fismedMedical Physics,
AI and Neurotechnology
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Medical Physics, AI and Neurotechnology · University of Rome Tor Vergata

fismed@uniroma2.it

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Scientific publications

Journal articles and preprints in artificial intelligence, biomedical imaging, neurotechnology and complex systems.

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The 50 most recent journal articles and preprints. Matching records are combined; links below each paper open its available source records.

2026

An Italian cartography of VEXAS-related thrombosis

HemaSphere · Giorgia Ranucci et al.

Thrombotic events (TEs) occur in up to 40% of patients with vacuoles, E1 enzyme, X-linked, autoinflammatory, and somatic (VEXAS) syndrome, but data on its clinical-genomics features and anticoagulation strategies are limited. To gain more insight into this, we conducted a two-step study evaluating the prevalence and outcome of TE in VEXAS. First, among 1086 patients followed for TEs, 198 were men aged >40 years with unprovoked thrombosis and no known thrombophilia; 21 also had at least one VEXAS-compatible feature and underwent UBA1 exon 3 testing. No UBA1 mutation was detected in these 21 patients. Next, we leveraged our Italian VEXAS network, and we accrued 87 molecularly confirmed Italian VEXAS cases (median age 70 years). Any history of TE was documented in 43/87 patients (49%), deep vein thrombosis being the most common (71%). Because follow-up varied, incident thrombosis was analyzed using a time-to-first-event framework from molecular VEXAS diagnosis, with death without prior TE treated as a competing event. Among 49 patients without prior/concomitant TE, five developed incident post-diagnosis TE; the 24-month cumulative incidence was 18.3%. Thrombophilia testing revealed a 15% co-occurrence, including heterozygous Factor V Leiden, Factor II G20210A, and anti-cardiolipin antibodies. Treatments comprised direct oral anticoagulants (DOACs) (51%), low molecular weight heparin (LMWH) (28%), Fondaparinux (14%), and vitamin K antagonists (AVKs) (7%). Notably, 27% experienced multiple TEs, of which 22% occurring despite anticoagulation during disease flares. Our findings provide an updated cartography of VEXAS-related TE, suggesting early screening for thrombophilia in these patients to inform both personalized anticoagulation and disease-control strategies.
PublisherPubMedScopus
Longitudinal modelling
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2026

Entropy, Inhibition and Memory in Balanced Spiking Reservoirs

Entropy (Basel, Switzerland) · Luigi Rosati et al.

Recurrent neural networks are studied along two largely parallel tracks: as machine-learning models evaluated by task performance and as computational-neuroscience models of cortical circuits evaluated by dynamical realism. Reservoir computing offers a meeting point, yet the link between dynamical regime and computational performance has not been systematically mapped in biologically constrained spiking architectures. We treat the Brunel balanced excitatory-inhibitory network as a reservoir and characterize separation capacity (kernel quality) and transient memory (corrected linear memory capacity, validated by non-parametric mutual information) across the full phase diagram. The analysis uses a four-state Markov source whose Shannon entropy rate is set in closed form by a single parameter at fixed marginal entropy. Both capabilities increase monotonically with the inhibitory ratio g, remaining jointly highest in the asynchronous irregular regime, with diminishing increments consistent with eventual saturation; the synchronous irregular regime, despite a network timescale three orders of magnitude longer, supports neither. Memory further requires sparse input coupling: dense coupling collapses the driven timescale and erases memory in every regime. Inhibitory balance thus emerges as a unified architectural control parameter, providing a quantitative design criterion for cortical-circuit modeling and reservoir computing applications.
PublisherPubMedScopus
Computational neuroscience
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Reservoir computing · Machine learning model · Recurrent neural network · Computational neuroscience

2026

NeuroFusion: A Unified Framework for Generalized Visual Stimulus Decoding from fMRI Across Datasets and Subjects

Neuroinformatics · Muhammad Kashif et al.

Recent advancements in neural decoding have shown promising results in reconstructing visual experiences from brain activity. However, existing approaches focus primarily on decoding within a single dataset or subject, which limits generalization across various sources of neuroimaging. In this work, we propose a novel framework for the decoding of visual stimuli between subjects and between data sets, integrating neural recordings from multiple publicly available fMRI datasets. To address inherent intersubject and interdataset variability, we introduce a contrastive learning-based alignment strategy using image embeddings from a pre-trained IP-Adapter model. Our approach learns a shared latent space by aligning subject-specific neural representations with image features, enabling generalized decoding across both subjects and datasets. In addition, we propose a simple yet effective data augmentation method using ridge regression. This method synthesizes realistic fMRI-like signals from novel images by predicting voxel activity and injecting learned noise distributions, thus enhancing training diversity and model robustness. To the best of our knowledge, while several recent studies have explored cross-subject decoding, we extend recent cross-subject decoding efforts by training a single unified framework jointly across multiple public fMRI datasets and subjects, enabling cross-dataset transfer in addition to cross-subject generalization. We distinguish this multi-dataset unified training setting, where each dataset contributes training data, from a stricter leave-one-dataset-out transfer setting in which the target dataset is excluded from source pretraining and used only for lightweight alignment-layer adaptation. Empirically, our unified model achieves strong semantic reconstruction across datasets (e.g., up to 94.8% CLIP similarity on NSD (AUG) and 0.403 SSIM on BOLD5000 after lightweight finetuning), demonstrating robust cross-subject and cross-dataset transfer.
PublisherPubMedScopus
AI and neural decoding
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Visual stimulus · FMRI datasets · Model achieves · Semantic reconstruction · Neural decoding

2026

A modular semantic-structural pipeline for visual decoding from primate spiking data via selective temporal integration

Imaging neuroscience (Cambridge, Mass.) · Matteo Ciferri et al.

Characterizing the information content of intracortical signals during visual processing is a central challenge in systems neuroscience. We address the problem of decoding visual information from high-density intracortical recordings in primates, using the THINGS Ventral Stream Spiking Dataset. We systematically evaluate the effects of model architecture, training objectives, and data scaling on decoding performance. Results show that decoding accuracy is jointly driven by non-linearity and selective temporal aggregation, rather than heavier sequence modelling in this data regime. A simple model combining temporal attention with a shallow MLP achieves up to 70% top-1 image retrieval accuracy, outperforming linear baselines as well as recurrent and convolutional approaches. Scaling analyses reveal predictable diminishing returns with increasing input dimensionality and dataset size. Building on these findings, we design a modular generative decoding pipeline that combines low-resolution latent reconstruction with semantically conditioned diffusion, generating plausible images from 200 ms of brain activity. This framework provides principles for brain-computer interfaces and semantic neural decoding.
PublisherPubMedScopusarXivarXiv
Brain computer interfaces
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Brain computer interfaces · Image retrieval · Plausible image · Decoding pipeline · Neural decoding

2026

Cross-subject decoding of human neural data for speech brain computer interfaces

Journal of neural engineering · Tommaso Boccato et al.

Objective.Brain-to-text systems have recently achieved impressive performance when trained on single-participant data, but remain limited by uninvestigated cross-subject generalization.Approach.We present the first neural-to-phoneme decoder trained jointly on the two largest intracortical speech datasets (Willettet al2023Nature6201031-6; Cardet al2024New Engl. J. Med.391609-18), introducing day- and dataset-specific affine transforms to align neural activity into a shared space. Additionally, a hierarchical GRU decoder with intermediate CTC supervision and feedback connections is designed to address the conditional-independence assumption of standard CTC loss.Main results.Our model matches or outperforms within-subject baselines while being trained across participants, and adapts to unseen subjects using only a linear transform or brief fine-tuning. On an independent inner-speech dataset (Kunzet al2025Cell1884658-4673.e17), our approach shows some initial evidence of generalization, by training only subject-, day-specific transforms.Significance.These results demonstrate the feasibility of cross-subject pretraining as a promising direction toward more scalable speech Brain Computer Interfaces.
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Brain computer interfaces
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Brain computer interfaces

2026

Bio-integrated μBots with overtone ultrawideband magnetoelectric antennas for wireless telemetry

Science advances · Mahdieh Shojaei Baghini et al.

Implantable and wearable devices require antennas that are both miniaturized and efficient, yet conventional designs are constrained by narrow bandwidth and orientation sensitivity. We report overtone ultrawideband magnetoelectric (OUWB-ME) antennas that exploit higher-order acoustic modes in polished silicon substrates to achieve a 22.6-gigahertz -10-decibel bandwidth and overtone capability in the 3- to 4-gigahertz range. Packaged into "μBots," these magnetoelectric heterostructures bonded with silver nanoparticle inks maintain stable operation under biological loading. In vitro assays confirm the biocompatibility of aluminum nitride and the protective role of parylene encapsulation for iron-gallium. Ex vivo rat and human tissues reshape transmission and reflection spectra, with reproducible frequency windows near 3.3 and 3.9 gigahertz. μBots enable real-time audiovisual telemetry using software-defined radios and exhibit compatibility with 7-tesla magnetic resonance imaging. By combining wideband response, robustness to misalignment, and biocompatible packaging, OUWB-ME μBots provide a scalable platform for wireless bio-integrated communication and telemetry.
PublisherPubMedScopusarXivarXiv
Stimulation and bioelectronics
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Magnetic resonance imaging

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2026

A new in silico model to precisely design focused ultrasound brain therapies

Medical physics · Allegra Conti et al.

BACKGROUND: Focused ultrasound (FUS) combined with microbubbles enables transient and noninvasive blood-brain barrier (BBB) opening, facilitating targeted drug delivery. However, accurate treatment planning remains difficult due to inter-patient anatomical variability and the common assumption in simulations that brain tissues behave like water. PURPOSE: To develop and evaluate MODFUS ( M O D e l t o p r e c i s e l y d e s i g n F o c u s e d U l t r a S o u n d e x p e r i m e n t s ), an in silico acoustic simulation framework that integrates a high-resolution anatomical head model to quantify the impact of intracranial tissue heterogeneity and probe alignment on transmitted acoustic pressure, supporting treatment planning for BBB opening. METHODS: MODFUS integrates an anatomical head model comprising 115 tissue types and Computed Tomography (CT)-derived skull properties. FUS simulations were conducted using a 250 kHz single-element transducer at 13 distinct skull entry locations. Two modeling approaches were evaluated: a classical model, in which the skull was embedded in water and brain tissues were homogenized as water, and an heterogeneous anatomical model. To further assess robustness, an additional set of 50 simulations introduced controlled perturbations in probe positioning, consisting of angular deviations ( ± 18 ∘ ) and translational offsets ( ± 7 mm) relative to the reference configuration. Model- and configuration-dependent differences were quantified using peak positive pressure (PPP), peak negative pressure (PNP), and potential therapeutic volume. Statistical significance was assessed using Wilcoxon rank-sum or Wilcoxon signed-rank tests ( α = 0.05). For Classical vs Realistic models, Wilcoxon rank-sum tests were applied to PPP, PNP, and BBB exposure volume, and Levene's test assessed variance differences across 13 positions. Multiple testing was controlled using the Holm-Bonferroni procedure ( α = 0.05) across all tests. Effect sizes were quantified using Cohen's d with 95% confidence intervals. RESULTS: Compared to the classical "skull+water" benchmark model, the heterogeneous model predicted up to 11% lower PPP, slightly lower PNP ( ∼ -5%), and 35% smaller potential BBB exposure volumes across the 13 paired sonication positions. After Bonferroni correction, Paired statistical testing (Wilcoxon signed-rank, two-sided) showed significant differences for PPP (p = 0.0270) and potential BBB exposure volume (p = 0.0015), while differences in PNP were not statistically significant (p = 0.8286). Levene's test for variance confirmed significant heteroscedasticity for PPP (p = 0.027) and PNP (p = 3.2 × 10), but not for BBB exposure volume (p = 0.2680). Cohen's d effect sizes indicated a large positive effect for PPP, a small negative effect for PNP, and a very large positive effect for BBB exposure volume. CONCLUSIONS: MODFUS demonstrates the influence of incorporating detailed tissue heterogeneity on simulation outcomes, including pressure distribution and potential BBB exposure volume. These results highlight the importance of realistic soft tissue modeling and stereotaxic probe alignment for safe and effective FUS treatment planning. The study serves as a preliminary proof-of-concept. Future studies incorporating in vivo experiments will be required to quantify the accuracy of this approach.
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Stimulation and bioelectronics
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Head model · Focused ultrasound · Computed tomography

2026

Hierarchical Pooling for Sheaf Neural Networks

arXiv · Dionisia Naddeo et al.

Sheaf Neural Networks (SNNs) generalize Graph Neural Networks (GNNs) by replacing scalar node signals with stalk-valued signals and by using restriction maps to measure compatibility across edges. Unlike standard graph diffusion, which encourages neighboring node features to become similar, sheaf diffusion promotes consistency through the restriction maps and can therefore model more general relationships between neighboring nodes. However, existing sheaf neural architectures mainly operate at a fixed graph resolution and do not provide a principled pooling mechanism for building hierarchical representations. In this paper, we introduce Hierarchical Sheaf Pool (HiSP), a sheaf-aware pooling framework based on local spectral coarsening. Given a partition of the graph, HiSP constructs each coarse stalk by projecting fine stalk-valued features onto the low-frequency eigenmodes of the cluster-internal sheaf Laplacian. These local modes define a cochain-level prolongation map, which allows the fine sheaf energy to be represented on the coarse space through a Galerkin operator. We further analyze the approximation induced by coarsening by separating truncation loss, due to discarded local modes, from realization loss, due to representing the projected operator as a coarse sheaf. Finally, we implement HiSP as a GNN pooling layer compatible with SNNs and provide a PyG implementation supporting batching, lifted sheaf Laplacians, and hierarchical architectures.
arXivarXiv
AI and neural decodingGraph neural network
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Graph neural network

2026

Ex vivo localization of wireless implantable microdevice using high-resolution 3D imaging techniques

Frontiers in bioengineering and biotechnology · Teresa Giannattasio et al.

The CROSSBRAIN EU project aims to address the heterogeneous nature of brain pathologies by developing wireless implantable microbots (µBots, planned dimensions 100 × 100 × 100 μm3) for highly localized neuromodulation. These devices are designed to precisely modulate brain activity with minimal invasiveness, enabling targeted resolution of specific spatiotemporal events, capabilities not currently achieved by existing neuromodulation technologies. A crucial step involves visualizing and ensuring the optimal placement of the µBots in the brain tissue, to study their functionality after implantation. In this preliminary ex vivo study, we used non-functional µBot silicon (Si) dummies matching the lateral dimensions of the intended µBots, with reduced thickness (100 × 100 × 50 μm3) to simplify fabrication and handling. Due to the intrinsic MRI incompatibility of the µBot platform, encompassing both the dummies used in this study and the future functional devices under development, and the limitations of standard histological approaches in reliably identifying and preserving the implant site during processing, we developed an integrated imaging workflow combining 2D and 3D techniques. While standard histological methods and tissue clearing presented substantial limitations in preserving the position of the dummies within the brain tissue, combining histological techniques with 3D X-ray tomography provided a robust strategy. In particular, synchrotron radiation-based X-ray Phase Contrast Tomography (XPCT), with its intrinsic high contrast and resolution, enabled detailed visualization of dummies within the surrounding vascular and cellular architecture. In contrast, conventional micro-Computed Tomography (micro-CT), although more widely accessible, enabled non-destructive guidance for targeted sectioning. Importantly, and in line with the scope of a Brief Research Report, this study presents a preliminary but technically robust investigation conducted within the CROSSBRAIN project, aimed at identifying and establishing an optimized imaging strategy for the visualization of implanted µBots in brain tissue. This methodological framework is intended as an initial step toward future in vivo studies, in which the validated imaging pipeline will be applied to track both dummy and functional devices and to enable subsequent evaluation of foreign body response under physiologically relevant conditions. This ex vivo workflow therefore provides the essential technical foundation for such future investigations and supports the clear positioning of this work as a feasibility and optimization study. This approach could be particularly valuable for new generations of implantable technologies incompatible with MRI and could support future development of personalized neuromodulation therapies by enabling precise device localization and structural tissue assessment.
PublisherPubMedScopus
Stimulation and bioelectronics
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3D imaging · Computed tomography

2026

Retrieval-Based Brain Decoding by Alignment, not Complexity

arXiv · Matteo Ciferri et al.

A prominent theory in cognitive science suggests that concepts in the brain are organized as high-dimensional vectors, with semantic meaning captured by directions and relative angles in this space. Brain decoding is the effort of reconstructing or retrieving stimuli (or their representations) from neural activity and involves finding a function that approximates how the brain represents concepts. This motivates the investigation of contrastive objectives as biologically plausible candidates to reverse the brain loss function. In this work, we study how functional MRI (fMRI) activity can generally be mapped with the embedding spaces of foundation models in vision, language, and audio. Although neural computations are highly non-linear at the microscale, fMRI measurements average signals across space and time, further smoothed by noise, effectively linearizing the observable representation. Consistent with these views, our experiments across multiple datasets demonstrate that linear contrastive decoders consistently outperform ridge regression and standard non-linear alternatives, and that these results generalize across images, text, and sound. These findings indicate that decoding gains arise more from the choice of training objective than from architectural complexity, pointing to contrastive-linear models as a principled strategy for brain decoding.
arXivarXiv
AI and neural decoding
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Brain decoding · Linear model · Foundation model · Functional MRI

2026

R&B - rhythm and brain: Cross-subject decoding of music from human brain activity

Neural networks : the official journal of the International Neural Network Society · Matteo Ciferri et al.

Music is a universal phenomenon that influences human experiences across cultures. We investigate whether music can be decoded from human brain activity measured with fMRI, by modeling mappings between neural data and latent representations of musical stimuli. Our approach integrates functional and anatomical alignment techniques to facilitate cross-subject decoding. Starting from the GTZan fMRI dataset, where five participants listened to 540 musical tracks from 10 genres, we used the CLAP model to extract latent representations of the musical stimuli and developed voxel-wise encoding models to identify brain regions responsive to these stimuli, by applying a threshold to the correlation between predicted and actual brain activity. Our decoding pipeline, primarily retrieval-based, employs a linear map to project back brain activity to the corresponding CLAP features. This enables us to retrieve the musical stimuli most similar to those that originated the fMRI data. Our results demonstrate state-of-the-art identification accuracy, outperforming existing approaches.
PublisherPubMedScopusarXivarXiv
AI and neural decodingBrain computer interfaces
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Encoding model · FMRI dataset · Decoding pipeline

2026

Mapping Whisper Representations to Human ECoG Responses with Interpretable Time-Resolved Neural Encoding

arXiv · Matteo Ciferri et al.

Understanding how speech foundation models relate to human cortical activity is a key challenge for computational neuroscience. Here, we investigate how internal representations from Whisper predict intracranial ECoG responses during naturalistic speech perception. We introduce a time-resolved neural encoder that combines speech embeddings with a recurrent temporal model and soft attention, allowing us to examine layer-wise brain alignment. Intermediate Whisper layers provide the strongest correspondence with neural activity, supporting a hierarchical match between model representations and cortical speech processing. Comparisons with baselines show that high-resolution ECoG responses benefit from temporally structured modelling beyond linear mappings from the same speech representations. In addition, attention maps reveal temporally local alignment between speech embeddings and neural responses, while a phonemic interpretability analysis identifies anatomically coherent phoneme-category organization among encoding-informative electrodes. Together, these results suggest that speech foundation models offer a useful framework for studying time-resolved cortical speech representations.
arXivarXiv
Computational neuroscience
2026

Stimulation success!? Improved response inhibition performance after prefrontal single-site and condition-and-perturb transcranial magnetic stimulation

Cognitive, affective & behavioral neuroscience · Maximilian A Friehs et al.

In everyday behaviour, the ability to stop an already initiated action is critical for ensuring both your safety and that of others; for example, when stopping a reaching movement towards a hot stove-top after realising it is hot. Neuroscientific evidence points towards the critical role of several regions in the right prefrontal cortex in the coordination and execution of this response inhibition-specifically the right inferior frontal gyrus (rIFG) and the right dorsolateral prefrontal cortex (rDLPFC). The present study investigated the effects of different transcranial magnetic stimulation (TMS) protocols on stop-signal task (SST) performance. We hypothesized that TMS over one or both of these areas would be detrimental to performance. However, contrary to our hypothesis, TMS significantly facilitated performance regardless of the stimulation condition. We applied both frequentist and Bayesian methods to assess the robustness of these effects, revealing consistent reductions in stop-signal reaction time (SSRT) across active conditions. Our results add to the growing body of results that suggest TMS effects may not be as straight-forward as usually assumed and that so-called "inhibitory protocols" can facilitate performance. This result could be explained by a shift in the signal-to-noise ratio depending on the pre-activation of the area. Put differently, TMS may have primed task-related activity in the target areas to a level that was optimal for task performance. Alternatively, the observed effect may reflect an (over)compensation by other parts of the network or disruption of competing resources. Future studies may provide further support for these hypotheses.
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Stimulation and bioelectronics
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Transcranial magnetic stimulation

2026

Ultrasound-Assisted multimodal neuromodulation via nanosystems

Journal of nanobiotechnology · Syed Bilal Nizami et al.

Neuromodulation techniques have emerged as transformative tools for treating several neurological and psychiatric disorders, offering alternatives to traditional pharmacological approaches often hindered by the blood-brain barrier and off-target effects. While conventional modalities like deep brain stimulation, transcranial magnetic stimulation, and optogenetics have shown promise, they each face limitations in invasiveness, spatial resolution, or clinical applicability. In recent years, low-intensity ultrasound has gained attention as a noninvasive, deep-penetrating modality capable of modulating brain circuits with millimeter-sized spatial precision. This review explores the synergistic integration of ultrasound with engineered nanosystems to achieve multimodal neuromodulation such as electrical, mechanical, optical, and chemical via acoustic energy. We describe how nanoscale materials can transduce ultrasound into localized bioelectric signals, mechanical stress, light emission, or controlled drug release. These interactions enable precise, on-demand stimulation or inhibition of neuronal activity, including in deep brain regions. Experimental studies have demonstrated neuromodulatory effects across a variety of models, with applications ranging from optogenetics and drug delivery to behavioral modulation in rodents and primates. The review concludes with a critical assessment of the translational challenges such as nanoparticle delivery, biocompatibility, long-term clearance, and safety thresholds for human use while outlining promising strategies like cell-based delivery, biodegradable materials, and closed-loop control systems. These innovations highlight the potential of ultrasound-assisted nanosystems as transformative tools for precise neuromodulation in both experimental neuroscience and clinical applications.
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Stimulation and bioelectronics
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Deep brain stimulation · Transcranial magnetic stimulation · Gained attention · Intensity ultrasound

2026

Interleukin-6-producing non-secreting cervical paraganglioma presenting with fever of unknown origin and systemic inflammatory response syndrome

BMJ case reports · Valentina Rusconi et al.

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours that usually present with symptoms related to catecholamine excess. However, a small subset may secrete cytokines such as interleukin-6 (IL-6), leading to atypical systemic manifestations and delayed recognition of a paraneoplastic inflammatory syndrome. We report the case of a middle-aged woman with a previously diagnosed non-secreting cervical paraganglioma who developed fever of unknown origin (FUO), anaemia and liver dysfunction 5 years after the initial diagnosis. Extensive investigations excluded infectious, autoimmune and haematological causes. Markedly elevated IL-6 levels suggested a paraneoplastic inflammatory syndrome. Corticosteroid therapy induced transient clinical improvement, while definitive surgical resection resulted in complete resolution of fever, normalisation of inflammatory markers and recovery of haematological and hepatic abnormalities. Histopathology confirmed IL-6 expression within tumour cells. This case highlights the importance of considering cytokine-secreting paragangliomas in patients with unexplained systemic inflammation, even in the absence of catecholamine hypersecretion.
PublisherPubMed
2026

Seeing the imagined: a latent functional alignment in visual imagery decoding from fMRI data

arXiv · Fabrizio Spera et al.

Recent progress in visual brain decoding from fMRI has been enabled by large-scale datasets such as the Natural Scenes Dataset (NSD) and powerful diffusion-based generative models. While current pipelines are primarily optimized for perception, their performance under mental-imagery remains less well understood. In this work, we study how a state-of-the-art (SOTA) perception decoder (DynaDiff) can be adapted to reconstruct imagined content from the Imagery-NSD benchmark. We propose a latent functional alignment approach that maps imagery-evoked activity into the pretrained model's conditioning space, while keeping the remaining components frozen. To mitigate the limited amount of matched imagery-perception supervision, we further introduce a retrieval-based augmentation strategy that selects semantically related NSD perception trials. Across four subjects, latent functional alignment consistently improves high-level semantic reconstruction metrics relative to the frozen pretrained baseline and a voxel-space ridge alignment baseline, and enables above-chance decoding from multiple cortical regions. These results suggest that semantic structure learned from perception can be leveraged to stabilize and improve visual imagery decoding under out-of-distribution conditions.
arXivarXiv
AI and neural decoding
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FMRI data · Pretrained model · Semantic reconstruction · Generative model · Brain decoding

2026

Therapeutic ultrasound for the treatment of demyelinating diseases

Progress in neurobiology · Marco Micali et al.

Demyelinating diseases, such as multiple sclerosis, result from the progressive loss of myelin sheaths in the central and peripheral nervous systems, leading to impaired neural conduction and disability. Current disease-modifying therapies focus on immunosuppression to limit inflammation but fail to restore lost myelin. This lack of regenerative capacity underscores the need for strategies that actively promote remyelination. Recent advances highlight neuromodulation, and in particular low-intensity ultrasound (US), as a promising approach to stimulate both neuronal activity and glial responses essential for myelin repair. Ultrasound noninvasively promotes remyelination through complementary mechanisms: indirectly, by enhancing activity-dependent myelination via neuronal firing, and directly, by exerting mechanical bioeffects on oligodendrocyte precursor cells, oligodendrocytes, astrocytes, microglia, and Schwann cells. Experimental studies show US activation of key signaling cascades (PI3K/Akt, MAPK/ERK, NF-κB, TGF-β1), promoting oligodendrocyte survival, differentiation, and myelin repair, alongside microglial polarization, astrocytic neurotrophic support, and functional recovery in central and peripheral models. Converging data from neuromodulation research indicate that activation of cholinergic and noradrenergic circuits-such as those engaged by vagus nerve stimulation-can enhance OPC differentiation, attenuate neuroinflammation, and support remyelination, raising the possibility that ultrasound-based stimulation of these pathways may synergistically amplify regenerative outcomes while avoiding the need for implanted devices. Ultrasound holds transformative potential for central nervous system repair and can also promote regenerative processes in the peripheral nervous system. Cutting-edge ultrasound technologies enable noninvasive penetration of the skull, precise modulation of deep brain circuits with millimeter accuracy, and fine temporal control without inducing systemic side effects. When combined with advanced imaging techniques (e.g., MR-guided US), ultrasound achieves increasingly effective therapeutic outcomes by enhancing beam-targeting precision and enabling real-time monitoring. Moreover, emerging approaches such as sonogenetics and magneto-acoustic stimulation further expand its specificity and therapeutic potential. Collectively, current evidence establishes therapeutic ultrasound as a transformative, noninvasive strategy for treating demyelinating diseases.
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Stimulation and bioelectronics
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Vagus nerve stimulation · Advanced imaging · Intensity ultrasound

2026

Magnetite nanodiscs as vortex-enhanced MRI contrast agents: a novel approach in medical imaging

Nanoscale advances · Elif Koçar et al.

Magnetic nanodiscs (MNDs) represent a transformative class of anisotropic magnetic nanoparticles with intrinsic vortex magnetization, enabling multifunctional applications in biomedical imaging and therapy. Here, we demonstrate their potential as dual-mode magnetic resonance (MR) contrast agents, a unique feature which is enabled by the high longitudinal relaxivity (r 1 ≈ 40 mM-1 s-1) at ultralow magnetic fields (<70 µT) in combination with strong transverse relaxivity (r 2 > 150 mM-1 s-1) at ultrahigh fields (>7 T). This field-dependent relaxivity profile uniquely positions MNDs as versatile T 1/T 2 agents compatible with emerging low-field MRI platforms and high-resolution clinical systems. Ex vivo and in vivo assessments confirmed clear anatomical localization and preferential hepatic accumulation, suggesting prolonged circulation times due to surface-mediated immune evasion. These properties highlight MNDs as promising candidates for next-generation theranostics, with tunable magnetic responses, high contrast efficiency, and the ability to synergize imaging and neurostimulation.
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Stimulation and bioelectronics
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Magnetic nanoparticles · Anisotropic magnetic · Magnetic fields · Magnetic resonance

2026

Brain Age in Conduct Disorder:: A Mega-Analysis of the ENIGMA Antisocial Behavior Working Group

bioRxiv : the preprint server for biology · Jules R Dugré et al.

Conduct disorder (CD) is the leading global cause of mental health burden in children and adolescents and has recently been hypothesized to be a neurodevelopmental disorder. Although prior research has identified neuroanatomical differences associated with CD, it remains unclear whether these differences reflect atypical brain development. Here, we investigated the difference between an individual's brain age and chronological age as a proxy for variations in brain maturation. Using a pretrained model, we estimated brain age from structural neuroimaging data obtained from 1,119 youth with CD and 1,183 typically developing controls across 14 international cohorts participating in the ENIGMA-Antisocial Behavior Working Group. Youth with CD exhibited a statistically robust but small acceleration in brain age compared to typically developing youth (around 0.50 years), which was restricted to the adolescence-onset subtype of the disorder. Our large-scale, coordinated analysis provides the first evidence of accelerated neurodevelopment as a potential mechanism underlying CD.
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Morphometry and behaviour
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Neuroimaging data · Pretrained model

2026

Hyperbolic Graph Neural Networks Under the Microscope: The Role of Geometry-Task Alignment

arXiv · Dionisia Naddeo et al.

Many complex networks exhibit hierarchical, tree-like structures, making hyperbolic space a natural candidate wherein to learn representations of them. Based on this observation, Hyperbolic Graph Neural Networks (HGNNs) have been widely adopted as a principled choice for representation learning on tree-like graphs. In this work, we question this paradigm by proposing the additional condition of geometry--task alignment, i.e., whether the metric structure of the target follows that of the input graph. We theoretically and empirically demonstrate the capability of HGNNs to recover low-distortion representations on regression problems, and show that their geometric inductive bias becomes helpful when the problem requires preserving metric structure. By jointly analyzing predictive performance and embedding distortion, we further show that HGNNs gain an advantage on link prediction, a naturally geometry-aligned task, whereas this advantage largely disappears on standard node classification benchmarks, which are typically not geometry--aligned. Overall, our findings shift the focus from only asking "Is the graph hyperbolic?" to also questioning "Is the task aligned with hyperbolic geometry?", showing that HGNNs consistently outperform Euclidean models under such alignment, while their advantage vanishes otherwise.
arXivarXiv
Graph neural network
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Graph neural network · Network exhibit · Complex network

2026

Towards neural foundation models for vision: Aligning EEG, MEG, and fMRI representations for decoding, encoding, and modality conversion

Information Fusion · Ferrante M. et al.

This paper presents a novel approach towards creating a foundational model for aligning neural data and visual stimuli across multimodal representationsof brain activity by leveraging contrastive learning. We used electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) data. Our framework's capabilities are demonstrated through three key experiments: decoding visual information from neural data, encoding images into neural representations, and converting between neural modalities. The results highlight the model's ability to accurately capture semantic information across different brain imaging techniques, illustrating its potential in decoding, encoding, and modality conversion tasks.
PublisherScopusarXivarXiv
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Foundation model · Functional magnetic resonance · Magnetic resonance imaging · Brain imaging · Contrastive learning

2026

Beam angle optimization for radiotherapy using LLMs via reinforcement-learning inspired iterative refinement

Medical physics · Sara Cammarota et al.

BACKGROUND: Radiotherapy treatment planning (TP) aims to maximize radiation dose delivered to tumors while minimizing exposure to surrounding healthy tissues. Beam angle optimization (BAO) is a crucial component of TP, characterized by high dimensionality and non-convexity, and is traditionally solved via heuristic or manual iterative approaches. These conventional methods are time-consuming and often yield suboptimal solutions due to incomplete exploration of the vast solution space. PURPOSE: This study introduces a novel framework integrating a general-purpose large language model (LLM) within a reinforcementlearning (RL)-inspired iterative strategy to automate BAO in radiotherapy planning. Taking advantage of the inherent knowledge embedded in LLMs, the method uses visual and scalar feedback to produce clinically meaningful treatment plans without requiring any domain-specific fine-tuning or additional training. METHODS: The proposed framework employs an off-the-shelf Generative Pre-trained Transformer, GPT-4 model (denoted GPT-4o) in an inference-only setting. At each iteration, GPT-4o suggests a set of gantry angles, which are subsequently input into the MatRad software to generate a dose distribution. A scalar reward is computed from this distribution using a custom reward function designed to balance target dose conformity and sparing of organs-at-risk (OARs). This reward, along with the corresponding dose maps, serves as feedback for the LLM to iteratively refine its suggestions. The refinement process consists of distinct exploration and exploitation phases inspired by classical RL paradigms. We evaluated six configurations that varied in exploration duration and in the Computed Tomography (CT) slice inputs provided to the LLM (Single-View vs. Multi-View). Performance was benchmarked against a random-angle selection baseline across three anatomical sites: prostate, head-and-neck, and liver. RESULTS: Across the liver and head-and-neck cases, all LLM-based configurations significantly outperformed the random baseline ( p < 0.05 ). In the prostate scenario, most strategies demonstrated statistically significant improvements, except for the Multi-View configurations with extended exploration phases (10 and 15 iterations). Rewards consistently increased during the exploitation phase, and the resulting dose-volume histograms and dose distributions exhibited improved conformity to target volumes with enhanced sparing of OARs. Notably, plans of clinically plausible quality were obtained within 20 iterative refinement steps in this proof-of-concept setting. CONCLUSIONS: This study demonstrates that general-purpose LLMs, operating without specialized model training or fine-tuning, can effectively serve as intelligent agents for automated radiotherapy TP, specifically addressing the BAO problem. This flexible and scalable framework has the potential to enhance clinical decision-making workflows in radiotherapy. Future research directions include exploring more comprehensive and clinically nuanced reward functions and extending the methodology to other components of radiotherapy TP.
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Image features and optimisation
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Beam angle · Large language model · Reinforcement learning · Computed tomography

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2026

Clustering Algorithm Reveals Dopamine-Motor Mismatch in Cognitively Preserved Parkinson's Disease

Annals of clinical and translational neurology · Rachele Malito et al.

OBJECTIVE: To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. METHODS: n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort. Clustering analysis stratified dopaminergic denervation, measured with 123I-FP-CIT-SPECT, and motor impairment into mild [D and M] and severe [D+ and M+]. Differences in terms of biomarkers and clinical progression were assessed across subgroups. Causal mediation analysis evaluated the effect of co-pathology on the relationship between subgroups and cognitive decline. RESULTS: Four subgroups were identified. Two subgroups showed concordant profiles: the severe dopaminergic and motor impairment subgroup [D+/M+] exhibited poorer memory performance, pathological Aβ1-42, as well as higher longitudinal Levodopa equivalent daily dose (LEDD) values and faster progression of motor disability; the mild dopaminergic and motor deficits [D/M] subgroup displayed a benign clinical profile and stable disease progression. Two subgroups exhibited dopaminergic and motor severity mismatch: the mild dopaminergic but severe motor impairment [D/M+] subgroup showed severe and rapidly progressive rigidity. CSF Aβ1-42 levels mediated the association between D+/M+ and cognitive decline in patients who were cognitively preserved at onset, accounting for 13% of the total effect. The external cohort supported the malignancy of D+/M+ and the presence of rigidity in D/M+. INTERPRETATION: Concordant severe impairment reflects a malignant profile linked to Aβ-related cognitive decline, while mild concordant cases show stable progression. Mismatch subgroups display distinct clinical patterns, underscoring the value of integrating imaging and motor features for early disease stratification.
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Longitudinal modelling
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2026

Dimensional Associations Between Conduct Problems and Brain Structure Across 18 International Cohorts in ENIGMA.

Journal of the American Academy of Child and Adolescent Psychiatry

OBJECTIVE: Although conduct problems (CPs) are continuously distributed, little is known about how dimensional measures of CPs map onto brain structure. Therefore a large sample was used to comprehensively assess associations between dimensionally measured CPs and brain structure. METHOD: T1-weighted structural brain magnetic resonance imaging scans from 14,160 youths (5-21 years old, 46.2% female) across 18 international case-control, community-based, and population-based cohorts were preprocessed using ENIGMA-standardized protocols. Regression models examined associations between CPs and cortical thickness, surface area, and subcortical volumes, adjusting for age, sex, and intracranial volume. Moderation by sex, age, and callous-unemotional traits was also investigated. RESULTS: Widespread but small (β = -0.02 to -0.07) negative associations were observed between CPs and surface area (total surface area, 23/34 regions), cortical thickness (average thickness, 15/34 regions), and amygdalar and hippocampal volumes. Sex was a key moderator, with many surface area associations limited to boys and some thickness associations limited to girls. Some associations were stronger in younger children and at lower levels of callous-unemotional traits. The impact of adjusting for IQ and other psychopathology varied by outcome (eg, most surface area findings survived IQ adjustment, whereas cortical thickness associations did not). CONCLUSION: CPs were associated with subtle, yet widespread, alterations in brain structure. Findings overlapped with differences observed in categorically measured conduct disorder, but novel associations with cortical thickness were identified. This provides further evidence that neuroanatomical differences are not limited to youth with clinically elevated CPs. Our findings have potential implications for neurocognitive models of CPs as they extend beyond the regions highlighted in these models. STUDY REGISTRATION INFORMATION: Investigating dimensional relationships between conduct problems and brain structure: an ENIGMA mega-analysis; https://osf.io/nzj3r/.
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Morphometry and behaviour
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Surface area · Cortical thickness · Brain magnetic resonance · Neurocognitive model · Magnetic resonance imaging

2026

Do We Need Curved Spaces? A Critical Look at Hyperbolic Graph Learning in Graph Classification

Communications in Computer and Information Science · Naddeo D.

Hyperbolic geometry has gained attention for its ability to naturally embed hierarchical and tree-like structures with low distortion, outperforming Euclidean spaces in various graph representation tasks. While previous work has demonstrated the advantage of graph neural networks embedding in hyperbolic space for link prediction and node classification, the benefits for graph classification remain less understood. Moreover, these studies typically attribute the benefits of hyperbolic models to the hierarchical or tree-like nature of graph structures, often neglecting the important role that node features play in leveraging these geometric advantages. With this in mind, we designed an experiment specifically aimed at evaluating the interplay between geometry and node features in graph classification, creating a dataset composed exclusively of tree-structured graphs. Each graph is generated by sampling the number of children per node at each level from a predefined range of branching factors, which varies across levels. The dataset defines two distinct classes based on these branching factor patterns. Node features are either random , structural embeddings obtained via node2vec , or layout-based embeddings derived from kamada-kawai algorithm, which provide a strong hierarchical prior. We evaluated a fully hyperbolic graph neural network against its Euclidean counterpart and a standard Graph Convolutional Network (GCN), using node embeddings learned in low-dimensional latent spaces. Across all feature types, the Euclidean counterpart consistently outperformed the hyperbolic one. This indicates that the benefits of hyperbolic geometry do not arise solely from its alignment with global graph structure. These findings call for a critical reassessment of hyperbolic models in graph classification tasks where preserving graph distances is not essential.
PublisherScopus
Graph neural network
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Graph neural network · Gained attention

2026

Exosomes as Advanced Nanocarriers: Overcoming the Blood-Brain Barrier for Targeted Therapeutic Delivery in Neurodegenerative Diseases.

Current drug delivery

INTRODUCTION: Exosomes, nanosized extracellular vesicles secreted by diverse cell types, have emerged as promising natural nanocarriers for therapeutic delivery. Their intrinsic ability to cross the Blood-Brain Barrier (BBB) positions them as valuable tools for treating neurodegenerative diseases. This review critically examines exosome biology, transport mechanisms, engineering strategies, and their clinical potential as drug-delivery platforms for the Central Nervous System (CNS). METHODS: We analyzed recent experimental, translational, and clinical studies on exosomes and engineered derivatives, focusing on BBB penetration, therapeutic cargo delivery, and applications in brain disorders. Key advances and landmark preclinical studies were synthesized to provide a comprehensive perspective. RESULTS: Exosomes cross the BBB through receptor-mediated transcytosis, lipid raft-associated uptake, and macropinocytosis, enabling bidirectional transport between circulation and brain. Their intrinsic cargo, including proteins, nucleic acids, and lipids, can reflect disease states and serve as predictive biomarkers. Engineered exosomes further enhance delivery potential, as surface functionalization and optimized cargo loading improve brain specificity and therapeutic efficacy in preclinical models. Collectively, both native and engineered exosomes surpass many synthetic carriers in stability, targeting, and BBB penetration. DISCUSSION: Versus previous reviews, this manuscript integrates exosome composition, engineering, isolation technologies, and administration routes, while also addressing patent and clinical translation challenges. Importantly, it highlights quantitative and mechanistic insights into BBB transport, offering a distinct framework for advancing exosome-based CNS therapies. CONCLUSION: Exosomes constitute a versatile platform for BBB-crossing drug delivery. By consolidating mechanistic, preclinical, and translational evidence, this review highlights their transformative potential in neurodegenerative disease therapy while outlining limitations and future directions.
PubMed
Stimulation and bioelectronics
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Preclinical model

2026

Functional brain connectivity in patients with de novo Parkinson's disease.

Neuroimage. Reports

INTRODUCTION: This study aims to identify early brain network changes in de novo Parkinson's disease (PD) using resting state-functional Magnetic Resonance Imaging (rs-fMRI), graph-theoretical analysis, and a functional brain network disruption index (k), applied here for the first time to de novo PD. MATERIALS AND METHODS: The study enrolled untreated de novo PD patients and age- and sex-matched healthy controls. PD patients underwent comprehensive clinical assessments (MDS-UPDRS III, H&Y, MMSE, MoCA, NMSS). MRI data were acquired on a 3T system, including 3D T1-weighted MPRAGE and rs-fMRI. rs-fMRI data were pre-processed and analysed using graph theory. RESULTS: The study included 30 de novo PD patients and 30 healthy controls. While global network metrics did not differ significantly, local metrics revealed a reduced disruption index k in de novo PD patients. The disruption index k was negatively correlated with MMSE scores and demonstrated strong discriminatory power between PD patients and healthy controls based on clustering coefficient metrics. Significant differences in hub regions were found, as some disappeared in PD patients while others emerged compared to healthy controls. CONCLUSIONS: This study provides evidence of widespread functional alterations in the local brain networks of de novo Parkinson's disease (PD) patients, suggesting early reorganization of brain connectivity. The disruption index (k) demonstrated the ability to detect early and subtle changes in functional brain networks in de novo Parkinson patients. SIGNIFICANCE: rs-fMRI can provide valuable insights into the early stages of PD pathophysiology helping to understand the complexity of PD.
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Networks and dynamics
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Functional brain network · Brain connectivity · Brain network disruption · Network disruption index · Functional magnetic resonance

2026

Low-dose Ionizing Radiation, a Protective Intervention to Mitigate Chronic Cerebral Hypoperfusion-induced Brain Damage and Cognitive Deficits in the Rat.

Radiation research

Vascular dementia is a leading cause of cognitive decline, with few effective treatment options currently available. Within the framework of hormesis, treatment with low-dose ionizing radiation (LDIR<0.1 Gy) has been proposed as a promising therapy for neurodegenerative diseases. This study investigates the effects of LDIR on cognitive decline, brain damage, blood-brain barrier permeability, and oxidative stress in a rat model of vascular dementia. After chronic cerebral hypoperfusion (CCH), the whole brain was exposed to 0.3 Gy of X rays, delivered in 0.1 Gy fractions over three consecutive days, beginning at day 1 or day 7 after occlusion. LDIR effects were evaluated using behavioral tests, MRI, post-mortem analyses, and in vitro analyses. Early exposure to LDIR attenuated recognition memory deficits induced by CCH. LDIR applied one day after occlusion reduced alterations in brain tissue integrity attributed to CCH and mitigated neuronal loss in the hippocampus. Moreover, LDIR counteracted CCH-induced blood-brain barrier permeabilization and improved survival of epithelial cells subjected to oxygen-glucose deprivation/reoxygenation. LDIR modulated oxidative stress by altering the reactive species interactome and activating antioxidant enzymes. Brain exposure to low-dose ionizing radiation modulates the events triggered by CCH and thus attenuates cognitive decline and brain damage in the rat.
PubMed
Stimulation and bioelectronics
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2026

Morpho-functional correlates of gait impairment in early Parkinson's disease patients: insights from a multimodal path modeling framework.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

OBJECTIVE: To deepen the underlying pathophysiology of gait impairment in early-stage Parkinson's disease (PD) through a multimodal assessment, including MR-based brainstem morphometry and EEG functional connectivity (FC). METHODS: 73 PD patients and 60 healthy controls (HC) were included. The MR Parkinsonism Index (MRPI) was calculated for each subject. FC was measured using HD-EEG in θ-α-β-low-γ-high-γ bands. Partial Least Squares Path Modeling (PLS-PM) was used to explore the causal relationships between MRPI, FC, and clinical data. RESULTS: MRPI was higher in PD than HC (p = 0.001) and correlated with MDS-UPDRS-III gait subscore (r = 0.54,p < 0.001). α-FC was significantly lower in PD than HC (t = -5.4,p < 0.001). α-FC in PD was negatively correlated with both MRPI (r = -0.31,p = 0.007) and gait subscore (r = -0.31,p = 0.007). PLS-PM revealed MRPI's negative direct effect on α-FC (-0.59,p < 0.001) and significant positive total effect on gait performance (0.53,p < 0.001), partially mediated by α-FC (0.19, p = 0.006). Additionally, α-FC had a direct negative impact on gait performance (-0.33,p = 0.004). CONCLUSIONS: The effect of MRPI on gait performance, partially mediated by α-FC, highlights a dual pattern in which brainstem structural alterations show an association with gait impairment both directly and indirectly through their relationship with cortical functional disruptions. SIGNIFICANCE: These findings support a multimodal framework for understanding and addressing gait disorders in PD, potentially opening avenues for personalized treatment.
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Longitudinal modelling
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Causal relationships · Functional connectivity

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2026

Training Neural Networks by Optimizing Neuron Positions

Lecture Notes in Computer Science · Erb L. et al.

The high computational complexity and increasing parameter counts of deep neural networks pose significant challenges for deployment in resource-constrained environments, such as edge devices or real-time systems. To address this, we propose a parameter-efficient neural architecture where neurons are embedded in Euclidean space. During training, their positions are optimized and synaptic weights are determined as the inverse of the spatial distance between connected neurons. These distance-dependent wiring rules replace traditional learnable weight matrices and significantly reduce the number of parameters while introducing a biologically inspired inductive bias: connection strength decreases with spatial distance, reflecting the brain’s embedding in three-dimensional space where connections tend to minimize wiring length. We validate this approach for both multi-layer perceptrons and spiking neural networks. Through a series of experiments, we demonstrate that these spatially embedded neural networks achieve a performance competitive with conventional architectures on the MNIST dataset. Additionally, the models maintain performance even at pruning rates exceeding 80% sparsity, outperforming traditional networks with the same number of parameters under similar conditions. Finally, the spatial embedding framework offers an intuitive visualization of the network structure.
PublisherScopusarXivarXiv
AI and neural decodingSpiking neural network
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Deep neural network · Spiking neural network

2025

Advancing Generalisable Neural Network-Based PET Quantification: A Multicenter [11C]PBR28 study

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · Ludovica Brusaferri et al.

Quantifying the volume of distribution (VT) in Positron Emission Tomography (PET) is widely considered the gold standard for assessing tracer binding. However, this process requires an accurate estimation of the tracer's input function (IF) obtained through arterial sampling and metabolite correction-procedures that are both invasive and technically demanding. To overcome these limitations, we introduce a neural network-based framework for estimating the IF directly from [11C]PBR28 dynamic PET data, exploring generalisability across datasets and scanners. The framework employs a patched variational autoencoder (pVAE) for dimensionality reduction, generating IFs with uncertainty (NNIF-dPET), and computes VT from the mean output signal. Additionally, we evaluate two alternative methods: NNIF-IDIF, which derives IFs from image-derived input functions, and NNIF-unBlood, which uses uncorrected blood signals as input. NNIF-dPET achieves accuracy comparable to true arterial IFs while outperforming IDIF-based methods, suggesting that latent space representations can effectively approximate whole-blood activity for parent plasma input function estimation, rather than relying on pre-selected voxels.Clinical Relevance-This approach highlights the potential for scalable, non-invasive PET quantification across diverse clinical settings.
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Quantitative imaging
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Dynamic PET data · Latent space representation · Neural network · Variational autoencoder

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2025

From Radiomics to Generative Models: Evaluating Early Radiation Effects in Metastatic Brain Lesions

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · Marianna Inglese et al.

Brain metastases (BM), along with primary central nervous system lymphomas and glioblastomas, represent the majority of malignant brain tumors encountered in clinical neuro-oncology, driving a need for advanced imaging techniques and post-processing methods to improve their characterization and treatment monitoring. In particular, stereotactic radiosurgery (SRS), a cornerstone treatment for BM, delivers high-dose, focused radiation (>20 Gy) to target lesions with minimal impact on surrounding tissues. Despite its efficacy, radiation-induced effects such as early radiation effects (ERE) and adverse radiation effects (ARE) complicate diagnosis and management, with ARE occurring in up to 30% of patients, often presenting as ring-enhancing T2/FLAIR hyperintensities. To address these challenges, we aimed to compare standard radiomics-based machine learning approaches with pretrained generative models for assessing ERE in BM lesions. A cohort of 21 patients for a total of 35 lesions (17 treatment-naïve and 18 post-SRS +/- combination therapy) who underwent multiparametric 18F-FPIA PET/MRI was analyzed. The study investigated: 1) Multiparametric analysis of PET and MRI diffusion/perfusion parameters (ADC, Ktrans, CBF, K1, vt); 2) MRI-based radiomics; 3) static PET radiomics; 4) Dynomics; 5) a combination of PET and MRI radiomics; and 6) low-level embeddings from a pretrained generative diffusion model applied to full T1, static PET, and their combination. Using manually contoured lesion masks for analyses 1-5 and lesion-free embeddings for analysis 6, multiple classifiers (SVM, XGBoost, Linear regressor) were applied after feature standardization and principal component analysis (retaining 90% variance). Fivefold cross-validation demonstrated comparable performances across radiomic approaches (Accuracy: 71.95±0.05%, AUC: 0.72±0.05%), while the pretrained generative model achieved significantly higher performance (Accuracy: 83.82±0.01%, AUC: 0.83±0.01%) without requiring lesion segmentation in assessing ERE in BM lesions.Clinical Relevance-This study shows the potential of generative models to streamline and enhance the assessment of early radiation effects in parenchymal metastatic lesions without need of lesion segmentation.
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Image features and optimisation
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Generative model · Advanced imaging · Model achieved · Fpia PET · Diffusion model

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2025

Multimodal Generative Modeling for DaT Scan Reconstruction in Parkinson's Disease

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · Marianna Inglese et al.

The creation of synthetic medical data that truly captures the statistical distribution of real-world patient information, while simultaneously protecting individual privacy, remains a formidable challenge for the clinical and scientific community. This challenge is especially pronounced in nuclear medicine research, where rigorous data sharing is hindered by tight regulations and ethical considerations. In this study, we introduce a multimodal deep learning model designed to reconstruct (and thereby enable future synthesis of) 123I-FPCIT SPECT (DaT) scans by leveraging both the target DaT and co-registered T1-weighted MRI scans. Through extensive experimentation on a large Parkinson's Progression Markers Initiative (PPMI) dataset, comprising healthy controls, Parkinson's disease (PD) patients, and individuals without imaging evidence of dopaminergic deficits (SWEDD), we demonstrate that the proposed framework yields DaT images that strongly preserve clinical signal distributions. Our findings show minimal intensity discrepancies, unbiased contrast-to-noise ratios, and robust region-based analyses across pathological and demographic subgroups, highlighting the feasibility of this approach for large-scale data augmentation in neurodegenerative research.Clinical Relevance-By enabling the reconstruction of DaT scans from multi-contrast inputs, this framework has the potential to enhance future generation of synthetic data that are applicable to early PD detection, disease progression studies, and model training in clinical scenarios where data are often limited, and privacy constraints are stringent.
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Longitudinal modelling
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Deep learning model · Model designed · MRI scans

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2025

NeuroSync: Generalized Brain Decoding of Visual Stimuli Across Subjects

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · Muhammad Kashif et al.

Decoding visual stimuli from neural activity poses significant challenges due to the complexity of cross-subject neural variability and the hierarchical nature of visual processing. This study introduces a novel cross-subject brain decoding framework that integrates structural and semantic information to reconstruct images from fMRI data. Using diffusion models, we align neural representations with visual and textual embeddings through a contrastive learning paradigm. Our framework employs a composite neural module to harmonize cross-subject fMRI signals into a unified latent space, while a dual-pathway architecture, combining VDVAE for structural reconstruction and IP-Adapter with BERT for semantic alignment, ensures hierarchical fidelity. Evaluated on the Natural Scenes Dataset (NSD), our method achieves state-of-the-art performance in structural (SSIM: 0.379) and semantic (EffNet-B: 0.571 and SwAV: 0.225) metrics, outperforming previous studies. The results demonstrate robust generalizability across four subjects, advancing the feasibility of cross-subject brain decoding and providing insights into distributed neural encoding mechanisms.Clinical relevance-This study advances foundational understanding of neural encoding by jointly modeling structural and semantic information derived from brain activity, a novel methodological contribution to brain decoding research. The integration of these complementary dimensions provides a more comprehensive framework for interpreting how the brain hierarchically processes visual stimuli, offering insight into neural representation mechanisms.
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AI and neural decoding
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Visual stimuli · Brain decoding · Neural encoding · Reconstruct image · FMRI signal

2025

Optimal Transport and Contrastive Learning for Brain Decoding of Musical Perception

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · Matteo Ciferri et al.

Brain decoding aims to reconstruct external stimuli from brain activity, providing insights into the neural representation of cognitive experiences. Music decoding from functional magnetic resonance imaging (fMRI) is particularly challenging due to the complexity of auditory processing and the temporal limitations of fMRI signals. In this study, we introduce a novel decoding framework that improves the alignment between fMRI activity and latent musical representations extracted using a pre-trained multimodal model (CLAP). We propose a dual-loss approach combining Optimal Transport and Contrastive Learning to enhance feature mapping and retrieval accuracy. The first loss ensures structural consistency between brain-predicted and true musical embeddings, while the contrastive loss refines the embedding space by maximizing similarities between corresponding pairs and minimizing non-correspondences. Using fMRI data from five subjects listening to music tracks from the GTZAN dataset, our method achieves improved decoding performance, surpassing traditional regression-based approaches from 22.1% top-1 accuracy to 29.3%. These results highlight the potential of integrating Optimal Transport and Contrastive Learning to improve brain decoding performance, paving the way for extending the approach to different sensory domains and applications in Brain-Computer Interfaces (BCI).Clinical relevance- This study could have clinical implications for understanding auditory processing disorders and developing neurorehabilitation strategies. By elucidating how the brain encodes complex auditory stimuli, this approach may contribute to BCI applications for speech and music perception restoration in individuals with hearing impairments or neurological conditions affecting auditory cognition.
PublisherPubMed
AI and neural decodingBrain computer interfaces
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Contrastive learning · Brain decoding · Brain computer interfaces · Functional magnetic resonance · Auditory stimuli

2025

Self-Supervised Transformer-Based Foundation Model for functional Magnetic resonance Imaging

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · Matteo Ferrante et al.

Functional Magnetic Resonance Imaging is a powerful tool for studying brain function but presents challenges due to high dimensionality and variability. We propose a self-supervised transformer-based foundation model using a masked autoencoder to learn generalizable representations of fMRI time series. Trained on the Human Connectome Project (HCP) S1200 dataset, the model is evaluated on cognitive task classification and neuroticism prediction using linear, MLP, and ConvLSTM probes under zero-shot and fine-tuning settings. Our model outperforms training from scratch, exceeding 90% accuracy in cognitive task classification and improving correlations in neuroticism prediction. Architectural enhancements, including contrastive loss and spatiotemporal attention, further refine representations. These results highlight the potential of self-supervised transformers for fMRI analysis, enabling scalable, generalizable models for neuroscience and clinical applications.
PublisherPubMed
AI and neural decoding
2025

Spiking Reservoir Computing Architectures for Model-based Epileptic Brain State Recognition

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · Luigi Rosati et al.

Bio-inspired networks offer rich dynamic capabilities with minimal energy demands, especially when implemented on neuromorphic hardware. In particular, the recurrence in brain circuits enables Recurrent Spiking Neural Networks (RSNNs) to generate complex spatio-temporal spike patterns, forming internal representations of time-varying signals. Despite this biological sophistication, such architectures are often applied to machine learning tasks with limited biological relevance. In this work, we present a spiking reservoir computing architecture, implemented as a fully spiking Liquid State Machine (LSM) with Leaky Integrate-and-Fire (LIF) neurons, designed to recognize and decode internal brain states. We simulate epileptic activity using a spiking model and develop a complete pipeline where a source generates Local Field Potentials (LFPs). These signals are encoded through population coding and processed by the LSM, which performs regression on the biophysical parameters controlling epileptic dynamics, thereby inferring the source state. To enhance the LSM's performance, we apply a biologically inspired synaptic plasticity mechanism to the RSNN. Our results demonstrate that a simple, unsupervised plasticity mechanism can optimize the internal parameters of the reservoir, particularly in smaller networks. This approach offers a hardware-efficient strategy for task-specific adaptation of general-purpose circuits, highlighting its suitability for edge-device implementations. Our findings emphasize the potential of spiking reservoir computing for real-time decoding of complex brain dynamics, such as epileptic activity, and underscore the advantages of biologically inspired, energy-efficient methods for neuromorphic systems in real-world applications.Clinical relevance- The ability to decode internal brain states from Local Field Potentials (LFPs) using a biologically inspired spiking reservoir computing architecture has significant implications for clinical neuroscience. By providing a real-time, energy-efficient method for tracking epileptic dynamics, this approach could aid in the development of advanced braincomputer interfaces (BCIs) and personalized neurostimulation therapies. Such a system may help clinicians monitor seizure progression, optimize treatment strategies, and improve patient outcomes in clinical epilepsy management.
PublisherPubMed
Spiking neural network
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Reservoir computing · Spiking neural network · Neuromorphic hardware · Machine learning

2025

Towards Intelligent Agents for Radiotherapy: Integrating Exploration-Exploitation with Foundation Models

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · Sara Cammarota et al.

This study proposes an automated approach to radiotherapy treatment planning by integrating a reinforcement-learning-style iterative framework with a multimodal Large Language Model (LLM). We specifically investigate the problem of Beam Angle Optimization, a high-dimensional and non-convex subproblem of Treatment Planning. Our system employs GPT-4V to select candidate beam angles and analyze three-dimensional dose distributions generated by Monte Carlo simulations within the MatRAD environment. Iterative plan refinement is guided by a reward function that encourages target dose conformity and penalizes excessive dose to organs at risk. We incorporate exploration-exploitation principles to strike a balance between investigating diverse action proposals and refining promising solutions. Experimental results on prostate cancer cases demonstrate that our LLM-based framework offers superior performance compared to random beam selection and can outperform the quality of deep reinforcement learning baselines, indicating the potential for LLMs to assist in complex radiotherapy treatment planning tasks.Clinical relevance-This approach is designed to alleviate the significant effort of manual treatment planning by assisting medical physicists in exploring beam configurations and systematically refining plans to improve dose coverage and protect healthy tissues.
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Image features and optimisation
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Large language model · Reinforcement learning · Foundation model · Beam angle

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2025

Big multiple sclerosis data network: novel modelling approaches for real-world data analysis

Journal of Neurology · Trojano M.

OBJECTIVE: The objective of this study is to present a report from the Big Multiple Sclerosis Data (BMSD) statistics workshop (Bari - Italy, June 2023) which focused on advanced statistical approaches for real-world data (RWD) analyses in multiple sclerosis (MS). The report emphasises the application of these approaches in predicting individual treatment response, assessing comparative effectiveness and safety of therapies and their sequences, and harmonizing data for large-scale federated analyses. METHODS: The BMSD network, comprising five national registries and the international MSBase database (> 350,000 total patients), convened in June 2023 in Bari (Italy) to review methodological advances in RWD analysis. Experts discussed strengths, limitations, and regulatory implications of frequentist, Bayesian, and machine learning (ML) approaches, with case studies on treatment response modelling, comparative effectiveness, safety surveillance, and Common Data Model (CDM)-based federated learning. RESULTS: Bayesian and ML techniques, integrated with causal inference frameworks, can improve personalized predictions of treatment benefit and risk by using high-dimensional longitudinal data. Propensity score-based methods and marginal structural models remain essential for minimizing confounding in comparative analyses, but require rigorous diagnostics and sensitivity analyses. Adoption of a CDM facilitates harmonization of heterogeneous datasets, while federated learning enables privacy-preserving, multi-jurisdictional collaboration. Together, these innovations address key challenges in studying treatment sequences, rare adverse events, and underrepresented patient groups. CONCLUSIONS: This workshop report highlights how advanced statistical and computational methodologies enhance the robustness, interpretability, and regulatory relevance of MS RWD studies. By promoting the integration of complementary statistical and computational approaches within harmonized data infrastructures, the BMSD network is positioned to accelerate the translation of real-world evidence into precision medicine for MS.
PublisherPubMedScopus
Networks and dynamics
2025

Reconstructing music perception from brain activity using a prior guided diffusion model

Scientific reports · Matteo Ciferri et al.

Reconstructing music directly from brain activity provides insight into the neural representations underlying auditory processing and paves the way for future brain-computer interfaces. We introduce a fully data-driven pipeline that combines cross-subject functional alignment with bayesian decoding in the latent space of a diffusion-based audio generator. Functional alignment projects individual fMRI responses onto a shared representational manifold, increasing the performance of cross-participant accuracy with respect to anatomically normalized baselines. A bayesian search over latent trajectories then selects the most plausible waveform candidate, stabilizing reconstructions against neural noise. Crucially, we bridge CLAP's multi-modal embeddings to music-domain latents through a dedicated aligner, eliminating the need for hand-crafted captions and preserving the intrinsic structure of musical features. Evaluated on ten diverse genres, the model achieves a cross-subject-averaged identification accuracy of [Formula: see text], and produces audio that human listeners recognize above chance in 85.7% of trials. Voxel-wise analyses locate the predictive signal within a bilateral circuit spanning early auditory, inferior-frontal, and premotor cortices, consistent with hierarchical and sensorimotor theories of music perception. The framework establishes a principled bridge between generative audio models and cognitive neuroscience.
PublisherPubMedScopus
Brain computer interfaces
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Brain computer interfaces · Diffusion model · FMRI responses · Model achieves · Latent space

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2025

Generation of synthetic TSPO PET maps from structural MRI images

Frontiers in neuroinformatics · Matteo Ferrante et al.

INTRODUCTION: Neuroinflammation, a pathophysiological process involved in numerous disorders, is typically imaged using [11C]PBR28 (or TSPO) PET. However, this technique is limited by high costs and ionizing radiation, restricting its widespread clinical use. MRI, a more accessible alternative, is commonly used for structural or functional imaging, but when used using traditional approaches has limited sensitivity to specific molecular processes. This study aims to develop a deep learning model to generate TSPO PET images from structural MRI data collected in human subjects. METHODS: A total of 204 scans, from participants with knee osteoarthritis (n = 15 scanned once, 15 scanned twice, 14 scanned three times), back pain (n = 40 scanned twice, 3 scanned three times), and healthy controls (n = 28, scanned once), underwent simultaneous 3 T MRI and [11C]PBR28 TSPO PET scans. A 3D U-Net model was trained on 80% of these PET-MRI pairs and validated using 5-fold cross-validation. The model's accuracy in reconstructed PET from MRI only was assessed using various intensity and noise metrics. RESULTS: The model achieved a low voxel-wise mean squared error (0.0033 ± 0.0010) across all folds and a median contrast-to-noise ratio of 0.0640 ± 0.2500 when comparing true to reconstructed PET images. The synthesized PET images accurately replicated the spatial patterns observed in the original PET data. Additionally, the reconstruction accuracy was maintained even after spatial normalization. DISCUSSION: This study demonstrates that deep learning can accurately synthesize TSPO PET images from conventional, T1-weighted MRI. This approach could enable low-cost, noninvasive neuroinflammation imaging, expanding the clinical applicability of this imaging method.
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Longitudinal modelling
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Structural MRI image · PET image · Structural MRI data · Deep learning model · Functional imaging

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2025

Causal contributions of left inferior and medial frontal cortex to semantic and executive control

Communications biology · Sandra Martin et al.

Semantic control enables context-guided retrieval from memory, yet its distinction from domain-general executive control remains debated. We applied transcranial magnetic stimulation (TMS) to the left inferior frontal gyrus (IFG) and pre-supplementary motor area (pre-SMA) to probe their functional relevance for semantic and executive control. Across four sessions, 24 participants received repetitive TMS, followed by semantic fluency, figural fluency, and picture naming tasks. Stimulation of either region broadly disrupted both semantic and figural fluency, suggesting shared functionality. However, electric field modeling of the induced stimulation strength revealed distinct specializations: The left IFG was primarily associated with semantic control, affecting primarily verbal fluency, while the pre-SMA played a domain-general role in executive functions, affecting non-verbal fluency and cognitive flexibility. Notably, only dual-site TMS impaired accuracy in figural fluency, providing unique evidence for successful compensation of executive functions through either the left IFG or pre-SMA following single-site perturbation. These findings underscore the multidimensionality of cognitive control and suggest a flexible contribution of the IFG to control processes, either as semantic-specific or general executive resource. Furthermore, they highlight the tightly interconnected network of executive control subserved by the left IFG and pre-SMA, advancing our understanding of the neural basis of cognitive control.
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Stimulation and bioelectronics
2026

Choroid Plexus Enlargement in Multiple Sclerosis Correlates with Cortical and Phase Rim Lesions on 7T MRI and Predicts Progression Independent of Relapse Activity

AJNR. American journal of neuroradiology · Elena Barbuti et al.

BACKGROUND AND PURPOSE: In multiple sclerosis, the choroid plexus is thought to promote and sustain the disease immunopathologic inflammatory process. However, its association with cortical pathology and disease progression is still uncertain. We aimed to characterize choroid plexus enlargement and evolution in MS and its relationship with imaging markers of compartmentalized inflammation and clinical outcome. MATERIALS AND METHODS: One hundred patients with MS (74 relapsing-remitting [RRMS]; 26 secondary-progressive MS [SPMS] and 41 matched healthy controls (HC) underwent 3T MRI to measure brain volumes and 7T MRI for cortical and WM lesions, including paramagnetic rim lesion (PRL) segmentation. Choroid plexus volumes of the lateral ventricles were manually edited from FreeSurfer segmentation by 1 neurologist and normalized by total intracranial volume to estimate the choroid plexus ratio (CPR). The Expanded Disability Status Scale was assessed at baseline and longitudinally in 71 patients, 43 of whom had a follow-up MRI at 1.5 years. An extreme gradient boosting algorithm estimated the importance of CPR, alongside other imaging markers, in predicting disease phenotype (RRMS versus SPMS) and progression independent of relapse activity. RESULTS: Relative to HC, CPR was higher in patients (11.66 [3.21] versus 14.98 [4.37] × 10-4, P = .001), both in RRMS and SPMS (P = .007, P = .003), and there was a mean ∼4% increase per year in CPR at follow-up, though it was not significant[ (P = .14). Choroid plexus enlargement correlated with greater PRL and cortical lesion volumes (r = 0.35, P < .001). CPR did not discriminate MS phenotype but predicted progression independent of relapse activity alongside CSF subarachnoid space volume, PRL, and cortical lesion volumes (median [median absolute deviation] area under the curve 0.71 [0.12], accuracy 0.74 [0.10], sensitivity 0.81 [0.12], and specificity of 0.52 [0.22]). CONCLUSIONS: In MS, a CPR increase relates to imaging markers of compartmentalized disease activity including cortical lesions and PRLs and is a critical predictor of disease progression. Our findings could provide the rationale for implementing CPR estimation for prognosis prediction in MS.
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Longitudinal modelling
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Imaging markers · Paramagnetic rim

2025

Evidence for compositionality in fMRI visual representations via Brain Algebra

Communications biology · Matteo Ferrante et al.

Electrophysiological and neuroimaging studies have revealed how the brain encodes various visual categories and concepts. An open question is how combinations of multiple visual concepts are represented in terms of the component brain patterns: are brain responses to individual concepts composed according to algebraic rules? To explore this, we generated "conceptual perturbations" in neural space by averaging fMRI responses to images with a shared concept (e.g., "winter" or "summer"). After thresholding to ensure specificity, we applied these perturbations to the neural pattern associated with a base image, forming new brain patterns that incorporate the added concept. These modified brain patterns were then decoded into images using a pretrained fMRI-to-image decoding model. Qualitative and quantitative inspection of the resulting images provides insight into how the brain might combine visual concepts. For example, adding a "winter" perturbation to the brain pattern of a man on a skateboard yields a new pattern representing a man on a snowboard in a winter scene-even when the perturbation modifies only a small subset of voxels. Our findings reveal that compositional processes in neural representations may lead to predictable perceptual outcomes, as interpreted by our decoding model. This suggests that the brain's combinatory encoding of concepts may follow a systematic, algebraic-like process-what we term "brain algebra." Although our study is model-driven, it opens avenues for future empirical work into the mechanisms of compositionality in the brain.
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AI and neural decoding
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Decoding model · Brain encodes · FMRI responses

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2026

Shared and Distinct Alterations in Brain Structure of Youth With Internalizing or Externalizing Disorders: Findings From the ENIGMA Antisocial Behavior, ADHD, Major Depressive Disorder, and Anxiety Working Groups

Biological psychiatry · None Sophie Townend et al.

BACKGROUND: Externalizing and internalizing disorders are common in youth but are often studied separately, preventing researchers from identifying shared (i.e., transdiagnostic) alterations in brain structure. Using data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium, we conducted a mega-analysis to identify shared and distinct cortical and subcortical brain alterations across internalizing (anxiety disorders and depression) and externalizing (attention-deficit/hyperactivity disorder [ADHD] and conduct disorder [CD]) disorders in youth. METHODS: 3D T1-weighted magnetic resonance imaging data from youths (age range 4-21 years) with anxiety disorders (n = 1044), depression (n = 504), ADHD (n = 1317), and CD (n = 1172) along with healthy control participants (n = 4743) were analyzed. We assessed group differences in regional cortical thickness, surface area (SA), and subcortical volume using linear models, adjusted for site, age, and sex, as well as total intracranial volume in the SA and subcortical volume models. RESULTS: We observed transdiagnostic associations, with both internalizing and externalizing disorders characterized by lower SA in the insula, entorhinal cortex, and middle temporal gyrus and lower amygdala volume (Cohen's ds = -0.07 to -0.24) as well as total SA and intracranial volume (ds = -0.11 to -0.25). Externalizing-specific reductions in SA were observed in frontoparietal regions (ds = -0.08 to -0.13), but no internalizing-specific associations were identified. Disorder-specific alterations were identified for ADHD, CD, and anxiety disorders but not depression. CONCLUSIONS: Both common and disorder-specific alterations were identified, with regions involved in salience attribution and emotion processing implicated across internalizing and externalizing disorders. These novel findings can guide future research targeting common biological processes across youth psychiatric disorders as well as features unique to individual disorders.
PublisherPubMedScopus
Morphometry and behaviour
2024

Sympathetic and parasympathetic central autonomic networks

Imaging neuroscience (Cambridge, Mass.) · Gaetano Valenza et al.

The central-autonomic network (CAN) comprises brain regions that are functionally linked to the activity of peripheral autonomic nerves. While parasympathetic CAN (i.e., the CAN projecting onto parasympathetic branches) has recently been investigated and is known to be involved in neurological and neuropsychiatric disorders, sympathetic CAN (i.e., the CAN projecting onto sympathetic nerves) has not been fully characterized. Using functional magnetic resonance imaging (fMRI) data from the Human Connectome Project in conjunction with heartbeat dynamics and its orthonormal autoregressive descriptors as a proxy for sympathetic activity estimation, namely, the sympathetic activity index (SAI), we uncover brain regions belonging to the sympathetic CAN at rest. We uncover a widespread CAN comprising both cortical (in all lobes) and subcortical areas, including the cerebellum and brainstem, which is functionally linked to sympathetic activity and overlaps with brain regions driving parasympathetic activity. These findings may constitute fundamental knowledge linking brain and bodily dynamics, including the link between neurological and psychiatric disorders and autonomic dysfunctions.
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Signals and physiological control
2024

Through their eyes: Multi-subject brain decoding with simple alignment techniques

Imaging neuroscience (Cambridge, Mass.) · Matteo Ferrante et al.

To-date, brain decoding literature has focused on single-subject studies, that is, reconstructing stimuli presented to a subject under fMRI acquisition from the fMRI activity of the same subject. The objective of this study is to introduce a generalization technique that enables the decoding of a subject's brain based on fMRI activity of another subject, that is, cross-subject brain decoding. To this end, we also explore cross-subject data alignment techniques. Data alignment is the attempt to register different subjects in a common anatomical or functional space for further and more general analysis. We utilized the Natural Scenes Dataset, a comprehensive 7T fMRI experiment focused on vision of natural images. The dataset contains fMRI data from multiple subjects exposed to 9,841 images, where 982 images have been viewed by all subjects. Our method involved training a decoding model on one subject's data, aligning new data from other subjects to this space, and testing the decoding on the second subject based on information aligned to the first subject. We also compared different techniques for fMRI data alignment, specifically ridge regression, hyper alignment, and anatomical alignment. We found that cross-subject brain decoding is possible, even with a small subset of the dataset, specifically, using the common data, which are around 10 % of the total data, namely 982 images, with performances in decoding comparable to the ones achieved by single-subject decoding. Cross-subject decoding is still feasible using half or a quarter of this number of images with slightly lower performances. Ridge regression emerged as the best method for functional alignment in fine-grained information decoding, outperforming all other techniques. By aligning multiple subjects, we achieved high-quality brain decoding and a potential reduction in scan time by 90 % . This substantial decrease in scan time could open up unprecedented opportunities for more efficient experiment execution and further advancements in the field, which commonly requires prohibitive (20 hours) scan time per subject.
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AI and neural decoding
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Brain decoding · FMRI data · Natural image · Decoding model

2025

Mindfulness-based stress reduction intervention during pregnancy changes maternal brain

Scientific reports · Yvan Gomez et al.

Our aim is to evaluate the effect of a structured stress reduction intervention based on mindfulness during pregnancy on the maternal brain. We report a secondary analysis of IMPACT BCN, a randomized clinical trial including pregnant women randomly allocated to 8-week Mindfulness-Based Stress Reduction (n = 41) or usual care (without any intervention, n = 35). Maternal magnetic resonance (MR) was performed in the third trimester, cluster-wise analysis was used to assess cortical morphometric differences, and proton magnetic resonance spectroscopy (1H-MRS) to evaluate the metabolic characteristics. Mindfulness status was evaluated using the Five Facet Mindfulness Questionnaire (FFMQ). Results showed that participants from Stress reduction group had significantly larger surface areas in the right superior frontal region as compared to the Usual care group (90%CI: 0.023-0.029, p = 0.03). The1H-MRS revealed that Stress reduction group participants, had higher concentrations of myo-inositol (adjusted mean difference D 0.37 mol/L, 95%CI 0.05-0.69) as compared to Usual care. Participants who had high mindfulness on FFMQ facets of non-judgmental (D 358.5 mm2, 95%CI 53.5-663.6) and non-reactivity (D 362.3 mm2, 95%CI 18.8-705.7) had larger right superior frontal area. In conclusion, Mindfulness-Based Stress Reduction program during pregnancy has a significant effect on maternal brain structure and is associated with metabolite concentration changes.
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Morphometry and behaviour