Add MRI postprocessing of SPM12 outputs as following Huang2013 (#31)

* add MRI postprocessing

* remove untapped function

docs/references.bib

@@ -1,4 +1,3 @@
-
 @article{Li2010A,
 	title = {Independent {Component} {Analysis} by {Entropy} {Bound} {Minimization}},
 	volume = {58},
@@ -49,15 +48,15 @@ @inproceedings{Li2010B
 
 
 @article{Pollonini2014,
-title = {Auditory cortex activation to natural speech and simulated cochlear implant speech measured with functional near-infrared spectroscopy},
-journal = {Hearing Research},
-volume = {309},
-pages = {84-93},
-year = {2014},
-issn = {0378-5955},
-doi = {https://doi.org/10.1016/j.heares.2013.11.007},
-author = {Luca Pollonini and Cristen Olds and Homer Abaya and Heather Bortfeld and Michael S. Beauchamp and John S. Oghalai},
-abstract = {The primary goal of most cochlear implant procedures is to improve a patient's ability to discriminate speech. To accomplish this, cochlear implants are programmed so as to maximize speech understanding. However, programming a cochlear implant can be an iterative, labor-intensive process that takes place over months. In this study, we sought to determine whether functional near-infrared spectroscopy (fNIRS), a non-invasive neuroimaging method which is safe to use repeatedly and for extended periods of time, can provide an objective measure of whether a subject is hearing normal speech or distorted speech. We used a 140 channel fNIRS system to measure activation within the auditory cortex in 19 normal hearing subjects while they listed to speech with different levels of intelligibility. Custom software was developed to analyze the data and compute topographic maps from the measured changes in oxyhemoglobin and deoxyhemoglobin concentration. Normal speech reliably evoked the strongest responses within the auditory cortex. Distorted speech produced less region-specific cortical activation. Environmental sounds were used as a control, and they produced the least cortical activation. These data collected using fNIRS are consistent with the fMRI literature and thus demonstrate the feasibility of using this technique to objectively detect differences in cortical responses to speech of different intelligibility.}
+  title = {Auditory cortex activation to natural speech and simulated cochlear implant speech measured with functional near-infrared spectroscopy},
+  journal = {Hearing Research},
+  volume = {309},
+  pages = {84-93},
+  year = {2014},
+  issn = {0378-5955},
+  doi = {https://doi.org/10.1016/j.heares.2013.11.007},
+  author = {Luca Pollonini and Cristen Olds and Homer Abaya and Heather Bortfeld and Michael S. Beauchamp and John S. Oghalai},
+  abstract = {The primary goal of most cochlear implant procedures is to improve a patient's ability to discriminate speech. To accomplish this, cochlear implants are programmed so as to maximize speech understanding. However, programming a cochlear implant can be an iterative, labor-intensive process that takes place over months. In this study, we sought to determine whether functional near-infrared spectroscopy (fNIRS), a non-invasive neuroimaging method which is safe to use repeatedly and for extended periods of time, can provide an objective measure of whether a subject is hearing normal speech or distorted speech. We used a 140 channel fNIRS system to measure activation within the auditory cortex in 19 normal hearing subjects while they listed to speech with different levels of intelligibility. Custom software was developed to analyze the data and compute topographic maps from the measured changes in oxyhemoglobin and deoxyhemoglobin concentration. Normal speech reliably evoked the strongest responses within the auditory cortex. Distorted speech produced less region-specific cortical activation. Environmental sounds were used as a control, and they produced the least cortical activation. These data collected using fNIRS are consistent with the fMRI literature and thus demonstrate the feasibility of using this technique to objectively detect differences in cortical responses to speech of different intelligibility.}
 }
 
 @article{Pollonini2016,
@@ -78,46 +77,76 @@ @article{Pollonini2016
 }
 
 @article{Huppert2009,
-author = {Theodore J. Huppert and Solomon G. Diamond and Maria A. Franceschini and David A. Boas},
-journal = {Appl. Opt.},
-keywords = {Fourier optics and signal processing ; Medical and biological imaging; Spectroscopy; Functional monitoring and imaging ; Absorption coefficient; Brain imaging; Laser sources; Optical absorption; Optical imaging; Tissue optics},
-number = {10},
-pages = {D280--D298},
-publisher = {Optica Publishing Group},
-title = {HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain},
-volume = {48},
-month = {Apr},
-year = {2009},
-doi = {https://doi.org/10.1364/AO.48.00D280},
-abstract = {Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data.},
-url = "https://github.com/BUNPC/Homer3"
+  author = {Theodore J. Huppert and Solomon G. Diamond and Maria A. Franceschini and David A. Boas},
+  journal = {Appl. Opt.},
+  keywords = {Fourier optics and signal processing ; Medical and biological imaging; Spectroscopy; Functional monitoring and imaging ; Absorption coefficient; Brain imaging; Laser sources; Optical absorption; Optical imaging; Tissue optics},
+  number = {10},
+  pages = {D280--D298},
+  publisher = {Optica Publishing Group},
+  title = {HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain},
+  volume = {48},
+  month = {Apr},
+  year = {2009},
+  doi = {https://doi.org/10.1364/AO.48.00D280},
+  abstract = {Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data.},
+  url = "https://github.com/BUNPC/Homer3"
 }
 
 @article{Oostenveld2001,
-title = {The five percent electrode system for high-resolution EEG and ERP measurements},
-journal = {Clinical Neurophysiology},
-volume = {112},
-number = {4},
-pages = {713-719},
-year = {2001},
-issn = {1388-2457},
-doi = {https://doi.org/10.1016/S1388-2457(00)00527-7},
-author = {Robert Oostenveld and Peter Praamstra},
-keywords = {Electrode placement, High resolution EEG, High resolution ERP, Nomenclature},
-abstract = {Objective: A system for electrode placement is described. It is designed for studies on topography and source analysis of spontaneous and evoked EEG activity. Method: The proposed system is based on the extended International 10–20 system which contains 74 electrodes, and extends this system up to 345 electrode locations. Results: The positioning and nomenclature of the electrode system is described, and a subset of locations is proposed as especially useful for modern EEG/ERP systems, often having 128 channels available. Conclusion: Similar to the extension of the 10–20 system to the 10–10 system (‘10% system’), proposed in 1985, the goal of this new extension to a 10–5 system is to further promote standardization in high-resolution EEG studies.}
+  title = {The five percent electrode system for high-resolution EEG and ERP measurements},
+  journal = {Clinical Neurophysiology},
+  volume = {112},
+  number = {4},
+  pages = {713-719},
+  year = {2001},
+  issn = {1388-2457},
+  doi = {https://doi.org/10.1016/S1388-2457(00)00527-7},
+  author = {Robert Oostenveld and Peter Praamstra},
+  keywords = {Electrode placement, High resolution EEG, High resolution ERP, Nomenclature},
+  abstract = {Objective: A system for electrode placement is described. It is designed for studies on topography and source analysis of spontaneous and evoked EEG activity. Method: The proposed system is based on the extended International 10–20 system which contains 74 electrodes, and extends this system up to 345 electrode locations. Results: The positioning and nomenclature of the electrode system is described, and a subset of locations is proposed as especially useful for modern EEG/ERP systems, often having 128 channels available. Conclusion: Similar to the extension of the 10–20 system to the 10–10 system (‘10% system’), proposed in 1985, the goal of this new extension to a 10–5 system is to further promote standardization in high-resolution EEG studies.}
+}
+
+@software{Luke_fNIRS_Finger_Tapping_2021,
+  author = {Luke, Robert and McAlpine, David},
+  doi = {10.5281/zenodo.5529797},
+  month = sep,
+  title = {{fNIRS Finger Tapping Data in BIDS Format}},
+  version = {v0.0.1},
+  year = {2021},
+  url = {https://github.com/rob-luke/BIDS-NIRS-Tapping}
 }
 
+@article{Huang2013,
+  author = {Huang, Yu and Dmochowski, Jacek and Su, Yuzhuo and Datta, Abhishek and Rorden, Chris and Parra, Lucas},
+  year = {2013},
+  month = {10},
+  pages = {066004},
+  title = {Automated MRI Segmentation for Individualized Modeling of Current Flow in the Human Head},
+  volume = {10},
+  journal = {Journal of neural engineering},
+  doi = {10.1088/1741-2560/10/6/066004}
+}
+
+@software{Harmening2022,
+  author = {Harmening, Nils and Miklody, Daniel}, 
+  doi = {10.5281/zenodo.7357674},   
+  month = {11},
+  title = {{MRIsegmentation}},      
+  url = {https://github.com/harmening/MRIsegmentation},
+  version = {1.0}, 
+  year = {2022}   
 
 @article{Gao2015,
-author={Gao, James S.  and Huth, Alexander G.  and Lescroart, Mark D.  and Gallant, Jack L. },
-title={Pycortex: an interactive surface visualizer for fMRI},
-journal={Frontiers in Neuroinformatics},
-volume={9},
-year={2015},
-url={https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2015.00023},
-doi={10.3389/fninf.2015.00023},
-issn={1662-5196},
-abstract={<p>Surface visualizations of fMRI provide a comprehensive view of cortical activity. However, surface visualizations are difficult to generate and most common visualization techniques rely on unnecessary interpolation which limits the fidelity of the resulting maps. Furthermore, it is difficult to understand the relationship between flattened cortical surfaces and the underlying 3D anatomy using tools available currently. To address these problems we have developed pycortex, a Python toolbox for interactive surface mapping and visualization. Pycortex exploits the power of modern graphics cards to sample volumetric data on a per-pixel basis, allowing dense and accurate mapping of the voxel grid across the surface. Anatomical and functional information can be projected onto the cortical surface. The surface can be inflated and flattened interactively, aiding interpretation of the correspondence between the anatomical surface and the flattened cortical sheet. The output of pycortex can be viewed using WebGL, a technology compatible with modern web browsers. This allows complex fMRI surface maps to be distributed broadly online without requiring installation of complex software.</p>}}
+  author={Gao, James S.  and Huth, Alexander G.  and Lescroart, Mark D.  and Gallant, Jack L. },
+  title={Pycortex: an interactive surface visualizer for fMRI},
+  journal={Frontiers in Neuroinformatics},
+  volume={9},
+  year={2015},
+  url={https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2015.00023},
+  doi={10.3389/fninf.2015.00023},
+  issn={1662-5196},
+  abstract={<p>Surface visualizations of fMRI provide a comprehensive view of cortical activity. However, surface visualizations are difficult to generate and most common visualization techniques rely on unnecessary interpolation which limits the fidelity of the resulting maps. Furthermore, it is difficult to understand the relationship between flattened cortical surfaces and the underlying 3D anatomy using tools available currently. To address these problems we have developed pycortex, a Python toolbox for interactive surface mapping and visualization. Pycortex exploits the power of modern graphics cards to sample volumetric data on a per-pixel basis, allowing dense and accurate mapping of the voxel grid across the surface. Anatomical and functional information can be projected onto the cortical surface. The surface can be inflated and flattened interactively, aiding interpretation of the correspondence between the anatomical surface and the flattened cortical sheet. The output of pycortex can be viewed using WebGL, a technology compatible with modern web browsers. This allows complex fMRI surface maps to be distributed broadly online without requiring installation of complex software.</p>}
+}
 
 @article{Fang2009,
   title={Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units},
@@ -164,27 +193,27 @@ @article{Dehghani2009
 }
 
 @article{Tucker2022,
-author = {Stephen Tucker and Jay Dubb and Sreekanth Kura and Alexander von L{\"u}hmann and Robert Franke and J{\"o}rn M. Horschig and Samuel Powell and Robert Oostenveld and Michael L{\"u}hrs and {\'E}douard Delaire and Zahra M. Aghajan and Hanseok Yun and Meryem A. Y{\"u}cel and Qianqian Fang and Theodore J. Huppert and Blaise deB. Frederick and Luca Pollonini and David A. Boas and Robert Luke},
-title = {{Introduction to the shared near infrared spectroscopy format}},
-volume = {10},
-journal = {Neurophotonics},
-number = {1},
-publisher = {SPIE},
-pages = {013507},
-keywords = {functional near-infrared spectroscopy, shared near-infrared spectroscopy format, standardization, data format, data sharing, software, Near infrared spectroscopy, Data acquisition, Neuroimaging, Software development, Standards development, Neurophotonics, Design and modelling, Compliance, Data storage, MATLAB},
-year = {2022},
-doi = {10.1117/1.NPh.10.1.013507},
-URL = {https://doi.org/10.1117/1.NPh.10.1.013507}
+  author = {Stephen Tucker and Jay Dubb and Sreekanth Kura and Alexander von L{\"u}hmann and Robert Franke and J{\"o}rn M. Horschig and Samuel Powell and Robert Oostenveld and Michael L{\"u}hrs and {\'E}douard Delaire and Zahra M. Aghajan and Hanseok Yun and Meryem A. Y{\"u}cel and Qianqian Fang and Theodore J. Huppert and Blaise deB. Frederick and Luca Pollonini and David A. Boas and Robert Luke},
+  title = {{Introduction to the shared near infrared spectroscopy format}},
+  volume = {10},
+  journal = {Neurophotonics},
+  number = {1},
+  publisher = {SPIE},
+  pages = {013507},
+  keywords = {functional near-infrared spectroscopy, shared near-infrared spectroscopy format, standardization, data format, data sharing, software, Near infrared spectroscopy, Data acquisition, Neuroimaging, Software development, Standards development, Neurophotonics, Design and modelling, Compliance, Data storage, MATLAB},
+  year = {2022},
+  doi = {10.1117/1.NPh.10.1.013507},
+  URL = {https://doi.org/10.1117/1.NPh.10.1.013507}
 }
 
 @misc{Luke2021,
-author = {Luke, Robert and McAlpine, David},
-doi = {10.5281/zenodo.5529797},
-month = sep,
-title = {{fNIRS Finger Tapping Data in BIDS Format}},
-version = {v0.0.1},
-year = {2021},
-url = {https://github.com/rob-luke/BIDS-NIRS-Tapping}
+  author = {Luke, Robert and McAlpine, David},
+  doi = {10.5281/zenodo.5529797},
+  month = sep,
+  title = {{fNIRS Finger Tapping Data in BIDS Format}},
+  version = {v0.0.1},
+  year = {2021},
+  url = {https://github.com/rob-luke/BIDS-NIRS-Tapping}
 }
 
 @article{Holmes1998,
@@ -220,4 +249,4 @@ @misc{Fishburn2018
 title = {TDDR},
 year = {2018},
 url = {https://github.com/frankfishburn/TDDR/}
-}
+}