Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images
Authors: Gabriel Herbert Sarch
Michael J. Tarr
Katerina Fragkiadaki
Leila Wehbe
Venue: NeurIPS 2023 poster (2023)
Keywords: Computational Neuroscience
Deep Neural Networks
Visual Neuroscience
Visual Streams
Scene Perception
Brain Imaging
TL;DR: Using fMRI response-optimized networks with explainable AI, we examine intermediate properties, including depth, surface normals, curvature, and object relations and find selectivity differences across the visual streams and scene areas.

Abstract: The alignment between deep neural network (DNN) features and cortical responses currently provides the most accurate quantitative explanation for higher visual areas. At the same time, these model features have been critiqued as uninterpretable explanations, trading one black box (the human brain) for another (a neural network). In this paper, we train networks to directly predict, from scratch, brain responses to images from a large-scale dataset of natural scenes (Allen et. al., 2021). We then use "network dissection" (Bau et. al., 2017), an explainable AI technique used for enhancing neural network interpretability by identifying and localizing the most significant features in images for individual units of a trained network, and which has been used to study category selectivity in the human brain (Khosla & Wehbe, 2022). We adapt this approach to create a hypothesis-neutral model that is then used to explore the tuning properties of specific visual regions beyond category selectivity, which we call "brain dissection". We use brain dissection to examine a range of ecologically important, intermediate properties, including depth, surface normals, curvature, and object relations across sub-regions of the parietal, lateral, and ventral visual streams, and scene-selective regions. Our findings reveal distinct preferences in brain regions for interpreting visual scenes, with ventro-lateral areas favoring closer and curvier features, medial and parietal areas opting for more varied and flatter 3D elements, and the parietal region uniquely preferring spatial relations. Scene-selective regions exhibit varied preferences, as the retrosplenial complex prefers distant and outdoor features, while the occipital and parahippocampal place areas favor proximity, verticality, and in the case of the OPA, indoor elements. Such findings show the potential of using explainable AI to uncover spatial feature selectivity across the visual cortex, contributing to a deeper, more fine-grained understanding of the functional characteristics of human visual cortex when viewing natural scenes.

Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images
Gabriel H. Sarch
Michael Tarr
Katerina Fragkiadaki"
Leila Wehbe"
Carnegie Mellon University
(gsarch,mtol wtw.:mtttttnttao..nomommm cmu edu, kateffcs cmu.edu, 1vehbe@cmu.edu
*Equal advising
brain-ddiseccioongiitbb.
Abstract
The alignment between deep neural network (DNN) features and cortical responses currently provides the most accurate quantitative explanation for higher visual At the same time, these model features have been critiqued uninterpretable explanations, trading one black box (the human brain) for another (a neural network). In this paper, train networks to directly predict, from scratch, brain responses to images from a large-scale dataset of natural scenes [5]. We then use "network dissection" (6), explainable Al technique used for enhancing neural network interpretability by identifying and localizing the most significant features in images for individual units of trained network, and which has been used study category selectivity in the human brain 77. We adapt this approach to create hypothesis-neutral model that is then used explore the tuning properties of specific visual regions beyond category selectivity, which we call "brain dissection". We use brain dissection to examine a range of ecologically important, intermediate properties, including depth, surface normals, curvature, and object relations across sub-regions of the parietal, lateral, and ventral visual streams, and ecene-selective regions. Our findings reveal distinct preferences in brain regions for interpreting visual scenes, with ventro-lateral areas favoring closer and curvier features, medial and parietal areas opting for more varied and flatter 3D elements, and the parietal region uniquely preferring spatial relations. Scene-selective regions exhibit varied preferences, the retrosplenial complex prefers distant and outdoor features, while the occipital and parahippocampal place areas favor proximity, verticality, and in the case of the OPA, indoor elements. Such findings show the potential using explainable AI to uncover spatial feature selectivity across the visual cortex, contributing to deeper, more fine- ine-grained understanding of the functional characteristics human visual cortex when viewing natural scenes.
areas [ 3 4
Introduction
Humans possess an extraordinary ability to process and interpret visual information; however, our understanding of how such information is represented and processed within the brain remains incomplete. While advances in modeling visual cortex have been made through the use of abstract stimuli and the study of evoked neuronal responses, these efforts have primarily focused on low-level visual features [8] or high-level categorical selectivity [Р]. In reality, the brain utilizes numerous coologically-reevaan intermediate features to perceive the world, such 3D information, physical relationships, and object properties (10
37th Conference on Neural Information Processing Systems (NcurIPS 2023).

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Characterizing high-level areas in the visual cortex presents challenges due to the circular problem of finding optimal stimuli. Although deductive [ T3 [3] [6] and inductive [7 I9 20
approaches have identified wide range of pnactionallyydefiine
brain regions, these methods are not scalable for exploring a wider range brain areas and diverse features. To address these limitations, we leverage "network dissection" [6]. method that enables exploring the feature selectivity of neural network units at scale. Combined with brain response-opimmized networks trained on fMRI data for subjects viewing natural images, this method offers hypothesis-neutra approach to uncovering feature tuning properties across the visual system 7 21. While Khosla and Wehbe 7 demonstrate the effectiveness of network dissection for validating known category selectivities, here report that this combined approach, brain dissection, can be extended to examine more granular features, such depth, curvature, and object relations.
We apply brain dissection across the visual cortex to uncover functional differences between regions. In particular, the human visual system is believed to be organized into distinctive processing streams: the parietal, lateral, and ventral visual streams. While previous experimental and modeling work has made progress in characterizing each of these streams [22] 23 24 25 26 at fine-grained level, their distinct roles visual processing remain under-specified. Functional networks within these streams likewise remain functionally under-specified. For example, precise differences among the three components of the "scene network", the occipital place area (OPA), parahippocampal place area (PPA), and the retrosplenial complex (RSC) [27] remain unclear for naturalistic. large-scale scale viewing of scenes with prior work providing primarily broad functional characterizations in terms
of the representation of scene layout 28 29 30 3D features (20 31 32 33), scene objects [[] 7
and navigational affordances
35
Utilizing pypothesis-neutrry approach with brain response optimization, we demonstrate feature selectivity differences across multiple visual streams and scene regions, encompassing wide variety of highly specific feature properties. Importantly, our findings based on subjects viewing natural images and not require separate experiment each category or visual feature.
Training
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Figure 1: A. IMRI Response- Response-Optimized Training Procedure. All voxels in an ROI share a convolutional backbone. Each voxel is assigned a set of learnable spatial and feature weights. The voxel weights applied to the output features of the backbone network to predict the voxel response for input image. Training minimizes the mean squared error between
predicted and actual voxel response NSD images. B. Dissection Procedure. For each voxel an ROI and test image, we obtain the voxel's selectivity binary mask by applying the voxel's feature weights to the output of the ROI-specific backbone and thresholding the resultant feature map. We examine the overlapping region between the mask and measurement of interest (e.g.. depth map).

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2
Brain Dissection
Brain dissection provides a hypothesss-neutral approach for identifying the most significant image features for predicting the response of specific voxel. The method trains a convolutional neural network tailored predict voxel responses within a defined sub-region. By training a backbone network for this sub-region and incorporating a linear readout for each voxel, the network learns to extract image features crucial for predicting each voxel (Fig. [T]. After training, we "dissect" the network by inputting held-out images and extracting regions that the network considers most relevant for each voxel (Fig. BB). Subsequently, we analyze properties of the voxel-selective regions within
the images. Our training and dissection procedure closely follows Khosla and Wehbe [7].
2.1 IMRI Response-Optimized Network Training
We trained set of neural network encoding models predict fMRI voxel responses to NSD images (Fig. [T]. Visual cortex was divided into sub-regions based on the Human Connectome Project (HCP) atlas [36]. with a separate convolutional neural network (CNN) backbone shared across all voxels within each sub-region. For each voxel in a sub-region, linear readout was applied to the backbone output features to predict individual voxel responses to the images. The linear readout was factorized into voxel-specific spatial and feature dimensions, correspond to the most responsive locations of a voxel an image, while feature weights represent the visual information to which voxel is most responsive.
described by Klindt et al. (37]. Spatial weights
Given the activations of the last layer of the ROI-specific backbone network (A E R the spatial weights for a voxel in the ROI (w, E IR "x=""" ) were multiplied channel-wise to A. The feature weights (wy E R) were applied to each spatial position of A. The output is voxel-specific feature map, which was summed and added to a learned bias to obtain the predicted voxel response. The network was trained using mean-squared error with the true voxel response. We trained each network using NSD) data from 8 subjects, including 68, 400 training images, and 3,600 validation images. We show predictivity performance of our networks in Appendix Section Se.2
While we use CNN for all the analysis in the main paper, we also report results for the scene regions using a transformer architecture, and attention heads for the voxel-specific feature maps with consistent results in the Appendix Section S3.1
2.2 Dissection Procedure
During dissection (see Fig. B]), the spatial weights were discarded and only the feature weights used to focus the analysis on visual selectivity for each voxel. For each voxel k; we obtained its corresponding ROI-specific backbone network froi (with 1 roi) and voxel-specifie feature weights w/ E C For every imput image æ in the evaluation dataset, we passed the image through froe to obtain activations A. The feature weights (w E C were then applied to each spatial position of A using a Ix convolution. The output is a voxel-specific feature map As E RR The top quantile level T3 is determined such that P(A Tx) 0.01 over every spatial location of the activation map.
To compare low-resolution voxel activation map to the input-resolution annotation Le for some concept or measure e (c.g., L. could be a category mask, depth map. surface normal map, etc.), the activation map is scaled up to the mask resolution S₁ from A& using bilinear interpolation. Sş is then thresholded into binary segmentation: M₁ = S Z Tk, selecting all regions for which the activation exceeds the threshold Tμ-
As in Bau et al. [6]. to score each voxel k for a discrete category concept e, we compute the date-set-wide intersection over union (IOU) score:
Σx [Ma(z)) (x)
10Uk,
(I)
Ex Ma(e) U (x)
To score the selectivity of each voxel against a continuous concept (e.g., depth), we compute the median value within the region defined by My, and take the average median across all images as the selective value for that voxel:
V2 Ex median (z) (M)))))
(2)
Σ X
3

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where (xx represents the values in (z) defined by the mask (z)...
We exclude voxels that lack selectivity across all evaluation images by comparing the distribution of I__ masked by M_k against a uniformly distributed (full image) mask. This exclusion used the Kolmogorov-Smingg Smiinov test, with significance threshold of p>0.01.
While we present results using the Network Dissection interpretability method [6] throughout the main paper, we demonstrate in Appendix Section [S3.1 the feasibility of using other interpretability techniques. These include gradCAM (38) and transformer raw attention consistent results for the scene regions.
[9], with which we obtain
3 Experiments
3.1 Brain Data
IMRI dataset. We used the Natural Scenes Dataset (NSD) (5]. which consists of high-resolution fMRI responses to naturalistic images from Microsoft COCO [40]. NSD contains 7T iMRI re- sponses (1.8 mm, 1.6 from 8 subjects who each viewed 1,0000-100000 distinct color natural scenes (22,000-30,000 trials). Subjects fixated centrally and performed a long-term continuous image recognition task. The noise ceiling (NC) was estimated in each voxel as described in Allen et al. 5 We only include voxels with NC 2 10% variance. Notably, the dataset contains complex and sometimes crowded images of various everyday objects in their natural contexts at varied viewpoints. The stimulus set is thus more typical of real-world vision and allows us to characterize neural representations and computations under more ecological conditions.
Regions of Interest (ROIs). We used the "streams" anatomical atlas from NSD to define three ROIs that cover the parictal, lateral, and ventral visual streams (mid- and high-level ROIs were grouped). We then split each of the ROIs into sub-regions using the HCP atlas, including any ROI in the HCP atlas with at least 50% voxel overlap with any of the streams. We added fourth ROI, which we call "Medial", and took all HCP regions overlapping with RSC. We further split the "Ventral" ROI into "Ventrolateral" and "Ventromedial", where "Ventromedial" included HCP regions overlapping with PPA. A separate network was trained for each of the HCP sub-regions. We also examined three scene ROIs RSC, OPA, and PPA defined by the category functional localizer.
3.2 Measures
Unless otherwise specified, we evaluate on the Places365 dataset 41 and use the top 1000 images per sub-region per subject as determined by the predicted responses the images by the sub-region network. Thus, the evaluation takes into account: 1) the sop-most-activatingg images for region; 2) the areas of the image that most contribute to the response as determined by network dissection. We perform brain dissection on the following measures:
Depth. We analyze the selectivity of depth, which refers the perceived distance between the viewer and different regions of the scene. To obtain depth maps for each evaluation image in Places365, we the ZeeDepth metric depth estimation network [42] which has been shown exhibit exceptional zero-shot generalization performance on indoor and outdoor scenes. We evaluate two measures of depth: 1) metric depth, which represents the absolute depth in meters for each pixel in the image, and 2) relative depth, which denotes depth maps normalized to a range of 0-1 for each image. Consequently, absolute depth captures the overall nearness or farness across the images, while relative depth focuses on the nearness or farness within an individual image.
Surface Normals. Surface normals, vectors perpendicular to surfaces, are essential for spatial cognition, as they indicate surface orientation and reflect the spatial coordinate system. Using the XTC network [43, estimate normals for each Places365 image. The x, y, and components segmented into 512 bins (8 per component: ga = 512 combinations), and voxel selectivity is reported the bin number.
3D Curvature. Curvature features provide information about structure and are invariant under rigid transformations. We use the XTC network (43) for estimating principal curvatures.

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Shading, One way to infer scene geometry is "shape from shading" 444 45 using the intrinsic image decomposition I AS, where S is shading function parameterized by lighting and depth. We use the XTC network
43
for estimating shading.
Segment Anything
46
used to obtain segmentation masks from the bounding boxes for each of
the annotations.
4 Results
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Figure 2: A. Depth, surface normals, Gaussian curvature, and shading flatmaps colored by sclectivity per voxel for S1 (large), S2, and S5 (maps for the remaining five subjects are in supplemental). B. Enlarged portions flatmaps from A. Medial areas show depth preference for far-reaching depths (top left). There is gradient from near to far depths from lateral to medial ventral (bottom left). Parietal and Medial areas show clusters of "upright" and "right/left" surface normals, while Lateral areas prefer "camera facing" surface normals (right).
We employed brain dissection to investigate the scene features represented throughout visual cortex. Our investigation focused on the Ventromedial, Ventrolateral, Lateral, Parietal, and Medial regions, well the scene network PPA. OPA, and RSC. Our initial examination centered on identifying and quantifying the differences between these regions based on four spatial measures, namely, depth, surface normals, 3D eurvature, and shading. These measures were chosen cover range of hallooically--reevvae spatial features. Following our spatial analysis. we explored how these identified differences are reflected in the processing of object categories, attributes. and spatial relations. This aspect of our study sheds light on the relationship between the observed variations in spatial features and the resultant impact on higher-order visual processing specifically how we perceive and interpret objects and their relationships within a given scene. Finally, we quantified the spatial clustering of voxels for each measure better understand the spatial organization for different visual features.
4.1
3D Spatial Dissection
4.1.1
Brain Flatmaps
To gain better understanding how 3D structure is represented in the brain, we used brain dissection to quantify the selectivity for each voxel across the high-level visual cortex for the four
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spatial measures. This process allowed us to create a detailed selectivity map for each voxel that reveals how 2D inputs are transformed into 3D representations that enable reasoning about the physical world. Figure 2 displays these selectivity maps on a cortical flatmap of the brain. We observe that Lateral areas demonstrate selectivity for close distances, aligning with the overlap of Lateral ROIs with regions selective for bodies, frequently featured in the foreground of images. Notably, we observe that the depth selectivity for the Lateral and Ventrolateral ROIs are similar, and stand in contrast with the rest of the ROIs This pattern is also true for the other spatial metrics (Surface Normals, Gaussian Curvature and Shading) we study, where the Lateral and Ventrolateral appear as a distinct cluster from the Ventromedial, Parietal and Medial ROIs. In contrast, we observed high selectivity for far distances in the Medial and Ventromedial areas, suggesting specialized role for behaviors that require processing and understanding far- far-reaching aspects of visual scene (e.g.. navigation). Additionally, we observed a strong gradient in the selectivity for depth in Ventral areas. The gradient transitioned from near-preferring depths in the Ventrolateral ROI to far-preferring depths in the Ventromedial ROI. Such a gradient is likely to be important for building up scene representations that capture wide variety of behaviorally relevant affordances. Finally, we observed distinct patterns related to the processing of surface normals. Large areas of the Lateral and Ventrolateral regions showed a preference for surface normals in the negative X direction, pointing back at the camera (again, consistent with the preference of these areas for bodies and objects). In contrast, the Parietal and Medial areas predominantly preferred surface normals oriented in the positive direction (pointing upwards) and along the Y direction (pointing right and left).
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Figure 3: Mean + standard deviation (NV subjects) for each of the high-level visual ROIs.
== significantly
different from all other ROIs, significantly different from
one other ROI, significantly different
from all but two other ROIs (p < 0.01: post hoc Tukey test; N = 8 subjects).
4.1.2 Spatial Dissection of High-level Visual Areas
We quantify the differences between the high-level visual ROIs in terms of mean + standard deviation for four 3D spatial measures: depth (metric & relative), surface normals, shading, and Gaussian curvature (Fig. 3). We observe differences in depth preferences between the Medial and Ventrolateral ROIs. Medial and Ventromedial areas exhibit significantly larger absolute and relative depth selectivity compared to the Ventrolateral and Lateral regions (p < 0.01; post hoc Tukey test; N = 8). Conversely, Ventrolateral and Lateral areas have significantly lower average absolute depths than the Medial and Ventromedial areas, as depicted in Figure B (p < 0.01: post hoc Tukey test; N = 8). This disparity highlights the specialized roles these regions play in processing natural images across varying depth distributions, catering to both nearby and distant objects. Concerning the processing of surface normals, we observe that the Ventromedial region significantly prefers normals indicative of surfaces pointing in the "up" direction (p < 0.01; post hoc Tukey test; N = 8). Parietal and Medial areas show a stronger preference for surface normals oriented the left and right compared to the other ROIS (p < 0.01; post hoc Tukey test; N = 8). In contrast, the Ventrolateral and Lateral regions have a preference for surface normals pointing back at the camera (p- 0.01; post hoc Tukey test; N 8).
Examining the shading preferences across the ROIs, we again notice a clear distinction. Ventrolateral areas prefer the lightest shading, while Medial and Ventromedial areas prefer the darkest shading (p < 0.01; post hoc Tukey test; N = 8). This observation reaffirms the notion of functional segregation in the visual cortex, with different regions specializing in processing specific visual attributes, in the case of shading, perhaps reflecting additional cues to scene depth.
I Lastly, we consider Gaussian curvature, a measure that captures the degree of 3D curvature of surface. Higher Gaussian curvature implies a more spherical geometry. Ventrolateral and I Lateral areas significantly prefer higher Gaussian curvature (Fig. 3 indicating a preference for more spherical

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surfaces (p < 0.01; post hoc Tukey test; N 8). In contrast, Parietal and Ventromedial areas prefer lower Gaussian curvature, suggesting preference for flatter surfaces (p 0.01; post hoc Tukey test; N = 8). Ventrolateral and Lateral areas may be encoding spatial features relevant to object appearance for both recognition and grasping, while Parietal and Ventromedial areas may encoding scene layout in terms of large-scale surfaces and scene elements. Such findings add another layer to our understanding how different areas of the visual cortex process and interpret 3D scene structure.
4.1.3 Spatial Dissection of Scene Areas
In this section, we delve into the differences between the scene ROIs (PPA, OPA, RSC) with respect to the four 3D spatial measures, providing quantified analysis of their specialized roles in scene perception. We illustrate the voxel-selectivity on brain flatmaps (Fig. 4 and plot the mean + standard deviation (N 8) for each of the each ROIs and measures (Fig. 4). These distributions
and mappings elucidate the distinct patterns of selectivity across the scene ROIs.
There is considerable increase in both metric and relative depth for RSC compared to both PPA and OPA, as evidenced in Figure 43 (p < 0,001; post hoc Tukey test; N = 8). This pattern indicates that RSC in processing far-reaching information in scenes, consistent with the claim that RSC is preferentially involved in navigation 47 Additionally, we observe a distinct shift in surface normal preference, with RSC showing a preference for more right/left surface normal bins compared to OPA and PPA, which favor upright surface normals (p < 0.001; post hoc Tukey test; = 8). This distinction underscores the functional segregation between these scene ROIs in interpreting surface orientation and suggest, again, that RSC may represent information supporting navigation, while OPA and PPA may represent information
relevant interacting within local region of space.
To illustrate these observations, we identified voxels that have the closest selectivity to the ROI mean for depth surface normals for PPA, OPA, and RSC, and plotted the voxel's top five images by predicted response (Fig.E) 5). We include corresponding dissection masks below these on images and either depth surface normal images. RSC exhibits preference for far depths and more "right/left" surface normals, indicative of vertical structures relevant to navigation. In contrast, OPA and PPA exhibit preference for "up" surface normals which are relevant to representing the local scene layout. Once more, these results highlight the distinct functional roles these areas play in scene perception.
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Figure 4: A. Flatmaps showing voxel-preferences in OPA, PPA, and RSC for the four spatial measures. B.
Mean + standard deviation (N subjects) for each of the high-level visual ROIs. = < 0.001, **
p < 0.01, = =p< < 0.05, n.s. significant p 0.05 (post hoc Tukey test; subjects).
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PPN
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RSC Metricc Depth)
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(Suriaae Normals)
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Voxels that have the closest selectivity (by euclidean distance) the ROI mean of depth (top) or surface normals (bottom) for PPA, OPA, and RSC. An example image is shown with a representative voxel (left) and the top five images by predicted response for the top four voxels (right) for each ROL. Dissection masks are visualized on the images and either the depth or surface normal images. RSC prefers far-reaching depth and vertical structures, while OPA and PPA prefers more local depths and a mix of vertical and horizontal structures.
Figure 5:
OPA
RSC
Ventromedial
Ventrolateral
Lateral
Parietal
Medial
Medion
Figure 6: Median IOU (mean 1 standard deviation; N = 8 subjects) for the GQA dataset 48 for each of the scene ROIs (lop) and high-level visual ROIs (bottom).
4.2 Object Relations, Attributes and Categories
We utilize the GQA dataset (48) in concert with brain dissection to gain insights into the selectivity differences for object relationship, attributes, and categories within an image. The median IOU (mean + standard deviation across 8 subjects) for the top 20 concepts for each ROI is reported in Figure 6 With respect to high-level visual ROIs, we observe distinct preferences: Parietal areas prefer inanimate and scene objects, such as walls and tables, that are more relevant to both navigation and physically interacting with objects. In contrast, Lateral areas exhibit a preference for person and animate objects, while Ventrolateral areas favor persons and objects representations that support social interactions and scene understanding. When examining object relations, Parietal areas showed a strong preference for spatial relations such as 'in front of,' 'near,' 'in,' and 'above'. Lateral areas favor relations involving people, such as 'wearing," 'holding,' and 'with'. In terms of attributes, Parictal areas show a preference for colors and materials, whereas Lateral areas favor animate attributes involving people, such as 'playing." "standing.' and 'happy". . These preferences are largely aligned with prior results showing Parietal preferentially processes spatial visual information 22] Lateral preferentially processes social visual information [24, and Ventral preferentially processes objects
and faces 24]
As illustrated in Figure within scene ROIS, OPA shows greater selectivity for indoor scene elements, such as chairs, tables, and floors. PPA exhibits mix of indoor and outdoor preferences, whereas RSC shows a clear preference for outdoor scene elements like trees, buildings, and the sky, once again reinforcing dichotomy between local space interactions and navigation. Furthermore, when
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considering attributes. RSC again demonstrates more selectivity for "outdoor" attributes compared PPA and OPA, which show higher selectivity for indoor elements. These findings underscore the distinct functional implications of these ROI selectivity profiles for processing visual scenes.
4.3 Topographic Organization of the Spatial Preferences
We investigated the topographical organization of the spatial measures within each ROI. High-level visual areas are topographically organized, comprising multiple hierarchically organized areas that are selective for specific domains such as faces and scenes [49 50 12 13 14
[5 16 [1]. However, whether and how this topographical organization applies to the spatial measures used here remains unclear. Given a specific type of spatial information, we explored: (a) if the voxels in an ROI are differently tuned for this type of information (e.g., sub-groups of voxels are tuned for different surface normal orientations); and if so, (b) does the tuning follow a smooth topographical organization with relatively large clusters processing similar information. To quantify the degree of clustering by selectivity similarity, for each measure we computed the average cluster proportion (cluster size normalized by total voxels in the ROI) (considering clusters > 5 voxels) that each voxel belongs to, and report the results compared to random permutation of the voxels (Fig. 7). We employed connected components 52 on the 3D voxel data to determine whether two neighboring clusters belong to the same cluster. For continuous measures, such as depth, we consider two voxels the same they fall within 5 of each other. We used 1/100th of the data range across all ROIs as the 5 for each of the continuous measures. For discrete concepts, such as object category, we consider two neighbors to be in the same cluster if they belong to the same discrete category.
A cluster analysis was run for each ROI. We report the average cluster size for given measure compared to the average cluster size for the measure when the voxels are randomly permuted ((Fig. 7 < 0.05 paired t-test; N = 8)). We visualize the spatial clusters on flatmaps: All of the high level visual areas showed clustering significantly different from random for all spatial measure, indicating that their voxels are tuned for different values of each measure in a way that is topographically organized. At the level of the scene areas however, RSC was not distinguishable from chance for metric depth, relative depth, shading, gaussian curvature and category. This suggests that except for surface normals, the voxels in RSC are tuned similarly. Similarly, the results suggest that the voxels in PPA are tuned differently only for metric depth, and the voxels in OPA are tuned differently for metric depth, relative depth and surface normals. These findings highlight the robust topographical organization within the ROIs, extending beyond traditional domain-specifie areas. Critically, this extended organization highlights the fact that, as a whole, visual cortex is representing a broad range of ecologically relevant properties that support complex behavior 5
Paratall Clastes (eeti Dechhi
Parets Clusters (Surface Normak)
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Figure 7: A. Metric depth (left) and surface normals (right) spatial clusters on flatmaps for example subject
S1, where each color is different cluster with (bottom) and without (top) random spatial permutations. B. Bar plots showing mean + standard deviation of average cluster size per voxel for each ROI. =p < 0.001, p < 0.01, p < 0.05, n.s. significant p > 0.05 (paired t-test; N 8).
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5 Conclusion Discussion
clearer understanding of representational progression in natural vision requires developing hypothesis-neunt neutral models and interpretable methods. We leverage advances in quantifying fea- ture selectivity of artificial neural networks [6], merged with response-optiminn fMRI data 77, to better characterize spatial properties of broad range of visual brain regions. This method, "brain dissection," " enables granular examination of feature preferences within multiple visual processing streams and functional sub-regions like the scene network. Contrary to earlier studies [7], our work focuses on characterizing visual sub-region preferences in terms of spatial properties and high-level categories (e.g., faces, places). Dissecting spatial properties provides complementary perspectives on visual processing: relationships, orientations, positions of objects, and surfaces within scene in terms of spatial features (see also [10).
models trained on
We observed specific preferences in regions like the RSC, OPA, and PPA. The RSC demonstrated a pronounced preference for greater depths, outdoor object categories, attributes, relations, and predominantly "right/left" surface normals. In contrast, the OPA exhibited preference for proximate depths, intricate 3D geometries, indoor scene object categories, relations, attributes, and higher inclination towards "upward" surface normals. These findings align with literature suggesting OPA primed for local navigational affordances 34, while RSC is geared towards facilitating landmark- based patial-memory retrieval (55) The PPA preferences spanned a middle ground between OPA and RSC, slightly leaning towards OPA, supporting its role in encoding scene structure at coarser scale than OPA [34]. A salient finding is RSC's pronounced selectivity for vertical surface normals, contrasting with PPA and OPA's preference for horizontal supporting structures, like tabletops and floors, highlighting PPA'S sensitivity to scene layout and surface arrangement [56].
Further distinguishing between high-level visual ROIs, our study revealed gradients from ventro- lateral medial areas. Ventro-lateral regions exhibited preference for closer depths, predominantly horizontal surface normals, and darker shading, while medial areas showed opposite preferences, aligning with distinet processing of foreground objects and distant background elements these pathways (54). Added granularity was evident in variability in depth and surface normal selectivity across voxels in medial and parictal regions, indicative of specialized regions for different 3D profiles and global shape processing (57 58). This pattern may also reflect differences in coordinate systems for spatial information representation. Ventral areas' preference for camera-oriented surface normals indicates egocentric frame of reference, while medial and parietal areas' preference for ground plane-oriented surface normals indicates an allocentric frame of reference. Parietal areas favored inanimate objects and spatial relations, Lateral areas preferred persons and animate objects, and Ventral areas favored both, aligning with previous literature. Furthermore, the parietal region's 's pronounced selectivity for spatial relations (like 'on', "near', etc.) underscores the significance of spatial relation encoding for this area, corroborating recent research (58)
Limitations. Our approach relies on network dissection and therefore inherits some of its limitations. Network dissection considers each output unit in the network (in our case corresponding to an fMRI voxel) in isolation during interpretation, thus it is not capable of identifying the selectivity of group of output units. We thus identify the selectivity of individual voxels, which is in line with the voxel-wise modeling school of thought, but not with multivariate pattern analysis. Furthermore, in natural images, there are inherent correlations between specific categories and their spatial attributes (e.g., scenes tend to be rectilinear, pointing up, with far away features, while bodies tend to be close up, facing the camera, etc. derive spatial measures from images, and this can introduce some estimation emrrs.. We mitigate this limitation by focusing on ROIs that are widely held to encode spatial properties.
IT) LTD. Lastly, our approach relies on pretrained networks to
A comprehensive understanding of natural vision should cover early visual processing, ecologically- based intermediate features, and high-level, semantically-rooudeed representations. Current knowl- edge largely focuses on the first and third areas. Brain dissection offers potential for uncovering data-driven insights into the full visual processing hierarchy.
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S1 Overview
Section S2 contains more details of the methods described in the main paper. Section S3 provides additional results from the experiments.
We include interactive project page, which includes exploratory brain flatmaps for each of our dissection measures, and code for training and evaluating the networks. Please visit the following link to access the project page: https://brainnddisecciio /isseectiooniiiiiubiooo
S2
Implementation details
S2.1 Network Architecture and Training
Our architecture and training largely follow Khosla et al. [5], except we use regular convolu- tions instead of E(2)-steerable convolutions. We found that using regular convolutions instead of E(2)-steerable convolutions does not significantly affect dissection performance while providing significantly improved training speed and memory efficiency. We train a separate convolutional neural network (CNN) for each ROI sub-region shared across all 8 subjects. The CNN consists of four "blocks", each with two convolutional layers followed by average pooling. We use batch normalization and early stopping. For each voxel in the ROI sub-region (across for all subjects), employ linear readout model on top of the feature space to predict the responses of individual voxels in specific brain region. The linear readout is factorized into spatial and feature dimensions following [37]. This allows us separate image spatial tuning (the "where") from feature tuning (the "what")). The spatial features have been shown to correlate with the population receptive fields (pRFs) of the voxels (2n. We train with mean squared error loss between the predicted voxel response and the true voxel response. During the dissection procedure, we only use the feature tuning. We train all models on a single Nvidia GeForee RTX 3090 GPU. The number of parameters the CNN is 784697 and the number of parameters in the transformer network (see next section) is 7053009.
S2.2 Training & Validation Curves
We provide encoding model training and validation curves for PPA, OPA and RSC functionally- defined ROIs Figure ST and Figure S2] We utilize early stopping to terminate training and obtaín an evaluation checkpoint by checking if the validation performance (Pearson correlation) is lower than the highest validation performance for 10 epochs.
S2.3 Additional Evaluation Details
For network dissection, we use the code repository from Bau et al. 6 We use pycortex to visualize voxel data [60]. Data are projected onto 2D brain flatmaps from 3D voxel data using trilinear interpo- lation for continuous data (e.g., depth) and nearest interpolation for discrete data (e.g., category).
S3
Additional Results
S3.1
Performance of Response-Optimized and Baseline Models
We have provided the Pearson correlation coefficient plotted on flatmap for all ROIs on a held-out test set of NSD data in Figure S3 for example Subject S1. Additionally, we compare our model's mean Pearson correlation to the features from Lescroart and Gallant [20] and ImageNet task-optimized features [6 on held-out NSD brain data using Pearson correlation. We the features fit to brain data via Ridge regression. We computed the features Lescroart and Gallant (20 for NSD images using the estimation networks. As shown in Figure S3 our brain dissection model demonstrates a significant improvement, nearly 2x across scene ROIs, its alignment with the brain responses compared to the baseline features from 20) Our model also aligns in performance with Alex ImageNet features, even though the AlexNet network having a significantly larger parameter size and trained on significantly more images.
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train/mse_iee /ase_/selsss loss
Sroin_streamsssuusss222 traain.streams_sii 13345678_PPA
0.2
Step
50k
100k
150k
200k
250k
Figure S1: Mean squared error training curves for encoding models for PPA (gray), OPA (pink), and RSC
(green) ROIs. X-axis is number of training steps.
validation/total_co corr
wrain_streamm__se suteemms_subss13345722 train streams streensssuuujeeeee subjs123345678.00 htt.nsstrranssusss224445555
0.2
0.1
Stop
150k
200k
250k
Figure S2: Pearson Correlation validation curves for encoding models for PPA (gray), OPA (pink), and RSC (green) ROIs. X-axis is number of training steps. Training terminated using early stopping based the validation curves.
S3.2 Results using Different Architectures and Interpretability Methods
We integrated both gradCAM 38 and raw attention techniques 33
The former utilizes input
features combined with network gradients, while the latter leverages attention scores from transformer architectures. As shown in Figure S4 our results affirm consistency across diverse interpretability methods and network architectures.
83.3 GQA Data Visualization
We show example images and annotations for the GQA dataset in Figure SS Anything 46 to obtain the segmentation masks from the GQA box annotations.
We used Segment
S3.4 Histograms of the Spatial Measures
We show the distribution of voxel preferences for each high-level visual ROI in Figure So and for scene ROIs Figure S7 We observe that subjects are largely consistent in their distributions for each ROI. We also observe that some ROIs and measures are multi-modal, such in several measures in Medial and Gaussian curvature Ventro-lateral and Lateral. This may indicate further specialization of voxel clusters in these areas.
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0.6
Lescroart Gall Gallant, 2019
CNN response-oplimized
transtormer response optimized
0.5
task optimized (ImageNett)
Pearson correlation
0.4
02
04
08
0.0
Figure S3: A. Pearson correlation coefficient plotted on flatmap for all ROIS held-out test set of NSD
data for example Subject SI for response-optimizer networks, B. Same A but for the features of Lescroart and Gallant (20). Mean Pearson correlation for our model, the features from Lescroart Gallant [20], and the ImageNet task- features [61] on held-out NSD brain data using Pearson correlation for the scene-sclective ROIs.
S3.5 Places365 Evaluation
In addition to the GQA dataset, we evaluate for category selectivity in the Places365 dataset, where segmentation masks are obtained using the Unified Perceptual Parsing image segmentation net- work [62 previously trained on 20,000 scene-centric images from the ADE20k dataset [63 We
report median IOU (mean standard deviation across subjects) Figure
S3.6 Unit Visualizations
We include additional single-voxel visualizations for RSC, OPA, and PPA in Figure 89 Figure STO and Figure SII respectively.
S3.7 Brain Flatmaps
In this section, we include brain flatmaps for all eight subjects for metric depth (Figure S12 relative depth (Figure ST3), surface normals (Figure S14). Guassian curvature (Figure S15 and shading (Figure 516
S3.8 WordCloud Visualizations
We offer WordCloud representation visualize the top 20 categories that ROI selects for. In this visualization, the category size represents the magnitude of the median IOU. We show WordClouds for all high-level visual ROIs and scene ROIs for the GQA dataset (Figure[S1) (S17) and the Places365 dataset (Figure S18).
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Network Dissection (CNN)
Absolute depth
Surface normals
Surface Normal Bin
150
Depth (meters
12.5
325
10.0
100
7.5
75
5.0
50
25
PPA
OFA
RSC
Raw Attention (Transformer)
12
Surface Normal Bin
Depth (meters)
1000
60
20
PPA
RSC
OPA
RSC
gradCAM (CNN)
Surface Normal Bin
7.
Depth (meters)
100
25
RSC
PPA
DRA
RSC
Figure S4: Depth surface normal results Network Dissection [6]
gradCAM
38
raw attention
techniques (39 for the scene-selective ROIs.
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BEACO
Figure S5: GQA image with bounding box annotations (left). Segment Anything 46 output given the box annotations input (right).
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Metric Depth
Relative Depth
Surface Normals
Shading
Guassian Curvature
Ventro-
sutject
medial
sajer
aucjoct
Ventro-
lateral
Lateral
Parietal
Medial
10
0.2
0.4
50
100
0.50
0.75
0.1
0.2
Figure S6: Histogram representation each measure high-level visual ROL. Each color represents different subject. Gray vertical dotted lines indicate the grand mean, and red vertical dotted lines indicate the 95% confidence intervals.
Metric Depth
Dapth
Surface
Shading
Guassian Curvature
PPA
Siject
Calgec3 g
OPA
10
0.2
0.4
50
100
0.50
0.75
0.1
0.2
Figure S7: Histogram representation cach measure cach scene ROI. Each color represents different subject. Gray vertical dotted lines indicate the grand mean, and red vertical dotted lines indicate the 95% confidence intervals.
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Ventromedial
Ventrolateral
Lateral
Parietal
Medial
Figure S8: The median IOU (mean + standard deviation across subjects) for the top concepts for each high-level visual ROI for the places365 dataset.
Unit visualization plots for four additional RSC units aligned with the mean depth selectivity of the ROI (note images down-sampled to memory). Each unit displays its top 50 images by predicted
Figure S9:
response for unit. For the 50 images. we show sets images (by row top to bottom): 1. original RGB;
Dissection mask overlaid on the RGB image; 3. Dissection mask overlaid on the surface normal map: 4. Dissection mask overlaid on the depth map: 5. Dissection mask overlaid on the Gaussian curvature map: 6. Disscction mask overlaid on the shading map; 7. heatmap visualization of the voxel feature map for the image.
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Figure S10: Unit visualization plots for four additional OPA units aligned with the mean depth selectivity of the ROI (note that images are down-sampled to save memory). Same format Figure S9
Figure S11: Unit visualization plots for four additional PPA units aligned with mean depth selectivity of the ROI (note that images are damp-sd to save memory). Same format as Figure S9
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S1
S2
S4
S6
S8
10 meters
2
6 8
Figure $12: Metric depth flatmaps colored by selectivity per voxel for subjects S1-S8.
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S1
S2
S3
S4
S5
S6
S7
0.1 0.15 0.2 0.25 0.3 0.35 0.4
Figure S13: Relative depth on flatmaps colored by selectivity per voxel for S1-S8.
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S2
S3
S4
S5
S6
S7
S8
40
60
80
100
120
Figure S S14: Surface normals on flatmaps colored by selectivity per voxel for S1-S8.
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S2
S3
S4
S6
S7
S8
0.05 0.10 0.15 0.20 0.25
Figure S15: Guassian curvature on flatmaps colored by selectivity per voxel for S1-S8.
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S1
S2
S3
S4
S5
S6
S7
S8
0.3
0.4
0.5
0.6
0.7
0.8
Figure S16: Shading flatmaps colored by selectivity per voxel for S1-S8.
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Ventrolateral
Ventromedial
sotting
hai r
holding
to
the
left
of
tall
near
boy on
face
bathroom
owwn
1ding
black
wall
bui
hat in Front of
O
wear
person
ng
behind
playing
standing
the
head
left of
man
to the right of
woman
happy
bus
to
window
the
right
white
wood
of
on
large
mirro
above
train
Lateral
Parietal
holding
p
laying
round orange to the
left
of
to the left of leg
white
hand
player
near
sky
red
table
wear
holding
on above
girl
to
the
right of
in
wood
Woman man
arn swinging shirt
hitting
blue wearing
boy
son
of
of to the right
white
plate bowl
man
Medial
RSC
sign left
to the
of
buildings
to the right of
train
sky
building
near
in front of
roof
building
trees
bus
tall
ng
on
wall
black
red
white
buildings
behind
whi
te
train
bus
large
brick window
to
large
the
1 left
of
behind
the
right
of
tall
brick
near
above window
OPA
PPA
in
black
to
the
left
brown et
of
luildng
white counter above
in front of
white
bathtub
floor
to the left of
to
]
wood
in floor toilet
front of on
plate
gray
table black
train
wall
table
desk
k
on
sink
near counter
to
the right of near
bed to the right of
desk
below Wood
bathroon oon
Figure S17: WordCloud for top categories that ROI selects for the GQA dataset. this visualization.
the category size represents the magnitude the median IOU
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Ventrolateral
Ventromedial
person
pon-r
son-r
pink
metal
building
wall
-r
green
head
painting
wood
metal
hair
hand
painted
bui
wall-t,
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WordCloud for top 20 categories that ROl selects for in the Places365 dataset. In this visualization, the category size represents the magnitude of median IOU
Figure S18:
29


Reviews:
[{"review_id": "CswJydjXIz", "reviewer": "Reviewer_HWnv", "summary": "This paper utilized network dissection model to understand how human brain is functionally mapped to perception of natural scenes. The proposed method is used to examine a range of ecologically important, intermediate properties, including depth, surface normals, curvature, and object relations and find consistent feature selectivity differences. ", "questions": "- As far as I know, there are various methods that provide interpretability, e.g., Chefer et al., CVPR 2021. Will the result remain the same if other models are adopted other than the network dissection (which is a bit outdated). \n- What would be the feature maps like if some naive models were to be deployed, e.g., MLP or simple CNNs with Class Activation Map?\n- Is NSD dataset the only dataset that connects fMRI to natural scene? If so, this would justify experimenting only on a single benchmark regardless of generalization issue. \n- What are OPA, RSC, PPA in the abstract? They should be fully spelled out. ", "limitations": "The authors address limitation of the paper. ", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "- The paper is well written and easy to understand. \n- The paper introduces interesting discussions by employing an AI model to a neuroimaging study. \n", "weaknesses": "- While the paper performs a very interesting experiment from neuroscience perspective, there is not much of a technical contribution. The paper employs an existing model, i.e., network dissection, and does not propose a new or novel approach. \n- There is no baseline experiment and there is no way to validate the gain or improvement from the proposed method. \n- I think this paper has novelty, but perhaps not in a way that the NeurIPS community expects. At least I would like to see a proposal of a novel approach that lead to novel and improved findings. ", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Lacking on machine learning technical contribution. Lacking novelty in a way that the NeurIPS community expects.**\n\nWe value the feedback provided. To enhance the clarity on the novel and improved findings:\n\n1. Our work has introduced a novel scale of examining pixel-level spatial feature selectivity during natural image viewing, revealing new insights into representational differences in spatial feature encoding in the visual cortex. For example, our findings suggest a preferential role for PPA and OPA in supporting surfaces. Our findings on depth and surface normal selectivity in the scene and visual stream networks indicate specialized pathways for the processing of scenes with different 3D geometries, such as object spatial relations, flat versus vertical surfaces, and outdoor versus indoor scenes. The implications of these findings range from understanding brain representation modularity to better informing potential advances in representation learning, brain-computer interface applications, and treatments of related disorders. \n\n2. In comparison to prior studies like [1], our model features align more closely with the mid- and high-level visual cortex representations (Figure R1). We've also introduced a brain dissection method offering pixel-level feature selectivity of spatial measures, a clear advancement over the full-image regression analysis of preceding work. We delve deeper into these distinctions in the global rebuttal response.\n\nWe'd also like to emphasize the alignment of our work with NeurIPS's scope:\n\n1. NeurIPS has historically published neuroscience-centric results. To illustrate this, consider the recent papers: [2,3,4,5].\n\n2. NeurIPS originated at the crossroads of biological and artificial neural networks. The conference’s Call for Papers explicitly promotes neuroscience findings, referencing its dedication to bridging disciplines such as machine learning and neuroscience.\n\n**\"There is no baseline experiment and there is no way to validate the gain or improvement from the proposed method.\"**\n\nAddressing the baseline concerns, we've compared our method with the model from relevant previous work Lescroart & Gallant (2019) [1]. **As shown in Figures R1, our brain dissection model demonstrates a significant improvement (nearly 2x across scene ROIs) in its alignment with the brain responses compared to the baseline features from [1].** Additionally, we've provided a textual explanation of how our brain dissection approach offers more detailed and interpretable insights compared to earlier studies on spatial selectivity in the global reviewer response.\n\n**\"Will the result remain the same if other models are adopted other than the network dissection?\"**\n\nThank you for your insightful comments.\n\n1. In response to the suggestion for varied interpretability methods, we expanded our experiments beyond network dissection. We integrated both gradCAM [6] and raw attention techniques [7]. The former utilizes input features combined with network gradients, while the latter leverages attention scores from transformer architectures. \n\n2. **Our updated results, detailed in Figure R2, affirm consistency across diverse interpretability methods and network architectures.** \n\nWe provide more details in the global reviewer response.\n\nIt's important to mention that we looked into the Chefer et al., CVPR 2021 method you referenced. However, integrating it with our custom architecture posed challenges. Both the GradCAM and raw attention methods we tried, which are baselines in that paper, performed relatively well, and are widely recognized for network interpretability [7,8].\n\n**\"What would be the feature maps like if some naive models were to be deployed, e.g., MLP or simple CNNs with Class Activation Map?\"**\n\nOur current architecture closely resembles a simple CNN with Class Activation Maps. We also experimented with a transformer architecture and analyzed its attention maps, finding its results consistent with our CNN-based network dissection (Figure R2). More details are available in the global reviewer response.\n\n**\"Is NSD dataset the only dataset that connects fMRI to natural scene?\"**\n\nThe NSD dataset is the only fMRI natural image dataset of its scale, with a total of 73000 COCO images and eight subjects. \n\n**\"What are OPA, RSC, PPA in the abstract? They should be fully spelled out.\"**\n\nWe apologize for not expanding the abbreviations of the scene-selective regions. We have updated the abstract with the names fully spelled out: occipital place area (OPA), retrosplenial complex (RSC), and parahippocampal place area (PPA).\n\n**References**\n\n1. Lescroart & Gallant (2019). Neuron, 101(1), 178-192.e7.\n\n2. Millet et al. (2022). NuerIPS, 35, 33428-33443.\n \n3. Wang, A., et al. (2019). NuerIPS, 32.\n\n4. Khosla et al. (2022). NuerIPS, 35, 9389-9402.\n\n5. Antonello et al. (2021). NuerIPS, 34, 8332-8344.\n\n6. Selvaraju et al. (2017). ICCV, 618-626.\n\n7. Caron et al. (2021). ICCV, 9650-9660.\n\n8. Linardatos et al. (2020). Entropy, 23(1), 18."}, {"review_id": "btZJS2zLN5", "reviewer": "Reviewer_c89B", "summary": "This study uses the network dissect method to investigate the feature selectivity of RSC, OFA, and PPA in the human brain. This method is called \"brian dissection\". In particular, this study focuses on some ecologically important intermediate features, such as depth, surface normals, curvatures, and object relations. Results showed that the three regions show distinct feature preferences.", "questions": "My major concern is why this method is better than previous methods. For example, a simple encoding model was developed in ref.[1] and capture several important properties of scene-selective regions. I hope the authors provide evidence why brain dissection is better than this simple encoding-regression model. I understand that brain dissection certainly gives different results and feature maps because they are different methods. But this is not the reason why brain dissection is superior. \n\nOr, different dissection methods could be used and show that the results are highly consistent??\n\n\n[1] Lescroart MD, Gallant JL. Human Scene-Selective Areas Represent 3D Configurations of Surfaces. Neuron. 2019 Jan 2;101(1):178-192.e7. doi: 10.1016/j.neuron.2018.11.004.", "limitations": "1. I trust the results. But the results are completely data-driven. I didn't see how the results add much to our understanding of the function of high-level visual regions.\n2. My feeling is that this type of result is suitable for neuroscience journals like Neuroimage. I didn't see any new algorithm-level novelty. It is certainly not interesting to the machine learning community. The results here may be of interests to neuroscientists. I mean the results are valid. But this study only displays some results but does not contribute to how the brain form such representation.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "1. To my best knowledge, this is the first study to apply the network dissect method to examine the voxel preferences.\n2. The overall method is clear and the presentation is good.", "weaknesses": "1. It is unclear to me why this study only focuses on some intermediate features, such as depth, surface normals, curvature. In theory, this method can be also used to study both low-level and high-level visual features.\n2. The theoretical contribution is unclear. I agree this study may be the first application of network dissection on voxel preferences. But the idea here is generally incremental. It can certainly obtain some new findings because the method per se is new. But I don't see clear progress being made as compared to previous methods. These types of results may be good for a neuroscience journal. The key point which is missing here is that how these representations are formed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**The theoretical contribution is unclear. These types of results may be good for a neuroscience journal. The key point which is missing here is how these representations are formed.**\n\nThank you for your insightful comments. To enhance the clarity on the theoretical contribution of our paper:\n\n1. Our work has introduced a novel scale of examining pixel-level spatial feature selectivity during natural image viewing, revealing new insights into representational differences in spatial feature encoding in the visual cortex. For example, our findings suggest a preferential role for PPA and OPA in supporting surfaces. Our findings on depth and surface normal selectivity in the scene and visual stream networks indicate specialized pathways for the processing of scenes with different 3D geometries, such as object spatial relations, flat versus vertical surfaces, and outdoor versus indoor scenes. The implications of these findings range from understanding brain representation modularity to better informing potential advances in representation learning, brain-computer interface applications, and treatments of related disorders. \n\n2. In comparison to prior studies like [1], our model features align more closely with the mid- and high-level visual cortex representations (Figure R1). We've also introduced a brain dissection method offering pixel-level feature selectivity of spatial measures, a clear advancement over the full-image regression analysis of preceding work. We delve deeper into these distinctions in the global rebuttal response.\n\nWe'd also like to emphasize the alignment of our work with NeurIPS's scope:\n\n1. NeurIPS has historically published neuroscience-centric results. To illustrate this, consider the recent papers: [2,3,4,5].\n\n2. NeurIPS originated at the crossroads of biological and artificial neural networks. The conference’s Call for Papers explicitly promotes neuroscience findings, referencing its dedication to bridging disciplines such as machine learning and neuroscience.\n\n**Why is this method better than previous methods?**\n\nAddressing your concerns, we have included a comparison of our model to that of the model used in Lescroart & Gallant (2019) [1]. **As shown in Figures R1, our brain dissection model demonstrates a significant improvement (nearly 2x across scene ROIs) in its alignment with the brain responses compared to the baseline features from [1].** Additionally, we've provided a textual explanation of how our brain dissection approach offers more detailed and interpretable insights compared to earlier studies on spatial selectivity in the global reviewer response.\n\n**Different dissection methods could be used and show that the results are highly consistent.**\n\nThank you for your comment. In response to the suggestion for varied interpretability methods:\n\n1. We expanded our experiments beyond network dissection. We integrated both gradCAM [6] and raw attention techniques [7]. The former utilizes input features combined with network gradients, while the latter leverages attention scores from transformer architectures. \n\n2. **Our updated results, detailed in Figure R2, affirm consistency across diverse interpretability methods and network architectures.** \n\nWe provide more details in the global reviewer response.\n\n**Why not also focus on low-level and high-level features?**\n\nIndeed, our method can handle features from low- to high-level. We focused on intermediate features like 3D spatial attributes and object relationships because the encodings for intermediate features have proven more challenging to describe compared to well-studied low-level [8,9] and high-level category [10,11] features. Our method enables effective study of these features using natural image data without requiring specialized stimuli or changes to brain imaging techniques. In Section 4.2, we also analyzed high-level features such as object relations, attributes, and categories, at a scale larger than most previous studies (1703 categories, 310 relations, 617 attributes).\n\n**References**\n\n1. Lescroart & Gallant (2019). Neuron, 101(1), 178-192.e7.\n\n2. Millet et al. (2022). Advances in Neural Information Processing Systems, 35, 33428-33443.\n \n3. Wang, A., et al. (2019). Advances in Neural Information Processing Systems, 32.\n\n4. Khosla et al. (2022). Advances in Neural Information Processing Systems, 35, 9389-9402.\n\n5. Antonello et al. (2021). Advances in Neural Information Processing Systems, 34, 8332-8344.\n\n6. Selvaraju et al. (2017). In Proceedings of the IEEE international conference on computer vision, 618-626.\n\n7. Caron et al. (2021). In Proceedings of the IEEE/CVF international conference on computer vision, 9650-9660.\n\n8. Carandini et al. (2005). Journal of Neuroscience, 25(46), 10577-10597.\n \n9. Hubel & Wiesel (1962). The Journal of physiology, 160(1), 106.\n\n10. Desimone et al. (1984). Journal of Neuroscience, 4(8), 2051-2062.\n\n11. Grill-Spector & Weiner (2014). Nature Reviews Neuroscience, 15(8), 536-548."}, {"review_id": "VNAK4bXY55", "reviewer": "Reviewer_9bSv", "summary": "Understanding the organization of higher level information representation in the brain is a challenging task in neuroscience. Modern deep learning methods together with big data of brain recording have opened up new opportunities for constructing large-scale models in a data-driven way and for gaining valuable insights about information processing in the brain. The present paper explores this idea by training a deep neural network model for predicting human brain fMRI from natural scene images. By analyzing the feature map generated by the model, the paper reveals spatial and functional organization of the brain for a wide range of high-level visual features. ", "questions": "Major comments\n1.\tThe paper mentions that the model is trained on the NSD dataset including 68400 training images and 3600 validation images. However, the performance of the trained network on estimating fMRI data on the training/validation dataset is missing. Since the main point of the paper is to use a DNN model to gain insights about feature representation in the brain, it is crucial to establish an understanding of how well the model can predict brain activity in the first place. I notice that in the supplementary material, the performance of the model is reported for 3 ROIs (PPA, OPA, RSC) in terms of the Pearson correlation coefficient. However, unless the performance of the model is clearly reported for all ROIs, it is difficult to meaningfully interpret the analysis based on the proposed brain dissection method. For instance, the correlation between the DNN prediction and the ground-truth fMRI data in each ROI could be presented as a flatmap.\n2.\tOne concern regarding the calculation of selectivity (Eq. 1-2) is that a voxel is assigned to a feature value disregarding whether the voxel is truly selective at all. For instance, if L_c masked by M_k across all images is uniformly distributed, then no selectivity can be said about this voxel. To remedy this, the author should rule out these non-selective voxels from the analysis, otherwise it could lead to misleading interpretations of the result.\n\n\nMinor comments\n1.\tThe dimensionality of the neural network model should be clearly stated for reproducibility.\n2.\tSince the paper is trained on human fMRI data, a discussion about the biological plausibility of the model would be appropriate. For instance, how much can we infer from the feature representation in the model about that in the real brain? Can we make any experimentally testable predictions?\n", "limitations": "The paper technically sounds. But the technical details are not clear and guarantee reproductivity. ", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Overall, I find that the paper is well written and that it addresses important questions at the intersection of machine learning and neuroscience.", "weaknesses": " I have a number of concerns about the results and particularly, technical details presented in the paper, which prevented from accepting it directly.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**The performance of the trained network on estimating fMRI data is missing.**\n\nThank you for highlighting the importance of performance reporting for all ROIs. Based on your recommendation, we've provided the Pearson correlation coefficient plotted on a flatmap for all ROis on a held-out test set of NSD data in Figure R1 for example Subject S1. Additionally, we compare our model's mean Pearson correlation to the features from [1] and ImageNet task-optimized features (fit to brain data via Ridge regression) for scene ROIs. Notably, our model surpasses the features from [1] and aligns with AlexNet ImageNet features, even though the AlexNet network has a 76x larger parameter size and is trained on 17.6x more images. We will include correlation flatmaps for all subjects in the revised version of the paper. \n\n**Exclusion of non-selective voxels in analysis**\n\nThank you for highlighting the potential issue of including non-selective voxels in our analysis. In response to your suggestion:\n\n1. We've reanalyzed our data by excluding voxels that lack selectivity across all evaluation set images, essentially comparing the distribution of L_c masked by M_k against a uniformly distributed (full image) mask. This exclusion used the Kolmogorov–Smirnov test, with a significance threshold of p>0.01.\n\n2. Updated findings can be seen in Figure R3 of the provided pdf. This figure presents the absolute depth both with and without the non-selective voxels. Our findings remain stable when excluding non-selective voxels. We'll apply this refined method to all metrics in the final manuscript.\n\n**Clarifying the model's dimensionality for reproducibility**\n\nThank you for this feedback. To address your concern:\n\n1. We have updated our paper to include the dimensionality of our network. The dimensionality of the CNN is 784697 and the dimensionality of the transformer network is 7053009. \n\n2. To ensure reproducibility, we have provided our full code for training and evaluation in the supplementary materials. We commit to open-sourcing our code upon publication. We have also expanded on any missing technical details in the paper.  \n\n**Discussing the biological relevance of the model. Can we make any experimentally testable predictions?**\n\n1. Our method indeed paves the way for several experimentally testable predictions. For example, our findings suggest potential studies of the encoding of outdoor versus indoor scenes, as well as differences in expansive versus enclosed space encoding between RSC, OPA, and PPA. Furthermore, the observed differences in horizontal versus vertical orientation preferences across the scene ROIs might hint at unique encodings, such as those of \"supporting\" structures or divergences in the reference frame. \n\n2. It's important to note that our model's architecture need not mirror the brain's intricacies. The primary objective is to accurately predict brain responses. By analyzing our model's activations, we can deduce which image features it is leveraging to make predictions, and thus are most relevant for the responses that emerge in that brain voxel. This ability does not necessarily equate to a biologically accurate architecture, as seen in certain studies like [2].\n\n**References**\n\n1. Lescroart MD, Gallant JL. Human Scene-Selective Areas Represent 3D Configurations of Surfaces. Neuron. 2019 Jan 2;101(1):178-192.e7. doi: 10.1016/j.neuron.2018.11.004.\n\n2. St-Yves, G., Allen, E.J., Wu, Y. et al. Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations. Nat Commun 14, 3329 (2023). https://doi.org/10.1038/s41467-023-38674-4"}, {"review_id": "s9Tsd9vNTL", "reviewer": "Reviewer_MstC", "summary": "The authors train CNNs to predict the responses of individual voxels across several ROIs to the natural scenes dataset (NSD). They then develop and employ a strategy termed “brain dissection” to uncover the properties/features of images for which specific visual regions are tuned. They highlight gradients within subregions of the visual stream that correspond to tuning variation along mid-level image characteristics including depth, curvature, and object relations. ", "questions": "As noted above, I would recommend expanding the discussion of related work and methodology to make for a clearer and more well-packaged reading. It would also be beneficial to tie in more related work supporting or refuting the hypotheses introduced about the subdivision of the “scene network”, such that these data can be appropriately placed into context with other findings and approaches. This limited connection to existing literature, context, and related findings is the work’s greatest weakness.", "limitations": "Methodological limitations are mostly well discussed. An additional sentence about limitations with this type of ROI modeling strategy in general, e.g., potential lack of transfer or generalization to real experiments, should be mentioned.", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "Originality: The methodology, as the authors note, is conceptually similar to Khosla & Wehbe (2022) but is applied towards evaluating sub-region tuning of mid-level visual features rather than reaffirming category selectivity. This is an effective use and reference of an existing computational strategy towards novel scientific investigation. My only comment would be to include some more comprehensive methods and related work description, instead of relying on the reader going back to this original work for the conceptual motivation and validation. A few sentences will suffice.\n\nQuality: This submission is technically sound, uses appropriate methods, and the claims are well supported for the most part. The authors should, however, be a bit more cautious about claims tying the trends they observe in RSC and OPA/PPA to specific functional preferences, e.g., in 4.1.3 and 4.3, as these are somewhat speculative hypotheses that were not explicitly tested, e.g., tying “up” normal to preferences for an “allocentric frame of reference.”\n\nClarity: The submission is clearly written and well organized.\n\nSignificance: The results are important, tie in with existing literature and discussions, and will inform other researchers interested in visual stream organization.", "weaknesses": "Wrapped in with above and discussed again below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**\"More comprehensive methods and related work description\" \"Tie in more related work supporting or refuting the hypotheses\"**\n\nThank you for your feedback on the need for comprehensive methods and an expanded discussion on related works. \n\n- We have included additional motivation points for the method as it relates to previous methods in the global reviewer response. We have added these to the paper.\n\n- In addition, we have added the following paragraphs to the discussion section:\n\n> We observed specific preferences in regions like the RSC, OPA, and PPA. The RSC demonstrated a pronounced preference for greater depths, outdoor object categories, attributes, and relations, and predominantly “right/left” surface normals. On the other hand, the OPA exhibited a preference for proximate depths, intricate 3D geometries, indoor scene object categories, relations, and attributes, along with a higher inclination towards “upward” surface normals. These findings align with existing literature suggesting that OPA is primed for local navigational affordances [1,2], whereas RSC is geared more towards facilitating landmark-based spatial-memory retrieval [3,4]. In the case of the PPA, its preferences spanned a middle ground between the OPA and RSC, albeit leaning slightly more towards the OPA. This supports the notion of PPA's role in encoding scene structure, albeit at a coarser scale than OPA [2]. A salient finding from our study is the pronounced selectivity of RSC for vertical surface normals when contrasted with PPA and OPA, implying its emphasis on encoding vertical structures. In contrast, PPA and OPA demonstrated a marked preference for horizontal supporting structures, such as tabletops and floors. Prior research has highlighted PPA's sensitivity to scene layout and consistent surface arrangement [5]. Our findings further indicate that such selectivity might be particularly driven by these supporting surfaces.\n> \n\n> Further distinguishing between high-level visual ROIs, our study revealed distinct gradients moving from ventro-lateral to medial areas. Ventro-lateral regions exhibited a preference for closer depths, predominantly horizontal surface normals, and darker shading. In contrast, medial areas showed the opposite preferences, which resonates with the idea of distinct processing of foreground objects and distant background elements in these pathways [3]. An added layer of granularity was evident in the observed variability in depth and surface normal selectivity across voxels in the medial and parietal regions. Such variability is indicative of specialized regions tailored for different 3D profiles and global shape processing [6,7]. Furthermore, the parietal region stood out in its pronounced selectivity for spatial relations (like 'on', 'near', etc.), underscoring the significance of spatial relation encoding for this area, a finding that corroborates recent research [7].\n\n**Be more clear about speculative conclusions in the paper.**\n\nWe appreciate your feedback. We've refined the paper to clarify speculative statements. For instance, we modified the statement “Surface normals oriented at the camera indicate an egocentric frame of reference...\" to \"It can be hypothesized, though not yet confirmed, that surface normals facing the camera might suggest an egocentric perspective, while those oriented from the ground could indicate an allocentric viewpoint.\" All speculative content has been relocated to the discussion section.\n\n**\"Limitations with this type of ROI modeling strategy in general\"**\n\nThank you for highlighting the need to discuss the limitations of our ROI modeling strategy in more depth. To expand on some of the limitations of our approach: \n\n1. While our analysis is primarily restricted to the ROIs hypothesis space, we've supplemented it with additional analyses that encompass entire streams, ensuring a broader scope of interpretation.\n \n2. We acknowledge that focusing solely on individual voxels might miss out on representations in networks or multiple voxels. This limitation forms the basis for future work, a dissection procedure considering multiple units, not just individual ones.\n\n3. Our approach relies on pretrained networks to derive spatial measures from images, and this can introduce some estimation errors. \n\n4. We are also aware that in natural images, certain categories inherently correlate with specific spatial attributes, like scenes predominantly showcasing rectilinear and faraway features, while bodies are usually depicted up close and facing the camera. Our methodology specifically targets ROIs that are widely accepted to encode these spatial properties, which helps to counteract this limitation. \n\n**References**\n\n1. Lescroart & Gallant (2019). Neuron, 101(1), 178-192.e7.\n\n2. Bonner & Epstein (2017). Proceedings of the National Academy of Sciences, 114(18), 4793-4798.\n\n3. Epstein (2008). Trends in Cognitive Sciences, 12, 388–396.\n\n4. Auger, Mullally, & Maguire (2012). PLoS One, 7, e43620.\n\n5. Epstein & Kanwisher (1998). Nature, 392(6676), 598-601.\n\n6. Welchman (2016). Annual Review of Vision Science, 2, 345-376.\n\n7. Ayzenberg & Behrmann (2022). Journal of Neuroscience, 42(23), 4693-4710."}]

Author rebuttal:
We sincerely appreciate the constructive feedback from the reviewers. We are grateful that reviewers HWnv, 9bSv, and MstC acknowledged the clarity and quality of our paper's writing. The novelty of our approach was positively highlighted by HWnv, and the pioneering application of the network dissect method to examine voxel spatial preferences was recognized by c89b. We are pleased that both 9bSv and MstC noted the significance of our work at the intersection of machine learning and neuroscience, emphasizing the depth of insights our research provides into the spatial and functional organization of the brain. The technical soundness of our work was underscored by 9bSv and MstC, with MstC further highlighting its importance in the context of existing literature. We'll address overarching questions here and specific reviewer concerns in their respective responses.

@Reviewers HWnv,c89B: **Lacking on machine learning technical contribution. Mostly relevant for neuroscience.**

1. NeurIPS has historically published neuroscience-centric results, evidenced by recent papers: [1,2,3,4].

2. NeurIPS originated at the intersection of biological and artificial neural networks. Its Call for Papers explicitly promotes neuroscience findings.

3. Our work has introduced a novel scale of examining pixel-level spatial feature selectivity during natural image viewing, revealing novel spatial feature encoding in the visual cortex. For example, our findings suggest a preferential role for PPA and OPA in supporting surfaces. Our findings on spatial selectivity in the scene and visual stream networks indicate specialized pathways for the processing of scenes with different 3D geometries, such as object spatial relations, flat versus vertical surfaces, and outdoor versus indoor scenes. The implications of these findings range from understanding brain representation modularity to better informing potential advances in representation learning, brain-computer interface applications, and treatments of related disorders.

4. In comparison to prior studies like [5], our model features align more closely with the mid- and high-level visual cortex representations (Figure R1). We've also introduced a brain dissection method offering pixel-level feature selectivity of spatial measures, a clear advancement over the full-image regression analysis of preceding work. 

@Reviewers HWnv,c89B,9bSv: **Why is brain dissection superior to baseline methods? What is the performance of your model?**

Thank you for your feedback. We recognize the value of having comparative baselines. Addressing the concerns raised:

1. We directly compared our model to the model from Lescroart & Gallant. (2019) [5] on held-out NSD brain data using Pearson correlation. We computed the features in [5] for NSD images using the estimation networks, and fit them to brain data using Ridge regression. **As shown in Figure R1, our brain dissection model demonstrates a significant improvement (nearly 2x across scene ROIs) in its alignment with the brain responses compared to the baseline features from [5].** 

2. Contrary to Reviewer c89B, our methodology markedly diverges from Lescroart & Gallant (2019). While they utilize a regression model for whole-image features like depth and surface normals, their approach limits deeper feature selectivity, and adding features requires new regression parameters. In contrast, our brain dissection method provides pixel and region-level selectivity across numerous natural image features, supported by brain-aligned neural networks. **This doesn't just mean different results; it signifies a leap in granularity and interpretability.** For example, where Lescroart & Gallant (2019) identified broad depth variations among OPA, PPA, and RSC, we detail fine spatial selectivity differences across the ROIs.

@Reviewers HWnv,c89B: **Do the results hold with other interpretability methods & network architectures?**

Thank you for your insightful comments. Addressing the concerns raised:

1. In response to the suggestion for varied interpretability methods, we expanded our experiments beyond network dissection. We integrated both gradCAM [6] and raw attention techniques [7]. The former utilizes input features combined with network gradients, while the latter leverages attention scores from transformer architectures. See below for implementation details.

2. **Our updated results, detailed in Figure R2, affirm consistency across diverse interpretability methods and network architectures.** Specifically:
    - The raw attention shows stable mean depth and surface normal selectivity when compared to network dissection. The method replicates significant depth metric increase and a preference for right/left surface normals in RSC compared to PPA and OPA, which prefer lower depths and upright surface normals.
    - GradCAM's depth selectivity aligns with network dissection, showing increased metric depth in RSC versus PPA and OPA. For surface normals, GradCAM mostly reflects prior findings but shows variations in RSC's selectivity. This might be attributed to its adaptation for regression gradients, and occasional inconsistencies as pointed out by Chefer et al. (CVPR 2021).

*Implementation Details*: We combined gradCAM with our initial CNN. For the raw attention method, we opted for the Vision Transformer instead of the paper's CNN. We substituted voxel-specific weights with unique attention heads for each voxel prediction. Attention scores from the [CLS] token in these heads were used to generate voxel-specific attention maps for assessment.

**References**

1. Millet et al. (2022). NeurIPS, 35, 33428-33443.
 
2. Wang, A., et al. (2019). NeurIPS, 32.

3. Khosla et al. (2022). NeurIPS, 35, 9389-9402.

4. Antonello et al. (2021). NeurIPS, 34, 8332-8344.

5. Lescroart & Gallant. (2019). Neuron, 101(1), 178-192.e7.

6. Selvaraju et al. (2017). ICCV, 618-626.

7. Caron et al. (2021). ICCV, 9650-9660.

Meta-review: The paper introduces the use if network dissect method to examine the voxel preferences.
The methodology, though conceptually similar to previous work (acknowledged in the paper), addresses an novel application consisting in evaluating sub-region tuning of mid-level visual features is novel.
The methodolgy is clear.
The paper is well written and easy to understand.
The claims are well supported for the most part.
The results are important, and will inform other researchers interested in visual stream organization.

The contribution is rather incremental from a  methodological perspective.
There is no baseline experiment and there is no way to validate the gain or improvement from the proposed method.

In short, the contribution is solid and interesting from a neuroscience perspective. As such it deserves publication at NeurIPS.
Decision: Accept (poster)