Biology subjects

Fukuma, R.

Publications and source records attributed to Fukuma, R..

4 recordsLinked to original sources

bioRxiv2024

A microendovascular system can record precise neural signals from cortical and deep vessels with minimal invasiveness

Minimally invasive intravascular electroencephalography (ivEEG) signals are a promising tool for developing clinically feasible brain-computer interfaces (BCIs) that restore communication and motor functions in paralyzed patients. However, current ivEEG techniques can only record signals from the superior sagittal sinus (SSS), making it challenging to record motor responses related to hand and mouth movements from brain regions distant from the SSS, despite their critical role in BCIs. Here, using micro intravascular electrodes, ivEEGs recorded from the cortical or deep veins of eight pigs could measure cortical activities with greater signal power and better spatial resolution than those recording in the SSS, thus allowing mapping of the sensorimotor and visual functional areas. Additionally, electrical stimulation in the cortical vein between the micro intravascular electrodes induced muscle contractions contralateral to the stimulated area in five anesthetized pigs. These results demonstrate that ivEEG using micro intravascular electrodes is a promising tool for developing BCIs.

neuroscience↗

bioRxiv2024

Image retrieval based on closed-loop visual-semantic neural decoding

Neural decoding via the latent space of deep neural network models can infer perceived and imagined images from neural activities, even when the image is novel for the subject and decoder. Brain-computer interfaces (BCIs) using the latent space enable a subject to retrieve intended image from a large dataset on the basis of their neural activities but have not yet been realized. Here, we used neural decoding in a closed-loop condition to retrieve images of the instructed categories from 2.3 million images on the basis of the latent vector inferred from electrocorticographic signals of visual cortices. Using a latent space of contrastive language-image pretraining (CLIP) model, two subjects retrieved images with significant accuracy exceeding 80% for two instructions. In contrast, the image retrieval failed using the latent space of another model, AlexNet. In another task to imagine an image while viewing a different image, the imagery made the inferred latent vector significantly closer to the vector of the imagined category in the CLIP latent space but significantly further away in the AlexNet latent space, although the same electrocorticographic signals from nine subjects were decoded. Humans can retrieve the intended information via a closed-loop BCI with an appropriate latent space.

neuroscience↗

bioRxiv2024

Accurate localization of cortical and subcortical sources of M/EEG signals by a convolutional neural network with a realistic head conductivity model: Validation with M/EEG simulation, evoked potentials, and invasive recordings

While electroencephalography (EEG) and magnetoencephalography (MEG) are well-established non-invasive methods in neuroscience and clinical medicine, they suffer from low spatial resolution. Particularly challenging is the accurate localization of subcortical sources of M/EEG, which remains a subject of debate. To address this issue, we propose a four-layered convolutional neural network (4LCNN) designed to precisely locate both cortical and subcortical source activity underlying M/EEG signals. The 4LCNN was trained using a vast dataset generated by forward M/EEG simulations based on a realistic head volume conductor model. The 4LCNN implicitly learns the characteristics of M/EEG and their sources from the training data without need for explicitly formulating and fine-tuning optimal priors, a common challenge in conventional M/EEG source imaging techniques. We evaluated the efficacy of the 4LCNN model on a validation dataset comprising forward M/EEG simulations and two types of real experimental data from humans: 1) somatosensory evoked potentials recorded by EEG, and 2) simultaneous recordings from invasive electrodes implanted in the brain and MEG signals. Our results demonstrate that the 4LCNN provides robust and superior estimation accuracy compared to conventional M/EEG source imaging methods, aligning well with established neuroscience knowledge. Notably, the accuracy of the subcortical regions was as accurate as that of the cortical regions. The 4LCNN method, as a data-driven approach, enables accurate source localization of M/EEG signals, including in subcortical regions, suggesting future contributions to various research endeavors such as contributions to the clinical diagnosis, understanding of the pathophysiology of various neuronal diseases and basic brain functions.

neuroscience↗

bioRxiv2023

Hippocampal sharp-wave ripples correlate with naturally occurring self-generated thoughts in humans

Core features of human cognition, for example, the experience of mind wandering, highlight the importance of the capacity to focus on information separate from the here and now. However, the brain mechanisms that underpin these self-generated states remain unclear. An emerging hypothesis is that self-generated states depend on the process of memory replay, which, in animals, is linked to sharp-wave ripples (SWRs) originating in the hippocampus. SWRs are transient high-frequency oscillations that exhibit circadian fluctuations and, in the laboratory, are important for memory and planning. Local field potentials were recorded from the hippocampus of 11 patients with epilepsy for up to 15 days, and experience sampling was used to describe their association with ongoing thought patterns. SWRs were correlated with patterns of vivid, intrusive ongoing thoughts unrelated to the task being performed, establishing their contribution to the ongoing thoughts that humans experience in daily life.

neuroscience↗