Machine learning and artificial intelligence in neuroscience: A primer for researchers.

Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute intelligence to. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. Both have seen a recent boom in usage, both in scientific and commercial fields. For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. ML can here identify patterns that could not have been found using traditional statistic approaches. However, ML comes with serious limitations that need to be kept in mind: their tendency to optimise solutions for the input data means it is of crucial importance to externally validate any findings before considering them more than a hypothesis. Their black-box nature implies that their decisions usually cannot be understood, which renders their use in medical decision making problematic and can lead to ethical issues. Here, we present an introduction for the curious to the field of ML/AI. We explain the principles as commonly used methods as well as recent methodological advancements before we discuss risks and what we see as future directions of the field. Finally, we show practical examples of neuroscience to illustrate the use and limitations of ML.

The impact of patients' pre-treatment expectations on immunosuppressive treatment outcomes in myasthenia gravis: A pilot correlational study.

INTRODUCTION/AIMS: The impact of treatment expectations on active treatment outcomes has not been specifically investigated in neuromuscular disorders. We thus explored in myasthenia gravis (MG) the contribution of patients' pre-treatment expectations combined with an immunosuppressant drug on treatment outcomes. METHODS: This pilot correlational study involved 17 patients with generalized MG, scheduled to start immunosuppressant azathioprine. At baseline, a healthcare professional administered: (i) the Stanford Expectations of Treatment Scale; (ii) a structured checklist paper form asking patients which side-effects they expected to develop after starting azathioprine, coupled with a standardized framing of statements. Quantitative Myasthenia Gravis (QMG) score and daily dose of concomitant drugs were assessed by neurologists as clinical outcomes. Clinical outcomes and side-effects were re-assessed at 3 and 6 months, and clinical outcomes were monitored at 18 months. RESULTS: Clinically significant improvement in the QMG scores was achieved at 3 or 6 months. The level of state anxiety appeared to act as moderator of pre-treatment negative expectations (strong, positive, indicative correlation, rs = .733, p = .001). The latter were, in turn, associated with the fulfillment of side-effects that patients expected to develop with the new treatment (moderate, positive, indicative correlation, rs = .699, p = .002). No significant correlation emerged between positive and negative expectations. DISCUSSION: Our findings show a very quick clinical response and also suggest that patients' expectations and anxiety contributed to treatment outcomes, highlighting the importance of promoting safety messages and education strategies around newly introduced treatments. Future goals include evaluating a larger cohort that includes a matched control group.

Pain after combat injury in male UK military personnel deployed to Afghanistan.

BACKGROUND: Chronic pain after injury poses a serious health burden. As a result of advances in medical technology, ever more military personnel survive severe combat injuries, but long-term pain outcomes are unknown. We aimed to assess rates of pain in a representative sample of UK military personnel with and without combat injuries. METHODS: We used data from the ADVANCE cohort study (ISRCTN57285353). Individuals deployed as UK armed forces to Afghanistan were recruited to include those with physical combat injuries, and a frequency-matched uninjured comparison group. Participants completed self-reported questionnaires, including 'overall' pain intensity and self-assessment of post-traumatic stress disorder, anxiety, and depression. RESULTS: A total of 579 participants with combat injury, including 161 with amputations, and 565 uninjured participants were included in the analysis (median 8 yr since injury/deployment). Frequency of moderate or severe pain was 18% (n=202), and was higher in the injured group (n=140, 24%) compared with the uninjured group (n=62, 11%, relative risk: 1.1, 95% confidence interval [CI]: 1.0-1.2, P<0.001), and lower in the amputation injury subgroup (n=31, 19%) compared with the non-amputation injury subgroup (n=109, 26%, relative risk: 0.9, 95% CI: 0.9-1.0, P=0.034). Presence of at least moderate pain was associated with higher rates of post-traumatic stress (RR: 3.7, 95% CI: 2.7-5.0), anxiety (RR: 3.2, 95% CI: 2.4-4.3), and depression (RR: 3.4, 95% CI: 2.7-4.5) after accounting for injury. CONCLUSION: Combat injury, but not amputation, was associated with a higher frequency of moderate to severe pain intensity in this cohort, and pain was associated with adverse mental health outcomes.

A systematic review of the prevalence of postamputation and chronic neuropathic pain associated with combat injury in military personnel.

ABSTRACT: Combat trauma can lead to widespread tissue damage and limb loss. This may result in chronic neuropathic and post amputation pain, including phantom limb pain (PLP) and residual limb pain (RLP). The military population is distinct with respect to demographic, injury, and social characteristics compared with other amputation and trauma cohorts. We undertook a systematic review of studies of military personnel, with a history of combat injury, that reported a prevalence of any type of postamputation pain or chronic neuropathic pain, identified from Embase and MEDLINE databases.Using the inverse variance method with a random-effects model, we undertook a meta-analysis to determine an overall prevalence and performed exploratory analyses to identify the effect of the type of pain, conflict, and time since injury on prevalence. Pain definitions and types of pain measurement tools used in studies were recorded. Thirty-one studies (14,738 participants) were included. The pooled prevalence of PLP, RLP, and chronic neuropathic pain were 57% (95% CI: 46-68), 61% (95% CI: 50-71), and 26% (95% CI: 10-54), respectively. Between-study heterogeneity was high (I 2 : 94%-98%). Characterisation of duration, frequency, and impact of pain was limited. Factors reported by included studies as being associated with PLP included the presence of RLP and psychological comorbidity. The prevalence of postamputation pain and chronic neuropathic pain after combat trauma is high. We highlight inconsistency of case definitions and terminology for pain and the need for consensus in future research of traumatic injury.

Beyond numbers: integrating qualitative analysis into quantitative sensory testing for neuropathic pain.

This article investigates the benefits of adopting qualitative and quantitative sensory testing (QQST) in sensory assessment, with a focus on understanding neuropathic pain. The innovative QQST method combines participant qualitative experiences with quantitative psychophysical measurements, offering a more varied interpretation of sensory abnormalities and normal sensory function. This article also explores the steps for the optimization of the method by identifying qualitative signs of sensory abnormalities and standardizing data collection. By leveraging the inherent subjectivity in the test design and participant responses, the QQST method contributes to a more holistic exploration of both normal and abnormal sensory experiences. This article positions the QQST approach as a foundational element within the Sensory Evaluation Network, uniting international experts to harmonize qualitative and quantitative sensory evaluation methods.

Patient engagement in designing, conducting, and disseminating clinical pain research: IMMPACT recommended considerations.

ABSTRACT: In the traditional clinical research model, patients are typically involved only as participants. However, there has been a shift in recent years highlighting the value and contributions that patients bring as members of the research team, across the clinical research lifecycle. It is becoming increasingly evident that to develop research that is both meaningful to people who have the targeted condition and is feasible, there are important benefits of involving patients in the planning, conduct, and dissemination of research from its earliest stages. In fact, research funders and regulatory agencies are now explicitly encouraging, and sometimes requiring, that patients are engaged as partners in research. Although this approach has become commonplace in some fields of clinical research, it remains the exception in clinical pain research. As such, the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials convened a meeting with patient partners and international representatives from academia, patient advocacy groups, government regulatory agencies, research funding organizations, academic journals, and the biopharmaceutical industry to develop consensus recommendations for advancing patient engagement in all stages of clinical pain research in an effective and purposeful manner. This article summarizes the results of this meeting and offers considerations for meaningful and authentic engagement of patient partners in clinical pain research, including recommendations for representation, timing, continuous engagement, measurement, reporting, and research dissemination.

Methods for pragmatic randomized clinical trials of pain therapies: IMMPACT statement.

ABSTRACT: Pragmatic, randomized, controlled trials hold the potential to directly inform clinical decision making and health policy regarding the treatment of people experiencing pain. Pragmatic trials are designed to replicate or are embedded within routine clinical care and are increasingly valued to bridge the gap between trial research and clinical practice, especially in multidimensional conditions, such as pain and in nonpharmacological intervention research. To maximize the potential of pragmatic trials in pain research, the careful consideration of each methodological decision is required. Trials aligned with routine practice pose several challenges, such as determining and enrolling appropriate study participants, deciding on the appropriate level of flexibility in treatment delivery, integrating information on concomitant treatments and adherence, and choosing comparator conditions and outcome measures. Ensuring data quality in real-world clinical settings is another challenging goal. Furthermore, current trials in the field would benefit from analysis methods that allow for a differentiated understanding of effects across patient subgroups and improved reporting of methods and context, which is required to assess the generalizability of findings. At the same time, a range of novel methodological approaches provide opportunities for enhanced efficiency and relevance of pragmatic trials to stakeholders and clinical decision making. In this study, best-practice considerations for these and other concerns in pragmatic trials of pain treatments are offered and a number of promising solutions discussed. The basis of these recommendations was an Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) meeting organized by the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks.