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Spatial considerations for achieving intermodal cross-referencing of fNIRS data
Ippeita ‘Pepe’ Dan(檀一平太)
Functional Brain Science Lab.Jichi Medical University,
Tochigi, Japan
Today’s slides are available at
http://www.jichi.ac.jp/brainlab
(Go to English site)
fMRIWhat is the best toolto study human brain?
Anatomy+ function
PET
Quantification
MEG
Temporal& spatial rez.
EEG/ERP
Temporal rez.& flexibility
TMS
Noninvasiveintervention
fNIRS
High-flexibility
No single toolNeed for integrative understanding
fMRIDo we have the common language?
Anatomy+ function
PET
Quantification
MEG
Temporal& spatial rez.
EEG/ERP
Temporal rez.& flexibility
TMS
High-flexibility
Tower of BabelPieter Bruegel, 1563
fNIRS
Noninvasiveintervention
An important issueis spatial data compatibility
Common spatial platform is necessary
This is the prerequisite for cross-modal data reference
fNIRS data should not exist in vacuum but be related to other imaging modality
This is either common coordinate system or macroanatomy
Are you sure that the data is appropriately registered?
TypicalfNIRS data
(visual processing)
Primary motor cortexPrimary somatosensory cortex
Parietal association cortex(spatial information processing
advanced somatosensory processing)
Temporal association cortex (hearing, advanced visual processing)
Secondary motor cortex
Prefrontal cortex
planning of movements,
(working memory,
inhibition of inappropriate behaviors)
Occipital association cortex
Primary visual cortex
Function Structure
What’s Human Brain Mapping
Incomplete tool for HBM
fNIRSprobes are placed on HEAD but not BRAIN surface
?
Function Structure
What’s Human Brain Mapping
Ideal solution: why don’t you use MRI
We would like to do without MRI
But How?
fNIRS measurement Structural image acquired by MRI
fNIRS data registered to MRI of the subject
Expensive, time consuming
Individual data: OK; Group data: needs some technique
Practical solution: registration to MNI space
in a probabilistic manner without acquiring MRI
+ + +
+ + +
Cannot see faces through masks
Normalize to a common coordinate system
Structural estimation through masks is possible
International 10-20 systemused in EEG
Stereotaxic brain coordinate system
Landmark measurement on head surface
Okamoto et al. NeuroImage 21, 99-111 (2004)
MNI (or Talairach) systemused in fMRI & PET
Link
International 10-20 system
Periauricular point
Nasion
Inion
10%
20%
20%
10%
20%
20%
20% 20%10%
Jasper, H. H. 1958. Electroenceph. Clin. Neurophysiol. 10: 367-380.
Primary reference points
Relative head surface division to set more landmarks
Normalized brain
MNI(Montreal Neurological Institute) standard brain coordinate system
X Y
Z
Subject’s Brain MNItemplate
(X, Y, Z )=(-55,33,18 )
Anterior
Commissure
Posterior
Commissure
Y
Z
X
Y
Z
Y
Z
Y
Z
Align
Warp
Affine
Before normalization
Template(MNI152)
After normalization
After normalizationCentral sulcus & Sylvian fissure
Standard coordinate system can realize the common spatial platform for probabilistic expression
Itnl 10-20 system
Head surface
Manual measurement for establishing transcranialcorrespondence
MNI coordinates
Cortical surface
Fp2Right MFG:65%Right SFG:35%
10-20 standard positions on MNI standard cortical surface
Virtual 10-20 measurement on MRI
Jurcak et al. NeuroImage (2005)
Reference database can be expanded if MRIs are available
Jurkak et al.NeuroImage 34, 1600-1611 (2007)
Extending cranio-cerebral correspondence to 10/10 and 10/5 systems
Now also incorporated in eLORETA
10/5 nomenclature
10/5 landmarkson MNI coordinate system
Practical way to use these data
• Place fNIRS probes, channels, or ROIs in reference to 10-20, 10-10, or 10-5 positions
• Describe fNIRS probes, channels, or ROIs in reference to 10-20, 10-10, or 10-5 positions
• Visit www.jichi.ac.jp/brainlab/tools.htmlfor 10-5 probe positions
• Cite Okamoto et al., 2003 NeuroImage for 10/20
• Cite Jurkak et al. 2007 NeuroImage for 10/10 and 10/5
More sophisticated way for using the landmark information
• Cranial landmarks for transformation to MNI space
• Estimation of fNIRS channel positions– by creating reference head and brain
database– performing “probabilistic” or “virtual”
registrations
Borrow other’s brain instead of yours
3D-magnetic digitizer for MRI-free probabilistic registration
Polhimus Fast-track
3D spatial measurement
10-20 standard positions
Arbitrary origin
fNIRS channel
Subject i without structural MRI
Estimation of fNIRS channel positions using reference database
Measuring 10-20 and fNIRS channel positions using 3D magnetic digitizer
Digitized dada inReal world coordinate system
(Subject i )
10-20 standard positions for subject ifNIRS channel position for subject i
10-20 standard positions for reference head j
Affine transformation
MNI coordinate system(Reference head j)
...
Ref. data 1
Sujbect iChannel estimation
and errorfor Subject i(Brain, MNI)
Reference database (MNI, m=17)
Channel (Head, MNI) Channel (Brain, MNI)
Channel (Head, RW)
Virtual registration of subject i’s data on reference brain and head database
Ref. data 2
Ref. data m
Ref. data 1
Ref. data 2
Ref. data m
Ref. data 1
Ref. data 2
Ref. data m
Projection
Within-subject error
...
Subject 1
Subject 2
Subject n
Channel
Channel
Channel
Virtual registration of subject i’s data on reference brain and head database
Subject 1
Subject 2
Subject n
Head, Real World Channel estimation
& errorMNI, Brain
Most likelychannel estimation
for the groupMNI, Brain
Between-subject error
+=
Between-subject errors
CMij i j ijP e 2, , ~ (0, )i j ije N
Pooled within-subject errorsTotal errors for channel estimation of the group
PCMij :most likely channel location; μ:expected value; αi :error fo rsubject i; βj:error for ref. nrain j ; eij: :residual
Summerizing spatial registration estimation errors for group analysis
fNIRS group analysis data can now be registered onto MNI space without MRI
Center of a circle: Most likely coordinate values
Radius of a circle: Standard deviationError (SD) <1cm
Singh AK et al.NeuroImage 27,842-851 (2005)
Probabilistic registration using reference database without MRI & with 3D-digitizer
Stable landmarks
Cz:Iz dependent
Stable landmarks
Subject’s real world space MNI space
Probabilistic registration Iz:unstable
Cz
Anchor-based probabilistic registration using any given scalp anchor point
Tsuzuki et al. Neurosci Res (2012)
Stable landmarks
Any given scalp anchor point
Stable landmarks
Corresponding point to the anchor
Anchor-basedprobabilistic registration
Much faster& easier
C3 Cz
T3
Can’t we do without a digitizer?Virtual registrationAssuming that probe setting and deformation are reproducible,we can simulate placement and deformation of probe holders
Tsuzuki, D et al. NeuroImage 34, 1600-1611 (2007)
Kinds of fNIRS holders
Fixed-size
Elastic (Hitachi Med. Co.) Flexible(Shimadzu)
Simulation for holder placement
Verification on a spherical phantom
Verification on a real head
C3 Cz
T3
Problem: holder size is fixed, but sizes and shapes of heads are different
Virtual registrationusing reference database
without MRI & without 3D-digitizer
Tsuzuki, D et al. NeuroImage 34, 1600-1611 (2007)
Example of virtual registration
Error of spatial registration is below 1cm
Gyrus-level estimation is possible
Atlas-guided DOT(diffused optical tomography) using probabilistic registration
Custo et al. NeuroImage (2010), Cooper et al. NeuroImage (2012)
MRI-based registration for group data
Continuous imaging data:You can depend on SPM procedureNIRS-SPM can deal with this issue (tentatively, tough)
Tsuzuki et al. Neurosci Res 72, 163-71 (2012)
MRI-based registration for group data
Discrete channel-wise data:SPM procedure is not straight-forward
Brain can be normalized to MNI152based on structural information
?fNIRS channels/probes cannot be directly normalized to MNI152due to lack of structural information
MRI-based registration for group data
Normalization to MNI152based on structural information
Adopt the deformation field to fNIRSchannel/probe locations
Extract deformation field(=Jacobian matrix for warping)
Merge with normalized subject’s brain
Subject’s own MRI
Subject’s own fNIRS channel/ probe locations
Prb X Y Z1 47 48 262 29 55 34| | | |9 53 31 39
MRI-based registration for group data
Prb X Y Z1 47 48 262 29 55 34| | | |9 53 31 39
Prb X Y Z1 48 49 252 29 56 32| | | |9 54 32 41
Prb X Y Z1 46 47 272 27 54 32| | | |9 53 32 38
Prb X Y Z1 49 50 262 30 55 31| | | |9 56 34 37
Prb X Y Z SD1 47.3 48.4 26.0 8.82 28.5 54.5 34.4 7.9| | | |9 53.3 31.2 39.8 6.8
Summary data In the standard brain
Averaged point tends to sink
Slight adjustment
Project back to the cortical surface (“shell”)
Sbjct1 Sbjct2 Sbjct3 SbjctN
・・・
Frequent misunderstanding in fMRI study
AAL (Automatic Anatomical Labeling)
fMRI can measure structure & function for INDIVIDUAL data
For GROUP analysis, you have to look at a representative atlas to estimate cortical mactoanatomy
Tzourio-Mazoyer et al., NeuroImage 2002
Link to Anatomical labeling tools in MNI space
AAL (Automatic Anatomical Labeling)
Brodmann Area Labeling via MRIcro
AAL (Automatic Anatomical Labeling)
Brodmann Area Labeling via MRIcro
Link to Anatomical labeling tools in MNI space
Other registration tools are also available
nfri_mni_plot
Virtual registration library
User-unfriendly development policyUser-friendly software needs sophisticated graphical interface which-is highly integrated-requires lots of energy to develop-is difficult to maintain-is hard to be transferred to other software packages
We chose developer-friendliness-open-source cords-fully public domain for academic use-Developed as Matlab functionsNow incorporated into-POTATo from Hitachi, Japan (by Dr. Katsura et al.)-NIRS-SPM from KAIST, Korea (by Drs. Ye & Tak)-HOMER from MGH, US (by Drs. Huppert & Boas)
Please feel free to steal
them !
Registration tools in POTATo
Platform for Optical Topography Analysis Tools (POTATo)by Dr. Katsura et al. at Hitachi Ltd. Japan
-Suitable for discrete channel-wise analysis-Highly flexible capacity for filtering in data-preprocessing-Capability for sophisticated signal processing e.g. ICA, PRCA-Suited for exploratory use (needs lots of thinking)-Flexible design to incorporate other tools-Batch process for 2nd level group analysis
Registration tools in NIRS-SPM
NIRS-SPMby Drs. Ye & Takat KAIST, Korea
-Continuous 2D image data generated from any given discrete channel data-Elaborated image reconstruction & FWEC by Sun’s tube formula at 1st level
-Mostly automatic with high affinity to GLM regression
Registration tools in HomER
Hemodynamic Evoked ResponseNIRS data analysis GUIby Drs. Huppert & Boas at MGH, US
-Highly flexible channel design with excellent GUI-Compatibility to common spatial platforms such as MNI, FreeSurfer-Affinity to 3D DOT analyses -Flexibility and capability for expansion-Suited for exploratory use (needs lots of thinking)
Our tools are available starting from HomER 2
What would I do as a user?
Discrete channel-wise data
Exploratory study
POTAToFWE correction
Group level analysis for selected channels
2D continuous data
NIRS-SPMROI extraction
Group level analysis for 2D ROI
3D continuous data
HomER ROI extraction
Group level analysis for 3D ROI
Flexible group analyses with SPSS, SAS etc
Where is activated by Strooptask in ADHD adults?
Is SMG activated by Strooptask in ADHD adults?
In any case
HomER
Strong functional hypothesis
Individual level analysis
Spatial registration profile of fNIRS data in MNI coordinate system
Okamoto M et al. NeuroImage 31, 796-806 (2006)
e.g. our taste encoding study
考察
Oro-lingual foci*
Verbal encoding (Visual)
Verbal encoding (Auditory)
Taste encoding vs verbal encoding
Comparison of NIRS/OT data with former fMRI and PET studies
考察
Oro-lingual foci*
Nonverbal encoding (Visual)
Nonverbal encoding (Auditory)
Nonverbal encoding (somatosensory)
Taste encoding vs non-verbal encoding
Comparison of NIRS/OT data with former fMRI and PET studies
Conclusion in spatial consideration of fNIRS
Cross-modal reference can be achieved using the same spatial platform: MNI space or Talairach space
Additional MRI measurement is omitted for fNIRS
Future agendaNeeds adjustment for Infant and children data (under development)Needs more direct probabilistic inference to macro-anatomyFine adjustment for POTATo, NIRS-SPM and HOMEREspecially expansion to 3D DOT-based analyses
More user-friendly manuals, demos at least…
Still, gyrus-level inference is possible
Related tools & today’s presentationare available
(or will be available)at
http://www.jichi.ac.jp/brainlab
or e-mail me at