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Background and HypothesisDynamics of the distributed sets of functionally synchronized brain regions, known as large-scale networks, are essential for the emotional state and cognitive processes. However, few studies were performed to elucidate the aberrant dynamics across the large-scale networks across multiple psychiatric disorders. In this paper, we aimed to investigate dynamic aspects of the aberrancy of the causal connections among the large-scale networks of the multiple psychiatric disorders.Study DesignWe applied dynamic causal modeling (DCM) to the large-sample multi-site dataset with 739 participants from 4 imaging sites including 4 different groups, healthy controls, schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD), to compare the causal relationships among the large-scale networks, including visual network, somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network, and default mode network.Study ResultsDCM showed that the decreased self-inhibitory connection of LIN was the common aberrant connection pattern across psychiatry disorders. Furthermore, increased causal connections from LIN to multiple networks, aberrant self-inhibitory connections of DAN and SMN, and increased self-inhibitory connection of SAN were disorder-specific patterns for SCZ, MDD, and BD, respectively.ConclusionsDCM revealed that LIN was the core abnormal network common to psychiatric disorders. Furthermore, DCM showed disorder-specific abnormal patterns of causal connections across the 7 networks. Our findings suggested that aberrant dynamics among the large-scale networks could be a key biomarker for these transdiagnostic psychiatric disorders.

Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA studies in terms of the correlation with the World Health Organization grades, risk stratification in gliomas, and the differentiation of gliomas from other brain tumors. Multiple MRTA studies in gliomas have repeatedly shown high performance of entropy, a measure of the randomness in image intensity values, of either histogram- or gray-level co-occurrence matrix parameters. Similarly, researchers have applied MRTA to other brain tumors, including meningiomas and pediatric posterior fossa tumors.However, the value of MRTA in the clinical use remains undetermined, probably because previous studies have shown only limited reproducibility of the result in the real world. The low-to-modest generalizability may be attributed to variations in MRTA methods, sampling bias that originates from single-institution studies, and overfitting problems to a limited number of samples.To enhance the reliability and reproducibility of MRTA studies, researchers have realized the importance of standardizing methods in the field of radiomics. Another advancement is the recent development of a comprehensive assessment system to ensure the quality of a radiomics study. These two-way approaches will secure the validity of upcoming MRTA studies. The clinical use of texture analysis in brain MRI will be accelerated by these continuous efforts.

Repeated injection of gadolinium-based contrast agents did not cause any motor or behavioral changes, whether the agents were linear or macrocyclic.

The present study examined the effects of unilateral stimulus presentation on the right hemisphere preponderance of the stimulus-preceding negativity (SPN) in the event-related potential (ERP) experiment, and aimed to elucidate whether unilateral stimulus presentation affected activations in the bilateral anterior insula in the functional magnetic resonance imaging (fMRI) experiment. Separate fMRI and ERP experiments were conducted using visual and auditory stimuli by manipulating the position of stimulus presentation (left side or right side) with the time estimation task. The ERP experiment revealed a significant right hemisphere preponderance during left stimulation and no laterality during the right stimulation. The fMRI experiment revealed that the left anterior insula was activated only in the right stimulation of auditory and visual stimuli whereas the right anterior insula was activated by both left and right stimulations. The visual condition retained a contralateral dominance, but the auditory condition showed a right hemisphere dominance in a localized area. The results of this study indicate that the SPN reflects perceptual anticipation, and also that the anterior insula is involved in its occurrence.

Purpose: To evaluate a new method that registers phase-contrast magnetic resonance angiography images to time-of-flight magnetic resonance angiography images. Methods: Magnetic resonance angiography datasets of 10 healthy volunteers obtained by using two modalities (phase-contrast magnetic resonance angiography and time-of-flight magnetic resonance angiography) were preprocessed. Specifically, vessel regions were extracted using the region growing method with a threshold of 10% of the signal intensity maximum or 50% of the signal intensity maximum for phase-contrast magnetic resonance angiography images and time-of-flight magnetic resonance angiography images, respectively. Then, the normalized mutual information method was used to determine spatial positions, and registration between non-preprocessed phase-contrast magnetic resonance angiography and time-of-flight magnetic resonance angiography was performed using the spatial position information. Misalignment of 3 anatomical points was used to compare the accuracy of registration in this data group (the proposed method group) to that in the data group without registration (the non-registration group) and that in the data group subjected to normalized mutual information-based registration without preprocessing (the non-preprocessing group). Results: The mean misalignment of 3 anatomical points ± standard error was 1.69 ± 0.11 mm in the proposed method group, and 2.77± 0.13 mm and 90.28 ± 8.24 mm in the non-registration group and non-preprocessing group, respectively. The mean misalignment of 3 anatomical points was significantly smaller in the proposed method group than in the non-registration group (p = 0 001). Conclusions: The proposed preprocessing and registration method improved the accuracy of normalized mutual information-based registration between phase-contrast magnetic resonance angiography images and time-of-flight magnetic resonance angiography images.

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.

To investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images. In 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. The estimated BMD values, according to the CNN model (BMDCNN), were significantly correlated with the BMD values obtained with DXA (r = 0.852 (p

We performed present study to investigate whether the localization of primary aldosteronism (PA) can be predicted using quantitative texture analysis on unenhanced computed tomography (CT). Plain CT data of 82 PA patients (54 unilateral (right-sided:left-sided = 24:30), 28 bilateral) were analyzed retrospectively. After semi-automatically setting the region of interest to include the whole adrenal gland, texture analyses were performed with or without a Laplacian of Gaussian filter with various spatial scaling factors (SSFs). Logistic regression analysis was performed using the extracted histogram-based texture features to identify parameters capable of predicting excessive aldosterone production. The result of adrenal venous sampling served as gold standard in present study. As a result, logistic regression analysis indicated that the mean gray level intensity (p = 0.026), the mean value of the positive pixels (p = 0.003) in the unfiltered image, and entropy (p = 0.027) in the filtered image (SSF: 2 mm) were significant parameters. Using the model constructed by logistic regression analysis and the optimum cutoff value, the localization of PA (three multiple choices of left, right or bilateral) was determined with an accuracy of 67.1% (55/82). CT texture analysis may provide a potential avenue for less invasive prediction of the localization of PA.

To assess the possibility of reducing the image acquisition time for diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) by denoising with deep learning-based reconstruction (dDLR).Seventeen patients with prostate cancer who underwent DWIBS by 1.5 T magnetic resonance imaging with a number of excitations of 2 (NEX2) and 8 (NEX8) were prospectively enrolled. The NEX2 image data were processed by dDLR (dDLR-NEX2), and the NEX2, dDLR-NEX2, and NEX8 image data were analyzed. In qualitative analysis, two radiologists rated the perceived coarseness, conspicuity of metastatic lesions (lymph nodes and bone), and overall image quality. The contrast-to-noise ratios (CNRs), contrast ratios, and mean apparent diffusion coefficients (ADCs) of metastatic lesions were calculated in a quantitative analysis.The image acquisition time of NEX2 was 2.8 times shorter than that of NEX8 (3 min 30 s vs 9 min 48 s). The perceived coarseness and overall image quality scores reported by both readers were significantly higher for dDLR-NEX2 than for NEX2 (P = 0.005-0.040). There was no significant difference between dDLR-NEX2 and NEX8 in the qualitative analysis. The CNR of bone metastasis was significantly greater for dDLR-NEX2 than for NEX2 and NEX8 (P = 0.012 for both comparisons). The contrast ratios and mean ADCs were not significantly different among the three image types.dDLR improved the image quality of DWIBS with NEX2. In the context of lymph node and bone metastasis evaluation with DWIBS in patients with prostate cancer, dDLR-NEX2 has potential to be an alternative to NEX8 and reduce the image acquisition time.

T2-weighted imaging (T2WI) is a key sequence of MRI studies of the pancreas. The single-shot fast spin echo (single-shot FSE) sequence is an accelerated form of T2WI. We hypothesized that denoising approach with deep learning-based reconstruction (dDLR) could facilitate accelerated breath-hold thin-slice single-shot FSE MRI, and reveal the pancreatic anatomy in detail.To assess the image quality of thin-slice (3 mm) respiratory-triggered FSE T2WI (Resp-FSE) and breath-hold fast advanced spin echo with and without dDLR (BH-dDLR-FASE and BH-FASE, respectively) at 1.5 T.MR images of 42 prospectively enrolled patients with suspected pancreaticobiliary disease were obtained at 1.5 T. We qualitatively and quantitatively evaluated image quality of BH-dDLR-FASE related to BH-FASE and Resp-FSE.The scan time of BH-FASE was significantly shorter than that of Resp-FSE (30 ± 4 s and 122 ± 25 s, p 0.001). Qualitatively, dDLR significantly improved BH-FASE image quality, and the image quality of BH-dDLR-FASE was significantly better than that of Resp-FSE; as quantitative parameters, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of BH-dDLR-FASE were also significantly better than those of Resp-FSE. The BH-dDLR-FASE sequence covered the entire pancreas and liver and provided overall image quality rated close to excellent.The dDLR technique enables accelerated thin-slice single-shot FSE, and BH-dDLR-FASE seems to be clinically feasible.

Purpose We aimed to examine the characteristics of imaging findings of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) in the lungs of smokers compared with those of non-smokers. Materials and methods We included seven cases of AIS and 20 cases of MIA in lungs of smokers (pack-years ≥ 20) and the same number of cases of AIS and MIA in lungs of non-smokers (pack-years = 0). We compared the diameter of the entire lesion and solid component measured on computed tomography (CT) images, pathological size and invasive component diameter measured from pathological specimens, and CT values of the entire lesion and ground-glass opacity (GGO) portions between the smoker and non-smoker groups. Results The diameters of AIS and MIA on CT images and pathological specimens of the smoker group were significantly larger than those of the non-smoker group (p = 0.036 and 0.008, respectively), whereas there was no significant difference in the diameter of the solid component on CT images or invasive component of pathological specimens between the two groups. Additionally, mean CT values of the entire lesion and GGO component of the lesions in the smoker group were significantly lower than those in the non-smoker group (p = 0.036 and 0.040, respectively). Conclusion AIS and MIA in smoker’s lung tended to have larger lesion diameter and lower internal CT values compared with lesions in non-smoker’s lung. This study calls an attention on smoking status in CT-based diagnosis for early stage adenocarcinoma.

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

Objectives We aimed to investigate the influence of magnetic resonance fingerprinting (MRF) dictionary design on radiomic features using in vivo human brain scans. Methods Scan-rescans of three-dimensional MRF and conventional T1-weighted imaging were performed on 21 healthy volunteers (9 males and 12 females; mean age, 41.3 ± 14.6 years; age range, 22–72 years). Five patients with multiple sclerosis (3 males and 2 females; mean age, 41.2 ± 7.3 years; age range, 32–53 years) were also included. MRF data were reconstructed using various dictionaries with different step sizes. First- and second-order radiomic features were extracted from each dataset. Intra-dictionary repeatability and inter-dictionary reproducibility were evaluated using intraclass correlation coefficients (ICCs). Features with ICCs > 0.90 were considered acceptable. Relative changes were calculated to assess inter-dictionary biases. Results The overall scan-rescan ICCs of MRF-based radiomics ranged from 0.86 to 0.95, depending on dictionary step size. No significant differences were observed in the overall scan-rescan repeatability of MRF-based radiomic features and conventional T1-weighted imaging (p = 1.00). Intra-dictionary repeatability was insensitive to dictionary step size differences. MRF-based radiomic features varied among dictionaries (overall ICC for inter-dictionary reproducibility, 0.62–0.99), especially when step sizes were large. First-order and gray level co-occurrence matrix features were the most reproducible feature classes among different step size dictionaries. T1 map-derived radiomic features provided higher repeatability and reproducibility among dictionaries than those obtained with T2 maps. Conclusion MRF-based radiomic features are highly repeatable in various dictionary step sizes. Caution is warranted when performing MRF-based radiomics using datasets containing maps generated from different dictionaries. Key Points • MRF-based radiomic features are highly repeatable in various dictionary step sizes. • Use of different MRF dictionaries may result in variable radiomic features, even when the same MRF acquisition data are used. • Caution is needed when performing radiomic analysis using data reconstructed from different dictionaries.

Posttraumatic stress disorder (PTSD) is a psychiatric condition that develops after a person experiences one or more traumatic events, characterized by intrusive recollection, avoidance of trauma-related events, hyperarousal, and negative cognitions and mood. Neurophysiological evidence suggests that the development of PTSD is ascribed to functional abnormalities in fear learning, threat detection, executive function and emotional regulation, and contextual processing. Magnetic resonance imaging (MRI) plays a primary role in both structural and functional neuroimaging for PTSD, demonstrating focal atrophy of the gray matter, altered fractional anisotropy, and altered focal neural activity and functional connectivity. MRI findings have implicated that brain regions associated with PTSD pathophysiology include the medial and dorsolateral prefrontal cortex, orbitofrontal cortex, insula, lentiform nucleus, amygdala, hippocampus and parahippocampus, anterior and posterior cingulate cortex, precuneus, cuneus, fusiform and lingual gyri, and the white matter tracts connecting these brain regions. Of these, alterations in the anterior cingulate, amygdala, hippocampus, and insula are highly reproducible across structural and functional MRI, supporting the hypothesis that abnormalities in fear learning and reactions to threat play an important role in the development of PTSD. In addition, most of these structures have been known to belong to one or more intrinsic brain networks regulating autobiographical memory retrieval and self-thought, salience detection and autonomic responses, or attention and emotional control. Altered functional brain networks have been shown in PTSD. Therefore, in PTSD MRI is expected to reflect disequilibrium among functional brain networks, malfunction within an individual network, and impaired brain structures closely interacting with the networks. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:380-396.

Purpose: Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classification by support vector machine, a method of traditional machine learning, using texture features computed from contrast-enhanced T1-weighted images. Methods: This retrospective study on preoperative brain tumor MRI included 76 consecutives, initially treated patients with glioblastoma (n = 55) or PCNSL (n = 21) from one institution, consisting of independent training group (n = 60: 44 glioblastomas and 16 PCNSLs) and test group (n = 16: 11 glioblastomas and 5 PCNSLs) sequentially separated by time periods. A total set of 67 texture features was computed on routine contrast-enhanced T1-weighted images of the training group, and the top four most discriminating features were selected as input variables to train support vector machine classifiers. These features were then evaluated on the test group with subsequent image classification. Results: The area under the receiver operating characteristic curves on the training data was calculated at 0.99 (95% confidence interval [CI]: 0.96–1.00) for the classifier with a Gaussian kernel and 0.87 (95% CI: 0.77–0.95) for the classifier with a linear kernel. On the test data, both of the classifiers showed prediction accuracy of 75% (12/16) of the test images. Conclusions: Although further improvement is needed, our preliminary results suggest that machine learning-based image classification may provide complementary diagnostic information on routine brain MRI.

Recent functional studies have reported that amygdala and anterior cingulate cortex (ACC) dysfunction is a reproducible and good biomarker of major depressive disorder. When we use the activation of these regions as biomarkers of major depressive disorder, a short and simple stimulation paradigm could be preferable to reduce the burden on patients. However, negativity bias, which is the phenomenon by which negative stimuli are processed noticeably faster than positive stimuli, might affect the activation of these regions in the short and simple stimulation paradigm. Few studies have reported the relationship between the length of the stimulation paradigm and activation in the amygdala and ACC from the viewpoint of negativity bias. The purpose of this study was to assess the effects of negativity bias on the amygdala and ACC as a result of manipulating the stimulation paradigm (short-simple vs. long-complex conditions) on presenting pleasant and unpleasant pictures. Image analyses showed that the amygdala was activated during unpleasant picture presentation, regardless of the task length, but no activation was observed during pleasant picture presentation under the short-simple condition. The ACC was deactivated in both the short-simple and long-complex conditions. Region of interest analyses showed that the effect of negativity bias was prominent for the amygdala in the short-simple condition and for the ACC in the long-complex condition. In conclusion, the effects of negativity bias depend on neural regions, including the amygdala and ACC, and therefore, we should consider these effects while designing stimulation paradigms.

To investigate the detectability of pancreatic cystic lesions and main pancreatic duct dilation by low-dose unenhanced computed tomography (CT).This study included 2684 patients who underwent low-dose unenhanced CT using iterative reconstruction and magnetic resonance imaging (MRI) as a part of a health-screening program between February 1, 2019 and December 31, 2019. Patients diagnosed with pancreatic cystic lesions and/or dilatations of the main pancreatic duct on MRI were identified. Detection rates by low dose CT in terms of lesion size were tested for significance by Fisher's exact test.Of the 2684 patients, 558 (20.8 %) had pancreatic cystic lesions and 22 (0.8 %) had main pancreatic duct dilatation on MRI. The low-dose CT detection rates among the pancreatic cystic lesions were as follows: 1-9-mm cysts, three (0.65 %) of 461; 10-19-mm cysts, 17 (21.25 %) of 80, and ≥20-mm cysts, eight (47.06 %) of 17. The detection rates were significantly higher in the 10-19-mm and the ≥20-mm cyst group than in the 1-9-mm cyst group (p 0.001). The detection rates among the main pancreatic duct dilatations were as follows: 3-5-mm dilatations, two (11.76 %) of 17 and ≥6-mm dilatations, four (80 %) of five, which were significantly higher rates than that for the 3-5-mm dilatations (p = 0.009).Small pancreatic cysts and slight main pancreatic duct dilatation were practically undetectable by low-dose unenhanced CT. The application of a low-dose CT protocol as a screening tool in the detection of pancreatic abnormalities is not recommended.

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.

Objectives To investigate the application of apparent diffusion coefficient (ADC) histogram analysis in differentiating between benign and malignant breast lesions detected as non-mass enhancement on MRI. Materials and Methods A retrospective study was conducted for 25 malignant and 26 benign breast lesions showing non-mass enhancement on breast MRI. An experienced radiologist without prior knowledge of the pathological results drew a region of interest (ROI) outlining the periphery of each lesion on the ADC map. A histogram was then made for each lesion. Following a univariate analysis of 18 summary statistics values, we conducted statistical discrimination after hierarchical clustering using Ward’s method. A comparison between the malignant and the benign groups was made using multiple logistic regression analysis and the Mann-Whitney U test. A P -value of less than 0.05 was considered statistically significant. Results Univariate analysis for the 18 summary statistics values showed the malignant group had greater entropy (P < 0.001) and lower uniformity (P < 0.001). While there was no significant difference in mean and skewness values, the malignant group tended to show a lower mean (P = 0.090) and a higher skewness (P = 0.065). Hierarchical clustering of the 18 summary statistics values identified four values (10th percentile, entropy, skewness, and uniformity) of which the 10th percentile values were significantly lower for the malignant group (P = 0.035). Conclusions Whole-lesion ADC histogram analysis may be useful for differentiating malignant from benign lesions which show non-mass enhancement on breast MRI.

Purpose To investigate whether Parkinson’s disease (PD) can be differentiated from healthy controls and to identify neural circuit disorders in PD by applying a deep learning technique to parameter-weighted and number of streamlines (NOS)–based structural connectome matrices calculated from diffusion-weighted MRI. Methods In this prospective study, 115 PD patients and 115 healthy controls were enrolled. NOS-based and parameter-weighted connectome matrices were calculated from MRI images obtained with a 3-T MRI unit. With 5-fold cross-validation, diagnostic performance of convolutional neural network (CNN) models using those connectome matrices in differentiating patients with PD from healthy controls was evaluated. To identify the important brain connections for diagnosing PD, gradient-weighted class activation mapping (Grad-CAM) was applied to the trained CNN models. Results CNN models based on some parameter-weighted structural matrices (diffusion kurtosis imaging (DKI)–weighted, neurite orientation dispersion and density imaging (NODDI)–weighted, and g-ratio-weighted connectome matrices) showed moderate performance (areas under the receiver operating characteristic curve (AUCs) = 0.895, 0.801, and 0.836, respectively) in discriminating PD patients from healthy controls. The DKI-weighted connectome matrix performed significantly better than the conventional NOS-based matrix (AUC = 0.761) (DeLong’s test, p g-ratio-weighted matrices. Conclusion Patients with PD can be differentiated from healthy controls by applying the deep learning technique to the parameter-weighted connectome matrices, and neural circuit disorders including those between the basal ganglia on one side and the cerebellum on the contralateral side were visualized.

To evaluate the effect of emphysema on tumor diameter measured on preoperative computed tomography (CT) images versus pathological specimens. We investigated patients who underwent primary lung cancer surgery: 55 patients (57 tumors) with severe emphysema and 57 patients (57 tumors) without emphysema. The tumor diameters measured in the postoperative pathological specimens were compared with those measured on the axial CT images and on multiplanar reconstruction (MPR) CT images by two independent radiologists; a subgroup analysis according to tumor size was also performed. A paired or unpaired t test was performed, depending on the tested subjects. In the emphysema group, the mean axial CT diameter was significantly smaller than the mean pathological diameter (p = 0.025/0.001 for reader 1/2), whereas in the non-emphysema group, the mean axial CT diameter was not significantly different from the pathological one for both readers. The difference between CT axial diameter and pathological diameter (= CT diameter − pathological diameter) was significantly smaller (i.e., had a stronger tendency toward underestimation on radiological measurements) in the emphysema group compared with the non-emphysema group (p = 0.014/0.008 for reader 1/2), and the difference was significantly smaller in tumors sized > 30 mm than tumors sized ≤ 20 mm in both groups. Tumor size is significantly smaller on preoperative CT in patients with severe emphysema compared to patients without emphysema, especially in the case of large tumors. MPR measurement using the widest of three dimensions should be used to select T-stage for patients with severe emphysema. • The presence of emphysema affects the accuracy of tumor size measurements on CT. • Compared to patients without emphysema, the tumor size in severe emphysema patients tends to be measured smaller in preoperative CT than the pathological specimen. • This trend is more evident when large tumors are measured on axial CT images alone.

Aim Liver dysfunction is sometimes observed in patients with coronavirus disease 2019 (COVID-19), but most studies are from China, and the frequency in other countries is unclear. In addition, previous studies suggested several mechanisms of liver damage, but precise or additional mechanisms are not clearly elucidated. Therefore, we examined COVID-19 patients to explore the proportion of patients with liver dysfunction and also the factors associated with liver dysfunction. Methods We retrospectively examined 60 COVID-19 patients hospitalized at the Hospital affiliated with The Institute of Medical Science, The University of Tokyo (Tokyo, Japan). Patients who presented ≥40 U/L alanine aminotransferase (ALT) levels at least once during their hospitalization were defined as high-ALT patients, and the others as normal-ALT patients. The worst values of physical and laboratory findings during hospitalization for each patient were extracted for the analyses. Univariable and multivariable logistic regression models with bootstrap (for 1000 times) were carried out. Results Among 60 patients, there were 31 (52%) high-ALT patients. The high-ALT patients were obese, and had significantly higher levels of D-dimer and fibrin/fibrinogen degradation products, as well as white blood cell count, and levels of C-reactive protein, ferritin, and fibrinogen. Multivariable analysis showed D-dimer and white blood cells as independent factors. Conclusions Considering that higher D-dimer level and white blood cell count were independently associated with ALT elevation, liver dysfunction in COVID-19 patients might be induced by microvascular thrombosis in addition to systemic inflammation.

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.

ObjectiveDisruptions in the dopamine system have been observed in psychiatric disorders. Since dopamine is mainly produced in the ventral tegmental area (VTA), elucidating the differences in the VTA neural network across psychiatric disorders would facilitate a greater understanding of the pathophysiological mechanisms underlying these disorders. However, no study has compared VTA-seed-based functional connectivity across psychiatric disorders. Therefore, we conducted a resting-state functional magnetic resonance imaging (rs-fMRI) study to perform a seed-based fMRI analysis, using the VTA as a seed.MethodsWe included participants with major depressive disorder (MDD; n = 45), schizophrenia (n = 32), and bipolar disorder (BPD; n = 30), along with healthy control participants (n = 46) who were matched for age, gender, and handedness.ResultsThe results showed that patients with MDD and BPD had altered VTA-related connectivity in the superior frontal gyrus, frontal pole regions, hippocampus, cerebellum, and posterior cingulate cortex. Some of these differences in connectivity were also found between affective disorders and schizophrenia; however, there were no differences between the schizophrenia and control groups. Connectivity between the VTA and the hippocampus was correlated with positive symptoms in the schizophrenia group. The connectivity was not associated with medication dose, and the results remained significant after controlling for dose.ConclusionsThe results suggest that altered brain functional connectivity related to VTA networks could be associated with the distinctive pathophysiologies of psychiatric disorders, especially affective disorders.

Accumulated experience supports the efficacy of allogenic haematopoietic stem cell transplantation in arresting the progression of childhood-onset cerebral form of adrenoleukodystrophy in early stages. For adulthood-onset cerebral form of adrenoleukodystrophy, however, there have been only a few reports on haematopoietic stem cell transplantation and the clinical efficacy and safety of that for adulthood-onset cerebral form of adrenoleukodystrophy remain to be established. To evaluate the clinical efficacy and safety of haematopoietic stem cell transplantation, we conducted haematopoietic stem cell transplantation on 12 patients with adolescent-/adult-onset cerebral form/cerebello-brainstem form of adrenoleukodystrophy in a single-institution-based prospective study. Through careful prospective follow-up of 45 male adrenoleukodystrophy patients, we aimed to enrol patients with adolescent-/adult-onset cerebral form/cerebello-brainstem form of adrenoleukodystrophy at early stages. Indications for haematopoietic stem cell transplantation included cerebral form of adrenoleukodystrophy or cerebello-brainstem form of adrenoleukodystrophy with Loes scores up to 13, the presence of progressively enlarging white matter lesions and/or lesions with gadolinium enhancement on brain MRI. Clinical outcomes of haematopoietic stem cell transplantation were evaluated by the survival rate as well as by serial evaluation of clinical rating scale scores and neurological and MRI findings. Clinical courses of eight patients who did not undergo haematopoietic stem cell transplantation were also evaluated for comparison of the survival rate. All the patients who underwent haematopoietic stem cell transplantation survived to date with a median follow-up period of 28.6 months (4.2–125.3 months) without fatality. Neurological findings attributable to cerebral/cerebellar/brainstem lesions became stable or partially improved in all the patients. Gadolinium-enhanced brain lesions disappeared or became obscure within 3.5 months and the white matter lesions of MRI became stable or small. The median Loes scores before haematopoietic stem cell transplantation and at the last follow-up visit were 6.0 and 5.25, respectively. Of the eight patients who did not undergo haematopoietic stem cell transplantation, six patients died 69.1 months (median period; range 16.0–104.1 months) after the onset of the cerebral/cerebellar/brainstem lesions, confirming that the survival probability was significantly higher in patients with haematopoietic stem cell transplantation compared with that in patients without haematopoietic stem cell transplantation (P = 0.0089). The present study showed that haematopoietic stem cell transplantation was conducted safely and arrested the inflammatory demyelination in all the patients with adolescent-/adult-onset cerebral form/cerebello-brainstem form of adrenoleukodystrophy when haematopoietic stem cell transplantation was conducted in the early stages. Further studies are warranted to optimize the procedures of haematopoietic stem cell transplantation for adolescent-/adult-onset cerebral form/cerebello-brainstem form of adrenoleukodystrophy., Presently, there are only a few reports on allogenic haematopoietic stem cell transplantation for adult-onset adrenoleukodystrophy. Matsukawa et al. report that survival probability was significantly higher in 12 patients with adolescent-/adult-onset cerebral/cerebello-brainstem form of adrenoleukodystrophy who underwent haematopoietic stem cell transplantation than that in 8 patients who did not undergo haematopoietic stem cell transplantation. Haematopoietic stem cell transplantation arrested the inflammatory demyelination in all the patients., Graphical Abstract Graphical Abstract

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.

BACKGROUND AND PURPOSE: Both clinical and imaging criteria must be met to diagnose neuromyelitis optica spectrum disorders and multiple sclerosis. However, neuromyelitis optica spectrum disorders are often misdiagnosed as MS because of an overlap in MR imaging features. The purpose of this study was to confirm imaging differences between neuromyelitis optica spectrum disorders and MS with visually detailed quantitative analyses of large-sample data. MATERIALS AND METHODS: We retrospectively examined 89 consecutive patients with neuromyelitis optica spectrum disorders (median age, 51 years; range, 16–85 years; females, 77; aquaporin 4 immunoglobulin G–positive, 93%) and 89 with MS (median age, 36 years; range, 18–67 years; females, 68; relapsing-remitting MS, 89%; primary-progressive MS, 7%; secondary-progressive MS, 2%) from 9 institutions across Japan (April 2008 to December 2012). Two neuroradiologists visually evaluated the number, location, and size of all lesions using the Mann-Whitney U test or the Fisher exact test. RESULTS: We enrolled 79 patients with neuromyelitis optica spectrum disorders and 87 with MS for brain analysis, 57 with neuromyelitis optica spectrum disorders and 55 with MS for spinal cord analysis, and 42 with neuromyelitis optica spectrum disorders and 14 with MS for optic nerve analysis. We identified 911 brain lesions in neuromyelitis optica spectrum disorders, 1659 brain lesions in MS, 86 spinal cord lesions in neuromyelitis optica spectrum disorders, and 102 spinal cord lesions in MS. The frequencies of periventricular white matter and deep white matter lesions were 17% and 68% in neuromyelitis optica spectrum disorders versus 41% and 42% in MS, respectively (location of brain lesions, P < .001). We found a significant difference in the distribution of spinal cord lesions between these 2 diseases (P = .024): More thoracic lesions than cervical lesions were present in neuromyelitis optica spectrum disorders (cervical versus thoracic, 29% versus 71%), whereas they were equally distributed in MS (46% versus 54%). Furthermore, thoracic lesions were significantly longer than cervical lesions in neuromyelitis optica spectrum disorders (P = .001), but not in MS (P = .80). CONCLUSIONS: Visually detailed quantitative analyses confirmed imaging differences, especially in brain and spinal cord lesions, between neuromyelitis optica spectrum disorders and MS. These observations may have clinical implications.

Purpose: To elucidate differences between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) with MR image-based texture features. Methods: This was an Institutional Review Board (IRB)-approved retrospective study. Consecutive, pathologically proven, initially treated 44 patients with GBM and 16 patients with PCNSL were enrolled. We calculated a total of 67 image texture features on the largest contrast-enhancing lesion in each patient on post-contrast T1-weighted images. Texture analyses included first-order features (histogram) and second-order features calculated with gray level co-occurrence matrix, gray level run length matrix (GLRLM), gray level size zone matrix, and multiple gray level size zone matrix. All texture features were measured by two neuroradiologists independently and the intraclass correlation coefficients were calculated. Reproducible features with the intraclass correlation coefficients of greater than 0.7 were used for hierarchical clustering between the cases and the features along with unpaired t statistics-based comparisons under the control of false discovery rate (FDR) < 0.05. Principal component analysis (PCA) was performed to find the predominant features in evaluating the differences between GBM and PCNSL. Results: Twenty-one out of the 67 features satisfied the acceptable intraclass correlation coefficient and the FDR constraints. PCA suggested first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage as the distinguished features. Compared with PCNSL, run percentage and median were significantly lower, and entropy and run length non-uniformity were significantly higher in GBM. Conclusions: Among MR image-based textures, first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage are considered to enhance differences between GBM and PCNSL.

Objective Previous work has shown that high body mass index (BMI) is associated with low grey matter volume. However, evidence on the relationship between waist circumference (WC) and brain volume is relatively scarce. Moreover, the influence of mild obesity (as indexed by WC and BMI) on brain volume remains unclear. This study explored the relationships between WC and BMI and grey matter volume in a large sample of Japanese adults. Methods The participants were 792 community-dwelling adults (523 men and 269 women). Brain magnetic resonance images were collected, and the correlation between WC or BMI and global grey matter volume were analysed. The relationships between WC or BMI and regional grey matter volume were also investigated using voxel-based morphometry. Results Global grey matter volume was not correlated with WC or BMI. Voxel-based morphometry analysis revealed significant negative correlations between both WC and BMI and regional grey matter volume. The areas correlated with each index were more widespread in men than in women. In women, the total area of the regions significantly correlated with WC was slightly greater than that of the regions significantly correlated with BMI. Conclusions Results show that both WC and BMI were inversely related to regional grey matter volume, even in Japanese adults with somewhat mild obesity. Especially in populations with less obesity, such as the female participants in current study, WC may be more sensitive than BMI as a marker of grey matter volume differences associated with obesity.

Spinal extradural arteriovenous fistulas (AVFs) may be more difficult to prospectively identify than dural AVFs because they are less common than dural AVFs. The primary purpose was to further characterize the diagnostic imaging of spinal extradural AVFs with intradural retrograde venous drainage. The magnetic resonance (MR) imaging and angiographic results of 23 patients with suspected spinal dural AVFs were analyzed in order to distinguish dural and extradural AVFs. The diagnostic accuracy of MR angiography was retrospectively compared between dural and extradural AVFs. All 23 patients showed high intensity in the spinal cord on T2-weighted MR images. Eighteen out of 23 patients were diagnosed with dural AVFs, while the remaining 5 were diagnosed with extradural AVFs by angiography. Extradural AVFs were fed by a branch of the segmental artery to the vertebral body, characterized by a fistula located in the ventral extradural space, and drained retrogradely via an epidural venous pouch into intradural veins. The segmental artery was localized within 1 vertebral level using MRA in 12 out of 18 patients (67%) with dural AVFs and in 1 out of 5 patients (20%) with extradural AVFs (p=0.09). The reasons behind the lower accuracy was mainly the image misinterpretation. Congestion of the spinal cord in spinal extradural AVFs with intradural retrograde venous drainage was similar to that in dural AVFs, whereas its angioarchitecture differed from that of dural AVFs. A clearer understanding of the imaging features of extradural AVFs is important for improving the diagnostic accuracy and clarifying treatment targets.

Parkinson’s disease (PD) is caused by a selective degeneration of dopamine neurons. The relationship between dopamine transporter (DAT) density and gray matter volume has been unclear. Here we investigated the voxelwise correlation between gray matter volume and DAT binding measured by 123I-N-ω-fluoropropyl-2β-carboxymethoxy-3β-(4-iodophenyl)nortropane (123I-FP-CIT) single-photon emission computed tomography (SPECT; DaTscan™ imaging) in PD. Thirty-one male patients with PD were examined with MRI and DaTscan. To measure nigrostriatal dopaminergic degeneration in PD, the specific binding ratio (SBR) of the striatum was obtained by DaTscan. Voxel-based morphometry (VBM) of 3D T1-weighted images was used to evaluate the relationships between the regional gray matter volume and the SBR in the striatum. There were significant positive correlations between the SBR and the gray matter volume in the right pulvinar and posterior middle temporal gyrus and a trend level in the left pulvinar, all of which are associated with the second visual pathway. The nigrostriatal dopaminergic degeneration might affect the secondary visual pathway, leading to visual dysfunctions in PD.