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The fNIRS glossary project: a consensus-based resource for functional near-infrared spectroscopy terminology.
(Neurophotonics, 2025-04-18) Stute, Katharina; Gossé, Louisa K; Montero-Hernandez, Samuel; Perkins, Guy A; Yücel, Meryem A; Cutini, Simone; Durduran, Turgut; Ehlis, Ann-Christine; Ferrari, Marco; Gervain, Judit; Mesquita, Rickson C; Orihuela-Espina, Felipe; Quaresima, Valentina; Scholkmann, Felix; Tachtsidis, Ilias; Torricelli, Alessandro; Wabnitz, Heidrun; Yodh, Arjun G; Carp, Stefan A; Dehghani, Hamid; Fang, Qianqian; Fantini, Sergio; Hoshi, Yoko; Niu, Haijing; Obrig, Hellmuth; Klein, Franziska; Artemenko, Christina; Bajracharya, Aahana; Barth, Beatrix; Bartkowski, Christian; Borot, Lénac; Bulgarelli, Chiara; Busch, David R; Chojak, Malgorzata; DeFreitas, Jason M; Diprossimo, Laura; Dresler, Thomas; Eken, Aykut; Elsherif, Mahmoud M; Emberson, Lauren L; Exner, Anna; Ferdous, Talukdar Raian; Fiske, Abigail; Forbes, Samuel H; Gemignani, Jessica; Gerloff, Christian; Guérin, Ségolène M R; Guevara, Edgar; Hamilton, Antonia F de C; Hadi Hosseini, S M; Jain, Divya; Kerr-German, Anastasia N; Kong, Haiyan; Kroczek, Agnes; Longhurst, Jason K; Lührs, Michael; MacLennan, Rob J; Mehler, David M A; Meidenbauer, Kimberly L; Moreau, David; Mutlu, Murat C; Orti, Renato; Paranawithana, Ishara; Pinti, Paola; Jounghani, Ali Rahimpour; Reindl, Vanessa; Ross, Nicholas A; Sanchez-Alonso, Sara; Seidel-Marzi, Oliver; Shukla, Mohinish; Usama, Syed A; Talati, Musa; Vergotte, Grégoire; Atif Yaqub, M; Yu, Chia-Chuan; Zainodini, Hanieh
A shared understanding of terminology is essential for clear scientific communication and minimizing misconceptions. This is particularly challenging in rapidly expanding, interdisciplinary domains that utilize functional near-infrared spectroscopy (fNIRS), where researchers come from diverse backgrounds and apply their expertise in fields such as engineering, neuroscience, and psychology.
Estimating sensor-space EEG connectivity PART 2: Identifying optimal artifact reduction techniques for functional connectivity in real data.
(Clinical Neurophysiology, 2025-04-08) Miljevic, Aleksandra; Murphy, Oscar W; Fitzgerald, Paul B; Bailey, Neil W
Electroencephalography (EEG) can be used to assess functional brain connectivity (FC). However, there is considerable variability in the methods used for FC measurement across different studies, which may contribute to heterogeneity in research outcomes. We aimed to assess how different EEG pre-processing steps impact EEG-FC measurement when applied to real EEG data.
Estimating sensor-space EEG connectivity PART 1: Identifying best performing methods for functional connectivity in simulated data.
(Clinical Neurophysiology, 2025-04-08) Miljevic, Aleksandra; Murphy, Oscar W; Fitzgerald, Paul B; Bailey, Neil W
Functional brain connectivity (FC) can be estimated using electroencephalography (EEG). However, there is considerable variability across studies in the FC measures used and in data (pre-)processing methods, leading to difficulties comparing and amalgamating results between studies. Thus, standardisation of EEG (pre-)processing for the measurement and reporting of FC is needed.We aimed to assess differences in FC estimates produced by different settings across multiple EEG pre-processing steps, (including re-referencing and epoching) to validate a reliable methodological pipeline for assessing EEG-FC in simulated EEG data.
Ensemble responses of auditory midbrain neurons in the cat to speech stimuli at different signal-to-noise ratios
(Hearing Research, 2024-12-03) Anu Sabu; Dexter Irvine; David B Grayden; James Fallon
Originally reserved for those who are profoundly deaf, cochlear implantation is now common for people with partial hearing loss, particularly when combined with a hearing aid. This combined intervention enhances speech comprehension and sound quality when compared to electrical stimulation alone, particularly in noisy environments, but the physiological basis for the benefits is not well understood. Our long-term aim is to elucidate the underlying physiological mechanisms of this improvement, and as a first step in this process, we have investigated in normal hearing cats, the degree to which the patterns of neural activity evoked in the inferior colliculus (IC) by speech sounds in various levels of noise allows discrimination between those sounds. Neuronal responses were recorded simultaneously from 32 sites across the tonotopic axis of the IC in anaesthetised normal hearing cats (n = 7). Speech sounds were presented at 20, 40 and 60 dB SPL in quiet and with increasing levels of additive noise (signal-to-noise ratios (SNRs) –20, –15, –10, –5, 0, +5, +10, +15, +20 dB). Neural discrimination was assessed using a Euclidean measure of distance between neural responses, resulting in a function reflecting speech sound differentiation across various SNRs. Responses of IC neurons reliably encoded the speech stimuli when presented in quiet, with optimal performance when an analysis bin-width of 5–10 ms was used. Discrimination thresholds did not depend on stimulus level and were best for shorter analysis binwidths. This study sheds light on how the auditory midbrain represents speech sounds and provides baseline data with which responses to electro-acoustic speech sounds in partially deafened animals can be compared.
Measuring Speech Discrimination Ability in Sleeping Infants Using fNIRS-A Proof of Principle
(Trends in Hearing, 2025) Onn Wah Lee; Demi Gao; Tommy Peng; Julia Wunderlich; Darren Mao; Gautam Balasubramanian; Colette M McKay
This study used functional near-infrared spectroscopy (fNIRS) to measure aspects of the speech discrimination ability of sleeping infants. We examined the morphology of the fNIRS response to three different speech contrasts, namely “Tea/Ba,”“Bee/ Ba,” and “Ga/Ba.” Sixteen infants aged between 3 and 13 months old were included in this study and their fNIRS data were recorded during natural sleep. The stimuli were presented using a nonsilence baseline paradigm, where repeated standard stimuli were presented between the novel stimuli blocks without any silence periods. The morphology of fNIRS responses varied between speech contrasts. The data were fit with a model in which the responses were the sum of two independent and concurrent response mechanisms that were derived from previously published fNIRS detection responses. These independent components were an oxyhemoglobin (HbO)-positive early-latency response and an HbO-negative late latency response, hypothesized to be related to an auditory canonical response and a brain arousal response, respectively. The goodness of fit of the model with the data was high with median goodness of fit of 81%. The data showed that both response components had later latency when the left ear was the test ear (p<.05) compared to the right ear and that the negative component, due to brain arousal, was smallest for the most subtle contrast, “Ga/Ba” (p=.003).