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Figure 2—figure supplement 2. The ‘bistable’ level cut (top) and rotated (bottom) mask conditions used in Experiment 1. ; The level cut contours of the masks in the top row are identical to those in the top row of Figure 2—figure supplement 1, but the masks now exclude all surface regions at a nearer depth than the level cut contours. The visible gradients are bistable: they can appear as concave dents illuminated from above or convex bumps illuminated from below (or neither). The bottom row depicts rotated versions of these masks that are no longer related to surface geometry. Note that the depth of the level cut now decreases from left to right to produce images with a greater percentage of visible gradients. The top-center image is also shown in Figure 2C.

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Figure 3—figure supplement 2. Results using re-sampled orientation preference masks designed to sample from all cortical depths equally. ; In our original analysis we selected voxels that were maximally responsive to and selective for our stimuli. This included restricting ROIs to only include voxels that exhibited a significant response to the stimuli presented in the stimulus localizer. Due to stronger overall signal in superficial cortex, this restriction created a sampling bias where our masks included more voxels that maximally overlapped with the superficial depth bin compared to other bins: on average in V1, 334 voxels (SD = 97) maximally overlapped with the superficial bin, compared to 141 (SD = 43) for middle and 175 (SD = 55) for deep. Similarly, for V2 there were 331 voxels (SD = 117) for superficial, 146 (SD = 50) for middle and 189 for deep (SD = 56) and in V3 there were 379 voxels (SD = 132) for superficial, 162 (SD = 55) for middle and 177 (SD = 54) for deep. Note that the total number of voxels reported here for each visual area do not add up to the total analyzed for each visual area (1000). This is because there were also a number of voxels that fell within white matter and CSF, which helped the spatial GLM estimate responses for these depth bins that fell outside the gray matter, but responses from these depths were not analyzed further. To check our results were not dependent on this sampling bias, we conducted the following control analysis. We resampled our orientation preference masks by randomly removing voxels until there was an equal number that maximally overlapped with each gray matter depth bin. This resulted in 132 voxels (SD = 40) for each depth bin in V1, 137 (SD = 46) for each depth in V2, and 141 (SD = 45) for each depth in V3. We recomputed our analyses using these control masks that sampled evenly from all cortical depths, which revealed very similar results. The effect of attention was significant during the highlighted time points (stats, panel A). The effect of attention varied across depth (F [46, 2]=3.55, p=0.037, panel B), being larger in superficial compared to middle (t [23]=2.05, p=0.052) and deep cortex (t [23]=2.25, p=0.034). The effect of stimulus contrast was significant in the highlighted time window (stats, panel C). The effect of contrast varied significantly across depth (F [46, 2]=5.73, p=0.006), being larger in the middle depth bin compared to deep (t [23]=3.20, p=0.004, panel D). The effect of attention was significantly stronger in the agranular layers compared to stimulus contrast (t [23]=2.37, p=0.026, panel E). Overall, the results of this control analysis were similar to our main analysis. All error bars depict within-subjects standard error.

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Figure 2—figure supplement 1. The ‘convex’ level cut (top) and rotated (bottom) mask conditions used in Experiment 1. ; The masks in the top row exclude all surface regions at a greater depth than the level cut contours, and the remaining visible gradients appear vividly convex. These masks have been rotated by 180° in the bottom row, which destroys the geometric relationship between the contours and the 3D surface. The percentage of visible gradients increases with the depth of the level cut from left to right. The top-center image is also shown in Figure 2A.

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Figure 4. Cspk responses measured in the same fields of view across learning. ; (A) Example of manual identification from a field of view containing several of the same PC dendrites on day one and day N+1. Left, fields of view for day 1 (top) and day N+1 (bottom). Right, magnified view from the white box at left. Red arrows indicate an example PC dendrite identified both pre and post learning. Scale bars are 200 μm (x) x 50 μm (y). (B) Cue aligned calcium transients averaged across trials extracted from the PC dendrite identified in (A) for day 1 (top) and day N+1 (bottom). (C) Mean images taken from day 1 (left) and day N+1 (right) after independent rigid motion registration on each dataset. Scale bars are 200 μm (x) x 50 μm (y). (D) Pixel masks of PC dendrites independently extracted from the datasets in (C) (Materials and methods). (E) The post-learning data set from (C) right, was motion registered to the pre-learning dataset from (C) left. The resulting x-y pixel shifts were applied to the post-learning dendrite pixel masks from (D) right. (F,G) Following registration, the overlap between pre and post learning pixel masks was compared quantitatively (F) and graphically (G) with pre-learning masks labeled red, post-learning masks labeled green, and overlap labeled yellow. (H) Dendritic masks from (G) that had >50% overlap. (I) Summary of Cspk firing rates for individual PC dendrites in the pre (left) and post (right) reward window for all dendrites that had a > 50% overlap in their dendritic masks across learning (n = 61). (J) Summary of Cspk rates for the same individual PC dendrites in (I) measured in the post-reward window on Day one and the pre-reward window on Day N+1.

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Figure 6—figure supplement 1. Analysis of light microscopy data, LHON neuropil projections, LHON and LHLN interactions. ; (A) Schematic of the light microscopy overlap analysis pipeline. An original high-resolution image (row one) for a given cell type is segmented into its dendritic and axonal segments guided by Synaptotagmin::smHA polarity marker (row two). For each compartment of each cell type, three compartmental segmentations from different samples were added together, underwent contrast enhancement, gaussian blur and then binarized into a cell type mask for axon or dendritic domains (row three). These masks were used for the overlap calculations in the heatmaps displayed in this paper. A similar pipeline was used to generate PN, LHIN, DAN and MBON compartmental masks in Figures 7–8. See Materials and methods for full details. (B) Heatmap of percentage overlap score between masks of LHON axons and masks generated of the different neuropil regions of the fly brain. The region nomenclature used for the columns is derived from Ito et al. (2014). (C) Volume rendering of LHON axon segmentations (one for each cell type) from different brains registered to the JFRC2013 template brain. Broad coverage in the SLP is observed (yellow bracket) (D) Correlation between pairwise overlaps of LHON axons against LHON axons (x-axis) and LHON dendrites against LHON dendrites (y-axis). R2 = 0.09402, p-value

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Figure 1. Histology-informed approach for the reconstruction of the frontal limb of the ventral amygdalofugal pathway. ; (A-C, left and middle panels) Coronal sections depicting reconstructions of the frontal limb of the amygdalofugal pathway (AmF) in the macaque brain using probabilistic tractography based on diffusion MRI data (middle panels) and published reconstructions of the tract using radio-labeled fibers identified using tract-tracing injections in macaque (left panels; adapted with permission from Springer Nature (Oler et al., 2017); These panels are not available under CC-BY and are exempt from the CC-BY 4.0 license.) The AmF tractography reconstruction showed marked correspondence in anatomy and in the course of the fibers with the histological tracing data. AmF fibers leave the amygdala caudal to the anterior commissure (ac) and turn medially towards the medial wall of the hemisphere (A) and rostrally towards the frontal lobe by coursing ventrally to the ventromedial striatum (B–C). (A-C, right panels) The histology-informed reconstruction of the AmF was then used to guide the tractographic algorithm in the human brain, including when the seed, exclusion and waypoint masks were used in both species (see Materials and methods for details on the masks). The human AmF follows a course matching the results observed in the macaque brain (A-C, middle panels) with fibers projecting both medially (A) and rostrally (B–C). T1-weighted images are shown in radiological convention. ac, anterior commissure; C, caudate nucleus; GP, globus pallidus; ic, internal capsule; P, putamen.

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Figure 8. People and Place memory areas of MPC correspond to the dorsal and ventral DMN subnetworks. ; (a) Medial views of both the left and right hemispheres are shown (TT n27 surface). Overlaid onto these surfaces is the effect of Category (p=1.0−4, q = 5.8−4). Cold colors represent regions of the brain more active during the recall of people (famous and personal), whereas hot colors represent regions of the brain more active during recall of places (famous and personal). Masks of the dorsal DMN (dDMN) are outlined in black and show a striking spatial correspondence to regions more active during recall of people. In contrast, masks of the ventral DMN (vDMN) are outlined in white and correspond to regions more active during recall of places. DMN masks were taken from 38. (b) The effect of Familiarity is overlaid onto the same surfaces (p=1.0−4, q = 5.8−4). Cold colors represent regions of the brain more active during the recall of personally familiar stimuli (places and people), whereas hot colors represent regions of the brain more active during the recall of famous stimuli (places or people). Unlike in (a), regions showing a Familiarity effect overlap with regions from both the dDMN and vDMN.

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Figure 8. Identifying interactions between the innate and learned olfactory processing centers. ; (A) Heatmap of percentage overlap score between masks of LHON axons and DAN dendrites. The tracks of the rows represent the neurotransmitter, as determined by immunohistochemistry for acetylcholine, GABA and glutamate. (B) Heatmap of percentage overlap score between masks of LHON axons and MBON axons. The tracks of the rows and columns represent the neurotransmitter for LHONs and MBONs respectively. White asterisks represent cell type pairs that are illustrated with volume renderings (see below). (C’) Volume rendering showing overlap of AD1a1/f1 (green) and PAM-β1 (magenta) (C’’) Overlap between PV6a1 and MBON-α1 (C’’’) PV5a1 and MBON-β2β′2a. All of C’-C’’’ are expression patterns from different brains registered to the JFRC2013 template brain. (D) EM reconstructions of AD1b2 (green) and MBONs (magenta), illustrating synaptic connectivity from MBON-α1, MBON-γ5β′2a, MBON-α′two and MBON-γ2α′one onto AD1b2. (D’) MBON-α1 forms synapses on AD1b2 dendrites which are outside of the LH. (D’’–D’’’’) MBON-γ5β′2a, MBON-α′two and MBON-γ2α form axoaxonic synapses onto AD1b2 in the SMP. The LH and MB volumes are labelled in orange and light grey respectively. Red spheres represent synapses from MBON onto AD1b2. Black circle highlights region of synapses. (D’’) Reconstruction of seven AD1b2 neurons (green) and MBON-γ2α1 (black) in the EM volume. The LH and MB volumes are labelled in red and light grey respectively. Red spheres represent synapses from MBON-γ2α1 onto the AD1b2 axonal compartment. (E) Heatmap of synaptic connectivity from MBONs onto each AD1b2 neuron revealing the variability of connectivity across individual AD1b2 neurons. Green dendrogram is a morphological clustering of individual AD1b2 neurons by NBLAST. (F) Cartoon summary of dendritic and axoaxonal connectivity from MBONs onto AD1b2 axons. Note that all MBONs also have other currently unknown downstream synaptic partners .

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Figure 7. Behavior-related calcium signals are organized in a distance-dependent manner within the dendritic tree. ; (A) Estimation of pairwise signal correlation after bAP subtraction for two example spines (denoted in (B)). Trial average responses for each epoch and trial type are calculated for each mask from non-overlapping trials (to exclude noise correlations). Signal correlation is the Pearson correlation coefficient between these sets of responses. (B) Pairwise signal correlation of three spines (green dots) with all other masks in an example session (same session as shown in Figure 4C). (C) Pairwise signal correlation as a function of traversal distance through the dendrite (see Materials and methods for binning; L2/3 dendrite: N = 22688 pairs, 1533 segments; L5 dendrite: N = 7468 pairs, 432 segments; L2/3 spines: N = 45993 pairs, 2058 spines; L5 spines: N = 2783 pairs, 285 spines). Shaded regions are ± SEM (see Materials and methods). Magenta point is the mean pairwise correlation of masks with Euclidean distance < 15 μm and traversal distance > 30 μm. Only masks with significant (p < 0.01) task-associated selectivity were included. p-values from nonparametric comparison to shuffle. λ is the mean length constant ± SEM (D) Estimation of pairwise noise correlation for two example spines (denoted in (B)). Each panel is an example epoch denoted by the colored text in the upper left. Each black point is a trial. Noise correlation for a pair is the mean of the correlations calculated across all epochs. (E) Same as (B), but for noise correlation. (F) Same as (C), but for noise correlation. All N same as in (C).

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Figure 6. Inter- and intra-LH interactions. ; (A) Volume renderings of fly brain (grey) with the SLP (magenta), SIP (green) and SMP (cyan) labelled. The LH in each panel is labelled in red. The brown inset is the region displayed in C and E. (B) Heatmap of percentage overlap score (white to black) between masks of LHON axons against themselves. Scores between the same LHON axon and itself are set from 100 to −1 for clarity. The tracks of the rows and columns both represent the neurotransmitter, as determined by immunohistochemistry for acetylcholine, GABA and glutamate. Two main clusters of axons emerged from the analysis (orange and blue bound). (C’) Volume rendering of AV4b8 (green) and AV6b1 (magenta), both are expression patterns from different brains registered to the JFRC2013 template brain. The orange dashed circle shows the axonal projections of these two cell types which coclustered in the heatmap orange box. (C’’) Volume rendering of PV4d1 (green) and PV5c1 (magenta), both are expression patterns from different brains registered to the JFRC2013 template brain. The blue dashed circle shows the axonal projections of these two cell types which coclustered in the heatmap blue box. (D) Heatmap of percentage overlap score (white to black) between masks of LHON dendrites against the neurites of LHLNs. The tracks of the rows and columns both represent the neurotransmitter, for LHLNs and LHONs respectively (see Figure 6B for neurotransmitter colour code). (E’) Volume rendering of PV5a1 LHON (green) and AV4a1 LHLN (magenta), both are expression patterns from different brains registered to the JFRC2013 template brain. (E’’) Volume rendering of AV7a1 LHON (green) and AV4a1 LHLN (magenta), both are expression patterns from different brains registered to the JFRC2013 template brain.

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