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Ji H, Zhang X, Chen B, Yuan Z, Zheng N, Keil A. Groupwise structural sparsity for discriminative voxels identification. Front Neurosci 2023; 17:1247315. [PMID: 37746136 PMCID: PMC10512739 DOI: 10.3389/fnins.2023.1247315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 08/18/2023] [Indexed: 09/26/2023] Open
Abstract
This paper investigates the selection of voxels for functional Magnetic Resonance Imaging (fMRI) brain data. We aim to identify a comprehensive set of discriminative voxels associated with human learning when exposed to a neutral visual stimulus that predicts an aversive outcome. However, due to the nature of the unconditioned stimuli (typically a noxious stimulus), it is challenging to obtain sufficient sample sizes for psychological experiments, given the tolerability of the subjects and ethical considerations. We propose a stable hierarchical voting (SHV) mechanism based on stability selection to address this challenge. This mechanism enables us to evaluate the quality of spatial random sampling and minimizes the risk of false and missed detections. We assess the performance of the proposed algorithm using simulated and publicly available datasets. The experiments demonstrate that the regularization strategy choice significantly affects the results' interpretability. When applying our algorithm to our collected fMRI dataset, it successfully identifies sparse and closely related patterns across subjects and displays stable weight maps for three experimental phases under the fear conditioning paradigm. These findings strongly support the causal role of aversive conditioning in altering visual-cortical activity.
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Affiliation(s)
- Hong Ji
- The Shaanxi Key Laboratory of Clothing Intelligence, School of Computer Science, Xi'an Polytechnic University, Xi'an, China
| | - Xiaowei Zhang
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong Univeristy, Xi'an, China
| | - Badong Chen
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong Univeristy, Xi'an, China
| | - Zejian Yuan
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong Univeristy, Xi'an, China
| | - Nanning Zheng
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong Univeristy, Xi'an, China
| | - Andreas Keil
- Center for the Study of Emotion and Attention, Department of Psychology, University of Florida, Gainesville, FL, United States
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Ji M, Xie W, Zhao M, Qian X, Chow CY, Lam KY, Yan J, Hao T. Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6722321. [PMID: 35463247 PMCID: PMC9033323 DOI: 10.1155/2022/6722321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/16/2022] [Accepted: 03/19/2022] [Indexed: 11/18/2022]
Abstract
Background Medication nonadherence represents a major burden on national health systems. According to the World Health Organization, increasing medication adherence may have a greater impact on public health than any improvement in specific medical treatments. More research is needed to better predict populations at risk of medication nonadherence. Objective To develop clinically informative, easy-to-interpret machine learning classifiers to predict people with psychiatric disorders at risk of medication nonadherence based on the syntactic and structural features of written posts on health forums. Methods All data were collected from posts between 2016 and 2021 on mental health forum, administered by Together 4 Change, a long-running not-for-profit organisation based in Oxford, UK. The original social media data were annotated using the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC) system. Through applying multiple feature optimisation techniques, we developed a best-performing model using relevance vector machine (RVM) for the probabilistic prediction of medication nonadherence among online mental health forum discussants. Results The best-performing RVM model reached a mean AUC of 0.762, accuracy of 0.763, sensitivity of 0.779, and specificity of 0.742 on the testing dataset. It outperformed competing classifiers with more complex feature sets with statistically significant improvement in sensitivity and specificity, after adjusting the alpha levels with Benjamini-Hochberg correction procedure. Discussion. We used the forest plot of multiple logistic regression to explore the association between written post features in the best-performing RVM model and the binary outcome of medication adherence among online post contributors with psychiatric disorders. We found that increased quantities of 3 syntactic complexity features were negatively associated with psychiatric medication adherence: "dobj_stdev" (standard deviation of dependents per direct object of nonpronouns) (OR, 1.486, 95% CI, 1.202-1.838, P < 0.001), "cl_av_deps" (dependents per clause) (OR, 1.597, 95% CI, 1.202-2.122, P, 0.001), and "VP_T" (verb phrases per T-unit) (OR, 2.23, 95% CI, 1.211-4.104, P, 0.010). Finally, we illustrated the clinical use of the classifier with Bayes' monograph which gives the posterior odds and their 95% CI of positive (nonadherence) versus negative (adherence) cases as predicted by the best-performing classifier. The odds ratio of the posterior probability of positive cases was 3.9, which means that around 10 in every 13 psychiatric patients with a positive result as predicted by our model were following their medication regime. The odds ratio of the posterior probability of true negative cases was 0.4, meaning that around 10 in every 14 psychiatric patients with a negative test result after screening by our classifier were not adhering to their medications. Conclusion Psychiatric medication nonadherence is a large and increasing burden on national health systems. Using Bayesian machine learning techniques and publicly accessible online health forum data, our study illustrates the viability of developing cost-effective, informative decision aids to support the monitoring and prediction of patients at risk of medication nonadherence.
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Affiliation(s)
- Meng Ji
- School of Languages and Cultures, University of Sydney, Sydney, Australia
| | - Wenxiu Xie
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, China
| | - Mengdan Zhao
- School of Languages and Cultures, University of Sydney, Sydney, Australia
| | - Xiaobo Qian
- School of Computer Science, South China Normal University, Guangzhou, Guangdong, China
| | - Chi-Yin Chow
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, China
| | - Kam-Yiu Lam
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, China
| | - Jun Yan
- AI Lab, Yidu Cloud (Beijing) Technology Co. Ltd., Beijing, China
| | - Tianyong Hao
- School of Computer Science, South China Normal University, Guangzhou, Guangdong, China
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Numssen O, Bzdok D, Hartwigsen G. Functional specialization within the inferior parietal lobes across cognitive domains. eLife 2021; 10:63591. [PMID: 33650486 PMCID: PMC7946436 DOI: 10.7554/elife.63591] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 03/01/2021] [Indexed: 11/13/2022] Open
Abstract
The inferior parietal lobe (IPL) is a key neural substrate underlying diverse mental processes, from basic attention to language and social cognition, that define human interactions. Its putative domain-global role appears to tie into poorly understood differences between cognitive domains in both hemispheres. Across attentional, semantic, and social cognitive tasks, our study explored functional specialization within the IPL. The task specificity of IPL subregion activity was substantiated by distinct predictive signatures identified by multivariate pattern-learning algorithms. Moreover, the left and right IPL exerted domain-specific modulation of effective connectivity among their subregions. Task-evoked functional interactions of the anterior and posterior IPL subregions involved recruitment of distributed cortical partners. While anterior IPL subregions were engaged in strongly lateralized coupling links, both posterior subregions showed more symmetric coupling patterns across hemispheres. Our collective results shed light on how under-appreciated hemispheric specialization in the IPL supports some of the most distinctive human mental capacities.
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Affiliation(s)
- Ole Numssen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Leipzig, Germany
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, McGill University, Montreal, Canada.,Mila - Quebec Artificial Intelligence Institute, Montreal, Canada
| | - Gesa Hartwigsen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Leipzig, Germany
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Klyuzhin IS, Fu JF, Hong A, Sacheli M, Shenkov N, Matarazzo M, Rahmim A, Stoessl AJ, Sossi V. Data-driven, voxel-based analysis of brain PET images: Application of PCA and LASSO methods to visualize and quantify patterns of neurodegeneration. PLoS One 2018; 13:e0206607. [PMID: 30395576 PMCID: PMC6218048 DOI: 10.1371/journal.pone.0206607] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 10/16/2018] [Indexed: 11/19/2022] Open
Abstract
Spatial patterns of radiotracer binding in positron emission tomography (PET) images may convey information related to the disease topology. However, this information is not captured by the standard PET image analysis that quantifies the mean radiotracer uptake within a region of interest (ROI). On the other hand, spatial analyses that use more advanced radiomic features may be difficult to interpret. Here we propose an alternative data-driven, voxel-based approach to spatial pattern analysis in brain PET, which can be easily interpreted. We apply principal component analysis (PCA) to identify voxel covariance patterns, and optimally combine several patterns using the least absolute shrinkage and selection operator (LASSO). The resulting models predict clinical disease metrics from raw voxel values, allowing for inclusion of clinical covariates. The analysis is performed on high-resolution PET images from healthy controls and subjects affected by Parkinson’s disease (PD), acquired with a pre-synaptic and a post-synaptic dopaminergic PET tracer. We demonstrate that PCA identifies robust and tracer-specific binding patterns in sub-cortical brain structures; the patterns evolve as a function of disease progression. Principal component LASSO (PC-LASSO) models of clinical disease metrics achieve higher predictive accuracy compared to the mean tracer binding ratio (BR) alone: the cross-validated test mean squared error of adjusted disease duration (motor impairment score) was 16.3 ± 0.17 years2 (9.7 ± 0.15) with mean BR, versus 14.4 ± 0.18 years2 (8.9 ± 0.16) with PC-LASSO. We interpret the best-performing PC-LASSO models in the spatial sense and discuss them with reference to the PD pathology and somatotopic organization of the striatum. PC-LASSO is thus shown to be a useful method to analyze clinically-relevant tracer binding patterns, and to construct interpretable, imaging-based predictive models of clinical metrics.
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Affiliation(s)
- Ivan S. Klyuzhin
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- * E-mail:
| | - Jessie F. Fu
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Andy Hong
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Matthew Sacheli
- Pacific Parkinson’s Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Nikolay Shenkov
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Michele Matarazzo
- Pacific Parkinson’s Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Arman Rahmim
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - A. Jon Stoessl
- Pacific Parkinson’s Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Vesna Sossi
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
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Dwyer DB, Falkai P, Koutsouleris N. Machine Learning Approaches for Clinical Psychology and Psychiatry. Annu Rev Clin Psychol 2018; 14:91-118. [PMID: 29401044 DOI: 10.1146/annurev-clinpsy-032816-045037] [Citation(s) in RCA: 395] [Impact Index Per Article: 65.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an accessible understanding of why this approach is important for future practice given its potential to augment decisions associated with the diagnosis, prognosis, and treatment of people suffering from mental illness using clinical and biological data. To this end, the limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to critical machine learning methods used in clinical studies. A selective literature review is then presented aiming to reinforce the usefulness of machine learning methods and provide evidence of their potential. In the context of promising initial results, the current limitations of machine learning approaches are addressed, and considerations for future clinical translation are outlined.
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Affiliation(s)
- Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich 80638, Germany; , ,
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich 80638, Germany; , ,
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich 80638, Germany; , ,
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Kia SM, Vega Pons S, Weisz N, Passerini A. Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects. Front Neurosci 2017; 10:619. [PMID: 28167896 PMCID: PMC5253369 DOI: 10.3389/fnins.2016.00619] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Accepted: 12/27/2016] [Indexed: 01/18/2023] Open
Abstract
Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future.
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Affiliation(s)
- Seyed Mostafa Kia
- Department of Information Engineering and Computer Science, University of Trento Trento, Italy
| | - Sandro Vega Pons
- Fondazione Bruno KesslerTrento, Italy; Pattern Analysis and Computer Vision, Istituto Italiano di TecnologiaGenova, Italy
| | - Nathan Weisz
- Division of Physiological Psychology, Centre for Cognitive Neuroscience, University of Salzburg Salzburg, Austria
| | - Andrea Passerini
- Department of Information Engineering and Computer Science, University of Trento Trento, Italy
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