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Although the identification of late adolescents with subthreshold depression (StD) may provide a basis for developing effective interventions that could lead to a reduction in the prevalence of StD and prevent the development of major depressive disorder, knowledge about the neural basis of StD remains limited. The purpose of this study was to develop a generalizable classifier for StD and to shed light on the underlying neural mechanisms of StD in late adolescents. Resting-state functional magnetic resonance imaging data of 91 individuals (30 StD subjects, 61 healthy controls) were included to build an StD classifier, and eight functional connections were selected by using the combination of two machine learning algorithms. We applied this biomarker to an independent cohort (n = 43) and confirmed that it showed generalization performance (area under the curve = 0.84/0.75 for the training/test datasets). Moreover, the most important functional connection was between the left and right pallidum, which may be related to clinically important dysfunctions in subjects with StD such as anhedonia and hyposensitivity to rewards. Investigation of whether modulation of the identified functional connections can be an effective treatment for StD may be an important topic of future research.

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; allP

Recent advances in genetic neuromodulation technology have enabled circuit-specific interventions in nonhuman primates (NHPs), thereby revealing the causal functions of specific neural circuits. Going forward, an important step is to use these findings to better understand neuropsychiatric and neurological disorders in humans, in which alterations in functional connectivity between brain regions are demonstrated. We recently identified the causal roles of the pathways from the dorsolateral prefrontal cortex (DLPFC) to the lateral part of the mediodorsal thalamic nucleus (MDl) and dorsal caudate nucleus (dCD) in working memory and decision-making, respectively. In the present study, we examined the resting-state functional connectivity of these two prefronto-subcortical circuits in healthy controls (HCs) and patients with various neuropsychiatric disorders including schizophrenia (SCZ), major depressive disorder (MDD), and autism spectrum disorders (ASD) in humans. We found that the functional connectivity of two pathways, DLPFC-MDl and DLPFC-dCD, was significantly reduced in the SCZ groups compared to HCs; however, this hypoconnectivity was not observed in the ASD or MDD groups, suggesting a disease-specific profile of altered prefronto-subcortical connectivity at rest. These results suggest that causal findings of pathway-specific functions revealed in NHPs can be effectively translated to identify the altered connectivity in neuropsychiatric disorders with related symptoms in humans.

Birth order is a crucial environmental factor for child development. For example, later-born children are relatively unlikely to feel secure due to sibling competition or diluted parental resources. The positive effect of being earlier-born on cognitive intelligence is well-established. However, whether birth order is linked to social behavior remains controversial, and the neural correlates of birth order effects in adolescence when social cognition develops remain unknown. Here, we explored the birth order effect on prosociality using a large-scale population-based adolescent cohort. Next, since the amygdala is a key region for sociality and environmental stress, we examined amygdala substrates of the association between birth order and prosociality using a subset neuroimaging cohort. We found enhanced prosociality in later-born adolescents (N = 3160), and observed the mediating role of larger amygdala volume (N = 208) and amygdala-prefrontal functional connectivity with sex-selective effects (N = 183). We found that birth order, a non-genetic environmental factor, affects adolescent social development via different neural substrates. Our findings may indicate the later-born people’s adaptive survival strategy in stressful environments.

The extrastriate body area (EBA) in the lateral occipito-temporal cortex has an important role in reciprocal interaction, as it detects congruence between self and other’s hand actions. However, it is unclear whether the EBA can detect congruence regardless of the type of action. In the present study, we examined the neural substrate underlying congruence detection of three types of actions: hand gestures, vocalizations, and facial expressions. A univariate analysis revealed a congruency effect, especially for imitating action, for all three types of actions in the EBA. A multi-voxel pattern analysis classifier in the EBA was able to distinguish between initiating interaction from responding to interaction in all experiments. Correspondingly, the congruency effect in the EBA revealed by univariate analysis was stronger for responding to than for initiating interaction. These findings suggest that the EBA might contribute to detect congruence regardless of the body part used (i.e. face or hand) and the type of action (i.e. gestural or vocal). Moreover, initiating and responding to interaction might be processed differently within the EBA. This study highlights the role of the EBA in comparing between self and other’s actions beyond hand actions.

異なる脳機能イメージング法であるfMRIとPETで撮像画像に共通する成分はあるのだろうか。共通する成分を抽出できれば、それを利用しfMRI画像とPET画像を相互に変換することが可能となる。本研究では、共通成分の抽出・相互変換を試みるため、深層学習により拡張した正順相関分析法を実装した。本発表において、シミュレーションデータに適応した予備結果を報告し、実データ適用に向けた課題・拡張について議論する。, 量子生命科学会 第4回大会

[Aim] Recently, a machine-learning (ML) technique has been used to create generalizable classifiers for psychiatric disorders based on information of functional connections (FCs) between brain regions at resting state. These classifiers predict diagnostic labels by a weighted linear sum (WLS) of the correlation values of a small number of selected FCs. We aimed to develop a generalizable classifier for gambling disorder (GD) from the information of FCs using the ML technique and examine relationships between WLS and clinical data. [Methods] As a training dataset for ML, data from 71 GD patients and 90 healthy controls (HCs) were obtained from two magnetic resonance imaging sites. We used an ML algorithm consisting of a cascade of an L1-regularized sparse canonical correlation analysis and a sparse logistic regression to create the classifier. The generalizability of the classifier was verified using an external dataset. This external dataset consisted of six GD patients and 14 HCs, and was collected at a different site from the sites of the training dataset. Correlations between WLS and South Oaks Gambling Screen (SOGS) and duration of illness were examined. [Results] The classifier distinguished between the GD patients and HCs with high accuracy in leave-one-out cross-validation (area under curve (AUC = 0.89)). This performance was confirmed in the external dataset (AUC = 0.81). There was no correlation between WLS, and SOGS and duration of illness in the GD patients. [Conclusion] We developed a generalizable classifier for GD based on information of functional connections between brain regions at resting state., 脳機能結合の情報からギャンブル障害の判別器を開発 --人工知能技術の応用により診断や治療に新たな道!--. 京都大学プレスリリース. 2022-04-15.

In evaluating the personality attributes and performance of the self, people are inclined to view themselves superior to others, a phenomenon known as superiority illusion (SI). This illusive outlook pervades people’s thoughts, creating hope for the future and promoting mental health. Although a specific cortico-striatal functional connectivity (FC) under dopaminergic modulation was previously shown to be implicated in SI, the underlying whole-brain mechanisms have remained unclarified. Herein, to reveal the neural network subserving individual’s SI, we conducted a data-driven, machine-learning investigation to explore the resting-state FC network across the whole brain. Using the locally-acquired resting-state functional magnetic resonance imaging data (n = 123), we identified a set of 15 FCs most informative in classifying individuals with higher-versus lower-than-average levels of SI in evaluating positive trait words (area under the curve [AUC] = 0.81). Among the 15 FCs, the contribution level to the classification was 11% by the previously-highlighted cortico-striatal FC alone, but 60% by the encompassing cortico-limbico-striatal network cluster. A newly-identified, cortico-thalamic FC and another FC cluster also demonstrated substantial contribution. The classification accuracy was generalized into an independent cohort (n = 36; AUC = 0.73). Importantly, using the same set of 15 FCs, we achieved prediction on an individual’s level of striatal dopamine D2 receptor availability (Pearson correlation, r = 0.46, P = 0.005). This is the first successful identification of the whole-brain neural network that simultaneously predicts the behavioral manifestation and molecular underpinning of an essential psychological process that promotes well-being and mental health.Significance StatementSuperiority illusion (SI) is a basic self-referential framework that pervades people’s thoughts and promotes well-being and mental health. An aberrant form of SI has been reported in psychiatric conditions such as depression. Our hypothesis-free, data-driven investigation revealed the spatially-distributed neural network that for the first time achieved prediction on an individual’s levels of SI and the striatal dopaminergic transmission simultaneously. In principle, this multiple-biological-layer framework can be applicable to any behavioral trait to establish a link with its underlying neural network and neurochemical properties, which could quantitatively present the relation of its aberrant form with the pathophysiology of neuropsychiatric disorders. Future clinical research may aid in deriving a diagnostic biomarker for examining the related behavioral and neurochemical characteristics within individuals.

In evaluating the personality attributes and performance of the self, people are inclined to view themselves superior to others, a phenomenon known as superiority illusion (SI). This illusive outlook pervades people’s thoughts, creating hope for the future and promoting mental health. Although a specific cortico-striatal functional connectivity (FC) under dopaminergic modulation was previously shown to be implicated in SI, the underlying whole-brain mechanisms have remained unclarified. Herein, to reveal the neural network subserving individual’s SI, we conducted a data-driven, machine-learning investigation to explore the resting-state FC network across the whole brain. Using the locally-acquired resting-state functional magnetic resonance imaging data (n = 123), we identified a set of 15 FCs most informative in classifying individuals with higher-versus lower-than-average levels of SI in evaluating positive trait words (area under the curve [AUC] = 0.81). Among the 15 FCs, the contribution level to the classification was 11% by the previously-highlighted cortico-striatal FC alone, but 60% by the encompassing cortico-limbico-striatal network cluster. A newly-identified, cortico-thalamic FC and another FC cluster also demonstrated substantial contribution. The classification accuracy was generalized into an independent cohort (n = 36; AUC = 0.73). Importantly, using the same set of 15 FCs, we achieved prediction on an individual’s level of striatal dopamine D2 receptor availability (Pearson correlation, r = 0.46, P = 0.005). This is the first successful identification of the whole-brain neural network that simultaneously predicts the behavioral manifestation and molecular underpinning of an essential psychological process that promotes well-being and mental health. Significance Statement Superiority illusion (SI) is a basic self-referential framework that pervades people’s thoughts and promotes well-being and mental health. An aberrant form of SI has been reported in psychiatric conditions such as depression. Our hypothesis-free, data-driven investigation revealed the spatially-distributed neural network that for the first time achieved prediction on an individual’s levels of SI and the striatal dopaminergic transmission simultaneously. In principle, this multiple-biological-layer framework can be applicable to any behavioral trait to establish a link with its underlying neural network and neurochemical properties, which could quantitatively present the relation of its aberrant form with the pathophysiology of neuropsychiatric disorders. Future clinical research may aid in deriving a diagnostic biomarker for examining the related behavioral and neurochemical characteristics within individuals.

Principal component analysis (PCA) is a widely used statistical tool for extracting low-dimensional structures underlying multivariate data. However, its application to high-dimensional data is limited due to its large computational time. While the conventional PCA algorithm requires polynomial time, using a quantum-inspired algorithm as a subroutine, we have implemented an algorithm that approximates it with computational time proportional to the logarithm of the input dimensionality. The computational efficiency and performance of the implemented algorithm, quantum-inspired PCA, are experimentally evaluated on synthetic and real datasets., 第31回 日本神経回路学会 全国大会（JNNS2021）

Introduction: Using stimuli (e.g., images, videos, products) labeled by a number of raters has recently become common in brain decoding analysis, where subjective emotion/impression for stimuli felt by the population is predicted from brain responses. However, there remains no established method for constructing a decoder using such multi-rater labels. In previous studies, the variability across multiple raters was assumed to reflect noise, and the answers for a binary judgment were averaged across raters. Then, the average scores (i.e., empirical probabilities) for individual stimuli were predicted using standard regression methods. While this procedure is a simple and popular approach, it is not appropriate because most of these regression methods ignore the fact that probability is the variable to be predicted. To address this in an appropriate manner, we present a new framework in this study. Methods: Here, we assume that individual answers for a binary judgment about a stimulus follow a Bernoulli distribution. We then predicted the probability of positive answers from the human functional magnetic resonance imaging (fMRI) response to the stimulus using probit regression. We also introduced sparse regularization into probit regression (sparse probit regression) to prevent overfitting. Results: In both simulation and real fMRI data analysis, sparse probit regression more accurately predicted the probabilities of positive answers for individual stimuli than probit regression without sparse regularization, indicating that sparseness results in better decoding performance. Sparse probit regression also outperformed linear regression using the same type of sparse regularization, reflecting the advantage of our appropriate treatment of probability. Discussion & Conclusion: Our results suggest that our framework using sparse probit regression provides an effective method for the population prediction of emotion/impression assessment based on brain activity., 第5回ヒト脳イメージング研究会

Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants (“traveling subjects”) visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset., Measurement(s)mental or behavioural disorder • brain measurement • Demographic DataTechnology Type(s)functional magnetic resonance imaging • magnetic resonance imaging • Resting State Functional Connectivity Magnetic Resonance ImagingFactor Type(s)age • sex • site • disorderSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14716329

The extrastriate body area (EBA) in the lateral occipito-temporal cortex has an important role in reciprocal interaction, as it detects congruence between self and other's hand actions. However, it is unclear whether the EBA can detect congruence regardless of the type of action. In the present study, we examined the neural substrate underlying congruence detection of three types of actions: hand gestures, vocalizations, and facial expressions. A univariate analysis revealed a congruency effect, especially for imitating action, for all three types of actions in the EBA. A multi-voxel pattern analysis classifier in the EBA was able to distinguish between initiating interaction from responding to interaction in all experiments. Correspondingly, the congruency effect in the EBA revealed by univariate analysis was stronger for responding to than for initiating interaction. These findings suggest that the EBA might contribute to detect congruence regardless of the body part used (i.e. face or hand) and the type of action (i.e. gestural or vocal). Moreover, initiating and responding to interaction might be processed differently within the EBA. This study highlights the role of the EBA in comparing between self and other's actions beyond hand actions.Running head: Function of EBA in reciprocal imitation.

主成分分析は多変量データから重要な低次元成分を抽出する統計手法である．しかし，主成分分析のアルゴリズムは特異値分解に基づくため，次元数（変量の数）が数百万を超えるデータには，計算時間の問題からしばしば適用が困難となる．我々は近年発案された「量子インスパイアアルゴリズム」を用い，計算時間を次元数の対数オーダーに抑えつつ，主成分分析を近似するアルゴリズムを計算機実装した．本報告において，複数の人工データ・実データを用いてその計算時間と性能を評価した結果を紹介する．, 第44回量子情報技術研究会（QIT44）

To interrogate particular neuronal pathways in non-human primates under natural and stress-free conditions, we applied designer receptors exclusively activated by designer drugs (DREADDs) technology to common marmosets. We injected adeno-associated virus vectors expressing the excitatory DREADD hM3Dq into the unilateral substantia nigra in three marmosets. Using multi-tracer positron emission tomography imaging, we detected DREADD expression in vivo, which was confirmed in nigrostriatal dopamine neurons by immunohisto-chemistry, and assessed activation of the substantia nigra and dopamine release following agonist administration. The marmosets rotated in a contralateral direction relative to the activated side 30–90 min after consuming food containing the highly potent DREADD agonist deschloroclozapine (DCZ), but not on the following days without DCZ. These results indicate that non-invasive and reversible DREADD manipulation will extend the utility of marmoset as a primate model for linking neuronal activity and natural behavior in various contexts.

Human prosocial behavior (PB) emerges in childhood and matures during adolescence. Previous task-related functional magnetic resonance imaging (fMRI) studies have reported involvement of the medial prefrontal cortex including the anterior cingulate cortex (ACC) in social cognition in adolescence. However, neurometabolic and functional connectivity (FC) basis of PB in early adolescence remains unclear. Here, we measured GABA levels in the ACC and FC in a subsample (aged 10.5–13.4 years) of a large-scale population-based cohort with MR spectroscopy (MEGA-PRESS) and resting-state fMRI. PB was negatively correlated with GABA levels in the ACC (N = 221), and positively correlated with right ACC-seeded FC with the right precentral gyrus and the bilateral middle and posterior cingulate gyrus (N = 187). Furthermore, GABA concentrations and this FC were negatively correlated, and the FC mediated the association between GABA levels and PB (N = 171). Our results from a minimally biased, large-scale sample provide new insights into the neurometabolic and neurofunctional correlates of prosocial development during early adolescence.

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability., Biomarkers for psychiatric disorders based on neuroimaging data have yet to be put to practical use. This study overcomes the problems of inter-site differences in fMRI data by using a novel harmonization method, thereby successfully constructing a generalizable brain network marker of major depressive disorder across multiple imaging sites.

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable major depressive disorder (MDD) brain network markers which would distinguish patients from healthy controls (a classifier) or would predict symptom severity (a prediction model) based on resting state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD brain network markers. The classifier achieved 70% generalization accuracy, and the prediction model moderately well predicted symptom severity for an independent validation dataset with 449 participants from 4 different imaging sites. Finally, we found common 2 functional connections between those related to MDD diagnosis and those related to depression symptoms. The successful generalization to the perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

The limited efficacy of available antidepressant therapies may be due to how they affect the underlying brain network. The purpose of this study was to develop a melancholic MDD biomarker to identify critically important functional connections (FCs), and explore their association to treatments. Resting state fMRI data of 130 individuals (65 melancholic major depressive disorder (MDD) patients, 65 healthy controls) were included to build a melancholic MDD classifier, and 10 FCs were selected by our sparse machine learning algorithm. This biomarker generalized to a drug-free independent cohort of melancholic MDD, and did not generalize to other MDD subtypes or other psychiatric disorders. Moreover, we found that antidepressants had a heterogeneous effect on the identified FCs of 25 melancholic MDDs. In particular, it did impact the FC between left dorsolateral prefrontal cortex (DLPFC)/inferior frontal gyrus (IFG) and posterior cingulate cortex (PCC)/precuneus, ranked as the second ‘most important’ FC based on the biomarker weights, whilst other eight FCs were normalized. Given that left DLPFC has been proposed as an explicit target of depression treatments, this suggest that the limited efficacy of antidepressants might be compensated by combining therapies with targeted treatment as an optimized approach in the future.

Contains fulltext : 219632.pdf (Publisher’s version ) (Open Access) Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories worldwide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a mouse default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. RSN spatial specificity was enhanced in datasets acquired at higher field strength, with cryoprobes, in ventilated animals, and under medetomidine-isoflurane combination sedation. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.

The child-parent relationship is a significant factor in an adolescent’s well-being and functional outcomes. Epidemiological evidence indicates that relationships with the father and mother are differentially associated with specific psychobehavioral problems that manifest differentially between boys and girls. Neuroimaging is expected to bridge the gap in understanding such a complicated mapping between the child-parent relationships and adolescents’ problems. However, possible differences in the effects of child-father and child-mother relationships on sexual dimorphism in children’s brains and psychobehavioral problems have not been examined yet. This study used a dataset of 10- to 13-year-old children (N ​= ​93) to reveal the triad of associations among child-parent relationship, brain, and psychobehavioral problems by separately estimating the respective effects of child-father and child-mother relationships on boys and girls. We first fitted general linear models to identify the effects of paternal and maternal relationships in largely different sets of children’s resting-state functional connectivity, which we term paternal and maternal functional brain connectomes (FBCs). We then performed connectome-based predictive modeling (CPM) to predict children’s externalizing and internalizing problems from these parental FBCs. The models significantly predicted a range of girls’ internalizing problems, whereas the prediction of boys’ aggression was also significant using a more liberal uncorrected threshold. A series of control analyses confirmed that CPMs using FBCs associated with peer relationship or family socioeconomic status failed to make significant predictions of psychobehavioral problems. Lastly, a causal discovery method identified causal paths from daughter-mother relationship to maternal FBC, and then to daughter’s internalizing problems. These observations indicate sex-dependent mechanisms linking child-parent relationship, brain, and psychobehavioral problems in the development of early adolescence.

Impulsive behaviours are common symptoms of attention-deficit hyperactivity disorder (ADHD). Although previous studies have suggested functional models of impulsive behaviour, a full explanation of impulsivity in ADHD remains elusive. To investigate the detailed mechanisms behind impulsive behaviour in ADHD, we applied an economic intertemporal choice task involving gains and losses to adults with ADHD and healthy controls and measured brain activity by functional magnetic resonance imaging. In the intertemporal choice of future gains, we observed no behavioural or neural difference between the two groups. In the intertemporal choice of future losses, adults with ADHD exhibited higher discount rates than the control participants. Furthermore, a comparison of brain activity representing the sensitivity of future loss in the two groups revealed significantly lower activity in the striatum and higher activity in the amygdala in adults with ADHD than in controls. Our preliminary findings suggest that an altered size sensitivity to future loss is involved in apparent impulsive choice behaviour in adults with ADHD and shed light on the multifaceted impulsivity underlying ADHD.