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...

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...

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...

CONCLUSIONS: Our work demonstrated that machine learning models trained with data calculated by a dose-tracking software can provide good estimates of the effective dose just from patient and scanner parameters, without the need for a Monte Carlo approach.