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dfc random walk analyses  (MathWorks Inc)


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    MathWorks Inc dfc random walk analyses
    <t>dFC</t> speed distributions and long-range correlations. (A) Distributions of resting state fMRI dFC speed, shown here for a representative subjects (log-log scale, pooled window sizes 12 s ≤ W < 31 s) displayed a peak at a value V typ ( typical dFC speed ) and a fat left tail, reflecting an increased probability with respect to chance level to observe short dFC flight lengths (95% confidence intervals are shaded: red, empirical; gray, chance level for shuffled surrogates, see <xref ref-type=Fig. 4 ). (B) The dFC speed V typ decreased with subject age (results are shown here for the specific window pooling used in panel A, but robust for other choices as well; cf. for details). The FC space was seemingly explored through an anomalous random process in which short steps were followed by short steps with large probability (sequential correlations), leading to “knotted” trajectories (panel C, top). This contrasts with a standard random process, visiting precisely the same FC configurations but without long-range correlations (panel C, bottom). (D) The presence of long-range sequential correlations (persistence) of dFC could be proved through a Detrended Fluctuation Analysis (DFA) adapted for dFC streams. We show here DFA log-log scaling plots for representative subjects (in the “young” group, in blue; or in the “older” group, in magenta) and a representative window sizes ( W = 150 s, in the middle of a broad range in which persistence is displayed, see ). The linearity of DFA scatter plots on the log-log plane (scale of coarse-graining vs total fluctuation strength) reveals that instantaneous increments along the dFC stream form a self-similar sequence. The black dashed lines indicate the slope that would be associated to α DFA = 0.5, i.e. the case of ordinary uncorrelated Gaussian random walk. Linear slopes are steeper than for ordinary random walk, indicating that dFC streams evaluated at this window size (and, generally, at W > ~20 s) follow a persistent stochastic walk. " width="250" height="auto" />
    Dfc Random Walk Analyses, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/dfc random walk analyses/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    dfc random walk analyses - by Bioz Stars, 2026-05
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    1) Product Images from "Dynamic Functional Connectivity as a complex random walk: Definitions and the dFCwalk toolbox"

    Article Title: Dynamic Functional Connectivity as a complex random walk: Definitions and the dFCwalk toolbox

    Journal: MethodsX

    doi: 10.1016/j.mex.2020.101168

    dFC speed distributions and long-range correlations. (A) Distributions of resting state fMRI dFC speed, shown here for a representative subjects (log-log scale, pooled window sizes 12 s ≤ W < 31 s) displayed a peak at a value V typ ( typical dFC speed ) and a fat left tail, reflecting an increased probability with respect to chance level to observe short dFC flight lengths (95% confidence intervals are shaded: red, empirical; gray, chance level for shuffled surrogates, see <xref ref-type=Fig. 4 ). (B) The dFC speed V typ decreased with subject age (results are shown here for the specific window pooling used in panel A, but robust for other choices as well; cf. for details). The FC space was seemingly explored through an anomalous random process in which short steps were followed by short steps with large probability (sequential correlations), leading to “knotted” trajectories (panel C, top). This contrasts with a standard random process, visiting precisely the same FC configurations but without long-range correlations (panel C, bottom). (D) The presence of long-range sequential correlations (persistence) of dFC could be proved through a Detrended Fluctuation Analysis (DFA) adapted for dFC streams. We show here DFA log-log scaling plots for representative subjects (in the “young” group, in blue; or in the “older” group, in magenta) and a representative window sizes ( W = 150 s, in the middle of a broad range in which persistence is displayed, see ). The linearity of DFA scatter plots on the log-log plane (scale of coarse-graining vs total fluctuation strength) reveals that instantaneous increments along the dFC stream form a self-similar sequence. The black dashed lines indicate the slope that would be associated to α DFA = 0.5, i.e. the case of ordinary uncorrelated Gaussian random walk. Linear slopes are steeper than for ordinary random walk, indicating that dFC streams evaluated at this window size (and, generally, at W > ~20 s) follow a persistent stochastic walk. " title="dFC speed distributions and long-range correlations. (A) Distributions of ..." property="contentUrl" width="100%" height="100%"/>
    Figure Legend Snippet: dFC speed distributions and long-range correlations. (A) Distributions of resting state fMRI dFC speed, shown here for a representative subjects (log-log scale, pooled window sizes 12 s ≤ W < 31 s) displayed a peak at a value V typ ( typical dFC speed ) and a fat left tail, reflecting an increased probability with respect to chance level to observe short dFC flight lengths (95% confidence intervals are shaded: red, empirical; gray, chance level for shuffled surrogates, see Fig. 4 ). (B) The dFC speed V typ decreased with subject age (results are shown here for the specific window pooling used in panel A, but robust for other choices as well; cf. for details). The FC space was seemingly explored through an anomalous random process in which short steps were followed by short steps with large probability (sequential correlations), leading to “knotted” trajectories (panel C, top). This contrasts with a standard random process, visiting precisely the same FC configurations but without long-range correlations (panel C, bottom). (D) The presence of long-range sequential correlations (persistence) of dFC could be proved through a Detrended Fluctuation Analysis (DFA) adapted for dFC streams. We show here DFA log-log scaling plots for representative subjects (in the “young” group, in blue; or in the “older” group, in magenta) and a representative window sizes ( W = 150 s, in the middle of a broad range in which persistence is displayed, see ). The linearity of DFA scatter plots on the log-log plane (scale of coarse-graining vs total fluctuation strength) reveals that instantaneous increments along the dFC stream form a self-similar sequence. The black dashed lines indicate the slope that would be associated to α DFA = 0.5, i.e. the case of ordinary uncorrelated Gaussian random walk. Linear slopes are steeper than for ordinary random walk, indicating that dFC streams evaluated at this window size (and, generally, at W > ~20 s) follow a persistent stochastic walk.

    Techniques Used: Sequencing


    Figure Legend Snippet:

    Techniques Used: Functional Assay, Marker



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    dfc random walk analyses  (MathWorks Inc)
    90
    MathWorks Inc dfc random walk analyses
    <t>dFC</t> speed distributions and long-range correlations. (A) Distributions of resting state fMRI dFC speed, shown here for a representative subjects (log-log scale, pooled window sizes 12 s ≤ W < 31 s) displayed a peak at a value V typ ( typical dFC speed ) and a fat left tail, reflecting an increased probability with respect to chance level to observe short dFC flight lengths (95% confidence intervals are shaded: red, empirical; gray, chance level for shuffled surrogates, see <xref ref-type=Fig. 4 ). (B) The dFC speed V typ decreased with subject age (results are shown here for the specific window pooling used in panel A, but robust for other choices as well; cf. for details). The FC space was seemingly explored through an anomalous random process in which short steps were followed by short steps with large probability (sequential correlations), leading to “knotted” trajectories (panel C, top). This contrasts with a standard random process, visiting precisely the same FC configurations but without long-range correlations (panel C, bottom). (D) The presence of long-range sequential correlations (persistence) of dFC could be proved through a Detrended Fluctuation Analysis (DFA) adapted for dFC streams. We show here DFA log-log scaling plots for representative subjects (in the “young” group, in blue; or in the “older” group, in magenta) and a representative window sizes ( W = 150 s, in the middle of a broad range in which persistence is displayed, see ). The linearity of DFA scatter plots on the log-log plane (scale of coarse-graining vs total fluctuation strength) reveals that instantaneous increments along the dFC stream form a self-similar sequence. The black dashed lines indicate the slope that would be associated to α DFA = 0.5, i.e. the case of ordinary uncorrelated Gaussian random walk. Linear slopes are steeper than for ordinary random walk, indicating that dFC streams evaluated at this window size (and, generally, at W > ~20 s) follow a persistent stochastic walk. " width="250" height="auto" />
    Dfc Random Walk Analyses, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/dfc random walk analyses/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    dfc random walk analyses - by Bioz Stars, 2026-05
    90/100 stars
    		  Buy from Supplier

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    dFC speed distributions and long-range correlations. (A) Distributions of resting state fMRI dFC speed, shown here for a representative subjects (log-log scale, pooled window sizes 12 s ≤ W < 31 s) displayed a peak at a value V typ ( typical dFC speed ) and a fat left tail, reflecting an increased probability with respect to chance level to observe short dFC flight lengths (95% confidence intervals are shaded: red, empirical; gray, chance level for shuffled surrogates, see <xref ref-type=Fig. 4 ). (B) The dFC speed V typ decreased with subject age (results are shown here for the specific window pooling used in panel A, but robust for other choices as well; cf. for details). The FC space was seemingly explored through an anomalous random process in which short steps were followed by short steps with large probability (sequential correlations), leading to “knotted” trajectories (panel C, top). This contrasts with a standard random process, visiting precisely the same FC configurations but without long-range correlations (panel C, bottom). (D) The presence of long-range sequential correlations (persistence) of dFC could be proved through a Detrended Fluctuation Analysis (DFA) adapted for dFC streams. We show here DFA log-log scaling plots for representative subjects (in the “young” group, in blue; or in the “older” group, in magenta) and a representative window sizes ( W = 150 s, in the middle of a broad range in which persistence is displayed, see ). The linearity of DFA scatter plots on the log-log plane (scale of coarse-graining vs total fluctuation strength) reveals that instantaneous increments along the dFC stream form a self-similar sequence. The black dashed lines indicate the slope that would be associated to α DFA = 0.5, i.e. the case of ordinary uncorrelated Gaussian random walk. Linear slopes are steeper than for ordinary random walk, indicating that dFC streams evaluated at this window size (and, generally, at W > ~20 s) follow a persistent stochastic walk. " width="100%" height="100%">

    Journal: MethodsX

    Article Title: Dynamic Functional Connectivity as a complex random walk: Definitions and the dFCwalk toolbox

    doi: 10.1016/j.mex.2020.101168

    Figure Lengend Snippet: dFC speed distributions and long-range correlations. (A) Distributions of resting state fMRI dFC speed, shown here for a representative subjects (log-log scale, pooled window sizes 12 s ≤ W < 31 s) displayed a peak at a value V typ ( typical dFC speed ) and a fat left tail, reflecting an increased probability with respect to chance level to observe short dFC flight lengths (95% confidence intervals are shaded: red, empirical; gray, chance level for shuffled surrogates, see Fig. 4 ). (B) The dFC speed V typ decreased with subject age (results are shown here for the specific window pooling used in panel A, but robust for other choices as well; cf. for details). The FC space was seemingly explored through an anomalous random process in which short steps were followed by short steps with large probability (sequential correlations), leading to “knotted” trajectories (panel C, top). This contrasts with a standard random process, visiting precisely the same FC configurations but without long-range correlations (panel C, bottom). (D) The presence of long-range sequential correlations (persistence) of dFC could be proved through a Detrended Fluctuation Analysis (DFA) adapted for dFC streams. We show here DFA log-log scaling plots for representative subjects (in the “young” group, in blue; or in the “older” group, in magenta) and a representative window sizes ( W = 150 s, in the middle of a broad range in which persistence is displayed, see ). The linearity of DFA scatter plots on the log-log plane (scale of coarse-graining vs total fluctuation strength) reveals that instantaneous increments along the dFC stream form a self-similar sequence. The black dashed lines indicate the slope that would be associated to α DFA = 0.5, i.e. the case of ordinary uncorrelated Gaussian random walk. Linear slopes are steeper than for ordinary random walk, indicating that dFC streams evaluated at this window size (and, generally, at W > ~20 s) follow a persistent stochastic walk.

    Article Snippet: • We present here blocks and pipelines for dFC random walk analyses that are made easily available through a dedicated MATLAB R toolbox ( dFCwalk ), openly downloadable.

    Techniques: Sequencing

    Journal: MethodsX

    Article Title: Dynamic Functional Connectivity as a complex random walk: Definitions and the dFCwalk toolbox

    doi: 10.1016/j.mex.2020.101168

    Figure Lengend Snippet:

    Article Snippet: • We present here blocks and pipelines for dFC random walk analyses that are made easily available through a dedicated MATLAB R toolbox ( dFCwalk ), openly downloadable.

    Techniques: Functional Assay, Marker