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Shared Near Infrared File Format Specification

License: This document is in the public domain.

The file format specification uses the extension *.snirf. These are HDF5 format files, renamed with the .snirf extension. For a program to be “SNIRF-compliant”, it must be able to read and write the SNIRF file.

The structure of each data file has a minimum of 5 required elements. For each element in the data structure, one of the 4 types is assigned, including

  • container: a structure containing sub-fields
  • string: a UTF-8 (ISO 10646) encoded string
  • integer: one of the integer types (1-byte, 2-byte, 4-byte, 8-byte)
  • numeric: one of the floating-point types (8-byte, 4-byte, 2-byte)

For integer and numeric data fields, users should use HDF5's Datatype Interface to query the byte-length stored in the file.

SNIRF file specification

Required fields

formatVersion
[Type: string]
This is a string that specifies the version of the file format. This document describes format version “1.0”
data(n)
[Type: container]
This is a structure containing the data dataTimeSeries, the time vector time of when the samples were acquired, and a description of the channels used to acquire the data. The data can be grouped in blocks indexed by index n. One convenient approach to blocking the data is to group all channels with the same time vector time.
data(n).dataTimeSeries
[Type: numeric]
This is the actual raw data variable. This variable has dimensions of <number of time points> x <number of channels>. Columns in dataTimeSeries are mapped to the measurement list (measurementList variable described below). The dataTimeSeries variable can be complex (as in the case of sine-cosine demodulation for the laser carrier frequencies).
data(n).time
[Type: numeric]
The time variable. This provides the acquisition time of the measurement relative to the time origin. This will usually be a straight line with slope equal to the acquisition frequency, but does not need to be equal spacing. The size of this variable is <number of time points> x 1.
data(n).measurementList
[Type: container]
The measurement list. This variable serves to map the data array onto the probe geometry (sources and detectors), data type, and wavelength. This variable is an array structure that has the size <number of channels> that describes the corresponding column in the data matrix.

For example, the measurementList(3) describes the third column of the data matrix (i.e. dataTimeSeries(:,3)).

Each element of the array is a structure which describes the measurement conditions for this data with the following fields:

  • measurementList(k).sourceIndex[Type: integer]: index (starting from 1) of the source
  • measurementList(k).detectorIndex[Type: integer]: index (starting from 1) of the detector
  • measurementList(k).wavelengthIndex[Type: integer]: index (starting from 1) of the wavelength
  • measurementList(k).dataType[Type: integer]: data-type identifier, see Appendix
  • measurementList(k).dataTypeIndex[Type: integer]: data-type specific parameter indices

Optional fields include:

  • measurementList(k).sourcePower[Type: numeric]: source power in milliwatt (mW)
  • measurementList(k).detectorGain[Type: numeric]: detector gain
  • measurementList(k).moduleIndex[Type: integer]: index (starting from 1) of a repeating module

For example, if measurementList(5) is a structure with sourceIndex=2, detectorIndex=3, wavelengthIndex=1, dataType=1, dataTypeIndex=1 would imply that the data in the 5th column of the dataTimeSeries variable was measured with source #2 and detector #3 at wavelength #1. Wavelengths (in nanometers) are described in the probe.wavelengths variable (described later). The data type in this case is 1, implying that it was a continuous wave measurement. The complete list of currently supported data types is found in the Appendix. The data type index specifies additional data type specific parameters that are further elaborated by other fields in the probe structure, as detailed below. Note that the Time Domain and Diffuse Correlation Spectroscopy data types have two additional parameters and so the data type index must be a vector with 2 elements that index the additional parameters.

sourcePower provides the option for information about the source power for that channel to be saved along with the data. The units are not defined, unless the user takes the option of using a metaDataTag described below to define, for instance, sourcePowerUnit. detectorGain provides the option for information about the detector gain for that channel to be saved along with the data.

Note: The source indices generally refer to the optode naming (probe positions) and not necessarily the physical laser numbers on the instrument. The same is true for the detector indices. Each source optode would generally, but not necessarily, have 2 or more wavelengths (hence lasers) plugged into it in order to calculate deoxy- and oxy-hemoglobin concentrations. The data from these two wavelengths will be indexed by the same source, detector, and data type values, but have different wavelength indices. Using the same source index for lasers at the same location but with different wavelengths simplifies the bookkeeping for converting intensity measurements into concentration changes. As described below, optional variables probe.sourceLabels and probe.detectorLabels are provided for indicating the instrument specific label for sources and detectors.

stim
[Type: container]
This is an array describing any stimulus conditions. Each element of the array has the following required fields.
stim(n).name
[Type: string]
This is a string describing the nth stimulus condition.
stim(n).data
[Type: numeric]
This is a three-column array specifying the stimulus time course for the nth condition. Each row corresponds with a specific stimulus trial. The three columns indicate [starttime duration value]. The starttime, in seconds, is the time relative to the time origin when the stimulus takes on a value; the duration is the time in seconds that the stimulus value continues, and value is the stimulus amplitude. The number of rows is not constrained. (see examples in the appendix).
probe
[Type: container]
This is a structured variable that describes the probe (source-detector) geometry. This variable has a number of required fields.
probe.wavelengths
[Type: numeric]
This field describes the wavelengths used. This is indexed by the wavelength index of the measurementList variable.

For example, probe.wavelengths = [690 780 830]; implies that the measurements were taken at three wavelengths (690nm, 780nm, and 830nm). The wavelength index of measurementList(k).wavelengthIndex variable refers to this field. measurementList(k).wavelengthIndex = 2 means the kth measurement was at 780nm.

The number of wavelengths is not limited (except that at least two are needed to calculate the two forms of hemoglobin). Each source-detector pair would generally have measurements at all wavelengths.

probe.wavelengthsEmission
[Type: numeric]
This field is required only for fluorescence data types, and describes the emission wavelengths used. The indexing of this variable is the same wavelength index in measurementList used for probe.wavelengths such that the excitation wavelength is paired with this emission wavelength for a given measurement.
probe.sourcePos
[Type: numeric]
This field describes the position (in spatialUnit units) of each source optode. This field has size <number of sources> x 3. For example, probe.sourcePos(1,:) = [1.4 1 0], and SpatialUnit='cm'; places source number 1 at x=1.4 cm and y=1 cm and z=0 cm.

Dimensions are relative coordinates (i.e. to some arbitrary defined origin).
The qform variable described below can be used to define the transformation between this SNIRF coordinate system and other coordinate systems.

probe.detectorPos
[Type: numeric]
Same as probe.sourcePos, but describing the detector positions.
There are additional required elements of the probe structure, depending on the data type of the measurement. These variables are indexed by measurementList(k).dataTypeIndex:
Continuous wave (Fluorescence or non-fluorescence):
  • None

Frequency Domain (Fluorescence or non-fluorescence):

  • probe.frequency[Type: numeric]: modulation frequency in Hz

Time domain – gated (Fluorescence or non-fluorescence):

  • probe.timeDelay[Type: numeric]
  • probe.timeDelayWidth[Type: numeric]

Time domain – moments (Fluorescence or non-fluorescence):

  • probe.momentOrder[Type: numeric]

Diffuse Correlation spectroscopy (Fluorescence or non-fluorescence):

  • probe.correlationTimeDelay[Type: numeric]
  • probe.correlationTimeDelayWidth[Type: numeric]

There are optional fields of the probe structure that can be used.

probe.sourceLabels
[Type: string]
This is a string array providing user friendly or instrument specific labels for each source. Each element of the array must be a unique string among both probe.sourceLabels and probe.detectorLabels. This can be of size <number of sources> x 1 or <number of sources> x <number of wavelengths>. This is indexed by measurementList(k).sourceIndex and measurementList(k).wavelengthIndex.
probe.detectorLabels
[Type: string]
This is a string array providing user friendly or instrument specific labels for each detector. Each element of the array must be a unique string among both probe.sourceLabels and probe.detectorLabels. This is indexed by measurementList(k).detectorIndex.
probe.landmark
[Type: numeric]
This is a 2-D array storing the neurological landmark positions measurement from 3-D digitization and tracking systems to facilitate the registration and mapping of optical data to brain anatomy. This array should contain a minimum of 3 columns, representing the x, y and z coordinates of the digitized landmark positions. If a 4th column presents, it stores the index to the labels of the given landmark. Label names are stored in the probe.landmarkLabels subfield. An label index of 0 refers to an undefined landmark.
probe.landmarkLabels
[Type: container]
This string array stores the names of the landmarks. The first string denotes the name of the landmarks with an index of 1 in the 4th column of probe.landmark, and so on. One can adopt the commonly used 10-20 landmark names, such as "Nasion", "Inion", "Cz" etc, or use user-defined landmark labels. The landmark label can also use the unique source and detector labels defined in probe.sourceLabels and probe.detectorLabels, respectively, to associate the given landmark to a specific source or detector. All strings are UTF-8 encoded.
probe.useLocalIndex
[Type: integer]
For modular fNIRS systems, setting this flag to a non-zero integer indicates that measurementList(k).sourceIndex and measurementList(k).detectorIndex are module-specific local-indices. One must also include measurementList(k).moduleIndex in the measurementList structure in order to restore the global indices of the sources/detectors.
metaDataTags
[Type: container]
This is a two column string array of arbitrary length consisting of any key/value pairs the user (or manufacturer) would like to put in. Each row of the array consists of two strings. Some possible examples:
['ManufacturerName','ISS'],
['Model','Imagent'],
['SubjectName', 'Pseudonym, I.M.A.'],
['DateOfBirth','20120401'],
['AcquisitionStartTime','150127.34']
['StudyID','Infant Brain Development']
['StudyDescription','We study infant cognitive development.']
['AccessionNumber','INA2S12']
['InstanceNumber','2']
['CalibrationFileName','phantomcal_121015.snirf']

While these tags are freeform, some conventions must be followed. Keys should use only alphanumeric characters with no spaces, with individual words capitalized. All values will be stored as strings, How strings are converted into numeric values is left to whoever defines the Key. However, it is required that dates be stored as YYYYMMDD, and clock times be stored as HHMMSS.SSSS… (24 hour format) for consistency. Time intervals must be in seconds.

The following metadata tags are required:

SubjectID
MeasurementDate
MeasurementTime
SpatialUnit    (allowed values are 'mm' and 'cm')

The metadata tags "StudyID" and "AccessionNumber" are unique strings that can be used to link the current dataset to a particular study and a particular procedure, respectively. The "StudyID" tag is similar to the DICOM tag "Study ID" (0020,0010) and "AccessionNumber" is similar to the DICOM tag "Accession Number"(0008,0050), as defined in the DICOM standard (ISO 12052).

The metadata tag "InstanceNumber" is defined similarly to the DICOM tag "Instance Number" (0020,0013), and can be used as the sequence number to group multiple datasets into a larger dataset - for example, concatenating streamed data segments during a long measurement session.

Optional variables:

These variables are not required for basic functions, but might be useful to get more out of your data sets.

aux
[Type: container]
This optional array specifies any recorded auxiliary data. Each element of aux has the following required fields:
aux(n).name
[Type: string]
This is string describing the nth auxiliary data timecourse.
aux(n).dataTimeSeries
[Type: numeric]
This is the aux data variable. This variable has dimensions of <number of time points> x 1.
aux(n).time
[Type: numeric]
The time variable. This provides the acquisition time of the aux measurement relative to the time origin. This will usually be a straight line with slope equal to the acquisition frequency, but does not need to be equal spacing. The size of this variable is <number of time points> x 1.
timeOffset
[Type: numeric]
This variable specifies the offset of the file time origin relative to absolute (clock) time in seconds.

Appendix

Supported data types for “dataTimeSeries”

  1. Raw - Continuous wave
  2. Raw - Frequency Domain
  3. Raw - Time domain - gated
  4. Raw - Time domain – moments
  5. Raw - Diffuse Correlation spectroscopy
  6. Raw - Fluorescence – continuous wave
  7. Raw - Fluorescence - Frequency Domain
  8. Raw - Fluorescence - Time domain - gated
  9. Raw - Fluorescence - Time domain – moments
  10. Raw - Bioluminescence – continuous wave

Examples of stimulus waveforms Assume there are 10 time points, starting at zero, spaced 0.1s apart. If we assume a stimulus to be a 0.2 second off, 0.2 second on repeating block, it would be specified as follows:

    [0.2 0.2 1.0]
    [0.6 0.2 1.0]

Acknowledgement

This document was originally drafted by Blaise Frederic (bbfrederick at mclean.harvard.edu) and David Boas (dboas at bu.edu).

Other significant contributors to this specification include:

  • Theodore Huppert (huppert1 at pitt.edu)
  • Jay Dubb (jdubb at bu.edu)
  • Qianqian Fang (q.fang at neu.edu)

The following individuals representing academic, industrial, software, and hardware interests are also contributing to and supporting the adoption of this specification:

Software

  • Adam Eggebrecht, University of Washington, neuroDOT
  • Felipe Orihuela-Espina, Instituto Nacional de Astrofísica, Óptica y Electrónica, ICNNA
  • Sungho Tak, Korea Basic Science Institute, NIR-SPM
  • Luca Pollonini, Houston Methodist, Phoebe
  • Hamid Deghani, University of Birmingham, NIRFAST
  • Stanislaw Wojtkiewicz, University of Birmingham, NIRFAST
  • Joe Culver, University of Washington, neuroDOT
  • Christophe Grova, McGill University, NIRS Storm
  • Ata Akin, Acıbadem University
  • Alessandro Torricelli, Politecnico di Milano

Hardware

  • Jorn Horschig, Artinis Inc
  • Hasan Ayaz, Biopac Inc
  • Rob Cooper, Gower Labs Inc
  • Rueben Hill, Gower Labs Inc
  • Hanseok Yun, OdeLab Inc
  • Hirokazu Asaka, Hitachi
  • Takumi Inakazu, Hitachi
  • Lamija Pasalic, NIRx
  • Mathieu Coursolle, Rogue
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