Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We leverage brain images from an unlabeled dataset to extract peaks, and train Peaks2Image to reconstruct images from the extracted peaks.

The review comment to be evaluated is:

This paper contains several controversial claims.

The paper uses Nilearn to estimate peaks and then uses these peaks to learn a model from data. However, there is no evaluation of the error associated with the selection of these peaks. Further, what evidence shows that you are more likely to see neural responses in these peaks than examining the whole brain data?

Another concern is the proposed formulation. As an example, what is the definition of the function f() in Equation 1? To learn f*, what constraints are considered? The same question can be asked for D in Equation 2. Due to the fact that D without any constraint may increase the chance of overfitting.

Empirical studies are another concern. The proposed method only compares with some classic machine learning approaches, such as Kernel Ridge or Space Gaussian. There are several groups of state-of-the-art techniques that aim at similar types of analysis and should be compared with the proposed approach, e.g., HTFA (Manning 2018), BRSA (Cai 2016), and DRD (Wu 2019).

In addition to the aforementioned concerns, the proposed approach should clarify some related concepts for multi-subject and multi-resource analysis. It seems that this study uses multi-subject data. However, I could not find anywhere that discusses how the author(s) functionally align brain responses from different subjects. Alternatively, Neuro Vault is a multi-resource repository where data are provided from different machines and environments. I also did not find any information on how the proposed method deals with bach effects to learn a model from multi-site data. You may provide additional information in a preprocessing/preparation section.

The evidence to be evaluated is:Thanks to the rise of large-scale databases of fMRI brain images such as NeuroVault (Gorgolewski et al., 2015) or OpenNeuro (Gorgolewski et al., 2017), decoding can now be performed across multiple studies (Mensch et al., 2017; Walters et al., 2022). Nonetheless, annotations associated with those images are often of low quality. Automatic strategies to improve this labeling are necessary to benefit properly from the scale of the data (Poldrack and Yarkoni, 2016).

Neuroscientists most frequently extract peaks (local maxima) above a statistical threshold from brain images. An image containing hundreds of thousands voxels is thus reduced to a few datapoints - less than 100 for most studies. We aim to reconstruct brain images from those few datapoints. This reconstruction problem is an inverse problem between the set of peaks and the image from which they were extracted. From a given set of \(l\) peaks \(\mathbf{P_{y}}\in\mathbb{R}^{3\times l}\) extracted from an image \(\mathbf{y}\in\mathbb{R}^{m}\) with \(m\) voxels, we learn the transformation \(f:\mathbb{R}^{3\times l}\mapsto\mathbb{R}^{m}\) by minimization of the \(\mathbf{L_{2}}\) error over the original image: \[f^{*}=\operatorname*{arg\,min}_{f}\mathbf{E_{y}}\|f(\mathbf{P_{y}})-\mathbf{ y}\|_{2}^{2} \tag{1}\] To solve the minimization problem in eq 1, we leverage a set of statistical maps taken from NeuroVault, an online repository of fMRI maps, from which we extract peak coordinates. This leads to a training set of peaks and the corresponding brain images to be reconstructed.

Peaks2Image consists of a neural network that reconstructs brain images from activity peaks.

Formally, we consider \(\mathbf{P}=(p_{1},..,\mathbf{p}_{l})\) peaks extracted from image \(\mathbf{y}\) where \(\forall i\in[1..l],\mathbf{p}_{i}\in\mathbb{R}^{3}\). Given a fixed sampling grid \(\mathbf{G}=(\mathbf{g}_{1},..,\mathbf{g}_{m})\) representing the voxels, where \(\forall j\in[1..m],\mathbf{g}_{j}\in\mathbb{R}^{3}\), and a Gaussian kernel \(\kappa\) we define the vector \(\mathbf{x_{y}}\) that samples \(\mathbf{P}\) on the grid \(\mathbf{G}\): \(\mathbf{x_{y}}=(x_{1},..,x_{m})\) with \(\forall j\in[1..m]:x_{j}=\sum_{i=1}^{l}\kappa(p_{i},g_{j})\).

We use the Dictionary of Functional Modes (DiFuMo) probabilistic atlas (Dadi et al., 2020), that efficiently reduces the \(m=50,000\) voxel values to \(k=1024\) components. DiFuMo has been trained on thousands of resting-state and task fMRI with non-negativity and sparsity constraints, making this atlas relevant to represent population-level information. In the rest of the paper, the term _brain image_ refers to its 1024-dimensional DiFuMo representation.

To train this reconstruction process, we use around 6k images from the #4438 NeuroVault collection ([https://neurovault.org/collections/4438](https://neurovault.org/collections/4438)). The reconstructed images are obtained from the predicted DiFuMo components by linear combinations of the dictionary components: \(\hat{\mathbf{y}}=\mathbf{D}f^{*}(\alpha(\mathbf{x}_{\mathbf{y}}))\).

By doing this on the most comprehensive public literature corpus, we reconstruct 13,000 brain images from the peak activation tables.

We leverage the NeuroVault database. We exclude a subset of images according to a set of rules such as having incoherent range of values (indicating that an image does not contain Z or T statistics), duplicated images or images with missing metadata to keep around 50k brain maps for our task.

We take collection #4438, without labels (6k maps), to train the reconstruction task.

The tool helps us parse the activation tables of those studies, totalling 400k peaks.

We generate pairs of matching (peaks, image) pairs from the collection (#4438) from NeuroVault images only.

We use Nileam's method for peak extraction, which first separatesthe images into statistically active clusters and retrieves peak coordinates from each cluster.

From a given set of peaks, we build a sparse Gaussian representation by placing Gaussian kernels at each peak position with a 9mm full-width at half-maximum, following what is done in Dockes et al. (2020).

The image reconstruction model is an MLP built from 3 dense layers with 1024 units and sigmoid activation implemented using PyTorch (Paszke et al., 2019). It is trained for 50 epochs using Adam optimizer and a learning rate of 1e-3. The loss is the mean-squared error (MSE) over the DiFuMo components. We select the best set of weights over the validation set, which is composed of 20% of collection #4438.

We evaluate the reconstruction produced by Peaks2Image on all NeuroVault except collection #4438 which represents a testing set of around 43k images. As Peaks2Image reconstructs a normalized image, we use as metrics both the mean-squared error that has been used at training time, and the correlation between the original and its reconstruction in the voxel space.

Peaks2Image significantly outperforms linear models.

We show the importance of the reconstruction process in the decoding performance by running the same decoding task over different representations of the input. In particular, we compare Peaks2Image reconstructions to the sparse Gaussian representations used in the literature. We show that, even though the correlation of sparse Gaussian representations remains close to Peaks2Image reconstruction, Peaks2Image largely outperforms the sparse Gaussian model on the decoding task.

We introduced Peaks2Image, a model for reconstructing brain images from peak activity coordinates. By reconstructing dense spatial representations, Peaks2Image bridges the gap between neuroscientific literature and brain image databases.

Most importantly, the decoding performance is reached without any labeling requirements on the images side, avoiding a time-consuming and error-prone task.

A promising avenue consists in relying on abundant resting-state data, that do not carry cognitive labels, yet display a rich repertoire of topographical maps, that can be extracted e.g. with Independent components analysis Smith et al. (2013). Peaks2Image paves the way towards better generation of large sets of images for neuroscientific studies.

For neuroscientific studies, we leveraged the data available at [https://github.com/neuroquery/neuroquery_data/tree/main/data](https://github.com/neuroquery/neuroquery_data/tree/main/data).

We show an example of the representation at different steps of the Peaks2Image model in figure 6. We also apply Peaks2Image to NeuroVault images that are associated to a set of cognitive terms. We compute the average of those reconstructions to show that Peaks2Image generates brain images that are consistent with expected patterns from the literature (fig 7).

Input: Figure 7: Reconstructions of NeuroVault samples: we show the average sparse Gaussian representation used in standard meta-analysis procedures (left) along with the Peaks2Image reconstruction (right) for NeuroVault samples containing certain terms. The reconstruction yields more extended networks (audition, face, language production) and sometimes biases the image away from the initial map (sadness). In some rare cases (visual) it focuses the activity patterns.

Input: Figure 6: Successive steps of Peaks2Image: we show the successive steps of Peaks2Image representations. From left to right, we collect brain images from NeuroVault from which we extract peaks above a statistical threshold, we transform the peaks into a sparse Gaussian representation from which we predict the Peaks2Image reconstruction.

Input: Figure 1: Reconstructing Brain Activation Maps from Activation Peaks (A) We learn the reconstruction process from NeuroVault collection #4438. After selection and preprocessing of the images, we extract the peaks that are above a statistical threshold. From those peaks, we build an intermediate representation by placing a Gaussian kernel at each peak as in Dockès et al. (2020); Ngo et al. (2021). We compress the representation using the DiFuMo atlas. We regress the original DiFuMo components of each image by leveraging a 3-layer multi-layer perceptron (MLP). (B) Once the reconstruction model is trained, we build intermediate Gaussian-kernel representations for the scraped studies, and reconstruct a brain map for each study using the above model, supporting further image-level computations such as decoding. From the text, we extract the tf-idf values for each word, and infer labels on a percentile-based threshold. (C) We perform decoding of NeuroVault images by training on the newly acquired representations of neuroscientific studies. We decode a broad set of cognitive processes benefiting from the large vocabulary.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim, evidence, and review comments, here is my assessment:

The claim "We leverage brain images from an unlabeled dataset to extract peaks, and train Peaks2Image to reconstruct images from the extracted peaks" appears to be well-stated and supported by the evidence. The paper clearly describes using NeuroVault collection #4438 (6k unlabeled maps) for training, details the peak extraction process using Nilearn, and demonstrates the reconstruction methodology through equations, architecture details, and visual results in Figures 1, 6 and 7. While the reviewer raises valid concerns about peak selection error and constraints, these don't directly challenge the basic claim about the training process and reconstruction capability.

<score>1</score>

Justification:
1. The evidence directly supports the claim's components:
- Use of unlabeled dataset (Collection #4438 with 6k maps)
- Peak extraction process is documented with Nilearn
- Training process and reconstruction are thoroughly detailed
- Visual results demonstrate the reconstruction

2. The claim is modest in scope - it simply states what was done without making grand assertions about performance or superiority

3. While the reviewer's concerns about peak selection error and constraints are valid methodological points, they don't make the basic descriptive claim overstated

4. The claim avoids exaggeration by:
- Not claiming perfect reconstruction
- Not claiming superiority over other methods
- Focusing on describing the process rather than its merits


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We leverage brain images from an unlabeled dataset to extract peaks, and train Peaks2Image to reconstruct images from the extracted peaks.

The review comment to be evaluated is:

Strength:
1.This paper is well-written and easy to read. The definition of tasks and problems are very clear.
2.For a decoding task, the idea that fully utilize the rich descriptions of the studied cognitive tasks in scientific report is novel.
Weaknesses
1.My major concern is the necessity of image reconstruction from peaks. As illustrated in the paper, the authors train a reconstruction network to convert the peak coordinates into images. In my opinion, this ill-posed problem is too hard to be reasonable. Reconstructing the fMRI from only a few peak coordinates (less than 100 according to the paper) seems impossible to recover the details of images. Thus, the reconstruction is unnecessary. If the peak coordinates contain enough information for the following decoding tasks, reconstruction would introduce inevitable noise. If the peak coordinates are not enough, reconstruction still can’t help the decoding since extra information comes from the ‘guessing’ of the trained network. Can the authors give more explanation about this?
2.Also, the experiments do not verify that image reconstruction can help in improving the decoding performance. The ablation study should compare with model using peak coordinates as input. So, I am wondering about the performance of model that uses peak coordinates as input.
3.Experimental results are all shown in figures, which is good for analysis. However, giving some extra tables with exact values can be better.

The evidence to be evaluated is:Thanks to the rise of large-scale databases of fMRI brain images such as NeuroVault (Gorgolewski et al., 2015) or OpenNeuro (Gorgolewski et al., 2017), decoding can now be performed across multiple studies (Mensch et al., 2017; Walters et al., 2022). Nonetheless, annotations associated with those images are often of low quality. Automatic strategies to improve this labeling are necessary to benefit properly from the scale of the data (Poldrack and Yarkoni, 2016).

Neuroscientists most frequently extract peaks (local maxima) above a statistical threshold from brain images. An image containing hundreds of thousands voxels is thus reduced to a few datapoints - less than 100 for most studies. We aim to reconstruct brain images from those few datapoints. This reconstruction problem is an inverse problem between the set of peaks and the image from which they were extracted. From a given set of \(l\) peaks \(\mathbf{P_{y}}\in\mathbb{R}^{3\times l}\) extracted from an image \(\mathbf{y}\in\mathbb{R}^{m}\) with \(m\) voxels, we learn the transformation \(f:\mathbb{R}^{3\times l}\mapsto\mathbb{R}^{m}\) by minimization of the \(\mathbf{L_{2}}\) error over the original image: \[f^{*}=\operatorname*{arg\,min}_{f}\mathbf{E_{y}}\|f(\mathbf{P_{y}})-\mathbf{ y}\|_{2}^{2} \tag{1}\] To solve the minimization problem in eq 1, we leverage a set of statistical maps taken from NeuroVault, an online repository of fMRI maps, from which we extract peak coordinates. This leads to a training set of peaks and the corresponding brain images to be reconstructed.

Peaks2Image consists of a neural network that reconstructs brain images from activity peaks.

Formally, we consider \(\mathbf{P}=(p_{1},..,\mathbf{p}_{l})\) peaks extracted from image \(\mathbf{y}\) where \(\forall i\in[1..l],\mathbf{p}_{i}\in\mathbb{R}^{3}\). Given a fixed sampling grid \(\mathbf{G}=(\mathbf{g}_{1},..,\mathbf{g}_{m})\) representing the voxels, where \(\forall j\in[1..m],\mathbf{g}_{j}\in\mathbb{R}^{3}\), and a Gaussian kernel \(\kappa\) we define the vector \(\mathbf{x_{y}}\) that samples \(\mathbf{P}\) on the grid \(\mathbf{G}\): \(\mathbf{x_{y}}=(x_{1},..,x_{m})\) with \(\forall j\in[1..m]:x_{j}=\sum_{i=1}^{l}\kappa(p_{i},g_{j})\).

We use the Dictionary of Functional Modes (DiFuMo) probabilistic atlas (Dadi et al., 2020), that efficiently reduces the \(m=50,000\) voxel values to \(k=1024\) components. DiFuMo has been trained on thousands of resting-state and task fMRI with non-negativity and sparsity constraints, making this atlas relevant to represent population-level information. In the rest of the paper, the term _brain image_ refers to its 1024-dimensional DiFuMo representation.

To train this reconstruction process, we use around 6k images from the #4438 NeuroVault collection ([https://neurovault.org/collections/4438](https://neurovault.org/collections/4438)). The reconstructed images are obtained from the predicted DiFuMo components by linear combinations of the dictionary components: \(\hat{\mathbf{y}}=\mathbf{D}f^{*}(\alpha(\mathbf{x}_{\mathbf{y}}))\).

By doing this on the most comprehensive public literature corpus, we reconstruct 13,000 brain images from the peak activation tables.

We leverage the NeuroVault database. We exclude a subset of images according to a set of rules such as having incoherent range of values (indicating that an image does not contain Z or T statistics), duplicated images or images with missing metadata to keep around 50k brain maps for our task.

We take collection #4438, without labels (6k maps), to train the reconstruction task.

The tool helps us parse the activation tables of those studies, totalling 400k peaks.

We generate pairs of matching (peaks, image) pairs from the collection (#4438) from NeuroVault images only.

We use Nileam's method for peak extraction, which first separatesthe images into statistically active clusters and retrieves peak coordinates from each cluster.

From a given set of peaks, we build a sparse Gaussian representation by placing Gaussian kernels at each peak position with a 9mm full-width at half-maximum, following what is done in Dockes et al. (2020).

The image reconstruction model is an MLP built from 3 dense layers with 1024 units and sigmoid activation implemented using PyTorch (Paszke et al., 2019). It is trained for 50 epochs using Adam optimizer and a learning rate of 1e-3. The loss is the mean-squared error (MSE) over the DiFuMo components. We select the best set of weights over the validation set, which is composed of 20% of collection #4438.

We evaluate the reconstruction produced by Peaks2Image on all NeuroVault except collection #4438 which represents a testing set of around 43k images. As Peaks2Image reconstructs a normalized image, we use as metrics both the mean-squared error that has been used at training time, and the correlation between the original and its reconstruction in the voxel space.

Peaks2Image significantly outperforms linear models.

We show the importance of the reconstruction process in the decoding performance by running the same decoding task over different representations of the input. In particular, we compare Peaks2Image reconstructions to the sparse Gaussian representations used in the literature. We show that, even though the correlation of sparse Gaussian representations remains close to Peaks2Image reconstruction, Peaks2Image largely outperforms the sparse Gaussian model on the decoding task.

We introduced Peaks2Image, a model for reconstructing brain images from peak activity coordinates. By reconstructing dense spatial representations, Peaks2Image bridges the gap between neuroscientific literature and brain image databases.

Most importantly, the decoding performance is reached without any labeling requirements on the images side, avoiding a time-consuming and error-prone task.

A promising avenue consists in relying on abundant resting-state data, that do not carry cognitive labels, yet display a rich repertoire of topographical maps, that can be extracted e.g. with Independent components analysis Smith et al. (2013). Peaks2Image paves the way towards better generation of large sets of images for neuroscientific studies.

For neuroscientific studies, we leveraged the data available at [https://github.com/neuroquery/neuroquery_data/tree/main/data](https://github.com/neuroquery/neuroquery_data/tree/main/data).

We show an example of the representation at different steps of the Peaks2Image model in figure 6. We also apply Peaks2Image to NeuroVault images that are associated to a set of cognitive terms. We compute the average of those reconstructions to show that Peaks2Image generates brain images that are consistent with expected patterns from the literature (fig 7).

Input: Figure 7: Reconstructions of NeuroVault samples: we show the average sparse Gaussian representation used in standard meta-analysis procedures (left) along with the Peaks2Image reconstruction (right) for NeuroVault samples containing certain terms. The reconstruction yields more extended networks (audition, face, language production) and sometimes biases the image away from the initial map (sadness). In some rare cases (visual) it focuses the activity patterns.

Input: Figure 6: Successive steps of Peaks2Image: we show the successive steps of Peaks2Image representations. From left to right, we collect brain images from NeuroVault from which we extract peaks above a statistical threshold, we transform the peaks into a sparse Gaussian representation from which we predict the Peaks2Image reconstruction.

Input: Figure 1: Reconstructing Brain Activation Maps from Activation Peaks (A) We learn the reconstruction process from NeuroVault collection #4438. After selection and preprocessing of the images, we extract the peaks that are above a statistical threshold. From those peaks, we build an intermediate representation by placing a Gaussian kernel at each peak as in Dockès et al. (2020); Ngo et al. (2021). We compress the representation using the DiFuMo atlas. We regress the original DiFuMo components of each image by leveraging a 3-layer multi-layer perceptron (MLP). (B) Once the reconstruction model is trained, we build intermediate Gaussian-kernel representations for the scraped studies, and reconstruct a brain map for each study using the above model, supporting further image-level computations such as decoding. From the text, we extract the tf-idf values for each word, and infer labels on a percentile-based threshold. (C) We perform decoding of NeuroVault images by training on the newly acquired representations of neuroscientific studies. We decode a broad set of cognitive processes benefiting from the large vocabulary.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I assess that it is well-stated and accurately reflects what was demonstrated. The evidence shows a clear methodology for extracting peaks from unlabeled NeuroVault images (collection #4438 containing 6k maps) and training a neural network (Peaks2Image) to reconstruct brain images from these peaks. The process is thoroughly documented with mathematical formulations, implementation details, and visual results demonstrating the reconstruction quality (Figure 6). The review comment raises valid concerns about reconstruction quality, but these limitations don't invalidate the factual claim about what was done.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We use Peaks2Image to obtain for the first time images associated with neuroscientific studies that only provide stereotactic coordinates.

The evidence to be evaluated is:Although there has been interest in enriching brain images to increase the amount of data (Zhuang et al., 2019), to generate or reconstruct fMRI images (Manning et al., 2018; Cai et al., 2016), there has been limited interest in generating reconstructions of statistical brain maps from peaks (Gorgolewski et al., 2019), probably due to the daunting data dimensionality. In particular, we found no attempt at merging rich textual information from neuroscientific studies with the dense spatial information of brain images. We focus on this particular aspect, proving that obtaining image representations for neuroscientific studies helps overcome the limitations of CBMA by generating a corpus with both rich textual annotations and dense spatial representations. Neuroscientists most frequently extract peaks (local maxima) above a statistical threshold from brain images. An image containing hundreds of thousands voxels is thus reduced to a few datapoints - less than 100 for most studies. We aim to reconstruct brain images from those few datapoints. This reconstruction problem is an inverse problem between the set of peaks and the image from which they were extracted. From a given set of \(l\) peaks \(\mathbf{P_{y}}\in\mathbb{R}^{3\times l}\) extracted from an image \(\mathbf{y}\in\mathbb{R}^{m}\) with \(m\) voxels, we learn the transformation \(f:\mathbb{R}^{3\times l}\mapsto\mathbb{R}^{m}\) by minimization of the \(\mathbf{L_{2}}\) error over the original image:

Once trained, Peaks2Image can be applied to any set of peak coordinates from neuroscientific publications. This leads to a combination of both continuous brain images and extensively annotated studies, which opens the possibility to decode a larger set of cognitive concepts.

Peaks2Image consists of a neural network that reconstructs brain images from activity peaks.

Peaks2Image outputs images from any set of positions. We apply it to neuroscientific publications to yield a set of dense brain maps accompanied by rich textual annotations, reducing the technical gap between CBMA and IBMA. By doing this on the most comprehensive public literature corpus, we reconstruct 13,000 brain images from the peak activation tables.

The model is trained from the sole neuroscientific studies, without supervision from any NeuroVault sample or its labels.

Peaks2Image could extend to a set of cognitive terms as large as the vocabulary size without any additional cost.

We take collection #4438, without labels (6k maps), to train the reconstruction task.

We generate pairs of matching (peaks, image) pairs from the collection (#4438) from NeuroVault images only.

From a given set of peaks, we build a sparse Gaussian representation by placing Gaussian kernels at each peak position with a 9mm full-width at half-maximum, following what is done in Dockes et al. (2020).

We evaluate the reconstruction produced by Peaks2Image on all NeuroVault except collection #4438 which represents a testing set of around 43k images. As Peaks2Image reconstructs a normalized image, we use as metrics both the mean-squared error that has been used at training time, and the correlation between the original and its reconstruction in the voxel space.

Peaks2Image performs well over all the main concept families of Cognitive Atlas. We show the importance of the reconstruction process in the decoding performance by running the same decoding task over different representations of the input. In particular, we compare Peaks2Image reconstructions to the sparse Gaussian representations used in the literature. We show that, even though the correlation of sparse Gaussian representations remains close to Peaks2Image reconstruction, Peaks2Image largely outperforms the sparse Gaussian model on the decoding task.

We introduced Peaks2Image, a model for reconstructing brain images from peak activity coordinates. By reconstructing dense spatial representations, Peaks2Image bridges the gap between neuroscientific literature and brain image databases. Compared to images-based models, it allows to decode a much broader set of cognitive processes by leveraging the rich textual information of neuroscientific studies, along with the reconstructions. Most importantly, the decoding performance is reached without any labeling requirements on the images side, avoiding a time-consuming and error-prone task.

Peaks2Image paves the way towards better generation of large sets of images for neuroscientific studies. It shows that one can outrun the limitations of large-scale poor labeling quality by performing zero-shot decoding. In particular, neuroscientific studies could become a challenging application for recent methods in text-to-image representation learning such as Radford et al. (2021).

We show an example of the representation at different steps of the Peaks2Image model in figure 6. We also apply Peaks2Image to NeuroVault images that are associated to a set of cognitive terms. We compute the average of those reconstructions to show that Peaks2Image generates brain images that are consistent with expected patterns from the literature (fig 7).

Input: Figure 7: Reconstructions of NeuroVault samples: we show the average sparse Gaussian representation used in standard meta-analysis procedures (left) along with the Peaks2Image reconstruction (right) for NeuroVault samples containing certain terms. The reconstruction yields more extended networks (audition, face, language production) and sometimes biases the image away from the initial map (sadness). In some rare cases (visual) it focuses the activity patterns.

Input: Figure 6: Successive steps of Peaks2Image: we show the successive steps of Peaks2Image representations. From left to right, we collect brain images from NeuroVault from which we extract peaks above a statistical threshold, we transform the peaks into a sparse Gaussian representation from which we predict the Peaks2Image reconstruction.

Input: Figure 1: Reconstructing Brain Activation Maps from Activation Peaks (A) We learn the reconstruction process from NeuroVault collection #4438. After selection and preprocessing of the images, we extract the peaks that are above a statistical threshold. From those peaks, we build an intermediate representation by placing a Gaussian kernel at each peak as in Dockès et al. (2020); Ngo et al. (2021). We compress the representation using the DiFuMo atlas. We regress the original DiFuMo components of each image by leveraging a 3-layer multi-layer perceptron (MLP). (B) Once the reconstruction model is trained, we build intermediate Gaussian-kernel representations for the scraped studies, and reconstruct a brain map for each study using the above model, supporting further image-level computations such as decoding. From the text, we extract the tf-idf values for each word, and infer labels on a percentile-based threshold. (C) We perform decoding of NeuroVault images by training on the newly acquired representations of neuroscientific studies. We decode a broad set of cognitive processes benefiting from the large vocabulary.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find this claim to be slightly overstated. While Peaks2Image does represent a novel approach to generating images from stereotactic coordinates, the evidence shows some prior work in generating reconstructions from peaks (e.g., "sparse Gaussian representations used in the literature"). The claim of being "first" is too strong, though the method does introduce innovative aspects by combining textual information with spatial data in a new way. The evidence demonstrates the method's effectiveness but should acknowledge existing approaches more explicitly.

<score>3</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We use Peaks2Image to obtain for the first time images associated with neuroscientific studies that only provide stereotactic coordinates.

The review comment to be evaluated is:

Strengths
1. The authors proposed an interesting workflow. If I understand it correctly, the authors are using the following priors: (1) fMRI brain maps is tightly correlated to the underlying cognitive states, and (2) distribution of peak locations in fMRI brain maps is highly correlated to the overall global pattern in fMRI brain maps. With these priors, the authors proposed to train a neural network model to predict the global fMRI pattern from sparse locations of peaks, and with that trained model they synthesized a paired dataset of (cognitive descriptions, global fMRI patterns) using the (cognitive descriptions, sparse locations of peaks) from the neuroscientific literatures. Finally, they trained a separate decoder with the (cognitive descriptions, global fMRI patterns) pairs to decode the former from the latter. The decoder is the ultimate piece of mastery that can describe the underlying cognitive states of an arbitrary fMRI image.

Weaknesses
1. In my humble opinion, the abstract may need to be better articulated. I was very disoriented and confused by the abstract until I finished reading the introduction section. Specifically, I was confused by (1) what the two complementary sources of data are, (2) why they are complementary, and (3) what is the proposed method trying to accomplish. For (1) and (2), even though the two sources of data are both explicitly mentioned, I did not have enough context to parse their relationship. In contrary, the introduction section has a very natural flow and is painless to follow.
2. It is a bit misleading that when the authors mentioned the word “image” they did not necessarily mean “image”, but rather the “1x1024 vector representation in the DeFuMo space of the image”. I believe it might be helpful to provide a clarifying statement somewhere early in the paper to avoid potential misunderstanding.
3. Figure 1 can be further polished. (1) Figure 1A did not clearly show what data format is provided and what are derived. It might be better if that information is illustrated more explicitly. (2) A lot of things are represented as “blue thin rectangle”, yet some of them are modules/operations while others are entities. It may be better to replace some of them. For example, “Reconstruction” and “decoder” can be replaced by sketches of the neural networks, and “Labels” can be replaced by some clipart object, etc. (3) The caption mentioned that in Figure 1B, the brain map is reconstructed using the trained model, so it would be better to show the sketch of model in an appropriate place and indicate that its weights are frozen.
4. The reconstruction performance (Section 5.1, Figure 2) is quite poor. a correlation of 0.3 does not seem too promising.

Minor Things
1. Is there some typo in the following sentence? Page 5, 3rd paragraph. “When the percentile corresponds to a To avoid selecting studies with a null tf-idf value, we replace the percentile threshold with a strictly positive tf-idf value criteria.”

The evidence to be evaluated is:Although there has been interest in enriching brain images to increase the amount of data (Zhuang et al., 2019), to generate or reconstruct fMRI images (Manning et al., 2018; Cai et al., 2016), there has been limited interest in generating reconstructions of statistical brain maps from peaks (Gorgolewski et al., 2019), probably due to the daunting data dimensionality. In particular, we found no attempt at merging rich textual information from neuroscientific studies with the dense spatial information of brain images. We focus on this particular aspect, proving that obtaining image representations for neuroscientific studies helps overcome the limitations of CBMA by generating a corpus with both rich textual annotations and dense spatial representations. Neuroscientists most frequently extract peaks (local maxima) above a statistical threshold from brain images. An image containing hundreds of thousands voxels is thus reduced to a few datapoints - less than 100 for most studies. We aim to reconstruct brain images from those few datapoints. This reconstruction problem is an inverse problem between the set of peaks and the image from which they were extracted. From a given set of \(l\) peaks \(\mathbf{P_{y}}\in\mathbb{R}^{3\times l}\) extracted from an image \(\mathbf{y}\in\mathbb{R}^{m}\) with \(m\) voxels, we learn the transformation \(f:\mathbb{R}^{3\times l}\mapsto\mathbb{R}^{m}\) by minimization of the \(\mathbf{L_{2}}\) error over the original image:

Once trained, Peaks2Image can be applied to any set of peak coordinates from neuroscientific publications. This leads to a combination of both continuous brain images and extensively annotated studies, which opens the possibility to decode a larger set of cognitive concepts.

Peaks2Image consists of a neural network that reconstructs brain images from activity peaks.

Peaks2Image outputs images from any set of positions. We apply it to neuroscientific publications to yield a set of dense brain maps accompanied by rich textual annotations, reducing the technical gap between CBMA and IBMA. By doing this on the most comprehensive public literature corpus, we reconstruct 13,000 brain images from the peak activation tables.

The model is trained from the sole neuroscientific studies, without supervision from any NeuroVault sample or its labels.

Peaks2Image could extend to a set of cognitive terms as large as the vocabulary size without any additional cost.

We take collection #4438, without labels (6k maps), to train the reconstruction task.

We generate pairs of matching (peaks, image) pairs from the collection (#4438) from NeuroVault images only.

From a given set of peaks, we build a sparse Gaussian representation by placing Gaussian kernels at each peak position with a 9mm full-width at half-maximum, following what is done in Dockes et al. (2020).

We evaluate the reconstruction produced by Peaks2Image on all NeuroVault except collection #4438 which represents a testing set of around 43k images. As Peaks2Image reconstructs a normalized image, we use as metrics both the mean-squared error that has been used at training time, and the correlation between the original and its reconstruction in the voxel space.

Peaks2Image performs well over all the main concept families of Cognitive Atlas. We show the importance of the reconstruction process in the decoding performance by running the same decoding task over different representations of the input. In particular, we compare Peaks2Image reconstructions to the sparse Gaussian representations used in the literature. We show that, even though the correlation of sparse Gaussian representations remains close to Peaks2Image reconstruction, Peaks2Image largely outperforms the sparse Gaussian model on the decoding task.

We introduced Peaks2Image, a model for reconstructing brain images from peak activity coordinates. By reconstructing dense spatial representations, Peaks2Image bridges the gap between neuroscientific literature and brain image databases. Compared to images-based models, it allows to decode a much broader set of cognitive processes by leveraging the rich textual information of neuroscientific studies, along with the reconstructions. Most importantly, the decoding performance is reached without any labeling requirements on the images side, avoiding a time-consuming and error-prone task.

Peaks2Image paves the way towards better generation of large sets of images for neuroscientific studies. It shows that one can outrun the limitations of large-scale poor labeling quality by performing zero-shot decoding. In particular, neuroscientific studies could become a challenging application for recent methods in text-to-image representation learning such as Radford et al. (2021).

We show an example of the representation at different steps of the Peaks2Image model in figure 6. We also apply Peaks2Image to NeuroVault images that are associated to a set of cognitive terms. We compute the average of those reconstructions to show that Peaks2Image generates brain images that are consistent with expected patterns from the literature (fig 7).

Input: Figure 7: Reconstructions of NeuroVault samples: we show the average sparse Gaussian representation used in standard meta-analysis procedures (left) along with the Peaks2Image reconstruction (right) for NeuroVault samples containing certain terms. The reconstruction yields more extended networks (audition, face, language production) and sometimes biases the image away from the initial map (sadness). In some rare cases (visual) it focuses the activity patterns.

Input: Figure 6: Successive steps of Peaks2Image: we show the successive steps of Peaks2Image representations. From left to right, we collect brain images from NeuroVault from which we extract peaks above a statistical threshold, we transform the peaks into a sparse Gaussian representation from which we predict the Peaks2Image reconstruction.

Input: Figure 1: Reconstructing Brain Activation Maps from Activation Peaks (A) We learn the reconstruction process from NeuroVault collection #4438. After selection and preprocessing of the images, we extract the peaks that are above a statistical threshold. From those peaks, we build an intermediate representation by placing a Gaussian kernel at each peak as in Dockès et al. (2020); Ngo et al. (2021). We compress the representation using the DiFuMo atlas. We regress the original DiFuMo components of each image by leveraging a 3-layer multi-layer perceptron (MLP). (B) Once the reconstruction model is trained, we build intermediate Gaussian-kernel representations for the scraped studies, and reconstruct a brain map for each study using the above model, supporting further image-level computations such as decoding. From the text, we extract the tf-idf values for each word, and infer labels on a percentile-based threshold. (C) We perform decoding of NeuroVault images by training on the newly acquired representations of neuroscientific studies. We decode a broad set of cognitive processes benefiting from the large vocabulary.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, the claim "We use Peaks2Image to obtain for the first time images associated with neuroscientific studies that only provide stereotactic coordinates" appears overstated. While the paper does present a novel approach to reconstructing brain images from peak coordinates, the evidence shows:

1. Previous work exists in generating reconstructions from peaks (referenced: Gorgolewski et al., 2019)
2. The reconstruction performance is noted as "quite poor" with correlations of only 0.3 (from reviewer comments)
3. The reconstructed images show clear differences from original patterns, sometimes biasing away from initial maps (evidenced in Figure 7)
4. The authors themselves use more measured language elsewhere, describing it as "bridging the gap" rather than being the first

<score>4</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We use Peaks2Image to obtain for the first time images associated with neuroscientific studies that only provide stereotactic coordinates.

The review comment to be evaluated is:

This paper contains several controversial claims.

The paper uses Nilearn to estimate peaks and then uses these peaks to learn a model from data. However, there is no evaluation of the error associated with the selection of these peaks. Further, what evidence shows that you are more likely to see neural responses in these peaks than examining the whole brain data?

Another concern is the proposed formulation. As an example, what is the definition of the function f() in Equation 1? To learn f*, what constraints are considered? The same question can be asked for D in Equation 2. Due to the fact that D without any constraint may increase the chance of overfitting.

Empirical studies are another concern. The proposed method only compares with some classic machine learning approaches, such as Kernel Ridge or Space Gaussian. There are several groups of state-of-the-art techniques that aim at similar types of analysis and should be compared with the proposed approach, e.g., HTFA (Manning 2018), BRSA (Cai 2016), and DRD (Wu 2019).

In addition to the aforementioned concerns, the proposed approach should clarify some related concepts for multi-subject and multi-resource analysis. It seems that this study uses multi-subject data. However, I could not find anywhere that discusses how the author(s) functionally align brain responses from different subjects. Alternatively, Neuro Vault is a multi-resource repository where data are provided from different machines and environments. I also did not find any information on how the proposed method deals with bach effects to learn a model from multi-site data. You may provide additional information in a preprocessing/preparation section.

The evidence to be evaluated is:Although there has been interest in enriching brain images to increase the amount of data (Zhuang et al., 2019), to generate or reconstruct fMRI images (Manning et al., 2018; Cai et al., 2016), there has been limited interest in generating reconstructions of statistical brain maps from peaks (Gorgolewski et al., 2019), probably due to the daunting data dimensionality. In particular, we found no attempt at merging rich textual information from neuroscientific studies with the dense spatial information of brain images. We focus on this particular aspect, proving that obtaining image representations for neuroscientific studies helps overcome the limitations of CBMA by generating a corpus with both rich textual annotations and dense spatial representations. Neuroscientists most frequently extract peaks (local maxima) above a statistical threshold from brain images. An image containing hundreds of thousands voxels is thus reduced to a few datapoints - less than 100 for most studies. We aim to reconstruct brain images from those few datapoints. This reconstruction problem is an inverse problem between the set of peaks and the image from which they were extracted. From a given set of \(l\) peaks \(\mathbf{P_{y}}\in\mathbb{R}^{3\times l}\) extracted from an image \(\mathbf{y}\in\mathbb{R}^{m}\) with \(m\) voxels, we learn the transformation \(f:\mathbb{R}^{3\times l}\mapsto\mathbb{R}^{m}\) by minimization of the \(\mathbf{L_{2}}\) error over the original image:

Once trained, Peaks2Image can be applied to any set of peak coordinates from neuroscientific publications. This leads to a combination of both continuous brain images and extensively annotated studies, which opens the possibility to decode a larger set of cognitive concepts.

Peaks2Image consists of a neural network that reconstructs brain images from activity peaks.

Peaks2Image outputs images from any set of positions. We apply it to neuroscientific publications to yield a set of dense brain maps accompanied by rich textual annotations, reducing the technical gap between CBMA and IBMA. By doing this on the most comprehensive public literature corpus, we reconstruct 13,000 brain images from the peak activation tables.

The model is trained from the sole neuroscientific studies, without supervision from any NeuroVault sample or its labels.

Peaks2Image could extend to a set of cognitive terms as large as the vocabulary size without any additional cost.

We take collection #4438, without labels (6k maps), to train the reconstruction task.

We generate pairs of matching (peaks, image) pairs from the collection (#4438) from NeuroVault images only.

From a given set of peaks, we build a sparse Gaussian representation by placing Gaussian kernels at each peak position with a 9mm full-width at half-maximum, following what is done in Dockes et al. (2020).

We evaluate the reconstruction produced by Peaks2Image on all NeuroVault except collection #4438 which represents a testing set of around 43k images. As Peaks2Image reconstructs a normalized image, we use as metrics both the mean-squared error that has been used at training time, and the correlation between the original and its reconstruction in the voxel space.

Peaks2Image performs well over all the main concept families of Cognitive Atlas. We show the importance of the reconstruction process in the decoding performance by running the same decoding task over different representations of the input. In particular, we compare Peaks2Image reconstructions to the sparse Gaussian representations used in the literature. We show that, even though the correlation of sparse Gaussian representations remains close to Peaks2Image reconstruction, Peaks2Image largely outperforms the sparse Gaussian model on the decoding task.

We introduced Peaks2Image, a model for reconstructing brain images from peak activity coordinates. By reconstructing dense spatial representations, Peaks2Image bridges the gap between neuroscientific literature and brain image databases. Compared to images-based models, it allows to decode a much broader set of cognitive processes by leveraging the rich textual information of neuroscientific studies, along with the reconstructions. Most importantly, the decoding performance is reached without any labeling requirements on the images side, avoiding a time-consuming and error-prone task.

Peaks2Image paves the way towards better generation of large sets of images for neuroscientific studies. It shows that one can outrun the limitations of large-scale poor labeling quality by performing zero-shot decoding. In particular, neuroscientific studies could become a challenging application for recent methods in text-to-image representation learning such as Radford et al. (2021).

We show an example of the representation at different steps of the Peaks2Image model in figure 6. We also apply Peaks2Image to NeuroVault images that are associated to a set of cognitive terms. We compute the average of those reconstructions to show that Peaks2Image generates brain images that are consistent with expected patterns from the literature (fig 7).

Input: Figure 7: Reconstructions of NeuroVault samples: we show the average sparse Gaussian representation used in standard meta-analysis procedures (left) along with the Peaks2Image reconstruction (right) for NeuroVault samples containing certain terms. The reconstruction yields more extended networks (audition, face, language production) and sometimes biases the image away from the initial map (sadness). In some rare cases (visual) it focuses the activity patterns.

Input: Figure 6: Successive steps of Peaks2Image: we show the successive steps of Peaks2Image representations. From left to right, we collect brain images from NeuroVault from which we extract peaks above a statistical threshold, we transform the peaks into a sparse Gaussian representation from which we predict the Peaks2Image reconstruction.

Input: Figure 1: Reconstructing Brain Activation Maps from Activation Peaks (A) We learn the reconstruction process from NeuroVault collection #4438. After selection and preprocessing of the images, we extract the peaks that are above a statistical threshold. From those peaks, we build an intermediate representation by placing a Gaussian kernel at each peak as in Dockès et al. (2020); Ngo et al. (2021). We compress the representation using the DiFuMo atlas. We regress the original DiFuMo components of each image by leveraging a 3-layer multi-layer perceptron (MLP). (B) Once the reconstruction model is trained, we build intermediate Gaussian-kernel representations for the scraped studies, and reconstruct a brain map for each study using the above model, supporting further image-level computations such as decoding. From the text, we extract the tf-idf values for each word, and infer labels on a percentile-based threshold. (C) We perform decoding of NeuroVault images by training on the newly acquired representations of neuroscientific studies. We decode a broad set of cognitive processes benefiting from the large vocabulary.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find the statement "We use Peaks2Image to obtain for the first time images associated with neuroscientific studies that only provide stereotactic coordinates" to be somewhat overstated.

While the evidence shows Peaks2Image is novel in combining textual information with spatial reconstruction from peaks, previous work has attempted to reconstruct or generate brain images (Manning 2018, Cai 2016). The reviewer points out that similar approaches exist (HTFA, BRSA, DRD) that weren't compared against. The claim of being "first" overlooks these prior contributions. Additionally, there are methodological concerns raised about peak selection validation and multi-subject alignment that weren't fully addressed.

The evidence demonstrates Peaks2Image makes meaningful contributions in bridging CBMA and IBMA approaches, but presenting it as the first method to generate images from coordinates overstates its novelty.

<score>4</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

We use Peaks2Image to obtain for the first time images associated with neuroscientific studies that only provide stereotactic coordinates.

The review comment to be evaluated is:

Strength:
1.This paper is well-written and easy to read. The definition of tasks and problems are very clear.
2.For a decoding task, the idea that fully utilize the rich descriptions of the studied cognitive tasks in scientific report is novel.
Weaknesses
1.My major concern is the necessity of image reconstruction from peaks. As illustrated in the paper, the authors train a reconstruction network to convert the peak coordinates into images. In my opinion, this ill-posed problem is too hard to be reasonable. Reconstructing the fMRI from only a few peak coordinates (less than 100 according to the paper) seems impossible to recover the details of images. Thus, the reconstruction is unnecessary. If the peak coordinates contain enough information for the following decoding tasks, reconstruction would introduce inevitable noise. If the peak coordinates are not enough, reconstruction still can’t help the decoding since extra information comes from the ‘guessing’ of the trained network. Can the authors give more explanation about this?
2.Also, the experiments do not verify that image reconstruction can help in improving the decoding performance. The ablation study should compare with model using peak coordinates as input. So, I am wondering about the performance of model that uses peak coordinates as input.
3.Experimental results are all shown in figures, which is good for analysis. However, giving some extra tables with exact values can be better.

The evidence to be evaluated is:Although there has been interest in enriching brain images to increase the amount of data (Zhuang et al., 2019), to generate or reconstruct fMRI images (Manning et al., 2018; Cai et al., 2016), there has been limited interest in generating reconstructions of statistical brain maps from peaks (Gorgolewski et al., 2019), probably due to the daunting data dimensionality. In particular, we found no attempt at merging rich textual information from neuroscientific studies with the dense spatial information of brain images. We focus on this particular aspect, proving that obtaining image representations for neuroscientific studies helps overcome the limitations of CBMA by generating a corpus with both rich textual annotations and dense spatial representations. Neuroscientists most frequently extract peaks (local maxima) above a statistical threshold from brain images. An image containing hundreds of thousands voxels is thus reduced to a few datapoints - less than 100 for most studies. We aim to reconstruct brain images from those few datapoints. This reconstruction problem is an inverse problem between the set of peaks and the image from which they were extracted. From a given set of \(l\) peaks \(\mathbf{P_{y}}\in\mathbb{R}^{3\times l}\) extracted from an image \(\mathbf{y}\in\mathbb{R}^{m}\) with \(m\) voxels, we learn the transformation \(f:\mathbb{R}^{3\times l}\mapsto\mathbb{R}^{m}\) by minimization of the \(\mathbf{L_{2}}\) error over the original image:

Once trained, Peaks2Image can be applied to any set of peak coordinates from neuroscientific publications. This leads to a combination of both continuous brain images and extensively annotated studies, which opens the possibility to decode a larger set of cognitive concepts.

Peaks2Image consists of a neural network that reconstructs brain images from activity peaks.

Peaks2Image outputs images from any set of positions. We apply it to neuroscientific publications to yield a set of dense brain maps accompanied by rich textual annotations, reducing the technical gap between CBMA and IBMA. By doing this on the most comprehensive public literature corpus, we reconstruct 13,000 brain images from the peak activation tables.

The model is trained from the sole neuroscientific studies, without supervision from any NeuroVault sample or its labels.

Peaks2Image could extend to a set of cognitive terms as large as the vocabulary size without any additional cost.

We take collection #4438, without labels (6k maps), to train the reconstruction task.

We generate pairs of matching (peaks, image) pairs from the collection (#4438) from NeuroVault images only.

From a given set of peaks, we build a sparse Gaussian representation by placing Gaussian kernels at each peak position with a 9mm full-width at half-maximum, following what is done in Dockes et al. (2020).

We evaluate the reconstruction produced by Peaks2Image on all NeuroVault except collection #4438 which represents a testing set of around 43k images. As Peaks2Image reconstructs a normalized image, we use as metrics both the mean-squared error that has been used at training time, and the correlation between the original and its reconstruction in the voxel space.

Peaks2Image performs well over all the main concept families of Cognitive Atlas. We show the importance of the reconstruction process in the decoding performance by running the same decoding task over different representations of the input. In particular, we compare Peaks2Image reconstructions to the sparse Gaussian representations used in the literature. We show that, even though the correlation of sparse Gaussian representations remains close to Peaks2Image reconstruction, Peaks2Image largely outperforms the sparse Gaussian model on the decoding task.

We introduced Peaks2Image, a model for reconstructing brain images from peak activity coordinates. By reconstructing dense spatial representations, Peaks2Image bridges the gap between neuroscientific literature and brain image databases. Compared to images-based models, it allows to decode a much broader set of cognitive processes by leveraging the rich textual information of neuroscientific studies, along with the reconstructions. Most importantly, the decoding performance is reached without any labeling requirements on the images side, avoiding a time-consuming and error-prone task.

Peaks2Image paves the way towards better generation of large sets of images for neuroscientific studies. It shows that one can outrun the limitations of large-scale poor labeling quality by performing zero-shot decoding. In particular, neuroscientific studies could become a challenging application for recent methods in text-to-image representation learning such as Radford et al. (2021).

We show an example of the representation at different steps of the Peaks2Image model in figure 6. We also apply Peaks2Image to NeuroVault images that are associated to a set of cognitive terms. We compute the average of those reconstructions to show that Peaks2Image generates brain images that are consistent with expected patterns from the literature (fig 7).

Input: Figure 7: Reconstructions of NeuroVault samples: we show the average sparse Gaussian representation used in standard meta-analysis procedures (left) along with the Peaks2Image reconstruction (right) for NeuroVault samples containing certain terms. The reconstruction yields more extended networks (audition, face, language production) and sometimes biases the image away from the initial map (sadness). In some rare cases (visual) it focuses the activity patterns.

Input: Figure 6: Successive steps of Peaks2Image: we show the successive steps of Peaks2Image representations. From left to right, we collect brain images from NeuroVault from which we extract peaks above a statistical threshold, we transform the peaks into a sparse Gaussian representation from which we predict the Peaks2Image reconstruction.

Input: Figure 1: Reconstructing Brain Activation Maps from Activation Peaks (A) We learn the reconstruction process from NeuroVault collection #4438. After selection and preprocessing of the images, we extract the peaks that are above a statistical threshold. From those peaks, we build an intermediate representation by placing a Gaussian kernel at each peak as in Dockès et al. (2020); Ngo et al. (2021). We compress the representation using the DiFuMo atlas. We regress the original DiFuMo components of each image by leveraging a 3-layer multi-layer perceptron (MLP). (B) Once the reconstruction model is trained, we build intermediate Gaussian-kernel representations for the scraped studies, and reconstruct a brain map for each study using the above model, supporting further image-level computations such as decoding. From the text, we extract the tf-idf values for each word, and infer labels on a percentile-based threshold. (C) We perform decoding of NeuroVault images by training on the newly acquired representations of neuroscientific studies. We decode a broad set of cognitive processes benefiting from the large vocabulary.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, the claim that Peaks2Image obtains "for the first time" images from stereotactic coordinates appears overstated. While the paper presents a novel approach, the evidence shows:

1. Previous work exists on generating/reconstructing fMRI images and enriching brain images (Manning et al., 2018; Cai et al., 2016).
2. The reviewer raises valid concerns about the reconstruction's necessity and reliability given the limited input data.
3. The authors themselves acknowledge "limited interest" rather than no prior attempts in generating reconstructions from peaks.
4. Claims of being "first" require extensive literature review documentation, which isn't provided.

<score>4</score>

Justification:
1. The claim lacks acknowledgment of existing related work in image reconstruction and generation
2. The reviewer highlights fundamental concerns about whether reconstructing from sparse peaks is feasible/beneficial
3. The evidence shows this is an advancement in combining textual and spatial information rather than a completely novel concept
4. The "first time" phrasing overstates the novelty without sufficient justification or literature review


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Peaks2Image succesfully decodes 65 of them on thousands of brain images from the NeuroVault database, without using any supervision from NeuroVault samples during training.

The evidence to be evaluated is:Thanks to the rise of large-scale databases of fMRI brain images such as NeuroVault (Gorgolewski et al., 2015) or OpenNeuro (Gorgolewski et al., 2017), decoding can now be performed across multiple studies (Mensch et al., 2017; Walters et al., 2022). Nonetheless, annotations associated with those images are often of low quality. Automatic strategies to improve this labeling are necessary to benefit properly from the scale of the data (Poldrack and Yarkoni, 2016). Menuet et al. (2022) leverages the Cognitive Atlas (Poldrack et al., 2011) to improve the label quality of NeuroVault images, enabling the decoding of a large set of concepts.

We aim to reconstruct brain images from those few datapoints. This reconstruction problem is an inverse problem between the set of peaks and the image from which they were extracted. From a given set of \(l\) peaks \(\mathbf{P_{y}}\in\mathbb{R}^{3\times l}\) extracted from an image \(\mathbf{y}\in\mathbb{R}^{m}\) with \(m\) voxels, we learn the transformation \(f:\mathbb{R}^{3\times l}\mapsto\mathbb{R}^{m}\) by minimization of the \(\mathbf{L_{2}}\) error over the original image: \[f^{*}=\operatorname*{arg\,min}_{f}\mathbf{E_{y}}\|f(\mathbf{P_{y}})-\mathbf{ y}\|_{2}^{2} \tag{1}\] To solve the minimization problem in eq 1, we leverage a set of statistical maps taken from NeuroVault, an online repository of fMRI maps, from which we extract peak coordinates. This leads to a training set of peaks and the corresponding brain images to be reconstructed. Once trained, Peaks2Image can be applied to any set of peak coordinates from neuroscientific publications.

Peaks2Image consists of a neural network that reconstructs brain images from activity peaks.

To train this reconstruction process, we use around 6k images from the #4438 NeuroVault collection ([https://neurovault.org/collections/4438](https://neurovault.org/collections/4438)).

By doing this on the most comprehensive public literature corpus, we reconstruct 13,000 brain images from the peak activation tables. We use these representations to decode cognitive processes in a zero-shot multi-label setting.

We evaluate the decoding performance on the NeuroVault database (#4438 excluded). We extract the term frequency-inverse document frequency (tf-idf) (Salton and Buckley, 1988) from the studies' text. The tf-idf reflects the relevance of a specific term to a study by giving higher power to terms that occurs repetitively in few studies. Based on it, we assign labels to studies, by setting a threshold at the 95th percentile of the tf-idf. This ensures that the few studies that are most relevant to each term get positive labels.

The model is trained from the sole neuroscientific studies, without supervision from any NeuroVault sample or its labels.

We leverage the NeuroVault database.

We take collection #4438, without labels (6k maps), to train the reconstruction task. We consider the remaining 43k brain maps from 2376 different collections to evaluate the decoding performances.

We evaluate the reconstruction produced by Peaks2Image on all NeuroVault except collection #4438 which represents a testing set of around 43k images.

Peaks2Image significantly outperforms linear models. As shown in the next section, although the correlations obtained with the different methods are comparable, the Peaks2Image reconstruction yields a much better performance on the downstream decoding task.

We validate the relevance of the reconstructions by decoding NeuroVault images using reconstructed neuroscientific studies as training set.

We exclude collection #4438 that has been used as training set in the reconstruction task, and evaluate the decoding of all remaining images from NeuroVault.

On terms that are shared with NeuroSynth (Yarkoni et al., 2011), we show that Peaks2Image outperforms the former, while largely expanding the number of cognitive processes decoded.

Compared to images-based models, it allows to decode a much broader set of cognitive processes by leveraging the rich textual information of neuroscientific studies, along with the reconstructions. Most importantly, the decoding performance is reached without any labeling requirements on the images side, avoiding a time-consuming and error-prone task.

Peaks2Image paves the way towards better generation of large sets of images for neuroscientific studies. It shows that one can outrun the limitations of large-scale poor labeling quality by performing zero-shot decoding.

We also apply Peaks2Image to NeuroVault images that are associated to a set of cognitive terms. We compute the average of those reconstructions to show that Peaks2Image generates brain images that are consistent with expected patterns from the literature (fig 7).

In figure 8, we report the per-label decoding performance along with the variance of the results. Most terms are decoded above chance.

Input: Figure 8: Zero-shot decoding performance on NeuroVault: we use Peaks2Image to produce dense brain images for neuroscientific studies. We train a decoder from the generated data on a broad set of cognitive terms. We evaluate the decoding performance on NeuroVault data. Peaks2Image succesfully decodes a large part of those terms in a zero-shot setting. Peaks2Image could extend to any word from the studies’ vocabulary, but could be evaluated on the NeuroVault annotations only.

Input: Figure 7: Reconstructions of NeuroVault samples: we show the average sparse Gaussian representation used in standard meta-analysis procedures (left) along with the Peaks2Image reconstruction (right) for NeuroVault samples containing certain terms. The reconstruction yields more extended networks (audition, face, language production) and sometimes biases the image away from the initial map (sadness). In some rare cases (visual) it focuses the activity patterns.

Input: Figure 3: Zero-shot decoding performance on NeuroVault: evaluation is performed on all NeuroVault except collection #4438. We exclude categories with less than 50 positive examples for stability. Peaks2Image decodes better than chance 65 out of 81 cognitive processes over 43k images (AUC > 0.55).

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find that it is generally well-stated but could be slightly more precise. The evidence shows in Figure 3 and the text that Peaks2Image successfully decodes cognitive processes above chance level (AUC > 0.55) for 65 out of 81 terms across 43k NeuroVault images, excluding collection #4438. The claim accurately represents the number decoded (65) and specifies that no supervision from NeuroVault samples was used during training. However, saying "successfully decodes" could be more precise by specifying "decodes above chance level (AUC > 0.55)."

<score>2</score>

Justification:
1. The claim accurately states the number of decoded terms (65)
2. It correctly notes the lack of NeuroVault supervision during training
3. The term "successfully" could be more specific by mentioning the AUC threshold
4. The evidence directly supports the claim through Figure 3 and text stating "Peaks2Image decodes better than chance 65 out of 81 cognitive processes over 43k images (AUC > 0.55)"
5. Only minor clarification needed, making it close to well-stated but not perfectly precise


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Peaks2Image succesfully decodes 65 of them on thousands of brain images from the NeuroVault database, without using any supervision from NeuroVault samples during training.

The review comment to be evaluated is:

Strengths
1. The authors proposed an interesting workflow. If I understand it correctly, the authors are using the following priors: (1) fMRI brain maps is tightly correlated to the underlying cognitive states, and (2) distribution of peak locations in fMRI brain maps is highly correlated to the overall global pattern in fMRI brain maps. With these priors, the authors proposed to train a neural network model to predict the global fMRI pattern from sparse locations of peaks, and with that trained model they synthesized a paired dataset of (cognitive descriptions, global fMRI patterns) using the (cognitive descriptions, sparse locations of peaks) from the neuroscientific literatures. Finally, they trained a separate decoder with the (cognitive descriptions, global fMRI patterns) pairs to decode the former from the latter. The decoder is the ultimate piece of mastery that can describe the underlying cognitive states of an arbitrary fMRI image.

Weaknesses
1. In my humble opinion, the abstract may need to be better articulated. I was very disoriented and confused by the abstract until I finished reading the introduction section. Specifically, I was confused by (1) what the two complementary sources of data are, (2) why they are complementary, and (3) what is the proposed method trying to accomplish. For (1) and (2), even though the two sources of data are both explicitly mentioned, I did not have enough context to parse their relationship. In contrary, the introduction section has a very natural flow and is painless to follow.
2. It is a bit misleading that when the authors mentioned the word “image” they did not necessarily mean “image”, but rather the “1x1024 vector representation in the DeFuMo space of the image”. I believe it might be helpful to provide a clarifying statement somewhere early in the paper to avoid potential misunderstanding.
3. Figure 1 can be further polished. (1) Figure 1A did not clearly show what data format is provided and what are derived. It might be better if that information is illustrated more explicitly. (2) A lot of things are represented as “blue thin rectangle”, yet some of them are modules/operations while others are entities. It may be better to replace some of them. For example, “Reconstruction” and “decoder” can be replaced by sketches of the neural networks, and “Labels” can be replaced by some clipart object, etc. (3) The caption mentioned that in Figure 1B, the brain map is reconstructed using the trained model, so it would be better to show the sketch of model in an appropriate place and indicate that its weights are frozen.
4. The reconstruction performance (Section 5.1, Figure 2) is quite poor. a correlation of 0.3 does not seem too promising.

Minor Things
1. Is there some typo in the following sentence? Page 5, 3rd paragraph. “When the percentile corresponds to a To avoid selecting studies with a null tf-idf value, we replace the percentile threshold with a strictly positive tf-idf value criteria.”

The evidence to be evaluated is:Thanks to the rise of large-scale databases of fMRI brain images such as NeuroVault (Gorgolewski et al., 2015) or OpenNeuro (Gorgolewski et al., 2017), decoding can now be performed across multiple studies (Mensch et al., 2017; Walters et al., 2022). Nonetheless, annotations associated with those images are often of low quality. Automatic strategies to improve this labeling are necessary to benefit properly from the scale of the data (Poldrack and Yarkoni, 2016). Menuet et al. (2022) leverages the Cognitive Atlas (Poldrack et al., 2011) to improve the label quality of NeuroVault images, enabling the decoding of a large set of concepts.

We aim to reconstruct brain images from those few datapoints. This reconstruction problem is an inverse problem between the set of peaks and the image from which they were extracted. From a given set of \(l\) peaks \(\mathbf{P_{y}}\in\mathbb{R}^{3\times l}\) extracted from an image \(\mathbf{y}\in\mathbb{R}^{m}\) with \(m\) voxels, we learn the transformation \(f:\mathbb{R}^{3\times l}\mapsto\mathbb{R}^{m}\) by minimization of the \(\mathbf{L_{2}}\) error over the original image: \[f^{*}=\operatorname*{arg\,min}_{f}\mathbf{E_{y}}\|f(\mathbf{P_{y}})-\mathbf{ y}\|_{2}^{2} \tag{1}\] To solve the minimization problem in eq 1, we leverage a set of statistical maps taken from NeuroVault, an online repository of fMRI maps, from which we extract peak coordinates. This leads to a training set of peaks and the corresponding brain images to be reconstructed. Once trained, Peaks2Image can be applied to any set of peak coordinates from neuroscientific publications.

Peaks2Image consists of a neural network that reconstructs brain images from activity peaks.

To train this reconstruction process, we use around 6k images from the #4438 NeuroVault collection ([https://neurovault.org/collections/4438](https://neurovault.org/collections/4438)).

By doing this on the most comprehensive public literature corpus, we reconstruct 13,000 brain images from the peak activation tables. We use these representations to decode cognitive processes in a zero-shot multi-label setting.

We evaluate the decoding performance on the NeuroVault database (#4438 excluded). We extract the term frequency-inverse document frequency (tf-idf) (Salton and Buckley, 1988) from the studies' text. The tf-idf reflects the relevance of a specific term to a study by giving higher power to terms that occurs repetitively in few studies. Based on it, we assign labels to studies, by setting a threshold at the 95th percentile of the tf-idf. This ensures that the few studies that are most relevant to each term get positive labels.

The model is trained from the sole neuroscientific studies, without supervision from any NeuroVault sample or its labels.

We leverage the NeuroVault database.

We take collection #4438, without labels (6k maps), to train the reconstruction task. We consider the remaining 43k brain maps from 2376 different collections to evaluate the decoding performances.

We evaluate the reconstruction produced by Peaks2Image on all NeuroVault except collection #4438 which represents a testing set of around 43k images.

Peaks2Image significantly outperforms linear models. As shown in the next section, although the correlations obtained with the different methods are comparable, the Peaks2Image reconstruction yields a much better performance on the downstream decoding task.

We validate the relevance of the reconstructions by decoding NeuroVault images using reconstructed neuroscientific studies as training set.

We exclude collection #4438 that has been used as training set in the reconstruction task, and evaluate the decoding of all remaining images from NeuroVault.

On terms that are shared with NeuroSynth (Yarkoni et al., 2011), we show that Peaks2Image outperforms the former, while largely expanding the number of cognitive processes decoded.

Compared to images-based models, it allows to decode a much broader set of cognitive processes by leveraging the rich textual information of neuroscientific studies, along with the reconstructions. Most importantly, the decoding performance is reached without any labeling requirements on the images side, avoiding a time-consuming and error-prone task.

Peaks2Image paves the way towards better generation of large sets of images for neuroscientific studies. It shows that one can outrun the limitations of large-scale poor labeling quality by performing zero-shot decoding.

We also apply Peaks2Image to NeuroVault images that are associated to a set of cognitive terms. We compute the average of those reconstructions to show that Peaks2Image generates brain images that are consistent with expected patterns from the literature (fig 7).

In figure 8, we report the per-label decoding performance along with the variance of the results. Most terms are decoded above chance.

Input: Figure 8: Zero-shot decoding performance on NeuroVault: we use Peaks2Image to produce dense brain images for neuroscientific studies. We train a decoder from the generated data on a broad set of cognitive terms. We evaluate the decoding performance on NeuroVault data. Peaks2Image succesfully decodes a large part of those terms in a zero-shot setting. Peaks2Image could extend to any word from the studies’ vocabulary, but could be evaluated on the NeuroVault annotations only.

Input: Figure 7: Reconstructions of NeuroVault samples: we show the average sparse Gaussian representation used in standard meta-analysis procedures (left) along with the Peaks2Image reconstruction (right) for NeuroVault samples containing certain terms. The reconstruction yields more extended networks (audition, face, language production) and sometimes biases the image away from the initial map (sadness). In some rare cases (visual) it focuses the activity patterns.

Input: Figure 3: Zero-shot decoding performance on NeuroVault: evaluation is performed on all NeuroVault except collection #4438. We exclude categories with less than 50 positive examples for stability. Peaks2Image decodes better than chance 65 out of 81 cognitive processes over 43k images (AUC > 0.55).

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find that the claim about Peaks2Image decoding "65 of them" successfully is largely accurate but could benefit from more context and precision. The evidence in Figure 3 explicitly shows that Peaks2Image decodes "65 out of 81 cognitive processes over 43k images (AUC > 0.55)." The claim omits the total number of processes (81) and the AUC threshold (0.55) used to determine successful decoding. The part about "without using any supervision from NeuroVault samples during training" is supported by multiple statements in the evidence.

<score>2</score>

Justification:
1. The claim is factually correct about the 65 successfully decoded processes and zero-shot nature
2. However, it would be more precise to:
- Specify "65 out of 81 cognitive processes"
- Include the AUC > 0.55 threshold used to define success
- Clarify that this was tested on 43k images
3. The reviewer's comments don't directly address this specific claim

The claim is generally well-stated with only minor omissions of contextual details that would make it more precise.