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Alzheimer’s Disease-resemblance atrophy index (AD-RAI) is an MRI-based machine learning derived biomarker that was developed to reflect the characteristic brain atrophy associated with AD. Recent study showed that AD-RAI (≥0.5) had the best performance in predicting conversion from mild cognitive impairment (MCI) to dementia and from cognitively unimpaired (CU) to MCI. We aimed to validate the performance of AD-RAI in detecting preclinical and prodromal AD. We recruited 128 subjects (MCI=50, CU=78) from two cohorts: CU-SEEDS and ADNI. Amyloid (A+) and tau (T+) status were confirmed by PET (11C-PIB, 18F-T807) or CSF analysis. We investigated the performance of AD-RAI in detecting preclinical and prodromal AD (i.e. A+T+) among MCI and CU subjects and compared its performance with that of hippocampal measures. AD-RAI achieved the best metrics among all subjects (sensitivity 0.74, specificity 0.91, accuracy 85.94%) and among MCI subjects (sensitivity 0.92, specificity 0.81, accuracy 86.00%) in detecting A+T+ subjects over other measures. Among CU subjects, AD-RAI yielded the best specificity (0.95) and accuracy (85.90%) over other measures, while hippocampal volume achieved a higher sensitivity (0.73) than AD-RAI (0.47) in detecting preclinical AD. These results showed the potential of AD-RAI in the detection of early AD, in particular at the prodromal stage.

The use of neuroimaging examinations is crucial in Alzheimer’s disease (AD), in both research and clinical settings. Over the years, magnetic resonance imaging (MRI)–based computer‐aided diagnosis has been shown to be helpful for early screening and predicting cognitive decline. Meanwhile, an increasing number of studies have adopted machine learning for the classification of AD, with promising results. In this review article, we focus on computerized MRI‐based biomarkers of AD by reviewing representative studies that used computerized techniques to identify AD patients and predict cognitive progression. We categorized these studies based on the following applications: (1) identifying AD from normal control; (2) identifying AD from other dementia types, including vascular dementia, dementia with Lewy bodies, and frontotemporal dementia; and (3) predicting conversion from NC to mild cognitive impairment (MCI) and from MCI to AD. This systematic review could act as a state‐of‐the‐art overview of this emerging field as well as a basis for designing future studies.

Background: Magnetic resonance imaging (MRI) provides objective information about brain structural atrophy in patients with Alzheimer’s disease (AD). This multi-structural atrophic information, when integrated as a single differential index, has the potential to further elevate the accuracy of AD identification from normal control (NC) compared to the conventional structure volumetric index. Objective: We herein investigated the performance of such an MRI-derived AD index, AD-Resemblance Atrophy Index (AD-RAI), as a neuroimaging biomarker in clinical scenario. Method: Fifty AD patients (19 with the Amyloid, Tau, Neurodegeneration (ATN) results assessed in cerebrospinal fluid) and 50 age- and gender-matched NC (19 with ATN results assessed using positron emission tomography) were recruited in this study. MRI-based imaging biomarkers, i.e., AD-RAI, were quantified using AccuBrain®. The accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of these MRI-based imaging biomarkers were evaluated with the diagnosis result according to clinical criteria for all subjects and ATN biological markers for the subgroup. Results: In the whole groups of AD and NC subjects, the accuracy of AD-RAI was 91%, sensitivity and specificity were 88% and 96%, respectively, and the AUC was 92%. In the subgroup of 19 AD and 19 NC with ATN results, AD-RAI results matched completely with ATN classification. AD-RAI outperforms the volume of any single brain structure measured. Conclusion: The finding supports the hypothesis that MRI-derived composite AD-RAI is a more accurate imaging biomarker than individual brain structure volumetry in the identification of AD from NC in the clinical scenario.

Background The differential diagnosis of frontotemporal dementia (FTD) and Alzheimer’s disease (AD) is difficult due to the overlaps of clinical symptoms. Structural magnetic resonance imaging (sMRI) presents distinct brain atrophy and potentially helps in their differentiation. In this study, we aim at deriving a novel integrated index by leveraging the volumetric measures in brain regions with significant difference between AD and FTD and developing an MRI-based strategy for the differentiation of FTD and AD. Methods In this study, the data were acquired from three different databases, including 47 subjects with FTD, 47 subjects with AD, and 47 normal controls in the NACC database; 50 subjects with AD in the ADNI database; and 50 subjects with FTD in the FTLDNI database. The MR images of all subjects were automatically segmented, and the brain atrophy, including the AD resemblance atrophy index (AD-RAI), was quantified using AccuBrain®. A novel MRI index, named the frontotemporal dementia index (FTDI), was derived as the ratio between the weighted sum of the volumetric indexes in “FTD dominant” structures over that obtained from “AD dominant” structures. The weights and the identification of “FTD/AD dominant” structures were acquired from the statistical analysis of NACC data. The differentiation performance of FTDI was validated using independent data from ADNI and FTLDNI databases. Results AD-RAI is a proven imaging biomarker to identify AD and FTD from NC with significantly higher values (p p = 0.647). FTDI showed excellent accuracy in identifying FTD from AD (AUC = 0.90; SEN = 89%, SPE = 75% with threshold value = 1.08). The validation using independent data from ADNI and FTLDNI datasets also confirmed the efficacy of FTDI (AUC = 0.93; SEN = 96%, SPE = 70% with threshold value = 1.08). Conclusions Brain atrophy in AD, FTD, and normal elderly shows distinct patterns. In addition to AD-RAI that is designed to detect abnormal brain atrophy in dementia, a novel index specific to FTD is proposed and validated. By combining AD-RAI and FTDI, an MRI-based decision strategy was further proposed as a promising solution for the differential diagnosis of AD and FTD in clinical practice.

Background Hippocampal sclerosis (HS) is associated with post-surgery outcome in patients with temporal lobe epilepsy (TLE), and an automated method that quantifies HS severity is still lacking. Here, we aim to propose an MRI-based HS index (HSI) that integrates hippocampal volume and FLAIR signal to measure the severity of HS. Methods Forty-two pre-surgery TLE patients were included retrospectively, with T1-weighted (T1W) and FLAIR images acquired from each subject. Two experienced neurosurgeons (W.D. and C.S.) and one neurologist (Q.L.) rated HS severity with a four-class grading scale (normal, mild, moderate and severe) based on both hippocampal volume loss and increased FLAIR signal. A consensus of HS severity for each subject was made by voting among the three visual rating results. Regarding the automatic quantification, the hippocampal volume was quantified by AccuBrain on T1W image, and the FLAIR signal of hippocampus was calculated as the mean intensity of hippocampal region on the FLAIR image (normalized by the mean intensity of gray matter). To fit the HSI from visual rating, we applied ordinal regression with the voted visual rating as the dependent variable, and hippocampal volume and FLAIR signal as the independent variables. The HSI was calculated by weighting the predicted probabilities of the four-class grading scales from ordinal regression. Results The intra-class correlation coefficient (single measure) of the three raters was 0.806. The generated HSI was significantly correlated with the visual rating scales of the three raters (W.D.: 0.823, Q.L.: 0.817, C.S.: 0.717). HSI scores well differentiated the different HS categories as defined by the agreed HS visual rating (normal vs. mild: p p p = 0.001). Conclusions The proposed HSI was consistent with visual rating scales from epileptologists and sensitive to HS severity. This MRI-based index may help to evaluate HS severity in clinical practice. Further validations are needed to associate HSI with post-surgery outcomes.

Background In utero fetal MRI has been developing in common medical prenatal practice for nearly two decades. But the applications and research on fetal MRI still lag behind due to the lack of specialized image processing and analysis tools. Brain extraction, as an initial preprocessing step for many brain MRI-based processing methods, is an important basis for accurate fetal MRI analysis. However, it is very challenging to automatically extract fetal brains from fetal MRI due to the large variation in fetal brains across different gestational weeks and complex maternal tissues surrounding the fetal brains. Method We proposed a novel two-step framework using the deep learning method for solving the challenging problem of automatic fetal brain extraction in 2D in utero fetal MRI slices. The proposed framework consisted of two fully convolutional network (FCN) models, i.e., a shallow FCN and an extra deep multi-scale FCN (M-FCN). The first shallow FCN rapidly located the fetal brain and extracted the region of interest (ROI) containing the brain. Then, within the brain ROI, the M-FCN further refined the segmentation and produced the final brain mask by leveraging the multi-scale information and residual learning blocks. Dilated convolutional layers were employed in both FCNs to control the size of feature maps and increase the field of view. Result Eighty-eight 2D fetal MRIs were collected for experiments. We compared our method with the state-of-the-art methods on extracting fetal brains. It has been evaluated that our proposed framework outperformed the other methods in both fetal brain localization and segmentation tasks. With the proposed method, we located the fetal brain with an accuracy of 100%. The brain segmentation performance was measured based on the overlap between the automatic segmentations and the manual segmentations. Our proposed method achieved an average of 0.958 Dice score, 0.950 sensitivity rate, and 0.968 precision on the testing dataset, and it took an average of 6 s to process one fetal MRI stack on a workstation with TITAN X GPU and i7-6700 CPU. Conclusion In this paper, we proposed an effective and efficient deep learning framework for automatic fetal brain extraction from fetal MRI. It has been validated with solid experiments that the proposed method can be used as a practical and useful tool in clinical practice and neuroscience research.

PurposeThis study aimed to determine if people living with HIV (PLWH) in preclinical human immunodeficiency virus (HIV)-associated neurocognitive disorder (HAND), with no clinical symptoms and without decreased daily functioning, suffer from brain volumetric alterations and its patterns.MethodFifty-nine male PLWH at the HAND preclinical stage were evaluated, including 19 subjects with asymptomatic neurocognitive impairment (ANI), 17 subjects with cognitive abnormality that does not reach ANI (Not reach ANI), and 23 subjects with cognitive integrity. Moreover, 23 healthy volunteers were set as the seronegative normal controls (NCs). These individuals underwent sagittal three-dimensional T1-weighted imaging (3D T1WI). Quantified data and volumetric measures of brain structures were automatically segmented and extracted using AccuBrain®. In addition, the multiple linear regression analysis was performed to analyze the relationship of volumes of brain structures and clinical variables in preclinical HAND, and the correlations of the brain volume parameters with different cognitive function states were assessed by Pearson’s correlation analysis.ResultsThe significant difference was shown in the relative volumes of the ventricular system, bilateral lateral ventricle, thalamus, caudate, and left parietal lobe gray matter between the preclinical HAND and NCs. Furthermore, the relative volumes of the bilateral thalamus in preclinical HAND were negatively correlated with attention/working memory (left: r = −0.271, p = 0.042; right: r = −0.273, p = 0.040). Higher age was associated with increased relative volumes of the bilateral lateral ventricle and ventricular system and reduced relative volumes of the left thalamus and parietal lobe gray matter. The lower CD4+/CD8+ ratio was associated with increased relative volumes of the left lateral ventricle and ventricular system. Longer disease course was associated with increased relative volumes of the bilateral thalamus. No significant difference was found among preclinical HAND subgroups in all indices, and the difference between the individual groups (Not reach ANI and Cognitive integrity groups) and NCs was also insignificant. However, there was a significant difference between ANI and NCs in the relative volumes of the bilateral caudate and lateral ventricle.ConclusionMale PLWH at the HAND preclinical stage suffer from brain volumetric alterations. AccuBrain® provides potential value in evaluating HIV-related neurocognitive dysfunction.

Current evidence supports the involvement of lipids in brain aging. A range of serum lipids is explored in association with brain structure and cognitive function amongst rural-dwelling older adults.This population-based cross-sectional study included 184 rural-dwelling adults (age ≥ 65 years, 39.1% women) in Shandong, China. In 2014-2016, data on demographics, lifestyle, health conditions and serum lipids were collected. Volumes of gray matter, white matter, ventricles, hippocampus and white matter hyperintensity were automatically estimated on brain magnetic resonance imaging. Global cognitive function was assessed with the Mini-Mental State Examination (MMSE), and mild cognitive impairment (MCI) was defined according to Petersen's criteria. Data were analyzed using the general linear regression, logistic regression and mediation models.Of the 184 participants, 47 were defined with MCI. Low high-density lipoprotein cholesterol (HDL-C;1.55 vs. ≥1.55 mmol/l) was significantly associated with reduced volumes of total white matter (multi-adjusted β = -9.77, 95% confidence interval -19.48-0.06) and hippocampus (-0.23, -0.46-0.01), a lower MMSE score (-1.49, -2.67-0.31) and a higher likelihood of MCI (multi-adjusted odds ratio 3.21, 95% confidence interval 1.42-7.29). The mediation effects of structural brain measures on the associations between a low level of HDL-C and MMSE score or MCI were not statistically significant (p 0.05).This study suggests that low HDL-C may be involved in structural brain aging and cognitive dysfunction amongst rural-dwelling older adults in China, but the association of low HDL-C with cognitive aging phenotypes appears not to be mediated by brain structure.

As an enrichment strategy supplemented by the diagnostic framework of subjective cognitive decline (SCD), SCD plus identifies features that may increase the likelihood of including future-Alzheimer’s disease (AD) patients. This study aimed to identify the shared and distinct atrophy patterns between patients specified by SCD plus and amnestic mild cognitive impairment (aMCI, a prodromal stage of AD) and to investigate the extent that automated brain magnetic resonance imaging (MRI) volumetry can differentiate patients with SCD from normal control (NC) participants and patients with aMCI. We acquired structural MRI brain scans from 44 patients with aMCI, 40 patients with SCD (who met the major criteria of SCD plus), and 48 NC participants. Automatic brain segmentation was performed to quantify the volumetric measures of cognitive-relevant areas. These volumetric measures were compared across the 3 groups with analysis of variance. In addition, we performed support vector machine analyses using volumetric measures of single regions or multiple regions to further evaluate the sensitivity of automated brain volumetry in differentiating a specific group from another. The atrophy patterns in patients with aMCI and SCD were similar. Using the regional volumetric measures, we achieved high performance in differentiating aMCI and SCD from NCs (average classification accuracy [ACC] > 90%). However, the performance was not ideal when differentiating aMCI from SCD (ACC < 63%). In conclusion, patients with SCD specified by SCD plus presented similar atrophy patterns as patients with aMCI, which was distinguishable from NC participants. Future studies should aim to associate the atrophy patterns of SCD with possible conversion to aMCI or AD in a longitudinal design.

To realize an individual-level risk evaluation of progression of early Alzheimer's disease (AD), we applied an AD resemblance atrophy index (AD-RAI) to differentiate the subjects at risk of progression from normal subjects (NC) to mild cognitive impairment (MCI) and from MCI to AD. We included 183 subjects with a two-year follow-up: 50 NC stable (NCs), 23 NC-to-MCI converters (NCc), 50 MCI stable (MCIs), 35 MCI-to-AD converters (MCIc), 25 AD stable (ADs). ANCOVA analyses were used to identify baseline brain atrophy in converters compared with non-converters. To explore the relative merits of AD-RAI over individual regional volumetric measures in prediction of disease progression, we searched for the optimal cutoff for each measure in logistic regressions and plotted the longitudinal trajectories of these brain volumetric measures in converters and non-converters. Baseline AD-RAI performed the best in differentiating NCc from NCs (odds ratio 26.35, AUC 0.740) and MCIc from MCIs (odds ratio 8.91, AUC 0.771). The AD-RAI presented greater increase in the second year for NCc vs. NCs but not for MCIc vs. MCIs. Baseline AD-RAIs were also associated with CSF-based and PET-based AD biomarkers. These results showed the potential of AD-RAI in early risk estimation before progression to MCI/AD at an individual-level.

BackgroundMagnetic resonance imaging (MRI) has a wide range of applications in medical imaging. Recently, studies based on deep learning algorithms have demonstrated powerful processing capabilities for medical imaging data. Previous studies have mostly focused on common diseases that usually have large scales of datasets and centralized the lesions in the brain. In this paper, we used deep learning models to process MRI images to differentiate the rare neuromyelitis optical spectrum disorder (NMOSD) from multiple sclerosis (MS) automatically, which are characterized by scattered and overlapping lesions.MethodsWe proposed a novel model structure to capture 3D MRI images’ essential information and converted them into lower dimensions. To empirically prove the efficiency of our model, firstly, we used a conventional 3-dimensional (3D) model to classify the T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) images and proved that the traditional 3D convolutional neural network (CNN) models lack the learning capacity to distinguish between NMOSD and MS. Then, we compressed the 3D T2-FLAIR images by a two-view compression block to apply two different depths (18 and 34 layers) of 2D models for disease diagnosis and also applied transfer learning by pre-training our model on ImageNet dataset.ResultsWe found that our models possess superior performance when our models were pre-trained on ImageNet dataset, in which the models’ average accuracies of 34 layers model and 18 layers model were 0.75 and 0.725, sensitivities were 0.707 and 0.708, and specificities were 0.759 and 0.719, respectively. Meanwhile, the traditional 3D CNN models lacked the learning capacity to distinguish between NMOSD and MS.ConclusionThe novel CNN model we proposed could automatically differentiate the rare NMOSD from MS, especially, our model showed better performance than traditional3D CNN models. It indicated that our 3D compressed CNN models are applicable in handling diseases with small-scale datasets and possess overlapping and scattered lesions.

Background: To compare brain morphological differences in progressive supranuclear palsy (PSP), multiple system atrophy with the parkinsonian variant (MSA-P), Parkinson’s disease (PD) and controls by manual and automated measurements and to explore the feasibility of these measurements in disease differentiation.Methods: Ninety-five PSP patients (48 males, mean age 67.9 y), 32 MSA-P patients (18 males, mean age 63.0 y), 136 PD patients (72 males, mean age 66.6 y) and 100 controls (50 males, mean age 66 y) were included. The 12 manual measurements were acquired. Relative brain structural volumes adjusted according to the intracranial volume (ICV) of different brain regions werealsoquantified. Differences among and between groups were evaluated. Receiver operating characteristic curve analysis was used to assess diagnostic performance and define cutoff values of these measures.Results: P/M area 2.0displayed the highest diagnostic performance (AUC: 0.801) for distinguishing PSP from MSA-P or PD (sensitivity69.5%, specificity 82.1%). Furthermore, the combination of morphological features in manual parameters (P/M area 2.0, MRPI and M/P diameter) and volume atrophy in the midbrain improved the PSP discrimination (AUC: 0.870, sensitivity 76.8%, specificity 83.9%). The relative volume of the putamen can better differentiate MSA-P from PSP and PD (AUC: 0.844, sensitivity 81.3%, specificity 75.3%). Similarly, the ability to differentially diagnose MSA-P increased most significantly (AUC: 0.927, sensitivity 87.5%, specificity 87.9%) when combing volume atrophy in the putamen with the caudate and manual parameter (M/P diameter).Conclusion: Manual and automated MR variables can reveal atrophy features of the brain and be helpful in the differential diagnosis.

Cushing's syndrome (CS) provides a unique model for assessing the neurotoxic effect of chronic hypercortisolism on human brains. With the ongoing development of different computer-assisted tools, four research stages emerged, each with its own pearls and pitfalls. This review summarizes current knowledge and describes the dynamic changes of views on the brain changes of CS, especially in the current era of the rapid development of artificial intelligence and big data. The adverse effects of GC on brain are proven to be on structural, functional and cellular levels at the same time.

Background Serum 2-oxoglutarate can reflect the severity of chronic heart failure (CHF) in patients without diabetes. Whether this predictive role persists in type 2 diabetes mellitus (T2DM) patients is unclear. In this study, we investigated this predictive role in T2DM patients and whether 2-oxoglutarate can indicate the diastolic or systolic function of left ventricle. Methods One hundred eighty CHF patients (76 with T2DM) and 66 healthy controls were studied. 2-Oxoglutarate was assayed by liquid chromatography-mass spectrometry/mass spectrometry. Echocardiographic parameters, N-terminal pro-B-type natriuretic peptide (NT-proBNP) and other parameters were measured. Results 2-Oxoglutarate was increased in CHF patients with or without T2DM compared with controls (both P Conclusions The levels of 2-oxoglutarate can reflect the clinical severity of CHF in nondiabetic patients but not in those with T2DM, and it can be used as a potential indicator of the systolic dysfunction of the left ventricle.

This study aims to compare the performance of an MRI-based composite index (HSI) with conventional MRI-based measures in hippocampal sclerosis (HS) detection and postoperative outcome estimation.Seventy-two temporal lobe epilepsy (TLE) patients with pathologically confirmed HS and fifteen TLE patients without HS were included retrospectively. The T1-weighted and FLAIR images of these patients were processed with AccuBrain to quantify the hippocampal volume (HV) and the hippocampal FLAIR signal. The HSI index that considered both HV and hippocampal FLAIR signal was also calculated. Two experienced neuropathologists rated the HS severity with the resected tissue and reached an agreement for all cases. The asymmetry indices of the MRI measures were used to lateralize the sclerotic side, and the original MRI measures were applied to detect HS vs. normal hippocampi. Operating characteristic curve (ROC) analyses were performed for these predictions. We also investigated the sensitivity of the ipsilateral MRI measures in characterizing the pathological severity of HS and the associations of the MRI measures with postoperative outcomes (Engel class categories).With the optimal cutoffs, the asymmetry indices of HSI and HV both achieved excellent performance in differentiating left vs. right HS (accuracy = 100 %), and the absolute value of the asymmetry index of HSI performed best in differentiating unilateral vs. bilateral HS (accuracy = 91.7 %). Regarding the detection of HS, HSI performed better in sensitivity (94.4 % vs. 87.5 %) while HV performed better in specificity (93.6 % vs. 89.4 %) when the contralateral site of unilateral HS and both sides of non-HS patients were considered as the normal reference, and HSI performed even better than HV when only both sides of non-HS patients were considered as the normal reference (AUC: 0.956 vs. 0.934, p = 0.038). The ipsilateral HSI presented the strongest association with the pathological rating of HS severity (r = 0.405, p0.001). None of the ipsilateral or contralateral MRI measures was associated with the postoperative outcomes. Among the asymmetry indices, only the absolute value of the asymmetry index of HV presented a significant association with the Engel classifications for the Year 2∼3 visit (r = -0.466, p = 0.004) or the latest visit with1 year follow-up (r = -0.374, p = 0.003) while controlling for disease duration and follow-up duration.The HSI index and HV presented comparable good performance in HS detection, and HSI may have better sensitivity than HV in differentiating pathological HS severity. Higher magnitude of HV dissymmetry may indicate better post-surgical outcomes for HS patients.

Objectives: To evaluate white matter hyperintensities (WMH) quantification reproducibility from multiple aspects of view and examine the effects of scan–rescan procedure, types of scanner, imaging protocols, scanner software upgrade, and automatic segmentation tools on WMH quantification results using magnetic resonance imaging (MRI). Methods: Six post-stroke subjects (4 males; mean age = 62.8, range = 58–72 years) were scanned and rescanned with both 3D T1-weighted, 2D and 3D T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) MRI across four different MRI scanners within 12 h. Two automated WMH segmentation and quantification tools were used to measure WMH volume based on each MR scan. Robustness was assessed using the coefficient of variation (CV), Dice similarity coefficient (DSC), and intra-class correlation (ICC). Results: Experimental results show that the best reproducibility was achieved by using 3D T2-FLAIR MRI under intra-scanner setting with CV ranging from 2.69 to 2.97%, while the largest variability resulted from comparing WMH volumes measured based on 2D T2-FLAIR MRI with those of 3D T2-FLAIR MRI, with CV values in the range of 15.62%–29.33%. The WMH quantification variability based on 2D MRIs is larger than 3D MRIs due to their large slice thickness. The DSC of WMH segmentation labels between intra-scanner MRIs ranges from 0.63 to 0.77, while that for inter-scanner MRIs is in the range of 0.63–0.65. In addition to image acquisition, the choice of automatic WMH segmentation tool also has a large impact on WMH quantification. Conclusion: WMH reproducibility is one of the primary issues to be considered in multicenter and longitudinal studies. The study provides solid guidance in assisting multicenter and longitudinal study design to achieve meaningful results with enough power.

OBJECTIVECushing’s disease (CD) involves brain impairments caused by excessive cortisol. Whether these impairments are reversible in remitted CD after surgery has long been controversial due to a lack of high-quality longitudinal studies. In this study the authors aimed to assess the reversibility of whole-brain changes in remitted CD after transsphenoidal surgery (TSS), and its correlations with clinical and hormonal parameters, in the largest longitudinal study cohort to date for CD patient brain analysis.METHODSFifty patients with pathologically diagnosed CD and 36 matched healthy controls (HCs) were enrolled in a tertiary comprehensive hospital and national pituitary disease registry center in China. 3-T MRI studies were analyzed using an artificial intelligence–assisted web-based autosegmentation tool to quantify 3D brain volumes. Clinical parameters as well as levels of serum cortisol, adrenocorticotrophic hormone (ACTH), and 24-hour urinary free cortisol were collected for the correlation analysis. All CD patients underwent TSS and 46 patients achieved remission. All clinical, hormonal, and MRI parameters were reevaluated at the 3-month follow-up after surgery.RESULTSWidespread brain volume loss was observed in active CD patients compared with HCs, including total gray matter (p = 0.003, with false discovery rate [FDR] correction) and the frontal, parietal, occipital, and temporal lobes; insula; cingulate lobe; and enlargement of lateral and third ventricles (p < 0.05, corrected with FDR). All affected brain regions improved significantly after TSS (p < 0.05, corrected with FDR). In patients with remitted CD, total gray matter and most brain regions (except the frontal and temporal lobes) showed full recovery of volume, with volumes that did not differ from those of HCs (p > 0.05, corrected with FDR). ACTH and serum cortisol changes were negatively correlated with brain volume changes during recovery (p < 0.05).CONCLUSIONSThis study demonstrates the rapid reversal of total gray matter loss in remitted CD. The combination of full recovery areas and partial recovery areas after TSS is consistent with the incomplete recovery of memory and cognitive function observed in CD patients in clinical practice. Correlation analyses suggest that ACTH and serum cortisol levels are reliable serum biomarkers of brain recovery for clinical use after surgery.

A network-level investigation of the volumetric changes of subcortical stroke patients is still lacking. Here, we explored the alterations of structural covariance caused by subcortical stroke with automated brain volumetry. T1-weighed brain MRI scans were obtained from 63 normal controls (NC), 46 stroke patients with infarct in left internal capsule (CI_L), 33 stroke patients with infarct in right internal capsule (CI_R). We performed automatic anatomical segmentation of the T1-weighted brain images with AccuBrain. Volumetric structural covariance analyses were first performed within the basal ganglia structures that were both identified by voxel-based morphometry with AAL atlas and AccuBrain. Subsequently, we additionally included the infratentorial regions that were particularly quantified by AccuBrain for the structural covariance analyses and investigated the alterations of anatomical connections within these subcortical regions in CI_L and CI_R compared with NC. The association between the regional brain volumetry and motor function was also evaluated in stroke groups. There were significant and extensive volumetric differences in stroke patients. These significant regions were generally symmetric for CI_L and CI_R group depending on the side of stroke, involving both regions close to lesions and remote regions. The structural covariance analyses revealed the synergy volume alteration in subcortical regions both in CI_L and CI_R group. In addition, the alterations of volumetric structural covariance were more extensive in CI_L group than CI_R group. Moreover, we found that the subcortical regions with atrophy contributed to the deficits of motor function in CI_R group but not CI_L group, indicating a lesion-side effect of brain volumetric changes after stroke. These findings indicated that the chronic subcortical stroke patients have extensive disordered anatomical connections involving the whole-brain level network, and the connections patterns depend on the lesion-side., Hightlights • Chronic subcortical stroke patients show extensive brain volumetric atrophy. • Subcortical stroke patients show disordered structural covariance network pattern. • Brain volumetric and connections patterns change depend on the lesion-side.

This study aims to investigate the alterations of brain volumetry and associated structural covariance at lobar level in cerebral small-vessel disease (CSVD) with and without cognitive impairment.Twenty-seven CSVD patients with mild cognitive impairment (CSVD-MCI), 37 CSVD patients with normal cognition (CSVD-NC), and 35 controls, underwent T1-weighted imaging of magnetic resonance. Volume of gray matter (GM) and white matter (WM) and a lobar atrophy index that measures the ratio of cerebrospinal fluid to brain parenchyma were quantified for each lobe. One-way ANOVA with multiple comparison corrections was performed to compare these volumetric measures. Volumetric structural covariance analyses were performed with lobar atrophy indexes to investigate the alterations of anatomical covariance within each pair of lobar regions in CSVD-NC and CSVD-MCI subjects compared with controls.CSVD-NC subjects presented no significant volumetric differences with controls in any of the lobar regions. Compared with controls, CSVD-MCI patients presented significantly smaller volume of GM in bilateral frontal and parietal lobes, significantly smaller volume of WM in right cingulate lobe, and significantly larger lobar atrophy indexes of bilateral temporal, insular lobes and left cingulate lobe (P 0.05). Both CSVD-NC group and CSVD-MCI group showed significant differences of structural covariance as measured by lobar atrophy index compared with controls. In particular, CSVD-MCI group showed even more extensive alterations of structural covariance, especially in bilateral cingulate and temporal lobes.There are alterations of brain volumetry and associated structural covariance within lobar regions in CSVD, which indicates the potential brain structural reorganization in CSVD.

The exact relationship between serum myostatin and the severity and prognosis of chronic heart failure (CHF) is unclear. In this study, we investigated the association between serum myostatin and the severity and prognosis in patients with CHF.Two hundred and eighty-eight CHF patients and 62 healthy controls were studied. Cardiac ultrasound and serum myostatin, N-terminal pro-B-type natriuretic peptide (NT-proBNP) and other parameters were detected. CHF patients were divided into 3 groups according to tertiles of NT-proBNP or myostatin levels respectively.Serum myostatin levels were higher in CHF patients than in controls. New York Heart Association (NYHA) class IV patients had the highest levels of serum myostatin among the four NYHA classes. Compared with the low tertile NT-proBNP group, serum myostatin levels were significantly higher in the moderate and high tertile groups (15.47 ± 4.25 vs. 14.18 ± 3.69 ng/mL, p = .026; 16.28 ± 5.34 vs. 14.18 ± 3.69 ng/mL, p = .002). During 51-months follow-up, of 173 patients there were 36 deaths. Compared to survivors, nonsurvivors had significantly higher serum myostatin (18.11 ± 4.52 vs. 14.85 ± 5.11 ng/mL, p .01). Patients in the high tertile myostatin group had lower survival rate (73.95% vs. 93.75%; p .05) and larger number of CHF rehospitalization than those in the low tertile group. Cox regression analysis showed that serum myostatin was an independent predictor of mortality.Serum myostatin levels can reflect the severity of CHF and be a predictor of adverse prognosis in CHF patients.

Background One significant barrier to incorporate Alzheimer’s disease (AD) imaging biomarkers into diagnostic criteria is the lack of standardized methods for biomarker quantification. The European Alzheimer’s Disease Consortium-Alzheimer’s Disease Neuroimaging Initiative (EADC-ADNI) Harmonization Protocol project provides the most authoritative guideline for hippocampal definition and has produced a manually segmented reference dataset for validation of automated methods. Purpose To validate automated hippocampal volumetry using AccuBrain™, against the EADC-ADNI dataset, and assess its diagnostic performance for differentiating AD and normal aging in an independent cohort. Material and Methods The EADC-ADNI reference dataset comprise of manually segmented hippocampal labels from 135 volumetric T1-weighted scans from various scanners. Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), and Pearson’s r were obtained for AccuBrain™ and FreeSurfer. The magnetic resonance imaging (MRI) of a separate cohort of 299 individuals (150 normal controls, 149 with AD) were obtained from the ADNI database and processed with AccuBrain™ to assess its diagnostic accuracy. Area under the curve (AUC) for total hippocampal volumes (HV) and hippocampal fraction (HF) were determined. Results Compared with EADC-ADNI dataset ground truths, AccuBrain™ had a mean DSC of 0.89/0.89/0.89, ICC of 0.94/0.96/0.95, and r of 0.95/0.96/0.95 for right/left/total HV. AccuBrain™ HV and HF had AUC of 0.76 and 0.80, respectively. Thresholds of ≤ 5.71 mL and ≤ 0.38% afforded 80% sensitivity for AD detection. Conclusion AccuBrain™ provides accurate automated hippocampus segmentation in accordance with the EADC-ADNI standard, with great potential value in assisting clinical diagnosis of AD.

BACKGROUND Carotid artery plaque is associated with coronary artery disease (CAD). Besides the presence of plaque, plaque characteristics is also related to the severity of CAD. So the characteristic difference of carotid plaque may affect this assessment role. However, it is unclear whether the maximum carotid plaque area can reflect the extents and severity of CAD. METHODS We enrolled 388 consecutive CAD patients and 45 controls, and 204 patients were studied after excluding 184 patients without carotid plaque or coronary angiogram. Carotid intima media thickness and carotid plaque were measured by carotid ultrasound. Coronary angiography was applied and Gensini score was calculated. Blood lipid and other parameters were also detected. RESULTS The total and right maximum carotid plaque area were greater in high Gensini Score group than those in low and moderate score groups (both P

White matter hyperintensities (WMH) are common in acute ischemic stroke patients. Although WMH volume has been reported to influence post-stroke cognition, it is still not clear whether WMH location, independent of acute ischemic lesion (AIL) volume and location, contributes to cognitive impairment after stroke. Here, we proposed a multiple-lesion symptom mapping model that considers both the presence of WMH and AIL to measure the additional contribution of WMH locations to post-stroke cognitive impairment. Seventy-six first-ever stroke patients with AILs in the left hemisphere were examined by Montreal Cognitive Assessment (MoCA) at baseline and 1 year after stroke. The association between the location of AIL and WMH and global cognition was investigated by a multiple-lesion symptom mapping (MLSM) model based on support vector regression (SVR). To explore the relative merits of MLSM over the existing lesion-symptom mapping approaches with only AIL considered (mass-univariate VLSM and SVR-LSM), we measured the contribution of the significant AIL and/or WMH clusters from these models to post-stroke cognitive impairment. In addition, we compared the significant WMH locations identified by the optimal SVR-MLSM model for cognitive impairment at baseline and 1 year post stroke. The identified strategic locations of WMH significantly contributed to the prediction of MoCA at baseline (short-term) and 1 year (long-term) after stroke independent of the strategic locations of AIL. The significant clusters of WMH for short-term and long-term post-stroke cognitive impairment were mainly in the corpus callosum, corona radiata, and posterior thalamic radiation. We noted that in some regions, the AIL clusters that were significant for short-term outcome were no longer significant for long-term outcome, and interestingly more WMH clusters in these regions became significant for long-term outcome compared to short-term outcome. This indicated that there are some regions where local WMH burden has larger impact than AIL burden on the long-term post-stroke cognitive impairment. In consequence, SVR-MLSM was effective in identifying the WMH locations that have additional impact on post-stroke cognition on top of AIL locations. Such a method can also be applied to other lesion-behavior studies where multiple types of lesions may have potential contributions to a specific behavior.

Given that the morphology of Chinese brains statistically differs from that of Caucasian, there is an urgent need to develop a Chinese brain template for neuroimaging studies in Chinese populations. Based on a multi-center dataset, we developed a statistical Chinese brain template, named as Chinese2020 (Liang et al., 2015). This new Chinese brain atlas has been validated in brain normalization and segmentation for anatomical Magnetic Resonance Imaging (MRI) studies, and is publicly available at http://www.chinese-brain-atlases.org/. In our previous study, we have demonstrated this Chinese atlas showed higher accuracy in segmentation and relatively smaller shape deformations during registration. Because the spatial normalization of functional images is mainly based on the segmentation and normalization of anatomical image, the population-specific brain atlas should also be more appropriate for functional studies involving Chinese populations. The aim of this technology report is to validate the performance of Chinsese2020 template in functional neuroimaging studies, and demonstrated that for Chinese population studies, the use of the Chinese2010 template produces more valid results. The steps of how to use the Chinese2020 template in SPM software were given in details in this technology report, and based on an example of finger tapping fMRI study, this technology report demonstrated the Chinese2020 template could improve the performance of the neuroimaging analysis of Chinese populations.

Constructing an atlas from a population of brain images is of vital importance to medical image analysis. Especially in neuroscience study, creating a brain atlas is useful for intra‐ and inter‐population comparison. Research on brain atlas construction has attracted great attention in recent years, but the research on pediatric population is still limited, mainly due to the limited availability and the relatively low quality of pediatric magnetic resonance brain images. This article is targeted at creating a high quality representative brain atlas for Chinese pediatric population. To achieve this goal, we have designed a set of preprocessing procedures to improve the image quality and developed an intensity and sulci landmark combined groupwise registration method to align the population of images for atlas construction. As demonstrated in experiments, the newly constructed atlas can better represent the size and shape of brains of Chinese pediatric population, and show better performance in Chinese pediatric brain image analysis compared with other standard atlases. Hum Brain Mapp 35:3880–3892, 2014. © 2014 Wiley Periodicals, Inc.

Background In dogs with heart failure (HF) induced by overload pressure, the role of renal sympathetic denervation (RSD) on heart failure and in the renal artery is unclear. Therefore, we investigated the efficacy and safety of RSD in dogs with pressure overload-induced heart failure. Methods Twenty mongrel dogs were divided into a sham-operated group, an HF group and an HF + RSD group. In the sham-operated group, the abdominal aorta was located but was not constricted, in the HF group, the abdominal aorta was constricted without RSD, and the HF+RSD group underwent RSD with constriction of the abdominal aorta after 10 weeks. Blood sampling assays, echocardiography, intravascular ultrasound (IVUS) measurement and histopathological examination were performed. Results Renal sympathetic denervation caused a significant reduction in the levels of noradrenaline (166.62±6.84 vs. 183.48±13.66 pg/ml, P 0.05), plasma renin activity (1.93±0.12 vs. 2.10±0.13 ng/mlh, P 0.05) and B-type natriuretic peptide (71.14±3.86 vs. 83.15±5.73 pg/ml, P 0.05) at eight weeks after RSD in the HF+RSD group. Compared with the HF group at eight weeks, the left ventricular internal dimension at end-diastole and end-systole were lower and the left ventricular ejection fraction was higher (all P 0.05) at eight weeks after RSD in the HF+RSD group. Intravenous ultrasound images showed no changes in the renal artery lumen, and intimal hyperplasia and vascular lumen stenosis were not observed after RSD. Conclusions Renal sympathetic denervation could improve cardiac function in dogs with HF induced by pressure overload; RSD had no adverse influence on the renal artery.

Moyamoya disease leads to the formation of stenosis in the cerebrovasculature. A superficial temporal artery to middle cerebral artery (STA-MCA) bypass is an effective treatment for the disease, yet it is usually associated with postoperative cerebral hyperperfusion syndrome (CHS). This study aimed to evaluate cerebral hemodynamic changes immediately after surgery and assess whether a semiquantitative analysis of an intraoperative magnetic resonance perfusion-weighted image (PWI) is useful for predicting postoperative CHS. Fourteen patients who underwent the STA-MCA bypass surgery were included in this study. An atlas-based registration method was employed for studying hemodynamics in different cerebral regions. Pre- versus intraoperative and group-wise comparisons were conducted to evaluate the hemodynamic changes. A postoperative increase in relative cerebral blood flow (CBF) at the terminal MCA territory (P = 0.035) and drop in relative mean-time-transit at the central MCA territory (P = 0.012) were observed in all patients. However, a significant raise in the increasing ratio of relative-CBF at the terminal MCA territory was only found in CHS patients (P = 0.023). The cerebrovascular changes of the patients after revascularization treatment were confirmed. Intraoperative PWI might be helpful in predicting the change in relative-CBF at MCA terminal territory which might indicate a risk of CHS.

Background Intensity normalization is an important preprocessing step in brain magnetic resonance image (MRI) analysis. During MR image acquisition, different scanners or parameters would be used for scanning different subjects or the same subject at a different time, which may result in large intensity variations. This intensity variation will greatly undermine the performance of subsequent MRI processing and population analysis, such as image registration, segmentation, and tissue volume measurement. Methods In this work, we proposed a new histogram normalization method to reduce the intensity variation between MRIs obtained from different acquisitions. In our experiment, we scanned each subject twice on two different scanners using different imaging parameters. With noise estimation, the image with lower noise level was determined and treated as the high-quality reference image. Then the histogram of the low-quality image was normalized to the histogram of the high-quality image. The normalization algorithm includes two main steps: (1) intensity scaling (IS), where, for the high-quality reference image, the intensities of the image are first rescaled to a range between the low intensity region (LIR) value and the high intensity region (HIR) value; and (2) histogram normalization (HN),where the histogram of low-quality image as input image is stretched to match the histogram of the reference image, so that the intensity range in the normalized image will also lie between LIR and HIR. Results We performed three sets of experiments to evaluate the proposed method, i.e., image registration, segmentation, and tissue volume measurement, and compared this with the existing intensity normalization method. It is then possible to validate that our histogram normalization framework can achieve better results in all the experiments. It is also demonstrated that the brain template with normalization preprocessing is of higher quality than the template with no normalization processing. Conclusions We have proposed a histogram-based MRI intensity normalization method. The method can normalize scans which were acquired on different MRI units. We have validated that the method can greatly improve the image analysis performance. Furthermore, it is demonstrated that with the help of our normalization method, we can create a higher quality Chinese brain template.

The quantitative analysis of diffusion tensor image (DTI) data has attracted increasing attention in recent decades for studying white matter (WM) integrity and development. Among the current DTI analysis methods, tract-based spatial statistics (TBSS), as a pioneering approach for the voxelwise analysis of DTI data, has gained a lot of popularity due to its user-friendly framework. However, in recent years, the reliability and interpretability of TBSS have been challenged by several works, and several improvements over the original TBSS pipeline have been suggested. In this paper, we propose a new DTI statistical analysis method, named tractography atlas-based spatial statistics (TABSS). It doesn't rely on the accurate alignment of fractional anisotropy (FA) images for population analysis and gets rid of the skeletonization procedures of TBSS, which have been indicated as the major sources of error. Furthermore, TABSS improves the interpretability of results by directly reporting the resulting statistics on WM tracts, waiving the need of a WM atlas in the interpretation of the results. The feasibility of TABSS was evaluated in an example study to show age-related FA alternation pattern of healthy human brain. Through this preliminary study, it is validated that TABSS can provide detailed statistical results in a comprehensive and easy-to-understand way.

The goal of nonrigid image registration is to find a suitable transformation such that the transformed moving image becomes similar to the reference image. The image registration problem can also be treated as an optimization problem, which tries to minimize an objective energy function that measures the differences between two involved images. In this paper, we consider image matching as the process of aligning object boundaries in two different images. The registration energy function can be defined based on the total energy associated with the object boundaries. The optimal transformation is obtained by finding the equilibrium state when the total energy is minimized, which indicates the object boundaries find their correspondences and stop deforming. We make an analogy between the above processes with the dislocation system in physics. The object boundaries are viewed as dislocations (line defects) in crystal. Then the well-developed dislocation energy is used to derive the energy assigned to object boundaries in images. The newly derived registration energy function takes the global gradient information of the entire image into consideration, and produces an orientation-dependent and long-range interaction between two images to drive the registration process. This property of interaction endows the new registration framework with both fast convergence rate and high registration accuracy. Moreover, the new energy function can be adapted to realize symmetric diffeomorphic transformation so as to ensure one-to-one matching between subjects. In this paper, the superiority of the new method is theoretically proven, experimentally tested and compared with the state-of-the-art SyN method. Experimental results with 3-D magnetic resonance brain images demonstrate that the proposed method outperforms the compared methods in terms of both registration accuracy and computation time.

Segmentation of deep brain structures is a challenging task for MRI images due to blurry structure boundaries, small object size and irregular shapes. In this paper, we present a new atlas-based segmentation method. It first uses a prior spatial dependency tree to constrain the relative positions between different deep brain structures and determine an optimal sequence for the structureby-structure segmentation. After positioning the structures, the segmentation result is further fine tuned by a non-rigid registration procedure between the atlas image and the target image using the histogram of the gradient magnitudes lying on the structure boundaries. The proposed method has been applied on a publicly available MRI brain database and can achieve comparatively high segmentation accuracy.

Vessel enhancement is a primary preprocessing step for vessel segmentation and visualization of vasculatures. In this paper, a new vessel enhancement technique is proposed in order to produce accurate vesselness measures and vessel direction estimations that are less subject to local intensity abnormalities. The proposed method is called vessel enhancement using local line integrals and variational optimization (VE-LLI-VO). First, vessel enhancement using local line integrals (VE-LLI) is introduced in which a vessel model is embedded by regarding a vessel segment as a straight line based upon the second order information of the local line integrals. Useful quantities similar to the eigenvalues and eigenvectors of the Hessian matrix are produced. Moreover, based upon the local line integrals, junctions can be detected and handled effectively. This can help deal with the bifurcation suppression problem which exists in the Hessian-based enhancement methods. Then a more generic curve model is embedded to model vessels and a variational optimization (VO) framework is introduced to generate optimized vesselness measures. Experiments have been conducted on both synthetic images and retinal images. It is experimentally demonstrated that VE-LLI-VO produces improved performance as compared with the widely used techniques in terms of both vesselness measurement and vessel direction estimation.

This paper proposes a new non-rigid image registration method based on the formulation of the Demons algorithm. The proposed method utilizes combined geometric moments of local histogram to form new feature images. It greatly improves the accuracy of the original Demons algorithm, which is easy to get trapped at local minima during optimization. The local histogram-based features are rotation invariant and can capture sufficient spatial image information. During the registration process, local histogram-based feature images are built to substitute the original intensity images. This can reduce the possibility of being trapped at local solutions, and consequently improve the registration accuracy. The experimental results on both the synthetic image and real MRI image show that the proposed method can achieve higher accuracy than the Demons algorithm especially when the images are noisy.

Bone-conducted ultrasound (BCU) has been used to develop a bone-conducted ultrasonic hearing aid (BCUHA) since it was reported that normal-hearing humans as well as some profoundly deaf people can perceive ultrasound through bone conduction (BC). In order to make a comparison between BCUHA and the conventional bone-conducted hearing aid (BCHA), finite difference time domain (FDTD) method is used to calculate the sound field in human head for both BCU and BC audible sound. The study shows that BCU stimulus is more efficient in transmitting sound to both cochleae than the BC audible sound stimulus, suggesting that BCUHA performs better than BCHA.