. Conclusions: MRSI-semiLASER improves spectral homogeneity, increases the proportion of usable spectra, and supports higher spatial detail. These technical improvements may enhance data yield per subject and may facilitate future applications such as more robust pattern recognition workflows and greater data efficiency in longitudinal studies, although such aspects were not evaluated here. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging, 2nd Edition)
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17 pages, 3640 KB  
Article
A 3D Global-Patch Transformer for Brain Age Prediction Using T1-Weighted MRI with Gray and White Matter Maps
by Seung-Jun Lee, Myungeun Lee, Yoo Ri Kim and Hyung-Jeong Yang
Appl. Sci. 2026, 16(6), 3004; https://doi.org/10.3390/app16063004 - 20 Mar 2026
Viewed by 315
Abstract
With the increasing prevalence of neurodegenerative diseases driven by population aging, imaging-based biomarkers are needed to quantify brain aging at an early stage. Brain age, which estimates structural brain aging relative to chronological age, has emerged as a useful indicator. Prior work has [...] Read more.
With the increasing prevalence of neurodegenerative diseases driven by population aging, imaging-based biomarkers are needed to quantify brain aging at an early stage. Brain age, which estimates structural brain aging relative to chronological age, has emerged as a useful indicator. Prior work has mainly used T1-weighted MRI with deep learning models such as convolutional neural networks (CNNs) or transformers; however, many approaches insufficiently capture three-dimensional structural continuity and localized anatomical patterns, and tissue-specific aging in gray matter (GM) and white matter (WM) is often treated as auxiliary. To address these limitations, we propose a 3D Global–Patch Transformer framework for brain age prediction that directly processes volumetric data while jointly learning global brain structure and local anatomical features. Our model runs global and patch pathways in parallel and explicitly incorporates GM and WM structural maps alongside T1-weighted MRI to encode tissue-specific aging signals. Experiments on multiple public datasets, including IXI and OASIS, show that the proposed method reduces mean absolute error (MAE) by approximately 10–15% compared with CNN-based and single-input transformer baselines, with notably improved performance in older populations, highlighting the value of tissue-level structural information for brain age estimation. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging, 2nd Edition)
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Review

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21 pages, 1747 KB  
Review
The Role of Advanced MR Imaging in Gliomas
by Anastasia K. Zikou, Eleni Romeo, George A. Alexiou, Marios Lampros, Spyridon Voulgaris, Loukas Astrakas and Maria I. Argyropoulou
Appl. Sci. 2026, 16(2), 1027; https://doi.org/10.3390/app16021027 - 20 Jan 2026
Viewed by 932
Abstract
Gliomas are a significant health problem with a lot of imaging challenges. The role of imaging is no longer limited to only providing anatomic details, but with the advancement of Magnetic Resonance Imaging (MRI) techniques, it now permits the assessment of the freedom [...] Read more.
Gliomas are a significant health problem with a lot of imaging challenges. The role of imaging is no longer limited to only providing anatomic details, but with the advancement of Magnetic Resonance Imaging (MRI) techniques, it now permits the assessment of the freedom of water molecule movement, the microvascular structure, the hemodynamic characteristics, and the chemical makeup of certain metabolites of lesions. These advanced imaging techniques include diffusion-weighted imaging, diffusion tensor imaging, dynamic contrast-enhanced MRI, Magnetic Resonance (MR) perfusion, MR angiography, and magnetic resonance spectroscopy. their role in the diagnosis, classification, and post-treatment follow-up of gliomas, as well as their application in radiogenomics and glioma analysis with the aid of artificial intelligence, is presented and discussed. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging, 2nd Edition)
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Other

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15 pages, 769 KB  
Perspective
Concurrent/Interleaved TMS–fMRI as an MR-Guided Framework for Target Engagement
by Chiara Di Fazio and Sara Palermo
Appl. Sci. 2026, 16(9), 4135; https://doi.org/10.3390/app16094135 - 23 Apr 2026
Viewed by 182
Abstract
Concurrent/interleaved transcranial magnetic stimulation combined with functional MRI (TMS–fMRI) enables causal perturbation of targeted cortical regions while measuring whole-brain MR-based responses during stimulation. This perspective argues that the main translational value of concurrent/interleaved TMS–fMRI lies in operationalizing target engagement and network-level propagation as [...] Read more.
Concurrent/interleaved transcranial magnetic stimulation combined with functional MRI (TMS–fMRI) enables causal perturbation of targeted cortical regions while measuring whole-brain MR-based responses during stimulation. This perspective argues that the main translational value of concurrent/interleaved TMS–fMRI lies in operationalizing target engagement and network-level propagation as measurable endpoints, bridging stimulation “dose” to clinically meaningful effects. Rather than proposing a validated gold-standard protocol, we frame concurrent/interleaved TMS–fMRI as a measurement-driven translational approach in which MRI-informed targeting and MR-based readouts can be integrated to quantify target engagement under clearly specified methodological and quality-control conditions. This perspective specifically aims to make explicit an intermediate verification step that remains only partially formalized in current clinical neuromodulation workflows. We propose that MRI-based neuronavigation should move beyond template coordinates toward individualized anatomical and network-informed targeting, with the aim of improving precision, reproducibility, and safety. Building on the field’s evolution from technical feasibility to emerging clinical applications, we outline a staged framework from feasibility to biomarker potential, summarize representative protocol archetypes, and provide pragmatic recommendations for reporting and study design to improve comparability. This framework is intended to guide future concurrent/interleaved TMS–fMRI studies toward biomarker-ready designs and more clinically informative network neuromodulation. We further distinguish offline MRI-informed targeting from potential future real-time or closed-loop implementations, and we emphasize that current biomarker claims should remain proportional to the still heterogeneous evidence base. Full article
(This article belongs to the Special Issue MR-Based Neuroimaging, 2nd Edition)
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