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. 2020 Sep 17;9:e55585. doi: 10.7554/eLife.55585

Figure 2. Whole-brain IC connectivity map.

(A) Comparison of inputs to excitatory and inhibitory IC neurons (left) and outputs of excitatory neurons of the IC (right) of all three IC subregions (aIC, red; mIC, green; pIC, blue) across the 17 major brain regions that displayed connectivity. Region values are given as percentage of total cells (RV) or of total pixels (AAV). Data is shown as average ± SEM. n = 3 mice per condition. Top panel shows cortical connectivity, bottom panel shows subcortical connectivity. One-way ANOVAs per subregion followed by Tuckey’s multiple comparison test were performed to generate p-values. Significant differences between inputs to excitatory or inhibitory neurons to IC subregions or between outputs from the IC subregions were labeled as ***p<0.001, **p<0.01, *p<0.05. For detailed statistics see Supplementary file 3. (B) Individual input-output maps for the three IC subdivisons highlighting selected brain regions. Weight of arrowhead and thickness of arrow shaft indicate strength of connection. Green arrowheads indicate inputs, red arrowheads indicate outputs.

Figure 2.

Figure 2—figure supplement 1. Brain-wide dataset for aIC.

Figure 2—figure supplement 1.

Brain-wide global datasets were divided into 75 subregions for comparison. Data shows excitatory (Camk2a) and inhibitory (Gad2) input strengths, and excitatory output strength (Camk2a) from each IC- subregion (aIC, mIC, and pIC, Figure 2—figure supplements 1, 2, and 3, respectively). Values are presented as normalized percentage of total cells (RV) or total pixels (AAV). Data shown as average ± SEM. N = 3 mice per condition. Top panel shows cortical connectivity, bottom panel shows subcortical connectivity.
Figure 2—figure supplement 2. Brain-wide dataset for mIC.

Figure 2—figure supplement 2.

Brain-wide global datasets were divided into 75 subregions for comparison. Data shows excitatory (Camk2a) and inhibitory (Gad2) input strengths, and excitatory output strength (Camk2a) from each IC- subregion (aIC, mIC, and pIC, Figure 2—figure supplements 1, 2, and 3, respectively). Values are presented as normalized percentage of total cells (RV) or total pixels (AAV). Data shown as average ± SEM. N = 3 mice per condition. Top panel shows cortical connectivity, bottom panel shows subcortical connectivity.
Figure 2—figure supplement 3. Brain-wide dataset for pIC.

Figure 2—figure supplement 3.

Brain-wide global datasets were divided into 75 subregions for comparison. Data shows excitatory (Camk2a) and inhibitory (Gad2) input strengths, and excitatory output strength (Camk2a) from each IC- subregion (aIC, mIC, and pIC, Figure 2—figure supplements 1, 2, and 3, respectively). Values are presented as normalized percentage of total cells (RV) or total pixels (AAV). Data shown as average ± SEM. N = 3 mice per condition. Top panel shows cortical connectivity, bottom panel shows subcortical connectivity.
Figure 2—figure supplement 4. Instructions to query the datasets with custom questions.

Figure 2—figure supplement 4.

From the accompanying excel resource sheet, all plots presented in this study can be recreated. In addition, the reader can query the dataset with his own questions, by creating pivot tables. The workflow, how to create such a pivot table in excel is described here. (1) After opening the excel sheet, navigate to the ‘RAW DATA’ tab. Then go to the ‘Insert’ tab and insert a pivot table. In the subsequent pop-up dialogue, make sure the entire range of the dataset is selected and click ‘OK’. Next, (2) set Filters for at least the tracing type (AAV or RV), as RV and AAV results do not share the same values. Optionally, you can set filters for the Genotype (Camk2a-Cre and Gad2-Cre) and from which part of the insula the tracings should be selected (aIC, mIC, pIC). Depending on your question and how you want the data to be plotted, you have to choose which values to use (3). In the manuscript we present the data as percent of total output or input, respectively. Additionally, we provide cell density and pixel density measurements for RV and AAV tracings, respectively. Important: (4) Because of the raw data structure, it is necessary to use ‘sum of percent_total_input or output’. Double-check that the percentages add up to 100% in the grand total fields. For density measurements, please use ‘average of’. (5) Depending on how you want the data to be arranged, drag and drop the fields into either columns or rows window. For example if you are interested in inputs from a region along the anterior-posterior axis, put ‘Bregma’ into either Columns or Rows. Then select the entire pivot table and insert a chart.