Global efficiency of the mouse connectome modelled by weighted and binary graphs.

The global efficiency of the thresholded mouse network increases as the weight threshold is reduced, for (blue) the weighted graph and (green) the binarized graph. Dashed line gives the fraction of nodes contained in the largest connected component. In the weighted graph (blue), the weaker weights have little impact on the global efficiency or connectedness of the network; whereas in the binary graph (green) the weakest edges incrementally increase topological integration of the network.</p

First two panels of Figure 9 from Shriki et al. [2].

These results seem to indicate that “PLI analysis yields similar results for human and empty scanner data.” The panels show the PLI distributions of a single human subject (A) and a single empty room recording (B), both at the Cambridge MEG facility.</p

Reanalysis results.

<p>Original wavelet-based method of PLI analysis (left panels; from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004175#pcbi.1004175.ref001" target="_blank">1</a>]) and simplified band-pass method of PLI analysis (right column; from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004175#pcbi.1004175.ref002" target="_blank">2</a>]) applied to same data as in [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004175#pcbi.1004175.ref002" target="_blank">2</a>]. The top row shows human subject data, the middle row shows empty-room data with line noise included, and the bottom row shows empty room data with line-noise artefacts removed.</p

Properties of the 5% weakest and 5% strongest edges of the mouse cortical network.

(A,B) Axial view of the mouse cortical networks, red dots represent brain regions, blue lines represent the connections between them. Drawn are the (A) 5% weakest or (B) 5% strongest edges. Dot size corresponds to degree, the total number of incoming and outgoing edges connected to a node. In (B), the three nodes with highest degree have been labeled: VISp, primary visual area; MOp, primary motor area; SSs, supplemental somatosensory area. The strong connections are spatially organized, mainly connecting spatially adjacent or contralaterally homologous regions. The weak connections span longer distances and are topologically more random than the strongest connections. (C) The distance distributions for (blue) the 5% weakest edges, (red) the 5% strongest edges, and (black) a random graph of the same size and connection density. (D) The degree distributions for the weakest and strongest connections of the mouse connectome, and a comparable random graph, color-coded as in (C).</p

Network density of the cortical brain network for mouse and macaque.

<p>Shown is the percentage of fractional weights stronger than a threshold, as a function of the threshold used, separated in two panels for clarity, for (dark blue) mouse intrahemispheric, (light blue) mouse interhemispheric and (green) multi-experiment macaque intra-hemispheric connections. The thick solid lines give the density of thresholds for all connections. This distribution is wide for mouse (blue), which features both high-weight co-injection thresholds and low-weight region-specific noise thresholds. It is much narrower for macaque (green), as the threshold is 1/(number of neurons measured per experiment), which changes over only one order of magnitude between experiments. The dashed vertical lines give an estimated lower bound of the mean contribution of a single projecting neuron, for (blue) mouse and (green) macaque.</p

Schematic illustration of an experiment <i>e</i>.

<p>Tracer is injected into a volume <i>I</i>_<i>ei</i> of the source region <i>i</i>. Signal <i>O</i>_<i>ej</i> is then algorithmically segmented for each target region <i>j</i>. Signal density <i>O</i>_<i>ej</i>/<i>V</i>_<i>j</i> can be found by dividing by target region volume <i>V</i>_<i>j</i>. The connection weight is found by further normalizing by the total injection volume: <i>O</i>_<i>ej</i>/<i>V</i>_<i>j</i>/∑_<i>i</i> <i>I</i>_<i>ei</i>, a metric known as normalized connection density [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005104#pcbi.1005104.ref009" target="_blank">9</a>]. Expert curation of algorithmically segmented data identified false positives for 20 experiments, such as illustrated for region 4.</p

Variability of connection weights.

<p>Fraction of signal is shown for all pairs of regions with at least two non-zero values, for (A) the AIBS mouse dataset, (B) the Markov et al (2012) macaque dataset, and (C) the meta-analytically collated macaque dataset (CoCoMac). Each vertical line represents one region pair, pairs are ordered by average weight of their connection. A thinner band indicates less variable values. The collated macaque dataset contains values in {1, 2, 3}; points have been jittered for visualisation purposes. (D-F) <i>V</i> = the variance of weights between repeated measures of the same connections divided by the overall variance of connection weights in the data.</p

Representation of connection weights (normalized connection density, NCD) and thresholds for (left) primary visual area and (right) dorsal anterior cingulate area.

<p>Connections to (top) ipsilateral and (bottom) contralateral target regions, sorted by ipsilateral weights. (Black) Log connection weights and 95% credibility intervals (CI) are overlaid on the (red) noise threshold, due to low specificity of the automated segmentation algorithm, and the (blue) co-injection threshold, due to co-injection of several regions in one experiment. The noise threshold is identical for contralateral homologue target regions. A connection weight can only be assessed when it is stronger than the two thresholds, which is reflected in the large CIs below the thresholds. Note the absence of any estimate for the right-most connection in the top right panel; a connection cannot be assessed if the target region is co-injected with the source region for every experiment.</p

Characterisation of false positive distribution in the AIBS mouse dataset.

<p>(A-B) Distribution of all 43 × 2 × 20 algorithmically derived values for the 43 cortical areas in both hemispheres for 20 evaluated experiments, assigned as (black solid) true positive, (grey solid) false positive or (striped) non-evaluated. (A) The signal density <i>O</i>_<i>ej</i>/<i>V</i>_<i>j</i> corresponds better to the manual assignments than (B) the normalized connection density <i>O</i>_<i>ej</i>/<i>V</i>_<i>j</i>/<i>I</i>_<i>e</i> (normalized Mann-Whitney U statistics: 0.88 and 0.81). (C-D) The same distribution for the 2 × 20 values from the primary visual area. Actual values are denoted by dots. (E) Comparison of median signal density for the 43 cortical regions and their contralateral homologue, for (black) true positive and (grey) false positive values. Data points for both assignments are strongly correlated across the two hemispheres (true positive: <i>r</i> = .60, <i>P</i> < 0.001, false positive: <i>r</i> = .73, <i>P</i> < 0.001). For true positives, right hemispheric values are significantly larger than left hemispheric (binomial test, <i>p</i> < .001). This is not the case for false positives (<i>P</i> > 0.1). (F) Density values for each of the 43 regions, combining the two hemispheres, ordered on median density. Values are distinct for different regions (Kruskal-Wallis: <i>P</i> < 0.001) but not for contralateral region pairs (uncorrected Mann-Whitney: <i>P</i> > 0.1 for all but one region).</p

Brain regions activated or deactivated by working memory task performance.

<p>Anatomical locations of maximal test statistics are specified in {x,y,z} coordinates (mm) in the stereotactic system of the MNI template image and the number of supra-threshold voxels comprising the 4 most significant clusters designated as activated regions and deactivated regions.</p