Biology subjects

Tanaka, S. C.

Publications and source records attributed to Tanaka, S. C..

13 recordsLinked to original sources

bioRxiv2026

A neural substrate for resistance to change in the ventral hippocampus

Strong prior beliefs can render individuals resistant to change, even when outcomes contradict expectations, yet the neural mechanisms supporting such stability remain poorly understood. Here we show that the ventral hippocampus (vCA1) actively stabilizes inferred contextual state (belief) during aversive memory extinction in a behavior-dependent manner. Using in vivo calcium imaging, we identify a subset of vCA1 neurons that exhibit selective reductions in activity when expected aversive outcomes do not occur during extinction. Axonal calcium imaging further reveals that reduced signals are broadcast to basal amygdala, nucleus accumbens, and medial prefrontal cortex. Optogenetic manipulations demonstrate that these signals actively suppress extinction learning and promote persistence of defensive response. Computational modeling indicates that vCA1 omission-related signals reflect discrepancies between beliefs and observed omission of the expected outcome, thereby maintaining the aversive belief. Crucially, convergent evidence from calcium imaging, modelling, and closed-loop optogenetic manipulations reveals that vCA1 omission-related signals are specific to the period during which animals execute defensive behavior driven by their inferred aversive state. These findings identify a novel mechanism in vCA1 that stabilizes inferred aversive state despite changes in stimulus contingencies and suggest a key role of vCA1 in belief-based control of learning.

neuroscience↗

bioRxiv2025

Unsupervised feature computation-based feature selection robustly extracted resting-state functional connectivity patterns related to mental disorders

Research on biomarkers for predicting psychiatric disorders from resting-state functional connectivity (FC) is advancing. While the focus has primarily been on the discriminative performance of biomarkers by machine learning, identification of abnormal FCs in psychiatric disorders has often been treated as a secondary goal. However, it is crucial to investigate the effect size and robustness of the selected FCs because they can be used as potential targets of neurofeedback training or transcranial magnetic stimulation therapy. Here, we incorporated approximately 5,000 runs of resting-state functional magnetic resonance imaging from six datasets, including individuals with three different psychiatric disorders (major depressive disorder [MDD], schizophrenia [SCZ], and autism spectrum disorder [ASD]). We demonstrated that an unsupervised feature-computation-based feature selection method can robustly extract FCs related to psychiatric disorders compared to other conventional supervised feature selection methods. We found that our proposed method robustly extracted FCs with larger effect sizes from the validation dataset compared to different types of feature selection methods based on supervised learning for MDD (Cohens d = 0.40 vs. 0.25), SCZ (0.37 vs. 0.28), and ASD (0.17 vs. 0.16). We found 78, 69, and 81 essential FCs for MDD, SCZ, and ASD, respectively, and these FCs were mainly thalamic and motor network FCs. The current study showed that the unsupervised feature-computation-based feature selection method robustly identified abnormal FCs in psychiatric disorders consistently across datasets. The discovery of such robust FCs will contribute to understanding neural mechanisms as abnormal brain signatures in psychiatric disorders. Furthermore, this finding can aid in developing precise therapeutic interventions, such as neurofeedback training or transcranial magnetic stimulation therapy.

neuroscience↗

bioRxiv2025

Continuous Neural Correlates of Imbalanced Reinforcement Learning in Obsessive-Compulsive Disorder and Healthy Individuals

AimObsessive-compulsive disorder (OCD) is characterized by imbalanced reinforcement learning. This study investigated neural correlates of this imbalance by examining resting-state functional connectivity (rs-fMRI) in non-medicated OCD patients and healthy controls (HCs) exhibiting varying degrees of memory trace imbalance. MethodsWe employed network-based statistics (NBS), suitable for identifying network-level alterations of rs-fMRI data from 49 OCD patients and 53 HCs (Core Discovery Dataset: Core-DS) to identify a significant network. We validated this network in an independent dataset of 10 OCD patients and 18 HCs (Independent Validation Dataset: IndV-DS). Additionally, we compared functional connectivity in the identified network between subgroups of HCs with imbalanced (OCD-like) and balanced learning profiles (Extension Dataset: Ext-DS; n=10 each). ResultsNBS identified an OCD network with increased connectivity in OCD patients (padjusted = 0.022), comprising regions in the dorsolateral prefrontal cortex (DLPFC), parietal cortex, retrosplenial cortex, and hippocampal formation. This networks increased connectivity in OCD patients was replicated in IndV-DS (p = 0.0027). Importantly, imbalanced HCs showed a significant increase in functional connectivity, particularly between the DLPFC and presubiculum, in the identified OCD network, compared to balanced HCs (p = 0.0051, padjusted = 0.066). ConclusionIncreased functional connectivity in the OCD network, especially between the DLPFC and presubiculum, is present in both OCD patients and a subgroup of HCs with learning imbalances. These findings provide evidence for a continuous neural basis of imbalanced reinforcement learning, suggesting a continuum of traits associated with OCD between patients and "healthy" individuals.

neuroscience↗

bioRxiv2024

Computational Mechanisms of Neuroimaging Biomarkers Uncovered by Multicenter Resting-State fMRI Connectivity Variation Profile

Resting-state functional connectivity (rsFC) is increasingly used to develop biomarkers for psychiatric disorders. Despite progress, development of the reliable and practical FC biomarker remains an unmet goal, particularly one that is clinically predictive at the individual level with generalizability, robustness, and accuracy. In this study, we propose a new approach to profile each connectivity from diverse perspective, encompassing not only disorder-related differences but also disorder-unrelated variations attributed to individual difference, within-subject across-runs, imaging protocol, and scanner factors. By leveraging over 1500 runs of 10-minute resting-state data from 84 traveling-subjects across 29 sites and 900 participants of the case-control study with three psychiatric disorders, the disorder-related and disorder-unrelated FC variations were estimated for each individual FC. Using the FC profile information, we evaluated the effects of the disorder-related and disorder-unrelated variations on the output of the multi-connectivity biomarker trained with ensemble sparse classifiers and generalizable to the multicenter data. Our analysis revealed hierarchical variations in individual functional connectivity, ranging from within-subject across-run variations, individual differences, disease effects, inter-scanner discrepancies, and protocol differences, which were drastically inverted by the sparse machine-learning algorithm. We found this inversion mainly attributed to suppression of both individual difference and within-subject across-runs variations relative to the disorder-related difference by weighted-averaging of the selected FCs and ensemble computing. This comprehensive approach will provide an analytical tool to delineate future directions for developing reliable individual-level biomarkers.

neuroscience↗

bioRxiv2024

Comprehensive evaluation of pipelines for diagnostic biomarkers of major depressive disorder using multi-site resting-state fMRI datasets

The objective diagnostic and stratification biomarkers developed with resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contribute to more effective treatment for mental disorders. Unfortunately, there are currently no widely accepted biomarkers, partially due to the large variety of analysis pipelines for developing them. In this study we comprehensively evaluated analysis pipelines using a large-scale, multi-site fMRI dataset for major depressive disorder (MDD) (1162 participants from eight imaging sites). We explored the combinations of options in four subprocesses of analysis pipelines: six types of brain parcellation, four types of estimations of functional connectivity (FC), three types of site difference harmonization, and five types of machine learning methods. 360 different MDD diagnostic biomarkers were constructed using the SRPBS dataset acquired with unified protocols (713 participants from four imaging sites) as a discovery dataset and evaluated with datasets from other projects acquired with heterogeneous protocols (449 participants from four imaging sites) for independent validation. To identify the optimal options regardless of the discovery dataset, we repeated the same procedure after swapping the roles of the two datasets. We found pipelines that included Glassers parcellation, tangent-covariance, no harmonization, and non-sparse machine learning methods tended to result in high classification performance. The diagnosis results of the top 10 biomarkers showed high similarity, and weight similarity was also observed between eight of the biomarkers, except two that used both data-driven parcellation and FC computation. We applied the top 10 pipelines to the datasets of other mental disorders (autism spectral disorder: ASD and schizophrenia: SCZ) and eight of the ten biomarkers showed sufficient classification performances for both disorders, except two pipelines that included Pearson correlation, ComBat harmonization and random forest classifier combination. HighlightsO_LIWe evaluated the analysis pipelines of rsFC biomarker development. C_LIO_LIFour subprocesses in them were investigated with two multi-site datasets. C_LIO_LIGlassers parcellation, tangent covariance, and non-sparse methods were preferred. C_LIO_LIThe weight patterns of eight of the top 10 biomarkers showed high commonality. C_LIO_LIEight of the top 10 pipelines were successful for developing SCZ/ASD biomarkers. C_LI

neuroscience↗

bioRxiv2023

Generalizable neuromarker for autism spectrum disorder across imaging sites and developmental stages: A multi-site study

Autism spectrum disorder (ASD) is a lifelong condition, and its underlying biological mechanisms remain elusive. The complexity of various factors, including inter-site and development-related differences, makes it challenging to develop generalizable neuroimaging-based biomarkers for ASD. This study used a large-scale, multi-site dataset of 730 Japanese adults to develop a generalizable neuromarker for ASD across independent sites (U.S., Belgium, and Japan) and different developmental stages (children and adolescents). Our adult ASD neuromarker achieved successful generalization for the US and Belgium adults (area under the curve [AUC] = 0.70) and Japanese adults (AUC = 0.81). The neuromarker demonstrated significant generalization for children (AUC = 0.66) and adolescents (AUC = 0.71; all P<0.05, family-wise-error corrected). We identified 141 functional connections (FCs) important for discriminating individuals with ASD from TDCs. These FCs largely centered on social brain regions such as the amygdala, hippocampus, dorsomedial and ventromedial prefrontal cortices, and temporal cortices. Finally, we mapped schizophrenia (SCZ) and major depressive disorder (MDD) onto the biological axis defined by the neuromarker and explored the biological continuity of ASD with SCZ and MDD. We observed that SCZ, but not MDD, was located proximate to ASD on the biological dimension defined by the ASD neuromarker. The successful generalization in multifarious datasets and the observed relations of ASD with SCZ on the biological dimensions provide new insights for a deeper understanding of ASD.

neuroscience↗

bioRxiv2021

Effects of upgrading acquisition-techniques and harmonization methods: A multi-modal MRI study with implications for longitudinal designs

A downside of upgrading MRI acquisition sequences is the discontinuity of technological homogeneity of the MRI data. It hampers combining new and old datasets, especially in a longitudinal design. Characterizing upgrading effects on multiple brain parameters and examining the efficacy of harmonization methods are essential. This study investigated the upgrading effects on three structural parameters, including cortical thickness (CT), surface area (SA), cortical volume (CV), and resting-state functional connectivity (rs-FC) collected from 64 healthy volunteers. We used two evaluation metrics, Cohens d and classification accuracy, to quantify the effects. In classification analyses, we built classifiers for differentiating the protocols from brain parameters. We investigated the efficacy of three harmonization methods, including traveling subject (TS), TS-ComBat, and ComBat methods, and the sufficient number of participants for eliminating the effects on the evaluation metrics. Finally, we performed age prediction as an example to confirm that harmonization methods retained biological information. The results without harmonization methods revealed small to large mean Cohens d values on brain parameters (CT:0.85, SA:0.66, CV:0.68, and rs-FC:0.24) with better classification accuracy (>92% accuracy). With harmonization methods, Cohens d values approached zero. Classification performance reached the chance level with TS-based techniques when data from less than 26 participants were used for estimating the effects, while the Combat method required more participants. Furthermore, harmonization methods improved age prediction performance, except for the ComBat method. These results suggest that acquiring TS data is essential to preserve the continuity of MRI data.

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bioRxiv2020

Trace Imbalance in Reinforcement and Punishment Systems Can Mis-reinforce Implicit Choices Leading to Anxiety

We may view most of our daily activities as rational action selections; however, we sometimes reinforce maladaptive behaviors despite having explicit environmental knowledge. In this study, we model obsessive-compulsive disorder (OCD) symptoms as implicitly learned maladaptive behaviors. Simulations in the reinforcement learning framework show that agents implicitly learn to respond to intrusive thoughts when the memory trace signal for past actions decays differently for positive and negative prediction errors. Moreover, this model extends our understanding of therapeutic effects of behavioral therapy in OCD. Using empirical data, we confirm that patients with OCD show extremely imbalanced traces, which are normalized by serotonin enhancers. We find that healthy participants also vary in their obsessive-compulsive tendencies, consistent with the degree of imbalanced traces. These behavioral characteristics can be generalized to variations in the healthy population beyond the spectrum of clinical phenotypes.

neuroscience↗

bioRxiv2020

Patterns of functional and structural brain information are preserved over generations

There is clear evidence of intergenerational transmission of life values, cognitive traits, psychiatric disorders, and even aspects of daily decision making. To investigate biological substrates of this phenomenon, brain has received increasing attention as a measurable biomarker and potential target for intervention. However, no previous study has quantitatively and comprehensively investigated the effects of intergenerational transmission on functional and structural brain networks from parents to their children. Here, by employing an unusually large cohort dataset, we show that patterns of functional and structural brain networks are preserved over a generation. We also demonstrate that several demographic and behavioural phenotypes have effects on brain similarity. Collectively, our results provide a comprehensive picture of neurobiological substrates of parent-child similarity, and demonstrate the usability of our dataset for investigating the neurobiological substrates of intergenerational transmission.

neuroscience↗

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

State-Unspecific Modes of Whole-Brain Functional Connectivity Predict Intelligence and Life Outcomes

Recent functional magnetic resonance imaging (fMRI) studies have increasingly revealed potential neural substrates of individual differences in diverse types of brain function and dysfunction. Although most previous studies have been inherently limited to state-specific characterizations of related brain networks and their functions, several recent studies have examined the potential state-unspecific nature of functional brain networks, such as their global similarities across different experimental conditions (i.e., states) including both task and rest. However, no previous studies have carried out direct, systematic characterizations of state-unspecific brain networks, or their functional implications. Here, we quantitatively identified several modes of state-unspecific individual variation in whole-brain functional connectivity patterns, called \"Common Neural Modes (CNMs)\", from a large fMRI dataset including eight task/rest states, obtained from the Human Connectome Project. Furthermore, we tested how CNMs account for variability in individual behavioral measures. The results revealed that three CNMs were robustly extracted under various different preprocessing conditions. Each of these CNMs was significantly correlated with different aspects of behavioral measures of both fluid and crystalized intelligence. The three CNMs were also able to predict several life outcomes, such as income and life satisfaction, achieving the highest performance when combined with behavioral intelligence measures as inputs. Our findings highlight the importance of state-unspecific brain networks to characterize fundamental individual variation.

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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

A common neural network among state, trait, and pathological anxiety from whole-brain functional connectivity

Anxiety is one of the most common mental states of humans. Although it drives us to avoid frightening situations and to achieve our goals, it may also impose significant suffering and burden if it becomes extreme. Because we experience anxiety in a variety of forms, previous studies investigated neural substrates of anxiety in a variety of ways. These studies revealed that individuals with high state, trait, or pathological anxiety showed altered neural substrates. However, no studies have directly investigated whether the different dimensions of anxiety share a common neural substrate, despite its theoretical and practical importance. Here, we investigated a neural network of anxiety shared by different dimensions of anxiety in a unified analytical framework using functional magnetic resonance imaging (fMRI). We analyzed different datasets in a single scale, which was defined by an anxiety-related neural network derived from whole brain. Through the fMRI task for provoking anxiety, we found a common neural network of state anxiety across participants (1,638 trials obtained from 10 participants). Then, using the resting-state fMRI in combination with the participants trait anxiety scale (879 participants from the Human Connectome Project), we demonstrated that trait anxiety also shared the same neural network as state anxiety. Furthermore, the common neural network between state and trait anxiety could detect patients with obsessive-compulsive disorder, which is characterized by pathological anxiety-driven behaviors (174 participants from multi-site datasets). Our findings provide direct evidence that different dimensions of anxiety are not completely independent but have a substantial biological inter-relationship. Our results also provide a biologically defined dimension of anxiety, which may promote further investigation of various human characteristics, including psychiatric disorders, from the perspective of anxiety.

neuroscience↗