Neurophotonics editor-in-chief Anna Devor discusses neuroethics topics related to rapidly advancing neurotechnology, in conversation with Institute of Neuroethics founder Karen Rommelfanger.

Thanks to reviewers who served Neurophotonics in 2021.

David Kleinfeld, the Dr. George Feher Endowed Chair in Experimental Biophysics at UC San Diego, shares his views on training and best practices in neurophotonics, offering words of wisdom for building and strengthening our scientific community.

Significance: Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool that can measure resting-state functional connectivity; however, non-neuronal components present in fNIRS signals introduce false discoveries in connectivity, which can impact interpretation of functional networks.

Aim: We investigated the effect of short channel correction on resting-state connectivity by removing non-neuronal signals from fNIRS long channel data. We hypothesized that false discoveries in connectivity can be reduced, hence improving the discriminability of functional networks of known, different connectivity strengths.

Approach: A principal component analysis-based short channel correction technique was applied to resting-state data of 10 healthy adult subjects. Connectivity was analyzed using magnitude-squared coherence of channel pairs in connectivity groups of homologous and control brain regions, which are known to differ in connectivity.

Results: By removing non-neuronal components using short channel correction, significant reduction of coherence was observed for oxy-hemoglobin concentration changes in frequency bands associated with resting-state connectivity that overlap with the Mayer wave frequencies. The results showed that short channel correction reduced spurious correlations in connectivity measures and improved the discriminability between homologous and control groups.

Conclusions: Resting-state functional connectivity analysis with short channel correction performs better than without correction in its ability to distinguish functional networks with distinct connectivity characteristics.

Significance: Functional near-infrared spectroscopy (fNIRS) has evaluated pain in awake and anesthetized states.

Aim: We evaluated fNIRS signals under general anesthesia in patients undergoing knee surgery for anterior cruciate ligament repair.

Approach: Patients were split into groups: those with regional nerve block (NB) and those without (non-NB). Continuous fNIRS measures came from three regions: the primary somatosensory cortex (S1), known to be involved in evaluation of nociception, the lateral prefrontal cortex (BA9), and the polar frontal cortex (BA10), both involved in higher cortical functions (such as cognition and emotion).

Results: Our results show three significant differences in fNIRS signals to incision procedures between groups: (1) NB compared with non-NB was associated with a greater net positive hemodynamic response to pain procedures in S1; (2) dynamic correlation between the prefrontal cortex (PreFC) and S1 within 1 min of painful procedures are anticorrelated in NB while positively correlated in non-NB; and (3) hemodynamic measures of activation were similar at two separate time points during surgery (i.e., first and last incisions) in PreFC and S1 but showed significant differences in their overlap. Comparing pain levels immediately after surgery and during discharge from postoperative care revealed no significant differences in the pain levels between NB and non-NB.

Conclusion: Our data suggest multiple pain events that occur during surgery using devised algorithms could potentially give a measure of “pain load.” This may allow for evaluation of central sensitization (i.e., a heightened state of the nervous system where noxious and non-noxious stimuli is perceived as painful) to postoperative pain levels and the resulting analgesic consumption. This evaluation could potentially predict postsurgical chronic neuropathic pain.

Significance: Hemodynamic responses (HRs) are typically averaged across experimental sessions based on the assumption that brain activation is consistent over multiple trials. This may not be a safe assumption, especially in pediatric populations, due to unaccounted effects of inattention, fatigue, or habituation.

Aim: The purpose of this study was to quantify the consistency of the HR over speech and language brain regions during speech production in typically developing school-aged children.

Approach: Brain activity over speech and language regions of interest (ROIs) was recorded with functional near-infrared spectroscopy during a picture description paradigm with 37 children (aged 7 to 12 years). We divided the 30 experimental trials, each 5 s long, into three segments of 10 trials each corresponding with early (trials 1 to 10), middle (trials 11 to 20), and late (trials 21 to 30) trials. We then compared oxygenated (HbO) and deoxygenated (HbR) hemoglobin concentrations averaged across each 10 trial segment to overall concentrations averaged across all 30 trials. We also compared differential hemoglobin (HbD) across ROIs.

Results: HbO and HbR averaged across all experimental trials most strongly correlated with HbO and HbR from early trials. HbD values from channels over most speech and language regions did not appreciably change throughout the experimental session. The exception was HbD values from channels over the dorsal inferior frontal gyrus (dIFG). This region showed significantly higher activation over the left hemisphere during the first segment of the experiment.

Conclusions: Our findings suggest that brain activity from speech and language ROIs was relatively consistent over the experimental session. The exception was increased activation of left dIFG during earlier experimental trials. We suggest that researchers critically evaluate the consistency of HRs from different brain regions to determine the reliability of HRs recorded during experimental sessions. This step is instrumental in ensuring that uncontrolled effects do not mask patterns of task-related activation.

Significance: Functional near-infrared spectroscopy (fNIRS) enables the measurement of brain activity noninvasively. Optical neuroimaging with fNIRS has been shown to be reproducible on the group level and hence is an excellent research tool, but the reproducibility on the single-subject level is still insufficient, challenging the use for clinical applications.

Aim: We investigated the effect of short-channel regression (SCR) as an approach to obtain fNIRS measurements with higher reproducibility on a single-subject level. SCR simultaneously considers contributions from long- and short-separation channels and removes confounding physiological changes through the regression of the short-separation channel information.

Approach: We performed a test-retest study with a hand grasping task in 15 healthy subjects using a wearable fNIRS device, optoHIVE. Relevant brain regions were localized with transcranial magnetic stimulation to ensure correct placement of the optodes. Reproducibility was assessed by intraclass correlation, correlation analysis, mixed effects modeling, and classification accuracy of the hand grasping task. Further, we characterized the influence of SCR on reproducibility.

Results: We found a high reproducibility of fNIRS measurements on a single-subject level (ICC_single  =  0.81 and correlation r  =  0.81). SCR increased the reproducibility from 0.64 to 0.81 (ICC_single) but did not affect classification (85% overall accuracy). Significant intersubject variability in the reproducibility was observed and was explained by Mayer wave oscillations and low raw signal strength. The raw signal-to-noise ratio (threshold at 40 dB) allowed for distinguishing between persons with weak and strong activations.

Conclusions: We report, for the first time, that fNIRS measurements are reproducible on a single-subject level using our optoHIVE fNIRS system and that SCR improves reproducibility. In addition, we give a benchmark to easily assess the ability of a subject to elicit sufficiently strong hemodynamic responses. With these insights, we pave the way for the reliable use of fNIRS neuroimaging in single subjects for neuroscientific research and clinical applications.

Significance: Differentiation of primary central nervous system lymphoma from glioblastoma is clinically crucial to minimize the risk of treatments, but current imaging modalities often misclassify glioblastoma and lymphoma. Therefore, there is a need for methods to achieve high differentiation power intraoperatively.

Aim: The aim is to develop and corroborate a method of classifying normal brain tissue, glioblastoma, and lymphoma using optical coherence tomography with deep learning algorithm in an ex vivo experimental design.

Approach: We collected tumor specimens from ordinal surgical operations and measured them with optical coherence tomography. An attention ResNet deep learning model was utilized to differentiate glioblastoma and lymphoma from normal brain tissues.

Results: Our model demonstrated a robust classification power of detecting tumoral tissues from normal tissues and moderate discrimination between lymphoma and glioblastoma. Moreover, our results showed good consistency with the previous histological findings in the pathological manifestation of lymphoma, and this could be important from the aspect of future clinical practice.

Conclusion: We proposed and demonstrated a quantitative approach to distinguish different brain tumor types. Using our method, both neoplasms can be identified and classified with high accuracy. Hopefully, the proposed method can finally assist surgeons with decision-making intraoperatively.

Significance: Transcranial photobiomodulation (tPBM) at 808 nm attenuates pentylenetetrazole (PTZ)-induced seizures and convulsive status epilepticus (CSE) in peripubertal rats by protecting neurons from injury and parvalbumin-positive interneurons from apoptosis, and preserving the integrity of perisomatic inhibitory networks. However, the effects of tPBM on neuroinflammation, astrogliosis, and microgliosis in epileptic rat brains are unknown. Thus, further study to unveil these aspects is needed for understanding the phenomena of tPBM on pediatric CSE prevention.

Aim: To evaluate the effects of tPBM on neuroinflammation, astrogliosis, and microgliosis in peripubertal rat hippocampus with PTZ-induced seizures and SE.

Approach: An 808-nm diode laser was applied transcranially to peripubertal rats prior to PTZ injection. Immunofluorescence staining of neuron-specific enolase (NSE) was used as a marker of neuroinflammation, glial fibrillary acid protein (GFAP) for astrogliosis, ionized calcium-binding adapter molecule 1 (Iba-1) for microgliosis, and mitochondrial cytochrome c oxidase subunit 1 (MT-CO1) for confirming the involvement of cytochrome c oxidase (CCO).

Results: tPBM significantly reduced NSE immunoreactivity in CA3 in PTZ-treated rats, GFAP immunoreactivity in CA1, and Iba-1 immunoreactivity in CA3. Enhancement of hippocampal MT-CO1 reflected that tPBM acted in CCO-dependent manner.

Conclusions: tPBM (808) attenuated PTZ-induced seizures and SE by suppressing neuroinflammation, astrogliosis, and microgliosis in peripubertal rats.

Significance: The expansion of functional near-infrared spectroscopy (fNIRS) systems toward broader utilities has led to the emergence of modular fNIRS systems composed of repeating optical source/detector modules. Compared to conventional fNIRS systems, modular fNIRS systems are more compact and flexible, making wearable and long-term monitoring possible. However, the large number of design parameters makes understanding their impact on a probe’s performance a daunting task.

Aim: We aim to create a systematic software platform to facilitate the design, characterization, and comparison of modular fNIRS probes.

Approach: Our software—modular optode configuration analyzer (MOCA)—implements semi-automatic algorithms that assist in tessellating user-specified regions-of-interest, in interconnecting modules of various shapes, and in quantitatively comparing probe performance using metrics, such as spatial channel distributions and average brain sensitivity of the resulting probes. There is also support for limited parameter sweeping capabilities.

Results: Through several examples, we show that users can use MOCA to design and optimize modular fNIRS probes, study trade-offs between several module shapes, improve brain sensitivity in probes via module re-orientation, and enhance probe performance via adjusting module spatial layouts.

Conclusion: Despite its simplicity, our modular probe design platform offers a framework to describe and quantitatively assess probes made by modules, opening a new door for the growing fNIRS user community to approach the challenging problem of module- and probe-parameter selection and fine-tuning.

The erratum corrects an error in Equation 1 of the published article.