T1-weighted Brain Images Research Articles

Differences in gut microbial composition correlate with regional brain volumes in irritable bowel syndrome

BackgroundPreclinical and clinical evidence supports the concept of bidirectional brain-gut microbiome interactions. We aimed to determine if subgroups of irritable bowel syndrome (IBS) subjects can be identified based on differences in gut microbial composition, and if there are correlations between gut microbial measures and structural brain signatures in IBS.MethodsBehavioral measures, stool samples, and structural brain images were collected from 29 adult IBS and 23 healthy control subjects (HCs). 16S ribosomal RNA (rRNA) gene sequencing was used to profile stool microbial communities, and various multivariate analysis approaches were used to quantitate microbial composition, abundance, and diversity. The metagenomic content of samples was inferred from 16S rRNA gene sequence data using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt). T1-weighted brain images were acquired on a Siemens Allegra 3T scanner, and morphological measures were computed for 165 brain regions.ResultsUsing unweighted Unifrac distances with hierarchical clustering on microbial data, samples were clustered into two IBS subgroups within the IBS population (IBS1 (n = 13) and HC-like IBS (n = 16)) and HCs (n = 23) (AUROC = 0.96, sensitivity 0.95, specificity 0.67). A Random Forest classifier provided further support for the differentiation of IBS1 and HC groups. Microbes belonging to the genera Faecalibacterium, Blautia, and Bacteroides contributed to this subclassification. Clinical features distinguishing the groups included a history of early life trauma and duration of symptoms (greater in IBS1), but not self-reported bowel habits, anxiety, depression, or medication use. Gut microbial composition correlated with structural measures of brain regions including sensory- and salience-related regions, and with a history of early life trauma.ConclusionsThe results confirm previous reports of gut microbiome-based IBS subgroups and identify for the first time brain structural alterations associated with these subgroups. They provide preliminary evidence for the involvement of specific microbes and their predicted metabolites in these correlations.

Gray matter anomalies in pedophiles with and without a history of child sexual offending

Pedophilia is a psychiatric disorder that is inter-related with but distinct from child sexual offending (CSO). Neural alterations reportedly contribute to both pedophilia and CSO, but until now, no study has distinguished the brain structural anomalies associated with pedophilia from those specifically associated with CSO in pedophilic men. Using high-resolution T1-weighted brain images and voxel-based morphometry, we analyzed the gray matter (GM) volume of the following 219 men recruited at four acquisition sites in Germany: 58 pedophiles with a history of CSO, 60 pedophiles without any history of CSO and 101 non-pedophilic, non-offending controls to control for the effects of age, education level, verbal IQ, sexual orientation and the acquisition site. Although there were no differences in the relative GM volume of the brain specifically associated with pedophilia, statistical parametric maps revealed a highly significant and CSO-related pattern of above vs below the ‘normal’ GM volume in the right temporal pole, with non-offending pedophiles exhibiting larger volumes than offending pedophiles. Moreover, regression analysis revealed that the lower GM volume of the dorsomedial prefrontal or anterior cingulate cortex was associated with a higher risk of re-offending in pedophilic child molesters. We believe our data provide the first evidence that CSO in pedophilia rather than pedophilia alone is associated with GM anomalies and thus shed new light on the results of previous studies on this topic. These results indicate the need for new neurobehavioral theories on pedophilia and CSO and may be potentially useful for treatment or prevention approaches that aim to reduce the risk of (re)offending in pedophilia.

Greater cerebellar gray matter volume in car drivers: an exploratory voxel-based morphometry study

Previous functional neuroimaging studies have identified multiple brain areas associated with distinct aspects of car driving in simulated traffic environments. Few studies, however, have examined brain morphology associated with everyday car-driving experience in real traffic. Thus, the aim of the current study was to identify gray matter volume differences between drivers and non-drivers. We collected T1-weighted structural brain images from 73 healthy young adults (36 drivers and 37 non-drivers). We performed a whole-brain voxel-based morphometry analysis to examine between-group differences in regional gray matter volume. Compared with non-drivers, drivers showed significantly greater gray matter volume in the left cerebellar hemisphere, which has been associated with cognitive rather than motor functioning. In contrast, we found no brain areas with significantly greater gray matter volume in non-drivers compared with drivers. Our findings indicate that experience with everyday car driving in real traffic is associated with greater gray matter volume in the left cerebellar hemisphere. This brain area may be involved in abilities that are critical for driving a car, but are not commonly or frequently used during other daily activities.

Brain morphology in children with nevoid basal cell carcinoma syndrome.

Brain morphology is tightly regulated by diverse signaling pathways. Hedgehog signaling is a candidate pathway considered responsible for regulating brain morphology. Nevoid basal cell carcinoma syndrome (NBCCS), caused by a PTCH1 mutation in the hedgehog signaling pathway, occasionally exhibits macrocephaly and medulloblastoma. Although cerebellar enlargement occurs in ptch1 heterozygous-deficient mice, its impact on human brain development remains unknown. We investigated the brain morphological characteristics of children with NBCCS. We evaluated brain T1-weighted images from nine children with NBCCS and 15 age-matched normal control (NC) children (mean [standard deviation], 12.2 [2.8] vs. 11.6 [2.3] years old). The diameters of the cerebrum, corpus callosum, and brain stem and the cerebellar volume were compared using two-tailed t-tests with Welch's correction. The transverse diameters (150.4 [9.9] vs. 136.0 [5.5] mm, P = 0.002) and longitudinal diameters (165.4 [8.0] vs. 151.3 [8.7] mm, P = 0.0007) of the cerebrum, cross-sectional area of the cerebellar vermis (18.7 [2.6] vs. 11.8 [1.7] cm2 , P = 0.0001), and total volume of the cerebellar hemispheres (185.1 [13.0] vs. 131.9 [10.4] cm3 , P = 0.0001) were significantly larger in the children with NBCCS than in NC children. Thinning of the corpus callosum and ventricular enlargement were also confirmed in children with NBCCS. We demonstrate that, on examination of the brain morphology, an increase in the size of the cerebrum, cerebellum, and cerebral ventricles is revealed in children with NBCCS compared to NC children. This suggests that constitutively active hedgehog signaling affects human brain morphology and the PI3K/AKT and RAS/MAPK pathways.

Closed-form expressions for flip angle variation that maximize total signal in T1-weighted rapid gradient echo MRI.

Magnetization-prepared rapid gradient-echo (MPRAGE) sequences are commonly employed for T1-weighted structural brain imaging. Following a contrast preparation radiofrequency (RF) pulse, the data acquisition proceeds under nonequilibrium conditions of the relaxing longitudinal magnetization. Variation of the flip angle can be used to maximize total available signal. Simulated annealing or greedy algorithms have so far been published to numerically solve this problem, with signal-to-noise ratios optimized for clinical imaging scenarios by adhering to a predefined shape of the signal evolution. We propose an unconstrained optimization of the MPRAGE experiment thatemploys techniques from resource allocation theory. A new dynamic programming solution is introduced that yields closed-form expressions for optimal flip angle variation. Flip angle series are proposed that maximize total transverse magnetization (Mxy) for a range of physiologic T1 values. A 3D MPRAGE sequence is modified to allow for a controlled variation of the excitation angle. Experiments employing a T1 contrast phantom are performed at 3T. 1D acquisitions without phase encoding permit measurement of the temporal development of Mxy. Image mean signal and standard deviation for reference flip angle trains are compared in 2D measurements. Signal profiles at sharp phantom edges are acquired to access image blurring related to nonuniform Mxy development. A novel closed-form expression for flip angle variation is found that constitutes the optimal policy to reach maximum total signal. It numerically equals previously published results of other authors when evaluated under their simplifying assumptions. Longitudinal magnetization (Mz) is exhaustively used without causing abrupt changes in the measured MR signal, which is a prerequisite for artifact free images. Phantom experiments at 3T verify the expected benefit for total accumulated k-space signal when compared with published flip angle series. Describing the MR signal collection in MPRAGE sequences as a Bellman problem is a new concept. By means of recursively solving a series of overlapping subproblems, this leads to an elegant solution for the problem of maximizing total available MR signal in k-space. A closed-form expression for flip angle variation avoids the complexity of numerical optimization and eases access to controlled variation in an attempt to identify potential clinical applications.

Cortical Thickness of Native Tibetans in the Qinghai-Tibetan Plateau.

High-altitude environmental factors and genetic variants together could have exerted their effects on the human brain. The present study was designed to investigate the cerebral morphology in high-altitude native Tibetans. T1-weighted brain images were obtained from 77 Tibetan adolescents on the Qinghai-Tibetan Plateau (altitude, 2300-5300 m) and 80 matched Han controls living at sea level. Cortical thickness, curvature, and sulcus were analyzed by using FreeSurfer. Cortical thickness was significantly decreased in the left posterior cingulate cortex, lingual gyrus, superior parietal cortex, precuneus, and rostral middle frontal cortex and the right medial orbitofrontal cortex, lateral occipital cortex, precuneus, and paracentral lobule. Curvature was significantly decreased in the left superior parietal cortex and right superior marginal gyrus; the depth of the sulcus was significantly increased in the left inferior temporal gyrus and significantly decreased in the right superior marginal gyrus, superior temporal gyrus, and insular cortex. Moreover, cortical thickness was negatively correlated with altitude in the left superior and middle temporal gyri, rostral middle frontal cortex, insular cortex, posterior cingulate cortex, precuneus, lingual gyrus, and the right superior temporal gyrus. Curvature was positively correlated with altitude in the left rostral middle frontal cortex, insular cortex, and middle temporal gyrus. The depth of the sulcus was negatively correlated with altitude in the left lingual gyrus and right medial orbitofrontal cortex. Differences in cortical morphometry in native Tibetans may reflect adaptations related to high altitude.

Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity

BackgroundLow-resolution images may be acquired in magnetic resonance imaging (MRI) due to limited data acquisition time or other physical constraints, and their resolutions can be improved with super-resolution methods. Since MRI can offer images of an object with different contrasts, e.g., T1-weighted or T2-weighted, the shared information between inter-contrast images can be used to benefit super-resolution.MethodsIn this study, an MRI image super-resolution approach to enhance in-plane resolution is proposed by exploring the statistical information estimated from another contrast MRI image that shares similar anatomical structures. We assume some edge structures are shown both in T1-weighted and T2-weighted MRI brain images acquired of the same subject, and the proposed approach aims to recover such kind of structures to generate a high-resolution image from its low-resolution counterpart.ResultsThe statistical information produces a local weight of image that are found to be nearly invariant to the image contrast and thus this weight can be used to transfer the shared information from one contrast to another. We analyze this property with comprehensive mathematics as well as numerical experiments.ConclusionExperimental results demonstrate that the image quality of low-resolution images can be remarkably improved with the proposed method if this weight is borrowed from a high resolution image with another contrast.Graphical Multi-contrast MRI Image Super-resolution with Contrast-invariant Regression Weights

Objectively measured sedentary behavior and brain volumetric measurements in multiple sclerosis.

This study examined the association between sedentary behavior patterns and whole brain gray matter (GM), white matter (WM) and subcortical GM structures in persons with multiple sclerosis (MS). 36 persons with MS wore an accelerometer and underwent a brain MRI. Whole brain GM and WM and deep GM structures were calculated from 3D T1-weighted structural brain images. There were statistically significant (p < 0.01) and moderate or large associations between number of sedentary bouts/day and brain volume measures. The primary result was a consistent negative association between number of sedentary bouts/day and whole brain GM and WM, and deep GM structures. We provide novel evidence for decreased brain volume as a correlate of a sedentary behavior pattern in persons with MS.

Automatic segmentation of infant brain MR images: With special reference to myelinated white matter

Automatic segmentation of infant brain images is faced with numerous challenges like poor image contrast, motion artifacts, and changes caused by progressive myelination of the infant brain. Since timely myelination points to normal brain maturity, monitoring the progress and degree of myelination is clinically significant. However, most of the existing segmentation methods do not segment myelinated portions of the infant brain. In this paper, we propose a segmentation approach focused on segmenting the myelinated white matter tissue in T1-weighted magnetic resonance images of the infant brain. The novelty of the algorithm lies in the introduction of a weighted localized Tsallis entropy based thresholding method. The proposed method is also tested on older babies beyond the one-year age mark to verify its utility and robustness. It is seen that the mean Dice coefficients obtained for myelin segmentation by the proposed weighted localized method are higher than that of the other methods, namely, the conventional Tsallis entropy thresholding and modified localized method.

Correlation between hippocampal volumes and medial temporal lobe atrophy in patients with Alzheimer's disease.

Introduction:Hippocampus undergoes atrophy in patients with Alzheimer's disease (AD). Calculation of hippocampal volumes can be done by a variety of methods using T1-weighted images of magnetic resonance imaging (MRI) of the brain. Medial temporal lobes atrophy (MTL) can be rated visually using T1-weighted MRI brain images. The present study was done to see if any correlation existed between hippocampal volumes and visual rating scores of the MTL using Scheltens Visual Rating Method.Materials and Methods:We screened 84 subjects presented to the Department of Neurology of a Tertiary Care Hospital and enrolled forty subjects meeting the National Institute of Neurological and Communicative Disorders and Stroke, AD related Disease Association criteria. Selected patients underwent MRI brain and T1-weighted images in a plane perpendicular to long axis of hippocampus were obtained. Hippocampal volumes were calculated manually using a standard protocol. The calculated hippocampal volumes were correlated with Scheltens Visual Rating Method for Rating MTL. A total of 32 cognitively normal age-matched subjects were selected to see the same correlation in the healthy subjects as well. Sensitivity and specificity of both methods was calculated and compared.Results:There was an insignificant correlation between the hippocampal volumes and MTL rating scores in cognitively normal elderly (n = 32; Pearson Correlation coefficient = 0.16, P > 0.05). In the AD Group, there was a moderately strong correlation between measured hippocampal volumes and MTL Rating (Pearson's correlation coefficient = −0.54; P < 0.05. There was a moderately strong correlation between hippocampal volume and Mini-Mental Status Examination in the AD group. Manual delineation was superior compared to the visual method (P < 0.05).Conclusions:Good correlation was present between manual hippocampal volume measurements and MTL scores. Sensitivity and specificity of manual measurement of hippocampus was higher compared to visual rating scores for MTL in patients with AD.

PCA based clustering for brain tumor segmentation of T1w MRI images

Background and objectiveMedical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. MethodsFive most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. ResultsThe obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. ConclusionAccording to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images.

Atypical Structural Asymmetry of the Planum Temporale is Related to Family History of Dyslexia.

Research on the neural correlates of developmental dyslexia indicates atypical anatomical lateralization of the planum temporale, a higher-order cortical auditory region. Yet whether this atypical lateralization precedes reading acquisition and is related to a familial risk for dyslexia is not currently known. In this study, we address these questions in 2 separate cohorts of young children and adolescents with and without a familial risk for dyslexia. Planum temporale surface area was manually labeled bilaterally, on the T1-weighted MR brain images of 54 pre-readers (mean age: 6.2 years, SD: 3.2 months; 33 males) and 28 adolescents (mean age: 14.7 years, SD: 3.3 months; 11 males). Half of the pre-readers and adolescents had a familial risk for dyslexia. In both pre-readers and adolescents, group comparisons of left and right planum temporale surface area showed a significant interaction between hemisphere and family history of dyslexia, with participants who had no family risk for dyslexia showing greater leftward asymmetry of the planum temporale. This effect was confirmed when analyses were restricted to normal reading participants. Altered planum temporale asymmetry thus seems to be related to family history of dyslexia.

Random forest regression for magnetic resonance image synthesis.

By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T2-weighted brain images that include the skull and FLuid Attenuated Inversion Recovery(FLAIR) images has been reported. The method described herein, called REPLICA, addresses these limitations. REPLICA is a supervised random forest image synthesis approach that learns a nonlinear regression to predict intensities of alternate tissue contrasts given specific input tissue contrasts. Experimental results include direct image comparisons between synthetic and real images, results from image analysis tasks on both synthetic and real images, and comparison against other state-of-the-art image synthesis methods. REPLICA is computationally fast, and is shown to be comparable to other methods on tasks they are able to perform. Additionally REPLICA has the capability to synthesize both T2-weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets.

Gaussian Mixture Model with the Inclusion of Spatial Factor and Pixel Re-labelling: Application to MR Brain Image Segmentation

A fully automatic and accurate brain image segmentation divided into the three tissue types—Grey Matter, White Matter and Cerebrospinal Fluid (CSF)—is necessary in medical analysis. Gaussian Mixture Model (GMM) is one of the most widely implemented methods for MR Brain Image Segmentation, and Expectation–Maximization algorithm is used to estimate the parameters of the model. The major drawback existing in conventional GMM is that it considers each pixel as independent and does not incorporate the spatial interaction among the neighbouring pixels. This will lead to poor segmentation accuracy in the presence of noise and intensity inhomogeneity. To overcome this drawback, in the proposed method, the posterior probability is weighted with a spatial factor which improves the segmentation accuracy and makes the convergence also faster. Also, in the automatic segmentation of the IBSR MR Brain Image Data, a new approach of re-labelling the voxels for Grey Matter and CSF is proposed. The proposed method is tested on 20 low-resolution T1-weighted IBSR Brain Images and BrainWeb Simulated MR Images. The results show that the proposed method can improve the performance of MR Brain Image Segmentation by around 4 % over the state-of-the-art algorithm.

Fuzzy C means integrated with spatial information and contrast enhancement for segmentation of MR brain images

This paper proposes a fully automated method for MR brain image segmentation into Gray Matter, White Matter and Cerebro-spinal Fluid. It is an extension of Fuzzy C Means Clustering Algorithm which overcomes its drawbacks, of sensitivity to noise and inhomogeneity. In the conventional FCM, the membership function is computed based on the Euclidean distance between the pixel and the cluster center. It does not take into consideration the spatial correlation among the neighboring pixels. This means that the membership values of adjacent pixels belonging to the same cluster may not have the same range of membership value due to the contamination of noise and hence misclassified. Hence, in the proposed method, the membership function is convolved with mean filter and thus the local spatial information is incorporated in the clustering process. The method further includes pixel re-labeling and contrast enhancement using non-linear mapping to improve the segmentation accuracy. The proposed method is applied to both simulated and real T1-weighted MR brain images from BrainWeb and IBSR database. Experiments show that there is an increase in segmentation accuracy of around 30% over the conventional methods and 6% over the state of the art methods.

Prediction of CT Substitutes from MR Images Based on Local Diffeomorphic Mapping for Brain PET Attenuation Correction.

This study presents a patch-based method for CT prediction from MR images, generating attenuation maps for PET reconstruction. Because no global relation exists between MR and CT intensities, we propose local diffeomorphic mapping (LDM) for CT prediction. In LDM, we assume that MR and CT patches are located on 2 nonlinear manifolds, and the mapping from the MR manifold to the CT manifold approximates a diffeomorphism under a local constraint. Locality is important in LDM and is constrained by the following techniques. The first is local dictionary construction, wherein, for each patch in the testing MR image, a local search window is used to extract patches from training MR/CT pairs to construct MR and CT dictionaries. The k-nearest neighbors and an outlier detection strategy are then used to constrain the locality in MR and CT dictionaries. Second is local linear representation, wherein, local anchor embedding is used to solve MR dictionary coefficients when representing the MR testing sample. Under these local constraints, dictionary coefficients are linearly transferred from the MR manifold to the CT manifold and used to combine CT training samples to generate CT predictions. Our dataset contains 13 healthy subjects, each with T1- and T2-weighted MR and CT brain images. This method provides CT predictions with a mean absolute error of 110.1 Hounsfield units, Pearson linear correlation of 0.82, peak signal-to-noise ratio of 24.81 dB, and Dice in bone regions of 0.84 as compared with real CTs. CT substitute-based PET reconstruction has a regression slope of 1.0084 and R2 of 0.9903 compared with real CT-based PET. In this method, no image segmentation or accurate registration is required. Our method demonstrates superior performance in CT prediction and PET reconstruction compared with competing methods.

An experiment model of obstructive hydrocephalus in the rats: injecting the astrocytes into the lateral ventricle

Objective To establish the animal model of obstructive hydrocephalus in rat by injecting lateral ventricle astrocytes from rat. Methods Three different concentrations of 20 μl astrocytes suspension with 5×107, 5×108, 5×109 cells per milliliter were injected into the right lateral ventricle of group A, B, C through stereotactic puncture, respectively, while 20 μl sterile phosphate buffer saline (PBS) solution was injected into the right lateral ventricle of control group. The magnetie resonance imaging (MRI) was performed to obtain T2 weighted brain images at 7, 14, 21, 28, 35 days after injection. Measure the maximum transverse diameter of lateral ventricle through coronary position images to determine the degree of hydrocephalus. Results The mean value of maximum transverse diameter in the control group, group A, group B and group C was (0.11±0.01), (1.01±0.78), (1.83±0.23), (1.89±0.21) mm at 35th day after injection, respectively. Compared with group A, group B and group C, the maximum transverse diameter of control group was statistically different(P 0.05). Conclusion The animal model of obstructive hydrocephalus in rat can be successfully established by injecting lateral ventricle astrocytes. Key words: Hydrocephalus; Rats; Models, animal; Astrocytes

Brain Cortical Thickness Differences in Adolescent Females with Substance Use Disorders.

Some youths develop multiple substance use disorders early in adolescence and have severe, persistent courses. Such youths often exhibit impulsivity, risk-taking, and problems of inhibition. However, relatively little is known about the possible brain bases of these behavioral traits, especially among females.MethodsWe recruited right-handed female patients, 14–19 years of age, from a university-based treatment program for youths with substance use disorders and community controls similar for age, race and zip code of residence. We obtained 43 T1-weighted structural brain images (22 patients and 21 controls) to examine group differences in cortical thickness across the entire brain as well as six a priori regions-of-interest: 1) medial orbitofrontal cortex; 2) rostral anterior cingulate cortex; and 3) middle frontal cortex, in each hemisphere. Age and IQ were entered as nuisance factors for all analyses.ResultsA priori region-of-interest analyses yielded no significant differences. However, whole-brain group comparisons revealed that the left pregenual rostral anterior cingulate cortex extending into the left medial orbitofrontal region (355.84 mm2 in size), a subset of two of our a priori regions-of-interest, was significantly thinner in patients compared to controls (vertex-level threshold p = 0.005 and cluster-level family wise error corrected threshold p = 0.05). The whole-brain group differences did not survive after adjusting for depression or externalizing scores. Whole-brain within-patient analyses demonstrated a positive association between cortical thickness in the left precuneus and behavioral disinhibition scores (458.23 mm2 in size).ConclusionsAdolescent females with substance use disorders have significant differences in brain cortical thickness in regions engaged by the default mode network and that have been associated with problems of emotional dysregulation, inhibition, and behavioral control in past studies.

Early cerebral volume reductions and their associations with reduced lupus disease activity in patients with newly-diagnosed systemic lupus erythematosus.

We examined if cerebral volume reduction occurs very early during the course of systemic lupus erythematosus (SLE), and observed prospectively whether gray (GMV) and white matter volumes (WMV) of the brain would improve with lowered SLE disease activity. T1-weighted MRI brain images were obtained from 14 healthy controls (HC) and 14 newly-diagnosed SLE patients within 5 months of diagnosis (S1) and after achieving low disease activity (S2). Whole brain voxel-based morphometry was used to detect differences in the GMV and WMV between SLE patients and HC and those between SLE patients at S1 and S2. SLE patients were found to have lower GMV than HC in the middle cingulate cortex, middle frontal gyrus and right supplementary motor area, and lower WMV in the superior longitudinal fasciculus, cingulum cingulate gyrus and inferior fronto-occipital fasciculus at both S1 and S2. Whole-brain voxel-wise analysis revealed increased GMV chiefly in the prefrontal regions at S2 compared to S1 in SLE patients. The GMV increase in the left superior frontal gyrus was significantly associated with lowered SLE disease activity. In conclusion, GMV and WMV reduced very early in SLE patients. Reduction of SLE disease activity was accompanied by region-specific GMV improvement in the prefrontal regions.

Computer-aided classification of Alzheimer's disease based on support vector machine with combination of cerebral image features in MRI

Several studies have differentiated Alzheimer's disease (AD) using cerebral image features derived from MR brain images. In this study, we were interested in combining hippocampus and amygdala volumes and entorhinal cortex thickness to improve the performance of AD differentiation. Thus, our objective was to investigate the useful features obtained from MRI for classification of AD patients using support vector machine (SVM). T1-weighted MR brain images of 100 AD patients and 100 normal subjects were processed using FreeSurfer software to measure hippocampus and amygdala volumes and entorhinal cortex thicknesses in both brain hemispheres. Relative volumes of hippocampus and amygdala were calculated to correct variation in individual head size. SVM was employed with five combinations of features (H: hippocampus relative volumes, A: amygdala relative volumes, E: entorhinal cortex thicknesses, HA: hippocampus and amygdala relative volumes and ALL: all features). Receiver operating characteristic (ROC) analysis was used to evaluate the method. AUC values of five combinations were 0.8575 (H), 0.8374 (A), 0.8422 (E), 0.8631 (HA) and 0.8906 (ALL). Although “ALL” provided the highest AUC, there were no statistically significant differences among them except for “A” feature. Our results showed that all suggested features may be feasible for computer-aided classification of AD patients.