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ObjectivesTo investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images.MethodsIn this Institutional Review Board–approved retrospective study, patients who received both unenhanced CT examinations and dual-energy X-ray absorptiometry (DXA) of the lumbar vertebrae, in two institutions (1 and 2), were included. Supervised deep learning was employed to obtain a convolutional neural network (CNN) model using axial CT images, including the lumbar vertebrae as input data and BMD values obtained with DXA as reference data. For this purpose, 1665 CT images from 183 patients in institution 1, which were augmented to 99,900 (= 1665 × 60) images (noise adding, parallel shift and rotation were performed), were used. Internal (by using data of 45 other patients in institution 1) and external validations (by using data of 50 patients in institution 2) were performed to evaluate the performance of the trained CNN model. Correlations and diagnostic performances were evaluated with Pearson’s correlation coefficient (r) and area under the receiver operating characteristic curve (AUC), respectively.ResultsThe estimated BMD values, according to the CNN model (BMDCNN), were significantly correlated with the BMD values obtained with DXA (r = 0.852 (p < 0.001) and 0.840 (p < 0.001) for the internal and external validation datasets, respectively). Using BMDCNN, osteoporosis was diagnosed with AUCs of 0.965 and 0.970 for the internal and external validation datasets, respectively.ConclusionsUsing deep learning, the BMD of lumbar vertebrae could be predicted from unenhanced abdominal CT images.Key Points• By applying a deep learning technique, the bone mineral density (BMD) of lumbar vertebrae can be estimated from unenhanced abdominal CT images.• A strong correlation was observed between the estimated BMD and the BMD obtained with DXA.• By using the estimated BMD, osteoporosis could be diagnosed with high performance.

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

ObjectivesTo investigate whether liver fibrosis can be staged by deep learning techniques based on CT images.MethodsThis clinical retrospective study, approved by our institutional review board, included 496 CT examinations of 286 patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. The 396 portal phase images with age and sex data of patients (F0/F1/F2/F3/F4 = 113/36/56/66/125) were used for training a deep convolutional neural network (DCNN); the data for the other 100 (F0/F1/F2/F3/F4 = 29/9/14/16/32) were utilised for testing the trained network, with the histopathological fibrosis stage used as reference. To improve robustness, additional images for training data were generated by rotating or parallel shifting the images, or adding Gaussian noise. Supervised training was used to minimise the difference between the liver fibrosis stage and the fibrosis score obtained from deep learning based on CT images (FDLCT score) output by the model. Testing data were input into the trained DCNNs to evaluate their performance.ResultsThe FDLCT scores showed a significant correlation with liver fibrosis stage (Spearman's correlation coefficient = 0.48, p < 0.001). The areas under the receiver operating characteristic curves (with 95% confidence intervals) for diagnosing significant fibrosis (≥ F2), advanced fibrosis (≥ F3) and cirrhosis (F4) by using FDLCT scores were 0.74 (0.64–0.85), 0.76 (0.66–0.85) and 0.73 (0.62–0.84), respectively.ConclusionsLiver fibrosis can be staged by using a deep learning model based on CT images, with moderate performance.Key Points• Liver fibrosis can be staged by a deep learning model based on magnified CT images including the liver surface, with moderate performance.• Scores from a trained deep learning model showed moderate correlation with histopathological liver fibrosis staging.• Further improvement are necessary before utilisation in clinical settings.

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 Data Technology Type(s) functional magnetic resonance imaging • magnetic resonance imaging • Resting State Functional Connectivity Magnetic Resonance Imaging Factor Type(s) age • sex • site • disorder Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14716329

A discrepancy in oxytocin’s behavioral effects between acute and repeated administrations indicates distinct underlying neurobiological mechanisms. The current study employed a combination of human clinical trial and animal study to compare neurochemical changes induced by acute and repeated oxytocin administrations. Human study analyzed medial prefrontal metabolite levels by using 1H-magnetic resonance spectroscopy, a secondary outcome in our randomized, double-blind, placebo-controlled crossover trial of 6 weeks intranasal administrations of oxytocin (48 IU/day) and placebo within-subject design in 17 psychotropic-free high-functioning men with autism spectrum disorder. Medial prefrontal transcript expression levels were analyzed in adult male C57BL/6J mice after intraperitoneal injection of oxytocin or saline either once (200 ng/100 μL/mouse, n = 12) or for 14 consecutive days (200 ng/100 μL/mouse/day, n = 16). As the results, repeated administration of oxytocin significantly decreased the medial prefrontal N-acetylaspartate (NAA; p = 0.043) and glutamate–glutamine levels (Glx; p = 0.001), unlike the acute oxytocin. The decreases were inversely and specifically associated (r = 0.680, p = 0.004 for NAA; r = 0.491, p = 0.053 for Glx) with oxytocin-induced improvements of medial prefrontal functional MRI activity during a social judgment task not with changes during placebo administrations. In wild-type mice, we found that repeated oxytocin administration reduced medial frontal transcript expression of N-methyl-d-aspartate receptor type 2B (p = 0.018), unlike the acute oxytocin, which instead changed the transcript expression associated with oxytocin (p = 0.0004) and neural activity (p = 0.0002). The present findings suggest that the unique sensitivity of the glutamatergic system to repeated oxytocin administration may explain the differential behavioral effects of oxytocin between acute and repeated administration.

When collecting large amounts of 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 a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of 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 a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.

Abstract Background and Hypothesis Machine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other disease spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could be applied to earlier clinical stages such as first-episode psychosis (FEP), ultra-high risk for psychosis (UHR), and autism spectrum disorders (ASDs). Study Design Total 359 T1-weighted MRI scans, including 154 individuals with schizophrenia spectrum (UHR, n = 37; FEP, n = 24; and ChSZ, n = 93), 64 with ASD, and 141 HCs, were obtained using three acquisition protocols. Of these, data regarding ChSZ (n = 75) and HC (n = 101) from two protocols were used to build a classifier (training dataset). The remainder was used to evaluate the classifier (test, independent confirmatory, and independent group datasets). Scanner and protocol effects were diminished using ComBat. Study Results The accuracy of the classifier for the test and independent confirmatory datasets were 75% and 76%, respectively. The bilateral pallidum and inferior frontal gyrus pars triangularis strongly contributed to classifying ChSZ. Schizophrenia spectrum individuals were more likely to be classified as ChSZ compared to ASD (classification rate to ChSZ: UHR, 41%; FEP, 54%; ChSZ, 70%; ASD, 19%; HC, 21%). Conclusion We built a classifier from multiple protocol structural brain images applicable to independent samples from different clinical stages and spectra. The predictive information of the classifier could be useful for applying neuroimaging techniques to clinical differential diagnosis and predicting disease onset earlier.

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.

PurposeTo describe a case of persistent trigeminal artery (PTA)–superior cerebellar artery (SCA) segmental fusion incidentally diagnosed on magnetic resonance (MR) angiography.Case reportA 53-year-old woman with a history of facial pain underwent cranial MR imaging and MR angiography. MR angiography showed a left lateral-type PTA arising from the precavernous portion of the left internal carotid artery (ICA). PTA branched into the left distal SCA and showed segmental fusion with the proximal SCA at the distal part of the PTA. We also diagnosed an unruptured cerebral aneurysm at the junction between the left ICA and PTA.DiscussionPTA is the most frequent type of carotid-vertebrobasilar anastomosis. The reported prevalence rate is 0.2% by angiography and 0.34% by MR angiography. There are two types of PTA—lateral (usual) and medial (intrasellar). SCA arising from the lateral-type PTA has rarely been reported. Further, a PTA from which the distal SCA branches and segmentally fuses with the proximal SCA at the distal part of the PTA has not been reported.ConclusionUsing MR angiography, we diagnosed a rare type of PTA that fused segmentally with SCA. No similar case has been reported in relevant English-language literature.

Voxel-based specific region analysis systems for Alzheimer’s disease (VSRAD) are clinically used to measure the atrophied hippocampus captured by magnetic resonance imaging (MRI). However, motion artifacts during acquisition of images may distort the results of the analysis. This study aims to evaluate the usefulness of the Pix2Pix network in motion correction for the input image of VSRAD analysis. Seventy-three patients examined with MRI were distinguished into the training group (n = 51) and the test group (n = 22). To create artifact images, the k-space images were manipulated. Supervised deep learning was employed to obtain a Pix2Pix that generates motion-corrected images, with artifact images as the input data and original images as the reference data. The results of the VSRAD analysis (severity of voxel of interest (VOI) atrophy, the extent of gray matter (GM) atrophy, and extent of VOI atrophy) were recorded for artifact images and motion-corrected images, and were then compared with the original images. For comparison, the image quality of Pix2Pix generated motion-corrected image was also compared with that of U-Net. The Bland-Altman analysis showed that the mean of the limits of agreement was smaller for the motion-corrected images compared to the artifact images, suggesting successful motion correction by the Pix2Pix. The Spearman’s rank correlation coefficients between original and motion-corrected images were almost perfect for all results (severity of VOI atrophy: 0.87–0.99, extent of GM atrophy: 0.88–00.98, extent of VOI atrophy: 0.90–1.00). Pix2Pix generated motion-corrected images that showed generally improved quantitative and qualitative image qualities compared with the U-net generated motion-corrected images. Our findings suggest that motion correction using Pix2Pix is a useful method for VSRAD analysis.