Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning

Hsiao, Fu Jung; Chen, Wei–Ta; Pan, Li; Liu, Hung‐Yu; Chen, Shih‐Pin; Lai, Kuan‐Lin; Wang, Shuu Jiun

doi:10.1186/s10194-022-01500-1

Cited by 11 publications

(12 citation statements)

References 56 publications

(86 reference statements)

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“…The classification model exhibited excellent performance in distinguishing patients with CM from HCs (accuracy ≥ 86.8%, area under the curve (AUC) ≥ 0.9) and from those with episodic migraine (EM) (accuracy: 94.5%, AUC: 0.96). The model also achieved high performance (accuracy: 89.1%, AUC: 0.91) in classifying CM from other pain disorders (Hsiao et al, 2022).…”

Section: Machine Learning and Deep Learningmentioning

confidence: 89%

Application of EEG in migraine

Zhang

Pan

Chen

et al. 2023

Front. Hum. Neurosci.

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Migraine is a common disease of the nervous system that seriously affects the quality of life of patients and constitutes a growing global health crisis. However, many limitations and challenges exist in migraine research, including the unclear etiology and the lack of specific biomarkers for diagnosis and treatment. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity. With the updating of data processing and analysis methods in recent years, EEG offers the possibility to explore altered brain functional patterns and brain network characteristics of migraines in depth. In this paper, we provide an overview of the methodology that can be applied to EEG data processing and analysis and a narrative review of EEG-based migraine-related research. To better understand the neural changes of migraine or to provide a new idea for the clinical diagnosis and treatment of migraine in the future, we discussed the study of EEG and evoked potential in migraine, compared the relevant research methods, and put forwards suggestions for future migraine EEG studies.

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Section: Machine Learning and Deep Learningmentioning

confidence: 89%

Application of EEG in migraine

Zhang

Pan

Chen

et al. 2023

Front. Hum. Neurosci.

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“…One recent study demonstrated that ML and MEG could classify healthy controls and chronic migraine patients with an accuracy greater than 86% (AUC > 0.9). Moreover, the study also demonstrated that ML could correctly classify patients into either chronic migraine and episodic migraine, as well as chronic migraine or fibromyalgia patients with high accuracies (Hsiao et al, 2022). Moreover, given the ongoing development of low-cost MEG that can operate at room temperature (Boto et al, 2017), future research should investigate the utility of MEG for pain intensity prediction, perhaps combining it with EEG for multimodal imaging (Singh, 2014;Yoshinaga et al, 2002), which may further improve performance.…”

Section: Discussionmentioning

confidence: 92%

External Validation of Machine Learning and EEG for Continuous Pain Intensity Prediction in Healthy Individuals

Mari,

Henderson,

Ali

et al. 2023

Preprint

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Previous research has predicted subjective pain intensity from electroencephalographic (EEG) data using machine learning (ML) models. However, there is a paucity of externally validated ML models for pain assessment, particularly for continuous pain prediction (e.g., decoding pain ratings on a 101-point scale). We aimed to conduct the first external validation paradigm for ML regression models for pain intensity prediction from EEG data. Ninety-one subjects were recruited across three samples. Sample one (n = 40) was used for model development, sample two (n = 51) was used as a cross-subject external validation set, whilst sample three (n = 25) was used as a within-subjects temporal external validation set. Pneumatic pressure stimuli were delivered to the left-hand index fingernail bed at 10 graded intensity levels. Single-trial time-frequency features of peri-stimulus EEG were used to train a Random Forest (RF) model and long short-term memory (LSTM) network to predict pain intensity responses. Results demonstrated that both the RF model and LSTM network predicted pain intensity significantly more accurately than a random prediction model, with the mean absolute error (MAE) of the RF (best performing model) at 19.59, 21.29, and 18.90 for internal validation, cross-subject external validation, and within-subject external validation, respectively. However, neither model was able to predict pain intensity better than a baseline dummy model, which predicted the mean behavioural rating of the training set and did not have access to neural data. Moreover, in a replication of our recent work, we developed a RF model for the classification of low and high-pain trials, which demonstrated internal and external validation accuracies up to 64% and 58%, respectively. Taken together, our results suggest that using ML and EEG to predict continuous pain ratings is not currently feasible. However, classification models demonstrate some potential, consistently outperforming chance across validation samples. Further improvements such as composite measures are required to elevate ML performance to a clinically meaningful level.

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“…Resting-state functional MRI connectivity data had an accuracy of 86.1% in identifying patients with migraine (31). In our previous study (10), the resting-state magnetoencephalographic functional connectivity within pain-related areas was used to establish a model that achieved excellent performance in distinguishing patients with CM from HCs. Generally, an ML approach combined with neuroimaging data might be capable of identifying of patients with migraine.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding pain processing, the pain generator or mediator in the peripheral trigeminal system, brainstem and midbrain (7,8), and cortical areas and networks are involved in the dysfunctional modulatory mechanisms of sensory perception. Accordingly, abnormal quantitative sensory tests (9), altered pain processing (4)(5)(6)(10)(11)(12), and reduced habituation (4,6,13) have been observed among patients with migraine. However, whether modulating cortical oscillations for painful (PF) and nonpainful (NP) sensory processing represents a potential brain signature that can be used to discern patients with CM from those without migraine remains debatable.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, neurologists still rely on traditional diagnostic tools to diagnose because clinical decisions must be made at the individual level (14). To achieve an individualised diagnosis, we applied a supervised machine-learning (ML) approach (10) to examining evoked oscillatory activities; algorithms have been developed to automatically detect patterns in the data, which are then used to predict or classify future data. This approach may provide a high level of individual characterisation suitable for routine clinical use.…”

Section: Introductionmentioning

confidence: 99%

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Identification of patients with chronic migraine by using sensory-evoked oscillations from the electroencephalogram classifier

et al. 2023

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Background To examine whether the modulating evoked cortical oscillations could be brain signatures among patients with chronic migraine, we investigated cortical modulation using an electroencephalogram with machine learning techniques. Methods We directly record evoked electroencephalogram activity during nonpainful, painful, and repetitive painful electrical stimulation tasks. Cortical modulation for experimental pain and habituation processing was analyzed and used to differentiate patients with chronic migraine from healthy controls using a validated machine-learning model. Results This study included 80 participants: 40 healthy controls and 40 patients with chronic migraine. Evoked somatosensory oscillations were dominant in the alpha band. Longer latency (nonpainful and repetitive painful) and augmented power (nonpainful and repetitive painful) were present among patients with chronic migraine. However, for painful tasks, alpha increases were observed among healthy controls. The oscillatory activity ratios between repetitive painful and painful tasks represented the frequency modulation and power habituation among healthy controls, respectively, but not among patients with chronic migraine. The classification models with oscillatory features exhibited high performance in differentiating patients with chronic migraine from healthy controls. Conclusion Altered oscillatory characteristics of sensory processing and cortical modulation reflected the neuropathology of patients with chronic migraine. These characteristics can be reliably used to identify patients with chronic migraine using a machine-learning approach.

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Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning

Cited by 11 publications

References 56 publications

Application of EEG in migraine

Application of EEG in migraine

External Validation of Machine Learning and EEG for Continuous Pain Intensity Prediction in Healthy Individuals

Identification of patients with chronic migraine by using sensory-evoked oscillations from the electroencephalogram classifier

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