Biology subjects

Sato, J. R.

Publications and source records attributed to Sato, J. R..

7 recordsLinked to original sources

bioRxiv2025

Ayahuasca Shifts Brain Dynamics Toward Higher Entropy: Persistent Elevation of Ising Temperature Correlates with Acute Subjective Effects

Serotonergic psychedelics profoundly alter high-order cognition, emotion, and sensory perception, creating dynamic, unpredictable brain states consistent with the entropic brain hypothesis. Although these acute effects are short-lived, users often experience lasting improvements in emotional and social functioning. Yet, few studies have quantitatively linked subjective experiences to neural dynamics. To bridge this gap, we investigate the acute and subacute effects of ayahuasca using fMRI data and the 2D Ising model, estimating the Ising brain temperature to quantify the shift from ordered to disordered resting state configurations and expose the critical transitions that characterize ayahuasca-induced brain dynamics. To complement our analysis, we investigated cortical functional connectivity changes through graph-theoretical metrics, quantifying how psychedelic ingestion alters functional network architecture in acute and subacute phases. Only the Ising temperature reliably differentiated the subacute phase. Applying a recently developed method that combines cortical connectivity analyses with Graph Neural Networks trained on Ising networks, our results robustly revealed significant Ising temperature increases during the acute (p < 0.001) and subacute (p < 0.05) phases compared with placebo, reflecting heightened brain entropy and a persistent shift toward a more disordered, paramagnetic state. To evaluate longer-term effects, the subacute temperature change correlated linearly with affective scores on the Hallucinogenic Rating Scale (HRS) captured during the acute session (p < 0.01), with HRS scores explaining substantially greater variance in the subacute group (adjusted R2 = 0.58) than in the placebo group (adjusted R2 = -0.05). These observations suggest that ayahuasca elevates system entropy and that the magnitude of lasting functional alterations scales with the intensity of the acute subjective experience.

neuroscience↗

bioRxiv2024

A Graph Neural Network Approach to Investigate Brain Critical States Over Neurodevelopment

Recent studies show that functional resting-state dynamics may be modelled by lattice models near criticality, such as the 2D Ising model. The Ising temperature, which is the control parameter dictating the phase transitions of the model, can provide insight into the large-scale dynamics and is being used to better understand different brain states and neurodevelopment. This period is categorized by intricated changes in the microcircuits to consolidate networks. These changes influence the macroscopic brain dynamics and also its functional relations, which can be observed in functional Magnetic Resonance Imaging (fMRI). Therefore, this work investigates neurodevelopment through a novel method to estimate the Ising temperature of the brain from fMRI data using functional connectivity and Graph Neural Networks (GNNs) trained on Ising model networks. The main finding indicates a statistically significant negative correlation between age and temperature for typically developing children ( =- 0. 48, < 0. 0001) and also children with Attention deficit/hyperactivity disorder (ADHD) ( =- 0. 49, < 0. 0001). This study suggests that the brain gets distant from criticality as age increases, leading to a more ordered state. Author SummaryIn this study, we employed Graph Neural Networks to investigate functional connectivity patterns of resting-state fMRI data using the 2D Ising model as a theoretical framework to access critical dynamics. Our key finding is the significant decrease in Ising temperature with age in both typically developing children and those with ADHD, indicating a developmental transition from a high-entropy, disordered state to a more ordered, decreasing the availability of dynamical states that the system can occupy and transit in different conditions. Moreover, this decrease in temperature is crucial as it implies that the brain is moving away from criticality, leading the system to a more sub-critical regime. The correlation coefficients across the groups showed no significant differences, indicating the same developmental trajectory.

neuroscience↗

bioRxiv2020

Normative modelling using deep autoencoders: a multi-cohort study on mild cognitive impairment and Alzheimer's disease

Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning technology, the use of deep neural networks has also been proposed. In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimers disease (n=206) and mild cognitive impairment (n=354). We first trained the autoencoder on an independent dataset (UK Biobank dataset) with 11,034 healthy controls. Then, we estimated how each patient deviated from this norm and established which brain regions were associated to this deviation. Finally, we compared the performance of our normative model against traditional classifiers. As expected, we found that patients exhibited deviations according to the severity of their clinical condition. The model identified medial temporal regions, including the hippocampus, and the ventricular system as critical regions for the calculation of the deviation score. Overall, the normative model had comparable cross-cohort generalizability to traditional classifiers. In order to promote open science, we are making all scripts and the trained models available to the wider research community.

neuroscience↗

bioRxiv2019

Dissociating decisional and temporal information in interval categorisation

Interval timing is fundamental for humans and non-human animals to interact with their environment. Several studies that investigate temporal processing combine behavioural tasks with neurophysiological methods, such as electrophysiological recordings (EEG). However, in the majority of these studies, it is hard to dissociate whether EEG activity reflects temporal or decisional information. In the present study, we investigated how time and decision is encoded in the EEG signal while human participants performed a temporal categorisation task with two different temporal references. Using a combination of evoked potentials and multivariate pattern analysis, we show that: (1) During the interval to-be-timed, both temporal and decisional information are encoded; (2) Activity evoked by the end of the interval encodes almost exclusively decisional information. These results suggest that decisional aspects of the task better explain EEG activity commonly related to temporal processing. The interplay between the encoding of time and decision is consistent with recent proposals that approximate temporal processing with decisional models.

neuroscience↗

bioRxiv2018

Unique individual factors shape brain hubs organization of the human functional connectome

Recent evidence suggests that the human functional connectome is stable at different time scales and unique. These characteristics posit the functional connectome not only as an individual marker but also as a powerful discriminatory measure characterized by high intersubject variability. Among distinct sources of intersubject variability, the long-term sources include functional patterns that emerge from genetic factors. Here, we sought to investigate the contribution of additive genetic factors to the variability of functional networks by determining the heritability of the connectivity strength in a multivariate fashion. First, we reproduced and extended the connectome fingerprinting analysis to the identification of twin pairs. Then, we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. Twin pairs were identified above chance level using connectome fingerprinting, with monozygotic twin identification accuracy equal to 57.2% on average for whole-brain connectome. Additionally, we found that a visual (0.37), the medial frontal (0.31) and the motor (0.30) functional networks were the most influenced by additive genetic factors. Our findings suggest that genetic factors not only partially determine intersubject variability of the functional connectome, such that twins can be identified using connectome fingerprinting, but also differentially influence connectivity strength in large-scale functional networks.

neuroscience↗

bioRxiv2017

Network supporting contextual fear learning after hippocampal damage has increased dependence on retrosplenial cortex

Hippocampal damage results in profound retrograde, but no anterograde amnesia in contextual fear conditioning (CFC). Although the content learned in the latter have been discussed, the compensating regions were seldom proposed and never empirically addressed. Here, we employed network analysis of pCREB expression quantified from brain slices of rats with dorsal hippocampal lesion (dHPC) after undergoing CFC session. Using inter-regional correlations of pCREB-positive nuclei between brain regions, we modelled functional networks using different thresholds. The dHPC network showed small-world topology, equivalent to SHAM (control) network. However, diverging hubs were identified in each network. In a direct comparison, hubs in both networks showed consistently higher centrality values compared to the other network. Further, the distribution of correlation coefficients was different between the groups, with most significantly stronger correlation coefficients belonging to the SHAM network. These results suggest that dHPC network engaged in CFC learning is partially different, and engage alternative hubs. We next tested if pre-training lesions of dHPC and one of the new dHPC network hubs (perirhinal, Per; or disgranular retrosplenial, RSC, cortices) would impair CFC. Only dHPC-RSC, but not dHPC-Per, impaired CFC. Interestingly, only RSC showed a consistently higher centrality in the dHPC network, suggesting that the increased centrality reflects an increased functional dependence on RSC. Our results provide evidence that, without hippocampus, the RSC, an anatomically central region in the medial temporal lobe memory system might support CFC learning and memory.\n\nAUTHOR SUMMARYWhen determined cognitive performances are not affected by brain lesions of regions generally involved in that performance, the interpretation is that the remaining regions can compensate the damaged one. In contextual fear conditioning, a memory model largely used in laboratory rodents, hippocampal lesions produce amnesia for events occurred before, but not after the lesion, although the hippocampus is known to be important for new learning. Addressing compensation in animal models has always been challenging as it requires large-scale brain mapping. Here, we quantified 30 brain regions and used mathematical tools to model how a brain network can compensate hippocampal loss and learn contextual fear. We described that the damaged network preserved general interactivity characteristics, although different brain regions were identified as highly important for the network (e.g. highly connected). Further, we empirically validated our network model by performing double lesions of the hippocampus and the alternative hubs observed in the network models. We verified that double lesion of the hippocampus and retrosplenial cortex, one of the hubs, impaired contextual fear learning. We provide evidence that without hippocampus, the remaining network relies on alternative important regions from the memory system to coordinate contextual fear learning.

neuroscience↗

bioRxiv2017

A common representation of time across visual andauditory modalities

Humans and non-human animals ability to process time on the scale of milliseconds and seconds is essential for adaptive behaviour. A central question of how brains keep track of time is how specific temporal information across different sensory modalities is. In the present study, we show that encoding of temporal intervals in auditory and visual modalities are qualitatively similar. Human participants were instructed to reproduce intervals in the range from 750 ms to 1500 ms marked by auditory or visual stimuli. Our behavioural results suggest that, although participants were more accurate in reproducing intervals marked by auditory stimuli, there was a strong correlation in performance between modalities. Using multivariate pattern analysis in scalp EEG, we show that activity during late periods of the intervals was similar within and between modalities. Critically, we show that a multivariate pattern classifier was able to accurately predict the elapsed interval, even when trained on an interval marked by a stimulus of a different sensory modality. Taken together, our results suggest that, while there are differences in the processing of intervals marked by auditory and visual stimuli, they also share a common neural representation.

neuroscience↗