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...

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...

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...

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...

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...

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.

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...

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...

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...

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,...

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...

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...

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...

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...

OBJECTIVE: To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts.

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...

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....

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...

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...

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...

Quantifying the volume of distribution (V(T)) 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 [^(11)C]PBR28 dynamic...

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...

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...

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...

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...