brainnet viewer  (MathWorks Inc)

Journal: Nature Communications

Article Title: Enhanced role of the entorhinal cortex in adapting to increased working memory load

doi: 10.1038/s41467-025-60681-w

Figure Lengend Snippet: A Schematic illustration of the WM task and intracranial EEG (iEEG) recording sites in the entorhinal cortex (EC), hippocampus (Hipp), and lateral temporal cortex (LTC). B Under medium-to-high load conditions, decoding accuracy based on EC power features was higher than that derived from the hippocampus or LTC. C Cross-regional decoding, in which decoders trained on one region’s data were tested on another, revealed that EC-based decoders demonstrated the highest generalization under medium-to-high load conditions. D Residual decoding analysis showed that removing neural activity shared with the EC significantly reduced decoding accuracy in the hippocampus and LTC under medium-to-high load conditions. E Functional connectivity analysis indicated that the phase locking value (PLV) between the EC and other regions increased with enhanced WM load. The brain ( A , C ) was visualized by the BrainNet Viewer toolbox ( www.nitrc.org/projects/bnv/ ) .

Article Snippet: We utilized BrainNet Viewer in MATLAB (MathWorks) to visualize all the recording sites, as depicted in Fig. .

Techniques: Derivative Assay, Activity Assay, Functional Assay

Journal: Nature Communications

Article Title: Enhanced role of the entorhinal cortex in adapting to increased working memory load

doi: 10.1038/s41467-025-60681-w

Figure Lengend Snippet: A Each trial began with a 1 s fixation screen, followed by a 2 s presentation of four, six, or eight letters. After letters disappeared, there was a 3 s maintenance period with a black square shown. Participants responded whether a probe letter was part of the original set by pressing “IN” or “OUT”. B Channel locations of all participants included 91 channels in the hippocampus (Hipp; light red), 46 channels in the entorhinal cortex (EC; light blue), and 136 channels in the lateral temporal cortex (LTC; light green). The brain was visualized by the BrainNet Viewer toolbox ( www.nitrc.org/projects/bnv/ ) . C We conducted binary classification (load 4 vs load 6, load 6 vs load 8). Time-frequency analysis was performed on each trial to obtain power spectra in the hippocampus, EC, and LTC. For each classification task, 70% of the data was used for training and 30% for testing with a linear SVM classifier. D The decoding accuracy for load 4 vs load 6 did not show significant differences among the hippocampus, EC, and LTC across all cross-validations ( n = 100, two-sided permutation t test: EC vs hippocampus: p = 0.768; EC vs LTC: p = 0.379; LTC vs hippocampus: p = 0.690; see distribution with 150 iterations in Supplementary Fig. ). The EC exhibited the highest decoding accuracy for load 6 vs load 8 ( n = 100 cross-validations; two-sided permutation t test: all ps < 0.001). *** p < 0.001. E Differences in decoding accuracy between low-to-medium and medium-to-high load conditions were smallest in the EC ( n = 100 cross-validations; two-sided permutation t test: hippocampus vs EC: p < 0.001; LTC vs EC: p = 0.005; hippocampus vs LTC: p = 0.047). * p < 0.05, ** p < 0.01, *** p < 0.001. In the box plots shown in ( D , E ), the center line represents the median, and the edges of the box correspond to the lower and upper quartiles, respectively. The whiskers extend to the minimum and maximum data points at most 1.5 times the interquartile range. Source data are provided as a Source Data file.

Article Snippet: We utilized BrainNet Viewer in MATLAB (MathWorks) to visualize all the recording sites, as depicted in Fig. .

Techniques:

Journal: Nature Communications

Article Title: Enhanced role of the entorhinal cortex in adapting to increased working memory load

doi: 10.1038/s41467-025-60681-w

Figure Lengend Snippet: A Schematic of cross-regional decoding analysis. Using the entorhinal cortex (EC) as an example, we trained classifiers using power features from EC for each trial and predicted the load using power from the hippocampus (Hipp) for both low-to-medium and medium-to-high load conditions. The specific decoding steps were the same as shown in Fig. . For all brain regions, models were trained using their own power features and tested on data from the other two brain regions. The generalization of each brain region was determined by averaging its accuracy when tested on data from the other two brain regions (hippocampus: light red; EC: light blue; lateral temporal cortex: LTC, light green). The brain was visualized by the BrainNet Viewer toolbox ( www.nitrc.org/projects/bnv/ ) . B Accuracy matrix of cross-regional decoding on low-to-medium load (left) and medium-to-high load (right). The rows of the matrix represented the regions used for training, while the columns denoted the regions employed for testing, with the values representing the average accuracy. C The averaged cross-regional decoding accuracy for load 4 vs load 6 did not differ significantly among hippocampus, EC, and LTC across all cross-validations ( n = 100; two-sided permutation t tests: EC vs hippocampus: p = 0.637; EC vs LTC: p = 0.128; LTC vs hippocampus: p = 0.141). The EC showed the highest cross-regional decoding accuracy for load 6 vs load 8 across all cross-validations ( n = 100; two-sided permutation t tests: EC vs hippocampus: p < 0.001; EC vs LTC: p = 0.002; LTC vs hippocampus: p < 0.001). ** p < 0.01, *** p < 0.001. The center line represents the median, and the edges of the box correspond to the lower and upper quartiles, respectively. The whiskers extend to the minimum and maximum data points at most 1.5 times the interquartile range. Source data are provided as a Source Data file.

Article Snippet: We utilized BrainNet Viewer in MATLAB (MathWorks) to visualize all the recording sites, as depicted in Fig. .

Techniques: