Biology subjects

Oishi, N.

Publications and source records attributed to Oishi, N..

3 recordsLinked to original sources

bioRxiv2025

Effects of Postmortem Intervals on Quantitative MRI in Unfixed and Fixed Swine Brain: Implications for Ex Vivo MRI Applications

PurposeThe postmortem interval (PMI) alters tissue properties that shape quantitative MRI (qMRI) signals. We systematically investigated the effects of PMI on relaxation times, depending on tissue characteristics, in both unfixed and fixed pig brains. MethodsTwelve pig brains (n = 12) were scanned in both unfixed and fixed states at three PMI windows ({approx}12, 24, 48 h). Quantitative T1, T2, and T2* maps were acquired with identical protocols at controlled room temperature ([~]24 {degrees}C). We assessed the PMI effects on relaxation times in the gray matter (GM) and the white matter (WM) at both group and sample levels, while fixation-induced effects and inter-sample variability were evaluated using pairwise rank correlation and coefficient of variation (CV) visualization. ResultsIn unfixed tissues, T1 significantly differed among PMI groups in GM (p = 0.0141) and WM (p = 0.0315) in the early window ([≤] 24 h). In the same window, PMI-T1 correlations were observed in both GM (r = 0.921, p = 0.007) and WM (r = 0.876, p = 0.013). T2 showed no group differences but exhibited an inverse correlation with PMI in WM (r = -0.849, p = 0.015). No significant PMI-T2* relationships were detected (all p > 0.05). At later PMI (20-50 h), PMI-qMRI correlations diminished. Fixation processes altered all qMRI parameters. Notably, the PMI effects on T1 in the unfixed brains were preserved even after fixation at an ordinal level, although fixation introduced substantial inter-sample variability. ConclusionsPMI exerts the most robust effects on T1. Fixation has a significant impact on qMRI values, mitigating the apparent PMI effect and increasing the inter-sample variability. However, it is still possible to retrieve the PMI effects from fixed brain at the level of rank order, providing practical guidance for further ex vivo qMRI studies on PMI.

bioengineering↗

bioRxiv2025

Neural Underpinnings of Olfactory Dysfunction across Parkinson's and Alzheimer's Spectra

Olfactory dysfunction is a frequent yet understudied feature of neurodegenerative spectrum disorders, including Alzheimers disease (AD) and Parkinsons disease (PD). To disentangle the neural substrates of hyposmia across disease spectra, we examined 222 participants from the Parkinsons and Alzheimers disease Dimensional Neuroimaging Initiative cohort. Participants were classified according to the presence of cognitive disturbance, movement disorder, or both. Olfactory testing disclosed that cognitive disturbance and movement disorder were independently associated with hyposmia, and having both cognitive disturbance and movement disorder was associated with severe hyposmia. Whole-brain voxel-based morphometry revealed that hyposmia was associated with atrophy in the medial temporal lobe (MTL) in individuals with cognitive disturbance, whereas an artificial intelligence-based segmentation model identified olfactory bulb atrophy in those with movement disorder. Regression analysis and structural equation modeling further confirmed that the MTL and olfactory bulb volume contributed to olfactory performance through distinct processes. Individuals with cognitive disturbance and movement disorder had atrophy in both the MTL and olfactory bulb ("double hit"). We identified dual processes underlying hyposmia in the AD and PD spectra: a process linking MTL degeneration to cognitive disturbance and a process linking olfactory bulb degeneration to movement disorder. Our transdiagnostic approach enhances strategy in identifying specific neural correlates underlying hyposmia, lending support to the development of biomarkers for early intervention in AD and PD.

neuroscience↗

bioRxiv2025

Harmonizing Inter-Site Differences in T1-Weighted Images Using CycleGAN

IntroductionWhen neuroimaging studies using magnetic resonance imaging (MRI) are conducted across multiple centers, they often encounter inter-site differences in MRI equipment and protocols leading to biases and confounding effects in MRI measurements. There are existing techniques for correcting these site effects, i.e., harmonization, but they have limitations, including the need for preprocessing of MRI data, which involves processes such as spatial normalization. Deep learning-based methods have emerged as potential alternatives that can handle site effects without the need for preprocessing steps. In this study, we propose a novel method based on the generative adversarial network (GAN) framework, CycleGAN, that effectively addresses inter-site differences in T1-weighted images with minimal preprocessing requirements. We compare the harmonization efficacy of CycleGAN with that of the commonly used method ComBat. MethodsWe trained the proposed CycleGAN method and the comparative ComBat method using data from 40 subjects at each of two sites. To evaluate the effectiveness of the two methods, we used data from nine subjects who underwent imaging at both sites. We assessed harmonization performance at the image level using the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). Additionally, we evaluated harmonization results at the feature level by analyzing regional cortical thickness and volume data. Cohens d was employed to quantify the differences between feature values. ResultsAt the image level, the ComBat method decreased the median baseline SSIM value from 0.86 (interquartile range [IQR], 0.02) to 0.84 (IQR, 0.02), whereas the proposed CycleGAN method maintained the SSIM value at 0.86 (IQR, 0.02). For PSNR, the baseline value was 18.33 (IQR, 1.78), which decreased to 15.30 (IQR, 2.20) after applying ComBat, but increased to 19.58 (IQR, 3.12) with the proposed CycleGAN method. These findings indicate that CycleGAN preserved the structural and signal similarity of the images. At the feature level, the effect size for cortical thickness decreased from 0.97 (IQR, 1.79) to 0.91 (IQR, 1.54) after applying ComBat, whereas the proposed CycleGAN method yielded an effect size of 1.05 (IQR, 1.14). For cortical volume, the effect size decreased from 0.95 (IQR, 1.78) to 0.69 (IQR, 1.00) after applying ComBat, and decreased to 0.88 (IQR, 0.74) with the CycleGAN method. Compared with baseline, Cohens d was significantly lower with both ComBat (p = 0.000002) and CycleGAN (p = 0.028) with no significant difference between the two methods, indicating similar performance of the two methods under the study conditions. ConclusionThe results underscore the ability of CycleGAN to harmonize data without explicit normalization and emphasize the potential impact of the normalization process on harmonization procedures. Our findings suggest that CycleGAN holds promise as a harmonization technique in multi-site neuroimaging studies.

neuroscience↗