Imperial College near infrared spectroscopy neuroimaging analysis framework

Use of neuroimaging to measure neurocognitive engagement in health professions education: a scoping review

PURPOSE

To map the current literature on functional neuroimaging use in medical education research as a novel measurement modality for neurocognitive engagement, learning, and expertise development.

METHOD

We searched PubMed, Embase, Cochrane, ERIC, and Web of Science, and hand-searched reference lists of relevant articles on April 4, 2019, and updated the search on July 7, 2020. Two authors screened the abstracts and then full-text articles for eligibility based on inclusion criteria. The data were then charted, synthesized, and analyzed descriptively.

RESULTS

Sixty-seven articles published between 2007 and 2020 were included in this scoping review. These studies used three main neuroimaging modalities: functional magnetic resonance imaging, functional near-infrared spectroscopy, and electroencephalography. Most of the publications (90%, n = 60) were from the last 10 years (2011-2020). Although these studies were conducted in 16 countries, 68.7% (n = 46) were from three countries: the USA (n = 21), UK (n = 15), and Canada (n = 10). These studies were mainly non-experimental (74.6%, n = 50). Most used neuroimaging techniques to examine psychomotor skill development (57%, n = 38), but several investigated neurocognitive correlates of clinical reasoning skills (22%, n = 15).

CONCLUSION

This scoping review maps the available literature on functional neuroimaging use in medical education. Despite the heterogeneity in research questions, study designs, and outcome measures, we identified a few common themes. Included studies are encouraging of the potential for neuroimaging to complement commonly used measures in education research and may help validate/challenge established theoretical assumptions and provide insight into training methods. This review highlighted several areas for further research. The use of these emerging technologies appears ripe for developing precision education, establishing viable study protocols for realistic operational settings, examining team dynamics, and exploring applications for real-time monitoring/intervention during critical clinical tasks.

Interpolated functional manifold for functional near-infrared spectroscopy analysis at group level

Significance: Solutions for group-level analysis of connectivity from fNIRS observations exist, but groupwise explorative analysis with classical solutions is often cumbersome. Manifold-based solutions excel at data exploration, but there are infinite surfaces crossing the observations cloud of points. Aim: We aim to provide a systematic choice of surface for a manifold-based analysis of connectivity at group level with small surface interpolation error. Approach: This research introduces interpolated functional manifold (IFM). IFM builds a manifold from reconstructed changes in concentrations of oxygenated Δ c HbO 2 and reduced Δ c HbR hemoglobin species by means of radial basis functions (RBF). We evaluate the root mean square error (RMSE) associated to four families of RBF. We validated our model against psychophysiological interactions (PPI) analysis using the Jaccard index (JI). We demonstrate the usability in an experimental dataset of surgical neuroergonomics. Results: Lowest interpolation RMSE was 1.26 e - 4 ± 1.32 e - 8 for Δ c HbO 2 [A.U.] and 4.30 e - 7 ± 2.50 e - 13 [A.U.] for Δ c HbR . Agreement with classical group analysis was JI = 0.89 ± 0.01 for Δ c HbO 2 . Agreement with PPI analysis was JI = 0.83 ± 0.07 for Δ c HbO 2 and JI = 0.77 ± 0.06 for Δ c HbR . IFM successfully decoded group differences [ANOVA: Δ cHbO 2 : F ( 2,117 ) = 3.07 ; p < 0.05 ; Δ c HbR : F ( 2,117 ) = 3.35 ; p < 0.05 ]. Conclusions: IFM provides a pragmatic solution to the problem of choosing the manifold associated to a cloud of points, facilitating the use of manifold-based solutions for the group analysis of fNIRS datasets.

Monte Carlo modeling of photon migration in realistic human thoracic tissues for noninvasive monitoring of cardiac hemodynamics

Noninvasive monitoring of cardiac hemodynamics remains challenging in cardiovascular medicine. The possibility of noninvasive optical monitoring of cardiac hemodynamics was theoretically investigated in this study. By utilizing the Monte Carlo simulation method for voxelized media (MCVM) and Visible Chinese Human dataset, we quantified and visualized the photon migration in human thoracic region. The light fluence distribution was showed to reach heart tissue (∼3 cm depth underbody surface) and 12% of the total fluence was absorbed by the myocardium. The proportion of spatial sensitivity distribution (SSD) in cardiac tissue to the total SSD reached 0.0195%. The portion of SSD increased following with cardiac diastole and diffuse reflectance deceased linearly with increasing cardiac volume. The optimal separation between the light source and detector was provided to be 3.5 to 4.0 cm for future development of noninvasive cardiac hemodynamics monitoring. A pilot experimental study was conducted to measure the diffuse reflectance light and fingertip photoplethysmography. These data suggest that the fluctuation period of near-infrared (NIR) diffuse reflectance was consistent with the cardiac cycle, while the fluctuation features of the NIR signal was not consistent with that of photoplethysmography. All results indicate the great potential of noninvasive optical monitoring of myocardial hemodynamics.

A Newcomer's Guide to Functional Near Infrared Spectroscopy Experiments

This review presents a practical primer for functional near-infrared spectroscopy (fNIRS) with respect to technology, experimentation, and analysis software. Its purpose is to jump-start interested practitioners considering utilizing a non-invasive, versatile, nevertheless challenging window into the brain using optical methods. We briefly recapitulate relevant anatomical and optical foundations and give a short historical overview. We describe competing types of illumination (trans-illumination, reflectance, and differential reflectance) and data collection methods (continuous wave, time domain and frequency domain). Basic components (light sources, detection, and recording components) of fNIRS systems are presented. Advantages and limitations of fNIRS techniques are offered, followed by a list of very practical recommendations for its use. A variety of experimental and clinical studies with fNIRS are sampled, shedding light on many brain-related ailments. Finally, we describe and discuss a number of freely available analysis and presentation packages suited for data analysis. In conclusion, we recommend fNIRS due to its ever-growing body of clinical applications, state-of-the-art neuroimaging technique and manageable hardware requirements. It can be safely concluded that fNIRS adds a new arrow to the quiver of neuro-medical examinations due to both its great versatility and limited costs.

Robotic Surgery Improves Technical Performance and Enhances Prefrontal Activation During High Temporal Demand

Robotic surgery may improve technical performance and reduce mental demands compared to laparoscopic surgery. However, no studies have directly compared the impact of robotic and laparoscopic techniques on surgeons' brain function. This study aimed to assess the effect of the operative platform (robotic surgery or conventional laparoscopy) on prefrontal cortical activation during a suturing task performed under temporal demand. Eight surgeons (mean age ± SD = 34.5 ± 2.9 years, male:female ratio = 7:1) performed an intracorporeal suturing task in a self-paced manner and under a 2 min time restriction using conventional laparoscopic and robotic techniques. Prefrontal activation was assessed using near-infrared spectroscopy, subjective workload was captured using SURG-TLX questionnaires, and a continuous heart rate monitor measured systemic stress responses. Task progression scores (au), error scores (au), leak volumes (mL) and knot tensile strengths (N) provided objective assessment of technical performance. Under time pressure, robotic suturing led to improved technical performance (median task progression score: laparoscopic suturing = 4.5 vs. robotic suturing = 5.0; z = - 2.107, p = 0.035; median error score: laparoscopic suturing = 3.0 mm vs. robotic suturing = 2.1 mm; z = - 2.488, p = 0.013). Compared to laparoscopic suturing, greater prefrontal activation was identified in seven channels located primarily in lateral prefrontal regions. These results suggest that robotic surgery improves performance during high workload conditions and is associated with enhanced activation in regions of attention, concentration and task engagement.

Imperial College near infrared spectroscopy neuroimaging analysis framework

This paper describes the Imperial College near infrared spectroscopy neuroimaging analysis (ICNNA) software tool for functional near infrared spectroscopy neuroimaging data. ICNNA is a MATLAB-based object-oriented framework encompassing an application programming interface and a graphical user interface. ICNNA incorporates reconstruction based on the modified Beer-Lambert law and basic processing and data validation capabilities. Emphasis is placed on the full experiment rather than individual neuroimages as the central element of analysis. The software offers three types of analyses including classical statistical methods based on comparison of changes in relative concentrations of hemoglobin between the task and baseline periods, graph theory-based metrics of connectivity and, distinctively, an analysis approach based on manifold embedding. This paper presents the different capabilities of ICNNA in its current version.