Biology subjects

Kitajo, K.

Publications and source records attributed to Kitajo, K..

18 recordsLinked to original sources

bioRxiv2026

The functional significance of EEG phase synchronization networks during information integration of left and right visual fields

Objects moving between the left and right visual hemifields are naturally perceived as continuous entities, although early visual processing independently transmits information from the two hemifields. Therefore, interhemispheric integration of visual information is essential for maintaining an object's identity. Additionally, brain function is thought to be maintained through a dynamic balance between integration and segregation. In this study, we investigated the functional neural architecture underlying visual hemifield integration in healthy adults, using electroencephalography (EEG) and a visual integration task. To capture neural oscillatory networks without relying on prior assumptions regarding electrode pairs or frequency bands, we applied a frequency-inclusive, data-driven network analysis based on an extended network-based statistic. This analysis identified a broadband EEG phase synchronization network that emerged specifically under task conditions with high interhemispheric integration demands. Furthermore, individual differences in behavioral performance were associated with modulation of interhemispheric synchronization, with this relationship differing according to participants' relative performance across task conditions. These findings suggest that visual hemifield integration is supported by large-scale phase synchronization networks spanning multiple frequencies and are consistent with the importance of a balance between integration and segregation.

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bioRxiv2026

Metastability in EEG phase synchronization networks is associated with autistic traits in a neurotypical cohort

Metastability is a fundamental dynamical property of large-scale brain networks and reflects the capacity of the brain to flexibly reorganize transient coordination patterns. In this study, we investigated whether metastable properties of resting-state electroencephalographic (EEG) phase synchronization networks are associated with individual differences in autistic traits. Resting-state EEG data from 88 neurotypical adults were analyzed using two complementary metrics: synchrony coalition entropy (SCE), which quantifies the diversity of transient phase synchronization patterns, and the metastability index (MSI), which quantifies temporal variance in global phase synchronization. SCE showed frequency-specific associations with the Autism-Spectrum Quotient (AQ) attention-switching subscore at 18-24 Hz and the communication subscore at 4-8 Hz, suggesting that frequency- and network-specific patterns of metastable synchronization are associated with distinct aspects of autistic traits. In contrast, MSI showed a modest association with the social-skill subscore in the lower-beta range, but this effect did not survive a cluster-based permutation test. This exploratory observation suggests that global synchronization variability may capture a weaker, complementary aspect of trait-related metastable dynamics. These findings suggest that, within a neurotypical population, individual differences in autistic traits may be more sensitively captured by the repertoire of transient phase synchronization patterns, as indexed by SCE, than by global phase synchronization variability, as indexed by MSI. Moreover, the associations of distinct AQ subscores with SCE in different frequency ranges suggest that different dimensions of autistic traits may be related to metastable network dynamics operating at different temporal scales. Author SummaryThe brain constantly coordinates activity across many regions, and this coordination changes over time rather than remaining constant. Understanding these dynamic patterns is important for explaining individual differences in cognition and behavior. In this study, we focused on a dynamical property called "metastability," which describes how brain activity flexibly shifts between different patterns of coordination. Instead of remaining in a stable state, the brain repeatedly forms and dissolves coordinated activity across regions. We analyzed brain signals recorded with resting-state electroencephalography (EEG) and examined whether these dynamic patterns were related to individual differences in autistic traits. We found that different aspects of time-varying coordination were linked to different dimensions of autistic traits in a neurotypical population. These findings suggest that examining how brain activity changes over time, rather than relying only on time-averaged measures, can reveal neural features associated with individual differences in autistic traits. Our study highlights metastability as a useful concept for understanding the flexible and dynamic nature of human brain function.

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bioRxiv2026

Pretraining Objective Shapes Cross-Category Generalization in Affective Image Prediction: A Geometric Comparison of Vision Transformer Encoders

The geometry of representations learned by deep neural networks is shaped jointly by architecture and pretraining objective, yet disentangling these two factors remains difficult. Here we isolate the contribution of pretraining objective by comparing two Vision Transformers from the same backbone family but trained under different objectives: language-image contrastive learning (CLIP) and ImageNet-21k classification. Using continuous Valence-Arousal prediction on the OASIS dataset as a probe of representational quality, we evaluated frozen features under Leave-One-Theme-Out and Leave-One-Category-Out cross-validation, the latter requiring extrapolation to entirely unseen semantic categories. The contrastively pretrained encoder generalized substantially better than the classification-pretrained encoder under both protocols, with the gap widening sharply when held-out categories required cross-category generalization. To characterize why the two representations differ, we developed a geometric analysis of prediction errors, treating per-image errors as vectors in the affective plane and quantifying their spatial structure via weighted phase-locking, trajectory-based occupancy entropy, and effective dimensionality. The classification-pretrained representation collapsed errors into a small number of attractor regions with a strong center-ward pull, whereas the language-aligned representation distributed errors broadly across the affective space. Layer-wise linear probing further revealed that affective information was distributed across depth in the contrastive encoder but increasingly concentrated in deeper layers of the classification encoder, mirroring the texture-bias and category-anchored statistics characteristic of ImageNet-trained representations. These results provide a representation-geometric account of how the choice of pretraining objective, holding architecture constant, determines whether learned features generalize across semantic boundaries or remain confined to category-bound visual regularities. HighlightsO_LIIsolate the effect of pretraining objective by holding the Vision Transformer backbone constant. C_LIO_LIContrastively pretrained features generalize across unseen semantic categories where classification-pretrained features fail. C_LIO_LIIntroduce a geometric analysis of prediction errors based on phase-locking and occupancy entropy. C_LIO_LIClassification pretraining produces concentrated error attractors and a rigid centerward bias. C_LIO_LIAffective information is distributed across depth in CLIP but localized in late layers of the classification ViT. C_LI

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bioRxiv2025

Numerical validation of 3D-VAR data assimilation for estimating network dynamics in multivariate EEGs

Data assimilation (DA) is widely used as a data-driven system identification method, enhancing our understanding of the generative mechanisms in the observational data based on the identified model and its parameters. Due to this advantage, DA has gradually begun to be applied in neuroscience studies. Recently, we introduced a DA-based method for reconstructing the internal state of the brain using the neural mass model and human scalp electroencephalography (EEG). This method allows us to estimate the balance of synaptic interactions between excitatory and inhibitory neurons (E/I balance) in the brain only from observed EEGs. Although we confirmed the neurophysiological validity of the proposed EEG-DA method in our recent works, this method cannot be applied to multivariate EEGs to parallelly infer the E/I balance changes and underlying network dynamics. In this study, to address this issue, we proposed an extended version of our DA method specified to multivariate observed EEGs. This method enables us to estimate sensor-level functional network and E/I balance changes of each EEG sensor in parallel. The method was validated by showing that it could parallelly estimate sensor-level network structures and E/I balance changes from synthetic multivariate EEGs. The results of this study indicate that it has the potential to quantify how the E/I balance changes functionally affect the brain network dynamics.

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bioRxiv2025

Validation of EEG data assimilation-based prefrontal excitation-inhibition balance estimation using TMS-EEG

The excitation and inhibition (E/I) balance of neural circuits is a crucial index reflecting neurophysiological homeostasis. Although several cutting-edge methods have been established to assess E/I balance in an intact brain, they have inherent limitations, such as difficulties in tracking changes in E/I balance over time. To tackle this issue, we have proposed neural-mass-model-based tracking of the brain states using a data assimilation (DA) scheme in our previous work. However, although we verified that sleep-dependent changes in E/I balance can be estimated from electroencephalography data, the neurophysiological validity of the method was not evaluated. Therefore, in the current study, we directly compared estimated E/I states based on the DA methods with the concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) based methods. The results showed that the E/I changes estimated by the DA-based method correlated significantly with E/I in the dorsolateral prefrontal cortex, as indexed by TMS-evoked EEG. These findings indicate that our proposed method can estimate neurophysiologically valid changes in E/I balance.

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bioRxiv2025

Electroencephalographic responses before, during, and after upper limb paired associative stimulation

Paired associative stimulation (PAS) is a non-invasive protocol involving repeated stimulus pairs to activate two cortical areas alternately, inducing Hebbian-like plasticity. However, its neurophysiological impacts remain unclear. To determine the changes that occur in the brain during PAS, brain activity during PAS must be measured and distinguished from the electromagnetic artifacts produced by the stimulation. Here, we present a novel dataset of electroencephalography (EEG) measurements during PAS with an inter-stimulus-interval of 25 ms (PAS25, expected to induce long-term potentiation-like changes) or 35 ms (PAS35, no expected change). This dataset includes raw data and pre-processed data with electromagnetic artefacts removed. he right ulnar nerves electrical stimulation preceded transcranial magnetic stimulation to the left primary motor cortex in both cases. EEG was measured before and after the PAS sessions, with only electrical or magnetic stimulation. To demonstrate the quality of the data, we summarize the stability of the stimulation site and the event-related potentials before, during, and after PAS. This dataset will enable observing brain dynamics due to the accumulation of stimulations during PAS and differences in responsiveness to stimulations before and after PAS.

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bioRxiv2024

Distinct roles of brain network flexibility in motor learning across age

Motor learning is a lifelong process, from infancy through old age. Acquiring new motor actions through repetitive practice requires adjusting motor output in response to sensory input and integrating them to facilitate learning. For this to occur, the central nervous system must flexibly predict and adapt to the dynamic interplay between sensory inputs and motor outputs. Although overall brain function changes with age, it remains unclear how flexible brain networks, reflecting moment-to-moment reconfiguration of large-scale brain networks, influence motor learning ability with aging. To address this, we designed a visuomotor learning task involving both younger and older adults and quantitatively assessed brain network flexibility, leveraging multichannel electroencephalography (EEG) in humans. We found age-group differences in motor learning properties, brain network flexibility, and their neural relationships. In younger adults, the learning aftereffect was associated with brain network flexibility during motor preparation in the learning task. However, this association was not observed in older adults. Together, our findings suggest that brain network flexibility during motor preparation was critical for acquiring and maintaining new motor actions in younger adults, but that this coupling was attenuated with aging.

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bioRxiv2024

Frequency-specific hemispheric bias in phase entrainment of neural oscillations in unilateral spatial neglect

Unilateral spatial neglect (USN) is a common consequence of right-hemisphere stroke, traditionally attributed to structural lesions and dysfunctional attention networks. However, the brain is fundamentally a rhythmic and dynamical system, and how disrupted neural synchronization underlies USN remains unknown. We recorded steady-state visual evoked potentials (SSVEPs; 3 - 30 Hz flicker) in stroke patients with USN, without USN, and healthy controls. Only the USN group exhibited significant hemispheric asymmetry at 9 Hz, driven by exaggerated responses in the intact hemisphere rather than suppression in the lesioned hemisphere. This effect appeared only during stimulation, not at rest, indicating its specificity to sensory processing. The enhanced 9 Hz entrainment in the intact hemisphere was accompanied by increased phase-amplitude coupling (PAC) between alpha phase and gamma amplitude, reflecting systematic coordination of high-frequency activity. Transfer entropy analysis further revealed increased feedforward information flow from the right visual to the left frontal cortex, highlighting large-scale asymmetry. To explore the mechanism underlying this frequency-specific bias, we implemented a coupled-oscillator model. The model showed that the hemispheric asymmetry arises from resonance between intrinsic alpha rhythms and external input, amplified by asymmetric right-to-left interhemispheric coupling. These findings suggest that USN arises from a selective impairment of alpha-band synchrony capacity. This study offers a novel framework conceptualizing USN as a disorder of disrupted oscillatory dynamics underlying spatial attention, and points toward frequency-specific neuromodulatory intervention as a potential therapeutic approach. Significant statementThe brain behaves as a metronome-like oscillator ensemble. When a rhythmic flicker--a cortical "tuning fork" matching the visual systems intrinsic alpha frequency--is applied, it serves as a probe that visualizes the brains internal state. Under normal conditions, neuronal oscillations in both hemispheres are moderately entrained at this frequency. In unilateral spatial neglect (USN), this resonance displays a distinctive asymmetry: the intact hemisphere becomes over-amplified, whereas the lesioned hemisphere shows no enhancement. A coupled-oscillator model shows that asymmetric interhemispheric coupling selectively boosts this intrinsic alpha resonance unilaterally. This biased resonance reflects a destabilization of the dynamic coordination of attention networks, offering a novel mechanistic view of spatial neglect in USN and pointing toward frequency-specific neuromodulation as a promising therapeutic strategy.

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bioRxiv2023

Selective consistency of recurrent neural networks induced by plasticity as a mechanism of unsupervised perceptual learning

Understanding the mechanism by which the brain overcomes its inherent inconsistency in activity to achieve consistent information processing is one of the major challenges in neuroscience. Recently, it has been reported that the consistency of neural responses to stimuli that are presented repeatedly is enhanced implicitly in an unsupervised way, and results in improved perceptual consistency. Here, we propose the term "selective consistency" to describe this input-dependent consistency and hypothesize that it will be acquired in a self-organizing manner by plasticity within the neural system. To test this, we investigated whether a reservoir-based plastic model could acquire selective consistency to repeated stimuli. We used white noise sequences randomly generated in each trial and referenced white noise sequences presented multiple times. The results showed that the plastic network was capable of acquiring selective consistency rapidly, with as little as five exposures to stimuli, even for white noise. The acquisition of selective consistency could occur independently of performance optimization, as the networks time-series prediction accuracy for referenced stimuli did not improve with repeated exposure and optimization. Furthermore, the network could only achieve selective consistency when in the region between order and chaos. These findings suggest that the neural system can acquire selective consistency in a self-organizing manner and that this may serve as a mechanism for certain types of learning.

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bioRxiv2022

A data assimilation method to track time-varying changes in the excitation-inhibition balance using scalp EEG

Recent neuroscience studies have suggested that controlling the excitation and inhibition (E/I) balance is essential for maintaining normal brain function. However, while control of time-varying E/I balance is considered essential for perceptual and motor learning, an efficient method for estimating E/I balance changes has yet to be established. To tackle this issue, we propose a new method to estimate E/I balance changes by applying neural-mass model-based tracking of the brain state using the Ensemble Kalman Filter. In this method, the parameters of synaptic E/I gains in the model are estimated from observed electroencephalography (EEG) signals. Moreover, the index of E/I balance was defined by calculating the ratio between synaptic E/I gains based on estimated parameters. The method was validated by showing that it could estimate E/I balance changes from human EEG data at the sub-second scale, indicating that it has the potential to quantify how time-varying changes in E/I balance influence changes in perceptual and motor learning. Furthermore, this method could be used to develop an E/I balance-based neurofeedback training method for clinical use.

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bioRxiv2021

A closed-loop stimulation approach with real-time estimation of the instantaneous phase of neural oscillations by a Kalman filter

We propose a novel method to estimate the instantaneous oscillatory phase to implement a real-time system for closed-loop sensory stimulation in electroencephalography (EEG) experiments. The method uses Kalman filter-based prediction to estimate current and future EEG signals. We tested the performance of our method in a real-time situation. We demonstrate that the performance of our method shows higher accuracy in predicting the EEG phase than the conventional autoregressive model-based method. A Kalman filter allows us to easily estimate the instantaneous phase of EEG oscillations based on the automatically estimated autoregressive model implemented in a real-time signal processing machine. The proposed method has a potential for versatile applications targeting the modulation of EEG phase dynamics and the plasticity of brain networks in relation to perceptual or cognitive functions.

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bioRxiv2020

Detection of the changes in dynamical structures insynchronous neural oscillations from a viewpoint of probabilistic inference

Recent neuroscience studies have suggested that cognitive functions and learning capacity are reflected in the time-evolving dynamics of brain networks. However, an efficient method to detect changes in dynamical brain structures using neural data has yet to be established. To address this issue, we developed a new model-based approach to detect change points in dynamical network structures by combining the model-based network estimation with a phase-coupled oscillator model and sequential Bayesian inference. By giving the model parameter as the prior distribution, applying Bayesian inference allows the extent of temporal changes in dynamic brain networks to be quantified by comparing the prior distribution with posterior distribution using information theoretical criteria. For this, we used the Kullback-Leibler divergence as an index of such changes. To validate our method, we applied it to numerical data and electroencephalography data. As a result, we confirmed that the Kullback-Leibler divergence only increased when changes in dynamical network structures occurred. Our proposed method successfully estimated both directed network couplings and change points of dynamical structures in the numerical and electroencephalography data. These results suggest that our proposed method can reveal the neural basis of dynamic brain networks.

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bioRxiv2019

The metastable human brain associated with autistic-like traits

Metastability in the brain is thought to be a mechanism involved in dynamic organization of cognitive and behavioral functions across multiple spatiotemporal scales. However, it is not clear how such organization is realized in underlying neural oscillations in a high-dimensional state space. It was shown that macroscopic oscillations often form phase-phase coupling (PPC) and phase-amplitude coupling (PAC) which result in synchronization and amplitude modulation, respectively, even without external stimuli. These oscillations can also make spontaneous transitions across synchronous states at rest. Using resting-state electroencephalographic signals and the autism-spectrum quotient scores acquired from healthy humans, we show experimental evidence that the PAC combined with PPC allows amplitude modulation to be transient, and that the metastable dynamics with this transient modulation is associated with autistic-like traits. In individuals with a longer attention span, such dynamics tended to show fewer transitions between states by forming delta-alpha PAC. We identified these states as two-dimensional metastable states that could share consistent patterns across individuals. Our findings suggest that the human brain dynamically organizes inter-individual differences in a hierarchy of macroscopic oscillations with multiple timescales by utilizing metastability. Author SummaryThe human brain organizes cognitive and behavioral functions dynamically. For decades, the dynamic organization of underlying neural oscillations has been a fundamental topic in neuroscience research. Even without external stimuli, macroscopic oscillations often form phase-phase coupling and phase-amplitude coupling (PAC) that result in synchronization and amplitude modulation, respectively, and can make spontaneous transitions across synchronous states at rest. Using resting-state electroencephalography signals acquired from healthy humans, we show evidence that these two neural couplings enable amplitude modulation to be transient, and that this transient modulation can be viewed as the transition among oscillatory states with different PAC strengths. We also demonstrate that such transition dynamics are associated with the ability to maintain attention to detail and to switch attention, as measured by autism-spectrum quotient scores. These individual dynamics were visualized as a trajectory among states with attracting tendencies, and involved consistent brain states across individuals. Our findings have significant implications for unraveling variability in the individual brains showing typical and atypical development.

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bioRxiv2019

Consistency in macroscopic human brain responses to noisy time-varying visual inputs

It is an open question as to whether macroscopic human brain responses to repeatedly presented external inputs show consistent patterns across trials. We here provide experimental evidence that human brain responses to noisy time-varying visual inputs, as measured by scalp electroencephalography (EEG), show a signature of consistency. The results indicate that the EEG-recorded responses are robust against fluctuating ongoing activity, and that they respond to visual stimuli in a repeatable manner. This consistency presumably mediates robust information processing in the brain. Moreover, the EEG response waveforms were discriminable between individuals, and were invariant over a number of days within individuals. We reveal that time-varying noisy visual inputs can harness macroscopic brain dynamics and can manifest hidden individual variations.

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bioRxiv2019

Probing dynamical cortical gating of attention with concurrent TMS-EEG

BackgroundAttention facilitates gating of information from the sending brain area to receiving areas, with this being achieved by dynamical change in effective connectivity between cortices. However, it is difficult to assess effective connectivity, which refers to the causal influence of one cortical area on another.\n\nObjectiveThe aim of this study is to directly probe the effective connectivity between cortical areas, which is modulated by covertly shifted attention, excluding the thalamic influence.\n\nMethodsTranscranial magnetic stimulation (TMS) was used to directly perturb the right retinotopic visual cortex in task-relevant (TR) and task-irrelevant (TIR) networks, and the impact of this was tracked to other areas by concurrent use of electroencephalography (EEG).\n\nResultsKey finding was that TMS to the TR hemisphere led to a stronger evoked potential than did stimulation to the TIR hemisphere. Moreover, stronger beta- and gamma-band effective connectivities assessed as lagged phase synchronizations between stimulated areas and other areas were observed when TMS was delivered to the TR area. These effects were more enhanced when they preceded more prominent alpha lateralization, which is known to be associated with gating information.\n\nConclusionOur results indicate that attention-regulated cortical excitability and feedforward dynamical effective connectivity can be probed by direct cortical stimulation to the TR or TIR area, thereby bypassing thalamic gating. These results bear out the idea that TMS-EEG could help to characterize changes in the functional architecture of brain networks that are required for rapid adaptation to the environment or due to brain diseases.\n\nHighlightsO_LIAttention-regulated cortical excitability and effective connectivity were probed by TMS-EEG.\nC_LIO_LIStronger TMS evoked potentials were observed in the task-relevant visual hemisphere.\nC_LIO_LIStrong effective connectivity between task-relevant visual cortex and other areas were found.\nC_LI

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bioRxiv2018

Frequency- and area-specific entrainment of intrinsic cortical oscillations by repetitive transcranial magnetic stimulation

Neural oscillations are ubiquitous throughout the cortex, but the frequency of oscillations differs from area to area. To elucidate the mechanistic architectures establishing various rhythmic activities, we tested whether spontaneous neural oscillations in different cortical modules can be entrained by direct perturbation with distinct frequencies of transcranial magnetic stimulation (TMS). While recording the electroencephalogram (EEG), we applied single-pulse TMS (sTMS) and repetitive TMS (rTMS) at 5, 11, and 23 Hz to motor or visual cortex. To assess entrainment, defined as phase locking of intrinsic oscillations to periodic external force , we examined local and global modulation of the phase-locking factor (PLF). sTMS triggered transient phase locking in a wide frequency band with distinct PLF peaks at 21 Hz in the motor cortex and 8 Hz in the visual cortex. With TMS pulse trains of 11 Hz over visual cortex and 23 Hz over motor cortex, phase locking was progressively enhanced at the stimulation frequency and lasted for a few cycles after the stimulation terminated. Moreover, such local entrainment propagated to other cortical regions, suggesting that rTMS entrained intrinsic neural oscillations locally and globally via network nodes. Because the entrainment was frequency-specific for each target site, these frequencies may correspond to the natural frequency of each cortical module and of the global networks. rTMS enables direct manipulation of the brain and is thus useful for investigating the causal roles of synchronous neural oscillations and synchrony in brain functions, and for the treatment of clinical symptoms associated with impaired oscillations and synchrony.\n\nSignificance StatementWe provide the first evidence for area- and frequency-specific entrainment by frequency-tuned repetitive transcranial magnetic stimulation (rTMS), and the propagation of this entrainment to other areas. Our results indicate that rTMS at the natural frequency of each cortical system is particularly effective for entraining oscillatory phase. Moreover, local entrainment led to global entrainment in functionally coupled areas. The ability to control brain rhythms in the intact human brain is highly beneficial for studying the causal roles of rhythmic activity in brain function. Moreover, this modulatory technique has the potential to treat patients with impaired rhythmic networks in disorders such as schizophrenia and stroke.

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bioRxiv2018

Phase-amplitude coupling of neural oscillations can be effectively probed with concurrent TMS-EEG

Despite the widespread use of transcranial magnetic stimulation (TMS), knowledge of its neurophysiological mode of action is still incomplete. Recently, TMS has been proposed to synchronise neural oscillators, and to thereby increase the detectability of corresponding oscillations at the population level. As oscillations in the human brain are known to interact within nested hierarchies via phase-amplitude coupling, TMS might also be able to increase the macroscopic detectability of such coupling. In a concurrent TMS-electroencephalography study, we therefore examined the techniques influence on theta-gamma, alpha-gamma and beta-gamma phase-amplitude coupling by delivering single-pulse TMS (sTMS) and repetitive TMS (rTMS) over the left motor cortex and right visual cortex of healthy participants. The rTMS pulse trains were of 5 Hz, 11 Hz and 23 Hz for the three coupling variations, respectively. Relative to sham stimulation, all conditions showed transient but significant increases in phase-amplitude coupling at the stimulation site. In addition, we observed enhanced coupling over various other cortical sites, with a more extensive propagation during rTMS than during sTMS. By indicating that scalp-recorded phase-amplitude coupling can be effectively probed with TMS, these findings open the door to the techniques application in manipulative dissections of such coupling during human cognition and behaviour in healthy and pathological conditions.

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bioRxiv2017

Tracking Individual Differences in Perception by TMS-EEG Intrinsic Effective Connectivity

Non-invasive human electroencephalography (EEG) coupled with transcranial magnetic stimulation (TMS) is currently used to measure coarse stimulus-response relationships in brain physiology during behavior. However, with key modifications, the TMS-EEG technique holds even greater promise for monitoring fine-scale neural signatures of human behavior. Here, we demonstrate that a novel TMS-EEG co-registration technique can dynamically monitor individual human variation in perception based solely on EEG resting-state intrinsic effective connectivity probed by TMS-based phase resetting of ongoing activity. We used a bistable stimulus task, where the percept is perceived as either horizontal or vertical apparent motion, to record gamma band interhemispheric integration of information. Fine-grained inter-individual behavioral differences in horizontal motion bias could be measured by tracking resting-state gamma-band effective connectivity from right hMT+ to left hMT+. Thus, our method of triggering intrinsic resting-state effective connectivity in oscillatory dynamics can monitor individual differences in perception via the long-range integration of information. This technique will be useful for the manipulative dissection of individual-scale human cognition mediated by neural dynamics and may also expand neurofeedback approaches.

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