Making sense: dopamine activates conscious self-monitoring through medial prefrontal cortex

When experiences become meaningful to the self, they are linked to synchronous activity in a paralimbic network of self-awareness and dopaminergic activity. This network includes medial prefrontal and medial parietal/posterior cingulate cortices, where transcranial magnetic stimulation may transiently impair self-awareness. Conversely, we hypothesize that dopaminergic stimulation may improve self-awareness and metacognition (i.e., the ability of the brain to consciously monitor its own cognitive processes). Here, we demonstrate improved noetic (conscious) metacognition by oral administration of 100 mg dopamine in minimal self-awareness. In a separate experiment with extended self-awareness dopamine improved the retrieval accuracy of memories of self-judgment (autonoetic, i.e., explicitly self-conscious) metacognition. Concomitantly, magnetoencephalography (MEG) showed increased amplitudes of oscillations (power) preferentially in the medial prefrontal cortex. Given that electromagnetic activity in this region is instrumental in self-awareness, this explains the specific effect of dopamine on explicit self-awareness and autonoetic metacognition

Automated Decision-Support System for Prediction of Treatment Responders in Acute Ischemic Stroke

MRI is widely used in the assessment of acute ischemic stroke. In particular, it identifies the mismatch between hypoperfused and the permanently damaged tissue, the PWI-DWI mismatch volume. It is used to help triage patients into active or supportive treatment pathways. COMBAT Stroke is an automated software tool for estimating the mismatch volume and ratio based on MRI. Herein, we validate the decision made by the software with actual clinical decision rendered. Furthermore, we evaluate the association between treatment decisions (both automated and actual) and outcomes. COMBAT Stroke was used to determine PWI-DWI mismatch volume and ratio in 228 patients from two European multi-center stroke databases. We performed confusion matrix analysis to summarize the agreement between the automated selection and the clinical decision. Finally, we evaluated the clinical and imaging outcomes of the patients in the four entries of the confusion matrix (true positive, true negative, false negative, and false positive). About 186 of 228 patients with acute stroke underwent thrombolytic treatment, with the remaining 42 receiving supportive treatment only. Selection based on radiographic criteria using COMBAT Stroke classified 142 patients as potential candidates for thrombolytic treatment and 86 for supportive treatment; 60% sensitivity and 29% specificity. The patients deemed eligible for thrombolytic treatment by COMBAT Stroke demonstrated significantly higher rates of compromised tissue salvage, less neurological deficit, and were more likely to experience thrombus dissolving and reestablishment of normal blood flow at 24 h follow-up compared to those who were treated without substantial PWI-DWI mismatch. These results provide evidence that COMBAT Stroke, in addition to clinical assessment, may offer an optimal framework for a fast, efficient, and standardized clinical support tool to select patients for thrombolysis in acute ischemic stroke

Liver fibrosis staging by deep learning:a visual-based explanation of diagnostic decisions of the model

OBJECTIVES: Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning. METHODS: The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage. RESULTS: The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2-F4), advanced fibrosis (F3-F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4). CONCLUSIONS: Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning-based liver fibrosis staging algorithms. KEY POINTS: • Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency. • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network. • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage

Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging

Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic–splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic–hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic–splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning

Seeing without Seeing? Degraded Conscious Vision in a Blindsight Patient

Blindsight patients, whose primary visual cortex is lesioned, exhibit preserved ability to discriminate visual stimuli presented in their “blind” field, yet report no visual awareness hereof. Blindsight is generally studied in experimental investigations of single patients, as very few patients have been given this “diagnosis”. In our single case study of patient GR, we ask whether blindsight is best described as unconscious vision, or rather as conscious, yet severely degraded vision. In experiment 1 and 2, we successfully replicate the typical findings of previous studies on blindsight. The third experiment, however, suggests that GR's ability to discriminate amongst visual stimuli does not reflect unconscious vision, but rather degraded, yet conscious vision. As our finding results from using a method for obtaining subjective reports that has not previously used in blindsight studies (but validated in studies of healthy subjects and other patients with brain injury), our results call for a reconsideration of blindsight, and, arguably also of many previous studies of unconscious perception in healthy subjects

The Co-Morbidity Burden of Children and Young Adults with Autism Spectrum Disorders

Objectives: Use electronic health records Autism Spectrum Disorder (ASD) to assess the comorbidity burden of ASD in children and young adults. Study Design: A retrospective prevalence study was performed using a distributed query system across three general hospitals and one pediatric hospital. Over 14,000 individuals under age 35 with ASD were characterized by their co-morbidities and conversely, the prevalence of ASD within these comorbidities was measured. The comorbidity prevalence of the younger (Age<18 years) and older (Age 18–34 years) individuals with ASD was compared. Results: 19.44% of ASD patients had epilepsy as compared to 2.19% in the overall hospital population (95% confidence interval for difference in percentages 13.58–14.69%), 2.43% of ASD with schizophrenia vs. 0.24% in the hospital population (95% CI 1.89–2.39%), inflammatory bowel disease (IBD) 0.83% vs. 0.54% (95% CI 0.13–0.43%), bowel disorders (without IBD) 11.74% vs. 4.5% (95% CI 5.72–6.68%), CNS/cranial anomalies 12.45% vs. 1.19% (95% CI 9.41–10.38%), diabetes mellitus type I (DM1) 0.79% vs. 0.34% (95% CI 0.3–0.6%), muscular dystrophy 0.47% vs 0.05% (95% CI 0.26–0.49%), sleep disorders 1.12% vs. 0.14% (95% CI 0.79–1.14%). Autoimmune disorders (excluding DM1 and IBD) were not significantly different at 0.67% vs. 0.68% (95% CI −0.14-0.13%). Three of the studied comorbidities increased significantly when comparing ages 0–17 vs 18–34 with p<0.001: Schizophrenia (1.43% vs. 8.76%), diabetes mellitus type I (0.67% vs. 2.08%), IBD (0.68% vs. 1.99%) whereas sleeping disorders, bowel disorders (without IBD) and epilepsy did not change significantly. Conclusions: The comorbidities of ASD encompass disease states that are significantly overrepresented in ASD with respect to even the patient populations of tertiary health centers. This burden of comorbidities goes well beyond those routinely managed in developmental medicine centers and requires broad multidisciplinary management that payors and providers will have to plan for

The impact of ADHD and conduct disorder in childhood on adult delinquency: A 30 years follow-up study using official crime records

<p>Abstract</p> <p>Background</p> <p>Few longitudinal studies have explored lifetime criminality in adults with a childhood history of severe mental disorders. In the present study, we wanted to explore the association between adult delinquency and several different childhood diagnoses in an in-patient population. Of special interest was the impact of disturbance of activity and attention (ADHD) and mixed disorder of conduct and emotions on later delinquency, as these disorders have been variously associated with delinquent development.</p> <p>Methods</p> <p>Former Norwegian child psychiatric in-patients (n = 541) were followed up 19-41 years after hospitalization by record linkage to the National Register of Criminality. On the basis of the hospital records, the patients were re-diagnosed according to ICD-10. The association between diagnoses and other baseline factors and later delinquency were investigated using univariate and multivariate Cox regression analyses.</p> <p>Results</p> <p>At follow-up, 24% of the participants had been convicted of criminal activity.</p> <p>In the multivariate Cox regression analysis, conduct disorder (RR = 2.0, 95%CI = 1.2-3.4) and hyperkinetic conduct disorder (RR = 2.7, 95% CI = 1.6-4.4) significantly increased the risk of future criminal behaviour. Pervasive developmental disorder (RR = 0.4, 95%CI = 0.2-0.9) and mental retardation (RR = 0.4, 95%CI = 0.3-0.8) reduced the risk for a criminal act. Male gender (RR = 3.6, 95%CI = 2.1-6.1) and chronic family difficulties (RR = 1.3, 95% CI = 1.1-1.5) both predicted future criminality.</p> <p>Conclusions</p> <p>Conduct disorder in childhood was highly associated with later delinquency both alone or in combination with hyperactivity, but less associated when combined with an emotional disorder. ADHD in childhood was no more associated with later delinquency than the rest of the disorders in the study population. Our finding strengthens the assumption that there is no direct association between ADHD and criminality.</p