Biology subjects

Kato, N.

Publications and source records attributed to Kato, N..

7 recordsLinked to original sources

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

High-level expression of STING restricts susceptibility to HBV by mediating type III IFN induction

Hepatitis B virus (HBV) is a hepatotropic DNA virus causing hepatic diseases such as chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma. To study HBV, human hepatoma HepG2 cells are currently used as an HBV infectious cell culture model worldwide. HepG2 cells exhibit susceptibility to HBV by exogenously expressing sodium taurocholate cotransporting polypeptide (NTCP). We herein demonstrated that human immortalized hepatocyte NKNT-3 cells exhibited susceptibility to HBV by exogenously expressing NTCP (NKNT-3/NTCP cells). By comparing the cGAS-STING signaling pathway in several NKNT-3/NTCP cell-derived cell clones, we found that STING was highly expressed in cell clones exhibiting resistance but not susceptibility to HBV. High-level expression of STING was implicated in HBV-triggered induction of type III IFN and a pro-inflammatory cytokine, IL-6. In contrast, RNAi-mediated knockdown of STING inhibited type III IFN induction and restored the levels of HBV total transcript in an HBV-infected cell clone exhibiting resistance to HBV. These results suggest that STING regulates susceptibility to HBV by its expression levels.\n\nSTING may thus be a novel target for anti-HBV strategies.

microbiology↗

bioRxiv2018

Machine learning approach to identify resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder

Endophenotype refers to a measurable and heritable component between genetics and diagnosis and exists in both individuals with a diagnosis and their unaffected siblings. We aimed to identify a pattern of endophenotype consisted of multiple connections. We enrolled adult male individuals with autism spectrum disorder (ASD) endophenotype (i.e., individuals with ASD and their unaffected siblings) and individuals without ASD endophenotype (i.e., pairs of typical development (TD) siblings) and utilized a machine learning approach to classify people with and without endophenotypes, based on resting-state functional connections (FCs). A sparse logistic regression successfully classified people as to the endophenotype (area under the curve=0.78, classification accuracy=75%), suggesting the existence of endophenotype pattern. A binomial test identified that nine FCs were consistently selected as inputs for the classifier. The least absolute shrinkage and selection operator with these nine FCs predicted severity of communication impairment among individuals with ASD (r=0.68, p=0.021). In addition, two of the nine FCs were statistically significantly correlated with the severity of communication impairment (r=0.81, p=0.0026 and r=-0.60, p=0.049). The current findings suggest that an ASD endophenotype pattern exists in FCs with a multivariate manner and is associated with clinical ASD phenotype.

neuroscience↗

bioRxiv2018

Four assessment methods to measure student gains in a graduate course on mathematical modeling in cell biology

The push for mathematics and quantitative skill development in biology is greater than ever before. The perceptions of these techniques are a barrier to their implementation on a grand scale. Methods to assess the efficacy of approaches to teaching quantitative skills are needed to determine if they effectively overcome social stereotypes and fears of math. In our project we study the effect of a mathematical modeling course in cell biology. We developed four assessments to understand if hands-on modeling increases student understanding of the concepts while simultaneously reducing fear of math. Two concept inventories track students learning gains both in general and specific knowledge to the course content. Two surveys quantify students quantitative confidence and opinions of the usefulness of these techniques in biology. We discuss the effectiveness of our methods and the implications for assessment development in mathematical biology education.

scientific communication and education↗

bioRxiv2017

Aberrant cerebellar-default-mode functional connectivity underlying auditory verbal hallucinations in schizophrenia revealed by multi-voxel pattern analysis of resting-state functional connectivity MRI data

Past neuroimaging studies have reported that aberrant functional connectivity (FC) underlying auditory verbal hallucinations (AVHs) in schizophrenia is highly distributed over multiple functional networks. There is thus a need for exploratory approaches without limiting analysis to particular seed regions or functional networks, to identify FC alterations underlying AVH. We applied a multi-voxel pattern analysis (MVPA) of FC together with a series of post-hoc FC analyses to resting-state fMRI data acquired from 25 patients with schizophrenia and 25 matched healthy controls. First, the MVPA revealed multiple clusters exhibiting altered FC patterns in schizophrenia. Subsequent multiple linear regression analysis using scores of these clusters identified that FC alteration in the right cerebellum crus I was significantly associated with the severity of AVH. Furthermore, post-hoc FC analysis with the right crus I as a seed revealed significant FC alterations with regions distributed across multiple functional networks, including speech, default-mode, thalamus, and cerebellum. Subsequent linear regression analyses further demonstrated that, among these regions, only reduced FC in the left precuneus was significantly associated with the severity of AVH. Our unbiased exploratory analysis of FC data revealed a novel evidence for the crucial role of FC between cerebellar and default-mode networks in AVH. (198 words)

neuroscience↗

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↗