Original: P EAKS 2I MAGE : R ECONSTRUCTING FMRI S TATISTICAL M APS FROM P EAKS
Revised: P EAKS 2I MAGE : R ECONSTRUCTING FMRI S TATISTICAL M APS FROM P EAKS

Original: Anonymous authors
Revised: Anonymous authors

Original: Paper under double-blind review
Revised: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: Neuroscience is striving to overcome the lack of power due to the small sample size of standard studies.
Revised: Neuroscience strives to overcome the lack of power due to the small sample size of imaging-based studies.

Original: An important step forward has been the creation of largescale public image repositories, such as NeuroVault.
Revised: An important step forward has been the creation of large-scale public image repositories, such as NeuroVault.

Original: Such repositories enable comparing images  across studies and automatically associating them with cognitive terms.
Revised: Such repositories allow images to be compared across studies and automatically associated with cognitive terms.

Original: Yet, this type of meta-analysis faces a major roadblock: the scarcity and inconsistency of image annotations and metadata.
Revised: Yet, this type of meta-analysis faces a major roadblock: the scarcity and inconsistency of image annotations and metadata.

Original: Another resource containing rich annotations is the neuroscientific literature.
Revised: Another resource containing rich annotations is the neuroscientific literature.

Original: However it only yields a handful of brain-space coordinates per publication, those of the main activity peaks reported in each study.
Revised: However it only yields a handful of brain-space coordinates per publication, those of the main activity peaks reported in each study.

Revised: This has led the community to mostly perform meta-analysis based on these reported coordinates.

Original: In this work, we propose Peaks2Image, a neural-network approach to reconstruct continuous spatial representations of brain activity from peak activation tables.
Revised: In this work, we propose Peaks2Image, a neuralnetwork approach to reconstruct continuous spatial representations of brain activity from peak activation tables.

Original: Using reconstructions of studies published in the neuroscientific literature, we train a decoder using tf-idf features as labels, leading to a much broader set of decoded terms than current image-based studies.
Revised: Peaks2Image thus associates rich annotations from the neuroscientific literature with dense brain reconstructions. Using those reconstructions, we train a decoder using tf-idf features as labels, leading to a much broader set of decoded terms than current image-based studies.

Original: We validate the decoder on 43,000 NeuroVault images, successfully decoding 58 out ofconcepts in a zero-shot setting.
Revised: We validate the decoder on 43,000 NeuroVault images, successfully decoding 65 out ofconcepts in a zero-shot setting.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Cognitive neuroscience aims to map cognitive processes onto brain regions.
Revised: Cognitive neuroscience aims to map cognitive processes onto brain regions.

Original: Functional Magnetic
Revised: Functional Magnetic

Original: Resonance Imaging (fMRI) is one of the most powerful techniques available to identify such associations.
Revised: Resonance Imaging (fMRI) is one of the most powerful techniques available to identify such associations.

Original: This approach measures brain activity while subjects perform cognitive tasks in an MRI scanner, and then contrasts brain signals associated with different mental conditions.
Revised: This approach measures brain activity while subjects perform cognitive tasks in an MRI scanner, and then contrasts brain signals associated with different mental conditions.

Original: Statistical testing of these contrasts then identifies brain regions where the neural activity elicited by the conditions probed is significantly different.
Revised: Statistical testing of these contrasts then identifies brain regions where the neural activity elicited by the conditions probed is significantly different.

Original: Unfortunately, the high cost of data acquisition limits the number of participants and tasks involved in each study (Poldrack et al., 2017).
Revised: Unfortunately, the high cost of data acquisition limits the number of participants and tasks involved in each study (Poldrack et al., 2017).

Original: A small number of participants causes low statistical power and a high proportion of false discoveries (Ioannidis, 2005; Button et al., 2013).
Revised: A small number of participants causes low statistical power and a high proportion of false discoveries (Ioannidis, 2005; Button et al., 2013).

Original: Analyzing a restricted set of cognitive tasks introduces the risk of over-interpreting statistical effects that are not specific to the mental functions under study (Poldrack, 2011).
Revised: Analyzing a restricted set of cognitive tasks introduces the risk of over-interpreting statistical effects that are not specific to the mental functions under study (Poldrack, 2011).

Original: Meta-analysis consists in aggregating the results of several studies to find effects that are reported consistently (Wager et al., 2007).
Revised: Meta-analysis consists in aggregating the results of several studies to find effects that are reported consistently (Wager et al., 2007).

Original: It helps overcome the challenge of small sample sizes and uncovers more reliable associations between brain activity and mental function.
Revised: It helps overcome the challenge of small sample sizes and uncovers more reliable associations between brain activity and mental function.

Original: When feasible, meta-analysis should use the full statistical brain maps produced by the original studies (Salimi-Khorshidi et al., 2009).
Revised: When feasible, meta-analysis should use the full statistical brain maps produced by the original studies (Salimi-Khorshidi et al., 2009).

Original: Unfortunately, the vast majority of studies does not share the actual brain images.
Revised: Unfortunately, the vast majority of studies does not share the actual brain images.

Original: Instead, only the locations of the peaks of activation are reported.
Revised: Instead, only the locations of the peaks of activation are reported.

Original: These are communicated in the form of tables, in scientific publications, containing stereotactic coordinates – 3D coordinates in a standard spatial referential for the brain.
Revised: These are communicated in the form of tables, in scientific publications, containing stereotactic coordinates – 3D coordinates in a standard spatial referential for the brain.

Original: This results in a poor representation of brain activity – most of the information contained in the original statistical maps is lost.
Revised: This results in a poor representation of brain activity – most of the information contained in the original statistical maps is lost.

Original: Meta-analyses that rely on peak activation coordinates reported in publications are called Coordinate-Based Meta-Analyses (CBMA).
Revised: Meta-analyses that rely on peak activation coordinates reported in publications are called Coordinate-Based Meta-Analyses (CBMA).

Original: Recent efforts to openly share full brain images and statistical maps such as NeuroVault (Gorgolewski et al., 2015) or OpenNeuro (Gorgolewski et al., 2017), could facilitate Image-Based MetaAnalysis (IBMA) – relying on the brain images rather than coordinates.
Revised: Recent efforts to openly share full brain images and statistical maps such as NeuroVault (Gorgolewski et al., 2015) or OpenNeuro (Gorgolewski et al., 2017), could facilitate Image-Based MetaAnalysis (IBMA) – relying on the brain images rather than coordinates.

Original: However, annotations available on those large databases are scarce and inconsistent, leading to difficulties to capture the semantics of the cognitive processes associated with images (Menuet et al., 2022).
Revised: However, annotations available on those large databases are scarce and inconsistent, leading to difficulties to capture the semantics of the cognitive processes associated with images (Menuet et al., 2022).

Original: There is no standard, agreed-upon ontology or vocabulary of mental functions.
Revised: There is no standard, agreed-upon ontology or vocabulary of mental functions.

Original: Moreover, all that is known is the task that the participant is performing, and ascribing mental functions to tasks is difficult (Poldrack et al., 
Revised: Moreover, all that is known is the task that the participant is performing, and ascribing mental functions to tasks is difficult (Poldrack et al., 2011).

Original: Therefore, formally describing the mental states that underlie a brain image remains an open problem.
Revised: Therefore, formally describing the mental states that underlie a brain image remains an open problem.

Original: Scientific literature and image repositories are therefore complementary: the literature provides rich descriptions of the studied cognitive tasks but poor spatial information, whereas image repositories contain complete representations of brain activity but lack useful annotations.
Revised: Scientific literature and image repositories are therefore complementary: the literature provides rich descriptions of the studied cognitive tasks but poor spatial information, whereas image repositories contain complete representations of brain activity but lack useful annotations.

Original: In the present study, we introduce a meta-analysis method that combines the strengths of both.
Revised: In the present study, we introduce a meta-analysis method that combines the strengths of both.

Original: Beyond the challenge of sample size, effects uncovered by individual neuroimaging studies suffer from a lack of specificity.
Revised: Beyond the challenge of sample size, effects uncovered by individual neuroimaging studies suffer from a lack of specificity.

Original: When a cognitive task is studied in isolation, there is no way of knowing if the observed brain activations are specific to the mental functions of interest, or associated with a broader set of mental processes.
Revised: When a cognitive task is studied in isolation, there is no way of knowing if the observed brain activations are specific to the mental functions of interest, or associated with a broader set of mental processes.

Original: Authors often over-interpret the observed associations, resulting in the fallacy of unwarranted reverse inference (Poldrack, 2011).
Revised: Authors often over-interpret the observed associations, resulting in the fallacy of unwarranted reverse inference (Poldrack, 2011).

Original: One way of identifying more specific associations between brain activity and mental function is decoding : inferring the mental processes at play, given a brain image of neural activity.
Revised: One way of identifying more specific associations between brain activity and mental function is decoding : inferring the mental processes at play, given a brain image of neural activity.

Original: Indeed, to discriminate between a wide variety of cognitive states, a decoding model must identify brain regions that characterize each state, rather than brain regions that are merely activated consistently.
Revised: Indeed, to discriminate between a wide variety of cognitive states, a decoding model must identify brain regions that characterize each state, rather than brain regions that are merely activated consistently.

Original: For the resulting associations to be specific, it is crucial that many and diverse cognitive states are decoded jointly.
Revised: For the resulting associations to be specific, it is crucial that many and diverse cognitive states are decoded jointly.

Original: Due to the difficulty of formalizing the mental states associated with a brain image, high-quality labels are lacking for this supervised task.
Revised: Due to the difficulty of formalizing the mental states associated with a brain image, high-quality labels are lacking for this supervised task.

Original: Performing such large-scale (sometimes called “open-ended”) decoding is therefore challenging, and in practice most studies that claim to perform “open-ended” decoding only discriminate a restricted set of cognitive concepts.
Revised: Performing such large-scale (sometimes called “open-ended”) decoding is therefore challenging, and in practice most studies that claim to perform “open-ended” decoding only discriminate a restricted set of cognitive concepts.

Original: In this work, Peaks2Image learns to discriminate dozens of cognitive terms on the largest available image repository, NeuroVault.
Revised: In this work, Peaks2Image learns to discriminate dozens of cognitive terms on the largest available image repository, NeuroVault.

Original: To do so, it leverages both the rich descriptions of mental processes found in the literature and the high-quality neural activity data found in full-brain statistical maps.
Revised: To do so, it leverages both the rich descriptions of mental processes found in the literature and the high-quality neural activity data found in full-brain statistical maps.

Original: Peaks2Image reduces the gap between IBMA and CBMA, by reconstructing brain maps from peaks coordinates contained in neuroscientific studies.
Revised: Peaks2Image reduces the gap between IBMA and CBMA, by reconstructing brain maps from peaks coordinates contained in neuroscientific publications.

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

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

Original: We evaluate whether those reconstructions are relevant by using them for brain image decoding.
Revised: We evaluate whether those reconstructions are relevant by using them for brain image decoding.

Original: We associate labels with the studies using some criteria on the termfrequency inverse-document-frequency of the text.
Revised: We associate labels with the studies using some criteria on the termfrequency inverse-document-frequency of the text.

Original: We use the decoding architecture from Neural
Revised: We use the decoding architecture from Neural

Original: Networks on Dictionaries (NNoD) (Menuet et al., 2022) trained on the neuroscientific corpus.
Revised: Networks on Dictionaries (NNoD) (Menuet et al., 2022) trained on the neuroscientific corpus.

Original: We evaluate the decoding performance against 81 terms from NeuroVault.
Revised: We evaluate the decoding performance against 81 terms from NeuroVault.

Original: Peaks2Image succesfully decodes 58 of them on thousands of brain images from the NeuroVault database, without using any supervision from NeuroVault samples during training.
Revised: Peaks2Image succesfully decodes 65 of them on thousands of brain images from the NeuroVault database, without using any supervision from NeuroVault samples during training.

Original: While the evaluation has been performed on a limited set of terms, Peaks2Image can decode in a zero-shot setting any term from its vocabulary.
Revised: While the evaluation has been performed on a limited set of terms, Peaks2Image can decode in a zero-shot setting any term from its vocabulary.

Original:  R ELATED W ORK
Revised:  R ELATED W ORK

Original: Automated meta-analysis (Laird et al., 2005; Yarkoni et al., 2011) has risen over the last few years to handle the growth of published neuroscientific studies.
Revised: Automated meta-analysis (Laird et al., 2005; Yarkoni et al., 2011) has risen over the last few years to handle the growth of published neuroscientific studies.

Original: Handling neuroscientific concepts properly has emerged as a challenge for CBMA, leading to the use of more complex textual features and models.
Revised: Handling neuroscientific concepts properly has emerged as a challenge for CBMA, leading to the use of more complex textual features and models.

Original: Dock`es et al.
Revised: Dock`es et al.

Original: (2020) broadened the spectrum of terms analyzed by mapping rare concepts to more common cognitive terms.
Revised: (2020) broadened the spectrum of terms analyzed by mapping rare concepts to more common cognitive terms.

Original: Ngo et al.
Revised: Ngo et al.

Original: (2021) leveraged language models (Beltagy et al., 2019) to capture term relationships semanticly, leading to the encoding of any query.
Revised: (2021) leveraged language models (Beltagy et al., 2019) to capture term relationships semantically, leading to the encoding of any query.

Original: However,
Revised: However,

Original: CBMA suffers from the drastic information reduction inherent to peak reporting.
Revised: CBMA suffers from the drastic information reduction inherent to peak reporting.

Original: Using dense images yields more information for meta-analysis (Salimi-Khorshidi et al., 2009).
Revised: Using dense images yields more information for meta-analysis (Salimi-Khorshidi et al., 2009).

Original: 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).
Revised: 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).

Original: Nonetheless, annotations associated with those images are often of low quality.
Revised: Nonetheless, annotations associated with those images are often of low quality.

Original: Automatic strategies to improve this labeling are necessary to benefit properly from the scale of the data (Poldrack and Yarkoni, 2016).
Revised: Automatic strategies to improve this labeling are necessary to benefit properly from the scale of the data (Poldrack and Yarkoni, 2016).

Original: Menuet et al.
Revised: Menuet et al.

Original: (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.
Revised: (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.

Original: Overall, neuroscience meta-analyses have increasingly relied on data-driven approaches, learning more efficiently across a large set of data (Beam et al., 2021).
Revised: Overall, neuroscience meta-analyses have increasingly relied on data-driven approaches, learning more efficiently across a large set of data (Beam et al., 2021).

Original: Although there has been interest in enriching brain images to increase the amount of data (Zhuang et al., 2019),  there has been limited interest in generating reconstructions of  brain maps from peaks (Gorgolewski et al., 2019), probably due to the daunting data dimensionality.
Revised: 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.

Original: In particular, we found no attempt at merging the richness of textual information contained in neuroscientific studies, with the continuous spatial information of brain images.
Revised: In particular, we found no attempt at merging rich textual information from neuroscientific studies with the dense spatial information of brain images.

Original: 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.
Revised: 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.

Original:  M ETHOD
Revised:  M ETHOD

Original: Neuroscientists most frequently extract peaks (local maxima) above a statistical threshold from brain images.
Revised: Neuroscientists most frequently extract peaks (local maxima) above a statistical threshold from brain images.

Original: An image containing hundreds of thousands voxels is thus reduced to a few datapoints – less than 100 for most studies.
Revised: An image containing hundreds of thousands voxels is thus reduced to a few datapoints – less than 100 for most studies.

Original: We aim to reconstruct brain images from those few datapoints.
Revised: We aim to reconstruct brain images from those few datapoints.

Original: This reconstruction problem is an inverse problem between the set of peaks and the image from which they were extracted.
Revised: This reconstruction problem is an inverse problem between the set of peaks and the image from which they were extracted.

Original: From a given set of l peaks P y ∈ R 3 × l extracted from an image y ∈ R m with m voxels, we minimize the reconstruction error compared with original image:
Revised: From a given set of l peaks P y ∈ R 3 × l extracted from an image y ∈ R m with m voxels, we learn the transformation f : R 3 × l (cid:55)→ R m by minimization of the L 2 error over the original image:

Original: [Equation]
Revised: [Equation]

Original: We leverage a set of statistical maps taken from NeuroVault, an online repository of fMRI maps, from which we extract peak coordinates.
Revised: 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.

Original: This leads to afully-supervised training set of peaks and their correspondingcontinuous brain images.
Revised: This leads to a training set of peaks and the corresponding brain images to be reconstructed.

Original: Once trained, Peaks2Image can be applied to any set of peak coordinates from neuroscientific publications.
Revised: Once trained, Peaks2Image can be applied to any set of peak coordinates from neuroscientific publications.

Original: 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.
Revised: 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.

Original: We show in Figure 1 a summary of Peaks2Image.
Revised: We show in Figure 1 a summary of Peaks2Image.

Original: The figure is divided between the learning of the reconstruction process from peaks (A),and the application  to meta-analyses (B), where brain imagesare decoded with a model trainedon images reconstructed from the literature in a zero-shot setting.
Revised: The figure is divided between the learning of the reconstruction process from peaks (A), the application of the reconstruction to neuroscientific studies (B), and the decoding of brain images with a model trained from those reconstructions in a zero-shot setting.

Original: 3. R ECONSTRUCTION
Revised: 3. R ECONSTRUCTION

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

Original: We tame the effect of outliers by rescaling image values to a standard range, thus losing the statistical scale of the effect but keeping the overall statistical distribution.
Revised: We tame the effect of outliers by rescaling image values to a standard range, thus losing the statistical scale of the effect but keeping the overall statistical distribution.

Original: Images are also resampled at a fixed resolution and masked to keep the brain volume only.
Revised: Images are also resampled at a fixed resolution and masked to keep the brain volume only.

Original: Instead of leveraging the peak coordinates directly as a sets problem (Zaheer et al., 2017),
Revised: Instead of leveraging the peak coordinates directly as a sets problem (Zaheer et al., 2017),

Original: Peaks2Image builds an internal sparse Gaussian representation similar to the literature as in Laird et al.
Revised: Peaks2Image builds an internal sparse Gaussian representation similar to the literature as by Laird et al.

Original: Dock`es et al.
Revised: Dock`es et al.

Original: ; Ngo et al.
Revised: ; Ngo et al.

Original: It performs a transformation between the
Revised: It performs a transformation between the

Original: Gaussian representation space and the image space.
Revised: Gaussian representation space and the image space.

Original: Formally, we consider P =
Revised: Formally, we consider P =

Original: ( p 1 , .., p l ) peaks extracted from image y where ∀ i ∈
Revised: ( p 1 , .., p l ) peaks extracted from image y where ∀ i ∈

Original: [1 ..l ] , p i ∈ R 3 .
Revised: [1 ..l ] , p i ∈ R 3 .

Original: Given a fixed sampling grid G =
Revised: Given a fixed sampling grid G =

Original: ( g 1 , .., g m ) representing the voxels, where ∀ j ∈ [1 ..m ] , g j ∈ R 3 , and a Gaussian kernel κ we define the vector x y that samples P on the grid G : x y =
Revised: ( g 1 , .., g m ) representing the voxels, where ∀ j ∈ [1 ..m ] , g j ∈ R 3 , and a Gaussian kernel κ we define the vector x y that samples P on the grid G : x y =

Original: ( x 1 , .., x m ) with ∀ j ∈ [1 ..m ] : x j = (cid:80) li =1 κ ( p i , g j ) .
Revised: ( x 1 , .., x m ) with ∀ j ∈ [1 ..m ] : x j = (cid:80) li =1 κ ( p i , g j ) .

Original: High-resolution is not needed here, as population-level images, that represent the average of individual data with variable shapes, are intrinsically smooth.
Revised: High-resolution is not needed here, as population-level images, that represent the average of individual data with variable shapes, are intrinsically smooth.

Original: We can thus perform a drastic dimension reduction of the data, which is welcome given that the sample size is inherently limited.
Revised: We can thus perform a drastic dimension reduction of the data, which is welcome given that the sample size is inherently limited.

Original: 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.
Revised: 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.

Original: DiFuMo has been trained on thousands of resting-state and task fMRI to produce sparse components, making this atlas relevant to represent population-level information.
Revised: DiFuMo has been trained on thousands of resting-state and task fMRI with non-negativity and sparsity constraints, making this atlas DiFuMoRepres. SparseGaussianRepres. Peak Extraction

Original: Mapping voxels x ∈ R m to coefficients α ∈ R k
Revised: Reconstruction MLP (Training) Reconstruction MLP (Inference) NeuroVaultCollection Decoding MLP

Revised: NeuroVaultexcluded

Revised: ReconstructedDiFuMo

Revised: X Y Z

Revised: TF-IDF

Revised: Label Binarization

Revised: TF-IDFper word

Revised: Labels

Revised: A. Training Reconstruction Task

Revised: B. Reconstruction of Neuroscientific Studies

Revised: T h

Revised: e o r y

Revised: o f M i n d

Revised: A u d

Revised: i t i o n V

Revised: i s u a l S e a r c h P a i n

Revised: C. Zero-Shot Decoding

Revised: ReconstructedNeuroscientific

Revised: Study

Revised: X Y Z

Revised: Neuroscientific Study

Revised: Training

Revised: Evaluation

Revised: L a n g u a g e P r

Revised: o

Revised: c

Revised: e

Revised: s s i n g relevant to represent population-level information.

Revised: In the rest of the paper, the term brain image refers to its 1024-dimensional DiFuMo representation.

Revised: Mapping voxels x ∈ R m to coefficients α ∈ R k

Original: along DiFuMo dictionary D ∈ R m × k of dimension k is a mere linear regression problem following equation 2.
Revised: along DiFuMo dictionary D ∈ R m × k of dimension k is a mere linear regression problem following equation 2.

Original: All the following steps are performed in the k -dimensional DiFuMo space.
Revised: All the following steps are performed in the k -dimensional DiFuMo space.

Original: [Equation]
Revised: [Equation]

Original: The DiFuMo-encoded sparse Gaussian representation is turned into a dense representation with a 3-layer neural network where the input and output are DiFuMo coefficients.
Revised: The DiFuMo-encoded sparse Gaussian representation is turned into a dense representation with a 3-layer neural network where the input and output are DiFuMo coefficients.

Original: Using the DiFuMo atlas instead of the raw voxels helps us keep a relatively low number of parameters for this model.
Revised: Using the DiFuMo atlas instead of the raw voxels helps us keep a relatively low number of parameters for this model.

Original: This transforms Eq 1 into:
Revised: This transforms Eq 1 into:

Original: [Equation]
Revised: [Equation]

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

Original: The reconstructed images are obtained from the predicted DiFuMo components by linear combinations of the dictionary components:
Revised: The reconstructed images are obtained from the predicted DiFuMo components by linear combinations of the dictionary components:

Original: ˆ y =
Revised: ˆ y =

Original: D f ∗ ( α ( x y )) .
Revised: D f ∗ ( α ( x y )) .

Original: [Figure]

Original: images by training on the newly acquired representations of neuroscientific studies.

Original: We decode a broad set of cognitive processes benefiting from the large vocabulary.

Original: 3. D ECODING FROM P EAKS
Revised: 3. D ECODING FROM P EAKS

Original: Peaks2Image outputs images from any set of positions.
Revised: Peaks2Image outputs images from any set of positions.

Original: 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.
Revised: 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.

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

Original: We use these representations to decode cognitive processes in a zero-shot multi-label setting.
Revised: We use these representations to decode cognitive processes in a zero-shot multi-label setting.

Original: Indeed, the ability to decode cognitive concepts from the produced database ensures its validity and circumvents the biases inherent to image reconstruction quality metrics, such as the confounding effect of smoothness.
Revised: Indeed, the ability to decode cognitive concepts from the produced database ensures its validity and circumvents the biases inherent to image reconstruction quality metrics, such as the confounding effect of smoothness.

Original: We evaluate the decoding performance on the NeuroVault database (# excluded).
Revised: We evaluate the decoding performance on the NeuroVault database (# excluded).

Original: We extract the term frequency-inverse document frequency (tf-idf)
Revised: We extract the term frequency-inverse document frequency (tf-idf)

Original: (Salton and Buckley, 1988) from the studies’ text.
Revised: (Salton and Buckley, 1988) from the studies’ text.

Original: 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.
Revised: 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.

Original: Based on it, we assign labels to studies, by setting a threshold at the 95th percentile of the tf-idf.
Revised: Based on it, we assign labels to studies, by setting a threshold at the 95th percentile of the tf-idf.

Original: This ensures that the few studies that are most relevant to each term get positive labels.
Revised: This ensures that the few studies that are most relevant to each term get positive labels.

Original: The computed percentile can be null for some extremely rare terms.
Revised: The computed percentile can be null for some extremely rare terms.

Original: When the percentile corresponds to aTo avoid selecting studies with a null tf-idf value, we replace the percentile threshold with a strictly positive tf-idf value criteria.
Revised: When the percentile corresponds to a null tf-idf value, we only consider strictly positive tf-idf values.

Original: We leverage a 3-layer dense neural network to predict the set of binary labels L = { 0 , 1 } 1 × · · · × { 0 , 1 } l , l denoting the total number of classes, from the input image X ∈ R d compressed in
Revised: We leverage a 3-layer dense neural network to predict the set of binary labels L = { 0 , 1 } 1 × · · · × { 0 , 1 } l , l denoting the total number of classes, from the input image X ∈ R d compressed in

Original: DiFuMo space.
Revised: DiFuMo space.

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

Original: We were limited for the evaluation by the annotations available in NeuroVault.
Revised: We were limited for the evaluation by the annotations available in NeuroVault.

Original: In the following experiments, we predict only l = 81 terms from the 6308 in the tf-idf vocabulary.
Revised: In the following experiments, we predict only l = 81 terms from the 6308 in the tf-idf vocabulary.

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

Original:  E XPERIMENTS 4. D ATASET D ESCRIPTION
Revised:  E XPERIMENTS 4. D ATASET D ESCRIPTION

Original: We leverage the NeuroVault database.
Revised: We leverage the NeuroVault database.

Original: 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.
Revised: 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.

Original: We split those maps into two different sets in a collection-wise manner.
Revised: We split those maps into two different sets in a collection-wise manner.

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

Original: We consider the remaining 43k brain maps from 2376 different collections to evaluate the decoding performances.
Revised: We consider the remaining 43k brain maps from 2376 different collections to evaluate the decoding performances.

Original: We use NeuroQuery Data Collection Dock ` es et al. (2020) to fetch around 13k neuroscientific studies from PubMed Central.
Revised: We use NeuroQuery Data Collection (Dock`es et al., 2020) to fetch around 13k neuroscientific studies from PubMed Central.

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

Original: It also transforms each study text into tf-idf features across a vocabulary of 6308 terms.
Revised: It also transforms each study text into tf-idf features across a vocabulary of 6308 terms.

Original: Forthe validation, we restrict the analysis to shared terms between the tags of NeuroVault images and the vocabulary from the tf-idf.
Revised: For validation, we restrict the analysis to shared terms between the tags of NeuroVault images and the vocabulary from the tf-idf.

Original: Among the 240 tags from the NeuroVault test collections,are present in the vocabulary.
Revised: Among the 240 tags from the NeuroVault test collections, 189 are present in the vocabulary.

Original: We focus on those 189 tags without adding any additional mappings between the left-out tags and words from the vocabulary.
Revised: We focus on those 189 tags without adding any additional mappings between the left-out tags and words from the vocabulary.

Original: Additionally, we filter out terms that contain less than 50 positive examples in the test set to prevent any non-significant effects, keeping 81 terms to decode.
Revised: Additionally, we filter out terms that contain less than 50 positive examples in the test set to prevent any non-significant effects, keeping 81 terms to decode.

Original: 4. D ATA P REPROCESSING
Revised: 4. D ATA P REPROCESSING

Original: Image preprocessing Images were fetched from NeuroVault using the Nilearn library (Abraham et al., 2014).
Revised: Image preprocessing Images were fetched from NeuroVault using the Nilearn library (Abraham et al., 2014).

Original: We resample all images to the standard MNI152 template and apply the MNI152 brain mask.
Revised: We resample all images to the standard MNI152 template and apply the MNI152 brain mask.

Original: We use the DiFuMo atlas in dimension 1024 available in Nilearn.
Revised: We use the DiFuMo atlas in dimension 1024 available in Nilearn.

Original: As the coefficients of the DiFuMo representation can range to large values, we scale them by a constant to facilitate the convergence of the neural network.
Revised: As the coefficients of the DiFuMo representation can range to large values, we scale them by a constant to facilitate the convergence of the neural network.

Original: Peak extraction We generate pairs of matching (peaks, image)  from the collection (#4438) from NeuroVault images only.
Revised: Peak extraction We generate pairs of matching (peaks, image) pairs from the collection (#4438) from NeuroVault images only.

Original: Similarly, we apply masking, resampling and DiFuMo compression without the normalization step.
Revised: Similarly, we apply masking, resampling and DiFuMo compression without the normalization step.

Original: We use Nilearn’s method for peak extraction, which first separates the images into statistically active clusters and retrieves peak coordinates from each cluster.
Revised: We use Nilearn’s method for peak extraction, which first separates the images into statistically active clusters and retrieves peak coordinates from each cluster.

Original: As these images represent statistical maps, equivalent to p-values of voxel-level tests, we keep peaks with a p-value lower than 10 − 3 , and with a minimal distance between peaks within the same cluster of 8mm.
Revised: As these images represent statistical maps, equivalent to p-values of voxel-level tests, we keep peaks with a p-value lower than 10 − 3 , and with a minimal distance between peaks within the same cluster of 8mm.

Original: These are standard parameters from peak extraction in brain imaging contexts.
Revised: These are standard parameters from peak extraction in brain imaging contexts.

Original: Sparse Gaussian Representations 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 Dock`es et al.
Revised: Sparse Gaussian Representations 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 Dock`es et al.

Original: Similarly to the NeuroVault images, we extract the
Revised: Similarly to the NeuroVault images, we extract the

Original: DiFuMo representations of the sparse Gaussian maps using Nilearn.
Revised: DiFuMo representations of the sparse Gaussian maps using Nilearn.

Original: 4. T RAINING DETAILS
Revised: 4. T RAINING DETAILS

Original: Reconstruction The image reconstruction model is an MLP built from 3 dense layers withunits and sigmoid activation implemented using PyTorch (Paszke et al., 2019).
Revised: Reconstruction The image reconstruction model is an MLP built from 3 dense layers withunits and sigmoid activation implemented using PyTorch (Paszke et al., 2019).

Original: It is trained forepochs using Adam optimizer and a learning rate of 1e-3.
Revised: It is trained forepochs using Adam optimizer and a learning rate of 1e-3.

Original: The loss is the mean-squared error (MSE) over the DiFuMo components.
Revised: The loss is the mean-squared error (MSE) over the DiFuMo components.

Original: We select the best set of weights over the validation set, which is composed of 20% of collection #4438.
Revised: We select the best set of weights over the validation set, which is composed of 20% of collection #4438.

Original: We compare Peaks2Image to linear models (linear regression, kernel Ridge).
Revised: We compare Peaks2Image to linear models (linear regression, kernel Ridge).

Original: We use implementations from scikit-learn (Pedregosa et al., 2011) with default parameters.
Revised: We use implementations from scikit-learn (Pedregosa et al., 2011) with default parameters.

Original: Decoding The decoder model is an MLP composed of one hidden layer of 300 units and ReLU activation.
Revised: Decoding The decoder model is an MLP composed of one hidden layer of 300 units and ReLU activation.

Original: It is trained for 200 epochs on the reconstructed neuroscientific studies using the Adam optimizer with a learning rate of 3e-3 on the binary cross-entropy loss.
Revised: It is trained for 200 epochs on the reconstructed neuroscientific studies using the Adam optimizer with a learning rate of 3e-3 on the binary cross-entropy loss.

Original: During evaluation, we apply an additional sigmoid operation to the model outputs.
Revised: During evaluation, we apply an additional sigmoid operation to the model outputs.

Original: We use the Area Under the Receiver Operating Characteristic Curve (ROC AUC) over each label of the testing set to evaluate the decoding performance.
Revised: We use the Area Under the Receiver Operating Characteristic Curve (ROC AUC) over each label of the testing set to evaluate the decoding performance.

Original: Chance level is at 0.5 AUC.
Revised: Chance level is 0.5.

Original: We performed 25 different runs of the experiments with different splits to obtainreasonable confidence intervals.
Revised: We performed 20 different runs of the experiments with different splits to obtain confidence intervals.

Original:  R ESULTS 5.R ECONSTRUCTION 
Revised:  R ESULTS 5.

Revised: R ECONSTRUCTION

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

Original: 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.
Revised: 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.

Original: Even though the model has been trained with respect to mean-squared error in DiFuMo space, we find correlation more appropriate to measure the effects in those maps.
Revised: Even though the model has been trained with respect to mean-squared error in DiFuMo space, we find correlation more appropriate to measure the effects in those maps.

Original: [Figure]
Revised: [Figure]

Original: [Figure] [Figure] [Figure]
Revised: Correlation in voxel space

Original: We show the reconstruction performance of each model in Fig.
Revised: We show the reconstruction performance of each model in Fig.

Original: Peaks2Image significantly outperforms linear models.
Revised: Peaks2Image significantly outperforms linear models.

Original: 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.
Revised: 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.

Original: This suggests that some aspects of the image reconstruction are not captured by simple metrics such as the MSE or correlation.
Revised: This suggests that some aspects of the image reconstruction are not captured by simple metrics such as the MSE or correlation.

Original: We plot examples of reconstructions for different labels in Fig.
Revised: We plot examples of reconstructions for different labels in Fig.

Original: 5. D ECODING PERFORMANCES
Revised: 5. D ECODING PERFORMANCE

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

Original: The problem is a multi-label classification, which we evaluate by computing the ROC AUC over each label.
Revised: The problem is a multi-label classification, which we evaluate by computing the ROC AUC over each label.

Original: 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.
Revised: 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.

Original: We apply label enrichment to the original image labels.
Revised: We apply label enrichment to the original image labels.

Original: This includes regular expressions to map different terminologies for a same concept, and a hierarchy-based inference of labels based on the Cognitive Atlas ontology.
Revised: This includes regular expressions to map different terminologies for a same concept, and a hierarchy-based inference of labels based on the Cognitive Atlas ontology.

Original: For a given term, we assign parent terms from the ontology  (Menuet et al., 2022).
Revised: For a given term, we assign parent terms from the ontology used in (Menuet et al., 2022).

Original: This results in 43k images, totalling around 200 different cognitive processes.
Revised: This results in 43k images, totalling around 200 different cognitive processes.

Original: We evaluate only cognitive processes with at least 50 positive examples to avoid results with a too small sample.
Revised: We evaluate only cognitive processes with at least 50 positive examples to avoid results with a too small sample.

Original: This reduces the evaluation to 81 terms.
Revised: This reduces the evaluation to 81 terms.

Original: The limitation of terms is induced by the evaluation against NeuroVault, but Peaks2Image could extend the prediction to any word included in the tf-idf vocabulary.
Revised: The limitation of terms is induced by the evaluation against NeuroVault, but Peaks2Image could extend the prediction to any word included in the tf-idf vocabulary.

Original: In Fig.
Revised: In Fig.

Original: 3, we summarize the decoding performance by aggregating over Cognitive Atlas categories.
Revised: 3, we summarize the decoding performance by aggregating over Cognitive Atlas categories.

Original: Peaks2Image performs well over all the main concept families of Cognitive Atlas.
Revised: Peaks2Image performs well over all the main concept families of Cognitive Atlas.

Original: [Figure]
Revised: [Figure]

Revised: performed on all NeuroVault except collection #4438.

Revised: We exclude categories with less than 50 positive examples for stability.

Original:  chance 58 out of 81 cognitive processes over 43k images (AUC > 0.55).
Revised: Peaks2Image decodes better than chance 65 out of 81 cognitive processes over 43k images (AUC > 0.55).

Original: We show the importance of the reconstruction process in the decoding performance by performing the same decoding task over different representations of the input.
Revised: We show the importance of the reconstruction process in the decoding performance by running the same decoding task over different representations of the input.

Original: In particular, we compare
Revised: In particular, we compare

Original: Peaks2Image reconstructions to the sparse Gaussian representations used in the literature.
Revised: Peaks2Image reconstructions to the sparse Gaussian representations used in the literature.

Original: We show that, even though the correlation of sparse Gaussian representations is on par with Peaks2Image reconstruction,the advantage of having real brain representations allow Peaks2Imageto largely outperform the sparse Gaussian model on the decoding task.
Revised: 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.

Original: We observe a statistically significant gain of 0.1 on the mean AUC across labels between Peaks2Image and sparse Gaussian representations.
Revised: We observe a statistically significant gain of 0.1 on the mean AUC across labels between Peaks2Image and sparse Gaussian representations.

Revised: We report the performance of each method in table 1.

Original: We also compare Peaks2Image to approaches from the literature.
Revised: We also compare Peaks2Image to approaches from the literature.

Original: On terms that are shared with
Revised: On terms that are shared with

Original: NeuroSynth
Revised: NeuroSynth

Original: (Yarkoni et al., 2011), we show that Peaks2Image outperforms the former, while largely expanding the number of cognitive processes decoded. 
Revised: (Yarkoni et al., 2011), we show that Peaks2Image outperforms the former, while largely expanding the number of cognitive processes decoded. [Table] Decoding [Table]

Revised: Sparse

Original: We plot in figure 5 the difference between NNoDperformance and Peaks2Image performance.
Revised: [Table] NNoD [Table] NNoD

Original: Particularly, we add to this graph the performance of Peaks2Image on unseen terms, showing that
Revised: [Table] Peaks2Image NNoD [Table]

Original: Peaks2Image enables the successful decoding of a broader set of terms.
Revised: vs NeuroSynth vs sparse Gaussians vs Kernel Ridge vs Logistic Regression =0.06 p=0. =0.08 p=3e- =0.06 p=3e-

Original:  Peaks2Image cannot com- NeuroSynth
Revised: [Table] gain We plot in figure 5 the difference between NNoD performance and Peaks2Image performance.

Original: Sparse Gaussians
Revised: Particularly, we add to this graph the performance of Peaks2Image on unseen terms, showing that Peaks2Image enables the successful decoding of a broader set of terms.

Original: Kernel Ridge Peaks2Image =0.07 p=5e- =0.1 p=1e- =0.05 p=2eAUC performance chance pete with NNoD on terms that are in the training set.
Revised: Peaks2Image cannot compete with NNoD on terms that are in the training set.

Original: NNoD has been trained specifically on NeuroVault images, leading logically to better decoding performances.
Revised: NNoD has been trained specifically on NeuroVault images, leading logically to better decoding performances.

Original: However, Peaks2Image’s value is that it makes it possible to decode unseen terms, leading to a larger set of decoded terms while keeping relatively good performances where NNoD outperforms it.
Revised: However, Peaks2Image’s value is that it makes it possible to decode unseen terms, leading to a larger set of decoded terms while keeping relatively good performances where NNoD outperforms it.

Original: We noted an effect of NNoD being able to decode some terms unseen during training.
Revised: We noted an effect of NNoD being able to decode some terms unseen during training.

Original: We made sure  no data leakage occurred and compared results with other linear models which reached an expected 0.5 AUC.
Revised: We made sure that no data leakage occurred and compared results with other linear models which reached an expected 0.5 AUC.

Original: Our intuition is that the multi-label setting duringthe training helps capture a compression effect akin to PCA, leading to above chance decoding levels for certain tasks.
Revised: Our intuition is that the multi-label setting during training and the multi-layer structure lead to a compression effect akin to PCA, leading to above chance decoding levels for certain tasks.

Original: This effect is negatively correlated with the number of positive samples in the testing set.
Revised: This effect is negatively correlated with the number of positive samples in the testing set.

Original:  D ISCUSSION
Revised:  D ISCUSSION

Original: We introduced Peaks2Image, a model for reconstructing brain images from peak activity coordinates.
Revised: We introduced Peaks2Image, a model for reconstructing brain images from peak activity coordinates.

Original: By reconstructing  spatial representations, Peaks2Image bridges the gap between neuroscientific literature and brain image databases.
Revised: By reconstructing dense spatial representations, Peaks2Image bridges the gap between neuroscientific literature and brain image databases.

Original: 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.
Revised: 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.

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

Original: A limitation of Peaks2Image is our strategy regarding the studies text.
Revised: A limitation of Peaks2Image is our strategy regarding the studies text.

Original: It inhibits comparisons by limitating to exact matches between vocabulary and tags from NeuroVault.
Revised: It hinders comparisons by limiting to exact matches between vocabulary and tags from NeuroVault.

Original: While Peaks2Image could extend to any word contained in the neuroscientific literature, evaluation was reduced to a limited number of concepts due to the lack of extensively annotated brain images. 
Revised: While Peaks2Image could extend to any word contained in the neuroscientific literature, evaluation was reduced to a limited number of concepts due to the lack of extensively annotated brain images. Exploration of more [Figure]

Original: Exploration of more powerful language models as in Ngo et al.
Revised: m a g e p e r f o r m a n c e [Figure] powerful language models as in Ngo et al.

Original: (2021) is a future direction to better benefit from the continuous brain images generated for the neuroscientific studies.
Revised: (2021) is a future direction to better benefit from the continuous brain images generated for the neuroscientific studies.

Original: Though, the poor quality of NeuroVault annotations would still limit our ability to evaluate those models.
Revised: Though, the poor quality of NeuroVault annotations would still limit our ability to evaluate those models.

Original: Further investigations are still necessary to study the impact of the dataset used for training.
Revised: Further investigations are still necessary to study the impact of the dataset used for training.

Original: 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.[Figure] f
Revised: 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.

Original: [Figure] r m a n [Figure] Peaks2Image paves the way towards better generation of large sets of images for neuroscientific studies.
Revised: Peaks2Image paves the way towards better generation of large sets of images for neuroscientific studies.

Original: It shows we can outrun the limitations of large-scale poor labeling quality by performing zero-shot decoding.
Revised: It shows that one can outrun the limitations of large-scale poor labeling quality by performing zero-shot decoding.

Original: In particular, neuroscientific studies could become a challenging application for recent methods in text-to-image representation learning such as Radford et al.
Revised: In particular, neuroscientific studies could become a challenging application for recent methods in text-to-image representation learning such as Radford et al.

Original:  R EPRODUCIBILITY S TATEMENT
Revised:  R EPRODUCIBILITY S TATEMENT

Original: Data was collected from public sources using either the NQDC module from NeuroQuery or the
Revised: Data was collected from public sources using either the NQDC module from NeuroQuery or the

Original: Nilearn library.
Revised: Nilearn library.

Original: All scripts to reproduce the experiments and figures will be released publicly after review, along with the downloading scripts and preprocessed data.
Revised: All scripts to reproduce the experiments and figures will be released publicly after review, along with the downloading scripts and preprocessed data.

Original: In particular, we will share the
Revised: In particular, we will share the

Original: DiFuMo components of all images from our current version of the NeuroVault dataset.
Revised: DiFuMo components of all images from our current version of the NeuroVault dataset.

Original: For neuroscientific studies, we leveraged the data available at https://github.com/neuroquery/ neuroquery_data/tree/main/data .
Revised: For neuroscientific studies, we leveraged the data available at https://github.com/neuroquery/ neuroquery_data/tree/main/data .

Original:  OF N EURO V AULT I MAGES
Revised: A. R ECONSTRUCTION OF N EURO V AULT I MAGES

Original: We show an example of the representation at different steps of the Peaks2Image model in figure 6. 
Revised: We show an example of the representation at different steps of the Peaks2Image model in figure 6. L R L

Original: We also apply Peaks2Image to NeuroVault images that are associated to a set of cognitive terms.
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Original:  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).
Revised: [Figure] Peaks2ImageReconstruction 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).

Original: A. P ER - LABEL DECODING PERFORMANCE
Revised: A. P ER - LABEL DECODING PERFORMANCE

Original: We ran 25 runs of the experiment.
Revised: We ran 20 runs of the experiment.

Original: In figure 8, we report the per-label decoding performance along with the variance of the results.
Revised: In figure 8, we report the per-label decoding performance along with the variance of the results.

Original: Most terms are decoded above chance.
Revised: Most terms are decoded above chance.

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Revised: learningprosodystrategy AUC Performance

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