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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: closed (30 August 2025) | Viewed by 10675

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Special Issue Editors

Dr. Valeria Sacca

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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

Dr. Fabiana Novellino

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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
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Special Issue Information

Dear Colleagues,

This Special Issue will be focused on advancements in MRI techniques and quantitative MRI analysis, which are central to neuroimaging research. Nowadays, contemporary and innovative analytical perspectives are 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 covers 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 within the research community 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 of 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

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Keywords

MRI
functional MRI
structural MRI
DTI
machine learning
deep learning
brain biomarkers
functional connectivity
neurological diseases

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Published Papers (9 papers)

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Research

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11 pages, 1051 KB

Open AccessArticle

White Matter Integrity and Anticoagulant Use: Age-Stratified Insights from MRI Diffusion-Weighted Imaging

by Teodora Anca Albu, Nicoleta Iacob and Daniela Susan-Resiga

Appl. Sci. 2025, 15(16), 9022; https://doi.org/10.3390/app15169022 - 15 Aug 2025

Viewed by 249

Abstract

Apparent diffusion coefficient (ADC) values, derived from diffusion-weighted magnetic resonance imaging (DW-MRI), increase with age, reflecting microstructural changes in white matter integrity. However, factors beyond chronological aging may influence cerebral diffusion characteristics. We investigated whether anticoagulant use is associated with favorable white matter ADC profiles, suggesting preserved microvascular health. ADC values were analyzed in cerebral white matter across four age-defined adult cohorts (20–59 years). Minimum, mean, and maximum ADC values were extracted. Patients at the lowest and highest ends of the ADC spectrum within each group were identified. The prevalence of anticoagulant use was compared between groups, and a logistic regression model adjusted for age was used to assess the independent association between anticoagulant use and lower ADC values. Across all cohorts (n = 892), anticoagulated patients (n = 89) were significantly overrepresented among individuals with low ADC values consistent with younger diffusion profiles. Of the anticoagulated patients, 93.3% had ADC values below the lower cut-off limit. In contrast, only 30% of non-anticoagulated patients exhibited such profiles. Anticoagulant use was independently associated with low ADC values after adjusting for age (OR = 4.89, p < 0.0001). Anticoagulation is strongly associated with lower, more favorable ADC values in cerebral white matter, independent of age. These findings support the potential neuroprotective role of anticoagulants and suggest that diffusion MRI may serve as a surrogate marker for early microvascular brain health. Full article

(This article belongs to the Special Issue MR-Based Neuroimaging)

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15 pages, 1028 KB

Open AccessArticle

DTI Histogram and Texture Features as Early Predictors of Post-Radiotherapy Cognitive Decline

by Jincheng Wang, Philip Kyeremeh Jnr Oppong, Maho Kitagawa, Hidefumi Aoyama, Shunsuke Onodera, Satoshi Terae and Khin Khin Tha

Appl. Sci. 2025, 15(12), 6794; https://doi.org/10.3390/app15126794 - 17 Jun 2025

Viewed by 473

Abstract

Background: Radiotherapy for brain tumors can induce cognitive decline, yet most studies examine white matter (WM) damage six months post-treatment, overlooking early microstructural changes. This study investigated whether early WM changes, as measured by diffusion tensor imaging (DTI) histogram and texture features, can predict later cognitive deficits. Methods: Nineteen adults with brain metastases underwent DTI before and immediately after radiotherapy. Ten features—eight histogram-based and two texture-based—were extracted from normal-appearing WM of major DTI indices. Changes (Δ) in these features, if any, were analyzed via multiple linear regression, correlating them with cognitive performance at four months after therapy. Results: Out of 40 features, four exhibited significant post-radiotherapy changes. These were the mean (AD_mean) and skewness (AD_skewness) of axial diffusivity and the kurtosis of mean diffusivity (MD_kurtosis) and radial diffusivity (RD_kurtosis). Regression identified ΔAD_mean (β = −3.303 × 10⁴, p = 0.002) as negatively and ΔAD_skewness (β = 4.642, p = 0.006) and ΔRD_kurtosis (β = −1.505, p = 0.027) as positively associated with semantic fluency. Conclusions: Early WM microstructural disruptions—particularly axonal damage and heterogeneous injury—correlate with declines in semantic fluency. DTI histogram and texture features may be promising as early non-invasive biomarkers for cognitive risk following radiotherapy. Full article

(This article belongs to the Special Issue MR-Based Neuroimaging)

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14 pages, 2140 KB

Open AccessCommunication

New Functional MRI Experiments Based on Fractional Diffusion Representation Show Independent and Complementary Contrast to Diffusion-Weighted and Blood-Oxygen-Level-Dependent Functional MRI

by Alessandra Maiuro, Marco Palombo, Emiliano Macaluso, Guglielmo Genovese, Marco Bozzali, Federico Giove and Silvia Capuani

Appl. Sci. 2025, 15(9), 4930; https://doi.org/10.3390/app15094930 - 29 Apr 2025

Viewed by 573

Abstract

A fundamental limitation of fMRI based on the BOLD effect is its limited spatial specificity. This is because the BOLD signal reflects neurovascular coupling, leading to macrovascular changes that are not strictly limited to areas of increased neural activity. However, neuronal activation also induces microstructural changes within the brain parenchyma by modifying the diffusion of extracellular biological water. Therefore, diffusion-weighted imaging (DWI) has been applied in fMRI to overcome BOLD limits and better explain the mechanisms of functional activation, but the results obtained so far are not clear. This is because a DWI signal depends on many experimental variables: instrumental, physiological, and microstructural. Here, we hypothesize that the γ parameter of the fractional diffusion representation could be of particular interest for DW-fMRI applications, due to its proven dependence on local magnetic susceptibility and diffusion multi-compartmentalization. BOLD fMRI and DW-fMRI experiments were performed at 3T using an exemplar application to task-based activation of the human visual cortex. The results, corroborated by simulation, highlight that γ provides complementary information to conventional diffusion fMRI and γ can quantify cellular morphology changes and neurovascular regulation during neuronal activation with higher sensitivity and specificity than conventional BOLD fMRI and DW-fMRI. Full article

(This article belongs to the Special Issue MR-Based Neuroimaging)

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18 pages, 7130 KB

Open AccessArticle

Improving Cerebrovascular Imaging with Deep Learning: Semantic Segmentation for Time-of-Flight Magnetic Resonance Angiography Maximum Intensity Projection Image Enhancement

by Tomonari Yamada, Takaaki Yoshimura, Shota Ichikawa and Hiroyuki Sugimori

Appl. Sci. 2025, 15(6), 3034; https://doi.org/10.3390/app15063034 - 11 Mar 2025

Viewed by 1155

Abstract

Magnetic Resonance Angiography (MRA) is widely used for cerebrovascular assessment, with Time-of-Flight (TOF) MRA being a common non-contrast imaging technique. However, maximum intensity projection (MIP) images generated from TOF-MRA often include non-essential vascular structures such as external carotid branches, requiring manual editing for accurate visualization of intracranial arteries. This study proposes a deep learning-based semantic segmentation approach to automate the removal of these structures, enhancing MIP image clarity while reducing manual workload. Using DeepLab v3+, a convolutional neural network model optimized for segmentation accuracy, the method achieved an average Dice Similarity Coefficient (DSC) of 0.9615 and an Intersection over Union (IoU) of 0.9261 across five-fold cross-validation. The developed system processed MRA datasets at an average speed of 16.61 frames per second, demonstrating real-time feasibility. A dedicated software tool was implemented to apply the segmentation model directly to DICOM images, enabling fully automated MIP image generation. While the model effectively removed most external carotid structures, further refinement is needed to improve venous structure suppression. These results indicate that deep learning can provide an efficient and reliable approach for automated cerebrovascular image processing, with potential applications in clinical workflows and neurovascular disease diagnosis. Full article

(This article belongs to the Special Issue MR-Based Neuroimaging)

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42 pages, 31756 KB

Open AccessArticle

Models to Identify Small Brain White Matter Hyperintensity Lesions

by Darwin Castillo, María José Rodríguez-Álvarez, René Samaniego and Vasudevan Lakshminarayanan

Appl. Sci. 2025, 15(5), 2830; https://doi.org/10.3390/app15052830 - 6 Mar 2025

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Abstract

According to the World Health Organization (WHO), peripheral and central neurological disorders affect approximately one billion people worldwide. Ischemic stroke and Alzheimer’s Disease and other dementias are the second and fifth leading causes of death, respectively. In this context, detecting and classifying brain lesions constitute a critical area of research in medical image processing, significantly impacting clinical practice. Traditional lesion detection, segmentation, and feature extraction methods are time-consuming and observer-dependent. In this sense, research in the machine and deep learning methods applied to medical image processing constitute one of the crucial tools for automatically learning hierarchical features to get better accuracy, quick diagnosis, treatment, and prognosis of diseases. This project aims to develop and implement deep learning models for detecting and classifying small brain White Matter hyperintensities (WMH) lesions in magnetic resonance images (MRI), specifically lesions concerning ischemic and demyelination diseases. The methods applied were the UNet and Segmenting Anything model (SAM) for segmentation, while YOLOV8 and Detectron2 (based on MaskRCNN) were also applied to detect and classify the lesions. Experimental results show a Dice coefficient (DSC) of 0.94, 0.50, 0.241, and 0.88 for segmentation of WMH lesions using the UNet, SAM, YOLOv8, and Detectron2, respectively. The Detectron2 model demonstrated an accuracy of 0.94 in detecting and 0.98 in classifying lesions, including small lesions where other models often fail. The methods developed give an outline for the detection, segmentation, and classification of small and irregular morphology brain lesions and could significantly aid clinical diagnostics, providing reliable support for physicians and improving patient outcomes. Full article

(This article belongs to the Special Issue MR-Based Neuroimaging)

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20 pages, 2360 KB

Open AccessArticle

Estimation and Validation of the “c” Factor for Overall Cerebral Functioning in the Philadelphia Neurodevelopmental Cohort

by Tyler M. Moore, Monica E. Calkins, Daniel H. Wolf, Theodore D. Satterthwaite, Ran Barzilay, J. Cobb Scott, Kosha Ruparel, Raquel E. Gur and Ruben C. Gur

Appl. Sci. 2025, 15(4), 1697; https://doi.org/10.3390/app15041697 - 7 Feb 2025

Viewed by 935

Abstract

While both psychopathology and cognitive deficits manifest in mental health disorders, the nature of their relationship remains poorly understood. Recent research suggests a potential common factor underlying both domains. Using data from the Philadelphia Neurodevelopmental Cohort (N = 9494, ages 8–21), we estimated and validated a “c” factor representing overall cerebral functioning through a structural model combining cognitive and psychopathology indicators. The model incorporated general factors of psychopathology (“p”) and cognitive ability (“g”), along with specific sub-domain factors. We evaluated the model’s criterion validity using external measures, including parent education, neighborhood socioeconomic status, global functioning, and intracranial volume, and assessed its predictive utility for longitudinal psychosis outcomes. The model demonstrated acceptable fit (CFI = 0.98, RMSEA = 0.021, SRMR = 0.030), and the “c” factor from this model showed stronger associations with parent education (r = 0.43), neighborhood SES (r = 0.47), and intracranial volume (r = 0.39) than “p” and “g” factors alone. Additionally, baseline “c” factor scores significantly predicted psychosis spectrum outcomes at follow-up (d = 0.30–0.57). These findings support the utility of a “c” factor in capturing overall cerebral function across cognitive and psychopathology domains, with potential implications for understanding brain function, improving clinical assessment, and optimally focusing interventions. Full article

(This article belongs to the Special Issue MR-Based Neuroimaging)

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14 pages, 2954 KB

Open AccessArticle

Towards Sustainable Magnetic Resonance Neuro Imaging: Pathways for Energy Optimization and Cost Reduction Strategies

by Zélie Alerte, Mateusz Chodorowski, Samy Ammari, Alex Rovira, Julien Ognard and Douraied Ben Salem

Appl. Sci. 2025, 15(3), 1305; https://doi.org/10.3390/app15031305 - 27 Jan 2025

Cited by 1 | Viewed by 2854

Abstract

We evaluated the energy consumption of a 3T MRI using a central monitoring system, focusing on hospital energy costs during peak winter months from 2021 to 2023. We analyzed consumption during non-productive phases like end-of-day standby and assessed their impact. For active use, we compared standard and AI-enhanced protocols on phantoms, scheduling high-demand protocols during off-peak hours to benefit from lower energy prices. Standard protocols consumed 3.4 to 15 kWh, while optimized protocols used 2.3 to 10.6 kWh, reducing consumption by 32% on average. Savings per scan ranged from EUR 0.03 to EUR 3.7. The electrical consumption of a brain MRI protocol is equivalent to that of 3–4 knee protocols or 2–3 lumbar spine protocols. Using AI-optimized protocols and management, 41 protocols can be completed in 12 h, up from 30, reducing daily costs by EUR 2.38 to EUR 29.18. Annually, AI-optimized protocols could save 7900 to 8800 kWh per MRI unit, totaling 10,500 to 11,600 MWh across France’s MRI fleet, equivalent to the yearly consumption of about 4700 to 5300 people. Optimizing MRI resource use can expand patient access while significantly reducing the associated energy footprint. These findings support the implementation of more sustainable practices in medical imaging without compromising care quality. Full article

(This article belongs to the Special Issue MR-Based Neuroimaging)

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11 pages, 5368 KB

Open AccessArticle

A Novel Method Combining Radial Projection with Simultaneous Multislice Imaging for Measuring Cerebrovascular Pulse Wave Velocity

by Jeong-Min Shim, Chang-Ki Kang and Young-Don Son

Appl. Sci. 2025, 15(2), 997; https://doi.org/10.3390/app15020997 - 20 Jan 2025

Viewed by 997

Abstract

Magnetic resonance imaging (MRI) using a simultaneous multislice technique can measure dynamic vascular elasticity over time. However, conventional k-space undersampling can cause signal interference, owing to vertical projection between blood vessels within the same hemisphere. Here, we proposed a radial projection method that can reduce signal interference between the blood vessels and aimed to verify the theoretical and practical effects of this method. A dataset from the internal and common carotid arteries (ICA and CCA) was used for both projection methods. Pulse wave velocity (PWV) was calculated using the ICA and CCA time series, and the methods were compared using the mean absolute error of PWV. The feasibility of the radial projection method in an actual MRI environment was also evaluated. PWVs of the radial projection method were statistically indistinguishable from the ground truth. And the radial projection method was less sensitive to background noise levels and showed similar results to the ground truth. This method could effectively avoid signal interference between vessels and was feasible for use in real MRI environments, maintaining high temporal resolution even with fewer sampling timepoints. Therefore, it can contribute to the early diagnosis and treatment of cerebrovascular diseases through accurate and dynamic PWV measurements. Full article

(This article belongs to the Special Issue MR-Based Neuroimaging)

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Review

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12 pages, 520 KB

Open AccessReview

Neuroimaging Features of GRIN-Related Epilepsies

by Marco Cocciante, Irma Minacapelli, Azzurra Almesberger, Rosa Pasquariello and Emanuele Bartolini

Appl. Sci. 2025, 15(17), 9520; https://doi.org/10.3390/app15179520 - 29 Aug 2025

Viewed by 198

Abstract

N-methyl-D-aspartate receptors (NMDARs) are ionotropic glutamate channels that play a pivotal role in brain development and the regulation of learning and memory processes. De novo pathogenic variants in four genes encoding NMDA receptor subunits (GRIN1, GRIN2A, GRIN2B, and GRIN2D) have been implicated in a broad spectrum of neurodevelopmental disorders, including developmental delay, intellectual disability, autism spectrum disorders, epilepsy, and movement disorders. Mutations in the GRIN1 and GRIN2B genes, which encode the GluN1 and GluN2B subunits, respectively, are strongly associated with malformations of cortical development, including diffuse dysgyria, bilateral polymicrogyria, hippocampal dysplasia, corpus callosum hypoplasia, and other findings such as ventricular enlargement and basal ganglia abnormalities. Conversely, GRIN2A mutations are associated with heterogeneous and less specific neuroimaging patterns. We reviewed the existing literature on the neuroradiological features associated with GRIN gene mutations, also providing pictorial representations from our patient cohort. The analysis revealed a more consistent association of malformations of cortical development with GRIN1 and GRIN2B variants, likely reflecting the critical role of these genes in neuronal migration and proper development of cortical structures. In comparison, GRIN2A mutations are associated with milder brain abnormalities. An integrated assessment of neuroimaging patterns and GRIN gene variants provides valuable insights for differential diagnosis and supports targeted genetic screening in patients presenting with epileptic encephalopathy, global developmental delay, and autism spectrum disorders. Full article

(This article belongs to the Special Issue MR-Based Neuroimaging)

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