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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Classification and Visualization of Chemotherapy-Induced Cognitive Impairment in Volumetric Convolutional Neural Networks.

Breast cancer is the most common female cancer worldwide, and breast cancer accounts for 30% of female cancers. Of all the treatment modalities, breast cancer survivors who have undergone chemotherapy might complain about cognitive impairment during and after cancer treatment. This phenomenon, chemo-brain, is used to describe the alterations in cognitive functions after receiving systemic chemotherapy. Few reports detect the chemotherapy-induced cognitive impairment (CICI) by performing functional MRI (fMRI) and a deep learning analysis. In this study, we recruited 55 postchemotherapy breast cancer survivors (C+ group) and 65 healthy controls (HC group) and extracted mean fractional amplitudes of low-frequency fluctuations (mfALFF) from resting-state fMRI as our input feature. Two state-of-the-art deep learning architectures, ResNet-50 and DenseNet-121, were transformed to 3D, embedded with squeeze and excitation (SE) blocks and then trained to differentiate cerebral alterations based on the effect of chemotherapy. An integrated gradient was applied to visualize the pattern that was recognized by our model. The average performance of SE-ResNet-50 models was an accuracy of 80%, precision of 78% and recall of 70%; on the other hand, the SE-DenseNet-121 model reached identical results with an average of 80% accuracy, 86% precision and 80% recall. The regions with the greatest contributions highlighted by the integrated gradients algorithm for differentiating chemo-brain were the frontal, temporal, parietal and occipital lobe. These regions were consistent with other studies and strongly associated with the default mode and dorsal attention networks. We constructed two volumetric state-of-the-art models and visualized the patterns that are critical for identifying chemo-brains from normal brains. We hope that these results will be helpful in clinically tracking chemo-brain in the future.

Assessment of Disrupted Brain Structural Connectome in Depressive Patients With Suicidal Ideation Using Generalized Q-Sampling MRI.

Suicide is one of the leading causes of mortality worldwide. Various factors could lead to suicidal ideation (SI), while depression is the predominant cause among all mental disorders. Studies have shown that alterations in brain structures and networks may be highly associated with suicidality. This study investigated both neurological structural variations and network alterations in depressed patients with suicidal ideation by using generalized q-sampling imaging (GQI) and Graph Theoretical Analysis (GTA). This study recruited 155 participants and divided them into three groups: 44 depressed patients with suicidal ideation (SI+; 20 males and 24 females with mean age = 42, SD = 12), 56 depressed patients without suicidal ideation (Depressed; 24 males and 32 females with mean age = 45, SD = 11) and 55 healthy controls (HC; nine males and 46 females with mean age = 39, SD = 11). Both the generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA) values were evaluated in a voxel-based statistical analysis by GQI. We analyzed different topological parameters in the graph theoretical analysis and the subnetwork interconnections in the Network-based Statistical (NBS) analysis. In the voxel-based statistical analysis, both the GFA and NQA values in the SI+ group were generally lower than those in the Depressed and HC groups in the corpus callosum and cingulate gyrus. Furthermore, we found that the SI+ group demonstrated higher global integration and lower local segregation among the three groups of participants. In the network-based statistical analysis, we discovered that the SI+ group had stronger connections of subnetworks in the frontal lobe than the HC group. We found significant structural differences in depressed patients with suicidal ideation compared to depressed patients without suicidal ideation and healthy controls and we also found several network alterations among these groups of participants, which indicated that white matter integrity and network alterations are associated with patients with depression as well as suicidal ideation.

Comparison of functional dorsal attention network alterations in breast cancer survivors before and after chemotherapy.

ABSTRACT: Breast cancer is the leading type of cancer among women worldwide, and a high number of breast cancer patients are suffering from psychological and cognitive disorders. This cross-sectional study used resting-state functional magnetic resonance imaging (rs-fMRI) and clinical neuropsychological tests to evaluate the possible underlying mechanisms.We enrolled 32 breast cancer patients without chemotherapy (BC), 32 breast cancer patients within 6 to 12 months after the completion of chemotherapy (BC_CTx) and 46 healthy controls. Participants underwent neuropsychological tests and rs-fMRI with mean fractional amplitude of low-frequency fluctuation and mean regional homogeneity analyses. Between groups whole-brain voxel-wise rs-fMRI comparisons were calculated using two-sample t test. rs-fMRI and neuropsychological tests correlation analyses were calculated using multiple regression. Age and years of education were used as covariates. A false discovery rate-corrected P-value of less than .05 was considered statistically significant.We found significantly alteration of mean fractional amplitude of low-frequency fluctuation and mean regional homogeneity in the frontoparietal lobe and occipital lobe in the BC group compared with the other 2 groups, indicating alteration of functional dorsal attention network (DAN). Furthermore, we found the DAN alteration was correlated with neuropsychological impairment.The majority of potential underlying mechanisms of DAN alteration in BC patients may due to insufficient frontoparietal lobe neural activity to drive DAN and may be related to the effects of neuropsychological distress. Further longitudinal studies with comprehensive images and neuropsychological tests correlations are recommended.

A CNN-Based Autoencoder and Machine Learning Model for Identifying Betel-Quid Chewers Using Functional MRI Features.

Betel quid (BQ) is one of the most commonly used psychoactive substances in some parts of Asia and the Pacific. Although some studies have shown brain function alterations in BQ chewers, it is virtually impossible for radiologists' to visually distinguish MRI maps of BQ chewers from others. In this study, we aimed to construct autoencoder and machine-learning models to discover brain alterations in BQ chewers based on the features of resting-state functional magnetic resonance imaging. Resting-state functional magnetic resonance imaging (rs-fMRI) was obtained from 16 BQ chewers, 15 tobacco- and alcohol-user controls (TA), and 17 healthy controls (HC). We used an autoencoder and machine learning model to identify BQ chewers among the three groups. A convolutional neural network (CNN)-based autoencoder model and supervised machine learning algorithm logistic regression (LR) were used to discriminate BQ chewers from TA and HC. Classifying the brain MRIs of HC, TA controls, and BQ chewers by conducting leave-one-out-cross-validation (LOOCV) resulted in the highest accuracy of 83%, which was attained by LR with two rs-fMRI feature sets. In our research, we constructed an autoencoder and machine-learning model that was able to identify BQ chewers from among TA controls and HC, which were based on data from rs-fMRI, and this might provide a helpful approach for tracking BQ chewers in the future.

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.

Prenatal Exposure to Endocrine-Disrupting Chemicals and Subsequent Brain Structure Changes Revealed by Voxel-Based Morphometry and Generalized Q-Sampling MRI.

Previous studies have indicated that prenatal exposure to endocrine-disrupting chemicals (EDCs) can cause adverse neuropsychiatric disorders in children and adolescents. This study aimed to determine the association between the concentrations of prenatal EDCs and brain structure changes in teenagers by using MRI. We recruited 49 mother-child pairs during the third trimester of pregnancy, and collected and examined the concentration of EDCs-including phthalate esters, perfluorochemicals (PFCs), and heavy metals (lead, arsenic, cadmium, and mercury)-in maternal urine and/or serum. MRI voxel-based morphometry (VBM) and generalized q-sampling imaging (GQI) mapping-including generalized fractional anisotropy (GFA), normalized quantitative anisotropy (NQA), and the isotropic value of the orientation distribution function (ISO)-were obtained in teenagers 13-16 years of age in order to find the association between maternal EDC concentrations and possible brain structure alterations in the teenagers' brains. We found that there are several specific vulnerable brain areas/structures associated with prenatal exposure to EDCs, including decreased focal brain volume, primarily in the frontal lobe; high frontoparietal lobe, temporooccipital lobe and cerebellum; and white matter structural alterations, which showed a negative association with GFA/NQA and a positive association with ISO, primarily in the corpus callosum, external and internal capsules, corona radiata, superior fronto-occipital fasciculus, and superior longitudinal fasciculus. Prenatal exposure to EDCs may be associated with specific brain structure alterations in teenagers.

Effect of medication on risk of traumatic brain injury in patients with bipolar disorder: A nationwide population-based cohort study.

BACKGROUND: Increased traumatic brain injury (TBI) risk was found in patients with bipolar disorder (BPD). Whether the medications for BPD and dosage moderate the risk of TBI is not clear. AIM: This study aimed to determine whether an association exists between BPD and TBI and whether the prescription of psychotropics moderates TBI risk. METHODS: A total of 5606 individuals who had received diagnoses of BPD between January 1, 1997 and December 31, 2013 and 56,060 matched controls without BPD were identified from Taiwan's National Health Insurance Research Database. Cases and controls were followed until the date of TBI diagnosis. RESULTS: BPD was associated with a high risk of TBI (adjusted hazard ratio (aHR): 1.85; 95% CI: 1.62-2.11). Patients with BPD, with or without a history of psychiatric hospitalization, had increased risks of TBI (aHR: 1.94, 95% CI: 1.57-2.4 and aHR: 1.82, 95% CI: 1.55-2.1, respectively). The prescription of typical antipsychotics (0 < defined daily dose (DDD) < 28: hazard ratio (HR) = 1.52, 95% CI: 1.19-1.94; ⩾28 DDD: HR = 1.54, 95% CI: 1.15-2.06) and tricyclic antidepressants (TCAs) (0 < DDD < 28: HR = 1.73, 95% CI: 1.26-2.39; ⩾28 DDD: HR = 1.52, 95% CI: 1.02-2.25) was associated with higher TBI risk. Patients receiving higher doses of benzodiazepines (BZDs) (cumulative dose ⩾28 DDD) had a higher TBI risk (HR = 1.53, 95% CI: 1.13-2.06). CONCLUSION: Patients with BPD have a higher risk of TBI. The use of typical antipsychotics, TCAs, or high-dose BZDs increases the risk of TBI in BPD.

Convolutional Neural Network-Based Deep Learning Model for Predicting Differential Suicidality in Depressive Patients Using Brain Generalized q-Sampling Imaging.

OBJECTIVE: Suicide is a priority health problem. Suicide assessment depends on imperfect clinician assessment with minimal ability to predict the risk of suicide. Machine learning/deep learning provides an opportunity to detect an individual at risk of suicide to a greater extent than clinician assessment. The present study aimed to use deep learning of structural magnetic resonance imaging (MRI) to create an algorithm for detecting suicidal ideation and suicidal attempts. METHODS: We recruited 4 groups comprising a total of 186 participants: 33 depressive patients with suicide attempt (SA), 41 depressive patients with suicidal ideation (SI), 54 depressive patients without suicidal thoughts (DP), and 58 healthy controls (HCs). The confirmation of depressive disorder, SA and SI was based on psychiatrists' diagnosis and Mini-International Neuropsychiatric Interview (MINI) interviews. In the generalized q-sampling imaging (GQI) dataset, indices of generalized fractional anisotropy (GFA), the isotropic value of the orientation distribution function (ISO), and normalized quantitative anisotropy (NQA) were separately trained in convolutional neural network (CNN)-based deep learning and DenseNet models. RESULTS: From the results of 5-fold cross-validation, the best accuracies of the CNN classifier for predicting SA, SI, and DP against HCs were 0.916, 0.792, and 0.589, respectively. In SA-ISO, DenseNet outperformed the simple CNNs with a best accuracy from 5-fold cross-validation of 0.937. In SA-NQA, the best accuracy was 0.915. CONCLUSIONS: The results showed that a deep learning method based on structural MRI can effectively detect individuals at different levels of suicide risk, from depression to suicidal ideation and attempted suicide. Further studies from different populations, larger sample sizes, and prospective follow-up studies are warranted to confirm the utility of deep learning methods for suicide prevention and intervention.

Usefulness of triggered non-contrast-enhanced magnetic resonance angiography in assessing lower extremity venous disease.

ABSTRACT: Although venous duplex ultrasonography (USG) is reliable for diagnosing lower extremity venous disease (LEVD), cross-sectional imaging studies were usually required before intervention or surgery. Patients of LEVD with renal insufficiency usually restrict the use of contrast-enhanced imaging modalities. In seeking an alternative imaging solution for these patients, we explore the clinical utility of triggered angiography non-contrast-enhanced magnetic resonance imaging (TRANCE-MRI) in the assessment of LEVD.We collected data from patients presenting to a tertiary wound-care center with symptoms of LEVD from April 2017-November 2019. Each participant underwent baseline USG followed by TRANCE-MRI on a 1.5T MR scanner (Philips Ingenia, Philips Healthcare, Best, The Netherlands). Inter-rater reliability was measured using Cohen's kappa (κ).All 80 participants (mean age, 61.9 ±â€Š14.8 years; 35 males, 45 females) were assessed and were classified into one of five disease groups, deep vein thrombosis (n = 38), venous static ulcer (n = 16), symptomatic varicose veins (n = 18), recurrent varicose veins (n = 3), and lymphoedema (n = 5). The inter-rater reliability between TRANCE-MRI and doppler USG showed substantial agreement (κ, 0.73). The sensitivity, specificity, and accuracy of TRANCE-MRI were 90.5%, 88.1%, and 88.8%, respectively. In 59 (73.8%) USG-negative patients, we were able to diagnose positive findings (deep venous thrombosis, n = 7; varicose veins, n = 15; lymphedema, n = 10; iliac vein compression with thrombosis, n = 6; external venous compression, n = 5; vena cava anomaly, n = 2; occult peripheral artery disease, n = 5; ccluded bypass graft, n = 1) by using TRANCE-MRI. Of these, 9 (15.3%) patients underwent additional vascular surgery based on positive TRANCE-MRI findings.TRANCE technique provides the limb's entire venous drainage in clear images without background contamination by associated arterial imaging. Additionally, simultaneous evaluation of bilateral lower extremities can help determine the lesion's exact site. Although TRANCE-MRI can provide MR arteriography and MR venography, we recommend performing only MR venography in symptomatic LEVD patients because the incidence of occult arterial disease is low.