Variance Based Measure for Optimization of Parametric Realignment Algorithms
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  • {"files"=>["https://ndownloader.figshare.com/files/5211727"], "description"=>"<p>A: Neuronal responses are related to the external event (in this example stimulus; S), but are triggered (R) by an internal process, which is not precisely time-locked to the onset of the event. B: A certain amount of noise is recorded together with the relevant neuronal responses. C: During the experiment, the external event occurs multiple times, while the neuronal activity is recorded. D: When neuronal responses are aligned on the response start, the trial average response (F: blue line) is a good approximation of the real neuronal response. However, the response onset is unknown. The trial-averaged response aligned on the event onset triggers (F: red line) does not correctly reproduce the real neuronal response. In addition, the standard deviation across trials calculated using the event onset triggers (F: blue and red shaded tubes) is an incorrect estimate of the variability of neuronal responses. This example was generated using Gaussian white noise with a standard deviation equal to 5% of the maximum response amplitude (SNR = 20). Differences between the response starts and stimulus onsets were modelled using a Gaussian distribution with a standard deviation equal to 1/7 of the response standard deviation (<i>σ</i><sub><i>R</i></sub> = 700ms, <i>σ</i><sub><i>J</i></sub> = 100ms).</p>", "links"=>[], "tags"=>["parameter values", "jittered", "influence", "interpretation", "data", "analysis", "efficacy", "trigger", "Such", "behaviour", "Optimization", "realignment algorithm", "performance", "Variance", "Temporal jitter", "realignment algorithms", "stimulus", "dTAV", "signal", "variance", "estimation", "Parametric Realignment Algorithms Neuronal responses", "stimuli"], "article_id"=>3369265, "categories"=>["Neuroscience", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Tomislav Milekovic", "Carsten Mehring"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153773.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Effect_of_single_trial_jitter_on_the_estimation_of_the_underlying_neuronal_response_/3369265", "title"=>"Effect of single trial jitter on the estimation of the underlying neuronal response.", "pos_in_sequence"=>1, "defined_type"=>1, "published_date"=>"2016-05-09 06:10:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/5211745"], "description"=>"<p>Data obtained by simulating 2000 100-trial experiments. A: Expectation of <i>dTAV</i> as a function of the reduction of jitter standard deviation. Lines drawn only for values of SNR of 0.2 and higher. For lower SNR, 2000 repetitions were insufficient to provide a reliable estimate of the expected value of <i>dTAV</i> due to the high noise level. For the shown SNR range, the expected value of <i>dTAV</i> is independent of the SNR. B: The standard deviation (std) of <i>dTAV</i> as a function of the amount of jitter reduction for different SNRs. Standard deviations of <i>dTAV</i> for SNR of 0.2 and lower are above 10<sup>−6</sup> and are, therefore, not shown. C: Probability of jitter reduction as a function of <i>ndTAV</i> for different SNRs. Panels A, B and C are shown for integration time <i>T</i><sub><i>I</i></sub> of 300ms. D: Values of jitter reduction and integration times for which the probability of correct <i>dTAV</i> prediction reaches 90%. For jitter reductions and integration times above the line, the probability for correct <i>dTAV</i> prediction, , is above 90%. For SNRs of 0.13 and lower, the probability of correct <i>dTAV</i> prediction never reached 90%.</p>", "links"=>[], "tags"=>["parameter values", "jittered", "influence", "interpretation", "data", "analysis", "efficacy", "trigger", "Such", "behaviour", "Optimization", "realignment algorithm", "performance", "Variance", "Temporal jitter", "realignment algorithms", "stimulus", "dTAV", "signal", "variance", "estimation", "Parametric Realignment Algorithms Neuronal responses", "stimuli"], "article_id"=>3369283, "categories"=>["Neuroscience", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Tomislav Milekovic", "Carsten Mehring"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153773.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Reliability_of_i_dTAV_i_as_a_measure_of_jitter_reduction_for_mono-phasic_neuronal_responses_/3369283", "title"=>"Reliability of <i>dTAV</i> as a measure of jitter reduction for mono-phasic neuronal responses.", "pos_in_sequence"=>3, "defined_type"=>1, "published_date"=>"2016-05-09 06:10:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/5211763"], "description"=>"<p>An integration time window of <i>T</i><sub><i>i</i></sub> = 300ms was used. For low SNR, <i>dTAV</i> is uninformative as a measure of jitter reduction. As the SNR increases, <i>dTAV</i> becomes more informative of the jitter reduction, i.e the ability to differentiate different levels of jitter reduction based on <i>dTAV</i> improves substantially.</p>", "links"=>[], "tags"=>["parameter values", "jittered", "influence", "interpretation", "data", "analysis", "efficacy", "trigger", "Such", "behaviour", "Optimization", "realignment algorithm", "performance", "Variance", "Temporal jitter", "realignment algorithms", "stimulus", "dTAV", "signal", "variance", "estimation", "Parametric Realignment Algorithms Neuronal responses", "stimuli"], "article_id"=>3369301, "categories"=>["Neuroscience", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Tomislav Milekovic", "Carsten Mehring"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153773.g005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Joint_probability_distribution_of_jitter_reduction_and_i_ndTAV_i_for_different_SNR_values_for_the_mono-phasic_neuronal_response_/3369301", "title"=>"Joint probability distribution of jitter reduction and <i>ndTAV</i> for different SNR values for the mono-phasic neuronal response.", "pos_in_sequence"=>5, "defined_type"=>1, "published_date"=>"2016-05-09 06:10:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/5211793"], "description"=>"<p>Main panels show maximum (black lines) and median (red lines) of jitter reduction obtained by using all permutations of options and parameter values used for <i>dTAV</i> optimization (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153773#pone.0153773.g006\" target=\"_blank\">Fig 6</a> for the list of options and parameter values). Blue lines show reduction of jitter standard deviation obtained using <i>dTAV</i> optimized parameter values. Insets show the percentage of maximum jitter reduction recovered when using <i>dTAV</i> optimization (grey line with black dots); and the percentage of jitter reduction values, obtained when using all option selections and parameter values, that are lower than the jitter reduction obtained using <i>dTAV</i> optimization (purple line with black dots). Both are shown only for SNRs for which the jitter reduction obtained using <i>dTAV</i> optimization was positive. All results are averaged over 100 simulation repetitions; error bars depict the standard errors of the mean. For some SNRs standard errors may be too small to be noticed on the plots.</p>", "links"=>[], "tags"=>["parameter values", "jittered", "influence", "interpretation", "data", "analysis", "efficacy", "trigger", "Such", "behaviour", "Optimization", "realignment algorithm", "performance", "Variance", "Temporal jitter", "realignment algorithms", "stimulus", "dTAV", "signal", "variance", "estimation", "Parametric Realignment Algorithms Neuronal responses", "stimuli"], "article_id"=>3369322, "categories"=>["Neuroscience", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Tomislav Milekovic", "Carsten Mehring"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153773.g007", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Maximum_median_and_i_dTAV_i_optimized_jitter_reduction_obtained_by_MaxCorr_algorithm_/3369322", "title"=>"Maximum, median and <i>dTAV</i> optimized jitter reduction obtained by MaxCorr algorithm.", "pos_in_sequence"=>7, "defined_type"=>1, "published_date"=>"2016-05-09 06:10:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/5211769"], "description"=>"<p>Jitter reduction is shown for monophasic (left panels) and biphasic (right panels) simulated neural responses and for jitter distributed according to Gaussian (top panels) and uniform distributions (bottom panels). Different lines show jitter reduction for simulated experiments containing different numbers of trials. MaxCorr algorithm options and parameters were optimized over the following values: maximum allowed correlation time lag Δ<i>λ</i><sub><i>MAX</i></sub>: 50ms, 100ms, 200ms, 400ms and 800ms; processing of the cross-correlation coefficients: “linear” and “logarithmic”; normalization of cross-correlation coefficients: “none”, “coefficient” and “unbiased”; number of consecutive iterations of the MaxCorr algorithm: 1 and 3; and the width of the filter window: 100ms, 250ms, 500ms and 1000ms. <i>σ’</i><sub><i>J</i></sub> and <i>σ”</i><sub><i>J</i></sub>−jitter standard deviation before and after realignment, respectively. All results are averaged over 100 simulation repetitions; error bars depict the standard errors of the mean. For SNR of 1.3 and 2, standard errors are too small to be noticed on the plots.</p>", "links"=>[], "tags"=>["parameter values", "jittered", "influence", "interpretation", "data", "analysis", "efficacy", "trigger", "Such", "behaviour", "Optimization", "realignment algorithm", "performance", "Variance", "Temporal jitter", "realignment algorithms", "stimulus", "dTAV", "signal", "variance", "estimation", "Parametric Realignment Algorithms Neuronal responses", "stimuli"], "article_id"=>3369307, "categories"=>["Neuroscience", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Tomislav Milekovic", "Carsten Mehring"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153773.g006", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Jitter_reduction_obtained_using_i_dTAV_i_-optimized_MaxCorr_/3369307", "title"=>"Jitter reduction obtained using <i>dTAV</i>-optimized MaxCorr.", "pos_in_sequence"=>6, "defined_type"=>1, "published_date"=>"2016-05-09 06:10:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/5211754"], "description"=>"<p>See caption of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153773#pone.0153773.g003\" target=\"_blank\">Fig 3</a> for details.</p>", "links"=>[], "tags"=>["parameter values", "jittered", "influence", "interpretation", "data", "analysis", "efficacy", "trigger", "Such", "behaviour", "Optimization", "realignment algorithm", "performance", "Variance", "Temporal jitter", "realignment algorithms", "stimulus", "dTAV", "signal", "variance", "estimation", "Parametric Realignment Algorithms Neuronal responses", "stimuli"], "article_id"=>3369292, "categories"=>["Neuroscience", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Tomislav Milekovic", "Carsten Mehring"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153773.g004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Reliability_of_i_dTAV_i_as_a_measure_of_jitter_reduction_for_bi-phasic_neuronal_response_/3369292", "title"=>"Reliability of <i>dTAV</i> as a measure of jitter reduction for bi-phasic neuronal response.", "pos_in_sequence"=>4, "defined_type"=>1, "published_date"=>"2016-05-09 06:10:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/5211736"], "description"=>"<p>Simulated mono-phasic (left) and bi-phasic (right) neuronal responses.</p>", "links"=>[], "tags"=>["parameter values", "jittered", "influence", "interpretation", "data", "analysis", "efficacy", "trigger", "Such", "behaviour", "Optimization", "realignment algorithm", "performance", "Variance", "Temporal jitter", "realignment algorithms", "stimulus", "dTAV", "signal", "variance", "estimation", "Parametric Realignment Algorithms Neuronal responses", "stimuli"], "article_id"=>3369274, "categories"=>["Neuroscience", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Tomislav Milekovic", "Carsten Mehring"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153773.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Simulated_mono-phasic_left_and_bi-phasic_right_neuronal_responses_/3369274", "title"=>"Simulated mono-phasic (left) and bi-phasic (right) neuronal responses.", "pos_in_sequence"=>2, "defined_type"=>1, "published_date"=>"2016-05-09 06:10:22"}

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  • {"unique-ip"=>"2", "full-text"=>"1", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"9"}
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  • {"unique-ip"=>"3", "full-text"=>"2", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"11"}
  • {"unique-ip"=>"3", "full-text"=>"2", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"4", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"8", "full-text"=>"6", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"8", "full-text"=>"4", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"1", "full-text"=>"0", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}

Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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