The NIRS Analysis Package: Noise Reduction and Statistical Inference
Publication Date
September 02, 2011
Journal
PLOS ONE
Authors
Tomer Fekete, Denis Rubin, Joshua M. Carlson & Lilianne R. Mujica Parodi
Volume
6
Issue
9
Pages
e24322
DOI
https://dx.plos.org/10.1371/journal.pone.0024322
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0024322
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/21912687
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166314
Europe PMC
http://europepmc.org/abstract/MED/21912687
Web of Science
000294686100034
Scopus
80052405494
Mendeley
http://www.mendeley.com/research/nirs-analysis-package-noise-reduction-statistical-inference
Events
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Mendeley | Further Information

{"title"=>"The nirs analysis package: Noise reduction and statistical inference", "type"=>"journal", "authors"=>[{"first_name"=>"Tomer", "last_name"=>"Fekete", "scopus_author_id"=>"37004346900"}, {"first_name"=>"Denis", "last_name"=>"Rubin", "scopus_author_id"=>"7202307226"}, {"first_name"=>"Joshua M.", "last_name"=>"Carlson", "scopus_author_id"=>"24757949100"}, {"first_name"=>"Lilianne R.", "last_name"=>"Mujica-Parodi", "scopus_author_id"=>"8568129700"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"362489451", "sgr"=>"80052405494", "issn"=>"19326203", "pmid"=>"21912687", "scopus"=>"2-s2.0-80052405494", "doi"=>"10.1371/journal.pone.0024322", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"5691476a-157a-39ac-8ca2-719d3c5b36ee", "abstract"=>"Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS.", "link"=>"http://www.mendeley.com/research/nirs-analysis-package-noise-reduction-statistical-inference", "reader_count"=>144, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>14, "Researcher"=>27, "Student > Doctoral Student"=>12, "Student > Ph. D. Student"=>45, "Student > Postgraduate"=>4, "Student > Master"=>25, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>8}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>14, "Researcher"=>27, "Student > Doctoral Student"=>12, "Student > Ph. D. Student"=>45, "Student > Postgraduate"=>4, "Student > Master"=>25, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>8}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>17, "Arts and Humanities"=>1, "Philosophy"=>2, "Business, Management and Accounting"=>2, "Computer Science"=>12, "Engineering"=>29, "Mathematics"=>1, "Medicine and Dentistry"=>17, "Neuroscience"=>16, "Sports and Recreations"=>7, "Physics and Astronomy"=>3, "Psychology"=>31, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>17}, "Social Sciences"=>{"Social Sciences"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>7}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Psychology"=>{"Psychology"=>31}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>5}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>29}, "Neuroscience"=>{"Neuroscience"=>16}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>17}, "Computer Science"=>{"Computer Science"=>12}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Philosophy"=>{"Philosophy"=>2}}, "reader_count_by_country"=>{"United States"=>3, "Japan"=>2, "Spain"=>1, "Canada"=>1, "Cuba"=>1, "Sweden"=>1, "Netherlands"=>2, "Austria"=>1, "Turkey"=>1, "Belgium"=>1, "Brazil"=>1, "Italy"=>1, "Germany"=>3}, "group_count"=>7}

CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/738956"], "description"=>"<p><b>(a)</b> A power spectrum of an activated NIRS channel (<i>red</i>) on a logarithmic scale. The spectrum exhibits excess energy in several frequency bands associated with periodic systemic artifacts: heart pulsations (1–2 Hz in adults), respiration (∼0.4 Hz in adults), and blood pressure (Mayer) waves (∼0.1 Hz). Superimposed on this are the spectra resulting from each successive application of the algorithm described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024322#s2\" target=\"_blank\">Methods</a> section 2.1: in <i>light gray</i> after heart beat is removed (i.e. using [0.6 2] Hz), in <i>dark gray</i> after the second sweep, for respiratory artifacts (i.e. using [0.15 0.4] Hz), and finally in <i>black</i> after using [0.05 0.2] Hz for blood pressure waves. <b>(b)</b> The same plot in linear scale. <b>(c)</b> After the first sweep the NIRS time series was averaged around the detected heart beat onsets. As can be seen in <i>gray</i>, the artifact is all but gone as compared to averaging the original time series in the same fashion. <b>(d)</b> The above time series was averaged around stimulus onset (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024322#s2\" target=\"_blank\">Methods</a> section 2.1 for details) before (<i>red</i>) and after (<i>black</i>) the procedure. As shown, the resulting response doesn't exhibit conspicuous oscillatory components.</p>", "links"=>[], "tags"=>["recursive", "algorithm", "systemic"], "article_id"=>409330, "categories"=>["Mathematics", "Biological Sciences", "Neuroscience", "Cell Biology"], "users"=>["Tomer Fekete", "Denis Rubin", "Joshua M. Carlson", "Lilianne R. Mujica-Parodi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024322.g003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_recursive_algorithm_for_systemic_artifact_reduction_/409330", "title"=>"A recursive algorithm for systemic artifact reduction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-02 02:35:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/738851"], "description"=>"<p><b>(a)</b> In <i>red</i>, a NIRS time series contaminated by a motion artifact. Superimposed on it in <i>black</i> is the time series resulting from artifact cancellation. <b>(b)</b> In <i>red</i>, a simulated NIRS time series (phase randomized noise, exponent −2) to which a simulated motion artifact is added, matched to the one in (a) by amplitude and duration. In <i>black</i> is the time series resulting from artifact cancellation. <b>(c)</b> The time series in (b) was analyzed using the SPM hemodynamic response model and the feasible generalized least squares FGLS (feasible generalized least squares – i.e. whitening according to a power law fit + precoloring; 0.017 Hz) method described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024322#s2\" target=\"_blank\">Methods</a> section 2.3, resulting in a p-value of 0.227. A set of 10000 artificial motion artifacts varying only in time of onset was generated and added to this time series and analyzed similarly. The resulting histogram of p-values demonstrates that motion artifact negation is imperative for valid statistical analysis. After motion artifact cancellation the correlation of these data to the original time series was 0.996±0.004, and the resulting p-value was 0.237±0.068 (reflecting the slight negative bias shown in 1(c)).</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "neurological disorders", "mathematics", "radiology and medical imaging"], "article_id"=>409219, "categories"=>["Mathematics", "Biological Sciences", "Neuroscience", "Cell Biology"], "users"=>["Tomer Fekete", "Denis Rubin", "Joshua M. Carlson", "Lilianne R. Mujica-Parodi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024322.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reduction_of_motion_artifact_/409219", "title"=>"Reduction of motion artifact.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-02 02:33:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/738705"], "description"=>"<p><b>(a)</b>: A representative power spectrum of a NIRS time series captured in the absence of stimulation (rest data in <i>red</i>) displayed on a logarithmic scale. The power spectrum follows a power law (exponent ∼ −1.8) tightly, apart from conspicuous deviations caused by systemic artifacts. The spectrum of the same time series after artifact reduction (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024322#s2\" target=\"_blank\">Methods</a> section 2.1) is plotted in black. <b>(b)</b> Data were simulated data to mimick NIRS rest data (exponent −2). Ten thousand random vectors of this type were generated and analyzed with a GLM representing the design of the task described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024322#s2\" target=\"_blank\">Methods</a> section 1, applying the precoloring method <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024322#pone.0024322-Friston2\" target=\"_blank\">[23]</a> to subdue autocorrelation. Next an ANOVA (for the FIR model) or a t-test (for the SPM-HRF model) wass carried out on the resulting coefficients, and the significance (p-value) for each derived. The resulting histogram of p-values is shown <b>(c)</b> A similar set of simulated noise was fitted with a FIR model representing the task design, and the FGLS (feasible generalized least squares- i.e. whitening according to a power law fit + precoloring (0.017 Hz)) method described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024322#s2\" target=\"_blank\">Methods</a> section 2.3 was applied. The resulting histogram of p-values is very close to the theoretical optimum. <b>(d)</b> Simulated noise was fitted with a single basis function – the SPM hemodynamic response function <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024322#pone.0024322-Friston1\" target=\"_blank\">[9]</a> and the FGLS method described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024322#s2\" target=\"_blank\">Methods</a> section 2.3 was applied. The resulting histogram of p-values exhibits some slight negative bias. <b>(e-f)</b> The same design vector was convolved with the SPM HRF. It then served as a model signal to which simulated noise of varying degrees was added. Next, FGLS was carried out using both the FIR and SPM HRF model. Probability of detection (average p-value) as a function of signal to noise (ratio of RMS (root means squared) squared) for the FIR (e) and SPM HRF model (f) are shown. As can be seen, both models are highly sensitive in the face of noise although, unsurprisingly, given that in the case of the SPM HRF model the model and signal are identical, the SPM HRF model is more sensitive in this case.</p>", "links"=>[], "tags"=>["characteristics", "nirs"], "article_id"=>409076, "categories"=>["Mathematics", "Biological Sciences", "Neuroscience", "Cell Biology"], "users"=>["Tomer Fekete", "Denis Rubin", "Joshua M. Carlson", "Lilianne R. Mujica-Parodi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024322.g001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Noise_characteristics_of_NIRS_DATA_/409076", "title"=>"Noise characteristics of NIRS DATA.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-02 02:31:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/739299"], "description"=>"<p><b>(a)</b> : a representative time series (Participant 8, channel 26). Data were band-passed filtered for purposes of presentation <b>(b)</b> the activation map (oxyhemoglobin data) for this participant, using a FIR model with precoloring (cutoff 0.017 Hz). 46 out of 52 channels are activated for p<0.01 (corrected for channel number). <b>(c)</b> the activation using a FIR model with whitening+precoloring (cutoff 0.017 Hz) for p<0.01 (corrected for channel number). Now activation is restricted to the visual areas. <b>(d)</b> the average activation for all participants (channel 26, n = 12). Note, that no residual oscillatory artifacts are present. <b>(e)</b> the activation map for all participants for the FIR model p<0.01 (corrected for channel number). Group analyses were carried out using the method described in Beckmann et al. (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024322#pone.0024322-Beckmann1\" target=\"_blank\">[20]</a>, see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024322#pone.0024322.s005\" target=\"_blank\">appendix S3</a>).</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "neurological disorders", "mathematics", "radiology and medical imaging"], "article_id"=>409670, "categories"=>["Mathematics", "Biological Sciences", "Neuroscience", "Cell Biology"], "users"=>["Tomer Fekete", "Denis Rubin", "Joshua M. Carlson", "Lilianne R. Mujica-Parodi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024322.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_results_of_the_visual_task_/409670", "title"=>"The results of the visual task.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-02 02:41:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/373021", "https://ndownloader.figshare.com/files/373083", "https://ndownloader.figshare.com/files/373125", "https://ndownloader.figshare.com/files/373171", "https://ndownloader.figshare.com/files/373272"], "description"=>"<div><p>Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS.</p> </div>", "links"=>[], "tags"=>["nirs", "inference"], "article_id"=>133673, "categories"=>["Mathematics", "Biological Sciences", "Neuroscience", "Cell Biology"], "users"=>["Tomer Fekete", "Denis Rubin", "Joshua M. Carlson", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0024322.s001", "https://dx.doi.org/10.1371/journal.pone.0024322.s002", "https://dx.doi.org/10.1371/journal.pone.0024322.s003", "https://dx.doi.org/10.1371/journal.pone.0024322.s004", "https://dx.doi.org/10.1371/journal.pone.0024322.s005"], "stats"=>{"downloads"=>22, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_NIRS_Analysis_Package_Noise_Reduction_and_Statistical_Inference/133673", "title"=>"The NIRS Analysis Package: Noise Reduction and Statistical Inference", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-09-02 01:01:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/739174"], "description"=>"<p><b>(a)</b> Results of residual analysis (channel 23, participant 5). In <i>red</i>: the empirical autocorrelation of the residuals after fitting the time series with an SPM HRF model with precoloring. In <i>gray</i>: the results of similar analysis, this time using whitening (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024322#s2\" target=\"_blank\">Methods</a> section 2.3). In <i>black</i>: the results using both. In this example, while whitening substantially outperforms precoloring, it is nevertheless improved upon by the combined approach. <b>(b)</b> Results of residual analysis (channel 46, participant 5). Same notation as (a). In this example, precoloring actually outperforms whitening, but again the combined approach is superior to both. <b>(c</b>–<b>f)</b> A histogram of all residual autocorrelation values (lag>0) for this participant using (c) precoloring (d) whitening (e) both. (f) histogram of all residual autocorrelation values (lag>0) resulting from applying the same design matrix with OLS to white noise. As shown, the combined the FGLS approach we propose is very close to the theoretical limit of efficiency.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "neurological disorders", "mathematics", "radiology and medical imaging"], "article_id"=>409546, "categories"=>["Mathematics", "Biological Sciences", "Neuroscience", "Cell Biology"], "users"=>["Tomer Fekete", "Denis Rubin", "Joshua M. Carlson", "Lilianne R. Mujica-Parodi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024322.g004", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_residual_analysis_/409546", "title"=>"residual analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-02 02:39:06"}

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Relative Metric

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