Exploring sex differences in alpha brain activity as a potential neuromarker associated with neuropathic pain

Fauchon, Camille; Kim, Junseok A.; El-Sayed, Rima; Osborne, Natalie R.; Rogachov, Anton; Cheng, Jui‐Ching; Hemington, Kasey S.; Bosma, Rachael L.; Dunkley, Benjamin T.; Oh, Jiwon; Bhatia, Anuj; Inman, Robert D.; Davis, Karen D.

doi:10.1097/j.pain.0000000000002491

Cited by 18 publications

(8 citation statements)

References 85 publications

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“…Finally, 76 studies were included. 1,5–8,10,11,15,16,18–27,30–33,35,37–49,51,56,59,60,63,66–69,74–76,79,80,82,89–91,95–98,100–105,109,110,112–115,120,122,124–126 Figure 1 depicts the PRISMA flow diagram of study selection along with reasons for study exclusion at every stage.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, 76 studies were included. 1,[5][6][7][8]10,11,15,16,[18][19][20][21][22][23][24][25][26][27][30][31][32][33]35,[37][38][39][40][41][42][43][44][45][46][47][48][49]51,56,59,60,63,[66][67][68][69][74][75][76]79,80,82,[89]…”

Section: Study Selectionmentioning

confidence: 99%

“…However, because included studies used heterogeneous statistical methods for hypothesis testing, we did not superimpose effect size estimation contours on the plots. Modified albatross plots were used for comparison of PAF, frequency-specific power, and frequency-specific connectivity at theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma frequencies in cross-sectional (diagnostic biomarker), longitudinal (monitoring biomarker), and descriptive studies (monitoring/diagnostic biomarker). For graphical presentation of longitudinal study results, we only included studies that reported pain relief.…”

Section: Introductionmentioning

confidence: 99%

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Resting-state electroencephalography and magnetoencephalography as biomarkers of chronic pain: a systematic review

Zebhauser¹,

Hohn²,

Ploner³

2022

Pain

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Reliable and objective biomarkers promise to improve the assessment and treatment of chronic pain. Resting-state electroencephalography (EEG) is broadly available, easy to use, and cost efficient and, therefore, appealing as a potential biomarker of chronic pain. However, results of EEG studies are heterogeneous. Therefore, we conducted a systematic review (PROSPERO CRD42021272622) of quantitative resting-state EEG and magnetoencephalography (MEG) studies in adult patients with different types of chronic pain. We excluded populations with severe psychiatric or neurologic comorbidity. Risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semiquantitative data synthesis was conducted using modified albatross plots. We included 76 studies after searching MEDLINE, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and EMBASE. For cross-sectional studies that can serve to develop diagnostic biomarkers, we found higher theta and beta power in patients with chronic pain than in healthy participants. For longitudinal studies, which can yield monitoring and/or predictive biomarkers, we found no clear associations of pain relief with M/EEG measures. Similarly, descriptive studies that can yield diagnostic or monitoring biomarkers showed no clear correlations of pain intensity with M/EEG measures. Risk of bias was high in many studies and domains. Together, this systematic review synthesizes evidence on how resting-state M/EEG might serve as a diagnostic biomarker of chronic pain. Beyond, this review might help to guide future M/EEG studies on the development of pain biomarkers.

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Section: Resultsmentioning

confidence: 99%

Section: Study Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Resting-state electroencephalography and magnetoencephalography as biomarkers of chronic pain: a systematic review

Zebhauser¹,

Hohn²,

Ploner³

2022

Pain

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“…Second, independent samples t tests were conducted to evaluate group differences in age, questionnaires, pain duration, and EEG data, whereas a x 2 was performed to investigate potential differences in sex. To investigate differences in global average IAF between our high and low fear of movement groups, we used an analysis of covariance (ANCOVA) and included the covariates of sex and age because both measures are known to affect brain activity in general and specifically within the alpha range 15,22,50 and average pain reported for the last week from the NPRS to help determine whether results are related to pain intensity as opposed to fear of movement. Significance was set at P , 0.05.…”

Section: Statistical Analysis 241 Primary Analysesmentioning

confidence: 99%

Lower individual alpha frequency in individuals with chronic low back pain and fear of movement

Ho,

Park,

Wang

et al. 2023

Pain

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Significant progress has been made in linking measures of individual alpha frequency (IAF) and pain. A lower IAF has been associated with chronic neuropathic pain and with an increased sensitivity to pain in healthy young adults. However, the translation of these findings to chronic low back pain (cLBP) are sparse and inconsistent. To address this limitation, we assessed IAFs in a cohort of 70 individuals with cLBP, implemented 3 different IAF calculations, and separated cLBP subjects based on psychological variables. We hypothesized that a higher fear movement in cLBP is associated with a lower IAF at rest. A total of 10 minutes of resting data were collected from 128 electroencephalography channels. Our results offer 3 novel contributions to the literature. First, the high fear group had a significantly lower peak alpha frequency. The high fear group also reported higher pain and higher disability. Second, we calculated individual alpha frequency using 3 different but established methods; the effect of fear on individual alpha frequency was robust across all methods. Third, fear of movement, pain intensity, and disability highly correlated with each other and together significantly predicted IAF. Our findings are the first to show that individuals with cLBP and high fear have a lower peak alpha frequency.

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“…13 Furthermore, another MEG study found that the power and frequency of peak alpha oscillations in the dynamic pain connectome are abnormal and manifest in a sex-specific manner. 4 The dynamic pain connectome represents a system of brain regions influencing the overarching experience of pain, including the salience network, default mode network, ascending nociceptive pathway, and descending nociceptive pathway. 15 If an individual's alpha frequency could be used to determine their pain sensitivity, it would pave the way for more personalized and effective pain management strategies.…”

mentioning

confidence: 99%

Fear and pain slow the brain

Mazaheri,

Furman,

Seminowicz

2023

Pain

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N europhysiological signals detected noninvasively at the scalp using techniques such as EEG/MEG contain rhythms (oscillatory activity) occurring at distinct frequency bands. The predominant rhythm in healthy humans occurs between the bandwidth of 8 to 14 Hz and is commonly referred to as the alpha rhythm. The frequency within the alpha band where power is maximal is called the peak alpha frequency (PAF) or individual alpha frequency. Peak alpha frequency varies considerably between individuals, 9,14 and this variability is believed to play a role in the individual differences in perceptual ability and temporal binding of sensory information. 1,8,21,23 The relationship of PAF to both acute and chronic pain is an exciting area currently under investigation by multiple laboratories. Patients with chronic pain often exhibit changes in alpha rhythms, particularly slower PAF, when compared with control subjects, 26 and the degree of PAF slowing has been found to correlate with chronic pain duration. 3 One interpretation of this PAF slowing is that it reflects brain processes that actively maintain chronic pain, such as excess "inhibition or disfacilitation" brought about by ongoing pain. 16 An alternative interpretation is that slower PAF reflects processes related to higher pain sensitivity and an increased disposition to develop chronic pain that predate disease onset. This latter interpretation is supported by a series of studies of acute pain in healthy subjects, which found that an individual's PAF recorded during a pain-free rest period was negatively correlated with the intensity of a future pain event. [5][6][7] This work has undergone initial clinical validation: In a cohort of patients undergoing thoracotomy, PAF collected before surgery was able to predict the severity of postsurgery pain. 20 Thus, PAF has been suggested as a biomarker of pain sensitivity, which has been hypothesized to be a key factor in the transition from acute to chronic pain. 10 Precisely how PAF fits into the myriad of biological, social, and psychological factors involved in the transition from acute to chronic pain has remained unexplored. One of these factors is the fear of movement, which has been demonstrated to be a significant predictor of the transition from acute low back pain to chronic low back pain (CLBP). 19,25

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Exploring sex differences in alpha brain activity as a potential neuromarker associated with neuropathic pain

Cited by 18 publications

References 85 publications

Resting-state electroencephalography and magnetoencephalography as biomarkers of chronic pain: a systematic review

Resting-state electroencephalography and magnetoencephalography as biomarkers of chronic pain: a systematic review

Lower individual alpha frequency in individuals with chronic low back pain and fear of movement

Fear and pain slow the brain

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