Exploring cortical morphology biomarkers of amnesic mild cognitive impairment using novel fractal dimension-based structural MRI analysis.

Amnestic mild cognitive impairment (aMCI) is considered as an intermediate stage of Alzheimer's disease, but no MRI biomarkers currently distinguish aMCI from healthy individuals effectively. Fractal dimension, a quantitative parameter, provides superior morphological information compared to conventional cortical thickness methods. Few studies have used cortical fractal dimension values to differentiate aMCI from healthy controls. In this study, we aim to build an automated discriminator for accurately distinguishing aMCI using fractal dimension measures of the cerebral cortex. Thirty aMCI patients and 30 health controls underwent structural MRI of the brain. First, the atrophy of participants' cortical sub-regions of Desikan-Killiany cortical atlas was assessed using fractal dimension and cortical thickness. The fractal dimension is more sensitive than cortical thickness in reducing dimensional effects and may accurately reflect morphological changes of the cortex in aMCI. The aMCI group had significantly lower fractal dimension values in the bilateral temporal lobes, right limbic lobe and right parietal lobe, whereas they showed significantly lower cortical thickness values only in the bilateral temporal lobes. Fractal dimension analysis was able to depict most of the significantly different focal regions detected by cortical thickness, but additionally with more regions. Second, applying the measured fractal dimensions (and cortical thickness) of both cerebral hemispheres, an unsupervised discriminator was built for the aMCI and healthy controls. The proposed fractal dimension-based method achieves 80.54% accuracy in discriminating aMCI from healthy controls. The fractal dimension appears to be a promising biomarker for cortical morphology changes that can discriminate patients with aMCI from healthy controls.

Deep Learning Detection and Segmentation of Brain Arteriovenous Malformation on Magnetic Resonance Angiography.

BACKGROUND: The delineation of brain arteriovenous malformations (bAVMs) is crucial for subsequent treatment planning. Manual segmentation is time-consuming and labor-intensive. Applying deep learning to automatically detect and segment bAVM might help to improve clinical practice efficiency. PURPOSE: To develop an approach for detecting bAVM and segmenting its nidus on Time-of-flight magnetic resonance angiography using deep learning methods. STUDY TYPE: Retrospective. SUBJECTS: 221 bAVM patients aged 7-79 underwent radiosurgery from 2003 to 2020. They were split into 177 training, 22 validation, and 22 test data. FIELD STRENGTH/SEQUENCE: 1.5 T, Time-of-flight magnetic resonance angiography based on 3D gradient echo. ASSESSMENT: The YOLOv5 and YOLOv8 algorithms were utilized to detect bAVM lesions and the U-Net and U-Net++ models to segment the nidus from the bounding boxes. The mean average precision, F1, precision, and recall were used to assess the model performance on the bAVM detection. To evaluate the model's performance on nidus segmentation, the Dice coefficient and balanced average Hausdorff distance (rbAHD) were employed. STATISTICAL TESTS: The Student's t-test was used to test the cross-validation results (P < 0.05). The Wilcoxon rank test was applied to compare the median for the reference values and the model inference results (P < 0.05). RESULTS: The detection results demonstrated that the model with pretraining and augmentation performed optimally. The U-Net++ with random dilation mechanism resulted in higher Dice and lower rbAHD, compared to that without that mechanism, across varying dilated bounding box conditions (P < 0.05). When combining detection and segmentation, the Dice and rbAHD were statistically different from the references calculated using the detected bounding boxes (P < 0.05). For the detected lesions in the test dataset, it showed the highest Dice of 0.82 and the lowest rbAHD of 5.3%. DATA CONCLUSION: This study showed that pretraining and data augmentation improved YOLO detection performance. Properly limiting lesion ranges allows for adequate bAVM segmentation. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 1.

Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.

PURPOSE: To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. MATERIALS AND METHODS: This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year. RESULTS: We included 20 studies on various aspects of lung cancer diagnosis on CT scans. Quantitatively, DL algorithms exhibited superior sensitivity (82%) and specificity (75%) compared to human experts (sensitivity 81%, specificity 69%). However, the difference in specificity was statistically significant, whereas the difference in sensitivity was not statistically significant. The DL algorithms' performance varied across different imaging modalities and tasks, demonstrating the need for tailored optimization of DL algorithms. Notably, DL algorithms matched experts in sensitivity on standard CT, surpassing them in specificity, but showed higher sensitivity with lower specificity on LDCT scans. CONCLUSION: DL algorithms demonstrated improved accuracy over human readers in malignancy and invasiveness classification on CT scans. However, their performance varies by imaging modality, underlining the importance of continued research to fully assess DL algorithms' diagnostic effectiveness in lung cancer. CLINICAL RELEVANCE STATEMENT: DL algorithms have the potential to refine lung cancer diagnosis on CT, matching human sensitivity and surpassing in specificity. These findings call for further DL optimization across imaging modalities, aiming to advance clinical diagnostics and patient outcomes. KEY POINTS: Lung cancer diagnosis by CT is challenging and can be improved with AI integration. DL shows higher accuracy in lung cancer detection on CT than human experts. Enhanced DL accuracy could lead to improved lung cancer diagnosis and outcomes.

Altered brainstem-cortex activation and interaction in migraine patients: somatosensory evoked EEG responses with machine learning.

BACKGROUND: To gain a comprehensive understanding of the altered sensory processing in patients with migraine, in this study, we developed an electroencephalography (EEG) protocol for examining brainstem and cortical responses to sensory stimulation. Furthermore, machine learning techniques were employed to identify neural signatures from evoked brainstem-cortex activation and their interactions, facilitating the identification of the presence and subtype of migraine. METHODS: This study analysed 1,000-epoch-averaged somatosensory evoked responses from 342 participants, comprising 113 healthy controls (HCs), 106 patients with chronic migraine (CM), and 123 patients with episodic migraine (EM). Activation amplitude and effective connectivity were obtained using weighted minimum norm estimates with spectral Granger causality analysis. This study used support vector machine algorithms to develop classification models; multimodal data (amplitude, connectivity, and scores of psychometric assessments) were applied to assess the reliability and generalisability of the identification results from the classification models. RESULTS: The findings revealed that patients with migraine exhibited reduced amplitudes for responses in both the brainstem and cortical regions and increased effective connectivity between these regions in the gamma and high-gamma frequency bands. The classification model with characteristic features performed well in distinguishing patients with CM from HCs, achieving an accuracy of 81.8% and an area under the curve (AUC) of 0.86 during training and an accuracy of 76.2% and an AUC of 0.89 during independent testing. Similarly, the model effectively identified patients with EM, with an accuracy of 77.5% and an AUC of 0.84 during training and an accuracy of 87% and an AUC of 0.88 during independent testing. Additionally, the model successfully differentiated patients with CM from patients with EM, with an accuracy of 70.5% and an AUC of 0.73 during training and an accuracy of 72.7% and an AUC of 0.74 during independent testing. CONCLUSION: Altered brainstem-cortex activation and interaction are characteristic of the abnormal sensory processing in migraine. Combining evoked activity analysis with machine learning offers a reliable and generalisable tool for identifying patients with migraine and for assessing the severity of their condition. Thus, this approach is an effective and rapid diagnostic tool for clinicians.

Federated Learning: A Cross-Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery.

BACKGROUND: Deep learning-based segmentation algorithms usually required large or multi-institute data sets to improve the performance and ability of generalization. However, protecting patient privacy is a key concern in the multi-institutional studies when conventional centralized learning (CL) is used. PURPOSE: To explores the feasibility of a proposed lesion delineation for stereotactic radiosurgery (SRS) scheme for federated learning (FL), which can solve decentralization and privacy protection concerns. STUDY TYPE: Retrospective. SUBJECTS: 506 and 118 vestibular schwannoma patients aged 15-88 and 22-85 from two institutes, respectively; 1069 and 256 meningioma patients aged 12-91 and 23-85, respectively; 574 and 705 brain metastasis patients aged 26-92 and 28-89, respectively. FIELD STRENGTH/SEQUENCE: 1.5T, spin-echo, and gradient-echo [Correction added after first online publication on 21 August 2023. Field Strength has been changed to "1.5T" from "5T" in this sentence.]. ASSESSMENT: The proposed lesion delineation method was integrated into an FL framework, and CL models were established as the baseline. The effect of image standardization strategies was also explored. The dice coefficient was used to evaluate the segmentation between the predicted delineation and the ground truth, which was manual delineated by neurosurgeons and a neuroradiologist. STATISTICAL TESTS: The paired t-test was applied to compare the mean for the evaluated dice scores (p < 0.05). RESULTS: FL performed the comparable mean dice coefficient to CL for the testing set of Taipei Veterans General Hospital regardless of standardization and parameter; for the Taichung Veterans General Hospital data, CL significantly (p < 0.05) outperformed FL while using bi-parameter, but comparable results while using single-parameter. For the non-SRS data, FL achieved the comparable applicability to CL with mean dice 0.78 versus 0.78 (without standardization), and outperformed to the baseline models of two institutes. DATA CONCLUSION: The proposed lesion delineation successfully implemented into an FL framework. The FL models were applicable on SRS data of each participating institute, and the FL exhibited comparable mean dice coefficient to CL on non-SRS dataset. Standardization strategies would be recommended when FL is used. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 1.

Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction.

BACKGROUND: Rapid evolution of artificial intelligence (AI) prompted its wide application in healthcare systems. Stereotactic radiosurgery served as a good candidate for AI model development and achieved encouraging result in recent years. This article aimed at demonstrating current AI application in radiosurgery. METHODS: Literatures published in PubMed during 2010-2022, discussing AI application in stereotactic radiosurgery were reviewed. RESULTS: AI algorithms, especially machine learning/deep learning models, have been administered to different aspect of stereotactic radiosurgery. Spontaneous tumor detection and automated lesion delineation or segmentation were two of the promising application, which could be further extended to longitudinal treatment follow-up. Outcome prediction utilized machine learning algorithms with radiomic-based analysis was another well-established application. CONCLUSIONS: Stereotactic radiosurgery has taken a lead role in AI development. Current achievement, limitation, and further investigation was summarized in this article.

Prediction of individual trigeminal pain sensitivity from gray matter structure within the sensorimotor network.

OBJECTIVE: To determine whether multivariate pattern regression analysis based on gray matter (GM) images constrained to the sensorimotor network could accurately predict trigeminal heat pain sensitivity in healthy individuals. BACKGROUND: Prediction of individual pain sensitivity is of clinical relevance as high pain sensitivity is associated with increased risks of postoperative pain, pain chronification, and a poor treatment response. However, as pain is a subjective experience accurate identification of such individuals can be difficult. GM structure of sensorimotor regions have been shown to vary with pain sensitivity. It is unclear whether GM structure within these regions can be used to predict pain sensitivity. METHODS: In this cross-sectional study, structural magnetic resonance images and pain thresholds in response to contact heat stimulation of the left supraorbital area were obtained from 79 healthy participants. Voxel-based morphometry was used to extract segmented and normalized GM images. These were then constrained to a mask encompassing the functionally defined resting-state sensorimotor network. The masked images and pain thresholds entered a multivariate relevance vector regression analysis for quantitative prediction of the individual pain thresholds. The correspondence between predicted and actual pain thresholds was indexed by the Pearson correlation coefficient (r) and the mean squared error (MSE). The generalizability of the model was assessed by 10-fold and 5-fold cross-validation. Non-parametric permutation tests were used to estimate significance levels. RESULTS: Trigeminal heat pain sensitivity could be predicted from GM structure within the sensorimotor network with significant accuracy (10-fold: r = 0.53, p < 0.001, MSE = 10.32, p = 0.001; 5-fold: r = 0.46, p = 0.001, MSE = 10.54, p < 0.001). The resulting multivariate weight maps revealed that accurate prediction relied on multiple widespread regions within the sensorimotor network. CONCLUSION: A multivariate pattern of GM structure within the sensorimotor network could be used to make accurate predictions about trigeminal heat pain sensitivity at the individual level in healthy participants. Widespread regions within the sensorimotor network contributed to the predictive model.

Characteristic oscillatory brain networks for predicting patients with chronic migraine.

To determine specific resting-state network patterns underlying alterations in chronic migraine, we employed oscillatory connectivity and machine learning techniques to distinguish patients with chronic migraine from healthy controls and patients with other pain disorders. This cross-sectional study included 350 participants (70 healthy controls, 100 patients with chronic migraine, 40 patients with chronic migraine with comorbid fibromyalgia, 35 patients with fibromyalgia, 30 patients with chronic tension-type headache, and 75 patients with episodic migraine). We collected resting-state magnetoencephalographic data for analysis. Source-based oscillatory connectivity within each network, including the pain-related network, default mode network, sensorimotor network, visual network, and insula to default mode network, was examined to determine intrinsic connectivity across a frequency range of 1-40 Hz. Features were extracted to establish and validate classification models constructed using machine learning algorithms. The findings indicated that oscillatory connectivity revealed brain network abnormalities in patients with chronic migraine compared with healthy controls, and that oscillatory connectivity exhibited distinct patterns between various pain disorders. After the incorporation of network features, the best classification model demonstrated excellent performance in distinguishing patients with chronic migraine from healthy controls, achieving high accuracy on both training and testing datasets (accuracy > 92.6% and area under the curve > 0.93). Moreover, in validation tests, classification models exhibited high accuracy in discriminating patients with chronic migraine from all other groups of patients (accuracy > 75.7% and area under the curve > 0.8). In conclusion, oscillatory synchrony within the pain-related network and default mode network corresponded to altered neurophysiological processes in patients with chronic migraine. Thus, these networks can serve as pivotal signatures in the model for identifying patients with chronic migraine, providing reliable and generalisable results. This approach may facilitate the objective and individualised diagnosis of migraine.

ECG Approximate Entropy in the Elderly during Cycling Exercise.

Approximate entropy (ApEn) is used as a nonlinear measure of heart-rate variability (HRV) in the analysis of ECG time-series recordings. Previous studies have reported that HRV can differentiate between frail and pre-frail people. In this study, EEGs and ECGs were recorded from 38 elderly adults while performing a three-stage cycling routine. Before and after cycling stages, 5-min resting-state EEGs (rs-EEGs) and ECGs were also recorded under the eyes-open condition. Applying the K-mean classifier to pre-exercise rs-ECG ApEn values and body weights revealed nine females with EEG power which was far higher than that of the other subjects in all cycling stages. The breathing of those females was more rapid than that of other subjects and their average heart rate was faster. Those females also presented higher degrees of asymmetry in the alpha and theta bands (stronger power levels in the right frontal electrode), indicating stressful responses during the experiment. It appears that EEG delta activity could be used in conjunction with a very low ECG frequency power as a predictor of bursts in the heart rate to facilitate the monitoring of elderly adults at risk of heart failure. A resting ECG ApEn index in conjunction with the subject's weight or BMI is recommended for screening high-risk candidates prior to exercise interventions.

Acceleration of Magnetic Resonance Fingerprinting Reconstruction Using Denoising and Self-Attention Pyramidal Convolutional Neural Network.

Magnetic resonance fingerprinting (MRF) based on echo-planar imaging (EPI) enables whole-brain imaging to rapidly obtain T1 and T2* relaxation time maps. Reconstructing parametric maps from the MRF scanned baselines by the inner-product method is computationally expensive. We aimed to accelerate the reconstruction of parametric maps for MRF-EPI by using a deep learning model. The proposed approach uses a two-stage model that first eliminates noise and then regresses the parametric maps. Parametric maps obtained by dictionary matching were used as a reference and compared with the prediction results of the two-stage model. MRF-EPI scans were collected from 32 subjects. The signal-to-noise ratio increased significantly after the noise removal by the denoising model. For prediction with scans in the testing dataset, the mean absolute percentage errors between the standard and the final two-stage model were 3.1%, 3.2%, and 1.9% for T1, and 2.6%, 2.3%, and 2.8% for T2* in gray matter, white matter, and lesion locations, respectively. Our proposed two-stage deep learning model can effectively remove noise and accurately reconstruct MRF-EPI parametric maps, increasing the speed of reconstruction and reducing the storage space required by dictionaries.

Application of Convolutional Neural Network for Fingerprint-Based Prediction of Gender, Finger Position, and Height.

Fingerprints are the most common personal identification feature and key evidence for crime scene investigators. The prediction of fingerprints features include gender, height range (tall or short), left or right hand, and finger position can effectively narrow down the list of suspects, increase the speed of comparison, and greatly improve the effectiveness of criminal investigations. In this study, we used three commonly used CNNs (VGG16, Inception-v3, and Resnet50) to perform biometric prediction on 1000 samples, and the results showed that VGG16 achieved the highest accuracy in identifying gender (79.2%), left- and right-hand fingerprints (94.4%), finger position (84.8%), and height range (69.8%, using the ring finger of male participants). In addition, we visualized the CNN classification basis by the Grad-CAM technique and compared the results with those predicted by experts and found that the CNN model outperformed experts in terms of classification accuracy and speed, and provided good reference for fingerprints that were difficult to determine manually.

FDA Approval Summary: Selinexor for Relapsed or Refractory Diffuse Large B-Cell Lymphoma.

In June 2020, the U.S. Food and Drug Administration granted accelerated approval to selinexor for the treatment of adult patients with relapsed or refractory diffuse large B-cell lymphoma (DLBCL), not otherwise specified, including DLBCL arising from follicular lymphoma, after at least two lines of systemic therapy. Approval was based on SADAL, a multicenter trial of selinexor monotherapy in patients with DLBCL after two to five systemic regimens. Efficacy was based on independent review committee-assessed objective response rate (ORR) and duration of response using Lugano criteria. In 134 patients treated with the approved dosage (60 mg orally on days 1 and 3 of each week), the ORR was 29% (95% confidence interval, 22-38), with complete response in 13% and with 38% of responses lasting at least 6 months. Gastrointestinal toxicity developed in 80% of patients, hyponatremia in 61%, central neurological toxicity (such as dizziness and mental status changes) in 25%, and ocular toxicity in 18%. New or worsening grade 3 or 4 thrombocytopenia, lymphopenia, neutropenia, anemia, or hyponatremia developed in ≥15%. Adverse reactions led to selinexor dose interruption in 61% of patients, dose reduction in 49%, and permanent discontinuation in 17%, with thrombocytopenia being the leading cause of dose modifications. Postmarketing studies will evaluate reduced dosages of selinexor and further evaluate clinical benefit in patients with relapsed or refractory DLBCL. IMPLICATIONS FOR PRACTICE: Selinexor is a new potential option for adults with relapsed or refractory diffuse large B-cell lymphoma, not otherwise specified, in the third-line setting or beyond. Toxicities are typically manageable but can be difficult to tolerate and necessitate close monitoring and supportive care.

Brain volume changes in spontaneous intracranial hypotension: Revisiting the Monro-Kellie doctrine.

OBJECTIVES: In the application of the Monro-Kellie doctrine in spontaneous intracranial hypotension, the brain tissue volume is generally considered as a fixed constant. Traditionally, cerebral venous dilation is thought to compensate for decreased cerebrospinal fluid. However, whether brain tissue volume is invariable has not yet been explored. The objective of this study is to evaluate whether brain tissue volume is fixed or variable in spontaneous intracranial hypotension patients using automatic quantitative methods. METHODS: This retrospective and longitudinal study analyzed spontaneous intracranial hypotension patients between 1 January 2007 and 31 July 2015. Voxel-based morphometry was used to examine brain volume changes during and after the resolution of spontaneous intracranial hypotension. Brain structure volume was analyzed using Statistical Parametric Mapping version 12 and FMRIB Software Library v6.0. Post-treatment neuroimages were used as surrogate baseline measures. RESULTS: Forty-four patients with spontaneous intracranial hypotension were analyzed (mean [standard deviation] age, 37.8 [8.5] years; 32 female and 12 male). The whole brain tissue volume was decreased during spontaneous intracranial hypotension compared to follow-up (1180.3 [103.5] mL vs. 1190.4 [93.1] mL, difference: -10.1 mL [95% confidence interval: -18.4 to -1.8 mL], p = 0.019). In addition, ventricular cerebrospinal fluid volume was decreased during spontaneous intracranial hypotension compared to follow-up (15.8 [6.1] mL vs. 18.9 [6.9] mL, difference: -3.2 mL [95% confidence interval: -4.5 to -1.8 mL], p < 0.001). Longer anterior epidural cerebrospinal fluid collections, as measured by number of vertebral segments, were associated with greater reduction of ventricular cerebrospinal fluid volume (Pearson's r = -0.32, p = 0.036). CONCLUSION: The current study found the brain tissue volume and ventricular cerebrospinal fluid are decreased in spontaneous intracranial hypotension patients. The change in ventricular cerebrospinal fluid volume, but not brain tissue volume change, was associated with the severity of spinal cerebrospinal fluid leakage. These results challenge the assumption that brain tissue volume is a fixed constant.

Differences in Physiological Signals Due to Age and Exercise Habits of Subjects during Cycling Exercise.

Numerous studies indicated the physical benefits of regular exercise, but the neurophysiological mechanisms of regular exercise in elders were less investigated. We aimed to compare changes in brain activity during exercise in elderly people and in young adults with and without regular exercise habits. A total of 36 healthy young adults (M/F:18/18) and 35 healthy elderly adults (M/F:20/15) participated in this study. According to exercise habits, each age group were classified into regular and occasional exerciser groups. ECG, EEG, and EMG signals were recorded using V-AMP with a 1-kHz sampling rate. The participants were instructed to perform three 5-min bicycle rides with different exercise loads. The EEG spectral power of elders who exercised regularly revealed the strongest positive correlation with their exercise intensity by using Pearson correlation analysis. The results demonstrate that exercise-induced significant cortical activation in the elderly participants who exercised regularly, and most of the p-values are less than 0.001. No significant correlation was observed between spectral power and exercise intensity in the elders who exercised occasionally. The young participants who exercised regularly had greater cardiac and neurobiological efficiency. Our results may provide a new exercise therapy reference for adult groups with different exercise habits, especially for the elders.

Alteration of the Intra- and Inter-Lobe Connectivity of the Brain Structural Network in Normal Aging.

The morphological changes in cortical parcellated regions during aging and whether these atrophies may cause brain structural network intra- and inter-lobe connectivity alterations are subjects that have been minimally explored. In this study, a novel fractal dimension-based structural network was proposed to measure atrophy of 68 parcellated cortical regions. Alterations of structural network parameters, including intra- and inter-lobe connectivity, were detected in a middle-aged group (30-45 years old) and an elderly group (50-65 years old). The elderly group exhibited significant lateralized atrophy in the left hemisphere, and most of these fractal dimension atrophied regions were included in the regions of the "last-in, first-out" model. Globally, the elderly group had lower modularity values, smaller component size modules, and fewer bilateral association fibers. They had lower intra-lobe connectivity in the frontal and parietal lobes, but higher intra-lobe connectivity in the temporal and occipital lobes. Both groups exhibited similar inter-lobe connecting pattern. The elderly group revealed separations, sparser long association fibers, commissural fibers, and lateral inter-lobe connectivity lost effect, mainly in the right hemisphere. New wiring and reconfiguring modules may have occurred within the brain structural network to compensate for connectivity, decreasing and preventing functional loss in cerebral intra- and inter-lobe connectivity.

Intra- and Inter-Modular Connectivity Alterations in the Brain Structural Network of Spinocerebellar Ataxia Type 3.

In addition to cerebellar degeneration symptoms, patients with spinocerebellar ataxia type 3 (SCA3) exhibit extensive involvements with damage in the prefrontal cortex. A network model has been proposed for investigating the structural organization and functional mechanisms of clinical brain disorders. For neural degenerative diseases, a cortical feature-based structural connectivity network can locate cortical atrophied regions and indicate how their connectivity and functions may change. The brain network of SCA3 has been minimally explored. In this study, we investigated this network by enrolling 48 patients with SCA3 and 48 healthy subjects. A novel three-dimensional fractal dimension-based network was proposed to detect differences in network parameters between the groups. Copula correlations and modular analysis were then employed to categorize and construct the structural networks. Patients with SCA3 exhibited significant lateralized atrophy in the left supratentorial regions and significantly lower modularity values. Their cerebellar regions were dissociated from higher-level brain networks, and demonstrated decreased intra-modular connectivity in all lobes, but increased inter-modular connectivity in the frontal and parietal lobes. Our results suggest that the brain networks of patients with SCA3 may be reorganized in these regions, with the introduction of certain compensatory mechanisms in the cerebral cortex to minimize their cognitive impairment syndrome.

Changing patterns of asthma medication use related to US Food and Drug Administration long-acting ß2-agonist regulation from 2005-2011.

BACKGROUND: Safety concerns associated with long-acting ß2-agonists (LABAs) have led to many US Food and Drug Administration (FDA) regulatory activities for this class of drugs. Little is known about the effect of these regulatory activities on use of LABA-containing agents or other asthma medications. METHODS: We created rolling cohorts of pediatric and adult asthmatic patients in the Mini-Sentinel Distributed Database between January 2005 and June 2011. The proportions of asthmatic patients using LABA-containing products, inhaled corticosteroids (ICSs), leukotriene modifiers, short-acting ß2-agonists, oral corticosteroids, other bronchodilators, and no medications were measured on a monthly basis, and the changes were evaluated by using interrupted time series with segmented regression analysis. RESULTS: When the 2005 regulatory activity was announced, there were statistically significant decreases in the use of fixed-dose ICS-LABA agents in children (-0.98 percentage points) and adults (-1.24 percentage points). Increased use of ICSs and leukotriene modifiers was observed just after the regulatory activities were announced in both children and adults. Although of smaller magnitude, continued favorable changes in the use of LABA agents were observed after the 2010 FDA regulatory activity. CONCLUSION: The 2005 and 2010 FDA regulatory activities might have contributed to reduced use of LABA agents, as intended; however, their effect, independent of other factors, cannot be determined. Use of other classes of asthma medications was similarly affected.

Alterations of neocortico-limbic association fibers and correlation with diet in prediabetes diagnosed by impaired fasting glucose.

PURPOSE: To assess the existence of alterations in the micro-integrity of the fasciculus in prediabetic subjects. The issue of micro-integrity in white matter tracts has not been adequately addressed in prediabetes. MATERIALS AND METHODS: Sixty-four prediabetic subjects and 54 controls were enrolled. All participants completed 24-hour diet records and 3-day diet records and received diffusion tensor imaging at 3T. The data for white matter micro-integrity were analyzed and compared between prediabetic subjects and controls with age and gender as covariates. In addition, voxel-wise regression between white matter micro-integrity, diet, and preprandial glucose levels were used to explore the relationship between white matter micro-integrity and diet or serum glucose levels. RESULTS: We found that prediabetic subjects had significant reductions in the micro-integrity of bilateral anterior thalamic radiation, left inferior longitudinal fasciculus, and left superior longitudinal fasciculus (corrected P < 0.05). In addition, total carbohydrate intake amount and preprandial serum glucose levels were negatively correlated with the micro-integrity in the left inferior longitudinal fasciculus and left anterior thalamic radiation (r: -0.47, corrected P < 0.05). CONCLUSION: Restrictive alterations in the white matter micro-integrity of the anterior thalamic radiation and inferior and superior longitudinal fasciculi might represent the initial "hot spots" for white matter tract alterations, which might play a role in the development of prediabetes. J. Magn. Reson. Imaging 2016;43:1500-1506.

Association of Genetic Polymorphisms of Renin-Angiotensin-Aldosterone System-Related Genes with Arterio-Venous Fistula Malfunction in Hemodialysis Patients.

Hemodialysis (HD) is the most commonly-used renal replacement therapy for patients with end-stage renal disease worldwide. Arterio-venous fistula (AVF) is the vascular access of choice for HD patients with lowest risk of infection and thrombosis. In addition to environmental factors, genetic factors may also contribute to malfunction of AVF. Previous studies have demonstrated the effect of genotype polymorphisms of angiotensin converting enzyme on vascular access malfunction. We conducted a multicenter, cross-sectional study to evaluate the association between genetic polymorphisms of renin-angiotensin-aldosterone system and AVF malfunction. Totally, 577 patients were enrolled. Their mean age was 60 years old and 53% were male. HD patients with AVF malfunction had longer duration of HD (92.5 ± 68.1 vs. 61.2 ± 51.9 months, p < 0.001), lower prevalence of hypertension (44.8% vs. 55.3%, p = 0.025), right-sided (31.8% vs. 18.4%, p = 0.002) and upper arm AVF (26.6% vs. 9.7%, p < 0.001), and higher mean dynamic venous pressure (DVP) (147.8 ± 28.3 vs. 139.8 ± 30.0, p = 0.021). In subgroup analysis of different genders, location of AVF and DVP remained significant clinical risk factors of AVF malfunction in univariate and multivariate binary logistic regression in female HD patients. Among male HD patients, univariate binary logistic regression analysis revealed that right-side AVF and upper arm location are two important clinical risk factors. In addition, two single nucleotide polymorphisms (SNPs), rs275653 (Odds ratio 1.90, p = 0.038) and rs1492099 (Odds ratio 2.29, p = 0.017) of angiotensin II receptor 1 (AGTR1), were associated with increased risk of AVF malfunction. After adjustment for age and other clinical factors, minor allele-containing genotype polymorphisms (AA and CA) of rs1492099 still remained to be a significant risk factor of AVF malfunction (Odds ratio 3.63, p = 0.005). In conclusion, we demonstrated that rs1492099, a SNP of AGTR1 gene, could be a potential genetic risk factor of AVF malfunction in male HD patients.

Decreased inter-hemispheric connectivity in anterior sub-network of default mode network and cerebellum: significant findings in major depressive disorder.

The issue of inter-hemispheric connectivity is an emerging new area in understanding the pathophysiology of depression. This study was designed to analyse the pattern of inter-hemispheric connectivity in patients with major depressive disorder (MDD). The resting-state functional magnetic resonance imaging (RFMRI) was acquired in all enrolled patients and controls. We used a method of voxel-mirrored homotopic connectivity (VMHC) to estimate the significant differences in inter-hemispheric connectivity between 44 patients with first-episode medication-naïve MDD and 27 normal controls. The patients and controls were matched for age and gender. The patients with first-episode medication-naïve MDD showed lower VMHC than normal controls in bilateral medial frontal cortex, anterior cingulate and cerebellar posterior lobe. The strength of inter-hemispheric connectivity VMHC value was negatively correlated with clinical severity of MDD. From the results, we suggested that decreased inter-hemispheric connectivity in the anterior sub-network of the default mode network and the cerebellar posterior lobe might represent an emerging finding in the pathophysiology for MDD.