Publications Highlights – October 2019 to February 2020

By Felipe Orihuela-Espina

This highlights corresponds to the publications list accompanying the newsletter issue of March 2020. It contains entries in the period from the beginning of October 2019 until end of February 2020. If you know of any other publication that you think it should be announced here, or you want an amendment to be made, please do contact us with the event information so that we can be more comprehensive. We encourage you to announce your publications in our Facebook group or in Tweeter using the hashtag #fNIRS. Entries have been organized in four sections according to the different methodologies to collect them. No efforts are made to eliminate duplicates.

fNIRS papers posted on Facebook and Twitter

Methodology. This section was constructed from your posts made to the social networks in the period from October 1, 2019 to February 29, 2020, and specifically in our Facebook group as well as those in Tweeter (with hashtag #fNIRS). As this is a manual task, errors may have occurred. Although we have done every effort to include all of the publications announced there, we apologize in advance if we have missed any of your posting. Please do let us know of those publications advertised in the social networks under the search criteria that we may have missed so that we can include it asap. In brackets, the date of posting and the person who made the post. When a publication is mentioned more than once, we only include the first post.

  • (25-Feb-FB, Jana Kainerstorfer) Ruesch et al. Estimating intracranial pressure using pulsatile cerebral blood flow measured with diffuse correlation spectroscopy. Biomedical Optics Express (2020) 11(3):1462-1476, doi: 10.1364/BOE.386612
  • (25-Feb-Tw, Felix Scholkmann) Rehain et al. Noise-tolerant single photon sensitive three-dimensional imager. Nature Communications, (2020) 11:921, doi: 10.1038/s41467-020-14591-8
  • (19-Feb-Tw, Brain Products) Rossi et al. How the Brain Understands Spoken and Sung Sentences. Brain Sci. 2020, 10(1):36, doi: 10.3390/brainsci10010036
  • (18-Feb-FB, Alex von Luhmann) von Luhmann et al. Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded CanonicalCorrelation Analysis, NeuroImage (2020) 208:1164722, doi: 10.1016/j.neuroimage.2019.116472
  • (7-Feb-FB, Rebecca Re) Dalla-Mora et al. Time-Gated Single-Photon Detection in Time-Domain Diffuse Optics: A Review. Appl. Sci. (2020), 10(3):1101; doi: 10.3390/app10031101
  • (20-Jan-Tw, Luca Pollonini) Fishell et al. Portable, field-based neuroimaging using high-density diffuse optical tomography. NeuroImage (2020) In press, doi: 10.1016/j.neuroimage.2020.116541
  • (20-Jan-FB, Turgut Durduran) Udina et al. Functional Near-Infrared Spectroscopy to Study Cerebral Hemodynamics in Older Adults During Cognitive and Motor Tasks: A Review. Front. Aging Neurosci. (2020) 11:367, doi: 10.3389/fnagi.2019.00367
  • (20-Jan-FB, Kevin Ferguson) Xiao et al. Transcranial brain atlas. Science Advances (2018) 4(9):eaar6904, doi: 10.1126/sciadv.aar6904
  • (20-Jan-FB, Baris Yesilyurt) Helmich et al. Reduced frontopolar brain activation characterizes concussed athletes with balance deficits. NeuroImage: Clinical (2020) 25:102164, doi: 10.1016/j.nicl.2020.102164
  • (17-Jan-FB, Baris Yesilyurt) Nguyen et al. The effects of interaction quality on neuralsynchrony during mother-child problem solving. Cortex, (2020) 124:235-249, doi: 10.1016/j.cortex.2019.11.020
  • (12-Jan-FB, Sergio Novi) Novi et al. Functional near-infrared spectroscopy for speech protocols: characterization of motion artifacts and guidelines for improving data analysis, Neurophotonics (2020) 7(1):015001, doi: 10.1117/1.NPh.7.1.015001
  • (10-Jan-FB, Rebecca Re) Pirovano et al. Instrument response function acquisition in reflectance geometry for time-resolved diffuse optical measurements. Biomedical Optics Express (2020) 11(1):240-250, doi: 10.1117/12.2526931
  • (6-Dec-FB, Felix Scholkmann) Scholkmann et al. Effects of psilocybin on functional connectivity measured with fNIRS: Insights from a single-subject pilot study, Matters (2019) In press
  • (6-Dec-FB, Hada Leong) Leong et al. Machine learning: assessing neurovascular signals in the prefrontal cortex with noninvasive bimodal electro-optical neuroimaging in opiate addiction. Nature Scientific Reports, (2019) 9:18262, doi: 10.1038/s41598-019-54316-6
  • (5-Dec-FB, Edgar Guevara) Guevara et al. Prediction of epileptic seizures using fNIRS and machine learning. Journal of Intelligent & Fuzzy Systems, (2020) 38(2):2055-2068, doi: 10.3233/JIFS-190738
  • (11-Nov-FB, Baris Yesilyurt) Carius et al. Characterizing cortical hemodynamic changes during climbing and itsrelation to climbing expertise. Neuroscience Letters (2020) 715:1346042, doi: 10.1016/j.neulet.2019.134604
  • (11-Nov-FB, Ilkka Nissilä) Ambika et al. Relationship between maternal pregnancy-related anxiety and infant brainresponses to emotional speech – a pilot study. Journal of Affective Disorders (2020) 262:62–7063, doi: 10.1016/j.jad.2019.10.047
  • (8-Nov-FB, Felix Scholkmann) Kleiser et al. Cerebral hemodynamic responses in preterm-born neonates to visual stimulation: classification according to subgroups and analysis of frontotemporal–occipital functional connectivity. Neurophotonics, (2019) 6 (4), 045005
  • (30-Oct-FB, Turgut Durduran) Giovanella et al. Validation of diffuse correlation spectroscopy against 15O-water PET for regional cerebral blood flow measurement in neonatal piglets. Journal of Cerebral Blood Flow & Metabolism (2019) In press, doi: 10.1177/0271678X19883751
  • (30-Oct-FB, Rebecca Re) Re et al. Time Domain Near Infrared Spectroscopy Device for Monitoring Muscle Oxidative Metabolism: Custom Probe and In Vivo Applications. Sensors (2018) 18(1):264, doi: 10.3390/s18010264
  • (28-Oct-FB, Efstratia Papoutselou) Harrison and Hartley. Shedding Light On The Human Auditory Cortex: A Review Of The Advances In Near Infrared Spectroscopy (NIRS), DOVE Reports in Medical Imaging, (2019) 12:31-42, doi: 10.2147/RMI.S174633
  • (27-Oct-FB, Kunal Mankodiya) Almajidy et al. A Newcomer’s Guide to Functional Near Infrared Spectroscopy Experiments, IEEE Reviews in Biomedical Engineering, (2020), 13:292-308, doi: 10.1109/RBME.2019.2944351
  • (12-Oct-FB, Turgut Durduran) Dragojevic et al. High-density speckle contrast optical tomography of cerebral blood flow response to functional stimuli in the rodent brain. Neurophotonics, (2019) 6(4):045001, doi: 10.1117/1.NPh.6.4.045001.

Special Issue “FNIRS in Neuroscience and its Emerging Applications” in Frontiers in Neuroscience (Brain Imaging Methods) and Frontiers in Human Neurosciences

  • (18-Feb-FB, Alex von Luhmann) von Luhmann et al. Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective, Front. Hum. Neurosci. (2020) 14:30, doi: 10.3389/fnhum.2020.00030
  • Abdalmalak et al. Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication. Front. Neurosci. (2020) 14:105, doi: 10.3389/fnins.2020.00105
  • Shin and Im. Performance improvement of near-infrared spectroscopy-based brain-computer interface using regularized linear discriminant analysis ensemble classifier based on bootstrap aggregating. Front. Neurosci. 2020) 14:168, doi: 10.3389/fnins.2020.00168
  • Urquhart et al. Differences in net information flow and dynamic connectivity metrics between physically active and inactive subjects measured by functional near-infrared spectroscopy (fNIRS) during a fatiguing handgrip task. Front. Neurosci. (2020) 14:167, doi: 10.3389/fnins.2020.00167
  • Xu et al. Altered Functional Connectivity in the Motor and Prefrontal Cortex for Children With Down’s Syndrome: An fNIRS Study. Front. Hum. Neurosci. (2020) 14:6, doi: 10.3389/fnhum.2020.00006
  • Sutoko et al. Atypical Dynamic-Connectivity Recruitment in Attention-Deficit/Hyperactivity Disorder Children: An Insight Into Task-Based Dynamic Connectivity Through an fNIRS Study. Front. Hum. Neurosci. (2020) 14:3, doi: 10.3389/fnhum.2020.00003
  • Aihara et al. Resting-State Functional Connectivity Estimated With Hierarchical Bayesian Diffuse Optical Tomography. Front. Neurosci. (2020) 14:32, doi: 10.3389/fnins.2020.00032
  • Zhang and Zhu. Assessing Brain Networks by Resting-State Dynamic Functional Connectivity: An fNIRS-EEG Study. Front. Neurosci. (2020) 13:1430, doi: 10.3389/fnins.2019.01430
  • Yang et al. Effects of Tai Chi Chuan on Inhibitory Control in Elderly Women: An fNIRS Study. Front. Hum. Neurosci. (2020) 13:476, doi: 10.3389/fnhum.2019.00476
  • Li et al. Reality Status Judgments of Real and Fantastical Events in Children’s Prefrontal Cortex: An fNIRS Study. Front. Hum. Neurosci. (2019) 13:444, doi: 10.3389/fnhum.2019.00444
  • Aksoy et al. Performance Monitoring via Functional Near Infrared Spectroscopy for Virtual Reality Based Basic Life Support Training. Front. Neurosci. (2019) 13:1336, doi: 10.3389/fnins.2019.01336
  • Zheng et al. Enhancing Attention by Synchronizing Respiration and Fingertip Pressure: A Pilot Study Using Functional Near-Infrared Spectroscopy. Front. Neurosci. (2019) 13:1209, doi: 10.3389/fnins.2019.01209
  • Holmes et al. Cognitive Enhancement by Transcranial Photobiomodulation Is Associated With Cerebrovascular Oxygenation of the Prefrontal Cortex. Front. Neurosci. (2019) 13:1129, doi: 10.3389/fnins.2019.01129
  • Cheng et al. Coordination Elicits Synchronous Brain Activity Between Co-actors: Frequency Ratio Matters. Front. Neurosci. (2019) 13:1071, doi: 10.3389/fnins.2019.01071

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From PubMed

Methodology. Searches were made in PubMed constraining the search period between October 1, 2019 and February, 29, 2020. These were later processed for readability but no records were otherwise added or removed. The following searches have been executed:

  • fNIRS