This paper is in the following e-collection/theme issue:

Non-Randomized Studies (funded, non-eHealth) (169) New Methods (1134) Chronic Conditions (434) Digital Pain Assessment and Management (232) Brain-Machine Interfaces (18) Cognitive and Neurorehabilitation (349) Neurotechnology in Clinical Studies (21) Methods and Feasibility Studies (450) Pilot studies (non-ehealth) (102)

Published on in Vol 14 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78806, first published .
Neurofeedback Training for Managing Neuropathic Pain–Like Features in Chronic Musculoskeletal Pain: Protocol for an Open-Label Pilot Feasibility Clinical Trial

Neurofeedback Training for Managing Neuropathic Pain–Like Features in Chronic Musculoskeletal Pain: Protocol for an Open-Label Pilot Feasibility Clinical Trial

Neurofeedback Training for Managing Neuropathic Pain–Like Features in Chronic Musculoskeletal Pain: Protocol for an Open-Label Pilot Feasibility Clinical Trial

Authors of this article:

Luke Spencer Bialostocki1, 2 Author Orcid Image ;   Divya Bharatkumar Adhia2 Author Orcid Image ;   Damith Rathnayake Mudiyanselage1 Author Orcid Image ;   Mark Llewellyn Smith3 Author Orcid Image ;   Yusuf Ozgur Cakmak1 Author Orcid Image ;   Dirk De Ridder2 Author Orcid Image ;   Ramakrishnan Mani4 Author Orcid Image ;   Jerin Mathew1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Protocol

    1Department of Anatomy, Faculty of Biomedical Sciences, University of Otago, Dunedin, New Zealand

    2Department of Surgical Sciences, Faculty of Medicine, University of Otago, Dunedin, New Zealand

    3Neurofeedback Services of New York, New York, NY, United States

    4Centre for Health, Activity, and Rehabilitation Research, School of Physiotherapy, University of Otago, Dunedin, New Zealand

    Corresponding Author:

    Jerin Mathew, PhD

    Department of Anatomy

    Faculty of Biomedical Sciences

    University of Otago

    270 Great King Street

    Dunedin

    New Zealand

    Phone: 64 035565036

    Email: jerin.mathew@otago.ac.nz


    Background: Neuropathic pain (NP) is characterized as pain arising from lesions of the somatosensory nervous system. However, NP-like features have been found in several chronic secondary musculoskeletal (MSK) pain conditions in the absence of detectable lesion or damage to the somatosensory pathways. Emerging evidence has demonstrated associations between NP-like symptoms and altered neural activity within brain regions implicated in sensory perception and affective-emotional processing of pain with consistent findings of abnormal activity in the right insula (RIns) cortex and dorsal anterior cingulate cortex (dACC). Electroencephalography neurofeedback (EEG-NF) is a brain-computer interface biofeedback technique that allows individuals to self-regulate the real-time cortical brain activities of the regions of interest.

    Objective: The primary objective of this study is to investigate the feasibility and safety of a novel EEG-NF intervention designed to simultaneously downtrain activity in the RIns and dACC in individuals with a chronic secondary MSK pain condition exhibiting NP-like features. In addition, this study will conduct secondary exploratory analyses to investigate EEG-derived neuronal changes and their associations with clinical and experimental pain outcomes following the EEG-NF training.

    Methods: We will design a single-arm, open-label, pilot-feasibility trial. We will recruit adults aged 35-75 years with a score of ≥19 using the PainDETECT questionnaire and an average pain score of ≥4 on the 11-point Numeric Pain Rating Scale over the last 3 months, with a minimum pain duration of 3 months, to receive active EEG-NF training. Participants will receive auditory feedback as a reward for achieving a predetermined activity threshold of the RIns and dACC. Primary outcomes will evaluate feasibility, acceptability, and safety using both self-reported questionnaires and monitoring data. Collected data will be summarized descriptively, with mean (SD) reported where appropriate. Secondary outcomes will include EEG parameters, self-reported measures, heart rate variability, and quantitative sensory testing. An exploratory within-group pre-post statistical comparison will be conducted for all secondary outcome measures, and correlation analysis will be performed to explore relationships between EEG measures, self-reported outcomes, heart rate variability, and quantitative sensory testing measures.

    Results: This study has received approval from the Health and Disability Ethics Committee and is registered with the Australian New Zealand Clinical Trials Registry. Participant recruitment began in April 2025 and is ongoing. As of October 2025, data collection has been completed, with a total of 5 participants enrolled, all of whom have completed the study to date. We expect to complete the study in March 2026. This study will generate data on feasibility, safety, acceptability, and preliminary data to inform a fully powered effectiveness clinical trial.

    Conclusions: The results and data generated will inform the design and sample size calculation for a fully powered randomized controlled trial aimed at evaluating the effectiveness of EEG-NF in targeting NP-like features in individuals with chronic MSK pain.

    Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12625000706471; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=389568&isReview=true

    International Registered Report Identifier (IRRID): DERR1-10.2196/78806

    JMIR Res Protoc 2025;14:e78806

    doi:10.2196/78806

    Keywords

    brain-computer interface; brain oscillations; chronic pain; electroencephalography; musculoskeletal; neuromodulation 


    Chronic musculoskeletal (MSK) pain is the leading cause of disability, affecting over 1.7 billion people globally [1]. In New Zealand, one-quarter of the population is affected by some form of MSK disorder, and one-fifth of the population lives with chronic pain [2,3]. A subgroup of chronic MSK pain exhibits neuropathic pain (NP)-like qualities, which include symptoms such as burning, shocking, shooting, numbness, and “pins and needles” [4-8]. According to the International Association for the Study of Pain, NP is caused by a lesion or disease affecting the somatosensory system and may involve either the central or peripheral nervous system [9,10]. However, chronic secondary MSK conditions, such as knee osteoarthritis, low back pain, shoulder pain, neck pain, and osteoporosis, have been reported to exhibit NP-like qualities with widespread pain localization in 5.4%-33% cases in the absence of a verifiable lesion affecting the somatosensory system [4,8,11-14]. Similar findings have been observed in chronic widespread pain conditions, like fibromyalgia, which exhibit NP-like symptoms despite the absence of direct somatosensory lesions [15,16]. Moreover, patients with chronic MSK pain experience higher degrees of pain unpleasantness and bothersomeness, leading to emotional distress, which are part of the affective dimensions of pain [17,18]. Concurrently, the severity of symptoms or pain intensity may not always correlate to the extent of the MSK injury in these individuals [19,20].

    Persistent pain associated with chronic MSK conditions and ongoing nociceptive input may lead to sensitization and widespread pain distribution, accompanied by altered sensory perception and changes in central processing within supraspinal structures [8,21]. Neuroimaging studies show that chronic MSK pain is associated with significant functional aberrations in brain regions involved in sensory processing, emotion and motivation, and pain inhibition [22,23]. Clinical aspects of pain correlate with these distinct anatomical brain regions and networks, as supported by functional neuroimaging data [24]. In particular, the insular cortex, specifically right insula (RIns) cortex and dorsal anterior cingulate cortex (dACC), plays a profound role in the sensory perception and emotional processing of pain and has been shown to exhibit alterations in chronic MSK pain conditions [25,26].

    The insular cortex acts as a hub for cortical processing, including sensory, emotional, social, and cognitive information [27]. In particular, the insula serves as a hub linking multiple networks, including regions involved in pain mediation, such as the salience and ventral frontoparietal attention networks [28,29]. It has been shown that pain experience is associated with salience network, with the insular cortex serving as a key hub. The insular cortex is lateralized in function, with the RIns being predominantly involved in pain perception compared with the left insula [25]. Increased activation of the RIns has been reported in individuals with chronic MSK pain in both functional magnetic resonance imaging studies and electroencephalogram (EEG) studies in the gamma frequency band [30-32]. The insular cortex is also involved in modulating pain intensity, as it has been linked to pain catastrophizing [27,33]. Studies suggest that both the insula and the dACC are involved in the emotional processing of pain [34-37]. The dACC is the main proxy for the medial suffering pain pathway and is associated with motivational-affective component of pain [38]. Alterations in the activity of the dACC, such as increased activity in the alpha and beta frequencies and decreased functional connectivity to other regions, including the pregenual anterior cingulate cortex and somatosensory cortex, have been shown in people with chronic NP [38-40]. Previous studies have demonstrated that NP-like symptoms in individuals with painful knee osteoarthritis are associated with altered infraslow frequency (ISF) activity in the RIns and dACC, with significant correlations observed between ISF fluctuations and pain measures [30,41-44]. Therefore, targeting the ISF activity of both the RIns and dACC, which are associated with sensory and affective dimensions of pain, may modulate NP-like symptoms and produce clinical improvements in individuals with chronic MSK pain [26,27,38].

    Currently, pharmacological treatments for NP conditions produce modest efficacy, potentially due to the variation in underlying mechanisms [45]. Furthermore, many available nonpharmacological treatments for NP conditions have demonstrated either low certainty of evidence or inconclusive results, highlighting the need for novel, innovative interventions [45,46]. Electroencephalography neurofeedback (EEG-NF) is a brain-computer interface biofeedback technique that allows individuals to self-regulate the real-time cortical brain activities of the regions of interest and reinforces learning using operant conditioning [47,48]. EEG-NF has been used successfully in several conditions, including chronic MSK pain, demonstrating clinical and EEG modulation [49-52]. EEG-NF training, which targets the ISF band in the brain, has been shown to provide clinical benefits [41,52]. Moreover, the ISF band has been shown to modulate higher-frequency brain oscillations through phase-locking mechanisms [53]. Previous studies have demonstrated that ISF EEG-NF training can influence resting-state brain networks and oscillations in various conditions, including targeting the dACC for chronic MSK pain [43,49,50,52,54,55]. However, to date, no studies have investigated the potential of ISF EEG-NF training for NP-like qualities in chronic MSK conditions. Furthermore, no research has explored the use of neurofeedback to simultaneously downtrain 2 distinct brain regions (dACC and RIns) as a targeted intervention for chronic MSK pain. This dual-target approach and ISF modulation for NP management highlight a novel strategy with the potential to achieve more comprehensive modulation of pain-related neural networks and clinical pain improvements.

    Therefore, this study aims, for the first time, to explore the potential of downregulating the ISF activity of both the RIns and dACC using a novel ISF EEG-NF protocol in individuals with NP-like symptoms associated with chronic MSK pain conditions. Given the novelty of the proposed ISF EEG-NF training protocol, pilot testing of the protocol and assessment of feasibility and safety are necessary. The primary objectives of this study are (1) to pilot test and investigate the feasibility of the EEG-NF program, and (2) to investigate the safety of the ISF EEG-NF training. The secondary objectives are (1) to estimate the variability of the outcome measures for informing the sample size of a fully powered clinical trial, (2) to explore the immediate and short-term trends of the effects of the targeted ISF EEG-NF training on pain measures, and (3) to explore the changes in EEG current density activity at the targeted cortical regions (dACC and RIns) and the functional connectivity between these regions following ISF EEG-NF training.


    Ethical Considerations

    We obtained Ethical approval from the Health & Disability Ethics Committee, New Zealand (Reference 2023 EXP 19190). Research consultation with Māori was obtained from the Ngāi Tahu Research Consultation Committee (Reference 24402). The trial has been prospectively registered in the Australian New Zealand Clinical Trials Registry (ACTRN12625000706471).

    All eligible participants will receive an information sheet with consent form outlining the study design and purpose. Participants who are enrolled will complete a digital consent form on the day before their baseline assessment and provide a signed paper copy upon attending in person. Participants may opt-out at any point during the study. All data for primary and secondary outcomes will be deidentified before analysis. In recognition of their time and contribution to the research, participants will receive NZ $200 (US $115) supermarket vouchers upon completion of all study sessions.

    Study Design

    A single-arm, open-label, pilot-feasibility clinical trial was designed according to the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT; Multimedia Appendix 1) and the Consolidated Standards of Reporting Trials (CONSORT; Multimedia Appendix 2) extension for pilot and feasibility trials. The trial will be conducted in Dunedin, New Zealand, and the study phases are summarized in Figure 1 [56,57]. This study was designed as an open-label trial to assess the feasibility, acceptability, safety, and preliminary data of the developed EEG-NF training. Given the pilot-feasibility design and exploratory nature of the study, inclusion of a control group was not necessary at this stage. The primary focus is to gather preliminary data on EEG-NF training implementation and variability in clinical outcomes, which will inform a future fully powered clinical trial [58-60]. A structured description of the intervention is summarized in Multimedia Appendix 3, following the Template for Intervention Description and Replication (TiDiR) guide (Multimedia Appendix 3) [61].

    ‎
    Figure 1. Diagram of participant flow for the study. NF: neurofeedback.

    Recruitment

    Convenience sampling will be used to recruit participants from the wider Dunedin community. Periodic advertising in community newspapers and social media will be carried out. Posters will be placed throughout the local community to advertise the study for participant recruitment.

    Interested participants will undergo eligibility screening by telephone or through a secure online survey using Research Electronic Data Capture (REDCap; Vanderbilt University) web application [62]. As this is a pilot-feasibility study, a formal power-based sample size calculation was not undertaken. Instead, the sample size was determined as 6-12 participants over a 4-month recruitment period, in accordance with recommendations for feasibility studies and previous literature, ensuring adequacy to evaluate safety and feasibility outcomes while also permitting exploratory analyses [63].

    Eligibility Criteria

    Participants aged 35-75 years with MSK pain, averaging a score of ≥4 on the 11-point Numeric Pain Rating Scale over the past 3 months, with a minimum pain duration of 3 months, and a score of at least 19 on the PainDETECT questionnaire will be eligible to participate in this study [6,14,64-67].

    Participants will be excluded if they have one of the following conditions or situations: systemic rheumatic conditions, neurological conditions, major psychiatric illness, cervical or lumbar radiculopathy, peripheral entrapment neuropathies, cognitive impairment, have undergone any surgery or intra-articular steroid injections in the 3 months before the study, have undergone any intra-articular hyaluronic acid injections in the 6 months before the study, are scheduled to undergo any surgery within 6 months of enrollment in the study, have undergone previous neurological procedures of the brain, are pregnant or up to 6 months postpartum, cardiovascular disease, peripheral arterial disease of limbs, uncontrolled hypertension (≥150/95 mm Hg), or uncontrolled diabetes.

    Screening and Enrollment

    Interested participants will undergo a brief preliminary screening for initial eligibility by telephone or through an online questionnaire using the REDCap online survey platform [68]. If the initial eligibility criteria are satisfied, participants will undergo a detailed screening based on their health information through a secure online survey. Participants will then be given an appointment for the final confirmatory screening.

    A paper-based Montreal Cognitive Assessment will be carried out to screen for cognitive impairments [69]. The maximum Montreal Cognitive Assessment score is 30 points, and only those who score 21 or above will be eligible to participate in this study, based on normative data for the population included in the study [69,70]. If the participant meets this threshold and is found to be eligible, baseline assessment will continue.

    Eligible participants will undergo 12 sessions (30 minutes each; 3 sessions per week) of ISF EEG-NF training and two 90-minute baseline and postintervention assessments at the Department of Anatomy–Research Clinic facility at the University of Otago, New Zealand. The intervention will be provided by a researcher experienced in the delivery of neurofeedback. Participants will be required to abstain from alcohol and caffeinated beverages for 24 hours and from food and drinks for at least 1 hour before any assessment sessions [71].

    Intervention: ISF EEG-NF Training

    At the start of each session, participants will be seated in a chair with back support and asked to remain relaxed for 10 minutes while the trainer prepares them for neurofeedback training. The ISF EEG-NF training will be administered using a 21-channel DC-coupled amplifier (BrainMaster Technologies Inc) with a Comby EEG lead cap containing Ag/AgCl sensors of an appropriate size, secured to the participant’s head. Reference electrodes will be placed at the mastoids [72]. The impedance of the active electrodes will be continuously monitored to remain below 5 kΩ [49,52,72]. Participants will be advised to minimize eyeball, head, and neck movements, swallowing, and clenching of teeth to reduce motion artifacts in the EEG data [73]. Treatment adherence will be ensured by directly observing participants throughout the training sessions.

    Participants will be instructed to relax, keep their eyes closed, and listen to the audio feedback. For this study, an ISF EEG-NF program to downtrain the RIns and dACC was developed using the BrainAvatar Live standardized low-resolution electromagnetic brain tomography projector software [52,74]. Standardized low-resolution electromagnetic brain tomography allows the selection of brain regions (region of interest [ROI]) for EEG-NF training based on the current density of the ROI and 3D brain mapping functionality [74-76]. A visual representation of the ROIs, direction of training, and an overview of outcome measures are provided in Figure 2. The figure was generated using BrainNet Viewer [77] and BioRender [78].

    ‎
    Figure 2. Regions of interest and outcome measures. dACC: dorsal anterior cingulate cortex; ECG: electrocardiogram; EEG-NF: electroencephalography neurofeedback; ISF: infraslow frequency; QST: quantitative sensory testing; RIns: right insula; ROI: region of interest.

    The BrainMaster Technologies software (BrainAvatar) will deliver real-time auditory feedback when participants’ brain activity is simultaneously downregulated within the ROIs—the dACC and RIns—in the ISF (0.0-0.1 Hz) band. The BrainAvatar auditory feedback is generated using Musical Instrument Digital Interface–based tones and sound effects. The auditory feedback serves as a primary reinforcement modality, providing immediate and intuitive cues that reflect real-time brain activity. The system incorporates a range of sounds, including simple tones, chimes, and naturalistic effects such as chirps or gongs, which vary in pitch, clarity, or occurrence depending on whether the individual’s brainwave activity meets the targeted thresholds. For example, reward tones or sound effects are triggered only when the desired neural patterns are achieved, whereas the absence, distortion, or reduction of sound indicates deviation from the training goals. This dynamic modulation allows the brain to receive continuous, nonverbal information about its own functioning, supporting operant conditioning and the self-regulation processes central to effective neurofeedback training [47,48].

    The auditory feedback (reward) will be captured using Audacity, a free and open-source audio recording and editing software [79]. Audacity can use the computer’s microphone and sound card as its own audio-to-digital converter, eliminating the need for additional equipment. The auditory feedback will then be used to calculate the duration of successful neurofeedback training, defined as the cumulative time that the participant received the auditory reward feedback during the 30-minute neurofeedback session [80]. The duration of successful neurofeedback training from each participant across the 12 sessions will be included in the analysis.

    The target ROIs were chosen based on previous literature related to EEG-NF and MSK pain conditions [30,31,50,52]. The reward threshold will be continuously monitored by the trainer and adjusted manually if cortical activity falls outside the desired threshold [49,52,72]. The duration and number of neurofeedback sessions were informed by recent studies using ISF EEG-NF for chronic pain management, which demonstrated changes in clinical pain outcomes, and were selected to ensure methodological rigor in this study [72,81].

    Baseline Assessment

    Once written informed consent is obtained, participants will complete questionnaires that include demographics and general health information, such as age, sex, and ethnicity. Assessment of resting-state EEG and clinical and experimental pain outcomes will be conducted by a researcher who is adequately trained in all procedures.

    Primary Outcomes

    The primary outcomes of this study will be the feasibility and safety measures [82]. These will be collected by the investigator throughout the study period, from recruitment until the last postintervention assessment session. Feasibility outcomes include the recruitment rate, defined as the number of participants attending a screening assessment each month; the enrollment rate, defined as the proportion of participants recruited from the total screened; the compliance rate, defined as the number of sessions attended by each participant out of the total sessions; and the dropout rate, defined as the number of participants who dropped out over course of the study [83,84]. All measures will be monitored through monthly audits of recruitment records during the first 4 months of the recruitment and intervention period.

    An adverse event is defined as any harmful event or symptom from the trial that could reasonably be linked to the procedure or EEG-NF training. EEG-NF is a safe technique; however, all participants will be asked about any adverse events experienced from the previous session at each visit, and any worsened side effects compared with previous training will be recorded. All participants will be instructed to complete the Discontinuation Emergent Sign and Symptom scale [85], a 43-item checklist consisting of emotional, behavioral, cognitive, and physical symptoms. These symptoms can be considered possible side effects from neurofeedback training and have been used in previous literature as a measure of safety monitoring for similar studies [52,72].

    Secondary Outcomes

    Secondary outcomes of the study include self-reported questionnaires, resting-state EEG, electrocardiogram (ECG), and quantitative sensory testing (QST), assessed at baseline (T1) and postintervention (T2). Table 1 briefly describes each of the measurement tools used and the time points throughout the study. All tools are reliable and validated for use in individuals with persistent pain and are consistent with previous literature and the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) recommendations for clinical trials in chronic pain [86-90].

    Table 1. Summary of secondary outcome measures, including descriptions and timeline used.
    Outcome domain and constructsBrief description of measurement toolsMeasurement time points
    Pain

    		Pain severity and interference [91,92]
    • The Brief Pain Inventory (BPI)–Short Form will be used to measure pain severity (0=no pain, 10=worst pain possible) and interference (0=does not interfere, 10=completely interfere) over the past 24 hours, past week, and past 4 weeks at T1 and T2. At each training session, a single-item BPI will measure pain severity and interference over the past 24 hours.
    		Full BPI: T1 and T2; Single-item BPI: every training session

    		Pain unpleasantness [93]
    • Measured using an 11-point numeric rating scale from 0 (not at all unpleasant) to 10 (most unpleasant imaginable) for the past 24 hours and past week at T1 and T2. At each training session, unpleasantness over the past 24 hours will be measured.
    		Before every session

    		Pain bothersomeness [41,94]
    • Measured using a 5-point scale ranging from “not at all” to “extremely” over the past 24 hours and past week at T1 and T2. Before every training session, bothersomeness over the past 24 hours will also be measured.
    		Before every session

    		Pain quality [6,8,66,95-100]
    • PainDETECT questionnaire: 12 items, each symptom rated on a 5-point scale. Scores <12 indicate nociceptive pain, 13-18 indicate possible neuropathic pain, and ≥19 indicate likely neuropathic pain.
    • Short-form McGill Pain Questionnaire: 22 items, each rated on a 10-point numeric scale to assess pain quality.
    • Leeds Assessment of Neuropathic Symptoms: 5 items with weighted scores ranging from 0 to 24, assessing signs of neuropathic pain.
    • Dolour Neuropathique 4 interview: assess the presence of neuropathic pain symptoms; a cut-off score of 4 or more positive items indicates neuropathic pain.
    		T1 and T2

    		Fatigue [101]
    • Measured using a 10-point numeric rating scale ranging from 0 (no fatigue) to 10 (fatigue as bad as it can be).
    		T1 and T2
    General health and well-being

    		Depression, anxiety, and stress [102,103]
    • The Depression, Anxiety, and Stress Scale, containing 21 items. Each item is rated on a scale from 0 (never) to 3 (almost always) to quantitatively measure psychological distress.
    		T1 and T2

    		Pain personification [104]
    • Pain personification questionnaire, containing 11 items scored on a 5-point Likert scale (1=strongly disagree, 5=strongly agree) to measure the participant’s experience of pain as an internal object.
    		T1

    		Intolerance of uncertainty [105,106]
    • Intolerance of Uncertainty Scale, containing 12 items. Each item is rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
    		T1

    		Sleep quality and disturbances [107,108]
    • Pittsburgh Sleep Quality Index, containing 19 items measuring sleep quality and disturbances over a 1-month interval. Total scores range from 0 to 21, with higher scores indicating poorer sleep quality.
    		T1 and T2
    Participant perceptions about intervention and its impact on health and pain

    		Credibility/expectancy of the intervention [109]
    • Credibility/Expectancy Questionnaire, containing 6 items to quantify how credible the participant perceived the intervention to be.
    		T2

    		Acceptability of interventions [110]
    • Numeric rating scale (0-7) assessing acceptability across 7 components of the theoretical framework of acceptability: affective attitude, burden, perceived effectiveness, ethicality, intervention coherence, opportunity costs, and self-efficacy.
    		T2

    		Perceived treatment satisfaction [111]
    • Single item numeric scale ranging from 0 (not satisfied at all) to 10 (highly satisfied).
    		T2

    		Patient Global impression of change [112,113]
    • The Patient Global Impression of Change score represents the participant’s perceived change in pain compared with baseline. It is rated on a 7-point scale, from 1 (very much improved) to 7 (very much worse).
    		T2

    		Mental fatigue [114]
    • Mental fatigue will be assessed using a single-item numeric scale from 0 (I do not feel fatigued at all) to 10 (I feel extremely fatigued).
    		T2

    		Mental strategies [72]
    • Patients will be asked whether they used any mental strategies during training sessions and if yes, to report which strategies they used.
    		T2

    		Level of motivation [52]
    • The Current Motivation–Brain Computer Interface will be used at T1. It uses a 7-point Likert scale across 4 components: mastery confidence, fear of incompetence, interest, and challenge related to electroencephalography neurofeedback training.
    • A single-item 11-point numeric scale will be used for motivation before each training session, ranging from 0 (not motivated at all) to 10 (extremely motivated).
    		T1 and before every training session

    		Mood [52,115]
    • Mood will be measured before every neurofeedback session using a single item from the Brief Mood Introspection Scale, ranging from 0 (very unpleasant) to 10 (very pleasant).
    		Before every training session

    		Level of engagement with the training [52]
    • A 10-point numeric scale will be used, ranging from 1 (least engaged) to 10 (highly engaged).
    		 After every training session

    Electroencephalographic Measures

    Resting-state EEG will be recorded (sampled at 256 Hz) using a 21-channel system (Fp1, Fp2, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8, O1, O2, A1, A2; BrainMaster Technologies, Inc) [43]. Raw EEG recordings will be collected for 10 minutes with participants’ eyes closed. To minimize potential artifacts, participants will be instructed to avoid facial movements, head and neck movements, and swallowing. Alertness will be monitored by observing the presence of spindles and slowing of the alpha rhythm in the EEG stream to prevent drowsiness-related increases in theta power during the recording [116].

    Each EEG file will be resampled to 128 Hz and bandpass filtered for 0.01-44 Hz in EEGLAB (MATLAB R2020a) [117]. Artifacts such as eye blinks, muscle artifacts, perspiration, and body movements will be filtered out using ICoN software [52,118].

    Exact low-resolution brain electromagnetic tomography (eLORETA) software will be used to extract the following EEG variables. First, ROI current density, for which frequency-specific data will be collected by performing a voxel-by-voxel analysis containing 6239 voxels for each of the following frequency bands at the dACC and RIns: infraslow (0.01-0.10 Hz), slow (0.2-1.5 Hz), delta (2-3.5 Hz), theta (4-7.5 Hz), alpha (8-12 Hz), beta (12.5-30 Hz), and gamma (30.5-44 Hz). Comparisons will be made between participants’ pretreatment and posttreatment measures using eLORETA statistical contrast maps. Multiple voxel-by-voxel comparisons in the logarithm of t-ratio with a threshold of P≤.05 will be used to compute the cortical 3D distribution of current density [49].

    Second, functional connectivity will be assessed using lagged linear connectivity, which is a statistical measure of coherence representing the phase synchronization between ROIs within the brain, as it combines the relationship between the phase and amplitude of the signals [23,24]. This measure reflects the level of communication between the ROIs [90]. Log-transformed lagged linear connectivity will be derived for all possible connections across the 7 frequency bands between the dACC and RIns.

    eLORETA is an EEG source localization software that computes current density across the full brain volume [119] to localize cortical brain structures with fewer localization errors for EEG data extraction and analysis [120]. eLORETA has been widely used in previous literature for EEG analysis [121-123].

    Cardiovascular Measures

    Heart rate variability (HRV) metrics will be obtained using a Polar V800 heart rate monitor and a Polar H10 Chest Pro Strap [124]. The ECG monitor will be placed below the sternum and secured to ensure a fixed position for the participant. Raw heart rate and R-R (peak-to-peak R wave) interval time series data will be downloaded from the Polar Flow software for further processing and analysis. Kubios HRV Premium (v3.2.0) software will be used to analyze HRV from the R-R interval record exported from the Polar Flow software [125]. The root-mean-square of successive differences between normal heartbeats and stress index (SI) will be extracted for HRV analysis. Root-mean-square of successive differences quantifies the variability between adjacent respiratory rate intervals, demonstrating a lesser susceptibility to influence by the respiratory system and, due to this, is regarded as the most dependable HRV for monitoring vagal response [125,126]. The SI will be calculated using the square root of the Baevsky SI model [127]. Given that the insular cortex and dACC are key regions involved in interoception—integrating internal bodily signals to regulate physiological and emotional states—any changes in sympathetic and parasympathetic activity can be effectively captured through HRV analysis [80,128,129].

    Quantitative Sensory Testing Measures

    The following QST measurements will be performed at T1 and T2. All QST measures were included based on previous literature demonstrating variability in these clinical measures among individuals with chronic MSK pain and NP [130-132].

    Pressure Pain Threshold

    A computerized algometer (AlgoMed, Medoc) will be used to measure pressure pain threshold on the dorsal aspect of the nondominant wrist of the participant for 3 trials [133,134]. Two familiarization trials will be performed at the mid-forearm before the formal trials. Pressure will be applied to the test site gradually at a rate of 30 kPa/s for each participant by placing a 1-cm2 probe perpendicular to the site until the participant reports that the pressure has become a pain sensation. Participants will be instructed to verbally inform the investigator immediately when they perceive pressure as pain. The average of the 3 tests will be used for analysis [135].

    Punctate Pain Intensity

    Punctate pain intensity will be assessed using a nylon filament (Semmens monofilament 6.65, 300 g) on the nondominant wrist of the participant for 3 trials [134,136]. The nylon monofilament will be applied perpendicular to the site on the nondominant wrist with enough force to bend the filament. Immediately after each trial, participants will report their pain intensity on a 101-point numeric pain rating scale (0 indicating no pain at all and 100 indicating the most intense pain they have ever experienced) [137]. The average across the 3 trials will be calculated.

    Mechanical Temporal Summation

    Mechanical temporal summation (MTS) will be assessed using a nylon filament (Semmens monofilament 6.65, 300 g) on the dorsal aspect of the nondominant wrist of the participant for 3 trials [134]. Participants will be instructed to provide a pain rating on a 101-point numeric pain rating scale. Immediately after this, 10 consecutive contacts with an interstimulus interval of 1 second will be performed, and participants will be asked to provide a pain rating on the 101-point numeric pain rating scale again. MTS will be calculated as the difference between the initial value after the first contact and the highest pain rating after the tenth contact. The average of these 3 trials will then be used in analysis.

    Vibration Detection Threshold

    A tuning fork (64 Hz, 8/8 scale) will be used to test a participant’s ability to detect vibration and will be placed on the dorsal aspect of the nondominant wrist with suprathreshold vibration intensity. It will be held in place until the participant can no longer feel the vibration [134,138,139]. This will be graded on an 8/8 scale, with 8 indicating the highest level of sensitivity to vibration [140]. The vibration detection threshold will be determined using the mean of 3 consecutive tests for each location [141].

    Medication and Other Treatment Monitoring

    Any concurrent treatments will be monitored, including regular general practitioner visits, use of physiotherapists, osteopaths, chiropractors, and self-management as well as current medications. Any medications that participants are taking regularly will be recorded at the baseline assessment. Changes in dosage, as well as the frequency of use of pain-relief medications during the study period, will be noted at each session.

    Analysis

    Primary Outcomes

    Participant demographics, feasibility, acceptability, credibility, and safety data over the course of the study will be summarized descriptively [142]. Mean (SD) or median (IQR) when distributional assumptions are not met and CI (75% and 95%) of the clinical and quantitative outcome measures for the participants will be derived using GraphPad Prism software [143]. Feasibility will be assessed against prespecified progression criteria: ≥50% of eligible participants consenting, recruitment meeting target within the planned window (~6 participants in 4 months), retention ≥80% at postintervention, adherence ≥75% of scheduled sessions, data completeness ≥85%, and mean acceptability ≥70%. Each metric will be reported as a point estimate with 95% CIs, and progression will be discussed in light of these thresholds [60,144,145].

    Secondary Outcomes

    A within-group pre-post statistical comparison (to be determined based on the normality of the data) will be conducted for all the secondary outcome measures using GraphPad Prism software (version 10.4.1; GraphPad Software) [143]. Similarly, correlation analysis will be performed to explore the relationship between EEG measures, self-reported outcomes, and HRV and QST measures. All analyses for this pilot-feasibility study will be unpowered due to sample size and are thus exploratory in nature.

    Secondary outcomes will be interpreted descriptively and exploratorily, focusing on patterns, effect size estimates, and CIs rather than formal hypothesis testing, to generate preliminary trends and inform the design of a future powered clinical trial [146]. Changes in clinical ratings before and after the intervention will be analyzed to inform the design of future efficacy trials, using effect sizes, paired t tests, and Wilcoxon signed-rank tests.


    This study was funded in January 2025. As of October 2025, data collection has been completed, with a total of 5 participants enrolled. The target sample size for the study was 12 participants. Data analysis will commence in November 2025 and will continue for 3 months following the completion of data collection. The results of the study are expected to be published once data collection and analysis are complete in late 2026 or early 2027. Any deviations from the protocol, regardless of the reason, will be documented in the Australian New Zealand Clinical Trials Registry and reported in the final publication.


    Principal Results

    The primary aim of this study is to pilot test and assess the feasibility of the novel ISF EEG-NF training protocol targeting the dACC and RIns for individuals with NP-like qualities. This study, for the first time, aims to simultaneously downregulate the ISF activity of the RIns and dACC in a chronic pain population. Therefore, a preliminary study assessing the feasibility, acceptability, and safety of the intervention is imperative before advancing to a fully powered clinical trial to evaluate its efficacy.

    Comparison With Previous Work

    Previous studies, using comparable protocols have conducted dual region source-localized ISF EEG-NF training in individuals with chronic low back pain and major depressive disorders [50,147]. These studies successfully targeted the dACC, showing clinical improvements and variability following the training. In addition, a recent study using a similar methodology successfully targeted the insular cortex, specifically training the left posterior insula in healthy volunteers. This study explored the relationship between the duration of successful neurofeedback training and clinical outcomes, aiming to understand the impact of EEG-NF training on gastric slow-wave activity [80]. Thus, this study adopts a similar methodological framework to develop the ISF EEG-NF program [75,120], using established and reliable methods that have been successfully implemented in previous research [43,52,72,80,89]. The ROIs (RIns and dACC) are created with all the nearby voxels with a localization of 5 mm [41,52,120]. Moreover, no studies have attempted ISF EEG-NF to manage NP-like qualities in individuals with chronic MSK pain, highlighting the novelty of this project and its potential for future clinical translation.

    This study will also introduce a novel approach to measuring EEG learning effects by assessing the duration of successful neurofeedback within each session, a method previously used in only one study [80]. The secondary aims will allow for exploratory analysis to better understand data variability and trends in EEG measures and clinical pain outcomes in individuals with NP-like qualities in chronic MSK conditions.

    Limitations

    During this study, several challenges may be encountered while assessing feasibility. One of the primary issues may be due to recruitment of participants. This could be attributed to the challenges faced by patients coming to the study location to receive treatment, as their pain may be debilitating, as highlighted in previous literature emphasizing activity limitations [148]. This study is also targeting a demographic that may be spread throughout the wider local community and thus may not encounter the flyers placed throughout the community. We plan to mitigate this by also including the study advertisement in newspapers, which may be more accessible for recruiting patients who are further away from the surrounding area. Since PainDETECT is being used as a screening tool, we are excluding patients with scores below 19, including those falling within the range of 14-18 that indicates a possible neuropathic component. This produces a challenge as it reduces recruitment and potentially excludes patients who may have a neuropathic component to their pain. However, only recruiting patients who score 19 or above strengthens the validity of the patients having a neuropathic component, as painDETECT has a high sensitivity and specificity [149]. That said, we acknowledge that this can impact patient recruitment and enrollment rates.

    Conclusions

    The data and findings of this study will aid in designing future fully powered clinical trials to further evaluate the efficacy of this novel ISF EEG-NF training for NP management. Additionally, the foundational data from this pilot open-label feasibility trial will provide the necessary information to enable future studies to perform power calculations for designing upcoming trials.

    Acknowledgments

    The authors would like to thank William Parton and Ravikash Prasad for their support and contributions in trialing the methodology before finalizing the study’s data collection procedures.

    This project is fully funded by the 2025 International Association for the Study of Pain Early Career Research Grant.

    Data Availability

    The data generated during this study will be available on reasonable request from the corresponding author.

    Authors' Contributions

    JM is responsible for the conceptualization of this study. LSB, DRM, and JM were involved in data curation. JM handled funding acquisition. LSB and DRM conducted the investigation. JM, DBA, YOC, MLS, RM, and DDR contributed to the methodology. JM, DBA, YOC, RM, and DDR provided resources. JM, MLS, and YOC were responsible for software. JM, DBA, YOC, RM, and DDR supervised the study. LSB wrote the original draft. LSB, JM, DBA, DRM, MLS, YOC, RM, and DDR contributed to writing, review, and editing.

    Conflicts of Interest

    MLS is the owner of Neurofeedback Therapy Services of New York, New York. The remaining authors have no conflicts of interest to declare.

    Multimedia Appendix 1

    SPIRIT checklist.

    PDF File (Adobe PDF File), 180 KB
    Multimedia Appendix 2

    CONSORT extension for pilot and feasibility trials.

    PDF File (Adobe PDF File), 183 KB
    Multimedia Appendix 3

    TiDiR checklist.

    PDF File (Adobe PDF File), 242 KB
    Multimedia Appendix 4

    Peer review report from the International Association for the Study of Pain (IASP) Early Career Researcher Grant Review Committee.

    PDF File (Adobe PDF File), 27 KB
    1. Blyth FM, Briggs AM, Schneider CH, Hoy DG, March LM. The global burden of musculoskeletal pain-where to from here? Am J Public Health. 2019;109(1):35-40. [CrossRef] [Medline]
    2. Abbott JH, Usiskin IM, Wilson R, Hansen P, Losina E. The quality-of-life burden of knee osteoarthritis in New Zealand adults: a model-based evaluation. PLoS One. 2017;12(10):e0185676. [FREE Full text] [CrossRef] [Medline]
    3. GBD 2021 Diseases and Injuries Collaborators. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the global burden of disease study 2021. Lancet. 2024;403(10440):2133-2161. [FREE Full text] [CrossRef] [Medline]
    4. El-Tallawy SN, Nalamasu R, Salem GI, LeQuang JAK, Pergolizzi JV, Christo PJ. Management of musculoskeletal pain: an update with emphasis on chronic musculoskeletal pain. Pain Ther. 2021;10(1):181-209. [FREE Full text] [CrossRef] [Medline]
    5. Schäfer AGM, Joos LJ, Roggemann K, Waldvogel-Röcker K, Pfingsten M, Petzke F. Pain experiences of patients with musculoskeletal pain + central sensitization: a comparative group Delphi study. PLoS One. 2017;12(8):e0182207. [FREE Full text] [CrossRef] [Medline]
    6. Bittencourt JV, Bezerra MC, Pina MR, Reis FJJ, de Sá Ferreira A, Nogueira LAC. Use of the painDETECT to discriminate musculoskeletal pain phenotypes. Arch Physiother. 2022;12(1):7. [FREE Full text] [CrossRef] [Medline]
    7. Rasmussen PV, Sindrup SH, Jensen TS, Bach FW. Symptoms and signs in patients with suspected neuropathic pain. Pain. 2004;110(1-2):461-469. [CrossRef] [Medline]
    8. Giske L, Bautz-Holter E, Sandvik L, Røe C. Relationship between pain and neuropathic symptoms in chronic musculoskeletal pain. Pain Med. 2009;10(5):910-917. [CrossRef] [Medline]
    9. Jensen TS, Baron R, Haanpää M, Kalso E, Loeser JD, Rice ASC, et al. A new definition of neuropathic pain. Pain. 2011;152(10):2204-2205. [CrossRef] [Medline]
    10. Terminology. IASP: International Association For The Study of Pain. 2025. URL: https://www.iasp-pain.org/resources/terminology/#pain [accessed 2025-04-01]
    11. Ohtori S, Orita S, Yamashita M, Ishikawa T, Ito T, Shigemura T, et al. Existence of a neuropathic pain component in patients with osteoarthritis of the knee. Yonsei Med J. 2012;53(4):801-805. [FREE Full text] [CrossRef] [Medline]
    12. Shigemura T, Ohtori S, Kishida S, Nakamura J, Takeshita M, Takazawa M, et al. Neuropathic pain in patients with osteoarthritis of hip joint. Eur Orthop Traumatol. 2011;2(3-4):73-77. [FREE Full text] [CrossRef]
    13. Povoroznyuk V, Shinkarenko T, Pryimych U. AB0952 neuropathic pain component under musculoskeletal diseases. Ann Rheum Dis. 2015;74:1217-1218. [FREE Full text] [CrossRef]
    14. Cancela-Cilleruelo I, Rodríguez-Jiménez J, Arias-Buría JL, Navarro-Santana MJ, Arendt-Nielsen L, Fernández-de-Las-Peñas C. Presence of neuropathic-like symptoms in individuals with painful tendinopathy/overuse injuries: a systematic review and meta-analysis. Clin J Pain. 2025;41(7):e1292. [CrossRef] [Medline]
    15. Fishbain DA, Lewis JE, Cutler R, Cole B, Rosomoff HL, Rosomoff RS. Can the neuropathic pain scale discriminate between non-neuropathic and neuropathic pain? Pain Med. 2008;9(2):149-160. [CrossRef] [Medline]
    16. Neeck G. Pathogenic mechanisms of fibromyalgia. Ageing Res Rev. 2002;1(2):243-255. [CrossRef] [Medline]
    17. Rainville P, Duncan GH, Price DD, Carrier B, Bushnell MC. Pain affect encoded in human anterior cingulate but not somatosensory cortex. Science. 1997;277(5328):968-971. [CrossRef] [Medline]
    18. Landmark L, Sunde HF, Fors EA, Kennair LEO, Sayadian A, Backelin C, et al. Associations between pain intensity, psychosocial factors, and pain-related disability in 4285 patients with chronic pain. Sci Rep. 2024;14(1):13477. [FREE Full text] [CrossRef] [Medline]
    19. Wang K, Kim HA, Felson DT, Xu L, Kim DH, Nevitt MC, et al. Radiographic knee osteoarthritis and knee pain: cross-sectional study from five different racial/ethnic populations. Sci Rep. 2018;8(1):1364. [FREE Full text] [CrossRef] [Medline]
    20. Bedson J, Croft PR. The discordance between clinical and radiographic knee osteoarthritis: a systematic search and summary of the literature. BMC Musculoskelet Disord. 2008;9:116. [FREE Full text] [CrossRef] [Medline]
    21. Arendt-Nielsen L, Morlion B, Perrot S, Dahan A, Dickenson A, Kress HG, et al. Assessment and manifestation of central sensitisation across different chronic pain conditions. Eur J Pain. 2018;22(2):216-241. [CrossRef] [Medline]
    22. Bushnell MC, Ceko M, Low LA. Cognitive and emotional control of pain and its disruption in chronic pain. Nat Rev Neurosci. 2013;14(7):502-511. [CrossRef] [Medline]
    23. Vanneste S, Ost J, Van Havenbergh T, De Ridder D. Resting state electrical brain activity and connectivity in fibromyalgia. PLoS One. 2017;12(6):e0178516. [FREE Full text] [CrossRef] [Medline]
    24. De Ridder D, Vanneste S. The Bayesian brain in imbalance: medial, lateral and descending pathways in tinnitus and pain: a perspective. Prog Brain Res. 2021;262:309-334. [CrossRef] [Medline]
    25. Öz F, Acer N, Katayıfçı N, Aytaç G, Karaali K, Sindel M. The role of lateralisation and sex on insular cortex: 3D volumetric analysis. Turk J Med Sci. 2021;51(3):1240-1248. [FREE Full text] [CrossRef] [Medline]
    26. De Ridder D, Vanneste S, Smith M, Adhia D. Pain and the triple network model. Front Neurol. 2022;13:757241. [FREE Full text] [CrossRef] [Medline]
    27. De Ridder D, Adhia D, Vanneste S. The anatomy of pain and suffering in the brain and its clinical implications. Neurosci Biobehav Rev. 2021;130:125-146. [CrossRef] [Medline]
    28. Chang LJ, Yarkoni T, Khaw MW, Sanfey AG. Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference. Cereb Cortex. 2013;23(3):739-749. [FREE Full text] [CrossRef] [Medline]
    29. Uddin LQ. Salience processing and insular cortical function and dysfunction. Nat Rev Neurosci. 2015;16(1):55-61. [CrossRef] [Medline]
    30. Cottam WJ, Iwabuchi SJ, Drabek MM, Reckziegel D, Auer DP. Altered connectivity of the right anterior insula drives the pain connectome changes in chronic knee osteoarthritis. Pain. 2018;159(5):929-938. [FREE Full text] [CrossRef] [Medline]
    31. Liu H, Chou K, Lee P, Wang Y, Chen S, Lai K, et al. Right anterior insula is associated with pain generalization in patients with fibromyalgia. Pain. 2022;163(4):e572-e579. [CrossRef] [Medline]
    32. Mathew J, Adhia DB, Smith ML, De Ridder D, Mani R. Closed-loop infraslow brain-computer interface can modulate cortical activity and connectivity in individuals with chronic painful knee osteoarthritis: A secondary analysis of a randomized placebo-controlled clinical trial. Clin EEG Neurosci. 2025;56(2):165-180. [FREE Full text] [CrossRef] [Medline]
    33. Mathur VA, Moayedi M, Keaser ML, Khan SA, Hubbard CS, Goyal M, et al. High frequency migraine is associated with lower acute pain sensitivity and abnormal insula activity related to migraine pain intensity, attack frequency, and pain catastrophizing. Front Hum Neurosci. 2016;10:489. [FREE Full text] [CrossRef] [Medline]
    34. Lamm C, Decety J, Singer T. Meta-analytic evidence for common and distinct neural networks associated with directly experienced pain and empathy for pain. Neuroimage. 2011;54(3):2492-2502. [CrossRef] [Medline]
    35. Singer T, Seymour B, O'Doherty J, Kaube H, Dolan RJ, Frith CD. Empathy for pain involves the affective but not sensory components of pain. Science. 2004;303(5661):1157-1162. [FREE Full text] [CrossRef] [Medline]
    36. Zaki J, Wager TD, Singer T, Keysers C, Gazzola V. The anatomy of suffering: understanding the relationship between nociceptive and empathic pain. Trends Cogn Sci. 2016;20(4):249-259. [FREE Full text] [CrossRef] [Medline]
    37. Corradi-Dell'Acqua C, Hofstetter C, Vuilleumier P. Felt and seen pain evoke the same local patterns of cortical activity in insular and cingulate cortex. J Neurosci. 2011;31(49):17996-18006. [FREE Full text] [CrossRef] [Medline]
    38. Vanneste S, De Ridder D. Chronic pain as a brain imbalance between pain input and pain suppression. Brain Commun. 2021;3(1):fcab014. [FREE Full text] [CrossRef] [Medline]
    39. Vanneste S, De Ridder D. BurstDR spinal cord stimulation rebalances pain input and pain suppression in the brain in chronic neuropathic pain. Brain Stimul. 2023;16(4):1186-1195. [FREE Full text] [CrossRef] [Medline]
    40. Cauda F, D'Agata F, Sacco K, Duca S, Cocito D, Paolasso I, et al. Altered resting state attentional networks in diabetic neuropathic pain. J Neurol Neurosurg Psychiatry. 2010;81(7):806-811. [FREE Full text] [CrossRef] [Medline]
    41. Mathew J. Neurofeedback Training for Pain Management in People With Knee Osteoarthritis. 2022. URL: https://hdl.handle.net/10523/13704 [accessed 2025-09-25]
    42. Moisset X, Bouhassira D. Brain imaging of neuropathic pain. Neuroimage. 2007;37 Suppl 1:S80-S88. [CrossRef] [Medline]
    43. Mathew J, Adhia DB, Smith ML, De Ridder D, Mani R. Closed-loop infraslow brain-computer interface can modulate cortical activity and connectivity in individuals with chronic painful knee osteoarthritis: a secondary analysis of a randomized placebo-controlled clinical trial. Clin EEG Neurosci. 2025;56(2):165-180. [FREE Full text] [CrossRef] [Medline]
    44. Mathew J, Adhia DB, Hall M, De Ridder D, Mani R. EEG-based cortical alterations in individuals with chronic knee pain secondary to osteoarthritis: a cross-sectional investigation. J Pain. 2024;25(5):104429. [FREE Full text] [CrossRef] [Medline]
    45. Soliman N, Moisset X, Ferraro MC, de Andrade DC, Baron R, Belton J, et al. NeuPSIG Review Update Study Group. Pharmacotherapy and non-invasive neuromodulation for neuropathic pain: a systematic review and meta-analysis. Lancet Neurol. 2025;24(5):413-428. [FREE Full text] [CrossRef] [Medline]
    46. Moisset X, Bouhassira D, Avez Couturier J, Alchaar H, Conradi S, Delmotte MH, et al. Pharmacological and non-pharmacological treatments for neuropathic pain: systematic review and French recommendations. Rev Neurol (Paris). 2020;176(5):325-352. [CrossRef] [Medline]
    47. Marzbani H, Marateb HR, Mansourian M. Neurofeedback: a comprehensive review on system design, methodology and clinical applications. Basic Clin Neurosci. 2016;7(2):143-158. [CrossRef] [Medline]
    48. Mathew J. Neurofeedback-based brain-computer interface for pain management: a research perspective. N Z J Physiothe. 2025;53(1):4-6. [FREE Full text] [CrossRef]
    49. Leong SL, Vanneste S, Lim J, Smith M, Manning P, De Ridder D. A randomised, double-blind, placebo-controlled parallel trial of closed-loop infraslow brain training in food addiction. Sci Rep. 2018;8(1):11659. [FREE Full text] [CrossRef] [Medline]
    50. Adhia DB, Mani R, Turner PR, Vanneste S, De Ridder D. Infraslow neurofeedback training alters effective connectivity in individuals with chronic low back pain: a secondary analysis of a pilot randomized placebo-controlled study. Brain Sci. 2022;12(11):1514. [FREE Full text] [CrossRef] [Medline]
    51. Vanneste S, Joos K, Ost J, De Ridder D. Influencing connectivity and cross-frequency coupling by real-time source localized neurofeedback of the posterior cingulate cortex reduces tinnitus related distress. Neurobiol Stress. 2018;8:211-224. [FREE Full text] [CrossRef] [Medline]
    52. Mathew J, Adhia DB, Smith ML, De Ridder D, Mani R. Source localized infraslow neurofeedback training in people with chronic painful knee osteoarthritis: a randomized, double-blind, sham-controlled feasibility clinical trial. Front Neurosci. 2022;16:899772. [FREE Full text] [CrossRef] [Medline]
    53. Picchioni D, Horovitz SG, Fukunaga M, Carr WS, Meltzer JA, Balkin TJ, et al. Infraslow EEG oscillations organize large-scale cortical-subcortical interactions during sleep: a combined EEG/fMRI study. Brain Res. 2011;1374:63-72. [FREE Full text] [CrossRef] [Medline]
    54. Perez TM, Adhia DB, Glue P, Zeng J, Dillingham P, Navid MS, et al. Infraslow closed-loop brain training for anxiety and depression (ISAD): a pilot randomised, sham-controlled trial in adult females with internalizing disorders. Cogn Affect Behav Neurosci. 2025;25(4):1147-1180. [CrossRef] [Medline]
    55. Mathew J, Adhia DB, Smith ML, De Ridder D, Mani R. Can EEG-neurofeedback training enhance effective connectivity in people with chronic secondary nusculoskeletal pain? A secondary analysis of a feasibility randomized controlled clinical trial. Brain Behav. 2025;15(6):e70541. [CrossRef] [Medline]
    56. Eldridge SM, Chan CL, Campbell MJ, Bond CM, Hopewell S, Thabane L, et al. PAFS consensus group. CONSORT 2010 statement: extension to randomised pilot and feasibility trials. Pilot Feasibility Stud. 2016;2:64. [FREE Full text] [CrossRef] [Medline]
    57. Chan A, Tetzlaff JM, Gøtzsche PC, Altman DG, Mann H, Berlin JA, et al. SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials. BMJ. 2013;346:e7586. [FREE Full text] [CrossRef] [Medline]
    58. Leon AC, Davis LL, Kraemer HC. The role and interpretation of pilot studies in clinical research. J Psychiatr Res. 2011;45(5):626-629. [FREE Full text] [CrossRef] [Medline]
    59. Arain M, Campbell MJ, Cooper CL, Lancaster GA. What is a pilot or feasibility study? A review of current practice and editorial policy. BMC Med Res Methodol. 2010;10:67. [FREE Full text] [CrossRef] [Medline]
    60. Teresi JA, Yu X, Stewart AL, Hays RD. Guidelines for designing and evaluating feasibility pilot studies. Med Care. 2022;60(1):95-103. [FREE Full text] [CrossRef] [Medline]
    61. Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014;348:g1687. [FREE Full text] [CrossRef] [Medline]
    62. Patridge EF, Bardyn TP. Research Electronic Data Capture (REDCap). J Med Libr Assoc. 2018;106(1):142. [CrossRef]
    63. Moore CG, Carter RE, Nietert PJ, Stewart PW. Recommendations for planning pilot studies in clinical and translational research. Clin Transl Sci. 2011;4(5):332-337. [FREE Full text] [CrossRef] [Medline]
    64. Childs JD, Piva SR, Fritz JM. Responsiveness of the numeric pain rating scale in patients with low back pain. Spine (Phila Pa 1976). 2005;30(11):1331-1334. [CrossRef] [Medline]
    65. Jensen MP, McFarland CA. Increasing the reliability and validity of pain intensity measurement in chronic pain patients. Pain. 1993;55(2):195-203. [CrossRef] [Medline]
    66. Turner KV, Moreton BM, Walsh DA, Lincoln NB. Reliability and responsiveness of measures of pain in people with osteoarthritis of the knee: a psychometric evaluation. Disabil Rehabil. 2017;39(8):822-829. [FREE Full text] [CrossRef] [Medline]
    67. Bartley EJ, King CD, Sibille KT, Cruz-Almeida Y, Riley JL, Glover TL, et al. Enhanced pain sensitivity among individuals with symptomatic knee osteoarthritis: potential sex differences in central sensitization. Arthritis Care Res (Hoboken). 2016;68(4):472-480. [FREE Full text] [CrossRef] [Medline]
    68. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, et al. REDCap Consortium. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;95:103208. [FREE Full text] [CrossRef] [Medline]
    69. Hobson J. The Montreal Cognitive Assessment (MoCA). Occup Med (Lond). 2015;65(9):764-765. [CrossRef] [Medline]
    70. Cheung G, Clugston A, Croucher M, Malone D, Mau E, Sims A, et al. Performance of three cognitive screening tools in a sample of older New Zealanders. Int Psychogeriatr. 2015;27(6):981-989. [FREE Full text] [CrossRef] [Medline]
    71. Jobert M, Wilson FJ, Ruigt GSF, Brunovsky M, Prichep LS, Drinkenburg WHIM, et al. IPEG Pharmaco-EEG Guidelines Committee. Guidelines for the recording and evaluation of pharmaco-EEG data in man: the international pharmaco-EEG society (IPEG). Neuropsychobiology. 2012;66(4):201-220. [CrossRef] [Medline]
    72. Adhia DB, Mani R, Mathew J, O'Leary F, Smith M, Vanneste S, et al. Exploring electroencephalographic infraslow neurofeedback treatment for chronic low back pain: a double-blinded safety and feasibility randomized placebo-controlled trial. Sci Rep. 2023;13(1):1177. [FREE Full text] [CrossRef] [Medline]
    73. Qi G, Zhao S, Ceder AA, Guan W, Yan X. Wielding and evaluating the removal composition of common artefacts in EEG signals for driving behaviour analysis. Accid Anal Prev. 2021;159:106223. [CrossRef] [Medline]
    74. Gracefire PA. Introduction to the concepts and clinical applications of multivariate live Z-Score training, PZOK and sLORETA feedback. In: Handbook of Clinical QEEG and Neurotherapy. United Kingdom. Routledge; 2016:358-416.
    75. Pascual-Marqui RD. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol. 2002;24 Suppl D:5-12. [Medline]
    76. Pascual-Marqui R. Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. ArXiv. Preprint posted online October 17, 2007. [FREE Full text]
    77. Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One. 2013;8(7):e68910. [FREE Full text] [CrossRef] [Medline]
    78. BioRender. 2025. URL: https://www.biorender.com/ [accessed 2025-06-01]
    79. Maheshkumar K, Dilara K, Maruthy KN, Sundareswaren L. Validation of PC-based sound card with biopac for digitalization of ECG recording in short-term HRV analysis. N Am J Med Sci. 2016;8(7):307-311. [FREE Full text] [CrossRef] [Medline]
    80. Mathew J, Galacgac J, Smith ML, Du P, Cakmak YO. The impact of alpha-neurofeedback training on gastric slow wave activity and heart rate variability in humans. Neurogastroenterol Motil. 2025;37(5):e15009. [CrossRef] [Medline]
    81. Anderson L, De Ridder D, Glue P, Mani R, van Sleeuwen C, Smith M, et al. A safety and feasibility randomized placebo controlled trial exploring electroencephalographic effective connectivity neurofeedback treatment for fibromyalgia. Sci Rep. 2025;15(1):209. [FREE Full text] [CrossRef] [Medline]
    82. Tickle-Degnen L. Nuts and bolts of conducting feasibility studies. Am J Occup Ther. 2013;67(2):171-176. [FREE Full text] [CrossRef] [Medline]
    83. Orsmond GI, Cohn ES. The distinctive features of a feasibility study: objectives and guiding questions. OTJR (Thorofare N J). 2015;35(3):169-177. [CrossRef] [Medline]
    84. Donald G. A brief summary of pilot and feasibility studies: exploring terminology, aims, and methods. European Journal of Integrative Medicine. 2018;24:65-70. [FREE Full text] [CrossRef]
    85. Rogel A, Guez J, Getter N, Keha E, Cohen T, Amor T, et al. Transient adverse side effects during neurofeedback training: a randomized, sham-controlled, double blind study. Appl Psychophysiol Biofeedback. 2015;40(3):209-218. [CrossRef] [Medline]
    86. Edwards RR, Dworkin RH, Turk DC, Angst MS, Dionne R, Freeman R, et al. Patient phenotyping in clinical trials of chronic pain treatments: IMMPACT recommendations. Pain. 2016;157(9):1851-1871. [FREE Full text] [CrossRef] [Medline]
    87. Taylor AM, Phillips K, Patel KV, Turk DC, Dworkin RH, Beaton D, et al. Assessment of physical function and participation in chronic pain clinical trials: IMMPACT/OMERACT recommendations. Pain. 2016;157(9):1836-1850. [FREE Full text] [CrossRef] [Medline]
    88. Yarnitsky D, Bouhassira D, Drewes AM, Fillingim RB, Granot M, Hansson P, et al. Recommendations on practice of conditioned pain modulation (CPM) testing. Eur J Pain. 2015;19(6):805-806. [CrossRef] [Medline]
    89. Mani R, Adhia DB, Awatere S, Gray AR, Mathew J, Wilson LC, et al. Self-regulation training for people with knee osteoarthritis: a protocol for a feasibility randomised control trial (MiNT trial). Front Pain Res (Lausanne). 2023;4:1271839. [FREE Full text] [CrossRef] [Medline]
    90. Mathew J, Adhia D, Smith M, De Ridder D, Mani R. Protocol for a pilot randomized sham-controlled clinical trial evaluating the feasibility, safety, and acceptability of Infraslow Electroencephalography neurofeedback training on experimental and clinical pain outcomes in people with chronic painful knee. NeuroRegulation. 2020;7(1):30-44. [FREE Full text] [CrossRef]
    91. Keller S, Bann CM, Dodd SL, Schein J, Mendoza TR, Cleeland CS. Validity of the brief pain inventory for use in documenting the outcomes of patients with noncancer pain. Clin J Pain. 2004;20(5):309-318. [CrossRef] [Medline]
    92. Mendoza T, Mayne T, Rublee D, Cleeland C. Reliability and validity of a modified Brief Pain Inventory short form in patients with osteoarthritis. Eur J Pain. 2006;10(4):353-361. [CrossRef] [Medline]
    93. Starr CJ, Houle TT, Coghill RC. Psychological and sensory predictors of experimental thermal pain: a multifactorial model. J Pain. 2010;11(12):1394-1402. [FREE Full text] [CrossRef] [Medline]
    94. Dunn KM, Croft PR. Classification of low back pain in primary care: using "bothersomeness" to identify the most severe cases. Spine (Phila Pa 1976). 2005;30(16):1887-1892. [CrossRef] [Medline]
    95. Cappelleri JC, Koduru V, Bienen EJ, Sadosky A. Characterizing neuropathic pain profiles: enriching interpretation of painDETECT. Patient Relat Outcome Meas. 2016;7:93-99. [FREE Full text] [CrossRef] [Medline]
    96. Mayer TG, Neblett R, Cohen H, Howard KJ, Choi YH, Williams MJ, et al. The development and psychometric validation of the central sensitization inventory. Pain Pract. 2012;12(4):276-285. [FREE Full text] [CrossRef] [Medline]
    97. Neblett R, Sanabria-Mazo JP, Luciano JV, Mirčić M, Čolović P, Bojanić M, et al. Is the central sensitization inventory (CSI) associated with quantitative sensory testing (QST)? A systematic review and meta-analysis. Neurosci Biobehav Rev. 2024;161:105612. [CrossRef] [Medline]
    98. Boljanovic-Susic D, Ziebart C, MacDermid J, de Beer J, Petruccelli D, Woodhouse LJ. The sensitivity and specificity of using the McGill pain subscale for diagnosing neuropathic and non-neuropathic chronic pain in the total joint arthroplasty population. Arch Physiother. 2023;13(1):9. [FREE Full text] [CrossRef] [Medline]
    99. van Wilgen CP, Konopka KH, Keizer D, Zwerver J, Dekker R. Do patients with chronic patellar tendinopathy have an altered somatosensory profile? A quantitative sensory testing (QST) study. Scand J Med Sci Sports. 2013;23(2):149-155. [CrossRef] [Medline]
    100. Bouhassira D, Attal N, Alchaar H, Boureau F, Brochet B, Bruxelle J, et al. Comparison of pain syndromes associated with nervous or somatic lesions and development of a new neuropathic pain diagnostic questionnaire (DN4). Pain. 2005;114(1-2):29-36. [CrossRef] [Medline]
    101. Stebbings S, Treharne GJ. Fatigue in rheumatic disease: an overview. Int J Clin Rheumatol. 2010;5(4):487-502. [CrossRef]
    102. Wood BM, Nicholas MK, Blyth F, Asghari A, Gibson S. The utility of the short version of the depression anxiety stress scales (DASS-21) in elderly patients with persistent pain: does age make a difference? Pain Med. 2010;11(12):1780-1790. [CrossRef] [Medline]
    103. Gloster AT, Rhoades HM, Novy D, Klotsche J, Senior A, Kunik M, et al. Psychometric properties of the depression anxiety and stress scale-21 in older primary care patients. J Affect Disord. 2008;110(3):248-259. [FREE Full text] [CrossRef] [Medline]
    104. Schattner E, Shahar G. Role of pain personification in pain-related depression: an object relations perspective. Psychiatry. 2011;74(1):14-20. [CrossRef] [Medline]
    105. Carleton RN, Norton MAPJ, Asmundson GJG. Fearing the unknown: a short version of the intolerance of uncertainty scale. J Anxiety Disord. 2007;21(1):105-117. [CrossRef] [Medline]
    106. Freeston MH, Rhéaume J, Letarte H, Dugas MJ, Ladouceur R. Why do people worry? Pers Individ Dif. 1994;17(6):791-802. [FREE Full text] [CrossRef]
    107. Omachi TA. Measures of sleep in rheumatologic diseases: Epworth Sleepiness Scale (ESS), functional outcome of sleep questionnaire (FOSQ), insomnia severity index (ISI), and Pittsburgh Sleep Quality Index (PSQI). Arthritis Care Res (Hoboken). 2011;63 Suppl 11(0 11):S287-S296. [FREE Full text] [CrossRef] [Medline]
    108. Cunningham JM, Blake C, Power CK, O'Keeffe D, Kelly V, Horan S, et al. The impact on sleep of a multidisciplinary cognitive behavioural pain management programme: a pilot study. BMC Musculoskelet Disord. 2011;12:5. [FREE Full text] [CrossRef] [Medline]
    109. Devilly GJ, Borkovec TD. Psychometric properties of the credibility/expectancy questionnaire. J Behav Ther Exp Psychiatry. 2000;31(2):73-86. [CrossRef] [Medline]
    110. Sekhon M, Cartwright M, Francis JJ. Acceptability of healthcare interventions: an overview of reviews and development of a theoretical framework. BMC Health Serv Res. 2017;17(1):88. [FREE Full text] [CrossRef] [Medline]
    111. Volpicelli Leonard K, Robertson C, Bhowmick A, Herbert LB. Perceived treatment satisfaction and effectiveness facilitators among patients with chronic health conditions: a self-reported survey. Interact J Med Res. 2020;9(1):e13029. [FREE Full text] [CrossRef] [Medline]
    112. Geisser ME, Clauw DJ, Strand V, Gendreau RM, Palmer R, Williams DA. Contributions of change in clinical status parameters to patient global impression of change (PGIC) scores among persons with fibromyalgia treated with milnacipran. Pain. 2010;149(2):373-378. [CrossRef] [Medline]
    113. Loncarić-Katušin M, Milošević M, Žilić A, Mišković P, Majerić-Kogler V, Žunić J. Practical chronic pain assessment tools in clinical practice. Acta Clin Croat. 2016;55 Suppl 1:19-26. [Medline]
    114. Marcos-Martínez D, Santamaría-Vázquez E, Martínez-Cagigal V, Pérez-Velasco S, Rodríguez-González V, Martín-Fernández A, et al. ITACA: An open-source framework for Neurofeedback based on brain-computer interfaces. Comput Biol Med. 2023;160:107011. [FREE Full text] [CrossRef] [Medline]
    115. Mayer JD, Gaschke YN. The experience and meta-experience of mood. J Pers Soc Psychol. 1988;55(1):102-111. [CrossRef] [Medline]
    116. Britton JW, Frey JC, Hopp JL, Korb P, Koubeissi MZ, Lievens WE, et al. Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants. Chicago. American Epilepsy Society; 2016.
    117. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9-21. [FREE Full text] [CrossRef] [Medline]
    118. Pascual-Marqui RD. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol. 2002;24 Suppl D:5-12. [Medline]
    119. Zhukov L, Weinstein D, Johnson C. Independent component analysis for EEG source localization. IEEE Eng Med Biol Mag. 2000;19(3):87-96. [CrossRef] [Medline]
    120. Jatoi MA, Kamel N, Malik AS, Faye I. EEG based brain source localization comparison of sLORETA and eLORETA. Australas Phys Eng Sci Med. 2014;37(4):713-721. [CrossRef] [Medline]
    121. Aoki Y, Hata M, Iwase M, Ishii R, Pascual-Marqui RD, Yanagisawa T, et al. Cortical electrical activity changes in healthy aging using EEG-eLORETA analysis. Neuroimage Rep. 2022;2(4):100143. [CrossRef] [Medline]
    122. Lee I, Kim KM, Lim MH. Theta and gamma activity differences in obsessive-compulsive disorder and panic disorder: insights from resting-state EEG with eLORETA. Brain Sci. 2023;13(10):1440. [FREE Full text] [CrossRef] [Medline]
    123. Ueda M, Ueno K, Yuri T, Aoki Y, Hata M, Inoue T, et al. EEG oscillatory activity and resting-state networks associated with neurocognitive function in mild traumatic brain injury. Clin EEG Neurosci. 2025;56(3):271-281. [CrossRef] [Medline]
    124. Giles D, Draper N, Neil W. Validity of the polar V800 heart rate monitor to measure RR intervals at rest. Eur J Appl Physiol. 2016;116(3):563-571. [FREE Full text] [CrossRef] [Medline]
    125. Shaffer F, Ginsberg JP. An overview of heart rate variability metrics and norms. Front Public Health. 2017;5:258. [FREE Full text] [CrossRef] [Medline]
    126. Laborde S, Mosley E, Thayer JF. Heart rate variability and cardiac vagal tone in psychophysiological research - recommendations for experiment planning, data analysis, and data reporting. Front Psychol. 2017;8:213. [FREE Full text] [CrossRef] [Medline]
    127. Baevsky RM, Chernikova AG. Heart rate variability analysis: physiological foundations and main methods. Cardiometry. 2017. [FREE Full text] [CrossRef]
    128. Babo-Rebelo M, Richter CG, Tallon-Baudry C. Neural responses to heartbeats in the default network encode the self in spontaneous thoughts. J Neurosci. 2016;36(30):7829-7840. [FREE Full text] [CrossRef] [Medline]
    129. Babo-Rebelo M, Wolpert N, Adam C, Hasboun D, Tallon-Baudry C. Is the cardiac monitoring function related to the self in both the default network and right anterior insula? Philos Trans R Soc Lond B Biol Sci. 2016;371(1708):20160004. [FREE Full text] [CrossRef] [Medline]
    130. Lindholm E, Löndahl M, Fagher K, Apelqvist J, Dahlin LB. Strong association between vibration perception thresholds at low frequencies (4 and 8 Hz), neuropathic symptoms and diabetic foot ulcers. PLoS One. 2019;14(2):e0212921. [FREE Full text] [CrossRef] [Medline]
    131. Uddin Z, MacDermid JC. Quantitative sensory testing in chronic musculoskeletal pain. Pain Med. 2016;17(9):1694-1703. [CrossRef] [Medline]
    132. Maier C, Baron R, Tölle TR, Binder A, Birbaumer N, Birklein F, et al. Quantitative sensory testing in the German research network on neuropathic pain (DFNS): somatosensory abnormalities in 1236 patients with different neuropathic pain syndromes. Pain. 2010;150(3):439-450. [CrossRef] [Medline]
    133. Mani R, Adhia DB, Leong SL, Vanneste S, De Ridder D. Sedentary behaviour facilitates conditioned pain modulation in middle-aged and older adults with persistent musculoskeletal pain: a cross-sectional investigation. Pain Rep. 2019;4(5):e773. [CrossRef] [Medline]
    134. Weaver KR, Griffioen MA, Klinedinst NJ, Galik E, Duarte AC, Colloca L, et al. Quantitative sensory testing across chronic pain conditions and use in special populations. Front Pain Res (Lausanne). 2021;2:779068. [FREE Full text] [CrossRef] [Medline]
    135. Rolke R, Magerl W, Campbell KA, Schalber C, Caspari S, Birklein F, et al. Quantitative sensory testing: a comprehensive protocol for clinical trials. Eur J Pain. 2006;10(1):77-88. [CrossRef] [Medline]
    136. Zhu GC, Böttger K, Slater H, Cook C, Farrell SF, Hailey L, et al. Concurrent validity of a low-cost and time-efficient clinical sensory test battery to evaluate somatosensory dysfunction. Eur J Pain. 2019;23(10):1826-1838. [FREE Full text] [CrossRef] [Medline]
    137. Greenspan JD, Slade GD, Bair E, Dubner R, Fillingim RB, Ohrbach R, et al. Pain sensitivity risk factors for chronic TMD: descriptive data and empirically identified domains from the OPPERA case control study. J Pain. 2011;12(11 Suppl):T61-T74. [FREE Full text] [CrossRef] [Medline]
    138. Baron R, Binder A, Wasner G. Neuropathic pain: diagnosis, pathophysiological mechanisms, and treatment. Lancet Neurol. 2010;9(8):807-819. [CrossRef] [Medline]
    139. Cruccu G, Sommer C, Anand P, Attal N, Baron R, Garcia-Larrea L, et al. EFNS guidelines on neuropathic pain assessment: revised 2009. Eur J Neurol. 2010;17(8):1010-1018. [CrossRef] [Medline]
    140. Fagius J, Wahren LK. Variability of sensory threshold determination in clinical use. J Neurol Sci. 1981;51(1):11-27. [CrossRef] [Medline]
    141. Jakorinne P, Haanpää M, Arokoski J. Reliability of pressure pain, vibration detection, and tactile detection threshold measurements in lower extremities in subjects with knee osteoarthritis and healthy controls. Scand J Rheumatol. 2018;47(6):491-500. [CrossRef] [Medline]
    142. Abbott JH. The distinction between randomized clinical trials (RCTs) and preliminary feasibility and pilot studies: what they are and are not. J Orthop Sports Phys Ther. 2014;44(8):555-558. [CrossRef] [Medline]
    143. Swift ML. GraphPad prism, data analysis, and scientific graphing. J Chem Inf Comput Sci. 1997;37(2):411-412. [CrossRef]
    144. Eldridge SM, Lancaster GA, Campbell MJ, Thabane L, Hopewell S, Coleman CL, et al. Defining feasibility and pilot studies in preparation for randomised controlled trials: development of a conceptual framework. PLoS One. 2016;11(3):e0150205. [FREE Full text] [CrossRef] [Medline]
    145. Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice. Hoboken. Pearson/Prentice Hall; 2009.
    146. Sim J. Should treatment effects be estimated in pilot and feasibility studies? Pilot Feasibility Stud. 2019;5:107. [FREE Full text] [CrossRef] [Medline]
    147. Perez TM, Glue P, Adhia DB, Navid MS, Zeng J, Dillingham P, et al. Infraslow closed-loop brain training for anxiety and depression (ISAD): a protocol for a randomized, double-blind, sham-controlled pilot trial in adult females with internalizing disorders. Trials. 2022;23(1):949. [FREE Full text] [CrossRef] [Medline]
    148. Vongsirinavarat M, Nilmart P, Somprasong S, Apinonkul B. Identification of knee osteoarthritis disability phenotypes regarding activity limitation: a cluster analysis. BMC Musculoskelet Disord. 2020;21(1):237. [FREE Full text] [CrossRef] [Medline]
    149. Hiyama A, Katoh H, Sakai D, Tanaka M, Sato M, Watanabe M. Clinical impact of JOABPEQ mental health scores in patients with low back pain: analysis using the neuropathic pain screening tool painDETECT. J Orthop Sci. 2017;22(6):1009-1014. [CrossRef] [Medline]

    ‎
    CONSORT: Consolidated Standards of Reporting Trials
    dACC: dorsal anterior cingulate cortex
    ECG: electrocardiogram
    EEG: electroencephalogram
    EEG-NF: electroencephalography neurofeedback
    eLORETA: exact low-resolution brain electromagnetic tomography
    HRV: heart rate variability
    IMMPACT: Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials
    ISF: infraslow frequency
    MSK: musculoskeletal
    MTS: mechanical temporal summation
    NP: neuropathic pain
    QST: quantitative sensory testing
    REDCap: Research Electronic Data Capture
    RIns: right insula
    ROI: region of interest
    R-R: peak-to-peak R wave
    SI: stress index
    SPIRIT: Standard Protocol Items: Recommendations for Interventional Trials
    TiDiR: Template for Intervention Description and Replication

    Edited by A Schwartz; The proposal for this study was peer-reviewed by: International Association for the Study of Pain (IASP) Early Career Researcher Grant Review Committee; See Multimedia Appendix 4 for the peer-review reportsubmitted 09.Jun.2025; accepted 23.Sep.2025; published 04.Nov.2025.

    Copyright

    ©Luke Spencer Bialostocki, Divya Bharatkumar Adhia, Damith Rathnayake Mudiyanselage, Mark Llewellyn Smith, Yusuf Ozgur Cakmak, Dirk De Ridder, Ramakrishnan Mani, Jerin Mathew. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 04.Nov.2025.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.