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Brain Sciences
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Applications of fMRI (Functional Magnetic Resonance Imaging) in Neuropsychiatry and...
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Applications of fMRI (Functional Magnetic Resonance Imaging) in Neuropsychiatry and Neurological Disease

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurotechnology and Neuroimaging".

Deadline for manuscript submissions: 20 January 2027 | Viewed by 6374

Editor

Dr. Vincenzo Alfano

E-Mail Website
Guest Editor
Department of Nuclear Medicine, IRCCS Synlab-SDN, Naples, Italy
Interests: brain imaging; neurosciences; psychiatry; radiology

Special Issue Information

Dear Colleagues,

Functional magnetic resonance imaging (fMRI) has emerged as an important tool in the study of brain function, offering non-invasive insights into the neural activity associated with both psychiatric and neurological disorders. This Special Issue will highlight advanced research that uses fMRI to explore the neural foundations of various conditions such as schizophrenia, depression, Alzheimer’s disease, epilepsy, Parkinson’s disease, and frontotemporal dementia. Contributions will explore advances in resting-state and task-based fMRI, functional connectivity analyses, and integration with other modalities such as EEG and PET. Emphasis will also be placed on the clinical translation of fMRI biomarkers for diagnosis, prognosis, and treatment monitoring. Innovative applications, including machine learning-based pattern recognition and individualized brain mapping, underscore the role of fMRI in precision medicine and, by encouraging interdisciplinary discussions among neuroscience, psychiatry, neurology, neuropsychology, and biomedical engineering, this Special Issue aims to advance our understanding of brain dysfunction and promote the development of fMRI-based tools for clinical decision-making.

Dr. Vincenzo Alfano
Guest Editor

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly 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 2200 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

  • functional MRI (fMRI)
  • neuropsychiatric disorders
  • neurological disease
  • brain connectivity
  • functional connectivity
  • neuroimaging
  • brain networks

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Published Papers (3 papers)

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Research

16 pages, 2035 KB  
Article
White Matter Infarct Detection with Transformer and Auto-ML-Derived Models
by Vitaly Dobromyslin and Wenjin Zhou
Brain Sci. 2026, 16(5), 529; https://doi.org/10.3390/brainsci16050529 - 15 May 2026
Viewed by 322
Abstract
Background: The past decade has seen a reversal in the U.S long-term decline in age-adjusted mortality rate from stroke. Timely stroke detection can boost the patient’s chances for recovery by enabling life-saving treatment and informing the patient of their increased risk of successive [...] Read more.
Background: The past decade has seen a reversal in the U.S long-term decline in age-adjusted mortality rate from stroke. Timely stroke detection can boost the patient’s chances for recovery by enabling life-saving treatment and informing the patient of their increased risk of successive infarcts. Since no single imaging modality can currently provide accurate and safe stroke detection at both acute and chronic stages, there is a need to develop novel imaging biomarkers with both diagnostic and prognostic value. Methods: We trained a U-shaped, nested hierarchical transformer model (UNesT) for T1-w white matter infarct segmentation using the ATLAS R2 dataset. Model reproducibility was independently evaluated on the Washington University (WU) stroke dataset. To boost T1-w UNesT stroke detection performance, automated machine learning techniques were used to extract 77 novel resting state fMRI (rs-fMRI) stroke biomarkers. Results: Stroke detection performance of the T1-w UNesT model degraded from Dice indices of 0.611 to 0.24 and 0.41 for the subacute and chronic timepoints respectively in the WU dataset. After UNesT re-optimization with the training portion of the WU dataset, the test set Dice index improved to 0.41–0.50. The spectral peak amplitude at the subacute timepoint increased the T1-w UNesT Dice index from 0.41 to 0.50 (p < 0.01) and correlated with language recovery. Conclusions: By training a UNesT model on the T1-w stroke data from one dataset and evaluating it on an independent dataset, we highlight the dataset drift concerns. Spectral peak amplitude is proposed as a novel rs-fMRI biomarker for improving stroke detection and predicting stroke recovery trajectory. Full article
(This article belongs to the Special Issue Applications of fMRI (Functional Magnetic Resonance Imaging) in Neuropsychiatry and Neurological Disease)
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17 pages, 4494 KB  
Article
What Can Neurosurgical Pediatric Populations Do in Functional Magnetic Resonance Imaging? Brain Activity Mapping Before Intervention Tasks, a Retrospective Study
by Ilaria Guarracino, Marta Maieron, Serena D’Agostini, Miran Skrap, Paola Cogo, Tamara Ius and Barbara Tomasino
Brain Sci. 2026, 16(4), 374; https://doi.org/10.3390/brainsci16040374 - 30 Mar 2026
Viewed by 642
Abstract
Background/Objectives: Performing presurgical functional magnetic resonance imaging (fMRI) mapping in young patients is considered a challenge for clinicians, as fMRI maps are the sole source of information about the functional organization of cognitive functions/areas, especially when an awake craniotomy is not possible, [...] Read more.
Background/Objectives: Performing presurgical functional magnetic resonance imaging (fMRI) mapping in young patients is considered a challenge for clinicians, as fMRI maps are the sole source of information about the functional organization of cognitive functions/areas, especially when an awake craniotomy is not possible, as is often the case for pediatric populations. The literature on the fMRI tasks used in pediatric populations with brain injuries shows a certain heterogeneity in the approaches (task-based or resting states) and tasks, with a preference for motor/language mapping: tasks assessing extra-language functions are lacking. Methods: We have designed fMRI tasks focused on language and extra-language functions, which can be easily be applied when clinicians need to perform presurgical mapping. We present a retrospective case series of 17 patients. Results: Seventeen young patients (13.4 ± 2.8 years; range 7–16) were included in the study, for whom fMRI was performed. All underwent successful fMRI mapping by completing fMRI tasks selected based on their lesion site. The number of tasks performed by each patient significantly correlated with their age (r(17) = 0.561, p = 0.019). The patients tolerated the assessment and had good motion control: their movement parameters were minimal (range of rotation of −0.015–0.01 degrees; range of translation of −0.8–0.2 mm). The most administered fMRI tasks were tongue motor localizer (60%) and object naming (70%), with some patients performing extra-language function mapping involving visuo-spatial processing, selective attention, memory, and inhibition. Conclusions: This is an exploratory study given the sample size. fMRI measurements were considered feasible, as patients were able to complete the tasks under clinically realistic conditions. We discuss the clinical implication/usefulness of administering tasks for a personalized functional assessment of the young patient before surgery. Full article
(This article belongs to the Special Issue Applications of fMRI (Functional Magnetic Resonance Imaging) in Neuropsychiatry and Neurological Disease)
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23 pages, 3402 KB  
Article
Resting-State and Task-Based Functional Connectivity Reveal Distinct mPFC and Hippocampal Network Alterations in Major Depressive Disorder
by Ekaete Ekpo, Lysianne Beynel, Bruce Luber, Zhi-De Deng, Timothy J. Strauman and Sarah H. Lisanby
Brain Sci. 2025, 15(11), 1133; https://doi.org/10.3390/brainsci15111133 - 22 Oct 2025
Cited by 2 | Viewed by 4540
Abstract
Background: Resting-state functional connectivity (RSFC) is widely used to identify abnormal brain function associated with depression. Resting-state functional magnetic resonance imaging (fMRI) scans have many potential confounds, and task-based FC might provide complementary information leading to better insight on brain function. Methods: We [...] Read more.
Background: Resting-state functional connectivity (RSFC) is widely used to identify abnormal brain function associated with depression. Resting-state functional magnetic resonance imaging (fMRI) scans have many potential confounds, and task-based FC might provide complementary information leading to better insight on brain function. Methods: We used MATLAB’s (version 2024b) CONN toolbox (version 22a) to evaluate FC in 40 adults with and without major depressive disorder (MDD) (nMDD = 23, nHC = 17). fMRI acquisition was performed while participants were at rest and while performing the Selves Task, an individualized goal priming task. Seed-based analyses were performed using two seeds: medial prefrontal cortex (mPFC) and left hippocampus. Results: Both groups showed strong positive RSFC between the mPFC and other DMN regions, including the anterior cingulate cortex and precuneus, which had more focal positive FC to the mPFC during the task in both groups. Additionally, the MDD group had significantly lower RSFC between the mPFC and several regions, including the right inferior temporal gyrus. The left hippocampus seed-based analysis revealed a pattern of hypoconnectivity to multiple brain regions in MDD, including the cerebellum, which was present at rest and during the task. Conclusions: Our results indicated multiple FC differences between adults with and without MDD, as well as distinct FC patterns and contrast results in resting state and task-based analyses, including differential FC between mPFC–cerebellum and hippocampus–cerebellum. These results emphasize that resting-state and task-based fMRI capture distinct patterns of brain connectivity. Further investigation into combining resting-state and task-based FC could inform future neuroimaging research. Full article
(This article belongs to the Special Issue Applications of fMRI (Functional Magnetic Resonance Imaging) in Neuropsychiatry and Neurological Disease)
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