Systematic reduction of gray matter volume in anorexia nervosa, but relative enlargement with clinical symptoms in the prefrontal and posterior insular cortices: a multicenter neuroimaging study.

Although brain morphological abnormalities have been reported in anorexia nervosa (AN), the reliability and reproducibility of previous studies were limited due to insufficient sample sizes, which prevented exploratory analysis of the whole brain as opposed to regions of interest (ROIs). Objective was to identify brain morphological abnormalities in AN and the association with severity of AN by brain structural magnetic resonance imaging (MRI) in a multicenter study, and to conduct exploratory analysis of the whole brain. Here, we conducted a cross-sectional multicenter study using T1-weighted imaging (T1WI) data collected between May 2014 and February 2019 in Japan. We analyzed MRI data from 103 female AN patients (58 anorexia nervosa restricting type [ANR] and 45 anorexia nervosa binge-purging type [ANBP]) and 102 age-matched female healthy controls (HC). MRI data from five centers were preprocessed using the latest harmonization method to correct for intercenter differences. Gray matter volume (GMV) was calculated from T1WI data of all participants. Of the 205 participants, we obtained severity of eating disorder symptom scores from 179 participants, including 87 in the AN group (51 ANR, 36 ANBP) and 92 HC using the Eating Disorder Examination Questionnaire (EDE-Q) 6.0. GMV reduction were observed in the AN brain, including the bilateral cerebellum, middle and posterior cingulate gyrus, supplementary motor cortex, precentral gyrus medial segment, and thalamus. In addition, the orbitofrontal cortex (OFC), ventromedial prefrontal cortex (vmPFC), rostral anterior cingulate cortex (ACC), and posterior insula volumes showed positive correlations with severity of symptoms. This multicenter study was conducted with a large sample size to identify brain morphological abnormalities in AN. The findings provide a better understanding of the pathogenesis of AN and have potential for the development of brain imaging biomarkers of AN. Trial Registration: UMIN000017456. https://center6.umin.ac.jp/cgi-open-bin/icdr/ctr_view.cgi?recptno=R000019303 .

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Neuroimaging-based brain-age prediction in diverse forms of epilepsy: a signature of psychosis and beyond.

Epilepsy is a diverse brain disorder, and the pathophysiology of its various forms and comorbidities is largely unknown. A recent machine learning method enables us to estimate an individual's "brain-age" from MRI; this brain-age prediction is expected as a novel individual biomarker of neuropsychiatric disorders. The aims of this study were to estimate the brain-age for various categories of epilepsy and to evaluate clinical discrimination by brain-age for (1) the effect of psychosis on temporal lobe epilepsy (TLE), (2) psychogenic nonepileptic seizures (PNESs) from MRI-negative epilepsies, and (3) progressive myoclonic epilepsy (PME) from juvenile myoclonic epilepsy (JME). In total, 1196 T1-weighted MRI scans from healthy controls (HCs) were used to build a brain-age prediction model with support vector regression. Using the model, we calculated the brain-predicted age difference (brain-PAD: predicted age-chronological age) of the HCs and 318 patients with epilepsy. We compared the brain-PAD values based on the research questions. As a result, all categories of patients except for extra-temporal lobe focal epilepsy showed a significant increase in brain-PAD. TLE with hippocampal sclerosis presented a significantly higher brain-PAD than several other categories. The mean brain-PAD in TLE with inter-ictal psychosis was 10.9 years, which was significantly higher than TLE without psychosis (5.3 years). PNES showed a comparable mean brain-PAD (10.6 years) to that of epilepsy patients. PME had a higher brain-PAD than JME (22.0 vs. 9.3 years). In conclusion, neuroimaging-based brain-age prediction can provide novel insight into or clinical usefulness for the diverse symptoms of epilepsy.

Comparison of traveling-subject and ComBat harmonization methods for assessing structural brain characteristics.

Multisite magnetic resonance imaging (MRI) is increasingly used in clinical research and development. Measurement biases-caused by site differences in scanner/image-acquisition protocols-negatively influence the reliability and reproducibility of image-analysis methods. Harmonization can reduce bias and improve the reproducibility of multisite datasets. Herein, a traveling-subject (TS) dataset including 56 T1-weighted MRI scans of 20 healthy participants in three different MRI procedures-20, 19, and 17 subjects in Procedures 1, 2, and 3, respectively-was considered to compare the reproducibility of TS-GLM, ComBat, and TS-ComBat harmonization methods. The minimum participant count required for harmonization was determined, and the Cohen's d between different MRI procedures was evaluated as a measurement-bias indicator. The measurement-bias reduction realized with different methods was evaluated by comparing test-retest scans for 20 healthy participants. Moreover, the minimum subject count for harmonization was determined by comparing test-retest datasets. The results revealed that TS-GLM and TS-ComBat reduced measurement bias by up to 85 and 81.3%, respectively. Meanwhile, ComBat showed a reduction of only 59.0%. At least 6 TSs were required to harmonize data obtained from different MRI scanners, complying with the imaging protocol predetermined for multisite investigations and operated with similar scan parameters. The results indicate that TS-based harmonization outperforms ComBat for measurement-bias reduction and is optimal for MRI data in well-prepared multisite investigations. One drawback is the small sample size used, potentially limiting the applicability of ComBat. Investigation on the number of subjects needed for a large-scale study is an interesting future problem.

Serial MRI alterations of pediatric patients with beta-propeller protein associated neurodegeneration (BPAN).

BACKGROUND AND PURPOSE: Beta-propeller protein-associated neurodegeneration (BPAN) is one subtype of neurodegeneration with brain iron accumulation. It is difficult to diagnose BPAN due to the non-specificity of their clinical findings and neuroimaging in early childhood. We experienced four pediatric patients with serial brain MRI and evaluated the alteration of the findings through their course. METHODS: We retrospectively reviewed the clinical findings and 21 MRI findings of the four patients with genetically confirmed pediatric BPAN. We also performed a quantitative MR assessment using the quantitative susceptibility mapping (QSM) values of the globus pallidus (GP), substantia nigra (SN), and deep cerebellar nuclei (DCN) compared to 10 age-matched disease controls. RESULTS: Only one patient was suspected of BPAN based on imaging findings before the genetic diagnosis was made. The other three patients could not be suspected until their Whole-exome sequencings (WES) done. In all four cases, no abnormal signals were noted in the GP and SN at the initial brain MRI, but hypointensities were observed after the ages of 4-7 years on T2-weighted images and after the ages of 2-7 years on susceptibility-weighted images. In three patients, T2 hyperintensity in the bilateral DCN was persistently observed throughout the observational period. Three patients showed transient T2 hyperintensity and swelling in the GP, SN and/or DCN during the episodes of pyrexia and seizures. The other findings included cerebral and cerebellar atrophy, thinning of the corpus callosum, and delayed myelination. The QSM values of the GP and SN were significantly higher in the patients compared to the controls (P=0.005, respectively), but that of the DCN did not differ significantly (P=0.16). CONCLUSION: Brain MRI is a useful method to establish the early diagnosis of BPAN.

Inter-method reliability between automatic region of interest analytic application with multi-atlas segmentation and FreeSurfer.

AIM: Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the aggregation of amyloid-ß and phosphorylated tau proteins. Magnetic resonance imaging (MRI) is a useful means of detecting hippocampal atrophy. However, instead of visual inspection, objective and time-saving tools for automated region of interest (ROI) analysis are needed. Advances in MRI segmentation techniques have enabled a multi-atlas approach with fewer errors than a conventional single-atlas approach. To support the clinical application of multi-atlas segmentation, an automated ROI analytic application consisting of multi-atlas segmentation with joint label fusion and corrective learning was developed: T-Proto. In the present study, we evaluated the inter-method reliability between T-Proto and a reference ROI analytic software, FreeSurfer. METHODS: This was a database study. MRI data from 30 patients with AD were selected, and the inter-method reliability was assessed in terms of the intra-class correlation coefficient (ICC). A post-hoc comparison according to the severity of AD was also performed. RESULTS: Almost all the regional volumes estimated with T-Proto were smaller than those estimated with FreeSurfer. The regional ICC values between the two methods showed moderate to excellent reliability. A post-hoc comparison revealed a similar t-value and effect size between both methods for the hippocampus. CONCLUSION: In the present study, we showed that automated regional analysis using T-Proto was reliable in the hippocampus in terms of ICC, compared with FreeSurfer.

Automated Volumetry of Medial Temporal Lobe Subregions in Mild Cognitive Impairment and Alzheimer Disease.

PURPOSE: Hippocampal subfield volumetry should be more useful than whole hippocampal (WH) volumetry for diagnosing Alzheimer disease (AD). This study sought to confirm this. METHODS: We investigated cognitively normal (CN) participants and patients with mild cognitive impairment (MCI) or AD using high-resolution T2-weighted and 3-dimensional T1-weighted magnetic resonance imaging. Using medial temporal subregion volumetry, we investigated discriminative power for MCI and AD versus CN. PATIENTS: We recruited 30 CN participants, 30 amnestic MCI patients, and 49 AD patients between April 2015 and October 2016. RESULTS: For AD, discriminative power of the combined volumes of the subiculum, entorhinal cortex, and cornu ammonis 1 was highest [area under the curve (AUC)=0.915; 85.7% sensitivity, 86.7% specificity, 86.1% accuracy], and was significantly higher than that of the WH volume (AUC=0.887; 90.0% sensitivity, 75.5% specificity, 84.5% accuracy) (P=0.019). For MCI, discriminative power of the subiculum volume was highest (AUC=0.747; 80.0% sensitivity, 73.3% specificity, 76.7% accuracy), but was only slightly higher than that of the WH volume (AUC=0.730; 56.7% sensitivity, 90.0% specificity, 73.3% accuracy). CONCLUSIONS: Using the combined volumes of the subiculum, entorhinal cortex, and cornu ammonis 1 may enable greater diagnostic accuracy compared with the WH volume or any single subfield in AD patients.

Effects of aging on brain volumes in healthy individuals across adulthood.

In this retrospective study, we analyzed the effects of age on brain volumes in healthy brains across adulthood. We investigated the correlations between brain volumes and age in the brains of 563 healthy individuals (age range: 20-86, 55% female) whose MRI scans and related information were drawn from the IXI database ( brain-development.org/ixi-dataset /). We conducted a regression analysis to assess the effect of age on whole-brain volumes as well as selected regional volumetric measures. The whole-brain analysis revealed a negative linear relationship between gray matter (GM) and age as well as nonlinear patterns of the relationship between age and the white matter (WM), cerebrospinal fluid (CSF), and the GM/WM ratio across adulthood. The regional volumetric analysis showed linear and non-linear age-related regional volumetric changes with aging. Our present findings contribute to the understanding of how structures in the human brain change over the adult years and will help address the pathological age-related neural changes in age-related neural disorders such as Parkinson disease and Alzheimer disease.

Japanese multicenter database of healthy controls for [123I]FP-CIT SPECT.

PURPOSE: The aim of this multicenter trial was to generate a [123I]FP-CIT SPECT database of healthy controls from the common SPECT systems available in Japan. METHODS: This study included 510 sets of SPECT data from 256 healthy controls (116 men and 140 women; age range, 30-83 years) acquired from eight different centers. Images were reconstructed without attenuation or scatter correction (NOACNOSC), with only attenuation correction using the Chang method (ChangACNOSC) or X-ray CT (CTACNOSC), and with both scatter and attenuation correction using the Chang method (ChangACSC) or X-ray CT (CTACSC). These SPECT images were analyzed using the Southampton method. The outcome measure was the specific binding ratio (SBR) in the striatum. These striatal SBRs were calibrated from prior experiments using a striatal phantom. RESULTS: The original SBRs gradually decreased in the order of ChangACSC, CTACSC, ChangACNOSC, CTACNOSC, and NOACNOSC. The SBRs for NOACNOSC were 46% lower than those for ChangACSC. In contrast, the calibrated SBRs were almost equal under no scatter correction (NOSC) conditions. A significant effect of age was found, with an SBR decline rate of 6.3% per decade. In the 30-39 age group, SBRs were 12.2% higher in women than in men, but this increase declined with age and was absent in the 70-79 age group. CONCLUSIONS: This study provided a large-scale quantitative database of [123I]FP-CIT SPECT scans from different scanners in healthy controls across a wide age range and with balanced sex representation. The phantom calibration effectively harmonizes SPECT data from different SPECT systems under NOSC conditions. The data collected in this study may serve as a reference database.

Intraventricular temperature measured by diffusion-weighted imaging compared with brain parenchymal temperature measured by MRS in vivo.

We examined and compared the temperatures of the intraventricular cerebrospinal fluid (Tv ) and the brain parenchyma (Tp ) using MRI, with reference to the tympanic membrane temperature (Tt ) in healthy subjects. We estimated Tv and Tp values from data gathered simultaneously by MR diffusion-weighted imaging (DWI) and MRS, respectively, in 35 healthy volunteers (17 males, 18 females; age 25-78 years). We also obtained Tt values just before each MR examination to evaluate the relationships among the three temperatures. There were significant positive correlations between Tv and Tp (R = 0.611, p < 0.001). The correlation was also significant after correction for Tt (R = 0.642, p < 0.001). There was no significant correlation between Tv and Tt or between Tp and Tt in the men or the women. Negative correlations were found between Tv and age and between Tp and age in the males but not females. DWI thermometry seems to reflect the intracranial environment as accurately as MRS thermometry. An age-dependent decline in temperature was evident in our male subjects by both DWI and MRS thermometry, probably due to the decrease in cerebral metabolism with age. Copyright © 2016 John Wiley & Sons, Ltd.

Impaired cerebral blood flow networks in temporal lobe epilepsy with hippocampal sclerosis: A graph theoretical approach.

Graph theory is an emerging method to investigate brain networks. Altered cerebral blood flow (CBF) has frequently been reported in temporal lobe epilepsy (TLE), but graph theoretical findings of CBF are poorly understood. Here, we explored graph theoretical networks of CBF in TLE using arterial spin labeling imaging. We recruited patients with TLE and unilateral hippocampal sclerosis (HS) (19 patients with left TLE, and 21 with right TLE) and 20 gender- and age-matched healthy control subjects. We obtained all participants' CBF maps using pseudo-continuous arterial spin labeling and analyzed them using the Graph Analysis Toolbox (GAT) software program. As a result, compared to the controls, the patients with left TLE showed a significantly low clustering coefficient (p=0.024), local efficiency (p=0.001), global efficiency (p=0.010), and high transitivity (p=0.015), whereas the patients with right TLE showed significantly high assortativity (p=0.046) and transitivity (p=0.011). The group with right TLE also had high characteristic path length values (p=0.085), low global efficiency (p=0.078), and low resilience to targeted attack (p=0.101) at a trend level. Lower normalized clustering coefficient (p=0.081) in the left TLE and higher normalized characteristic path length (p=0.089) in the right TLE were found also at a trend level. Both the patients with left and right TLE showed significantly decreased clustering in similar areas, i.e., the cingulate gyri, precuneus, and occipital lobe. Our findings revealed differing left-right network metrics in which an inefficient CBF network in left TLE and vulnerability to irritation in right TLE are suggested. The left-right common finding of regional decreased clustering might reflect impaired default-mode networks in TLE.

Hippocampal Structures Among Japanese Adolescents Before and After the COVID-19 Pandemic.

Importance: Few studies have used a large-sample, longitudinal, population-based cohort study to examine whether the COVID-19 pandemic as a global major life event is associated with structural plasticity of the adolescent hippocampus. Objective: To examine whether Japan's first state of emergency (SoE) during the COVID-19 pandemic was associated with alterations in the macrostructures and microstructures of the hippocampus during its development. Design, Setting, and Participants: The population-neuroscience Tokyo TEEN Cohort study is a prospective cohort study with 4 consecutive waves in Tokyo, Japan. Due to the SoE, data collection was suspended between March 27, 2020, and July 30, 2020. Analyzed data, comprising 1149 brain structural scans obtained from 479 participants, of whom 336 participants had undergone 2 or more scans, were collected between October 2013 and November 2021. Data were analyzed from August 2022 to December 2023. Exposures: Japan's first SoE (April 7 to May 25, 2020). Main Outcomes and Measures: Hippocampal volume, 12 hippocampal subfield volumes, and 7 microstructural measures of the hippocampus. Results: A total of 1060 brain scans from 459 participants (214 female participants [47%]) including 246 participants from wave 1 (median [IQR] age, 11.3 [11.1-11.7] years), 358 from wave 2 (median [IQR] age, 13.8 [13.3-14.5] years), 304 from wave 3 (median [IQR] age, 15.9 [15.4-16.5] years), and 152 from wave 4 (median [IQR] age, 17.9 [17.5-18.4] years) were included in the final main analysis. The generalized additive mixed model showed a significant associations of the SoE with the mean hippocampal volume (ß = 102.19; 95% CI, 0.61-203.77; P = .049). The generalized linear mixed models showed the main associations of the SoE with hippocampal subfield volume (granule cell and molecular layer of the dentate gyrus: ß = 18.19; 95% CI, 2.97-33.41; uncorrected P = .02; CA4: ß = 12.75; 95% CI, 0.38-25.12; uncorrected P = .04; hippocampus-amygdala transition area: ß = 5.67; 95% CI, 1.18-10.17; uncorrected P = .01), and fractional anisotropy (ß = 0.03; 95% CI, 0.00-0.06; uncorrected P = .04). Conclusions and Relevance: After the first SoE, a volumetric increase in the hippocampus and trend increase in 3 subfield volumes and microstructural integration of the hippocampus were observed, suggesting that the transient plasticity of the adolescent hippocampus was affected by a major life event along with the typical developmental trajectory.

Assessment of Gray Matter Microstructural Alterations in Alzheimer's Disease by Free Water Imaging.

Background: Cortical neurodegenerative processes may precede the emergence of disease symptoms in patients with Alzheimer's disease (AD) by many years. No study has evaluated the free water of patients with AD using gray matter-based spatial statistics. Objective: The aim of this study was to explore cortical microstructural changes within the gray matter in AD by using free water imaging with gray matter-based spatial statistics. Methods: Seventy-one participants underwent multi-shell diffusion magnetic resonance imaging, 11C-Pittsburgh compound B positron emission tomography, and neuropsychological evaluations. The patients were divided into two groups: healthy controls (n = 40) and the AD spectrum group (n = 31). Differences between the groups were analyzed using voxel-based morphometry, diffusion tensor imaging, and free water imaging with gray matter-based spatial statistics. Results: Voxel-based morphometry analysis revealed gray matter volume loss in the hippocampus of patients with AD spectrum compared to that in controls. Furthermore, patients with AD spectrum exhibited significantly greater free water, mean diffusivity, and radial diffusivity in the limbic areas, precuneus, frontal lobe, temporal lobe, right putamen, and cerebellum than did the healthy controls. Overall, the effect sizes of free water were greater than those of mean diffusivity and radial diffusivity, and the larger effect sizes of free water were thought to be strongly correlated with AD pathology. Conclusions: This study demonstrates the utility of applying voxel-based morphometry, gray matter-based spatial statistics, free water imaging and diffusion tensor imaging to assess AD pathology and detect changes in gray matter.

Multimodal imaging analyses in neuromyelitis optica spectrum disorder with or without visual disturbance.

BACKGROUND AND PURPOSE: Neuromyelitis optica spectrum disorder is a demyelinating and inflammatory affliction that often leads to visual disturbance. Various imaging techniques, including free-water imaging, have been used to determine neuroinflammation and degeneration. Therefore, this study aimed at determining multimodal imaging differences between patients with neuromyelitis optica spectrum disorder, especially those with visual disturbance, and healthy controls. MATERIALS AND METHODS: Eighty-five neuromyelitis optica spectrum disorder patients and 89 age- and sex-matched healthy controls underwent 3-T magnetic resonance imaging (MRI). We analyzed adjusted brain-predicted age difference, voxel-based morphometry, and free-water-corrected diffusion tensor imaging (DTI) by tract-based spatial statistics in each patient group (MRI-positive/negative neuromyelitis optica spectrum disorder patients with or without a history of visual disturbance) compared with the healthy control group. RESULTS: MRI-positive neuromyelitis optica spectrum disorder patients exhibited reduced volumes of the bilateral thalamus. Tract-based spatial statistics showed diffuse white matter abnormalities in all DTI metrics in MRI-positive neuromyelitis optica spectrum disorder patients with a history of visual disturbance. In MRI-negative neuromyelitis optica spectrum disorder patients with a history of visual disturbance, voxel-based morphometry showed volume reduction of bilateral thalami and optic radiations, and tract-based spatial statistics revealed significantly lower free-water-corrected fractional anisotropy and higher mean diffusivity in the posterior dominant distributions, including the optic nerve radiation. CONCLUSION: Free-water-corrected DTI and voxel-based morphometry analyses may reflect symptoms of visual disturbance in neuromyelitis optica spectrum disorder.

Alterations in subcortical magnetic susceptibility and disease-specific relationship with brain volume in major depressive disorder and schizophrenia.

Quantitative susceptibility mapping is a magnetic resonance imaging technique that measures brain tissues' magnetic susceptibility, including iron deposition and myelination. This study examines the relationship between subcortical volume and magnetic susceptibility and determines specific differences in these measures among patients with major depressive disorder (MDD), patients with schizophrenia, and healthy controls (HCs). This was a cross-sectional study. Sex- and age- matched patients with MDD (n = 49), patients with schizophrenia (n = 24), and HCs (n = 50) were included. Magnetic resonance imaging was conducted using quantitative susceptibility mapping and T1-weighted imaging to measure subcortical susceptibility and volume. The acquired brain measurements were compared among groups using analyses of variance and post hoc comparisons. Finally, a general linear model examined the susceptibility-volume relationship. Significant group-level differences were found in the magnetic susceptibility of the nucleus accumbens and amygdala (p = 0.045). Post-hoc analyses indicated that the magnetic susceptibility of the nucleus accumbens and amygdala for the MDD group was significantly higher than that for the HC group (p = 0.0054, p = 0.0065, respectively). However, no significant differences in subcortical volume were found between the groups. The general linear model indicated a significant interaction between group and volume for the nucleus accumbens in MDD group but not schizophrenia or HC groups. This study showed susceptibility alterations in the nucleus accumbens and amygdala in MDD patients. A significant relationship was observed between subcortical susceptibility and volume in the MDD group's nucleus accumbens, which indicated abnormalities in myelination and the dopaminergic system related to iron deposition.

Free-water-corrected diffusion and adrenergic/muscarinic antibodies in myalgic encephalomyelitis/chronic fatigue syndrome.

BACKGROUND AND PURPOSE: Free-water-corrected diffusion tensor imaging (FW-DTI), a new analysis method for diffusion MRI, can indicate neuroinflammation and degeneration. There is increasing evidence of autoimmune etiology in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). We used FW-DTI and conventional DTI to investigate microstructural brain changes related to autoantibody titers in patients with ME/CFS. METHODS: We prospectively examined 58 consecutive right-handed ME/CFS patients who underwent both brain MRI including FW-DTI and a blood analysis of autoantibody titers against ß1 adrenergic receptor (ß1 AdR-Ab), ß2 AdR-Ab, M3 acetylcholine receptor (M3 AchR-Ab), and M4 AchR-Ab. We investigated the correlations between these four autoantibody titers and three FW-DTI indices-free water (FW), FW-corrected fractional anisotropy (FAt), and FW-corrected mean diffusivity-as well as two conventional DTI indices-fractional anisotropy (FA) and mean diffusivity. The patients' age and gender were considered as nuisance covariates. We also evaluated the correlations between the FW-DTI indices and the performance status and disease duration. RESULTS: Significant negative correlations between the serum levels of several autoantibody titers and DTI indices were identified, mainly in the right frontal operculum. The disease duration showed significant negative correlations with both FAt and FA in the right frontal operculum. The changes in the FW-corrected DTI indices were observed over a wider extent compared to the conventional DTI indices. CONCLUSIONS: These results demonstrate the value of using DTI to assess the microstructure of ME/CFS. The abnormalities of right frontal operculum may be a diagnostic marker for ME/CFS.

Age and Sex-Related Effects on Single-Subject Gray Matter Networks in Healthy Participants.

Recent developments in image analysis have enabled an individual's brain network to be evaluated and brain age to be predicted from gray matter images. Our study aimed to investigate the effects of age and sex on single-subject gray matter networks using a large sample of healthy participants. We recruited 812 healthy individuals (59.3 ± 14.0 years, 407 females, and 405 males) who underwent three-dimensional T1-weighted magnetic resonance imaging. Similarity-based gray matter networks were constructed, and the following network properties were calculated: normalized clustering, normalized path length, and small-world coefficients. The predicted brain age was computed using a support-vector regression model. We evaluated the network alterations related to age and sex. Additionally, we examined the correlations between the network properties and predicted brain age and compared them with the correlations between the network properties and chronological age. The brain network retained efficient small-world properties regardless of age; however, reduced small-world properties were observed with advancing age. Although women exhibited higher network properties than men and similar age-related network declines as men in the subjects aged < 70 years, faster age-related network declines were observed in women, leading to no differences in sex among the participants aged ≥ 70 years. Brain age correlated well with network properties compared to chronological age in participants aged ≥ 70 years. Although the brain network retained small-world properties, it moved towards randomized networks with aging. Faster age-related network disruptions in women were observed than in men among the elderly. Our findings provide new insights into network alterations underlying aging.

Traveling Subject-Informed Harmonization Increases Reliability of Brain Diffusion Tensor and Neurite Mapping.

Diffusion-weighted magnetic resonance imaging (dMRI) of brain has helped elucidate the microstructural changes of psychiatric and neurodegenerative disorders. Inconsistency between MRI models has hampered clinical application of dMRI-based metrics. Using harmonized dMRI data of 300 scans from 69 traveling subjects (TS) scanning the same individuals at multiple conditions with 13 MRI models and 2 protocols, the widely-used metrics such as diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) were evaluated before and after harmonization with a combined association test (ComBat) or TS-based general linear model (TS-GLM). Results showed that both ComBat and TS-GLM significantly reduced the effects of the MRI site, model, and protocol for diffusion metrics while maintaining the intersubject biological effects. The harmonization power of TS-GLM based on TS data model is more powerful than that of ComBat. In conclusion, our research demonstrated that although ComBat and TS-GLM harmonization approaches were effective at reducing the scanner effects of the site, model, and protocol for DTI and NODDI metrics in WM, they exhibited high retainability of biological effects. Therefore, we suggest that, after harmonizing DTI and NODDI metrics, a multisite study with large cohorts can accurately detect small pathological changes by retaining pathological effects.

A Brain Morphometry Study with Across-Site Harmonization Using a ComBat-Generalized Additive Model in Children and Adolescents.

Regional anatomical structures of the brain are intimately connected to functions corresponding to specific regions and the temporospatial pattern of genetic expression and their functions from the fetal period to old age. Therefore, quantitative brain morphometry has often been employed in neuroscience investigations, while controlling for the scanner effect of the scanner is a critical issue for ensuring accuracy in brain morphometric studies of rare orphan diseases due to the lack of normal reference values available for multicenter studies. This study aimed to provide across-site normal reference values of global and regional brain volumes for each sex and age group in children and adolescents. We collected magnetic resonance imaging (MRI) examinations of 846 neurotypical participants aged 6.0-17.9 years (339 male and 507 female participants) from 5 institutions comprising healthy volunteers or neurotypical patients without neurological disorders, neuropsychological disorders, or epilepsy. Regional-based analysis using the CIVET 2.1.0. pipeline provided regional brain volumes, and the measurements were across-site combined using ComBat-GAM harmonization. The normal reference values of global and regional brain volumes and lateral indices in our study could be helpful for evaluating the characteristics of the brain morphology of each individual in a clinical setting and investigating the brain morphology of ultra-rare diseases.

Tohoku Medical Megabank Brain Magnetic Resonance Imaging Study: Rationale, Design, and Background.

The Tohoku Medical Megabank Brain Magnetic Resonance Imaging Study (TMM Brain MRI Study) was established to collect multimodal information through neuroimaging and neuropsychological assessments to evaluate the cognitive function and mental health of residents who experienced the Great East Japan Earthquake (GEJE) and associated tsunami. The study also aimed to promote advances in personalized healthcare and medicine related to mental health and cognitive function among the general population. We recruited participants for the first (baseline) survey starting in July 2014, enrolling individuals who were participating in either the TMM Community-Based Cohort Study (TMM CommCohort Study) or the TMM Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study). We collected multiple magnetic resonance imaging (MRI) sequences, including 3D T1-weighted sequences, magnetic resonance angiography (MRA), diffusion tensor imaging (DTI), pseudo-continuous arterial spin labeling (pCASL), and three-dimensional fluid-attenuated inversion recovery (FLAIR) sequences. To assess neuropsychological status, we used both questionnaire- and interview-based rating scales. The former assessments included the Tri-axial Coping Scale, Impact of Event Scale in Japanese, Profile of Mood States, and 15-item Depression, Anxiety, and Stress Scale, whereas the latter assessments included the Mini-Mental State Examination, Japanese version. A total of 12,164 individuals were recruited for the first (baseline) survey, including those unable to complete all assessments. In parallel, we returned the MRI results to the participants and subsequently shared the MRI data through the TMM Biobank. At present, the second (first follow-up) survey of the study started in October 2019 is underway. In this study, we established a large and comprehensive database that included robust neuroimaging data as well as psychological and cognitive assessment data. In combination with genomic and omics data already contained in the TMM Biobank database, these data could provide new insights into the relationships of pathological processes with neuropsychological disorders, including age-related cognitive impairment.