Biology subjects

Itahashi, T.

Publications and source records attributed to Itahashi, T..

3 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

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↗

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↗