Outline 3.3 Matrix Construction
- Core Argument dieses Kapitels definieren
To-Do’s
- check wording
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3.3 Matrix Construction
3.3.1 Functional Connectivity
Resting-state functional connectivity (RSFC) is defined as the temporal correlation between neurophysiological events in spatially distinct brain regions Friston (1994). In this study, RSFC is operationalized as the statistical dependency between BOLD (Blood Oxygen Level Dependent) signal time series, reflecting the intrinsic functional architecture of the brain during resting-state Biswal (1995). Unlike EC, which models directed causal influences, RSFC is a symmetric, undirected measure that captures the degree to which two regions show correlated activity over time, independent of any explicit task or stimulus.
3.3.2 Time Series Extraction
We utilized the preprocessed dense CIFTI timeseries from the HCP S1200 release. For each subject, the BOLD signal was spatially averaged across all vertices within each of the 360 parcels (180 per hemisphere) of the HCP MMP1 atlas (Glasser et al. (2016) - Nature). Mean BOLD time series were extracted for all seed regions (FEF, IFJa, IFJp) [WELCHE noch? 55b, 44, 45?], as well as all auditory target regions defined in section 3.2.
3.3.3 Connectivity Matrix Construction
Pairwise RSFC values were computed as Pearson correlation coefficients between the extracted BOLD time series of each seed region and all parcels of the HCP-MMP1 atlas - consistent with the HCP’s own connectivity analyses (Glasser et al. (2016) - Nature). This led to a connectivity vector for each subject representing the full RSFC profile for each seed across the cortex. All matrix construction steps were implemented using a custom MATLAB toolbox developed within the Baldauf Research Group. [TOOLBOX Ref - Daniel fragen]
3.3.4 Single Seed vs. Contrast Analysis
We used two complementary analysis modes. In the single-seed analysis, the functional connectivity of a given seed region (FEF or IFJa) was assessed against the mean whole-brain connectivity of that subject, detecting parcels that are significantly more correlated with the seed region than the average cortical region. In the contrast analysis, the connectivity profiles of two seed regions were directly compared, revealing parcels with preferential connectivity to one seed over the other.
3.3.5 Partial vs Full Correlation
When characterizing functional brain networks, a critical distinction needs to be made between full and partial correlation. Full Correlation calculates the pairwise statistical relationship between two regions A and B without controlling for other variables. Because any two regions that both correlate with a third region C will appear correlated with each other - therefore displaying indirect correlation without true connectivity. Partial Correlation on the other hand, estimates the relationship between A and B after regressing out the shared variance to all other simultaneously measured regions (e.g. region C, Marrelec 2006 - NeuroImage, Smith (2011)). As a result, the partial correlation connectome is considerably sparser than its full correlation counterpart, while retaining the most direct functional connections (Glasser et al. (2016) - Nature).
In this study, both measures are applied: full correlation provides a global view of each seed’s connectivity landscape, while partial correlation isolates direct connections after filtering out contributions from the other regions included in the analysis.
3.3.6 Statistical Testing: Wilcoxon Signed-Rank Test & FDR Correction
Statistical significance of RSFC estimates was assessed using the paired Wilcoxon signed-rank test, an alternative to the Gaussian-based paired t-test. This test was chosen because RSFC values derived from Pearson correlations do not necessarily follow a Gaussian distribution across subjects (Soyuhos, O., & Baldauf, D. (2022)) For the single-seed analyses, a one-tailed test was applied, evaluating whether connectivity with the seed region exceeded the mean whole-brain connectivity. For contrast analyses, a two-tailed test was used to determine whether connectivity was significantly higher for one seed than for the other.
Each seed region (FEF or IFJa) was tested independently; their connectivity profiles were not averaged before statistical testing, preserving each region’s functional fingerprint.
To control for the elevated risk of false positives arising from testing all 360 cortical parcels simultaneously, all p-values were corrected for multiple comparisons using the False Discovery Rate (FDR) procedure (Benjamini, Y., & Hochberg, Y. (1995)). In contrast to the more stringent Bonferroni correction, FDR correction maintains higher statistical power by controlling the expected proportion of false positives among all rejected null hypotheses, rather than the probability of any single false positives. A significance threshold of q < 0.05 (FDR-corrected) was applied in all analyses.
For visualization, significant p-values were converted to z-scores. The z-scores were summed across all seeds for which a given parcel reached significance, and then divided by the total number of seeds showing significance for that parcel. The normalization generates a score that weights connectivity strength by consistency of the effect across seeds.
Notes & Scrapbook
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see also
3.0 Methods
3.1 Data Acquisition & Preprocessing
3.2 Selection of Regions of Interest (ROIs)
3.4 Brain Behavior Correlation
Quellen für die BrainRest Toolbox
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Soyuhos, O., & Baldauf, D. (2023). Functional connectivity fingerprints of the frontal eye field and inferior frontal junction suggest spatial versus nonspatial processing in the prefrontal cortex. The European journal of neuroscience, 57(7), 1114–1140. https://doi.org/10.1111/ejn.15936
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Soyuhos, O., Scarpa, A., & Baldauf, D. (2025). Distinct resting-state connectomes for face and scene perception predict individual task performance. bioRxiv, 2025-07. https://doi.org/10.1101/2025.07.09.663812