Biology subjects

Yanagida, T.

Publications and source records attributed to Yanagida, T..

8 recordsLinked to original sources

bioRxiv2026

Modeling Time to Visual Insight in Mooney Image Recognition with a Chaotic Recurrent Neural Network

Insight is often described as a sudden shift in or formation of a conceptual representation, enabling humans to restructure existing knowledge and solve problems beyond conventional analytical approaches. Although prior computational studies have modeled aspects of insight using deep neural networks (DNNs) or reinforcement learning, few have captured the dynamic emergence of insight through autonomous neural computation. Here, we present a neural network model that simulates the time required to reach visual insight in the Mooney image recognition task, a widely used paradigm for studying visual insight and perceptual reorganization. The model couples a DNN module for perceptual feature extraction with a recurrent neural network (RNN) that implements a chaotic search process for recognition. The RNN is formulated as a continuous-time dynamical system, autonomously explores internal states, and stabilizes when the missing visual features required for recognition are internally reconstructed. Using the same image set as in human psychophysical experiments, the model reproduces key statistical properties of human search times (STs), including (i) lognormal-like ST distributions across participants for each image, (ii) a proportional relationship between the log-scale mean and standard deviation estimated from lognormal fits across images, and (iii) discrete levels of the fitted log-scale mean across images (a proxy for image difficulty). Importantly, these properties emerge without assuming any lognormal distribution for participant-to-participant variability, whereas previous models reproduced similar signatures by positing lognormal-distributed individual differences. We further show that lognormal-like signatures can arise from exponential search dynamics when combined with both standard experimental preprocessing and finite observation windows, highlighting the need to distinguish generative mechanisms from measurement and analysis effects. Together, these results motivate a mechanistic link between chaotic neural dynamics and insight-related search and provide a computational framework for implementing insight in artificial systems.

neuroscience↗

bioRxiv2025

Superior colliculus coordinates whole-brain dynamics for sudden unconscious visual insight

The human capacity for sudden insight, often marked by abrupt and illuminating "eureka" moments, has long intrigued neuroscientists seeking to understand its elusive neural basis1-6. Mooney image recognition offers a compelling behavioral paradigm of this phenomenon, in which ambiguous black-and-white patterns are abruptly perceived as coherent objects7-13. Whole-brain cortical involvement, which is mainly revealed by the contrast between recognition and nonrecognition, is well established8,14-27. However, the neural dynamics underlying the transition from nonrecognition to recognition, particularly the contributions of noncortical structures, have remained largely unknown. Here, we show that the transition in whole-brain dynamics can be effectively characterized by three large-scale activity patterns and that the superior colliculus (SC) emerges as a structure with a distinct temporal profile that is potentially critical for insight. Using functional clustering of functional magnetic resonance imaging data comprising 41,446 time points from 14 human participants performing this insight task, we identified three distinct functional clusters exhibiting stimulus-driven activation, suppression, and recognition-associated patterns. Notably, the SC in the third cluster displayed a distinct activation peak immediately before the recognition response. Furthermore, empirical dynamic modeling revealed that the SC was the only region in the whole brain that exhibited mutual positive directed interactions with all three clusters and was functionally embedded within higher-order cortical interactions mediating the transformation from stimulus input to motor output. Our findings provide compelling experimental evidence that the SC plays a critical role in orchestrating whole-brain dynamics for sudden insight, calling for a reappraisal of this evolutionarily conserved structure as a key player supporting unconscious but high-level cognitive processing.

neuroscience↗

bioRxiv2023

Dynamic coordination of the lever-arm swing of human myosin II in thick filaments on actin

Muscle myosins work in motor ensembles and must adapt their power stroke in response to mechanical actions by surrounding motors. Understanding the coordination of power strokes is essential for bridging microscopic molecular functions and macroscopic muscle contractions, but the details of this phenomenon remain elusive. Here we used high-speed atomic force microscopy to visualize the individual dynamics (lever-arm swing) of the myosin head bound to actin in DNA origami-based synthetic thick filaments. We observed spatially local lever-arm coordination, and our three-dimensional numerical model explained how mechanical communication between myosins achieved coordination. In a sarcomere model, the local coordination was spatially periodic and propagated toward the contraction direction. We confirmed that a structural mismatch between myosin head spacing (42.8 nm) and the actin helical pitch (37 nm) caused the coordination while improving contraction speed and energy efficiency. Our findings reveal a key physical basis of efficient muscle contraction.

biophysics↗

bioRxiv2021

Functional cortical localization of the tongue using corticokinematic coherence with a deep learning-assisted motion capture system

Measuring the corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer can evaluate the functional localization of the primary sensorimotor cortex (SM1) of the upper limbs. However, it is difficult to determine the tongue CKC because an accelerometer yields excessive magnetic artifacts. We introduce and validate a novel approach for measuring the tongue CKC using a deep learning-assisted motion capture system with videography, and compare it with an accelerometer in a control task measuring finger movement. Twelve healthy volunteers performed rhythmical side-to-side tongue movements in the whole-head magnetoencephalographic system, which were simultaneously recorded using a video camera and examined offline using a deep learning-assisted motion capture system. In the control task, right finger CKC measurements were simultaneously evaluated via motion capture and an accelerometer. The right finger CKC with motion capture was significant at the movement frequency peaks or its harmonics over the contralateral hemisphere; the motion-captured CKC was 84.9% similar to that with the accelerometer. The tongue CKC was significant at the movement frequency peaks or its harmonics over both hemispheres, with no difference between the left and right hemispheres. The CKC sources of the tongue were considerably lateral and inferior to those of the finger. Thus, the CKC based on deep learning-assisted motion capture can evaluate the functional localization of the tongue SM1. In this approach, because no devices are placed on the tongue, magnetic noise, disturbances due to tongue movements, risk of aspiration of the device, and risk of infection to the experimenter are eliminated.

neuroscience↗

bioRxiv2021

The synergic role of actomyosin architecture and biased detachment in muscle energetics: insights in cross bridge mechanism beyond the lever-arm swing

Muscle energetics reflects the ability of myosin motors to convert chemical energy into mechanical energy. How this process takes place remains one of the most elusive questions in the field. Here we combined experimental measurements of in vitro sliding velocity based on DNA-origami built filaments carrying myosins with different lever arm length and simulations based on a Monte-Carlo model which accounts for three basic components: (i) the geometrical hindrance, (ii) the mechano-sensing mechanism, and (iii) the biased kinetics for stretched or compressed motors. The model simulations showed that the geometrical hindrance due to acto-myosin spatial mismatching and the preferential detachment of compressed motors are synergic in generating the rapid increase in the ATP-ase rate from isometric to moderate velocities of contraction, thus acting as an energy-conservation strategy in muscle contraction. The velocity measurements on a DNA-origami filament that preserves the motors distribution showed that geometrical hindrance and biased detachment generate a non-zero sliding velocity even without rotation of the myosin lever-arm, which is widely recognized as the basic event in muscle contraction. Because biased detachment is a mechanism for the rectification of thermal fluctuations, in the Brownian-ratchet framework, we predict that it requires a non-negligible amount of energy to preserve the second law of thermodynamics. Taken together, our theoretical and experimental results elucidate non-conventional components in the chemo-mechanical energy transduction in muscle.

biophysics↗

bioRxiv2019

Remote control of neural function by X-ray-induced scintillation

Scintillators emit visible luminescence when irradiated with X-rays. Given the unlimited tissue penetration of X-rays, the employment of scintillators could enable remote optogenetic control of neural functions at any depth of the brain. Here we show that a yellow-emitting inorganic scintillator, Ce-doped Gd3(Al,Ga)5O12 (Ce:GAGG), could effectively activate red-shifted excitatory and inhibitory opsins, ChRmine and GtACR1, respectively. Using injectable Ce:GAGG microparticles, we successfully activated and inhibited midbrain dopamine neurons in freely moving mice by X-ray irradiation, producing bidirectional modulation of place preference behavior. Ce:GAGG microparticles were non-cytotoxic and biocompatible, allowing for chronic implantation. Pulsed X-ray irradiation at a clinical dose level was sufficient to elicit behavioral changes without reducing the number of radiosensitive cells in the brain and bone marrow. Thus, scintillator-mediated optogenetics enables less invasive, wireless control of cellular functions at any tissue depth in living animals, expanding X-ray applications to functional studies of biology and medicine.

neuroscience↗

bioRxiv2019

Direct visualization of human myosin II force generation using DNA origami-based thick filaments

Muscle contraction can be explained by the swinging lever-arm model. However, the dynamic features of how the myosin head swings the lever-arm and its initial interactions with actin are not well understood even though they are essential for the muscle force generation, contraction speed, heat production, and response to mechanical perturbations. This is because myosin heads during force generation have not been directly visualized. Here, we engineered thick filaments composed of DNA origami and recombinant human muscle myosin, and directly visualized the heads during force generation using nanometer-precision single-molecule imaging. We found that when the head diffuses, it weakly interacts with actin filaments and then strongly binds preferentially to the forward region as a Brownian ratchet. Upon strong binding, the head two-step lever-arm swing dominantly halts at the first step and occasionally reverses direction. These results can explain all mechanical characteristics of muscle contraction and suggest that our DNA origami-based assay system can be used to dissect the mechanistic details of motor proteins.

biophysics↗

bioRxiv2017

Classification and characterisation of brain network changes in chronic back pain: a multicenter study.

Chronic pain is a common and often disabling condition, and is thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Here, we investigated brain network architecture using resting state fMRI data collected from chronic back pain patients in UK and Japan (41 patients, 56 controls). Using a machine learning approach (support vector machine), we found that brain network patterns reliably classified chronic pain patients in a third, independent open data set with an accuracy of 63%, whilst 68% was attained in cross validation of all data. We then developed a deep learning classifier using a conditional variational autoencoder, which also yield yielded 63% generalisation and 68% cross-validation accuracy. Given the existence of reliable network changes, we next studied the graph topology of the network, and found consistent evidence of hub disruption based on clustering and betweenness centrality of brain nodes in pain patients. To examine this in more detail, we developed a multislice modularity algorithm to identify a consensus pattern of modular reorganisation of brain nodes across the entire data set. This revealed evidence of significant changes in the modular identity of several brain regions, most notably including broad regions of bilateral sensorimotor cortex, subregions of which also contributed to classifier performance. These results provide evidence of consistent and characteristic brain network changes in chronic pain, and highlight extensive reorganisaton of the network architecture of sensorimotor cortex.

neuroscience↗