A behavioral and brain imaging dataset with focus on emotion regulation of women with fibromyalgia

Balducci, Thania; Rasgado-Toledo, Jalil; Valencia, Alely; Tol, Marie-José van; Alemán, André; Garza‐Villarreal, Eduardo A.

doi:10.1038/s41597-022-01677-9

Cited by 6 publications

(3 citation statements)

References 60 publications

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“…We have created a live on-line demo (on-line demo available at https://aorliche.github.io/ImageNomer/live/ ) and a Docker image containing an example open-access dataset of Fibromyalgia patients. This dataset is available as accession number ds004144 from OpenNeuro ( Balducci et al, 2022 ; Markiewicz et al, 2021 ). Instructions for using the Docker images, as well as a tutorial based around the Fibromyalgia dataset, can be found online (documentation and tutorial link: https://imagenomer.readthedocs.io/en/latest/index.html ).…”

Section: Methodsmentioning

confidence: 99%

ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound

Orlichenko,

Daly,

Zhou

et al. 2023

Neuroimage: Reports

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Section: Methodsmentioning

confidence: 99%

ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound

Orlichenko,

Daly,

Zhou

et al. 2023

Neuroimage: Reports

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“…Cancer pain research and investigation on APA should prioritize examining the underlying pathophysiological mechanisms and evaluating the effectiveness of study models for different types of pain [ 97 ]. Cancer-induced neuropathic pain, for instance, has distinct pathophysiology, and this issue should be carefully addressed during the collection and analysis of physiological signals [ 98 ]. Moreover, distinct autonomic dysfunctions can be produced by anticancer therapy [ 99 , 100 ], and a bias in the analysis.…”

Section: Research Perspectives and Issuesmentioning

confidence: 99%

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

Cascella,

Schiavo,

Cuomo

et al. 2023

Pain Research and Management

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Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.

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“…We include a 66-subject dataset of 33 female fibromyalgia patients and 33 female healthy controls from the OpenNeuro repository, 54 study identifier ds004144. 55 Out of the entire cohort, 65 subjects have two different scans: resting state and epr. A variety of medication, demographic, and questionnaire data are available.…”

Section: Openneuro Fibromyalgia Dataset (Fibro)mentioning

confidence: 99%

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results

Orlichenko,

Qu,

et al. 2023

Preprint

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Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.

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A behavioral and brain imaging dataset with focus on emotion regulation of women with fibromyalgia

Cited by 6 publications

References 60 publications

ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound

ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results

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