The influence of semantic top-down processing in auditory verbal hallucinations

AbstractBackgroundAuditory verbal hallucinations (AVH) are one of the most prominent symptoms of schizophrenia but have also been reported in the general population. Several cognitive models have tried to elucidate the mechanism behind auditory verbal hallucinations, among which a top-down model. According to this model, perception is biased towards top-down information (e.g., expectations), reducing the influence of bottom-up information coming from the sense organs. This bias predisposes to false perceptions, i.e., hallucinations.MethodsThe current study investigated this hypothesis in non-psychotic individuals with frequent AVH, psychotic patients with AVH and healthy control subjects by applying a semantic top-down task. In this task, top-down processes are manipulated through the semantic context of a sentence. In addition, the association between hallucination proneness and semantic top-down errors was investigated.ResultsNon-psychotic individuals with AVH made significantly more top-down errors compared to healthy controls, while overall accuracy was similar. The number of top-down errors, corrected for overall accuracy, in the patient group was in between those of the other two groups and did not differ significantly from either the non-psychotic individuals with AVH or the healthy controls. The severity of hallucination proneness correlated with the number of top-down errors.DiscussionThese findings confirm that non-psychotic individuals with AVH are stronger influenced by top-down processing (i.e., perceptual expectations) than healthy controls. In contrast, our data suggest that in psychotic patients semantic expectations do not play a role in the etiology of AVH. This finding may point towards different cognitive mechanisms for pathological and nonpathological hallucinations

AI-Based Quantification of Planned Radiation Therapy Dose to Cardiac Structures and Coronary Arteries in Patients With Breast Cancer

Purpose: The purpose of this work is to develop and evaluate an automatic deep learning method for segmentation of cardiac chambers and large arteries, and localization of the 3 main coronary arteries in radiation therapy planning on computed tomography (CT). In addition, a second purpose is to determine the planned radiation therapy dose to cardiac structures for breast cancer therapy. Methods and Materials: Eighteen contrast-enhanced cardiac scans acquired with a dual-layer-detector CT scanner were included for method development. Manual reference annotations of cardiac chambers, large arteries, and coronary artery locations were made in the contrast scans and transferred to virtual noncontrast images, mimicking noncontrast-enhanced CT. In addition, 31 noncontrast-enhanced radiation therapy treatment planning CTs with corresponding dose-distribution maps of breast cancer cases were included for evaluation. For reference, cardiac chambers and large vessels were manually annotated in two 2-dimensional (2D) slices per scan (26 scans, totaling 52 slices) and in 3-dimensional (3D) scan volumes in 5 scans. Coronary artery locations were annotated on 3D imaging. The method uses an ensemble of convolutional neural networks with 2 output branches that perform 2 distinct tasks: (1) segmentation of the cardiac chambers and large arteries and (2) localization of coronary arteries. Training was performed using reference annotations and virtual noncontrast cardiac scans. Automatic segmentation of the cardiac chambers and large vessels and the coronary artery locations was evaluated in radiation therapy planning CT with Dice score (DSC) and average symmetrical surface distance (ASSD). The correlation between dosimetric parameters derived from the automatic and reference segmentations was evaluated with R2. Results: For cardiac chambers and large arteries, median DSC was 0.76 to 0.88, and the median ASSD was 0.17 to 0.27 cm in 2D slice evaluation. 3D evaluation found a DSC of 0.87 to 0.93 and an ASSD of 0.07 to 0.10 cm. Median DSC of the coronary artery locations ranged from 0.80 to 0.91. R2 values of dosimetric parameters were 0.77 to 1.00 for the cardiac chambers and large vessels, and 0.76 to 0.95 for the coronary arteries. Conclusions: The developed and evaluated method can automatically obtain accurate estimates of planned radiation dose and dosimetric parameters for the cardiac chambers, large arteries, and coronary arteries

Generative Models for Reproducible Coronary Calcium Scoring

Purpose: Coronary artery calcium (CAC) score, i.e. the amount of CAC quantified in CT, is a strong and independent predictor of coronary heart disease (CHD) events. However, CAC scoring suffers from limited interscan reproducibility, which is mainly due to the clinical definition requiring application of a fixed intensity level threshold for segmentation of calcifications. This limitation is especially pronounced in non-ECG-synchronized CT where lesions are more impacted by cardiac motion and partial volume effects. Therefore, we propose a CAC quantification method that does not require a threshold for segmentation of CAC. Approach: Our method utilizes a generative adversarial network where a CT with CAC is decomposed into an image without CAC and an image showing only CAC. The method, using a CycleGAN, was trained using 626 low-dose chest CTs and 514 radiotherapy treatment planning CTs. Interscan reproducibility was compared to clinical calcium scoring in radiotherapy treatment planning CTs of 1,662 patients, each having two scans. Results: A lower relative interscan difference in CAC mass was achieved by the proposed method: 47% compared to 89% manual clinical calcium scoring. The intraclass correlation coefficient of Agatston scores was 0.96 for the proposed method compared to 0.91 for automatic clinical calcium scoring. Conclusions: The increased interscan reproducibility achieved by our method may lead to increased reliability of CHD risk categorization and improved accuracy of CHD event prediction.Comment: In pres

Generative models for reproducible coronary calcium scoring

Purpose: Coronary artery calcium (CAC) score, i.e., the amount of CAC quantified in CT, is a strong and independent predictor of coronary heart disease (CHD) events. However, CAC scoring suffers from limited interscan reproducibility, which is mainly due to the clinical definition requiring application of a fixed intensity level threshold for segmentation of calcifications. This limitation is especially pronounced in non-electrocardiogram-synchronized computed tomography (CT) where lesions are more impacted by cardiac motion and partial volume effects. Therefore, we propose a CAC quantification method that does not require a threshold for segmentation of CAC. Approach: Our method utilizes a generative adversarial network (GAN) where a CT with CAC is decomposed into an image without CAC and an image showing only CAC. The method, using a cycle-consistent GAN, was trained using 626 low-dose chest CTs and 514 radiotherapy treatment planning (RTP) CTs. Interscan reproducibility was compared to clinical calcium scoring in RTP CTs of 1662 patients, each having two scans. Results: A lower relative interscan difference in CAC mass was achieved by the proposed method: 47% compared to 89% manual clinical calcium scoring. The intraclass correlation coefficient of Agatston scores was 0.96 for the proposed method compared to 0.91 for automatic clinical calcium scoring. Conclusions: The increased interscan reproducibility achieved by our method may lead to increased reliability of CHD risk categorization and improved accuracy of CHD event prediction

White matter disruptions in patients with bipolar disorder

Bipolar disorder (BD) patients show aberrant white matter microstructure compared to healthy controls but little is known about the relation with clinical characteristics. We therefore investigated the relation of white matter microstructure with the main pharmacological treatments as well its relation with IQ. Patients with BD (N = 257) and controls (N = 167) underwent diffusion tensor imaging (DTI) and comprehensive clinically assessments including IQ estimates. DTI images were analyzed using tract-based spatial statistics. Fractional anisotropy (FA) and Mean Diffusivity (MD) were determined. Patients had significantly lower FA and higher MD values throughout the white matter skeleton compared to controls. Within the BD patients, lithium use was associated with higher FA and lower MD. Antipsychotic medication use in the BD patients was not associated with FA but, in contrast to lithium, was associated with higher MD. IQ was significantly positively correlated with FA and negatively with MD in patients as well as in controls. In this large DTI study we found evidence for marked differences in FA and MD particularly in (but not restricted to) corpus callosum, between BD patients and controls. This effect was most pronounced in lithium-free patients, implicating that lithium affects white matter microstructure and attenuates differences associated with bipolar disorder. Effects of antipsychotic medication intake were absent in FA and only subtle in MD relative to those of lithium. The abnormal white matter microstructure was associated with IQ but not specifically for either group

The relationship between brain volumes and intelligence in bipolar disorder

Objectives Bipolar disorder type-I (BD-I) patients show a lower Intelligence Quotient (IQ) and smaller brain volumes as compared with healthy controls. Considering that in healthy individuals lower IQ is related to smaller total brain volume, it is of interest to investigate whether IQ deficits in BD-I patients are related to smaller brain volumes and to what extent smaller brain volumes can explain differences between premorbid IQ estimates and IQ after a diagnosis of BD-I. Methods Magnetic resonance imaging brain scans, IQ and premorbid IQ scores were obtained from 195 BDI patients and 160 controls. We studied the relationship of (global, cortical and subcortical) brain volumes with IQ and IQ change. Additionally, we investigated the relationship between childhood trauma, lithium- and antipsychotic use and IQ. Results Total brain volume and IQ were positively correlated in the entire sample. This correlation did not differ between patients and controls. Although brain volumes mediated the relationship between BD-I and IQ in part, the direct relationship between the diagnosis and IQ remained significant. Childhood trauma and use of lithium and antipsychotic medication did not affect the relationship between brain volumes and IQ. However, current lithium use was related to lower IQ in patients. Conclusions Our data suggest a similar relationship between brain volume and IQ in BD-I patients and controls. Smaller brain volumes only partially explain IQ deficits in patients. Therefore, our findings indicate that in addition to brain volumes and lithium use other disease factors play a role in IQ deficits in BD-I patients