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...

INTRODUCTION: Neuroinflammation, a pathophysiological process involved in numerous disorders, is typically imaged using [^(11)C]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...

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...

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.

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...

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.

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...

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...

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...

Dysregulated expression of human endogenous retrovirus K (HERV-K) has been found in many types of tumors. Previously, we demonstrated the concomitant expression of HERVs and embryonic genes in cancer cells with aggressive and stemness features. In the field of onco-hematology, some studies have described alterations of HERV expression in chronic lymphocytic leukemia (CLL), the most common adult leukemia in the Western world. Despite numerous achievements in CLL clinical research, given the...

CONCLUSION: Hypo- and hyper-connectivity, especially in early stages, may have different roles in AD neurodegenerative processes, with metabolism in hyper-connected regions acting as a mediator on the neurodegeneration of core regions of AD pathology.

Recent evidence suggests that chronic pain patients exhibit elevated brain levels of the neuroinflammation marker 18 kDa translocator protein (TSPO). However, the clinical significance of brain TSPO elevations, and their responses to pain interventions, remain unknown. To explore these questions, we studied patients with knee osteoarthritis (KOA) undergoing total knee arthroplasty (TKA), a procedure which is curative for most, but carries a relatively high risk of persistent post-surgical pain....

Real-world evidence (RWE) can complement clinical trials by addressing gaps in how approved anti-amyloid therapies for early Alzheimer's disease (AD) are used in everyday practice. This article outlines strategies to generate RWE that bridge three key challenges in AD care: low detection rates of mild cognitive impairment (MCI), limited data on long-term safety and effectiveness, and a lack of personalized treatment strategies. With MCI detection rates among primary care providers as low as...

Neuroimaging studies suggest that resilience to adversity is linked to reduced emotional reactivity or enhanced emotion regulation. However, such studies are scarce and mainly use adult samples and categorical definitions of resilience. Using a novel, data-driven approach to define resilience dimensionally, based on cumulative adversity exposure across childhood and psychopathology, we investigated associations between resilience and brain activation during facial emotion processing in youth. We...