Medical Physics,
AI and Neurotechnology

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.

Visual decoding from fMRI is typically siloed by participant and experiment, obscuring whether heterogeneous neural measurements can be organized within a common computational geometry. Here we introduce BRAID-fMRI (Brain Representation Alignment across Individuals and Datasets), a shared CLIP-supervised decoding framework. BRAID-fMRI uses a single ROI-wise Transformer with optional participant conditioning across eight visual-fMRI datasets comprising 93 dataset-specific participant entries, 430,007 single-trial responses and 162,839 unique stimuli. Regional brain activity is aligned with 512-dimensional CLIP ViT-B/32 representations using a multi-positive contrastive objective that treats repeated stimuli across participants and datasets as positives. BRAID-fMRI supports retrieval across seven evaluation datasets. On eight matched participant entries, it achieves 35.0 +/- 11.1% Top-10 accuracy, exceeding the observed mean accuracy of the two evaluated baselines – the MindEye-style pooled-CLIP decoder (27.1 +/- 5.1%) and ridge regression (21.1 +/- 9.1%) – and attaining the highest observed accuracy for seven of eight entries. In separately trained participant-agnostic models, expanding the source pool increased target-dataset holdout accuracy by up to 92.1% relative to the initial source-training condition. The learned space preserves graded semantic structure, while ablations and saliency highlight ventral and early visual cortex and category-specific motion and attentional systems. These results support scalable cross-dataset decoding into a common CLIP-aligned space, with model sensitivity concentrated in ventral and early visual inputs.

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.