Usefulness of Olfactory Bulb Measurement in 3D-FIESTA in Differentiating Parkinson Disease from Atypical Parkinsonism.

BACKGROUND AND PURPOSE: Parkinson disease is a prevalent disease, with olfactory dysfunction recognized as an early nonmotor manifestation. It is sometimes difficult to differentiate Parkinson disease from atypical parkinsonism using conventional MR imaging and motor symptoms. It is also known that olfactory loss occurs to a lesser extent or is absent in atypical parkinsonism. To the best of our knowledge, no study has examined olfactory bulb changes to differentiate Parkinson disease from atypical parkinsonism, even in an early diagnosis, and its association with conventional MR imaging findings. Hence, we aimed to assess the utility of olfactory bulb measurements in differentiating Parkinson disease from atypical parkinsonism even in the early stage. MATERIALS AND METHODS: In this retrospective study, we enrolled 108 patients with Parkinson disease, 13 with corticobasal syndrome, 15 with multiple system atrophy, and 17 with progressive supranuclear palsy who developed parkinsonism. Thirty-nine age-matched healthy subjects served as controls. All subjects underwent conventional MR imaging and 3D FIESTA for olfactory bulb measurements using manual ROI quantification of the cross-sectional olfactory bulb area using the coronal plane. Bilateral olfactory bulb measurements were averaged. For group comparisons, we used the Welch t test, and we assessed diagnostic accuracy using receiver operating characteristic analysis. RESULTS: Patients with Parkinson disease had a mean olfactory bulb area of 4.2 (SD, 1.0 mm2), significantly smaller than in age-matched healthy subjects (6.6 [SD, 1.7 mm2], P < .001), and those with corticobasal syndrome (5.4 [SD, 1.2 mm2], P < .001), multiple system atrophy (6.5 [SD, 1.2 mm2], P < .001), and progressive supranuclear palsy (5.4 [SD, 1.2 mm2], P < .001). The receiver operating characteristic analysis for the olfactory bulb area measurements showed good diagnostic performance in differentiating Parkinson disease from atypical parkinsonism, with an area under the curve of 0.87, an optimal cutoff value of 5.1 mm2, and a false-positive rate of 18%. When we compared within 2 years of symptom onset, the olfactory bulb in Parkinson disease (4.2 [SD, 1.1 mm2]) remained significantly smaller than in atypical parkinsonism (versus corticobasal syndrome (6.1 [SD, 0.7 mm2]), P < .001; multiple system atrophy (6.3 [SD, 1.4 mm2]), P < .001; and progressive supranuclear palsy (5.2 [1.3 mm2], P = .003, respectively). CONCLUSIONS: 3D FIESTA-based olfactory bulb measurement holds promise for distinguishing Parkinson disease from atypical parkinsonism, especially in the early stage.

Disturbed hippocampal intra-network in first-episode of drug-naïve major depressive disorder.

Complex networks inside the hippocampus could provide new insights into hippocampal abnormalities in various psychiatric disorders and dementia. However, evaluating intra-networks in the hippocampus using MRI is challenging. Here, we employed a high spatial resolution of conventional structural imaging and independent component analysis to investigate intra-networks structural covariance in the hippocampus. We extracted the intra-networks based on the intrinsic connectivity of each 0.9 mm isotropic voxel to every other voxel using a data-driven approach. With a total volume of 3 cc, the hippocampus contains 4115 voxels for a 0.9 mm isotropic voxel size or 375 voxels for a 2 mm isotropic voxel of high-resolution functional or diffusion tensor imaging. Therefore, the novel method presented in the current study could evaluate the hippocampal intra-networks in detail. Furthermore, we investigated the abnormality of the intra-networks in major depressive disorders. A total of 77 patients with first-episode drug-naïve major depressive disorder and 79 healthy subjects were recruited. The independent component analysis extracted seven intra-networks from hippocampal structural images, which were divided into four bilateral networks and three networks along the longitudinal axis. A significant difference was observed in the bilateral hippocampal tail network between patients with major depressive disorder and healthy subjects. In the logistic regression analysis, two bilateral networks were significant predictors of major depressive disorder, with an accuracy of 78.1%. In conclusion, we present a novel method for evaluating intra-networks in the hippocampus. One advantage of this method is that a detailed network can be estimated using conventional structural imaging. In addition, we found novel bilateral networks in the hippocampus that were disturbed in patients with major depressive disorders, and these bilateral networks could predict major depressive disorders.

The effect of CT texture-based analysis using machine learning approaches on radiologists' performance in differentiating focal-type autoimmune pancreatitis and pancreatic duct carcinoma.

PURPOSE: To develop a support vector machine (SVM) classifier using CT texture-based analysis in differentiating focal-type autoimmune pancreatitis (AIP) and pancreatic duct carcinoma (PD), and to assess the radiologists' diagnostic performance with or without SVM. MATERIALS AND METHODS: This retrospective study included 50 patients (20 patients with focal-type AIP and 30 patients with PD) who underwent dynamic contrast-enhanced CT. Sixty-two CT texture-based features were extracted from 2D images of the arterial and portal phase CTs. We conducted data compression and feature selections using principal component analysis (PCA) and produced the SVM classifier. Four readers participated in this observer performance study and the statistical significance of differences with and without the SVM was assessed by receiver operating characteristic (ROC) analysis. RESULTS: The SVM performance indicated a high performance in differentiating focal-type AIP and PD (AUC = 0.920). The AUC for all 4 readers increased significantly from 0.827 to 0.911 when using the SVM outputs (p = 0.010). The AUC for inexperienced readers increased significantly from 0.781 to 0.905 when using the SVM outputs (p = 0.310). The AUC for experienced readers increased from 0.875 to 0.912 when using the SVM outputs, however, there was no significant difference (p = 0.018). CONCLUSION: The use of SVM classifier using CT texture-based features improved the diagnostic performance for differentiating focal-type AIP and PD on CT.

Preferential tumor localization in relation to 18F-FDOPA uptake for lower-grade gliomas.

PURPOSE: Although tumor localization and 3,4-dihydroxy-6-18F-fluoro-L-phenylalanine (FDOPA) uptake may have an association, preferential tumor localization in relation to FDOPA uptake is yet to be investigated in lower-grade gliomas (LGGs). This study aimed to identify differences in the frequency of tumor localization between FDOPA hypometabolic and hypermetabolic LGGs using a probabilistic radiographic atlas. METHODS: Fifty-one patients with newly diagnosed LGG (WHO grade II, 29; III, 22; isocitrate dehydrogenase wild-type, 21; mutant 1p19q non-codeleted,16; mutant codeleted, 14) who underwent FDOPA positron emission tomography (PET) were retrospectively selected. Semiautomated tumor segmentation on FLAIR was performed. Patients with LGGs were separated into two groups (FDOPA hypometabolic and hypermetabolic LGGs) according to the normalized maximum standardized uptake value of FDOPA PET (a threshold of the uptake in the striatum) within the segmented regions. Spatial normalization procedures to build a 3D MRI-based atlas using each segmented region were validated by an analysis of differential involvement statistical mapping. RESULTS: Superimposition of regions of interest showed a high number of hypometabolic LGGs localized in the frontal lobe, while a high number of hypermetabolic LGGs was localized in the insula, putamen, and temporal lobe. The statistical mapping revealed that hypometabolic LGGs occurred more frequently in the superior frontal gyrus (close to the supplementary motor area), while hypermetabolic LGGs occurred more frequently in the insula. CONCLUSION: Radiographic atlases revealed preferential frontal lobe localization for FDOPA hypometabolic LGGs, which may be associated with relatively early detection.

Voxelwise and Patientwise Correlation of 18F-FDOPA PET, Relative Cerebral Blood Volume, and Apparent Diffusion Coefficient in Treatment-Naïve Diffuse Gliomas with Different Molecular Subtypes.

Our purpose was to identify correlations between 18F-fluorodihydroxyphenylalanine (18F-FDOPA) uptake and physiologic MRI, including relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC), in gliomas with different molecular subtypes and to evaluate their prognostic values. Methods: Sixty-eight treatment-naïve glioma patients who underwent 18F-FDOPA PET and physiologic MRI were retrospectively selected (36 with isocitrate dehydrogenase wild-type [IDHwt], 16 with mutant 1p/19q noncodeleted [IDHm-noncodel], and 16 with mutant codeleted [IDHm-codel]). Fluid-attenuated inversion recovery hyperintense areas were segmented and used as regions of interest. For voxelwise and patientwise analyses, Pearson correlation coefficients (rvoxelwise and rpatientwise) between the normalized SUV (nSUV), rCBV, and ADC were evaluated. Cox regression analysis was performed to investigate the associations between overall survival and rvoxelwise, maximum or median nSUV, median rCBV, or median ADC. Results: For IDHwt and IDHm-noncodel gliomas, nSUV demonstrated significant positive correlations with rCBV (rvoxelwise = 0.25 and 0.31, respectively; rpatientwise = 0.50 and 0.70, respectively) and negative correlations with ADC (rvoxelwise = -0.19 and -0.19, respectively; rpatientwise = -0.58 and -0.61, respectively) in both voxelwise and patientwise analyses. IDHm-codel gliomas demonstrated a significant positive correlation between nSUV and ADC only in voxelwise analysis (rvoxelwise = 0.18). In Cox regression analysis, rvoxelwise between nSUV and rCBV (hazard ratio, 28.82) or ADC (hazard ratio, 0.085) had significant associations with overall survival for only IDHwt gliomas. Conclusion: IDHm-codel gliomas showed distinctive patterns of correlations between amino acid PET and physiologic MRI. Stronger correlations between nSUV and rCBV or ADC may result in a worse prognosis for IDHwt gliomas.

The brain-derived neurotrophic factor Val66Met polymorphism increases segregation of structural correlation networks in healthy adult brains.

BACKGROUND: Although structural correlation network (SCN) analysis is an approach to evaluate brain networks, the neurobiological interpretation of SCNs is still problematic. Brain-derived neurotrophic factor (BDNF) is well-established as a representative protein related to neuronal differentiation, maturation, and survival. Since a valine-to-methionine substitution at codon 66 of the BDNF gene (BDNF Val66Met single nucleotide polymorphism (SNP)) is well-known to have effects on brain structure and function, we hypothesized that SCNs are affected by the BDNF Val66Met SNP. To gain insight into SCN analysis, we investigated potential differences between BDNF valine (Val) homozygotes and methionine (Met) carriers in the organization of their SCNs derived from inter-regional cortical thickness correlations. METHODS: Forty-nine healthy adult subjects (mean age = 41.1 years old) were divided into two groups according to their genotype (n: Val homozygotes = 16, Met carriers = 33). We obtained regional cortical thickness from their brain T1 weighted images. Based on the inter-regional cortical thickness correlations, we generated SCNs and used graph theoretical measures to assess differences between the two groups in terms of network integration, segregation, and modularity. RESULTS: The average local efficiency, a measure of network segregation, of BDNF Met carriers' network was significantly higher than that of the Val homozygotes' (permutation p-value = 0.002). Average shortest path lengths (a measure of integration), average local clustering coefficient (another measure of network segregation), small-worldness (a balance between integration and segregation), and modularity (a representative measure for modular architecture) were not significantly different between group (permutation p-values ≧ 0.01). DISCUSSION AND CONCLUSION: Our results suggest that the BDNF Val66Met polymorphism may potentially influence the pattern of brain regional morphometric (cortical thickness) correlations. Comparing networks derived from inter-regional cortical thickness correlations, Met carrier SCNs have denser connections with neighbors and are more distant from random networks than Val homozygote networks. Thus, it may be necessary to consider potential effects of BDNF gene mutations in SCN analyses. This is the first study to demonstrate a difference between Val homozygotes and Met carriers in brain SCNs.

The effect of deep convolutional neural networks on radiologists' performance in the detection of hip fractures on digital pelvic radiographs.

PURPOSE: The purpose of our study is to develop deep convolutional neural network (DCNN) for detecting hip fractures using CT and MRI as a gold standard, and to evaluate the diagnostic performance of 7 readers with and without DCNN. METHODS: The study population consisted of 327 patients who underwent pelvic CT or MRI and were diagnosed with proximal femoral fractures. All radiographs were manually checked and annotated by radiologists referring to CT and MRI for selecting ROI. At first, a DCNN with the GoogLeNet model was trained by 302 cases. The remaining 25 cases and 25 control subjects were used for the observer performance study and for the testing of DCNN. Seven readers took part in this study. A continuous rating scale was used to record each observer's confidence level. Subsequently, each observer interpreted with the DCNN outputs and rated them again. The area under the curve (AUC) was used to compare the fracture detection. RESULTS: The average AUC of the 7 readers was 0.832. The AUC of DCNN alone was 0.905. The average AUC of the 7 readers with DCNN outputs was 0.876. The AUC of readers with DCNN output were higher than those without(p < 0.05). The AUC of the 2 experienced readers with DCNN output exceeded the AUC of DCNN alone. CONCLUSION: For detecting the hip fractures on radiographs, DCNN developed using CT and MRI as a gold standard by radiologists improved the diagnostic performance including the experienced readers.

Hippocampal sclerosis without visually detectable hippocampal MRI abnormalities: automated subfield volumetric analysis.

PURPOSE: This study aims to investigate hippocampal subfield volumes in patients with hippocampal sclerosis (HS) without visually detectable MRI abnormalities and to determine the diagnostic accuracy using hippocampal subfield volumes. MATERIALS AND METHODS: We examined 46 patients with unilateral HS who had a histopathological diagnosis, and 54 controls. The patients were divided into two groups; visually detectable HS (n = 26) and undetectable HS (n = 20) on MRI. The volumes of hippocampal subfield using FreeSurfer were compared among the three groups. Diagnostic accuracy was calculated as the AUC of ROC using cutoff values for each individual subfield. RESULTS: Compared with the controls, visually detectable HS showed significantly reduced volumes of all the hippocampal subfields and entire hippocampus, whereas visually undetectable HS showed significant atrophy only in the CA3 and hippocampus-amygdala-transition-area. To diagnose visually undetectable HS, the CA3 volumes had AUC of 0.719, which was higher than AUC of 0.614 based on the entire hippocampal volumes. CONCLUSION: Visually undetectable HS demonstrated volume reductions in the CA3. Further, the CA3 volumes was more useful to diagnose visually undetectable HS compared with the entire hippocampal volumes.

Whole-brain structural covariance network abnormality in first-episode and drug-naïve major depressive disorder.

There has been a growing interest in the abnormality of networks across the brain in major depressive disorder (MDD). We aimed to investigate the structural covariance networks in patients with first-episode and drug-naïve MDD using structural imaging. A total of 77 patients with first-episode and drug-naïve MDD and 79 healthy subjects (HS) were recruited, from whom high-resolution T1-weighted images were analysed. Incident component analysis was used to calculate the brain networks based on grey matter volume covariance. There were significant differences in salience network, medial temporal lobe network, default mode network and central executive network between MDD and HS (p < 0.05). Further, the disturbance of medial temporal lobe network was significantly correlated with the severity of depressive symptoms (p < 0.05). In conclusion, we found a novel abnormality in the brain network in the medial temporal lobe primarily involving the hippocampus and parahippocampal gyrus in patients with first-episode and treatment-naïve MDD.

Correction to: The usefulness of full-iterative reconstruction algorithm for the visualization of cystic artery on CT angiography.

The authors wish to replace the Table 1.

Early volume reduction of the hippocampus after whole-brain radiation therapy: an automated brain structure segmentation study.

PURPOSE: To assess atrophy differences among brain regions and time-dependent changes after whole-brain radiation therapy (WBRT). MATERIALS AND METHODS: Twenty patients with lung cancer who underwent both WBRT and chemotherapy (WBRT group) and 18 patients with lung cancer who underwent only chemotherapy (control group) were recruited. Three-dimensional T1WI were analyzed to calculate volume reduction ratio after WBRT in various brain structures. The volume reduction ratio of the hippocampus was compared among following 3 periods: 0-3, 4-7, and 8-11 months after WBRT. RESULTS: The volume reduction ratio of the hippocampus was significantly higher in the WBRT group than in the control group (p < 0.05). In WBRT group, the volume reduction ratio of the hippocampus was significantly higher than that of the cortex and white matter (p < 0.05). There were significant differences in the volume reduction ratio between of 0-3 months and that of 4-7 months (p = 0.02) and between 4-7 months and that of 8-11 months (p = 0.01). CONCLUSION: The hippocampus is more vulnerable to the radiation compared with other brain regions and may become atrophic even in the early stage after WBRT.

A single-nucleotide polymorphism influences brain morphology in drug-naïve patients with major depressive disorder.

OBJECTIVE: Recently, a genome-wide association study successfully identified genetic variants associated with major depressive disorder (MDD). The study identified 17 independent single-nucleotide polymorphisms (SNPs) significantly associated with diagnosis of MDD. These SNPs were predicted to be enriched in genes that are expressed in the central nervous system and function in transcriptional regulation associated with neurodevelopment. The study aimed to investigate associations between 17 SNPs and brain morphometry using magnetic resonance imaging (MRI) in drug-naïve patients with MDD and healthy controls (HCs). METHODS: Forty-seven patients with MDD and 42 HCs were included. All participants underwent T1-weighted structural MRI and genotyping. The genotype-diagnosis interactions associated with regional cortical thicknesses were evaluated using voxel-based morphometry for the 17 SNPs. RESULTS: Regarding rs301806, an SNP in the RERE genomic regions, we found a significant difference in a genotype effect in the right-lateral orbitofrontal and postcentral lobes between diagnosis groups. After testing every possible diagnostic comparison, the genotype-diagnosis interaction in these areas revealed that the cortical thickness reductions in the MDD group relative to those in the HC group were significantly larger in T/T individuals than in C-carrier ones. For the other SNPs, no brain area was noted where a genotype effect significantly differed between the two groups. CONCLUSIONS: We found that a RERE gene SNP was associated with cortical thickness reductions in the right-lateral orbitofrontal and postcentral lobes in drug-naïve patients with MDD. The effects of RERE gene polymorphism and gene-environment interactions may exist in brain structures of patients with MDD.

COMT polymorphism regulates the hippocampal subfield volumes in first-episode, drug-naive patients with major depressive disorder.

Purpose: Compared with healthy subjects (HS), patients with major depressive disorder (MDD) exhibit volume differences that affect the volume changes in several areas such as the limbic, cortical, subcortical, and white matter. Catechol-O-methyltransferase (COMT) is a methylation enzyme that catalyzes endogenous catecholamines. The Val158Met polymorphism of COMT has been reported to affect the dopamine (DA) levels, which plays an important role in psychiatric diseases. However, the relationships among both DA levels, COMT genotype, and brain morphology are complicated and controversial. In previous studies that investigated the hippocampal subfields, the greatest brain abnormalities in MDD patients were observed in Cornu Ammonis (CA)1 and the subiculum, followed by that in CA2-3. We have prospectively demonstrated the relationship between the single-nucleotide polymorphism of the Val158Met COMT gene (rs4680) and the hippocampal subfields in drug-naive MDD patients. Patients and methods: In this study, we compared 27 MDD patients and 42 HS who were divided into groups based on their COMT genotype. The effects of the diagnosis, genotype, and genotype-diagnosis interaction related to CA1 and the subiculum volumes, as well as the whole-brain cortical thickness, were evaluated by performing a FreeSurfer statistical analysis of high-resolution magnetic resonance imaging (MRI) findings. Results: The results revealed that there was a statistically significant interaction between the effects of diagnosis and genotype on the right subiculum (a component of the hippocampus). Conclusion: This Val158Met COMT polymorphism may influence the subiculum volume in drug-naive, first-episode MDD patients.

The usefulness of full-iterative reconstruction algorithm for the visualization of cystic artery on CT angiography.

PURPOSE: To evaluate the potential of full-iterative reconstruction (IR) for improving image quality of the cystic artery on CT angiography and to assess observer performance. METHODS: Thirty patients who underwent both liver dynamic CT and conventional angiography were included in this retrospective study. All CT data were reconstructed through filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR3D), and forward-projected, model-based, iterative reconstruction solution (FIRST), respectively. In objective study, we analyzed mean ΔCT numbers (the difference between the HU peak of the vessel and the background) and full-width at tenth-maximum (FWTM) of three parts of the cystic artery by profile curve method comparing the three reconstructions. Subjectively, visualization was evaluated using a four-point scale performed by two blinded observers. ANOVA was used for statistical analysis. RESULTS: In all parts of the cystic artery, the mean ΔCT number of FIRST was shown to be significantly better than that of FBP and AIDR3D (p < 0.05). FWTM in FIRST was the smallest in all of the vessels. The mean visualization score was significantly better with FIRST than with other CT reconstructions (p < 0.05). CONCLUSIONS: The FIRST algorithm led to improved CTA visualization of the cystic artery.

Genetic effects on white matter integrity in drug-naive patients with major depressive disorder: a diffusion tensor imaging study of 17 genetic loci associated with depressive symptoms.

BACKGROUND: A genome-wide association study using megadata identified 17 single-nucleotide polymorphisms (SNPs) in candidate genes for major depressive disorder (MDD). These MDD susceptibility polymorphisms may affect white matter (WM) integrity. This study aimed to investigate the relationship between WM alterations and 17 SNPs in candidate genes for MDD in the first depressive episode of drug-naive MDD patients using a tract-based spatial statistics (TBSS) method. METHODS: Thirty-five drug-naive MDD patients with a first depressive episode and 47 age-and sex-matched healthy subjects underwent diffusion tensor imaging scans and genotyping. The genotype-diagnosis interactions related to WM integrity were evaluated using TBSS for the 17 SNPs. RESULTS: For the anterior thalamic radiation, cingulum, corticospinal tract, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus, uncinate fasciculus, forceps major, and forceps minor, the genotype effect significantly differed between diagnosis groups (P<0.05, family-wise error corrected) in only one SNP, rs301806, in the arginine-glutamic acid dipeptide (RE) repeats (RERE) gene. CONCLUSION: The RERE polymorphism was associated with WM alterations in first-episode and drug-naive MDD patients, which may be at least partially related to the manifestation of MDD. Future studies are needed to explore the gene-environment interactions with regard to individual WM integrity.

Relationship between VEGF-related gene polymorphisms and brain morphology in treatment-naïve patients with first-episode major depressive disorder.

Vascular endothelial growth factor (VEGF) is involved in the development of major depressive disorder (MDD). Recently, a genome-wide association study has revealed that four VEGF-related single nucleotide polymorphisms (SNPs) (i.e., rs4416670, rs6921438, rs6993770 and rs10738760) were independently associated with circulating VEGF levels. The current study investigated the relationship between brain volume and these four SNPs in first-episode drug-naïve MDD patients. A total of 38 first-episode drug-naïve MDD patients and 39 healthy subjects (HS) were recruited and underwent high-resolution T1-weighted imaging. Blood samples were collected from all the participants for serum VEGF assays and VEGF-related SNPs genotyping. Genotype-diagnosis interactions related to whole-brain cortical thickness and hippocampal subfield volumes were evaluated for the four SNPs. The results revealed a genotype-diagnosis interaction only for rs6921438 (i.e., the MDD patients and HS with the G/G genotype versus the MDD patients and HS with A-carrier genotype) in the subiculum of the left hippocampus (p < 0.05), and not the other SNPs. There was a volume reduction in the left subiculum of G/G genotype patients compared with the other groups. The "hypochondriasis" scores of the HAMD-17 scale were significantly higher in the G/G genotype patients than the A-carrier genotype patients. The association was observed between VEGF-related SNP rs6921438 and subiculum atrophy in first-episode drug-naïve MDD patients.

Brain structural connectivity and neuroticism in healthy adults.

Understanding the neural correlates of the neurotic brain is important because neuroticism is a risk factor for the development of psychopathology. We examined the correlation between brain structural networks and neuroticism based on NEO Five-Factor Inventory (NEO-FFI) scores. Fifty-one healthy participants (female, n = 18; male, n = 33; mean age, 38.5 ± 11.7 years) underwent the NEO-FFI test and magnetic resonance imaging (MRI), including diffusion tensor imaging and 3D T1WI. Using MRI data, for each participant, we constructed whole-brain interregional connectivity matrices by deterministic tractography and calculated the graph theoretical network measures, including the characteristic path length, global clustering coefficient, small-worldness, and betweenness centrality (BET) in 83 brain regions from the Desikan-Killiany atlas with subcortical segmentation using FreeSurfer. In relation to the BET, neuroticism score had a negative correlation in the left isthmus cingulate cortex, left superior parietal, left superior temporal, right caudal middle frontal, and right entorhinal cortices, and a positive correlation in the bilateral frontal pole, left caudal anterior cingulate cortex, and left fusiform gyrus. No other measurements showed significant correlations. Our results imply that the brain regions related to neuroticism exist in various regions, and that the neuroticism trait is likely formed as a result of interactions among these regions. This work was supported by a Grant-in-Aid for Scientific Research on Innovative Areas (Comprehensive Brain Science Network) from the Ministry of Education, Science, Sports and Culture of Japan.

Relationship between white matter integrity and serum inflammatory cytokine levels in drug-naive patients with major depressive disorder: diffusion tensor imaging study using tract-based spatial statistics.

Recently, accumulated evidence has indicated a role of inflammation in the pathogenesis of major depressive disorder (MDD). Therefore, we evaluated the relationship between white matter integrity and serum cytokine levels during the first depressive episode in drug-naive MDD patients, using a tract-based spatial statistics (TBSS) method. A total of 35 drug-naive MDD patients with a first depressive episode and 35 healthy subjects (HS) underwent diffusion tensor imaging, and an analysis was conducted using TBSS. We measured serum cytokine levels (interleukin [IL]-1ß, IL-6, interferon-γ, and tumor necrosis factor-α). Fractional anisotropy (FA) values of the bilateral inferior fronto-occipital fasciculus (IFOF) and genu of the corpus callosum in MDD patients were decreased significantly to the HS (p < 0.05 with family-wise error [FWE] correction) and were significantly inversely correlated with the IL-1ß levels (p < 0.05, with FWE correction). No regions showed a correlation between FA values and other serum cytokine levels. Our results suggested that the microstructural changes in IFOF and genu of the corpus callosum are associated with the high IL-1ß levels in the early stage of MDD.

Relationship between interleukin (IL)-6 and brain morphology in drug-naïve, first-episode major depressive disorder using surface-based morphometry.

There is a growing body of evidence to support the involvement of proinflammatory cytokines in the pathophysiology of depression; however, no previous studies have examined the relationship between cytokines and the brain morphology of patients with major depressive disorder (MDD). We therefore evaluated the relationship between serum cytokine levels and cortical thinning during the first depressive episode in drug-naïve patients with MDD. We measured the serum cytokine levels (IL-1ß, IL-6, IFN-γ, and TNFα), and whole-brain cortical thickness and hippocampal subfield volumes on brain magnetic resonance imaging (MRI) using surface-based morphometry in 40 patients with MDD and 47 healthy volunteers (controls). Only the serum IL-6 level was significantly higher in patients with MDD than in controls. The prefrontal cortex (PFC) thickness was significantly reduced in patients with MDD, and showed a significant inverse correlation with the serum IL-6 level. Although high serum IL-6 levels were correlated with reduced left subiculum and right CA1, CA3, CA4, GC-DG, subiculum, and whole hippocampus volumes, the presence or absence of MDD had no effect on the volume of any hippocampal subfields. Our results suggest that IL-6 may play a key role in the morphological changes in the PFC during the early stage of MDD.