First-in-human prediction of chronic pain state using intracranial neural biomarkers

Shirvalkar, Prasad; Prosky, Jordan; Chin, Gregory T.; Ahmadipour, Parima; Sani, Omid G.; Desai, Maansi; Schmitgen, Ashlyn; Dawes, Heather E.; Shanechi, Maryam M.; Starr, Philip A.; Chang, Edward F.

doi:10.1038/s41593-023-01338-z

Cited by 32 publications

(18 citation statements)

References 57 publications

(82 reference statements)

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“…Some studies suggest that OFC may inhibit pain by participating in reward-related circuits (Rolls, 2023) and connecting to subcortical centers like the periaqueductal gray involved in pain modulation (Huang et al, 2021). Additionally, Shirvalkar et al ( 2023) recently reported that OFC signals could be used in predicting chronic pain (Shirvalkar et al, 2023). However, animal and human data on the OFC's role in pain and TTH mechanisms remain limited, necessitating further research to elaborate on its contributions.…”

Section: Discussionmentioning

confidence: 99%

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Brain structural and functional abnormalities in patients with tension‐type headache: A systematic review of magnetic resonance imaging studies

Parsaei,

Taebi,

Arvin

et al. 2024

J of Neuroscience Research

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Tension‐type headache (TTH) stands as the most prevalent form of headache, yet an adequate understanding of its underlying mechanisms remains elusive. This article endeavors to comprehensively review structural and functional magnetic resonance imaging (MRI) studies investigating TTH patients, to gain valuable insights into the pathophysiology of TTH, and to explore new avenues for enhanced treatment strategies. We conducted a systematic search to identify relevant articles examining brain MRI disparities between TTH individuals and headache‐free controls (HFC). Fourteen studies, encompassing 312 diagnosed TTH patients, were selected for inclusion. Among these, eight studies utilized conventional MRI, one employed diffusion tensor imaging, and five implemented various functional MRI modalities. Consistent findings across these studies revealed a notable increase in white matter hyperintensity (WMH) in TTH patients. Furthermore, the potential involvement of the specific brain areas recognized to be involved in different dimensions of pain perception including cortical regions (anterior and posterior cingulate cortex, prefrontal cortex, anterior and posterior insular cortex), subcortical regions (thalamus, caudate, putamen, and parahippocampus), cerebellum in TTH pathogenesis was identified. However, no significant association was established between TTH and intracranial abnormalities or total intracranial volume. In conclusion, these findings support the hypotheses regarding the role of central mechanisms in TTH pathophysiology and offer probable brain regions implicated in these mechanisms. Due to the scarce data on the precise role of these regions in the TTH, further preclinical and clinical investigations should be done to advance our knowledge and enhance targeted therapeutic options of TTH.

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Section: Discussionmentioning

confidence: 99%

“…Additionally, Shirvalkar et al. (2023) recently reported that OFC signals could be used in predicting chronic pain (Shirvalkar et al., 2023). However, animal and human data on the OFC's role in pain and TTH mechanisms remain limited, necessitating further research to elaborate on its contributions.…”

Section: Discussionmentioning

confidence: 99%

Brain structural and functional abnormalities in patients with tension‐type headache: A systematic review of magnetic resonance imaging studies

Parsaei,

Taebi,

Arvin

et al. 2024

J of Neuroscience Research

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“…Overall, ML and EEG remain promising for pain classification, but improved performance from robustly designed studies remains imperative. Finally, whilst continuous pain intensity prediction is desirable for finer prediction resolution which would enable improved pain assessment and treatment monitoring/recommendations (Shirvalkar et al, 2023), it appears unrealistic within the current approach.…”

Section: Discussionmentioning

confidence: 99%

“…Obtaining more precise pain estimates may enable improved pain management. For example, predicting continuous ratings enables finer monitoring of pain over time and allows for changes after treatment to be more accurately assessed (e.g., small changes in pain intensity can be identified, which is not possible with broad binary classification; Shirvalkar et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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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“…The MED can also help extend multimodal motor BCIs to naturalistic setups where task-related events, such as movement onset, must be detected before decoding is possible. In addition to spiking and LFP, the application of MED to other neural modalities such as intracranial EEG or electrocorticography, which have high potential for translation, is also an important future direction [22,74,75,[84][85][86][87][88][89][90][91][92][93][94][95][96]. Finally, the combination of MED with various learning algorithms for neural population modeling and decoding, even in the presence of external input stimuli (e.g.…”

Section: Applications and Future Directionsmentioning

confidence: 99%

Event detection and classification from multimodal time series with application to neural data

Sadras,

Pesaran,

Shanechi

2024

J. Neural Eng.

Self Cite

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Objective: The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments for example can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need. Approach: Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets. Main results: We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded from a macaque monkey performing an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance. Significance: This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic setups without constrained tasks or prior knowledge of event times.

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First-in-human prediction of chronic pain state using intracranial neural biomarkers

Cited by 32 publications

References 57 publications

Brain structural and functional abnormalities in patients with tension‐type headache: A systematic review of magnetic resonance imaging studies

Brain structural and functional abnormalities in patients with tension‐type headache: A systematic review of magnetic resonance imaging studies

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

Event detection and classification from multimodal time series with application to neural data

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