SlowGuess/NeurIPS_2023_OCR huggingface dataset:
In this experiment, we investigate if our model can identify novel sub-divisions within existing ROIs. We first perform clustering on normalized per-voxel encoder weights using vmf-clustering [71]. We find consistent cosine difference between the cluster centers in the food-selective ROI as well as in the occipital place area (OPA), clusters shown in Figure 6. In all four subjects, we observe a relatively consistent anterior-posterior split of OPA. While the clusters within the food ROI vary more anatomically, each subject appears to have a more medial and a more lateral cluster. We visualize the images for the two food clusters in Figure 7, and for the two OPA clusters in Figure 8. We observe that for both the food ROI and OPA, the BrainDiVE-generated images from each cluster have noticeable differences in their visual and semantic properties. In particular, the BrainDiVE images from food cluster-2 have much higher color saturation than those from cluster-1, and also have more objects
Food cluster 1, NSD (fMPL)
S1
Food cluster 2 NSD (fMRI)
Figure 7: Comparing results across the food clusters. We visualize top-10 NSD fMRI (out of 10,000) and diffusion images (out of 500) for each cluster. While the first cluster largely consists of processed foods, the second cluster has more visible high color saturation foods, and more vegetables/fruit like objects. BrainDiVE helps highlight the differences between clusters.
Which cluster is more ...	vegetables/fruits	healthy	colorful	far away
S1	S2	S5	S7	S1	S2	S5	S7	S1	S2	S5	S7	S1	S2	S5	S7
Food-1 NSD	17	21	27	36	28	22	29	40	19	18	13	27	32	24	23	28
Food-2 NSD	65	56	56	49	50	47	54	45	42	52	53	42	34	39	36	42
Food-1 BrainDiVE	11	10	8	11	15	16	20	17	6	9	11	16	24	18	27	18
Food-2 BrainDiVE	80	75	67	64	68	68	46	51	79	82	65	61	39	51	39	40
Table 3: Human evaluation of the difference between food clusters. Evaluators compare groups of images corresponding to food cluster 1 (Food-1) and food cluster 2 (Food-2), with questions posed as "Which group of images has/is more X?". Comparisons are done within NSD and generated images respectively. Note that the "Same" responses are not shown; responses across all three options sum to 100. Results are in %.
that resemble fruits and vegetables. In contrast, food cluster-1 generally lacks vegetables and mostly consist of bread-like foods. In OPA, cluster-1 is dominated by indoor scenes (rooms, hallways), while 2 is overwhelmingly outdoor scenes, with a mixture of natural and man-made structures viewed from a far perspective. Some of these differences are also present in the NSD images, but the differences appear to be highlighted in the generated images.
To confirm these effects, we perform a human study (Table 3, Table 4) comparing the images from different clusters in each ROI, for both NSD and generated images. As expected from visual inspection of the images, we find that food cluster-2 is evaluated to have higher vegetable/fruit content, judged to be healthier, more colorful, and slightly more distant than food cluster-1. We find that OPA cluster-1 is evaluated to be more angular/geometric, include more indoor scenes, to be less natural and consisting of less distant scenes. Again, while these trends are present in the NSD images, they are more pronounced with the BrainDiVE images. This not only suggests that our method has uncovered differences in semantic selectivity within pre-existing ROIs, but also reinforces the ability of BrainDiVE to identify and highlight core functional differences across visual cortex regions.
Figure 8: Comparing results across the OPA clusters. We visualize top-10 NSD fMRI (out of 10,000) and diffusion images (out of 500) for each cluster. While both consist of scene images, the first cluster have more indoor scenes, while the second has more outdoor scenes. The BrainDiVE images help highlight the differences in semantic properties.
Which cluster is more...	angular/geometric	indoor	natural	far away
S1	S2	S5	S7	S1	S2	S5	S7	S1	S2	S5	S7	S1	S2	S5	S7
OPA-1 NSD	45	58	49	51	71	88	80	79	14	3	9	10	10	1	6	8
OPA-2 NSD	13	12	14	16	7	8	11	14	73	89	71	81	69	93	81	85
OPA-1 BrainDiVE	76	87	88	76	89	90	90	85	6	6	9	6	1	3	3	8
OPA-2 BrainDiVE	12	3	4	10	7	7	5	8	91	91	83	90	97	92	91	88
Table 4: Human evaluation of the difference between OPA clusters. Evaluators compare groups of images corresponding to OPA cluster 1 (OPA-1) and OPA cluster 2 (OPA-2), with questions posed as "Which group of images is more X?". Comparisons are done within NSD and generated images respectively. Note that the "Same" responses are not shown; responses across all three options sum to 100. Results are in %.
Limitations and Future Work Here, we show that BrainDiVE generates diverse and realistic images that can probe the human visual pathway. This approach relies on existing large datasets of natural images paired with brain recordings. In that the evaluation of synthesized images is necessarily qualitative, it will be important to validate whether our generated images and candidate features derived from these images indeed maximize responses in their respective brain areas. As such, future work should involve the collection of human fMRI recordings using both our synthesized images and more focused stimuli designed to test our qualitative observations. Future work may also explore the images generated when BrainDiVE is applied to additional sub-region, new ROIs, or mixtures of ROIs.
Conclusion We introduce a novel method for guiding diffusion models using brain activations - BrainDIVE - enabling us to leverage generative models trained on internet-scale image datasets for
data driven explorations of the brain. This allows us to better characterize fine-grained preferences across the visual system. We demonstrate that BrainDiVE can accurately capture the semantic selectivity of existing characterized regions. We further show that BrainDiVE can capture subtle differences between ROIs within the face selective network. Finally, we identify and highlight fine-grained subdivisions within existing food and place ROIs, differing in their selectivity for mid-level image features and semantic scene content. We validate our conclusions with extensive human evaluation of the images.
This work used Bridges-2 at Pittsburgh Supercomputing Center through allocation SOC220017 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296. We also thank the Carnegie Mellon University Neuroscience Institute for support.
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Supplementary Material: Brain Diffusion for Visual Exploration
1. Broader Impacts (section 7)
2. Visualization of each subject's category selective voxels (section 8)
3. CLIP zero-shot classification results for all subjects (section 9)
4. Image gradients and synthesis process (section 10)
5. Standard error for human behavioral studies (section 11)
6. Brain Encoder  $R^2$  (section 12)
7. Additional OFA and FFA visualizations (section 13)
8. Additional OPA and food clustering visualizations (section 14)
9. Training, inference, and experiment details (section 15)
7 Broader impacts
Our work introduces a method where brain responses - as measured by fMRI - can be used to guide diffusion models for image synthesis (BrainDiVE). We applied BrainDiVE to probe the representation of high-level semantic information in the human visual cortex. BrainDiVE relies on pretrained stable-diffusion-2-1 and will necessarily reflect the biases in the data used to train these models. However, given the size and diversity of this training data, BrainDiVE may reveal data-driven principles of cortical organization that are unlikely to have been identified using more constrained, hypothesis-driven experiments. As such, our work advances our current understanding of the human visual cortex and, with larger and more sensitive neuroscience datasets, may be utilized to facilitate future fine-grained discoveries regarding neural coding, which can then be validated using hypothesis-driven experiments.
8 Visualization of each subject's category selective voxel images
Figure S.1: Results for category selective voxels (S1). We identify the top-5 images from the stimulus set or generated by our method with highest average activation in each set of category selective voxels for the face/place/word/body categories, and the top-10 images for the food selective voxels. Note the top NSD body voxel image for S1 was omitted from the main paper due to content.
Figure S.2: Results for category selective voxels (S2). We identify the top-5 images from the stimulus set or generated by our method with highest average activation in each set of category selective voxels for the face/place/word/body categories, and the top-10 images for the food selective voxels.
Figure S.3: Results for category selective voxels (S3). We identify the top-5 images from the stimulus set or generated by our method with highest average activation in each set of category selective voxels for the face/place/word/body categories, and the top-10 images for the food selective voxels.
Figure S.4: Results for category selective voxels (S4). We identify the top-5 images from the stimulus set or generated by our method with highest average activation in each set of category selective voxels for the face/place/word/body categories, and the top-10 images for the food selective voxels.
Figure S.5: Results for category selective voxels (S5). We identify the top-5 images from the stimulus set or generated by our method with highest average activation in each set of category selective voxels for the face/place/word/body categories, and the top-10 images for the food selective voxels.
Figure S.6: Results for category selective voxels (S6). We identify the top-5 images from the stimulus set or generated by our method with highest average activation in each set of category selective voxels for the face/place/word/body categories, and the top-10 images for the food selective voxels.
Figure S.7: Results for category selective voxels (S7). We identify the top-5 images from the stimulus set or generated by our method with highest average activation in each set of category selective voxels for the face/place/word/body categories, and the top-10 images for the food selective voxels.
Figure S.8: Results for category selective voxels (S8). We identify the top-5 images from the stimulus set or generated by our method with highest average activation in each set of category selective voxels for the face/place/word/body categories, and the top-10 images for the food selective voxels.
9 CLIP zero-shot classification
In this section we show the CLIP classification results for S1 – S8, where Table S.1 in this Supplementary material matches that of Table 1 in the main paper. We use CLIP [63] to classify images from each ROI into five semantic categories: face/place/body/word/food. Shown is the percentage where the classified category of the image matches the preferred category of the brain region. We show this for the top-200 and top-100 images (top-2% and top-1%) evaluated by mean true fMRI beta, and the top-200 and top-100 (20% and 10%) of BrainDiVE images as self-evaluated by the encoding component of BrainDiVE. Please see Supplementary Section 15 for the prompts we use for CLIP classification.
Faces	Places	Bodies	Words	Food	Mean
S1↑	S2↑	S1↑	S2↑	S1↑	S2↑	S1↑	S2↑	S1↑	S2↑	S1↑	S2↑
NSD top-200	42.5	41.5	66.5	80.0	56.0	65.0	31.5	34.5	68.0	85.5	52.9	61.3
NSD top-100	40.0	45.0	68.0	79.0	49.0	60.0	30.0	49.0	78.0	85.0	53.0	63.6
BrainDiVE-200	69.5	70.0	97.5	100	75.5	68.5	60.0	57.5	89.0	94.0	78.3	75.8
BrainDiVE-100	61.0	68.0	97.0	100	75.0	69.0	60.0	62.0	92.0	95.0	77.0	78.8
Table S.1: Evaluating semantic specificity with zero-shot CLIP classification for S1 and S2
Faces	Places	Bodies	Words	Food	Mean
S3↑	S4↑	S3↑	S4↑	S3↑	S4↑	S3↑	S4↑	S3↑	S4↑	S3↑	S4↑
NSD top-200	33.0	39.0	74.5	71.5	57.9	47.5	27.0	20.5	49.5	53.5	48.4	46.4
NSD top-100	38.0	41.0	81.0	72.0	60.0	49.0	30.0	25.0	46.0	57.9	51.0	49.0
BrainDiVE-200	67.5	73.5	99.0	100	59.0	66.5	61.0	31.0	85.0	89.0	74.3	72.0
BrainDiVE-100	67.0	71.0	100	100	59.0	72.0	61.0	34.0	89.0	93.0	75.2	74.0
Table S.2: Evaluating semantic specificity with zero-shot CLIP classification for S3 and S4
Faces	Places	Bodies	Words	Food	Mean
S5↑	S6↑	S5↑	S6↑	S5↑	S6↑	S5↑	S6↑	S5↑	S6↑	S5↑	S6↑
NSD top-200	41.0	38.5	89.5	56.9	57.9	56.5	33.5	34.0	77.0	55.5	59.8	48.3
NSD top-100	45.0	46.0	93.0	55.0	54.0	61.0	33.0	32.0	85.0	56.9	62.0	50.2
BrainDiVE-200	67.0	63.0	99.5	96.0	74.0	66.0	75.0	68.0	83.5	79.0	79.8	74.4
BrainDiVE-100	64.0	57.9	100	99.0	77.0	72.0	80.0	75.0	87.0	83.0	81.6	77.4
Table S.3: Evaluating semantic specificity with zero-shot CLIP classification for S5 and S6
Faces	Places	Bodies	Words	Food	Mean
S7↑	S8↑	S7↑	S8↑	S7↑	S8↑	S7↑	S8↑	S7↑	S8↑	S7↑	S8↑
NSD top-200	38.5	34.0	71.0	57.5	61.0	56.5	20.5	24.5	52.0	36.5	48.6	41.8
NSD top-100	35.0	36.0	76.0	48.0	63.0	61.0	26.0	21.0	56.0	37.0	51.2	40.6
BrainDiVE-200	73.0	77.5	93.5	94.5	65.0	64.5	31.0	56.5	85.5	55.5	69.6	69.7
BrainDiVE-100	69.0	72.0	94.0	94.0	65.0	67.0	25.0	56.0	92.0	74.0	69.0	72.6
Table S.4: Evaluating semantic specificity with zero-shot CLIP classification for S7 and S8.
10 Image gradients and synthesis process
In this section, we show examples of the image at each step of the synthesis process. We perform this visualization for face-, place-, body-, word-, and food- selective voxels. Two visualizations are shown for each set of voxels, we use S1 for all visualizations in this section. The diffusion model is guided only by the objective of maximizing a given set of voxels. We observe that coarse image structure emerges very early on from brain guidance. Furthermore, the gradient and diffusion model sometimes work against each other. For example in Figure S.14 for body voxels, the brain gradient induces the addition of an extra arm, while the diffusion has already generated three natural bodies. Or in Figure S.15 for word voxels, where the brain gradient attempts to add horizontal words, but they are warped by the diffusion model. Future work could explore early guidance only, as described in "SDEdit" and "MagicMix" [72, 73].
10.1 Face voxels
We show examples where the end result contains multiple faces (Figure S.9), or a single face (Figure S.10).
Figure S.9: Example 1 of face voxel guided image synthesis for S1. We utilize 50 steps of Multistep DPM-Solver++. We visualize the gradient magnitude w.r.t. the latent (top, normalized at each step for visualization) and the weighted euler RGB image that the brain encoder accepts (bottom).
Figure S.10: Example 2 of face voxel guided image synthesis for S1. We utilize 50 steps of Multistep DPM-Solver++. We visualize the gradient magnitude w.r.t. the latent (top, normalized at each step for visualization) and the weighted euler RGB image that the brain encoder accepts (bottom)
10.2 Place voxels
We show examples where the end result contains an indoor scene (Figure S.11), or an outdoor scene (Figure S.12).
Figure S.11: Example 1 of place voxel guided image synthesis for S1. We utilize 50 steps of Multistep DPM-Solver++. We visualize the gradient magnitude w.r.t. the latent (top, normalized at each step for visualization) and the weighted euler RGB image that the brain encoder accepts (bottom).
Figure S.12: Example 2 of place voxel guided image synthesis for S1. We utilize 50 steps of Multistep DPM-Solver++. We visualize the gradient magnitude w.r.t. the latent (top, normalized at each step for visualization) and the weighted euler RGB image that the brain encoder accepts (bottom).
10.3 Body voxels
We show examples where the end result contains an single person's body (Figure S.13), or an multiple people (Figure S.14).
Figure S.13: Example 1 of body voxel guided image synthesis for S1. We utilize 50 steps of Multistep DPM-Solver++. We visualize the gradient magnitude w.r.t. the latent (top, normalized at each step for visualization) and the weighted euler RGB image that the brain encoder accepts (bottom).
Figure S.14: Example 2 of body voxel guided image synthesis for S1. We utilize 50 steps of Multistep DPM-Solver++. We visualize the gradient magnitude w.r.t. the latent (top, normalized at each step for visualization) and the weighted euler RGB image that the brain encoder accepts (bottom).
10.4 Word voxels
We show examples where the end result contains recognizable words (Figure S.15), or glyph like objects (Figure S.16).
Figure S.15: Example 1 of word voxel guided image synthesis for S1. We utilize 50 steps of Multistep DPM-Solver++. We visualize the gradient magnitude w.r.t. the latent (top, normalized at each step for visualization) and the weighted euler RGB image that the brain encoder accepts (bottom).
Figure S.16: Example 2 of word voxel guided image synthesis for S1. We utilize 50 steps of Multistep DPM-Solver++. We visualize the gradient magnitude w.r.t. the latent (top, normalized at each step for visualization) and the weighted euler RGB image that the brain encoder accepts (bottom).
10.5 Food voxels
We show examples where the end result contains highly processed foods (Figure S.17, showing what appears to be a cake), or cooked food containing vegetables (Figure S.18).
Figure S.17: Example 1 of food voxel guided image synthesis for S1. We utilize 50 steps of Multistep DPM-Solver++. We visualize the gradient magnitude w.r.t. the latent (top, normalized at each step for visualization) and the weighted euler RGB image that the brain encoder accepts (bottom).
Figure S.18: Example 2 of food voxel guided image synthesis for S1. We utilize 50 steps of Multistep DPM-Solver++. We visualize the gradient magnitude w.r.t. the latent (top, normalized at each step for visualization) and the weighted euler RGB image that the brain encoder accepts (bottom).
11 Human behavioral study standard error
In this section, we show the human behavioral study results along with the standard error of the responses. Each question was answered by exactly 10 subjects from prolific.co. In each table, the results are show in the following format: Mean(SEM). Where Mean is the average response, while SEM is the standard error of the mean ratio across 10 subjects:  $(\mathrm{SEM} = \frac{\sigma}{\sqrt{10}})$
Which ROI has more...	photorealistic faces	animals	abstract shapes/lines
S1	S2	S5	S7	S1	S2	S5	S7	S1	S2	S5	S7
FFA-NSD	45(7.2)	43(8.3)	34(6.2)	41(6.5)	34(4.5)	34(3.5)	17(4.0)	15(3.8)	21(6.8)	6(4.0)	14(2.9)	22(6.6)
OFA-NSD	25(5.1)	22(6.4)	21(5.6)	18(5.3)	47(3.2)	36(2.5)	65(5.7)	65(6.4)	24(8.5)	44(9.2)	28(8.1)	25(6.4)
FFA-BrainDiVE	79(7.8)	89(4.8)	60(5.3)	52(5.3)	17(5.6)	13(3.5)	21(3.9)	19(2.2)	6(3.2)	11(6.4)	18(4.9)	20(6.6)
OFA-BrainDiVE	11(5.7)	4(2.5)	15(2.9)	22(5.1)	71(8.4)	61(8.2)	52(5.1)	50(3.5)	80(5.8)	79(7.4)	40(5.8)	39(7.1)