Resting-State Functional Connectivity and Brain Network Abnormalities in Depressive Patients with Suicidal Ideation.

Our study aimed to investigate whether changes in brain function measured with functional magnetic resonance imaging (fMRI) can be detected among individuals with depressive disorders and suicidal ideation. The association between depression severity and brain images is also discussed. Our study recruited 111 participants in three groups: 35 depressive patients with suicidal ideation (SI), 32 depressive patients without suicidal ideation (NS), and 44 healthy controls (HCs). All participants were scanned using 3T MRI to obtain resting-state functional images, and functional connectivity (FC), amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and graph theoretical analysis (GTA) were performed. We found functional activity differences, such as the hippocampus and thalamus, in the SI group compared with the NS group. We also concluded lower activity in the thalamus and cuneus regions were related to suicidal ideation. We also found several functional connectivity of the brain areas, such as hippocampus, cuneus, and frontal regions, in the SI group correlated with Hamilton Depression Rating Scale (HAM-D) and Hospital Anxiety and Depression Scale (HADS). A graph theoretical analysis (GTA) and network-based statistical (NBS) analysis revealed different topological organization and slightly better local segregation of the brain network in healthy participants compared with those in depressive patients with suicidal ideation. We suggest that brain functional connectivity may be affected in depressive patients with suicidal ideation.

Cedrus atlantica Extract Suppress Glioblastoma Growth through Promotion of Genotoxicity and Apoptosis: In Vitro and In Vivo Studies.

Glioblastoma (GBM) is the most common malignant primary brain tumor in humans, exhibiting highly infiltrative growth and drug resistance to conventional chemotherapy. Cedrus atlantica (CAt) extract has been shown to decrease postoperative pain and inhibit the growth of K562 leukemia cells. The aim of this study was to assess the anti-GBM activity and molecular mechanism of CAt extract in vitro and in vivo. The results showed that CAt extract greatly suppressed GBM cells both in vitro and in vivo and enhanced the survival rate in subcutaneous and orthotopic animal models. Moreover, CAt extract increased the level of ROS and induced DNA damage, resulting in cell cycle arrest at the G0/G1 phase and cell apoptosis. Western blotting results indicated that CAt extract regulates p53/p21 and CDK4/cyclin D1 protein expression and activates extrinsic and intrinsic apoptosis. Furthermore, CAt extract enhanced the cytotoxicity of Temozolomide and decreased AKT/mTOR signaling by combination treatment. In toxicity assays, CAt extract exhibited low cytotoxicity toward normal cells or organs in vitro and in vivo. CAt extract suppresses the growth of GBM by induction of genotoxicity and activation of apoptosis. The results of this study suggest that CAt extract can be developed as a therapeutic agent or adjuvant for GBM treatment in the future.

Disrupted white matter connectivity and organization of brain structural connectomes in tuberous sclerosis complex patients with neuropsychiatric disorders using diffusion tensor imaging.

OBJECTIVE: Tuberous sclerosis complex (TSC) is a genetic neurocutaneous syndrome with variable and unpredictable neurological comorbidity that includes epilepsy, intellectual disability (ID), autism spectrum disorder, and neurobehavioral abnormalities. The degree of white matter involvement is believed to be associated with the severity of neurological impairment. The goal of the present study was to evaluate diffusion characteristics of tubers, white matter lesions, and brain structural network alterations in TSC patients using diffusion tensor imaging (DTI), graph theoretical analysis (GTA), and network-based statistical (NBS) analysis. MATERIALS AND METHODS: Forty-two patients with a definitive diagnosis of TSC were recruited for this study. All patients underwent brain DTI examination using a 3 T magnetic resonance imaging system. Mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) values, and fractional anisotropy (FA) mapping in 52 tubers and white matter lesions were measured and compared with those of contralateral normal regions. GTA was performed on the inter-regional connectivity matrix, and NBS analysis was used to identify the significance of any connected subnetworks evident in the set of altered connections. For neurological severity subgrouping, a neurological severity score was assigned to TSC patients including those with ID, seizure, autism, and other neuropsychiatric disorders (NPDs). RESULTS: Significantly higher MD, AD, and RD, and lower FA values, were found in TSC lesions compared with those measured in contralateral normal regions for tubers (P < 0.05). GTA and NBS analysis provided better local segregation but worse global integration of the structural network (regular-like network) in TSC patients with ID, seizure, and higher Neurological Severity Score. Disrupted subnetworks in TSC patients with severe status included connections from the frontal lobe to the parietal lobe, temporal lobe to the caudate, and temporal lobe to the insula. DISCUSSION: DTI has the potential to provide valuable information about cytoarchitectural changes in TSC lesions beyond morphological MRI findings alone. Using GTA and NBS, current results provide the information of disrupted white matter connectivity and organization in TSC patients with different neuropsychological impairments.

Differential associations of proinflammatory and anti-inflammatory cytokines with depression severity from noncancer status to breast cancer course and subsequent chemotherapy.

BACKGROUND: In this study, we examined the differential associations of various proinflammatory and anti-inflammatory cytokines with depression severity from the development of breast cancer to subsequent chemotherapy treatment. METHODS: A cross-sectional study was conducted on a sample of 116 women: 29 controls without cancer, 55 patients with breast cancer who were not receiving chemotherapy, and 32 patients with breast cancer who were receiving chemotherapy. Blood samples were assayed to evaluate serum levels of the following cytokines: interferon-γ, interleukin (IL)-12 (p70), IL-1ß, IL-2, tumor necrosis factor (TNF)-α, IL-4, IL-5, IL-10, IL-13, IL-6, and IL-17A. Depression severity was assessed using the Patient Health Questionnaire. RESULTS: After adjustment for sociodemographics, consistent patterns of the association between cytokine and depression were noted in the different groups. No significant associations were observed in the controls. Inverse associations were observed between cytokines levels and depression severity in patients with breast cancer who were not receiving chemotherapy, whereas positive associations were noted in patients with breast cancer who were receiving chemotherapy. Specific differential relationships between IL-5 levels and depression severity were found between patients with breast cancer who were receiving and not receiving chemotherapy. CONCLUSIONS: Our study revealed differential relationships between cytokine levels and depression severity with the development of cancer. Immunostimulation and immunosuppression in breast cancer and cancer treatment may account for the differential responses with the development of breast cancer.

Extract Derived from Cedrus atlantica Acts as an Antitumor Agent on Hepatocellular Carcinoma Growth In Vitro and In Vivo.

Cedrus atlantica is widely used in herbal medicine. However, the anti-cancer activity of C. atlantica extract (CAt extract) has not been clarified in hepatocellular carcinoma. In the study, we elucidated the anti-hepatoma capacity of CAt extract on HCC in vitro and in vivo. To explore the anti-hepatoma mechanisms of the CAt extract in vitro, HCC and normal cells were treated with the CAt extract, which showed marked inhibitory effects on HCC cells in a dose-dependent manner; in contrast, the CAt extract treatment was less cytotoxic to normal cells. In addition, our results indicate that the CAt extract induced apoptosis via caspase-dependent and independent apoptosis pathways. Furthermore, the CAt extract inhibited HCC tumor cell growth by restraining cell cycle progression, and it reduced the signaling of the AKT, ERK1/2, and p38 pathways. In the xenograft model, the CAt extract suppressed HCC tumor cell growth and prolonged lifespan by inhibiting PCNA protein expression, repressing part of the VEGF-induced autocrine pathway, and triggering strong expression of cleaved caspase-3, which contributed to cell apoptosis. Moreover, the CAt extract did not induce any obvious changes in pathological morphology or body weight, suggesting it had no toxicity. CAt extract exerted anti-tumor effects on HCC in vitro and in vivo. Thus, CAt extract could be used as a potential anti-cancer therapeutic agent against HCC.

Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning.

PURPOSE: Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherapy drugs may result in brain damage during treatment. When a patient's brain is changed in response to chemo drugs, it is termed chemo-brain. In this study, we aimed to construct machine-learning models to detect the subtle alternations of the brain in postchemotherapy BC patients. METHODS: Nineteen BC patients undergoing chemotherapy and 20 healthy controls (HCs) were recruited for this study. Both groups underwent resting-state functional MRI and generalized q-sampling imaging (GQI). RESULTS: Logistic regression (LR) with GQI indices in standardized voxel-wise analysis, LR with mean regional homogeneity in regional summation analysis, decision tree classifier (CART) with generalized fractional anisotropy in voxel-wise analysis, and XGBoost (XGB) with normalized quantitative anisotropy had formidable performances in classifying subjects into a chemo-brain group or an HC group. Classifying the brain MRIs of HC and postchemotherapy patients by conducting leave-one-out cross-validation resulted in the highest accuracy of 84%, which was attained by LR, CART, and XGB with multiple feature sets. CONCLUSIONS: In our study, we constructed the machine-learning models that were able to identify chemo-brains from normal brains. We are hopeful that these results will be helpful in clinically tracking chemo-brains in the future.

Investigation of chemotherapy-induced brain structural alterations in breast cancer patients with generalized q-sampling MRI and graph theoretical analysis.

BACKGROUND: Breast neoplasms are the most common cancer among women in Taiwan. Cognitive deficits are common complications of breast cancer survivors treated with chemotherapy. The most frequently observed disorders involve executive function and memory impairment. With improvements in tumor intervention and the consequent increase in the number of cancer survivors, the quality of life of patients has become an important issue. We are interested in the early effects of chemotherapy on the brain structures of patients. In addition, generalized q-sampling imaging (GQI), a wide range of q-space datasets for a more accurate and sophisticated diffusion MR approach, was first used in this topic. METHODS: As diffusion tensor imaging (DTI) is associated with restrictions in the resolution of crossing fibers, we attempted to use GQI, which can overcome these difficulties and is advantageous over DTI for tractography of the crossing fibers. This cross-sectional study included two groups: breast cancer survivors who had completed their chemotherapy (n = 19) and healthy controls (n = 20). All participants underwent diffusion MRI exams and neuropsychological assessments. We included four parts in our image analysis, i.e., voxel-based statistical analysis, multiple regression analysis, graph theoretical analysis and network-based statistical analysis. RESULTS: The results from the voxel-based statistical analysis showed significantly lower GFA and NQA values in the breast cancer group than those in the control group. We found significant positive correlations between the FACT-Cog and GQI indices. In the graph theoretical analysis, the breast cancer group demonstrated significantly longer characteristic path length. Adjuvant chemotherapy affected the integrity of white matter and resulted in poor cognitive performance, as indicated by the correlations between the neuropsychological assessment scales and the GQI indices. In addition, it was found that the characteristic path lengths in the breast cancer group increased, indicating that the brain network integration became worse. CONCLUSIONS: Our study demonstrated alterations in structural brain networks and associated neuropsychological deficits among breast cancer survivors.

Antitumor Effect of n-Butylidenephthalide Encapsulated on B16/F10 Melanoma Cells In Vitro with a Polycationic Liposome Containing PEI and Polyethylene Glycol Complex.

Advanced melanoma can metastasize to distal organs from the skin and yield an aggressive disease and poor prognosis even after treatment with chemotherapeutic agents. The compound n-Butylidenephthalide (BP) is isolated from Angelica sinensis, which is used to treat anemia and gynecological dysfunction in traditional Chinese medicine. Studies have indicated that BP can inhibit cancers, including brain, lung, prostate, liver, and colon cancers. However, because BP is a natural hydrophobic compound, it is quickly metabolized by the liver within 24 h, and thus has limited potential for development in cancer therapy. This study investigated the anticancer mechanisms of BP through encapsulation with a novel polycationic liposome containing polyethylenimine (PEI) and polyethylene glycol complex (LPPC) in melanoma cells. The results demonstrated that BP/LPPC had higher cytotoxicity than BP alone and induced cell cycle arrest at the G0/G1 phase in B16/F10 melanoma cells. The BP/LPPC-treated cell indicated an increase in subG1 percentage and TUNEL positive apoptotic morphology through induction of extrinsic and intrinsic apoptosis pathways. The combination of BP and LPPC and clinical drug 5-Fluorouracil had a greater synergistic inhibition effect than did a single drug. Moreover, LPPC encapsulation improved the uptake of BP values through enhancement of cell endocytosis and maintained BP cytotoxicity activity within 24 h. In conclusion, BP/LPPC can inhibit growth of melanoma cells and induce cell arrest and apoptosis, indicating that BP/LPPC has great potential for development of melanoma therapy agents.

Association of Longitudinal Changes in Cerebral Microstructure with Cognitive Functioning in Breast Cancer Survivors after Adjuvant Chemotherapy.

Background: Adjuvant chemotherapy for breast cancer might impact cognitive function and brain structure. Methods: In this study, we investigated the cerebral microstructural changes in breast cancer survivors after adjuvant chemotherapy and the correlation with cognitive function with both cross-sectional and longitudinal study designs. All participants underwent structural MRI. In total, we recruited 67 prechemotherapy patients (BB), 67 postchemotherapy patients (BA), and 77 healthy controls (BH). For the follow-up study, 28 participants in the BH and 28 in the BB groups returned for imaging and assessment (BHF, BBF). Voxel-based morphometry analysis was performed to evaluate differences in brain volume; vertex-based shape analysis was used to assess the shape alterations of subcortical regions. Moreover, multiple regression was applied to assess the association between the changes in neuropsychological assessment and brain volume. Results: The results showed brain volume reduction in the temporal and parietal gyrus in BB and BA patients. Among each group, we also found significant shape alterations in the caudate and thalamus. Volume reductions in the temporal regions and shape changes in the caudate and hippocampus were also observed in patients from time point 1 to time point 2 (postchemotherapy). An association between brain volume and cognitive performance was also found in the limbic system. Conclusions: Based on our findings, we can provide a better understanding of the cerebral structural changes in breast cancer survivors, establish a subsequent prediction model, and serve as a reference for subsequent treatment.

Longitudinal assessment of chemotherapy-induced brain connectivity changes in cerebral white matter and its correlation with cognitive functioning using the GQI.

Objective: Breast cancer was the most prevalent type of cancer and had the highest incidence rate among women worldwide. The wide use of adjuvant chemotherapy might have a detrimental effect on the human brain and result in chemotherapy-related cognitive impairment (CICI) among breast cancer patients. Furthermore, prior to chemotherapy, patients reported cancer-related cognitive impairment (CRCI), which might be due to physiological factors or mood symptoms. The present longitudinal study aimed to investigate microstructural and macroscale white matter alterations by generalized q-sampling imaging (GQI). Methods: The participants were categorized into a pre-chemotherapy group (BB) if they were diagnosed with primary breast cancer and an age-matched noncancer control group (HC). Some participants returned for follow-up assessment. In the present follow up study, 28 matched pairs of BB/BBF (follow up after chemotherapy) individuals and 28 matched pairs of HC/HCF (follow up) individuals were included. We then used GQI and graph theoretical analysis (GTA) to detect microstructural alterations in the whole brain. In addition, we evaluated the relationship between longitudinal changes in GQI indices and neuropsychological tests as well as psychiatric comorbidity. Findings: The results showed that disruption of white matter integrity occurred in the default mode network (DMN) of patients after chemotherapy, such as in the corpus callosum (CC) and middle frontal gyrus (MFG). Furthermore, weaker connections between brain regions and lower segregation ability were observed in the post-chemotherapy group. Significant correlations were observed between neuropsychological tests and white matter tracts of the CC, MFG, posterior limb of the internal capsule (PLIC) and superior longitudinal fasciculus (SLF). Conclusion: The results provided evidence of white matter alterations in breast cancer patients, and they may serve as potential imaging markers of cognitive changes. In the future, the study may be beneficial to create and evaluate strategies designed to maintain or improve cognitive function in breast cancer patients undergoing chemotherapy.

Investigating the effect of hypertension on vascular cognitive impairment by using the resting-state functional connectome.

Hypertension (HTN) affects over 1.2 billion individuals worldwide and is defined as systolic blood pressure (BP) ≥ 140 mmHg and diastolic BP ≥ 90 mmHg. Hypertension is also considered a high risk factor for cerebrovascular diseases, which may lead to vascular cognitive impairment (VCI). VCI is associated with executive dysfunction and is also a transitional stage between hypertension and vascular dementia. Hence, it is essential to establish a reliable approach to diagnosing the severity of VCI. In 28 HTN (51-83 yrs; 18 males, 10 females) and 28 healthy controls (HC) (51-75 yrs; 7 males, 21 females), we investigated which regions demonstrate alterations in the resting-state functional connectome due to vascular cognitive impairment in HTN by using the amplitude of the low-frequency fluctuations (ALFF), regional homogeneity (ReHo), graph theoretical analysis (GTA), and network-based statistic (NBS) methods. In the group comparison between ALFF/ReHo, HTN showed reduced spontaneous activity in the regions corresponding to vascular or metabolic dysfunction and enhanced brain activity, mainly in the primary somatosensory cortex and prefrontal areas. We also observed cognitive dysfunction in HTN, such as executive function, processing speed, and memory. Both the GTA and NBS analyses indicated that the HTN demonstrated complex local segregation, worse global integration, and weak functional connectivity. Our findings show that resting-state functional connectivity was altered, particularly in the frontal and parietal regions, by hypertensive individuals with potential vascular cognitive impairment.

Visualizing oxidative stress-induced depression of cardiac vagal baroreflex by MRI/DTI in a mouse neurogenic hypertension model.

A clinical hallmark of hypertension is impairment of the cardiac vagal baroreflex, which maintains stable blood pressure and heart rate under physiological conditions. There is also evidence that oxidative stress in the brain is associated with neurogenic hypertension. We tested the hypothesis that an augmented superoxide level in the nucleus tractus solitarii (NTS), the terminal site of baroreceptor afferents, contributes to the depression of cardiac vagal baroreflex by disrupting the connectivity between the NTS and the nucleus ambiguus (NA), the origin of the vagus nerve, during neurogenic hypertension. An experimental model of neurogenic hypertension that employed intracerebroventricular infusion of angiotensin II in male adult C57BL/6 mice was used. Based on tractographic evaluations using magnetic resonance imaging/diffusion tensor imaging of the medulla oblongata in the brain stem, we found that the connectivity between the NTS and NA was disrupted in neurogenic hypertension, concurrent with impairment of the cardiac vagal baroreflex as detected by radiotelemetry. We further found that the disrupted NTS-NA connectivity was reversible, and was related to oxidative stress induced by augmented levels of NADPH oxidase-generated superoxide in the NTS. We conclude that depression of the cardiac vagal baroreflex induced by oxidative stress in the NTS in the context of neurogenic hypertension may be manifested in the form of dynamic alterations in the connectivity between the NTS and NA.

Improving the brain image resolution of generalized q-sampling MRI revealed by a three-dimensional CNN-based method.

Background: Understanding neural connections facilitates the neuroscience and cognitive behavioral research. There are many nerve fiber intersections in the brain that need to be observed, and the size is between 30 and 50 nanometers. Improving image resolution has become an important issue for mapping the neural connections non-invasively. Generalized q-sampling imaging (GQI) was used to reveal the fiber geometry of straight and crossing. In this work, we attempted to achieve super-resolution with a deep learning method on diffusion weighted imaging (DWI). Materials and methods: A three-dimensional super-resolution convolutional neural network (3D SRCNN) was utilized to achieve super-resolution on DWI. Then, generalized fractional anisotropy (GFA), normalized quantitative anisotropy (NQA), and the isotropic value of the orientation distribution function (ISO) mapping were reconstructed using GQI with super-resolution DWI. We also reconstructed the orientation distribution function (ODF) of brain fibers using GQI. Results: With the proposed super-resolution method, the reconstructed DWI was closer to the target image than the interpolation method. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were also significantly improved. The diffusion index mapping reconstructed by GQI also had higher performance. The ventricles and white matter regions were much clearer. Conclusion: This super-resolution method can assist in postprocessing low-resolution images. With SRCNN, high-resolution images can be effectively and accurately generated. The method can clearly reconstruct the intersection structure in the brain connectome and has the potential to accurately describe the fiber geometry on a subvoxel scale.

Detecting microstructural alterations of cerebral white matter associated with breast cancer and chemotherapy revealed by generalized q-sampling MRI.

Objective: Previous studies have discussed the impact of chemotherapy on the brain microstructure. There is no evidence of the impact regarding cancer-related psychiatric comorbidity on cancer survivors. We aimed to evaluate the impact of both chemotherapy and mental health problem on brain microstructural alterations and consequent cognitive dysfunction in breast cancer survivors. Methods: In this cross-sectional study conducted in a tertiary center, data from 125 female breast cancer survivors who had not received chemotherapy (BB = 65; 49.86 ± 8.23 years) and had received chemotherapy (BA = 60; 49.82 ± 7.89 years) as well as from 71 age-matched healthy controls (47.18 ± 8.08 years) was collected. Chemotherapeutic agents used were docetaxel and epirubicin. We used neuropsychological testing and questionnaire to evaluate psychiatric comorbidity, cognitive dysfunction as well as generalized sampling imaging (GQI) and graph theoretical analysis (GTA) to detect microstructural alterations in the brain. Findings: Cross-comparison between groups revealed that neurotoxicity caused by chemotherapy and cancer-related psychiatric comorbidity may affect the corpus callosum and middle frontal gyrus. In addition, GQI indices were correlated with the testing scores of cognitive function, quality of life, anxiety, and depression. Furthermore, weaker connections between brain regions and lower segregated ability were found in the post-treatment group. Conclusion: This study suggests that chemotherapy and cancer-related mental health problem both play an important role in the development of white matter alterations and cognitive dysfunction.

Lower Risk of Burn Injury in Children and Adolescents with Autism Spectrum Disorder: A Nationwide Population-Based Study.

Little research has examined burn injury in the pediatric population with autism spectrum disorder (ASD). We used data from Taiwan's National Health Insurance Research Database to identify 15,844 participants aged <18 years with ASD and 130,860 participants without ASD. Our results revealed that the hazard ratios differed across three age ranges. The ASD group had a lower risk of burn injury than the non-ASD group when they were less than 6 years of age, a higher risk from 6 years to 12 years of age, and no difference when they were older than 12 years of age. More research is required to study the characteristics and causes of burn injury in the pediatric population with ASD.

Disrupted white matter network of brain structural connectomes in bipolar disorder patients revealed by q-ball imaging.

BACKGROUND: Structural and functional brain changes have been found to be associated with altered emotion and cognition in patients with bipolar disorder (BD). Widespread microstructural white matter abnormalities have been observed using traditional structural imaging in BD. q-Ball imaging (QBI) and graph theoretical analysis (GTA) improve the specificity and sensitivity and high accuracy of fiber tracking. We applied QBI and GTA to investigate and compare the structural connectivity alterations and network alterations in patients with and without BD. METHODS: Sixty-two patients with BD and 62 healthy controls (HCs) completed a MR scan. We evaluated the group differences in generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA) values by voxel-based statistical analysis with QBI. We also evaluated the group differences in topological parameters of GTA and subnetwork interconnections in network-based statistical analysis (NBS). RESULTS: The QBI indices in the BD group were significantly lower than those in the HC group in the corpus callosum, cingulate gyrus, and caudate. The GTA indices indicated that the BD group demonstrated less global integration and higher local segregation than the HC group, but they retained small-world properties. NBS evaluation showed that the majority of the more connected subnetworks in BD occurred in thalamo-temporal/parietal connectivity. CONCLUSION: Our findings supported white matter integrity with network alterations in BD.

Deep learning network for integrated coil inhomogeneity correction and brain extraction of mixed MRI data.

Magnetic Resonance Imaging (MRI) has been widely used to acquire structural and functional information about the brain. In a group- or voxel-wise analysis, it is essential to correct the bias field of the radiofrequency coil and to extract the brain for accurate registration to the brain template. Although automatic methods have been developed, manual editing is still required, particularly for echo-planar imaging (EPI) due to its lower spatial resolution and larger geometric distortion. The needs of user interventions slow down data processing and lead to variable results between operators. Deep learning networks have been successfully used for automatic postprocessing. However, most networks are only designed for a specific processing and/or single image contrast (e.g., spin-echo or gradient-echo). This limitation markedly restricts the application and generalization of deep learning tools. To address these limitations, we developed a deep learning network based on the generative adversarial net (GAN) to automatically correct coil inhomogeneity and extract the brain from both spin- and gradient-echo EPI without user intervention. Using various quantitative indices, we show that this method achieved high similarity to the reference target and performed consistently across datasets acquired from rodents. These results highlight the potential of deep networks to integrate different postprocessing methods and adapt to different image contrasts. The use of the same network to process multimodality data would be a critical step toward a fully automatic postprocessing pipeline that could facilitate the analysis of large datasets with high consistency.

Assessment of disrupted brain functional connectome in tuberous sclerosis complex using resting-state fMRI.

ABSTRACT: Tuberous sclerosis complex (TSC) is a rare genetic disorder with multisystem involvement. TSC is characterized by benign hamartomas in multiple organs, including the brain, and its clinical phenotypes may be associated with abnormal functional connections. We aimed to use resting-state functional connectivity to provide findings of disrupted functional brain networks in TSC patients using graph theoretical analysis (GTA) and network-based statistic (NBS) analysis.Forty TSC patients (age = 24.11+/-11.44 years old) and 18 age-matched (25.13+/- 10.01 years old) healthy controls were recruited; they underwent resting-state functional magnetic resonance imaging using a 3T magnetic resonance imaging scanner. After image preprocessing and removing physiological noises, GTA was used to calculate the topological parameters of the brain network. NBS analysis was then used to determine the differences in cerebrum functional connectivity between the 2 groups.In GTA, several topological parameters, including the clustering coefficient, local efficiency, transitivity, and modularity, were better in controls than in TSC patients (P < .05). In NBS analysis, the edges of the brain networks between the groups were compared. One subnetwork showed more edges in controls than in TSC patients (P < .05), including the connections from the frontal lobe to the temporal and parietal lobe.The study results provide the findings on disrupted functional connectivity and organization in TSC patients compared with controls. The findings may help better understand the underlying physiological mechanisms of brain connection in TSC.

Assessment of brain connectome alterations in male chronic smokers using structural and generalized q-sampling MRI.

An association has been shown between chronic cigarette smoking and structural abnormalities in the brain areas related to several functions relevant to addictive behavior. However, few studies have focused on the structural alternations of chronic smoking by using magnetic resonance imaging (MRI). Also, it remains unclear how structural alternations are associated with tobacco-dependence severity and the positive/negative outcome expectances. The q-sampling imaging (GQI) is an advanced diffusion MRI technique that can reconstruct more precise and consistent images of complex oriented fibers than other methods. We aimed to use GQI to evaluate the impact of the neurological structure caused by chronic smoking. Sixty-seven chronic smokers and 43 nonsmokers underwent a MRI scan. The tobacco dependence severity and the positive/negative outcome expectancies were assessed via self-report. We used GQI with voxel-based statistical analysis (VBA) to evaluate structural brain and connectivity abnormalities. Graph theoretical analysis (GTA) and network-based statistical (NBS) analysis were also performed to identify the structural network differences among groups. Chronic smokers had smaller GM and WM volumes in the bilateral frontal lobe and bilateral frontal region. The GM/WM volumes correlated with dependence severity and outcome expectancies in the brain areas involving high-level functions. Chronic smokers had shape changes in the left hippocampal head and tail and the inferior brain stem. Poorer WM integrity in chronic smokers was found in the left middle frontal region, the right superior fronto-occipital fasciculus, the right temporal region, the left parahippocampus, the left anterior internal capsule, and the right inferior parietal region. WM integrity correlated with dependence severity and outcome expectancies in brain areas involving high-level functions. Chronic smokers had decreased local segregation and global integration among the brain regions and networks. Our results provide further evidence indicating that chronic smoking may be associated with brain structure and connectivity changes.

Magnetic Resonance-Based Synthetic Computed Tomography Using Generative Adversarial Networks for Intracranial Tumor Radiotherapy Treatment Planning.

The purpose of this work is to develop a reliable deep-learning-based method that is capable of synthesizing needed CT from MRI for radiotherapy treatment planning. Simultaneously, we try to enhance the resolution of synthetic CT. We adopted pix2pix with a 3D framework, which is a conditional generative adversarial network, to map the MRI data domain into the CT data domain of our dataset. The original dataset contains paired MRI and CT images of 31 subjects; 26 pairs were used for model training and 5 were used for model validation. To identify the correctness of the synthetic CT of models, all of the synthetic CTs were calculated by the quantized image similarity formulas: cosine angle distance, Euclidean distance, mean square error, peak signal-to-noise ratio, and mean structural similarity. Two radiologists independently evaluated the satisfaction score, including spatial, detail, contrast, noise, and artifacts, for each imaging attribute. The mean (±standard deviation) of the structural similarity indices (CAD, L2 norm, MSE, PSNR, and MSSIM) between five real CT scans and the synthetic CT scans were 0.96 ± 0.015, 76.83 ± 12.06, 0.00118 ± 0.00037, 29.47 ± 1.35, and 0.84 ± 0.036, respectively. For synthetic CT, radiologists rated the results as evincing excellent satisfaction in spatial geometry and noise level, good satisfaction in contrast and artifacts, and fair imaging details. The similarity index and clinical evaluation results between synthetic CT and original CT guarantee the usability of the proposed method.