Journal: eLife

Article Title: Cell type-specific connectome predicts distributed working memory activity in the mouse brain

doi: 10.7554/eLife.85442

Figure Lengend Snippet: ( A ) Flattened view of mouse cortical areas. Figure adapted from . ( B ) Normalized parvalbumin (PV) cell fraction for each brain area, visualized on a 3D surface of the mouse brain. Five areas are highlighted: VISp, primary somatosensory area, barrel field (SSp-bfd), primary motor (MOp), MOs, and PL. ( C ) The PV cell fraction for each cortical area, ordered. Each area belongs to one of five modules, shown in color . ( D ) Hierarchical position for each area on a 3D brain surface. Five areas are highlighted as in ( B ), and color represents the hierarchy position. ( E ) Hierarchical positions for each cortical area. The hierarchical position is normalized and the hierarchical position of VISp is set to be 0. As in ( C ), the colors represent the module that an area belongs to. ( F ) Correlation between PV cell fraction and hierarchy (Pearson correlation coefficient r = –0.35, p<0.05).

Article Snippet: Firing rate, PV cell fraction, and hierarchy are plotted on a 3D brain surface using the website Scalable Brain Atlas ( https://scalablebrainatlas.incf.org/index.php ).

Techniques:

Journal: eLife

Article Title: Cell type-specific connectome predicts distributed working memory activity in the mouse brain

doi: 10.7554/eLife.85442

Figure Lengend Snippet: ( A–C ) Example attractor patterns with a fixed parameter set. Each attractor pattern can be reached via different external input patterns applied to the brain network. Delay activity is shown on a 3D brain surface. Color represents the firing rate of each area. ( D, E ) The distribution of attractor fractions (left) and number of attractors as a function of size (right) for different parameter combinations are shown. Attractor fraction of an area is the ratio between the number of attractors that include the area and the total number of identified attractors. In ( D ), local excitatory strengths are fixed ( g E , s e l f =0.44 nA) while long-range connection strengths vary in the range μ E E = 0.01–0.05 nA. Left and right panels of ( D ) show one specific parameter μ E E = 0.03 nA. Inset panel of ( D ) shows the number of attractors under different long-range connection strengths while g E , s e l f is fixed at 0.44 nA. In ( E ), long-range connection strengths are fixed ( μ E E =0.02 nA) while local excitatory strengths varies in the range g E , s e l f = 0.4–0.44 nA. Left and right panels of ( E ) show one specific parameter g E , s e l f = 0.43 nA. Inset panel of ( E ) shows the number of attractors under different local excitatory strengths, while μ E E is fixed at 0.02 nA. ( F ) Prediction of the delay-period firing rate using input strength and cell type-specific input strength for each attractor state identified under μ E E = 0.04 nA and g E , s e l f = 0.44 nA. A total of 143 distinct attractors were identified and the average correlation coefficient using cell type-specific input strength is better than that using input strength. ( G ) A example attractor state identified under the parameter regime μ E E = 0.03 nA and g E , s e l f = 0.44 nA. The five areas with persistent activity are shown in red. ( H ) Effect of single area inhibition analysis for the attractor state in ( G ). For a regime where five areas exhibit persistent activity during the delay period, inactivation of the premotor area MOs yields a strong inhibition effect (<0.95 orange dashed line) and is therefore a core area for the attractor state in ( G ). ( I ) Cell type-specific loop strength (blue) is plotted alongside core areas (orange) for the attractor state in ( G ). Only five areas with persistent activity are used to calculate the loop strength. Loop strength is normalized to be within the range of 0 and 1. High cell type-specific loop measures predict that an area is a Core area (prediction accuracy is 100% correct). The number of areas is limited, so prediction accuracy is very high.

Article Snippet: Firing rate, PV cell fraction, and hierarchy are plotted on a 3D brain surface using the website Scalable Brain Atlas ( https://scalablebrainatlas.incf.org/index.php ).

Techniques: Activity Assay, Inhibition

Journal: eLife

Article Title: Cell type-specific connectome predicts distributed working memory activity in the mouse brain

doi: 10.7554/eLife.85442

Figure Lengend Snippet: ( A ) Model design of the large-scale model for distributed working memory. Top: connectivity map of the cortical network. Each node corresponds to a cortical area and an edge is a connection, where the thickness of the edge represents the strength of the connection. Only strong connections are shown (without directionality for the sake of clarity). Bottom: local and long-range circuit design. Each local circuit contains two excitatory populations (red), each selective to a particular stimulus and one inhibitory population (blue). Long-range connections are scaled by mesoscopic connectivity strength and follows counterstream inhibitory bias (CIB) . ( B ) The activity of six selected areas during a working memory task is shown. A visual input of 500 ms is applied to area VISp, which propagates to the rest of the large-scale network. ( C ) Delay-period firing rate for each area on a 3D brain surface. Similar to , the positions of five areas are labeled. ( D ) Delay-period firing rate is positively correlated with cortical hierarchy ( r = 0.91, p<0.05). ( E ) Delay-period firing rate is negatively correlated with PV cell fraction ( r = –0.43, p<0.05).

Article Snippet: Firing rate, PV cell fraction, and hierarchy are plotted on a 3D brain surface using the website Scalable Brain Atlas ( https://scalablebrainatlas.incf.org/index.php ).

Techniques: Activity Assay, Labeling