Significance: Functional near-infrared spectroscopy (fNIRS) is a popular neuroimaging technique with proliferating hardware platforms, analysis approaches, and software tools. There has not been a standardized file format for storing fNIRS data, which has hindered the sharing of data as well as the adoption and development of software tools.
Aim: We endeavored to design a file format to facilitate the analysis and sharing of fNIRS data that is flexible enough to meet the community's needs and sufficiently defined to be implemented consistently across various hardware and software platforms.
Approach: The shared NIRS format (SNIRF) specification was developed in consultation with the academic and commercial fNIRS community and the Society for functional Near Infrared Spectroscopy.
Results: The SNIRF specification defines a format for fNIRS data acquired using continuous wave, frequency domain, time domain, and diffuse correlation spectroscopy devices.
Conclusions: We present the SNIRF along with validation software and example datasets. Support for reading and writing SNIRF data has been implemented by major hardware and software platforms, and the format has found widespread use in the fNIRS community.
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http://dx.doi.org/10.1117/1.NPh.10.1.013507 | DOI Listing |
Front Psychiatry
January 2025
Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
Background: This study aims to evaluate the intervention effect of intermittent Theta burst stimulation (iTBS) on bilateral dorsomedial prefrontal cortex (DMPFC) for negative symptoms in schizophrenia using functional near-infrared spectroscopy (fNIRS) to confirm the therapeutic significance of DMPFC in treating negative symptoms and provide new evidence for schizophrenia treatment and research.
Method: Thirty-nine schizophrenia patients with negative symptoms and mild cognitive impairment were randomly divided into a treatment group (n=20) and a control group (n=19). The treatment group received iTBS in bilateral DMPFC.
Neuroimage
January 2025
Institute for Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China. Electronic address:
Observational fear learning delineates the process by which individuals learn about potential threats through observing others' reactions. Prior research indicates that individuals with high trait anxiety (HTA) manifest pronounced fear responses in direct fear learning scenarios. However, the specific influence of trait anxiety on observational fear learning remains insufficiently explored.
View Article and Find Full Text PDFBiomed Opt Express
January 2025
School of Psychology, Shenzhen University, Shenzhen, China.
Functional near-infrared spectroscopy (fNIRS) -based hyperscanning is a popular new technology in the field of social neuroscience research. In recent years, studying human social interaction from the perspective of inter-brain networks has received increasing attention. In the present study, we proposed a new approach named the hyper-brain independent component analysis (HB-ICA) for detecting the inter-brain networks from fNIRS-hyperscanning data.
View Article and Find Full Text PDFNeuroimage
January 2025
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China. Electronic address:
Functional near-infrared spectroscopy (fNIRS) is a widely-used transcranial brain imaging technique in neuroscience research. Nevertheless, the lack of anatomical information from recordings poses challenges for designing appropriate optode montages and for localizing fNIRS signals to underlying anatomical regions. The photon measurement density function (PMDF) is often employed to address these issues, as it accurately measures the sensitivity of an fNIRS channel to perturbations of absorption coefficients at any brain location.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, PR China; Shanghai Yangpu Mental Health Center, Shanghai, 200093, PR China. Electronic address:
Background And Objective: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.
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