Advances in Neuroimaging for Human Cognition, Behavior, Brain Modulation and Prediction

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Neuroimaging and Neuroinformatics".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 430

Special Issue Editor

Special Issue Information

Dear Colleagues,

In recent decades, research on cortical activity has shifted from being primarily descriptive to playing a more disruptive role: discovering the underlying mechanisms of cortical activity and the ways to modulate it precisely and non-invasively. The ability to intervene in specific brain neural networks expands the horizons of clinical and technological innovation. However, achieving effective modulation depends primarily on neuroimaging techniques that can capture the spatiotemporal dynamics of cortical activity with high resolution. This Special Issue welcomes contributions that connect recent advances in neuroimaging with applications in the modulation of cortical activity. We invite submissions of studies that employ neuroimaging modalities, such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG), functional near-infrared spectroscopy (fNIRS), diffusion imaging, positron emission tomography (PET), and other multimodal tools. Of particular interest is research integrating these imaging tools with neuromodulation techniques, such as transcranial magnetic or electrical stimulation, to investigate how they influence cortical organization and plasticity. In that sense, this Special Issue calls for recent research in visual neuroscience, visual cognition, and human behavior that employs neuroimaging as a fundamental methodological approach for understanding the mechanisms underlying perception, decision-making, and interaction with the environment. Submissions are especially encouraged in emerging areas such as:

  • Human–computer interaction;
  • Brain–computer interfaces;
  • Design of computational models to analyze and predict cortical activity in conjunction with behavioral aspects.

This Special Issue aims to focus on interdisciplinary work among neuroimaging, neuromodulation, and computational modeling that will take us beyond image observation and analysis, toward a deeper understanding of brain function.

Dr. Francisco Ávila Gómez
Guest Editor

Manuscript Submission Information

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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. Journal of Imaging 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 1800 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

  • neuroimaging
  • human cognition
  • behavior prediction
  • brain stimulation
  • cortical modulation
  • multimodal integration
  • machine learning
  • human–machine interaction
  • predictive modeling
  • cognitive neuroscience

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Published Papers (1 paper)

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Research

18 pages, 6263 KB  
Article
Beyond GLM: Inter-Subject Variability as a Complementary Approach to Detect Longitudinal Changes in Emotion Processing in Multiple Sclerosis
by Alice Pirastru, Valeria Blasi, Diego Michael Cacciatore, Marco Rovaris, Elena Toselli, Francesco Pagnini, Cesare Cavalera, Fabrizio Esposito, Giuseppe Baselli and Francesca Baglio
J. Imaging 2026, 12(5), 210; https://doi.org/10.3390/jimaging12050210 - 15 May 2026
Viewed by 232
Abstract
Understanding how to reliably capture neural changes induced by treatments in neurological patients remains a major methodological challenge. This issue is particularly evident in the emotional domain—frequently impaired in conditions such as multiple sclerosis (MS) and a key target of rehabilitation—yet not limited [...] Read more.
Understanding how to reliably capture neural changes induced by treatments in neurological patients remains a major methodological challenge. This issue is particularly evident in the emotional domain—frequently impaired in conditions such as multiple sclerosis (MS) and a key target of rehabilitation—yet not limited to it. Longitudinal neuroimaging studies predominantly rely on group-level analyses (e.g., General Linear Model, GLM), which assume inter-subject homogeneity and treat inter-subject variability (ISV) as noise. Such assumptions may obscure treatment-related neuroplastic changes, especially in domains like emotion processing, where neural responses are intrinsically variable and highly individualized in clinical populations. This study investigates whether modeling ISV can better capture treatment-related neural changes, using emotion-focused rehabilitation as a representative case. We compared GLM with threshold-weighted overlap maps (OMthw), which quantify spatial consistency across individuals. Thirty healthy controls (HCs) and thirteen people with MS (pwMS) undergoing EMDR for depression performed an emotional fMRI task (pwMS pre/post-treatment). GLM revealed no longitudinal effects, whereas OMthw showed reduced variability in pwMS after treatment, alongside decreased depressive symptoms (p < 0.001). These findings highlight the value of variability-based approaches as a complementary framework to conventional GLM analyses for detecting treatment-related neuroplasticity in neurological populations. Full article
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