Biology subjects

Yanagisawa, T.

Publications and source records attributed to Yanagisawa, T..

12 recordsLinked to original sources

bioRxiv2026

Ripples precede insight in the right human hippocampus

Insight is a sudden transition from uncertainty to awareness of a solution, often preceded by non-conscious processing. Despite the ubiquity of insights in human cognition, the neural mechanisms underlying this pre-conscious restructuring phase remain largely unknown. Recent theoretical frameworks suggest that the hippocampus, through its capacity for rapid associative retrieval and flexible recombination of stored representations, may facilitate the non-conscious exploration that precedes insight. Hippocampal ripples associated with memory replay and consolidation, represent a promising candidate mechanism for this process. We recorded intracranial local field potentials from depth electrodes in the hippocampus of ten patients with drug-resistant epilepsy while they performed non-verbal reasoning tasks. Participants indicated the moment of solution awareness via button press, enabling precise temporal alignment of neural activity to subjective insight. We observed a significant increase in ripple rate during a pre-response window. Critically, this effect was lateralized to the right hippocampus. These findings provide the first direct human electrophysiological evidence linking right-lateralized hippocampal ripples to the non-conscious phase of insight. We propose that right hippocampal ripples support rapid retrieval and flexible recombination of representations, potentially triggering cortical processes that culminate in conscious solution awareness.

neuroscience↗

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↗

bioRxiv2024

Structural basis of eukaryotic transcription termination by the Rat1 exonuclease complex

The 5-3 exoribonuclease Rat1 (or Xrn2) plays a pivotal role in termination of mRNA transcription by eukaryotic RNA polymerase II (RNAPII). Rat1 forms a complex with its partner proteins, Rai1 and Rtt103, and acts as a "torpedo" to bind transcribing RNAPII and dissociate DNA/RNA from it. Here we report the cryo-electron microscopy structures of a Rat1-Rai1-Rtt103 complex and three Rat1-Rai1-associated RNAPII complexes (type-1, type-1b, and type-2) from the yeast Komagataella phaffii. The Rat1-Rai1-Rtt103 complex structure revealed that Rat1 and Rai1 form a heterotetramer, with a single Rtt103 bound between two Rai1 molecules. In the type-1 complex, Rat1-Rai1 forms a heterodimer and directly binds to the RNA exit site of transcribing RNAPII to extract RNA into the Rat1 nuclease active site. This interaction changes the RNA path in favor of termination (the "pre-termination" state). The type-1b and type-2 complexes have no bound DNA/RNA, likely representing the "post-termination" states. These structures shed light on the mechanism of eukaryotic mRNA transcription termination.

biochemistry↗

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↗

bioRxiv2023

Hippocampal neural fluctuation between memory encoding and retrieval states during a working memory task in humans

BackgroundWorking memory (WM) is essential for everyday life, yet its neural mechanism remains unclear. Although the hippocampus plays a critical role in memory consolidation and retrieval, its role in WM tasks has yet to be fully elucidated. We hypothesized that multiunit activities in the hippocampus alter their representations depending on the memory load and phase of a WM task, particularly in relation to sharp-wave ripple complexes (SWRs), which are known as various cognitive biomarkers. MethodsWe used an open dataset of intracranial electroencephalogram (iEEG) data and multiunit activity recorded from the medial temporal lobe (MTL) of nine patients with epilepsy. The MTL includes the hippocampus, entorhinal cortex, and amygdala. During the recording, all subjects performed an eight-second Sternberg test, in which they memorized sets of four, six, or eight letters (encoding phase), waited for three seconds (maintenance phase), and recalled whether a probe letter was included (Match IN task) or not (Mismatch OUT task) (retrieval phase). We used Gaussian-process factor analysis to visualize the neural trajectories of multiunit activity in MTL regions during the task. We also detected SWRs from the iEEG data in the hippocampus. FindingsWe found that the trajectory distance between phases of the Sternberg task was larger in the hippocampus compared to the entorhinal cortex and amygdala. Additionally, the trajectory distance between the encoding and retrieval phases was memory load dependent. Moreover, a transient trajectory increase was detected during SWRs. Finally, the trajectory direction of the hippocampus fluctuated between the encoding and retrieval states, and the balance of the fluctuation was shifted to the retrieval state during SWR periods. InterpretationOur results demonstrate the involvement of the hippocampus during a WM task. Furthermore, it is suggested that SWR in the retrieval phase plays a role in memory retrieval for a WM task. Our results provide new insight into the two-stage model of memory formation.

neuroscience↗

bioRxiv2021

Frequency band coupling with high-frequency activities during seizures shifts from θ band in tonic phase to δ band in clonic phase

ObjectiveTo clarify variations in the relationship between high-frequency activities (HFAs) and low-frequency bands from the tonic to the clonic phase in focal to bilateral tonic-clonic seizures (FBTCS), using phase-amplitude coupling. MethodsThis retrospective study enrolled six patients with drug-resistant focal epilepsy who underwent intracranial electrode placement for presurgical invasive electroencephalography at Osaka University Hospital (July 2018-July 2019). We used intracranial electrodes to record seizures in focal epilepsy (11 FBTCS). The magnitude of synchronization index (SIm) and receiver-operating characteristic (ROC) analysis were used to analyze the coupling between HFA amplitude (80-250 Hz) and lower frequencies phase. ResultsThe {theta} (4-8 Hz)-HFA SIm peaked in the tonic phase, whereas the {delta} (2-4 Hz)-HFA SIm peaked in the clonic phase. ROC analysis indicated that the {delta}-HFA SIm discriminated well the clonic from the tonic phase. ConclusionsThe main low-frequency band modulating the HFA shifted from the {theta} band in the tonic phase to the {delta} band in the clonic phase. SignificanceIn FBTCS, low-frequency band coupling with HFA amplitude varies temporally. Especially, the {delta} band is specific to the clonic phase. These results suggest dynamically neurophysiological changes in the thalamus or basal ganglia throughout FBTCS. HighlightsO_LIThe {theta} band (4-8 Hz) was mainly coupled with high-frequency activity (HFA) in the tonic phase of focal to bilateral tonic-clonic seizures (FBTCS). C_LIO_LIThe {delta} band (2-4 Hz) was mainly coupled with HFA in the clonic phase of FBTCS. C_LIO_LIThe magnitude of the synchronization index related to {delta}-HFA phase-amplitude coupling discriminated well the clonic from the tonic phase. C_LI

physiology↗

bioRxiv2020

Swallowing-related neural oscillation: An intracranial EEG study

Swallowing is a unique movement due to the indispensable orchestration of voluntary and involuntary movement. The transition from voluntary to involuntary swallowing is executed on the order of milliseconds. We hypothesized that its neural mechanism is revealed by high frequency cortical activities. Eight epileptic participants fitted with intracranial electrodes over the orofacial cortex were asked to swallow a water bolus, and cortical oscillatory changes, including high {gamma} band (75-150 Hz) and {beta} band (13-30 Hz) were investigated at the time of mouth-opening, water-injection, and swallowing. High {gamma} power increases associated with mouth-opening were observed in the ventrolateral prefrontal cortex with water-injection in the lateral central sulcus and with swallowing in the region along the Sylvian fissure. Mouth-opening induced a {beta} power decrease, which continued until the completion of swallowing. The high {gamma} burst activity was focal and specific to swallowing, however, the {beta} activities were extensive and not specific to swallowing. At the boundary time between voluntary and involuntary swallowing, swallowing-related high {gamma} power achieved the peak, and subsequently, the power decreased. We demonstrated three distinct activities related to mouth-opening, water-injection, and swallowing induced at different timings, using high {gamma} activities. The peak of high {gamma} power related to swallowing suggests that during voluntary swallowing phases, the cortex is the main driving force for swallowing rather than the brain stem.

neuroscience↗

bioRxiv2020

Visualization of Phase-Amplitude Coupling Using Rhythmic High-Frequency Activity

ObjectiveHigh-frequency activities (HFAs) and phase-amplitude coupling (PAC) are gaining attention as key neurophysiological biomarkers for studying human epilepsy. We aimed to clarify and visualize how HFAs are modulated by the phase of low-frequency bands during seizures. MethodsWe used intracranial electrodes to record seizures of symptomatic focal epilepsy (15 seizures in seven patients). Ripples (80-250 Hz), as representative of HFAs, were evaluated along with PAC. The synchronization index (SI), representing PAC, was used to analyze the coupling between the amplitude of ripples and the phase of lower frequencies. We created a video in which the intracranial electrode contacts were represented by circles that were scaled linearly to the power changes of ripple. ResultsThe main low frequency band modulating ictal-ripple activities was the {theta} band (4-8 Hz), and after completion of ictal-ripple burst, {delta} (1-4 Hz)-ripple PAC occurred. The video showed that fluctuation of the diameter of these circles indicated the rhythmic changes during significant high values of {theta}-ripple PAC. ConclusionsWe inferred that ripple activities occurring during seizure evolution were modulated by {theta} rhythm. In addition, we concluded that rhythmic circles fluctuation presented in the video represents the PAC phenomenon. Our video is thus a useful tool for understanding how ripple activity is modulated by the low-frequency phase in relation with PAC.

neuroscience↗

bioRxiv2020

Motor and Sensory Cortical Processing of Neural Oscillatory Activities revealed by Human Swallowing using Intracranial Electrodes

Swallowing, a unique movement, is attributed to the indispensable orchestration of motor-output and sensory-input. We hypothesized that swallowing can illustrate differences between motor and sensory neural processing. Eight epileptic participants fitted with intracranial electrodes over the orofacial cortex were asked to swallow a water bolus. Mouth-opening and swallowing were treated as motor tasks, while water-injection as sensory tasks. Phase-amplitude coupling between lower frequency and high {gamma} (HG) band (75-150 Hz) was investigated. An (10-16 Hz) -HG coupling appeared before motor-related HG power increase (burst), and a {theta} (5-9 Hz) -HG coupling appeared during sensory-related HG burst. The motor- and sensory-related HG amplitude were modulated at the trough of oscillations and peak of {theta} oscillations, respectively. These contrasting results acquired from the orofacial cortex can help to fully elucidate the sensory-motor function in the brain.

neuroscience↗

bioRxiv2018

Endogenous controllability of closed-loop brain-machine interfaces for pain.

The ultimate aim of closed-loop brain-machine systems for pain is to directly titrate the ongoing level of an intervention to pain-related neural activity. However pain is highly susceptible to endogenous modulation, raising the possibility that active or passive changes in neural activity provoked by the operation of the system could enhance or interfere with the signals upon which it is based. We studied healthy subjects receiving intermittent pain stimuli in a real-time fMRI-based closed-loop feedback-stimulation task. We showed that multi-voxel pattern decoding of pain intensity could be used to train a control algorithm to learn to deliver less painful stimuli (adaptive decoded neurofeedback). However, the system engaged two types of endogenous processes in the brain. First, despite the inherent incentive for subjects to enhance the neural decodability of pain, decodability was either reduced or unchanged in classic pain-processing regions, including insula, dorsolateral prefrontal, and somatosensory cortices. However, increased decodability was observed in a putative pain modulatory region - the pregenual anterior cingulate cortex (pgACC). Second, we found that pain perception itself was modulated by an endogenous computational uncertainty signal engaged as subjects learned the success rate of the system in reducing pain - an effect that also correlated with pgACC responses. The results illustrate how regionally and computationally specific co-adaptive brain-machine learning influences the efficacy of closed-loop systems for pain, and shows that pgACC acts as a key hub in the endogenous controllability of pain.

neuroscience↗