Elosegi, P. | bioRxiv Science

bioRxiv2023

PyDecNef: An open-source framework for fMRI-based decoded neurofeedback

Real time fMRI research has suffered from inaccessible analysis pipelines, hindering collaboration and reproducibility. Here we present PyDecNef, a Python-based platform designed to advance real-time fMRI analysis and fuel exploration of close-loop neuroimaging for cognitive neuroscience studies. Creating a real-time fMRI analysis pipeline from scratch poses formidable technical challenges, involving data transfer, experimental software, and machine learning classifier preparation. Existing tools like FRIEND, Brain-Voyant, and OpenNFT demand expensive licenses or rely on proprietary software, impeding accessibility and customizability. PyDecNef offers a solution: a transparent, versatile, and open workflow for real-time fMRI decoding protocols. This open-source platform simplifies decoder construction, real-time preprocessing, decoding, and feedback signal generation. It also supports co-adaptive decoders that update in real time based on decoded neurofeedback performance, enabling researchers to conduct more precise and efficient DecNef experiments. Moreover, its openness promotes collaboration, enhancing research quality, replicability, and impact. With PyDecNef, the path to advancing DecNef studies becomes more accessible and collaborative. PyDecNef resources for real-time fMRI analysis can be found at https://github.com/pydecnef/PydecNef.

neuroscience↗

bioRxiv2023

Unconscious manipulation of conceptual representations with decoded neurofeedback impacts search behaviour

The necessity of conscious awareness in human learning has been a long-standing topic in psychology and neuroscience. Previous research on non-conscious associative learning is limited by the low signal-to-noise ratio of the subliminal stimulus, and the evidence remains controversial, including failures to replicate. Using functional MRI decoded neurofeedback (fMRI-DecNef) we guided participants from both sexes to generate neural patterns akin to those observed when visually perceiving real-world entities (e.g., dogs). Importantly, participants remained unaware of the actual content represented by these patterns. We utilized an associative DecNef approach to imbue perceptual meaning (e.g., dogs) into Japanese hiragana characters that held no inherent meaning for our participants, bypassing a conscious link between the characters and the dogs concept. Despite their lack of awareness regarding the neurofeedback objective, participants successfully learned to activate the target perceptual representations in the bilateral fusiform. The behavioural significance of our training was evaluated in a visual search task. DecNef and control participants searched for dogs or scissors targets that were pre-cued by the hiragana used during DecNef training or by a control hiragana. The DecNef hiragana did not prime search for its associated target but, strikingly, participants were impaired at searching for the targeted perceptual category. Hence, conscious awareness may function to support higher-order associative learning. Meanwhile, lower-level forms of re-learning, modification, or plasticity in existing neural representations can occur unconsciously, with behavioural consequences outside the original training context. The work also provides an account of DecNef effects in terms of neural representational drift. Significance StatementThis study examined the role of conscious awareness in human learning by using fMRI-DecNef. These techniques enabled participants to self-regulate their brain activity to align with the perceptual representations generated by a real-world entity (i.e., dogs), without awareness of the content they represented. We demonstrated that established brain conceptual representations can be unconsciously modified, influencing visual search behaviour for the targeted perceptual content through the neural representational drift mechanism. Nonetheless, our research suggests that conscious awareness plays a role in more advanced forms of associative learning. Further, this study offers methodological insights for improving DecNef protocols and suggests potential for personalized interventions, including guidance to correct maladaptive conceptual representations.

neuroscience↗