Biology subjects

Kawato, M.

Publications and source records attributed to Kawato, M..

32 records · Page 2Linked to original sources

bioRxiv2019

A reciprocal inhibition model of alternations between non-dissociative and dissociative states in patients with PTSD

ObjectiveTraumatic life-events can leave individuals with contrasting posttraumatic stress disorder (PTSD) symptoms, including re-experiencing and avoidance. Notably, patients with PTSD are known to periodically switch between two opposing attentional biases; namely, toward threat and away from threat. We hypothesized that reciprocal inhibition between the amygdala and ventromedial prefrontal cortex (vmPFC) may induce alternations between these attentional biases, which in turn may contribute to the re-experiencing and avoidance symptoms, respectively.\n\nMethodsTo test this reciprocal inhibition model, we performed an experiment to measure the attentional biases of patients with PTSD. We examined the differential relationships between PTSD symptom clusters (re-experiencing/avoidance) and attentional biases (toward/away from threat). Additionally, we performed a meta-regression analysis to examine the role of amygdala reactivity in the imbalance between re-experiencing and avoidance symptoms.\n\nResultsWe found that attentional bias toward threat was selectively associated with re-experiencing symptoms whereas attentional bias away from threat was selectively associated with avoidance symptoms. Meta-regression analysis based on twelve participant populations (total N = 316) revealed that left amygdala activity was positively correlated with the severity of re-experiencing symptoms relative to avoidance symptoms.\n\nConclusionsOur findings support the hypothesis that reciprocal inhibition of common neural circuits may underlie the switch between attentional biases toward and away from threat as well as that between re-experiencing and avoidance symptoms. Re-experiencing and avoidance/emotional numbing are the core symptoms used to distinguish between the non-dissociative and dissociative PTSD subtypes. The reciprocal inhibition mechanism may help elucidate the mechanisms underlying those PTSD subtypes.

neuroscience↗

bioRxiv2019

Precise hyperacuity estimation of spike timing from calcium imaging

Two-photon imaging is a major recording technique in neuroscience, but it suffers from several limitations, including a low sampling rate, the nonlinearity of calcium responses, the slow dynamics of calcium dyes and a low signal-to-noise ratio, all of which impose a severe limitation on the application of two-photon imaging in elucidating neuronal dynamics with high temporal resolution. Here, we developed a hyperacuity algorithm (HA_time) based on an approach combining a generative model and machine learning to improve spike detection and the precision of spike time inference. First, Bayesian inference estimates the calcium spike model by assuming the constancy of the spike shape and size. A support vector machine employs this information and detects spikes with higher temporal precision than the sampling rate. Compared with conventional thresholding, HA_time improved the precision of spike time estimation up to 20-fold for simulated calcium data. Furthermore, the benchmark analysis of experimental data from different brain regions and simulation of a broader range of experimental conditions showed that our algorithm was among the best in a class of hyperacuity algorithms. We encourage experimenters to use the proposed algorithm to precisely estimate hyperacuity spike times from two-photon imaging.

neuroscience↗

bioRxiv2019

Multivoxel patterns for perceptual confidence are associated with false color detection

While it has been proposed that metacognition and conscious perception are related, the mechanistic relationship between the two is unclear. To address this question, we combined decoded neurofeedback (DecNef) in functional magnetic resonance imaging (fMRI) with concurrent psychophysics. Participants were rewarded for activating multivoxel patterns for color discrimination confidence while they detected color in mostly achromatic stimuli. We found that occurences of voxel patterns for high color discrimination confidence were associated with false alarms in the concurrent color detection task, suggesting a link between discrimination confidence and consciousness.

neuroscience↗

bioRxiv2019

Metacognition facilitates the exploitation of unconscious brain states

Can humans be trained to make strategic use of unconscious representations in their own brains? We investigated how one can derive reward-maximizing choices from latent high-dimensional information represented stochastically in neural activity. In a novel decision-making task, reinforcement learning contingencies were defined in real-time by fMRI multivoxel pattern analysis; optimal action policies thereby depended on multidimensional brain activity that took place below the threshold of consciousness. We found that subjects could solve the task, when their reinforcement learning processes were boosted by implicit metacognition to estimate the relevant brain states. With these results we identified a frontal-striatal mechanism by which the brain can untangle tasks of great dimensionality, and can do so much more flexibly than current artificial intelligence.

neuroscience↗

bioRxiv2019

Electrical coupling controls dimensionality and chaotic firing of inferior olive neurons

One of the main challenges facing online neural learning systems with numerous modifiable parameters (or "degrees-of-freedom") such as the cerebellum, is how to avoid "overfitting". We previously proposed that the cerebellum controls the degree-of-freedoms during learning by gradually modulating the electric coupling strength between inferior olive neurons. Here, we develop a modeling technique to estimate effective coupling strengths between inferior olive neurons from in vivo recordings of Purkinje cell complex spike activity in three different coupling conditions. We show that high coupling strengths induce synchronous firing and decrease the dimensionality of inferior olive firing dynamics. In contrast, intermediate coupling strengths induce chaotic firing and increase the dimensionality of firing dynamics. Our results thus support the hypothesis that effective coupling controls the dimensionality of inferior olive firing, which may allow the olivocerebellar system to learn effectively from a small training sample set despite the low firing frequency of inferior olive neurons.

neuroscience↗

bioRxiv2019

Multivoxel Pattern Analysis Reveals Dissociations Between Subjective Fear and Its Physiological Correlates

In studies of anxiety and other affective disorders, objectively measured physiological responses have commonly been used as a proxy for measuring subjective experiences associated with pathology. However, this commonly adopted biosignal approach has recently been called into question on the grounds that subjective experiences and objective physiological responses may dissociate. We performed machine-learning based analysis on functional magnetic resonance imaging (fMRI) data to assess this issue in the case of fear. Participants were presented with pictures of commonly feared animals in an fMRI experiment. Multivoxel brain activity decoders were trained to predict participants subjective fear ratings and their skin conductance reactivity, respectively. While subjective fear and objective physiological responses were correlated in general, the respective whole-brain multivoxel decoders for the two measures were not identical. Some key brain regions such as the amygdala and insula appear to be primarily involved in the prediction of physiological reactivity, while some regions previously associated with metacognition and conscious perception, including some areas in the prefrontal cortex, appear to be primarily predictive of the subjective experience of fear. The present findings are in support of the recent call for caution in assuming a one-to-one mapping between subjective sufferings and their putative biosignals, despite the clear advantages in the latters being objectively and continuously measurable in physiological terms.

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bioRxiv2018

Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias

When collecting large neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent the greatest barrier when acquiring multi-site neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multi-site, multi-disorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. Effects on resting-state functional MRI connectivity because of both bias types were greater than or equal to those because of psychiatric disorders. Furthermore, our findings indicated that each site can sample only from among a subpopulation of participants. This result suggests that it is essential to collect large neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using traveling-subject dataset and achieved the reduction of the measurement bias by 29% and the improvement of the signal to noise ratios by 40%.

neuroscience↗

bioRxiv2018

Overlapping but asymmetrical relationships between schizophrenia and autism revealed by brain connectivity

(Abstract included 248 words)Although the relationship between schizophrenia spectrum disorder (SSD) and autism spectrum disorder (ASD) has long been debated, it has not yet been fully elucidated. To address this issue, we took advantage of dual (ASD and SSD) classifiers that discriminate patients from their controls based on resting state brain functional connectivity. An SSD classifier using sophisticated machine-learning algorithms that automatically selected SSD- specific functional connections was applied to Japanese datasets including adult patients with SSD in a chronic stage. We demonstrated good performance of the SSD classification for independent validation cohorts. The generalizability was tested by USA and European cohorts in a chronic stage, and one USA cohort including first episode schizophrenia. The specificity was tested by two adult Japanese cohorts of ASD and major depressive disorder, and one European cohort of attention-deficit hyperactivity disorder. The weighted linear summation of the classifiers functional connections constituted the biological dimensions representing neural liability to the disorders. Our previously developed robust ASD classifier constituted the ASD dimension. Distributions of individuals with SSD, ASD and healthy controls were examined on the SSD and ASD biological dimensions. The SSD and ASD populations exhibited overlapping but asymmetrical patterns on the two biological dimensions. That is, the SSD population showed increased liability on the ASD dimension, but not vice versa. Furthermore, the two dimensions were correlated within the ASD population but not the SSD population. Using the two biological dimensions based on resting-state functional connectivity enabled us to quantify and visualize the relationships between SSD and ASD.

neuroscience↗

bioRxiv2018

Computational model for human 3D shape perception from a single specular image

In natural conditions the human visual system can estimate the 3D shape of specular objects even from a single image. Although previous studies suggested that the orientation field plays a key role for 3D shape perception from specular reflections, its computational plausibility and possible mechanisms have not been investigated. In this study, to complement the orientation field information, we first add prior knowledge that objects are illuminated from above and utilize the vertical polarity of the intensity gradient. Then we construct an algorithm that incorporates these two image cues to estimate 3D shapes from a single specular image. We evaluated the algorithm with glossy and mirrored surfaces and found that 3D shapes can be recovered with a high correlation coefficient of around 0.8 with true surface shapes. Moreover, under a specific condition, the algorithms errors resembled those made by human observers. These findings show that the combination of the orientation field and the vertical polarity of the intensity gradient is computationally sufficient and probably reproduces essential representations used in human shape perception from specular reflections.

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.

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bioRxiv2017

A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity

Individual differences in cognitive function have been shown to correlate with brain-wide functional connectivity, suggesting a common foundation relating connectivity to cognitive function across healthy populations. However, it remains unknown whether this relationship is preserved in cognitive deficits seen in a range of psychiatric disorders. Using machine learning methods, we built a prediction model of working memory function from whole-brain functional connectivity among a healthy population (N = 17, age 19-24 years). We applied this normative model to a series of independently collected resting state functional connectivity datasets (N = 968), involving multiple psychiatric diagnoses, sites, ages (18-65 years), and ethnicities. We found that predicted working memory ability was correlated with actually measured working memory performance in both schizophrenia patients (partial correlation,{rho} = 0.25, P = 0.033, N = 58) and a healthy population (partial correlation,{rho} = 0.11, P = 0.0072, N = 474). Moreover, the model predicted diagnosis-specific severity of working memory impairments in schizophrenia (N = 58, with 60 controls), major depressive disorder (N = 77, with 63 controls), obsessive-compulsive disorder (N = 46, with 50 controls), and autism spectrum disorder (N = 69, with 71 controls) with effect sizes g = -0.68, -0.29, -0.19, and 0.09, respectively. According to the model, each diagnosiss working memory impairment resulted from the accumulation of distinct functional connectivity differences that characterizes each diagnosis, including both diagnosis-specific and diagnosis-invariant functional connectivity differences. Severe working memory impairment in schizophrenia was related not only with fronto-parietal, but also widespread network changes. Autism spectrum disorder showed greater negative connectivity that related to improved working memory function, suggesting that some non-normative functional connections can be behaviorally advantageous. Our results suggest that the relationship between brain connectivity and working memory function in healthy populations can be generalized across multiple psychiatric diagnoses. This approach may shed new light on behavioral variances in psychiatric disease and suggests that whole-brain functional connectivity can provide an individual quantitative behavioral profile in a range of psychiatric disorders.

neuroscience↗

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↗

bioRxiv2017

The Control of Tonic Pain by Active Relief Learning

Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning so that the cause of the pain can be reduced if possible. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system also uses an uncertainty ( associability) signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that allows reduction of ongoing pain when learning about potential relief.

neuroscience↗

bioRxiv2017

Towards an Unconscious Neurotherapy for Common Fears

Can evolutionarily hardwired fear responses, e.g. for spiders and snakes, be reprogramed unconsciously in the human brain? Currently, exposure therapy is amongst the most effective treatments for anxiety disorders1, but this intervention is subjectively aversive to patients, and rates of premature attrition from treatment have been reported to be as high as 70%2. Here we introduce a novel method to bypass the subjective unpleasantness in conscious exposure, by directly pairing monetary reward with unconscious occurrences of decoded representations of naturally feared objects in the brain. The typical way to identify multivoxel functional magnetic resonance imaging (fMRI) representations for feared objects involves repeated presentations of the relevant images explicitly to subjects. However, for our potential treatment method to be effective in actual clinical settings, we need to decode fear representations without triggering excessively aversive reactions which may cause patients to dropout from treatments prematurely. Here we overcome this challenge by capitalizing on recent advancements in fMRI decoding techniques: We employed a method called hyperalignment3,4 to infer the relevant representations of feared objects for a designated participant based on data from other surrogate participants. This way the procedure completely bypasses the need for the conscious encountering of feared objects. We demonstrate that our method can lead to reliable reductions in physiological fear responses measured by skin conductance as well as amygdala hemodynamic activity. Not only do these results raise the intriguing possibility that naturally occurring fear can be re-programmed outside of conscious awareness, importantly they also created the rare opportunity for a psychological intervention of this nature to be tested rigorously in a double-blind placebo-controlled fashion. This may pave the way for a novel treatment method, combining the appealing rationale and proven efficacy of conventional psychotherapy with the rigor and leverage of clinical neuroscience.

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