scholarly journals Emotional and Motivational Processes in Bipolar Disorder: A Neural Network Perspective

10.5772/51890 ◽  
2013 ◽  
Author(s):  
Michle Wessa ◽  
Julia Linke
Keyword(s):  
Neural Network ◽  
Bipolar Disorder

scholarly journals Bipolar disorder: A neural network perspective on a disorder of emotion and motivation

2014 ◽  
Vol 32 (1) ◽  
pp. 51-62 ◽  
Author(s):  
Michèle Wessa ◽  
Philipp Kanske ◽  
Julia Linke
Keyword(s):  
Neural Network ◽  
Bipolar Disorder

scholarly journals Predicting The Risk and Timing of Bipolar Disorder In Offspring of Bipolar Parents: Exploring The Utility of a Neural Network Approach

2020 ◽  
Author(s):  
Alysha Cooper ◽  
Julie Horrocks ◽  
Sarah Margaret Goodday ◽  
Charles Keown-Stoneman ◽  
Anne Duffy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
At Risk ◽  
Bipolar Disorder ◽  
High Risk ◽  
Logistic Model ◽  
Predictive Performance ◽  
Future Research ◽  
Routine Practice ◽  
Neural Network Approach

Abstract BackgroundBipolar disorder onset peaks over early adulthood and confirmed family history is a robust risk factor. However, penetrance within families varies and most children of bipolar parents will not develop the illness. Individualized risk prediction would be helpful for identifying those young people most at risk and to inform targeted intervention. Using prospectively collected data from the Canadian Flourish High-Risk Offspring cohort study available in routine practice, we explored the use of a neural network, known as the Partial Logistic Artificial Neural Network (PLANN) to predict the time to diagnosis of bipolar spectrum disorders Results Overall, for predictive performance, PLANN outperformed the more traditional logistic model for one year, three year and five-year predictions. PLANN was better able to discriminate or rank individuals based on their risk of developing bipolar disorder, better able to predict the probability of developing bipolar disorder and had higher accuracy than the logistic model. ConclusionsThis evaluation of PLANN is a useful step in the investigation of using neural networks as tools in the prediction of diagnosis of mental health for at-risk individuals and demonstrated the potential that neural networks have in this field. Future research is needed to replicate these findings in a separate high-risk sample.

Convolutional Neural Network Visualization for Identification of Risk Genes in Bipolar Disorder

2020 ◽  
Vol 20 (6) ◽  
pp. 429-441
Author(s):  
Qixuan Yue ◽  
Jie Yang ◽  
Qian Shu ◽  
Mingze Bai ◽  
Kunxian Shu
Keyword(s):  
Neural Network ◽  
Bipolar Disorder ◽  
High Risk ◽  
Convolutional Neural Network ◽  
Model Prediction ◽  
Genome Wide Association Study ◽  
Gene Annotation ◽  
Test Accuracy ◽  
Risk Genes ◽  
Risk Snps

Background : Bipolar disorder (BD) is a type of chronic emotional disorder with a complex genetic structure. However, its genetic molecular mechanism is still unclear, which makes it insufficient to be diagnosed and treated. Methods and Results: In this paper, we proposed a model for predicting BD based on single nucleotide polymorphisms (SNPs) screening by genome-wide association study (GWAS), which was constructed by a convolutional neural network (CNN) that predicted the probability of the disease. According to the difference of GWAS threshold, two sets of data were named: group P001 and group P005. And different convolutional neural networks are set for the two sets of data. The training accuracy of the model trained with group P001 data is 96%, and the test accuracy is 91%. The training accuracy of the model trained with group P005 data is 94.5%, and the test accuracy is 92%. At the same time, we used gradient weighted class activation mapping (Grad-CAM) to interpret the prediction model, indirectly to identify high-risk SNPs of BD. In the end, we compared these high-risk SNPs with human gene annotation information. Conclusion: The model prediction results of the group P001 yielded 137 risk genes, of which 22 were reported to be associated with the occurrence of BD. The model prediction results of the group P005 yielded 407 risk genes, of which 51 were reported to be associated with the occurrence of BD.

41.4 DISSOCIABLE NEURAL NETWORK MARKERS OF RISK AND RESILIENCE IN PEDIATRIC BIPOLAR DISORDER

2019 ◽  
Vol 58 (10) ◽  
pp. S362
Author(s):  
Adina Fischer ◽  
Akua Nimarko ◽  
Corrina Fonseca ◽  
Sarthak Angal ◽  
Manpreet K. Singh
Keyword(s):  
Neural Network ◽  
Bipolar Disorder ◽  
Pediatric Bipolar Disorder ◽  
Risk And Resilience

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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