Technologies like magnetic resonance imaging (MRI) provide unique insights into macroscopic brain structure and function in vivo. Modern research increasingly emphasizes how microscale attributes, such as gene expression, influence these imaging-derived phenotypes (Fornito et al., 2019; Arnatkeviciute et al., 2019; Arnatkevičiūtė et al., 2021). Gene expression is particularly useful as it is a fundamental molecular phenotype that can be plausibly linked to the function of biological pathways (Whitaker et al., 2016; Seidlitz et al., 2018), protein synthesis (Zheng et al., 2019), receptor distributions (Beliveau et al., 2017; Nørgaard et al., 2021; Shine et al., 2019; Deco et al., 2020; Preller et al., 2018), and cell types (Hansen et al., 2021; Anderson et al., 2020b; Anderson et al., 2018; Seidlitz et al., 2020; Gao et al., 2020). However, researchers looking to bridge these macro- and microscopic phenotypes must overcome multiple challenges. Although there are numerous technical and analytic considerations, one foundational issue is that acquiring high-quality transcriptomic data from the human brain is both costly and highly invasive, requiring budgets far greater than most typical neuroimaging studies and restrictive access to tissue from post-mortem donors or cranial surgical patients. As such, researchers must often rely on freely available repositories of gene expression data.

There exist multiple open-access repositories for gene expression in the human brain, including BrainSpan (Miller et al., 2014; Kang et al., 2011) and PsychENCODE (Gandal et al., 2018; Li et al., 2018; Wang et al., 2018; among others: Sousa et al., 2017; Darmanis et al., 2015; Lake et al., 2016); however, these datasets generally provide relatively sparse anatomical coverage, limiting the types of analyses that can be performed. Thus, researchers who aim to compare transcriptomic expression with whole-brain imaging-derived phenotypes have primarily relied on the Allen Human Brain Atlas (AHBA; Hawrylycz et al., 2012; Hawrylycz et al., 2015). Initially released in 2010, the AHBA remains the most spatially comprehensive dataset of its kind. Derived from bulk microarray analysis of tissue samples obtained from six donors, the AHBA provides expression data for more than 20,000 genes across 3702 brain areas in MRI-derived stereotactic space. With its superior resolution, the AHBA has significantly contributed to the emergence of the field of imaging transcriptomics (Fornito et al., 2019), enabling dozens of studies over the past decade examining relationships between gene expression and an array of macroscale imaging attributes, including cortical thickness (Shin et al., 2018), myelination (Burt et al., 2018), developmental brain maturation (Whitaker et al., 2016; Kirsch and Chechik, 2016), structural brain networks (Seidlitz et al., 2018; Romero-Garcia et al., 2018; Arnatkevičiūtė et al., 2020), functional brain networks (Richiardi et al., 2015; Krienen et al., 2016; Vértes et al., 2016), and human cognition (Fox et al., 2014; Hansen et al., 2021). The AHBA has also highlighted the importance of whole-brain gene expression in neurological and psychiatric diseases, where it has become increasingly clear that transcriptional pathways play a critical role in shaping the broader dynamics of disease progression and emergent symptomatology (Zheng et al., 2019; Shafiei et al., 2021; Henderson et al., 2019; Vogel et al., 2020; Rittman et al., 2016; Anderson et al., 2020a; Romme et al., 2017; McColgan et al., 2018; Morgan et al., 2019).

Since its release, several software toolboxes have been developed to help researchers use transcriptional data from the AHBA (French and Paus, 2015; Gorgolewski et al., 2015; Rittman et al., 2017; Rizzo et al., 2016); however, these tools often focus primarily on facilitating integration of the AHBA with neuroimaging data, offering limited if any functionality for modifying how the data are processed prior to analysis. Instead, a recent comprehensive review revealed that many research groups have opted to develop their own processing pipelines for the AHBA (Arnatkeviciute et al., 2019). Unfortunately, as there are no field-accepted standards for processing imaging transcriptomic data, the generated pipelines vary substantially across groups.

The extent to which such processing variability affects analytic outcomes from the AHBA remains unknown. Indeed, over the past decade neuroimaging research has shown that methodological variability can have broad influences on analyses using structural MRI (Bhagwat et al., 2021; Kharabian Masouleh et al., 2020), diffusion MRI (Oldham et al., 2020; Maier-Hein et al., 2017; Schilling et al., 2019), task fMRI (Carp, 2012; Botvinik-Nezer et al., 2020), and resting-state fMRI (Parkes et al., 2018; Ciric et al., 2017). Although researchers are beginning to grapple with the consequences of this variability, the lack of baseline gene expression datasets against which to compare new results impedes the development of standardized practices. In these situations, some researchers have proposed performing ‘multiverse’ analyses (Steegen et al., 2016; Dragicevic et al., 2019), wherein all possible permutations of data processing are analyzed and the full range of analytic results reported. Although such analyses can be computationally intensive, they offer a path to understand how processing choices impact statistical inferences and conclusions, and provide a mechanism by which to help researchers converge on an optimal pipeline.

Here, we comprehensively investigate how different processing choices influence the results of analyses using the AHBA. First, we develop an open-source Python toolbox, abagen, that collates all possible processing parameters into a set of turn-key workflows, optimized for flexibility and ease-of-use. We then use the toolbox to process the AHBA through approximately 750,000 unique pipelines. Across three prototypical imaging transcriptomic analyses, we examine whether and how these different processing options modify derived statistical estimates and quantify the relative importance of each option. Next, we replicate a curated set of processing pipelines from the literature to assess how previously reported findings compare to the full range of potential outcomes observed across all examined pipelines. Finally, we end with a set of recommendations, integrated directly into the developed abagen toolbox, to promote standardized use of the AHBA in future work.

We introduce the abagen toolbox, an open-access software package designed to streamline processing and preparation of the AHBA for integration with neuroimaging data (Markello et al., 2021c, available at https://github.com/rmarkello/abagen; Markello, 2021b copy archived at swh:1:rev:2aeab5bd0f147fa76b488645e148a1c18095378d). Supporting several workflows, abagen offers functionality for an array of analyses and has already been used in several peer-reviewed publications and preprints (Shafiei et al., 2020; Hansen et al., 2021; Shafiei et al., 2021; Brown et al., 2021; Park et al., 2021; Valk et al., 2021; Zhao et al., 2020; Benkarim et al., 2020; Ding et al., 2021; Park et al., 2020; Lariviere et al., 2020; Martins et al., 2021). The primary workflow, used to generate regional gene expression matrices, integrates 17 distinct processing steps that have previously been employed by research groups throughout the published literature (Table 1). We refer to each unique set of processing choices and parameters as a ‘pipeline’. The following results use abagen to investigate how variable application of these processing steps can impact analyses of AHBA data.

Processing choices influence transcriptomic analyses

To understand how choices made during the processing of AHBA data impact downstream analyses, we enumerated 17 decision points (i.e. processing steps or options) that have been modified and used in the literature (Table 1). From these 17 steps we implemented 746,496 distinct processing pipelines, where each pipeline parcellated microarray expression from the AHBA with the Desikan-Killiany atlas (Desikan et al., 2006) to generate a unique brain region-by-gene expression matrix.

Analyses of expression data from the AHBA can be grouped into one of three broad classes (Fornito et al., 2019): correlated gene expression analyses, gene co-expression analyses, and regional gene expression analyses. Correlated gene expression analyses examine the correlation between brain regions across genes, yielding a symmetric region × region matrix (similar to a functional connectivity matrix). Gene co-expression analyses, on the other hand, examine the correlation between genes across brain regions, yielding a symmetric gene × gene matrix. Finally, regional gene expression analyses examine the expression patterns of specific genes or gene sets in relation to other imaging-derived phenotypes.

To examine how differences in processing choices may impact both the expression matrices generated from the different pipelines and derived statistical estimates we ran one analysis from each of these classes on the matrices generated by each processing pipeline. Notably, these analyses are either direct reproductions or variations of analyses that have been previously published (Arnatkeviciute et al., 2019; Oldham et al., 2008; Hawrylycz et al., 2012; Burt et al., 2018). Although there is no ground truth for any of these analyses, findings from previous work offer some context for interpreting the observed results (i.e. data from other species and other modalities; Lau et al., 2021). Nonetheless, we primarily focus on highlighting the potential variability resulting from different processing pipelines.

Correlated gene expression (CGE)

First, we separately correlated the rows of each expression matrix to generate symmetric region × region ‘correlated gene expression’ matrices, indicating the similarity of gene expression profiles between different brain regions (Figure 1a). Previous work in other species has reliably observed that transcriptional similarity in the brain decays with increasing separation distance (Fulcher et al., 2019; Lau et al., 2021). This distance-dependent relationship is an expected feature due to the functional specialization of brain regions, and is consistent with other imaging-derived phenotypes in humans (Roberts et al., 2016; Goulas et al., 2019; Betzel and Bassett, 2018; Mišić et al., 2014; Shafiei et al., 2020; Horvát et al., 2016). We assessed this relationship by extracting the upper triangle of the correlated gene expression matrices and correlating them with the upper triangle of a regional distance matrix, derived by computing the average Euclidean distance between brain region centroids in the Desikan-Killiany atlas (Figure 1a, left panel). Although previous work has highlighted that this relationship is exponential (Arnatkeviciute et al., 2019), we computed the Spearman correlation as both statistics should exhibit similar variability across pipelines and the latter is less computationally expensive.

Figure 1

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Gene co-expression (GCE)

For the second type of analysis we separately correlated the columns of each expression matrix to generate gene × gene ‘co-expression’ (GCE) matrices, indicating the similarity in spatial expression patterns between all pairs of genes (Figure 1a). A significant body of research has shown that genes tend to form functional communities, exhibiting synchronized expression patterns across space and time (Oldham et al., 2008), such that gene co-expression patterns tend to be more similar within than between such communities. Here, we obtained a set of gene community assignments derived for the brain from a previously studied human transcriptomic dataset (Oldham et al., 2008). We used these community assignments to calculate a silhouette score (Rousseeuw, 1987) for the gene co-expression matrices generated by each pipeline, measuring how well these communities represented the derived co-expression patterns (Figure 1a, middle panel).

Regional gene expression (RGE)

For the third type of transcriptomic analysis, we focused on regional correlations between gene expression measures and an MRI-derived phenotype. Our regional expression measure was defined by computing the first principal component of the region-by-gene expression matrix, representing the axis of maximum spatial variation of gene expression in the brain observed under a given AHBA processing pipeline. As gene expression fundamentally shapes the structure and function of the human brain, it is likely that this principal component may exhibit similar spatial variability to other imaging-derived measures. Recent work has highlighted that the T1w/T2w ratio is a robust phenotype that exhibits patterns of regional variation consistent with other microstructural and functional properties (Gao et al., 2020; Burt et al., 2018; Demirtaş et al., 2019; Fulcher et al., 2019). We therefore correlated the first principal component of gene expression with the whole-brain T1w/T2w ratio (Figure 1a, right panel), measuring the extent to which these values covary across the cortex.

Pipeline distributions

Results from these three analyses reveal that choice of processing pipeline dramatically influences derived statistical estimates (i.e. the CGE-distance correlation, the gene co-expression silhouette score, and the spatial correlations between gene PC1 and whole-brain T1w/T2w ratio; Figure 1b). We observe that all three of the generated distributions of statistical estimates across the 746,496 pipelines have wide ranges (correlated gene expression: [-0.51,–0.13]; gene co-expression: [-0.78,–0.18]; regional gene expression: [0.00, 0.90]) and are either bimodal (Figure 1b, left/middle panels) or heavily skewed (Figure 1b, right panel).

Since there is no ground truth for these analyses we cannot quantitatively assess whether some pipelines are more or less accurate than others. However, there is strong qualitative evidence to suggest that correlated gene expression should be lower between brain regions that are farther apart (Arnatkeviciute et al., 2019; Krienen et al., 2016; Richiardi et al., 2015; Fulcher et al., 2019; Lau et al., 2021). It is notable, then, that the distribution of distance-dependent estimates is so strongly bimodal (splitting at $r\approx -0.4$), suggesting two very different perspectives on the size of this effect (Figure 1a and b, left panels). As increasingly-detailed single-cell transcriptional data become available (e.g. Yao et al., 2021) we may be able to use these estimates to determine accuracy; for now, we simply note that even for this estimate with strong biological priors we see considerable variability.

Similar variability can be observed for the other two analyses. While all the pipelines demonstrate relatively poor fit of gene communities to the derived gene co-expression matrices (refer to Materials and methods: Analytic approaches for information on why this is not unexpected), we observe that a portion of the pipelines yield far worse correspondence (Figure 1a and b, middle panels). Moreover, while the correlations between gene PC1 and whole-brain T1w/T2w ratio are largely consistent across pipelines, there are a small group of pipelines that yield correlations that deviate by $\rho \approx 1.0$. Notably, the parameter choices for these pipelines are not pathological—that is, their use could be justified—and, as we discuss later (see Results: Variability in parameter importance), modifying just one parameter setting can yield changes in effect sizes within this range.

Collectively, we find that for all three of these analyses there is substantial variability in the statistical estimates generated by different processing pipelines, and this variability is large enough that, across pipelines, it has a meaningful difference in the potential inferences and conclusions that can be drawn.

Variability in parameter importance

Next, we quantified the relative importance of different processing steps and parameters on our three derived statistical estimates. While researchers must ultimately make choices for each of the steps individually when processing AHBA data, we wanted to investigate whether unique choices have distinct influences. Moreover, which parameters are most important may differ based on the type of analysis performed.

We investigated parameter importance by calculating a distribution of difference scores for each parameter, measuring the extent to which changing each parameter—holding all other parameters constant—influences the derived statistical metrics from each of the three analyses. For example, given a processing parameter with two choices this procedure yielded a distribution of $N/2$ difference scores per analysis, where $N$ is the total number of pipelines (i.e. $746,496/2=373,248$). We averaged these distributions separately for each analysis to generate a single, summary ‘impact score’ for each processing step, which we then rank-ordered independently for each analysis.

We find considerable agreement in which parameters are the most impactful across analyses (Figure 2a): the most influential processing steps often involve procedures that influence the gene normalization process in some way (e.g. gene normalization method, normalizing only matched samples; Figure 2b). On the other hand, among the least impactful parameters are choices concerning donor-specific probe selection and handling of missing data. It is worth noting that of the probe selection methods tested in the current manuscript (i.e. max intensity, correlation intensity, correlation variance, differential stability, RNAseq correlation, and averaging), three of the six all render the choice of donor-specific probe selection redundant. In other words, these three methods are mutually exclusive with choice of donor-specific probe selection, potentially confounding our ability to measure the real influence of this parameter. We also highlight that choice of atlas may influence the impact of missing data handling: since the Desikan-Killiany atlas is a relatively low-resolution atlas (68 nodes), expression matrices generated from the tested pipelines are missing, at most, data for two brain regions. It is possible that handling of missing data may be more important when higher-resolution parcellations are employed. That is, while some parameters do not appear to affect our results in aggregate, there are potentially specific research questions where these parameters could play an important and impactful role.

Figure 2

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To investigate those parameters that did play an influential role in the current analyses, we visualized their impact by examining the statistical distributions from each analysis separated by the different parameter choices (shown in Figure 2b for gene normalization method). Dividing the distributions in this way highlights how strongly parameter choice can influence the outcomes of the analyses: for example, when no gene normalization is employed the resulting estimates are dramatically shifted from those generated by pipelines that employed some form of normalization (Figure 2b; no normalization: purple distribution). Indeed, the bimodality and skew observed in the full statistical distributions for the analyses (Figure 1b) is almost entirely explained by this single parameter choice.

To investigate more qualitative differences in how parameter choice influences the processing pipelines we performed a principal component analysis (PCA) on the matrix of statistical estimates from the three analyses (i.e. the $746,496\times 3$ pipeline-by-analysis matrix). We extracted the first two principal components from the statistical estimate matrix (variance explained: PC1 = 70%, PC2 = 26%) and examined how pipeline scores were distributed along these axes (Figure 2c). Delineating the distribution of pipelines based on parameter choice underscores how these options impact the separability of resulting statistical estimates. Reinforcing results presented above, we find that the choice of gene normalization method distinguishes the one-third of pipelines with no normalization (purple) from the remaining two-thirds that applied some form of normalization (blue and brown; Figure 2c, left). It is clear from the distribution of pipelines, however, that other processing choices interact with this parameter. For example, plotting the pipelines by whether the gene normalization was performed separately on samples within each structural class (i.e. cerebral cortex, subcortex, cerebellum) rather than across all tissue samples further delineates the pipelines that applied gene normalization into two distinct clusters (Figure 2c, right).

These results reveal how different processing steps are grouped in terms of their importance to analyses of the AHBA, with some groups demonstrating greater potential impact. Broadly, parameters modifying normalization are the most important, followed by parameters influencing how tissue samples are matched to brain regions, and finally parameters impacting probe selection. Moreover, we find that choices within each processing step do not all have an equivalent impact on derived estimates (i.e. performing no gene normalization has a much greater influence than choosing between the two other forms of normalization tested).

Reproducing published analyses

The previous subsections demonstrate variability across the complete range of reasonable processing pipelines; however, many of these pipelines have not yet been used in practice. To investigate whether the subset of pipelines that have already been implemented in the published literature display similar variability, we used abagen to reproduce the processing procedures from nine peer-reviewed articles that (1) are highly-cited within the field, (2) highlight a wide range of processing options, and (3) sufficiently describe their processing pipelines such that they could be reproduced. We explored how different the gene expression values and statistical outcomes generated by these published pipelines were (Hawrylycz et al., 2015; French and Paus, 2015; Whitaker et al., 2016; Krienen et al., 2016; Anderson et al., 2018; Burt et al., 2018; Romero-Garcia et al., 2018; Anderson et al., 2020b; Liu et al., 2020). To ensure comparability, we standardized the choice of brain parcellation across pipelines, using the Desikan-Killiany atlas in all instances. The pipelines were used to generate nine region-by-gene expression matrices, which were then subjected to the same three analyses described previously.

In reproducing the pipelines we note important differences in processing parameter selection (Figure 3a), and find that this variability results in slight discrepancies between gene expression values generated by the pipelines. For example, looking at the distribution of cortical somatostatin (SST), a gene discussed heavily in Anderson et al., 2020b where it used as a proxy for somatostatin interneuron density (Fulcher, 2019), we observe some variation between pipelines (Figure 3b and c). Although we find moderate consistency in the statistical estimates generated by the pipelines, there are important differences (ranges: correlated gene expression [-0.49,–0.28], gene co-expression [-0.70,–0.24], regional gene expression [0.34, 0.88]; Figure 3c). One outlier is the single pipeline that did not appear to implement any form of gene normalization (French and Paus, 2015), supporting earlier results demonstrating the importance of this processing step on downstream expression estimates. This is potentially notable as the processed expression data from this pipeline were made openly available and have been used in analyses by other researchers (e.g. Sepulcre et al., 2018; Beliveau et al., 2017).

Figure 3

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Given that imaging transcriptomics is still relatively new and there has been limited work addressing best practices in the field (Arnatkeviciute et al., 2019), these results stress the importance of standardization in use of the AHBA among research groups. Although variation in processing can ostensibly lead to similar inferences in specific analyses, even minor differences in processing choices consistently yield measurable discrepancies in derived expression data. Without proper standardization, these differences will compound and become more problematic as the field continues to grow.

Standardized processing and reporting with the abagen toolbox

Across all of our analyses we find that choice of processing steps and parameters can have a strong influence on the statistical outcomes of research with the AHBA. Here, we briefly highlight features that we have integrated into the abagen toolbox to facilitate standardization in future research.

The abagen toolbox supports two use-case driven workflows: (1) a workflow that accepts an atlas and returns a parcellated, preprocessed regional gene expression matrix (Figure 4a); and, (2) a workflow that accepts a mask and returns preprocessed expression data for all tissue samples within the mask (Figure 4b). Workflows can be called via a single line of code from either the command line or Python terminal, and take approximately one minute to run with default settings using the Desikan-Killiany atlas. The main output of abagen is a single brain region (or tissue sample) × gene expression matrix. Changing the parameters may modify the shape of the matrix (e.g. different atlases will yield different numbers of regions or samples) or different values (e.g. different processing choices may yield different numbers of genes), but not the structure. The outputs of these workflows can be used generally to examine the three prototypical research questions enabled by the AHBA: correlated gene expression, gene co-expression, and regional expression of genes of interest more broadly (Fornito et al., 2019). Beyond its primary workflows, abagen has additional functionality for post-processing the AHBA data (e.g. removing distance-dependent effects from expression data, calculating differential stability estimates; Hawrylycz et al., 2015), and for accessing data from the companion Allen Mouse Brain Atlas (e.g. providing interfaces for querying the Allen Mouse API; https://mouse.brain-map.org/; Lein et al., 2007).

Figure 4

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Although these workflows support the entire range of processing options that we assessed in the current manuscript (Figure 4c), we have set the default options for all steps based on best practice recommendations developed in Arnatkeviciute et al., 2019 and further informed by the results presented above (see Supplementary file 1 for a full list). We believe the default settings in abagen will provide a reasonable starting point for researchers beginning to work with the AHBA; however, as we have continually noted, the appropriate choices for some parameters will vary based on research question. As such, to make it easier for researchers to report exactly what parameters they use, we have integrated an automated reporting mechanism into the abagen workflows (Figure 5). The generated reports provide manuscript-ready step-by-step documentation describing all the processing done to the AHBA data in the workflow, and are licensed CC0 (https://creativecommons.org/share-your-work/public-domain/cc0/) so that they can be freely used without restriction.

Figure 5

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Creation of the toolbox has followed best-practices in software development, including version control, continuous integration testing, and modular code design. To encourage further use by new research groups we provide comprehensive documentation on installing and working with the abagen toolbox online (https://abagen.readthedocs.io/).

In the present report, we introduced the abagen toolbox, an open-source Python library for processing transcriptomic data. Using abagen, we conducted a comprehensive analysis examining whether and how different processing options modify statistical estimates derived from analyses using the AHBA. We investigated how processing pipelines used in the literature compare to those we tested, and provide recommendations for improving standardization and reporting of analyses using the AHBA, highlighting how the abagen toolbox can facilitate future developments in this space.

Testing nearly 750,000 unique processing pipelines, we find that choice of processing parameters can strongly influence statistical estimates derived from analyses of the AHBA, and that these choices interact with the type of analysis performed (Figure 1). We observe significant variability with regard to which parameters are most influential, finding that procedures modifying gene expression normalization have a far greater impact on downstream analyses than other processing steps (Figure 2). Looking to the literature, we reproduce nine pipelines from published articles and find that, despite notable inconsistencies in their processing choices, there is moderate consistency in their produced statistical estimates (Figure 3). We demonstrate, however, that these summary estimates may obscure meaningful differences in gene expression values derived by the pipelines, cautioning researchers to be aware of how analytic choices may impact their findings.

Altogether, the present report provides a comprehensive assessment of how processing variability can impact analyses in the field of imaging transcriptomics. Our results demonstrate how researcher choices (or ‘researcher degrees of freedom’; Simmons et al., 2011) can play a meaningful role in analyses of the AHBA. However, these findings are not necessarily limited to the AHBA. Indeed, increasing reliance on open-access datasets has begun to reveal unique challenges associated with data reuse (Thompson et al., 2020). Improved standardization and reporting among research groups using (and re-using) openly available datasets may help to mitigate some of these challenges. We believe that functionality in the abagen toolbox can support future researchers in overcoming these pitfalls and improve reproducibility in processing and analyzing AHBA data.

Our results also show that not all processing choices are equal: that is, we find a hierarchy of processing parameters, wherein procedures modifying gene normalization have the greatest impact on analyses, followed by steps more broadly influencing the matching of tissue samples to brain regions and finally by parameters that determine probe selection. Furthermore, we find that within processing steps certain parameter choices may lead to more reasonable statistical estimates. In particular, applying some form of gene normalization tends to improve the behavior of processed expression data when compared to instances in which no normalization is applied (Figure 1), but there appear to be limited differences in the type of normalization used. Although we only considered cortical tissue samples in the current analyses, we expect that including non-cortical samples would further reinforce these results (Arnatkeviciute et al., 2019) known differences in microarray expression values between cortex and subcortical structures will likely emphasize the impact of different normalization procedures across pipelines. Critically, these findings largely agree with previous recommendations developed by Arnatkeviciute et al., 2019, and we have chosen default parameter choices for abagen workflows accordingly.

Note that there are some processing steps that should be performed in a specific sequence, and others whose order could potentially be interchanged. For example, intensity-based filtering of probes must always be performed before probe selection—reversing the order of these operations would, in the majority of cases, be problematic because it would potentially result in the selection of noisy probes to be carried through to analysis. However, the order of other steps (i.e. sample versus gene normalization) could arguably be reversed with no ostensible detriment. This procedural ambiguity is a salient example of the need to standardize workflows.

More broadly, this work builds on increasing efforts to examine the importance of methodological choices and analytical flexibility in human neuroimaging research (Bhagwat et al., 2021; Kharabian Masouleh et al., 2020; Oldham et al., 2020; Maier-Hein et al., 2017; Schilling et al., 2019; Carp, 2012; Botvinik-Nezer et al., 2020; Parkes et al., 2018; Ciric et al., 2017). Thankfully, emerging technical solutions have begun to tackle these issues via the development of tools that aim to abstract away sources of variation (e.g. fMRIPrep, Esteban et al., 2019; QSIPrep, Cieslak et al., 2020). While results from the present study reinforce the importance of methodological choices in research, abagen draws significant inspiration from these software packages in providing a set of tools designed to overcome such concerns when working with the AHBA.

While the AHBA dataset remains the only one of its kind, the abagen toolbox is designed to be used more broadly as similar datasets become available. That is, the preprocessing functions in abagen can be applied to other microarray expression datasets assuming, for example, availability of stereotactic coordinates. As new imaging transcriptomic datasets are developed and become more widely used, abagen functionality for creating standardized processing pipelines will only become more important. By developing the toolbox openly on GitHub (https://github.com/rmarkello/abagen), it is our hope that abagen can serve as a foundational, community tool for use in imaging transcriptomics research.

One consideration for future work on this topic is that the pipelines tested cover only a portion of the potential variability possible when processing AHBA data (Table 1). For example, a growing body of research has begun to examine how choice of brain parcellation may impact imaging analyses (e.g. Craddock et al., 2012; Thirion et al., 2014; Messé, 2020; Markello and Misic, 2021). While we only assessed processing pipelines using the Desikan-Killiany atlas, many other atlases have been used with the AHBA and it remains unclear how this variation may impact research findings. We also did not investigate whether donor-specific parcellations may impact analyses, a processing choice used in several published research findings (Anderson et al., 2020b; Romero-Garcia et al., 2018; Burt et al., 2018). Although there is significant evidence suggesting inter-individual variability in brain region definition (e.g. Gordon et al., 2017; Kong et al., 2019; Dickie et al., 2018), the process of generating individualized brain parcellations is fraught with methodological choices and requires careful data processing. Given the quality of the MRI data provided alongside the transcriptomic data in the AHBA—including important differences in scanning protocol and procedures between donors—creating donor-specific parcellations may be a large source of variability between pipelines.

Another limitation of the presented results is that we are unable to make categorical statements about which processing options are ’best’ for the AHBA. First, there is no ground truth against which one can assess what the optimal set of processing parameters. One potential solution to this could be to examine the robustness of pipelines based on a leave-one-donor-out strategy (e.g. Arnatkeviciute et al., 2019; Vogel et al., 2020), wherein analyses are repeated six times, omitting one donor each time, to ensure that none of the donors are unduly influencing analytic estimates. This approach is likely to become more useful as data from more individuals becomes available, but at present may be a worthwhile approach for assessing whether chosen processing parameters are appropriate. Moreover, the optimal set of processing parameters may vary based on research question. For instance, in most applications gene normalization is appropriate, as it ensures that downstream analyses are not driven by a small subset of highly expressed genes. However, in other applications it may be desirable to retain the variance contributed by genes to accurately reflect their relative expression levels. For example, many genes in AHBA are not brain-specific, so normalization will amplify their expression patterns, potentially obscuring more relevant expression information. This can be avoided by sub-selecting genes in a hypothesis-driven manner and skipping the normalization step altogether.

Nonetheless, we offer two alternative solutions for researchers who want to continue using the AHBA data. First, similar to the current report, researchers can conduct a comprehensive analysis with the AHBA, running multiple processing pipelines and showing the entire distribution of generated statistical estimates; however, this process can be computationally prohibitive and may impair researchers’ abilities to interpret their findings (Steegen et al., 2016). A less costly alternative, then, is for the imaging transcriptomic research community to converge on a set of data-driven processing pipeline for the AHBA that can be used across research groups. We believe the abagen toolbox—with its comprehensive workflows, well-informed default parameter choices, and detailed documentation—can facilitate this process. While we acknowledge that some research groups may have strong reasons for wanting to use specific (i.e. non-default) processing choices, in these instances we urge clear and detailed reporting of the methods used—such as via the automated reporting functionality from the abagen toolbox.

Altogether, the current report highlights the problem of processing variability in analyses using the AHBA, impacting many research studies in the burgeoning field of imaging transcriptomics. We demonstrate how different processing options can influence statistical estimates of analyses relating data from the AHBA to imaging-derived phenotypes, and present the abagen toolbox as a promising potential solution to this issue.

All datasets used in this study are publicly available. Detailed information about the datasets and how to access them are described in the manuscript.

1. Software

   1. Allen Institute for Brain Science

   (2013) Allen Human Brain Atlas online documentation

   Allen Institute Publications for Brain Science.

   https://help.brain-map.org/display/humanbrain/Documentation
2. 1. Anderson KM
   2. Krienen FM
   3. Choi EY
   4. Reinen JM
   5. Yeo BTT
   6. Holmes AJ

   (2018) Gene expression links functional networks across cortex and striatum

   Nature Communications 9:1428.

   https://doi.org/10.1038/s41467-018-03811-x

   • PubMed
   • Google Scholar
3. 1. Anderson KM
   2. Collins MA
   3. Chin R
   4. Ge T
   5. Rosenberg MD
   6. Holmes AJ

   (2020a) Transcriptional and imaging-genetic association of cortical interneurons, brain function, and schizophrenia risk

   Nature Communications 11:2889.

   https://doi.org/10.1038/s41467-020-16710-x

   • PubMed
   • Google Scholar
4. 1. Anderson KM
   2. Collins MA
   3. Kong R
   4. Fang K
   5. Li J
   6. He T
   7. Chekroud AM
   8. Yeo BTT
   9. Holmes AJ

   (2020b) Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder

   PNAS 117:25138–25149.

   https://doi.org/10.1073/pnas.2008004117

   • PubMed
   • Google Scholar
5. 1. Arnatkeviciute A
   2. Fulcher BD
   3. Fornito A

   (2019) A practical guide to linking brain-wide gene expression and neuroimaging data

   NeuroImage 189:353–367.

   https://doi.org/10.1016/j.neuroimage.2019.01.011

   • PubMed
   • Google Scholar
6. Preprint

   1. Arnatkevičiūtė A
   2. Fulcher B
   3. Oldham S
   4. Tiego J
   5. Paquola C
   6. Gerring Z
   7. Aquino K
   8. Hawi Z
   9. Johnson B
   10. Ball G
   11. Klein M
   12. Deco G
   13. Franke B
   14. Bellgrove M
   15. Fornito A

   (2020) Genetic Influences on Hub Connectivity of the Human Connectome

   bioRxiv.

   https://doi.org/10.1101/2020.06.21.163915

   • Google Scholar
7. Preprint

   1. Arnatkevičiūtė A
   2. Fulcher B
   3. Bellgrove M
   4. Fornito A

   (2021) Where the Genome Meets the Connectome: Understanding How Genes Shape Human Brain Connectivity

   PsyArXiv.

   https://doi.org/10.31234/osf.io/hqgz7

   • Google Scholar
8. 1. Avants BB
   2. Tustison NJ
   3. Song G
   4. Cook PA
   5. Klein A
   6. Gee JC

   (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration

   NeuroImage 54:2033–2044.

   https://doi.org/10.1016/j.neuroimage.2010.09.025

   • PubMed
   • Google Scholar
9. 1. Beliveau V
   2. Ganz M
   3. Feng L
   4. Ozenne B
   5. Højgaard L
   6. Fisher PM
   7. Svarer C
   8. Greve DN
   9. Knudsen GM

   (2017) A High-Resolution In Vivo Atlas of the Human Brain’s Serotonin System

   The Journal of Neuroscience 37:120–128.

   https://doi.org/10.1523/JNEUROSCI.2830-16.2016

   • PubMed
   • Google Scholar
10. Preprint

    1. Benkarim O
    2. Paquola C
    3. Park B -y
    4. Hong SJ
    5. Royer J
    6. de Wael R
    7. Larivière S
    8. Valk S
    9. Bzdok D
    10. Mottron L

    (2020) Functional Idiosyncrasy Has a Shared Topography with Group-Level Connectivity Alterations in Autism

    bioRxiv.

    https://doi.org/10.1101/2020.12.18.423291

    • Google Scholar
11. 1. Betzel RF
    2. Bassett DS

    (2018) Specificity and robustness of long-distance connections in weighted, interareal connectomes

    PNAS 115:E4880–E4889.

    https://doi.org/10.1073/pnas.1720186115

    • PubMed
    • Google Scholar
12. 1. Bhagwat N
    2. Barry A
    3. Dickie EW
    4. Brown ST
    5. Devenyi GA
    6. Hatano K
    7. DuPre E
    8. Dagher A
    9. Chakravarty M
    10. Greenwood CMT
    11. Misic B
    12. Kennedy DN
    13. Poline J-B

    (2021) Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses

    GigaScience 10:giaa155.

    https://doi.org/10.1093/gigascience/giaa155

    • PubMed
    • Google Scholar
13. 1. Botvinik-Nezer R
    2. Holzmeister F
    3. Camerer CF
    4. Dreber A
    5. Huber J
    6. Johannesson M
    7. Kirchler M
    8. Iwanir R
    9. Mumford JA
    10. Adcock RA
    11. Avesani P
    12. Baczkowski BM
    13. Bajracharya A
    14. Bakst L
    15. Ball S
    16. Barilari M
    17. Bault N
    18. Beaton D
    19. Beitner J
    20. Benoit RG
    21. Berkers RMWJ
    22. Bhanji JP
    23. Biswal BB
    24. Bobadilla-Suarez S
    25. Bortolini T
    26. Bottenhorn KL
    27. Bowring A
    28. Braem S
    29. Brooks HR
    30. Brudner EG
    31. Calderon CB
    32. Camilleri JA
    33. Castrellon JJ
    34. Cecchetti L
    35. Cieslik EC
    36. Cole ZJ
    37. Collignon O
    38. Cox RW
    39. Cunningham WA
    40. Czoschke S
    41. Dadi K
    42. Davis CP
    43. Luca AD
    44. Delgado MR
    45. Demetriou L
    46. Dennison JB
    47. Di X
    48. Dickie EW
    49. Dobryakova E
    50. Donnat CL
    51. Dukart J
    52. Duncan NW
    53. Durnez J
    54. Eed A
    55. Eickhoff SB
    56. Erhart A
    57. Fontanesi L
    58. Fricke GM
    59. Fu S
    60. Galván A
    61. Gau R
    62. Genon S
    63. Glatard T
    64. Glerean E
    65. Goeman JJ
    66. Golowin SAE
    67. González-García C
    68. Gorgolewski KJ
    69. Grady CL
    70. Green MA
    71. Guassi Moreira JF
    72. Guest O
    73. Hakimi S
    74. Hamilton JP
    75. Hancock R
    76. Handjaras G
    77. Harry BB
    78. Hawco C
    79. Herholz P
    80. Herman G
    81. Heunis S
    82. Hoffstaedter F
    83. Hogeveen J
    84. Holmes S
    85. Hu C-P
    86. Huettel SA
    87. Hughes ME
    88. Iacovella V
    89. Iordan AD
    90. Isager PM
    91. Isik AI
    92. Jahn A
    93. Johnson MR
    94. Johnstone T
    95. Joseph MJE
    96. Juliano AC
    97. Kable JW
    98. Kassinopoulos M
    99. Koba C
    100. Kong X-Z
    101. Koscik TR
    102. Kucukboyaci NE
    103. Kuhl BA
    104. Kupek S
    105. Laird AR
    106. Lamm C
    107. Langner R
    108. Lauharatanahirun N
    109. Lee H
    110. Lee S
    111. Leemans A
    112. Leo A
    113. Lesage E
    114. Li F
    115. Li MYC
    116. Lim PC
    117. Lintz EN
    118. Liphardt SW
    119. Losecaat Vermeer AB
    120. Love BC
    121. Mack ML
    122. Malpica N
    123. Marins T
    124. Maumet C
    125. McDonald K
    126. McGuire JT
    127. Melero H
    128. Méndez Leal AS
    129. Meyer B
    130. Meyer KN
    131. Mihai G
    132. Mitsis GD
    133. Moll J
    134. Nielson DM
    135. Nilsonne G
    136. Notter MP
    137. Olivetti E
    138. Onicas AI
    139. Papale P
    140. Patil KR
    141. Peelle JE
    142. Pérez A
    143. Pischedda D
    144. Poline J-B
    145. Prystauka Y
    146. Ray S
    147. Reuter-Lorenz PA
    148. Reynolds RC
    149. Ricciardi E
    150. Rieck JR
    151. Rodriguez-Thompson AM
    152. Romyn A
    153. Salo T
    154. Samanez-Larkin GR
    155. Sanz-Morales E
    156. Schlichting ML
    157. Schultz DH
    158. Shen Q
    159. Sheridan MA
    160. Silvers JA
    161. Skagerlund K
    162. Smith A
    163. Smith DV
    164. Sokol-Hessner P
    165. Steinkamp SR
    166. Tashjian SM
    167. Thirion B
    168. Thorp JN
    169. Tinghög G
    170. Tisdall L
    171. Tompson SH
    172. Toro-Serey C
    173. Torre Tresols JJ
    174. Tozzi L
    175. Truong V
    176. Turella L
    177. van ’t Veer AE
    178. Verguts T
    179. Vettel JM
    180. Vijayarajah S
    181. Vo K
    182. Wall MB
    183. Weeda WD
    184. Weis S
    185. White DJ
    186. Wisniewski D
    187. Xifra-Porxas A
    188. Yearling EA
    189. Yoon S
    190. Yuan R
    191. Yuen KSL
    192. Zhang L
    193. Zhang X
    194. Zosky JE
    195. Nichols TE
    196. Poldrack RA
    197. Schonberg T

    (2020) Variability in the analysis of a single neuroimaging dataset by many teams

    Nature 582:84–88.

    https://doi.org/10.1038/s41586-020-2314-9

    • PubMed
    • Google Scholar
14. Data

    1. Brett M
    2. Markiewicz CJ
    3. Hanke M
    4. Côté MA
    5. Cipollini B
    6. McCarthy P
    7. Cheng CP
    8. Halchenko YO
    9. Cottaar M
    10. Ghosh S

    (authors) (2019) Nipy/Nibabel

    Zenodo.

    https://doi.org/10.5281/zenodo.591597
15. Preprint

    1. Brown JA
    2. Lee AJ
    3. Pasquini L
    4. Seeley WW

    (2021) A Dynamic Gradient Architecture Generates Brain Activity States

    bioRxiv.

    https://doi.org/10.1101/2020.08.12.248112

    • Google Scholar
16. 1. Burt JB
    2. Demirtaş M
    3. Eckner WJ
    4. Navejar NM
    5. Ji JL
    6. Martin WJ
    7. Bernacchia A
    8. Anticevic A
    9. Murray JD

    (2018) Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography

    Nature Neuroscience 21:1251–1259.

    https://doi.org/10.1038/s41593-018-0195-0

    • PubMed
    • Google Scholar
17. 1. Carp J

    (2012) On the plurality of (methodological) worlds: estimating the analytic flexibility of FMRI experiments

    Frontiers in Neuroscience 6:149.

    https://doi.org/10.3389/fnins.2012.00149

    • PubMed
    • Google Scholar
18. Preprint

    1. Cieslak M
    2. Cook PA
    3. He X
    4. Yeh FC
    5. Dhollander T
    6. Adebimpe A
    7. Aguirre GK
    8. Bassett DS
    9. Betzel RF
    10. Bourque J

    (2020) QSIPrep: An Integrative Platform for Preprocessing and Reconstructing Diffusion MRI

    bioRxiv.

    https://doi.org/10.1101/2020.09.04.282269

    • Google Scholar
19. 1. Ciric R
    2. Wolf DH
    3. Power JD
    4. Roalf DR
    5. Baum GL
    6. Ruparel K
    7. Shinohara RT
    8. Elliott MA
    9. Eickhoff SB
    10. Davatzikos C
    11. Gur RC
    12. Gur RE
    13. Bassett DS
    14. Satterthwaite TD

    (2017) Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity

    NeuroImage 154:174–187.

    https://doi.org/10.1016/j.neuroimage.2017.03.020

    • PubMed
    • Google Scholar
20. Conference

    1. Collins DL
    2. Zijdenbos AP
    3. Baaré WF
    4. Evans AC

    (1999) ANIMAL+INSECT: improved cortical structure segmentation

    DBLP.

    https://doi.org/10.1007/3-540-48714-X_16

    • Google Scholar
21. 1. Craddock RC
    2. James GA
    3. Holtzheimer PE
    4. Hu XP
    5. Mayberg HS

    (2012) A whole brain fMRI atlas generated via spatially constrained spectral clustering

    Human Brain Mapping 33:1914–1928.

    https://doi.org/10.1002/hbm.21333

    • PubMed
    • Google Scholar
22. 1. Darmanis S
    2. Sloan SA
    3. Zhang Y
    4. Enge M
    5. Caneda C
    6. Shuer LM
    7. Hayden Gephart MG
    8. Barres BA
    9. Quake SR

    (2015) A survey of human brain transcriptome diversity at the single cell level

    PNAS 112:7285–7290.

    https://doi.org/10.1073/pnas.1507125112

    • PubMed
    • Google Scholar
23. Preprint

    1. Deco G
    2. Aquino KM
    3. Arnatkeviciute A
    4. Oldham S
    5. Sabaroedin K
    6. Rogasch NC
    7. Kringelbach ML
    8. Fornito A

    (2020) Dynamical Consequences of Regional Heterogeneity in the Brains Transcriptional Landscape

    bioRxiv.

    https://doi.org/10.1101/2020.10.28.359943

    • Google Scholar
24. 1. Demirtaş M
    2. Burt JB
    3. Helmer M
    4. Ji JL
    5. Adkinson BD
    6. Glasser MF
    7. Van Essen DC
    8. Sotiropoulos SN
    9. Anticevic A
    10. Murray JD

    (2019) Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics

    Neuron 101:1181–1194.

    https://doi.org/10.1016/j.neuron.2019.01.017

    • PubMed
    • Google Scholar
25. 1. Desikan RS
    2. Ségonne F
    3. Fischl B
    4. Quinn BT
    5. Dickerson BC
    6. Blacker D
    7. Buckner RL
    8. Dale AM
    9. Maguire RP
    10. Hyman BT
    11. Albert MS
    12. Killiany RJ

    (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

    NeuroImage 31:968–980.

    https://doi.org/10.1016/j.neuroimage.2006.01.021

    • PubMed
    • Google Scholar
26. 1. Dickie EW
    2. Ameis SH
    3. Shahab S
    4. Calarco N
    5. Smith DE
    6. Miranda D
    7. Viviano JD
    8. Voineskos AN

    (2018) Personalized Intrinsic Network Topography Mapping and Functional Connectivity Deficits in Autism Spectrum Disorder

    Biological Psychiatry 84:278–286.

    https://doi.org/10.1016/j.biopsych.2018.02.1174

    • PubMed
    • Google Scholar
27. 1. Ding Y
    2. Zhao K
    3. Che T
    4. Du K
    5. Sun H
    6. Liu S
    7. Zheng Y
    8. Li S
    9. Liu B
    10. Liu Y
    11. Alzheimer’s Disease Neuroimaging Initiative

    (2021) Quantitative Radiomic Features as New Biomarkers for Alzheimer’s Disease: An Amyloid PET Study

    Cerebral Cortex 31:3950–3961.

    https://doi.org/10.1093/cercor/bhab061

    • PubMed
    • Google Scholar
28. Conference

    1. Dragicevic P
    2. Jansen Y
    3. Sarma A
    4. Kay M
    5. Chevalier F

    (2019) Increasing the Transparency of Research Papers with Explorable Multiverse Analyses

    The 2019 CHI Conference.

    https://doi.org/10.1145/3290605.3300295

    • Google Scholar
29. 1. Esteban O
    2. Markiewicz CJ
    3. Blair RW
    4. Moodie CA
    5. Isik AI
    6. Erramuzpe A
    7. Kent JD
    8. Goncalves M
    9. DuPre E
    10. Snyder M
    11. Oya H
    12. Ghosh SS
    13. Wright J
    14. Durnez J
    15. Poldrack RA
    16. Gorgolewski KJ

    (2019) fMRIPrep: a robust preprocessing pipeline for functional MRI

    Nature Methods 16:111–116.

    https://doi.org/10.1038/s41592-018-0235-4

    • PubMed
    • Google Scholar
30. 1. Fonov VS
    2. Evans AC
    3. McKinstry RC
    4. Almli C
    5. Collins D

    (2009) Unbiased nonlinear average age-appropriate brain templates from birth to adulthood

    NeuroImage 47:S102.

    https://doi.org/10.1016/S1053-8119(09)70884-5

    • Google Scholar
31. 1. Fonov V
    2. Evans AC
    3. Botteron K
    4. Almli CR
    5. McKinstry RC
    6. Collins DL
    7. Group BDC

    (2011) Unbiased average age-appropriate atlases for pediatric studies

    NeuroImage 54:313–327.

    https://doi.org/10.1016/j.neuroimage.2010.07.033

    • PubMed
    • Google Scholar
32. 1. Fornito A
    2. Arnatkevičiūtė A
    3. Fulcher BD

    (2019) Bridging the Gap between Connectome and Transcriptome

    Trends in Cognitive Sciences 23:34–50.

    https://doi.org/10.1016/j.tics.2018.10.005

    • PubMed
    • Google Scholar
33. Preprint

    1. Fox AS
    2. Chang LJ
    3. Gorgolewski KJ
    4. Yarkoni T

    (2014) Bridging Psychology and Genetics Using Large-Scale Spatial Analysis of Neuroimaging and Neurogenetic Data

    bioRxiv.

    https://doi.org/10.1101/012310

    • Google Scholar
34. 1. French L
    2. Paus T

    (2015) A FreeSurfer view of the cortical transcriptome generated from the Allen Human Brain Atlas

    Frontiers in Neuroscience 9:323.

    https://doi.org/10.3389/fnins.2015.00323

    • PubMed
    • Google Scholar
35. 1. Fulcher BD
    2. Little MA
    3. Jones NS

    (2013) Highly comparative time-series analysis: the empirical structure of time series and their methods

    Journal of the Royal Society, Interface 10:20130048.

    https://doi.org/10.1098/rsif.2013.0048

    • PubMed
    • Google Scholar
36. 1. Fulcher BD

    (2019) Discovering Conserved Properties of Brain Organization Through Multimodal Integration and Interspecies Comparison

    Journal of Experimental Neuroscience 13:1179069519862047.

    https://doi.org/10.1177/1179069519862047

    • PubMed
    • Google Scholar
37. 1. Fulcher BD
    2. Murray JD
    3. Zerbi V
    4. Wang XJ

    (2019) Multimodal gradients across mouse cortex

    PNAS 116:4689–4695.

    https://doi.org/10.1073/pnas.1814144116

    • PubMed
    • Google Scholar
38. 1. Gandal MJ
    2. Zhang P
    3. Hadjimichael E
    4. Walker RL
    5. Chen C
    6. Liu S
    7. Won H
    8. van Bakel H
    9. Varghese M
    10. Wang Y
    11. Shieh AW
    12. Haney J
    13. Parhami S
    14. Belmont J
    15. Kim M
    16. Moran Losada P
    17. Khan Z
    18. Mleczko J
    19. Xia Y
    20. Dai R
    21. Wang D
    22. Yang YT
    23. Xu M
    24. Fish K
    25. Hof PR
    26. Warrell J
    27. Fitzgerald D
    28. White K
    29. Jaffe AE
    30. PsychENCODE Consortium
    31. Peters MA
    32. Gerstein M
    33. Liu C
    34. Iakoucheva LM
    35. Pinto D
    36. Geschwind DH

    (2018) Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder

    Science 362:eaat8127.

    https://doi.org/10.1126/science.aat8127

    • PubMed
    • Google Scholar
39. 1. Gao R
    2. van den Brink RL
    3. Pfeffer T
    4. Voytek B

    (2020) Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture

    eLife 9:e61277.

    https://doi.org/10.7554/eLife.61277

    • PubMed
    • Google Scholar
40. 1. Gordon EM
    2. Laumann TO
    3. Gilmore AW
    4. Newbold DJ
    5. Greene DJ
    6. Berg JJ
    7. Ortega M
    8. Hoyt-Drazen C
    9. Gratton C
    10. Sun H
    11. Hampton JM
    12. Coalson RS
    13. Nguyen AL
    14. McDermott KB
    15. Shimony JS
    16. Snyder AZ
    17. Schlaggar BL
    18. Petersen SE
    19. Nelson SM
    20. Dosenbach NUF

    (2017) Precision Functional Mapping of Individual Human Brains

    Neuron 95:791–807.

    https://doi.org/10.1016/j.neuron.2017.07.011

    • PubMed
    • Google Scholar
41. 1. Gorgolewski KJ
    2. Fox AS
    3. Chang L
    4. Schäfer A
    5. Arélin K
    6. Burmann I
    7. Sacher J
    8. Margulies DS

    (2014) Tight fitting genes: finding relations between statistical maps and gene expression patterns

    F1000Research 5:1.

    https://doi.org/10.7490/F1000RESEARCH.1097120.1

    • Google Scholar
42. 1. Gorgolewski KJ
    2. Varoquaux G
    3. Rivera G
    4. Schwarz Y
    5. Ghosh SS
    6. Maumet C
    7. Sochat VV
    8. Nichols TE
    9. Poldrack RA
    10. Poline J-B
    11. Yarkoni T
    12. Margulies DS

    (2015) NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain

    Frontiers in Neuroinformatics 9:8.

    https://doi.org/10.3389/fninf.2015.00008

    • PubMed
    • Google Scholar
43. 1. Goulas A
    2. Betzel RF
    3. Hilgetag CC

    (2019) Spatiotemporal ontogeny of brain wiring

    Science Advances 5:eaav9694.

    https://doi.org/10.1126/sciadv.aav9694

    • PubMed
    • Google Scholar
44. 1. Hansen JY
    2. Markello RD
    3. Vogel JW
    4. Seidlitz J
    5. Bzdok D
    6. Misic B

    (2021) Mapping gene transcription and neurocognition across human neocortex

    Nature Human Behaviour 5:1240–1250.

    https://doi.org/10.1038/s41562-021-01082-z

    • Google Scholar
45. 1. Harris CR
    2. Millman KJ
    3. van der Walt SJ
    4. Gommers R
    5. Virtanen P
    6. Cournapeau D
    7. Wieser E
    8. Taylor J
    9. Berg S
    10. Smith NJ
    11. Kern R
    12. Picus M
    13. Hoyer S
    14. van Kerkwijk MH
    15. Brett M
    16. Haldane A
    17. Del Río JF
    18. Wiebe M
    19. Peterson P
    20. Gérard-Marchant P
    21. Sheppard K
    22. Reddy T
    23. Weckesser W
    24. Abbasi H
    25. Gohlke C
    26. Oliphant TE

    (2020) Array programming with NumPy

    Nature 585:357–362.

    https://doi.org/10.1038/s41586-020-2649-2

    • PubMed
    • Google Scholar
46. 1. Hawrylycz MJ
    2. Lein ES
    3. Guillozet-Bongaarts AL
    4. Shen EH
    5. Ng L
    6. Miller JA
    7. van de Lagemaat LN
    8. Smith KA
    9. Ebbert A
    10. Riley ZL
    11. Abajian C
    12. Beckmann CF
    13. Bernard A
    14. Bertagnolli D
    15. Boe AF
    16. Cartagena PM
    17. Chakravarty MM
    18. Chapin M
    19. Chong J
    20. Dalley RA
    21. David Daly B
    22. Dang C
    23. Datta S
    24. Dee N
    25. Dolbeare TA
    26. Faber V
    27. Feng D
    28. Fowler DR
    29. Goldy J
    30. Gregor BW
    31. Haradon Z
    32. Haynor DR
    33. Hohmann JG
    34. Horvath S
    35. Howard RE
    36. Jeromin A
    37. Jochim JM
    38. Kinnunen M
    39. Lau C
    40. Lazarz ET
    41. Lee C
    42. Lemon TA
    43. Li L
    44. Li Y
    45. Morris JA
    46. Overly CC
    47. Parker PD
    48. Parry SE
    49. Reding M
    50. Royall JJ
    51. Schulkin J
    52. Sequeira PA
    53. Slaughterbeck CR
    54. Smith SC
    55. Sodt AJ
    56. Sunkin SM
    57. Swanson BE
    58. Vawter MP
    59. Williams D
    60. Wohnoutka P
    61. Zielke HR
    62. Geschwind DH
    63. Hof PR
    64. Smith SM
    65. Koch C
    66. Grant SGN
    67. Jones AR

    (2012) An anatomically comprehensive atlas of the adult human brain transcriptome

    Nature 489:391–399.

    https://doi.org/10.1038/nature11405

    • PubMed
    • Google Scholar
47. 1. Hawrylycz M
    2. Miller JA
    3. Menon V
    4. Feng D
    5. Dolbeare T
    6. Guillozet-Bongaarts AL
    7. Jegga AG
    8. Aronow BJ
    9. Lee C-K
    10. Bernard A
    11. Glasser MF
    12. Dierker DL
    13. Menche J
    14. Szafer A
    15. Collman F
    16. Grange P
    17. Berman KA
    18. Mihalas S
    19. Yao Z
    20. Stewart L
    21. Barabási A-L
    22. Schulkin J
    23. Phillips J
    24. Ng L
    25. Dang C
    26. Haynor DR
    27. Jones A
    28. Van Essen DC
    29. Koch C
    30. Lein E

    (2015) Canonical genetic signatures of the adult human brain

    Nature Neuroscience 18:1832–1844.

    https://doi.org/10.1038/nn.4171

    • Google Scholar
48. 1. Henderson MX
    2. Cornblath EJ
    3. Darwich A
    4. Zhang B
    5. Brown H
    6. Gathagan RJ
    7. Sandler RM
    8. Bassett DS
    9. Trojanowski JQ
    10. Lee VMY

    (2019) Spread of α-synuclein pathology through the brain connectome is modulated by selective vulnerability and predicted by network analysis

    Nature Neuroscience 22:1248–1257.

    https://doi.org/10.1038/s41593-019-0457-5

    • PubMed
    • Google Scholar
49. 1. Horvát S
    2. Gămănuț R
    3. Ercsey-Ravasz M
    4. Magrou L
    5. Gămănuț B
    6. Van Essen DC
    7. Burkhalter A
    8. Knoblauch K
    9. Toroczkai Z
    10. Kennedy H

    (2016) Spatial Embedding and Wiring Cost Constrain the Functional Layout of the Cortical Network of Rodents and Primates

    PLOS Biology 14:e1002512.

    https://doi.org/10.1371/journal.pbio.1002512

    • PubMed
    • Google Scholar
50. 1. Hunter JD

    (2007) Matplotlib: A 2D Graphics Environment

    Computing in Science & Engineering 9:90–95.

    https://doi.org/10.1109/MCSE.2007.55

    • Google Scholar
51. 1. Kang HJ
    2. Kawasawa YI
    3. Cheng F
    4. Zhu Y
    5. Xu X
    6. Li M
    7. Sousa AMM
    8. Pletikos M
    9. Meyer KA
    10. Sedmak G
    11. Guennel T
    12. Shin Y
    13. Johnson MB
    14. Krsnik Z
    15. Mayer S
    16. Fertuzinhos S
    17. Umlauf S
    18. Lisgo SN
    19. Vortmeyer A
    20. Weinberger DR
    21. Mane S
    22. Hyde TM
    23. Huttner A
    24. Reimers M
    25. Kleinman JE
    26. Sestan N

    (2011) Spatio-temporal transcriptome of the human brain

    Nature 478:483–489.

    https://doi.org/10.1038/nature10523

    • PubMed
    • Google Scholar
52. 1. Kharabian Masouleh S
    2. Eickhoff SB
    3. Zeighami Y
    4. Lewis LB
    5. Dahnke R
    6. Gaser C
    7. Chouinard-Decorte F
    8. Lepage C
    9. Scholtens LH
    10. Hoffstaedter F
    11. Glahn DC
    12. Blangero J
    13. Evans AC
    14. Genon S
    15. Valk SL

    (2020) Influence of Processing Pipeline on Cortical Thickness Measurement

    Cerebral Cortex 30:5014–5027.

    https://doi.org/10.1093/cercor/bhaa097

    • PubMed
    • Google Scholar
53. 1. Kirsch L
    2. Chechik G

    (2016) On Expression Patterns and Developmental Origin of Human Brain Regions

    PLOS Computational Biology 12:e1005064.

    https://doi.org/10.1371/journal.pcbi.1005064

    • PubMed
    • Google Scholar
54. Book

    1. Kluyver T
    2. Ragan-Kelley B
    3. Pérez F
    4. Granger BE
    5. Bussonnier M
    6. Frederic J
    7. Kelley K
    8. Hamrick JB
    9. Grout J
    10. Corlay S

    (2016)

    Jupyter Notebooks–A publishing format for reproducible computational workflows

    In: Loizides F, Scmidt B, editors. Positioning and Power in Academic Publishing: Players, Agents and Agendas. IOS Press. pp. 1–164.

    • Google Scholar
55. 1. Kong R
    2. Li J
    3. Orban C
    4. Sabuncu MR
    5. Liu H
    6. Schaefer A
    7. Sun N
    8. Zuo X-N
    9. Holmes AJ
    10. Eickhoff SB
    11. Yeo BTT

    (2019) Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion

    Cerebral Cortex 29:2533–2551.

    https://doi.org/10.1093/cercor/bhy123

    • PubMed
    • Google Scholar
56. 1. Krienen FM
    2. Yeo BTT
    3. Ge T
    4. Buckner RL
    5. Sherwood CC

    (2016) Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain

    PNAS 113:E469–E478.

    https://doi.org/10.1073/pnas.1510903113

    • PubMed
    • Google Scholar
57. 1. Lake BB
    2. Ai R
    3. Kaeser GE
    4. Salathia NS
    5. Yung YC
    6. Liu R
    7. Wildberg A
    8. Gao D
    9. Fung H-L
    10. Chen S
    11. Vijayaraghavan R
    12. Wong J
    13. Chen A
    14. Sheng X
    15. Kaper F
    16. Shen R
    17. Ronaghi M
    18. Fan J-B
    19. Wang W
    20. Chun J
    21. Zhang K

    (2016) Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain

    Science 352:1586–1590.

    https://doi.org/10.1126/science.aaf1204

    • PubMed
    • Google Scholar
58. Preprint

    1. Lariviere S
    2. Paquola C
    3. Park B -y
    4. Royer J
    5. Wang Y
    6. Benkarim O
    7. de Wael R
    8. Valk SL
    9. Thomopoulos S
    10. Kirschner M

    (2020) The ENIGMA Toolbox: Cross-Disorder Integration and Multiscale Neural Contextualization of Multisite Neuroimaging Datasets

    bioRxiv.

    https://doi.org/10.1101/2020.12.21.423838

    • Google Scholar
59. 1. Lau HYG
    2. Fornito A
    3. Fulcher BD

    (2021) Scaling of gene transcriptional gradients with brain size across mouse development

    NeuroImage 224:117395.

    https://doi.org/10.1016/j.neuroimage.2020.117395

    • PubMed
    • Google Scholar
60. 1. Lein ES
    2. Hawrylycz MJ
    3. Ao N
    4. Ayres M
    5. Bensinger A
    6. Bernard A
    7. Boe AF
    8. Boguski MS
    9. Brockway KS
    10. Byrnes EJ
    11. Chen L
    12. Chen L
    13. Chen T-M
    14. Chin MC
    15. Chong J
    16. Crook BE
    17. Czaplinska A
    18. Dang CN
    19. Datta S
    20. Dee NR
    21. Desaki AL
    22. Desta T
    23. Diep E
    24. Dolbeare TA
    25. Donelan MJ
    26. Dong H-W
    27. Dougherty JG
    28. Duncan BJ
    29. Ebbert AJ
    30. Eichele G
    31. Estin LK
    32. Faber C
    33. Facer BA
    34. Fields R
    35. Fischer SR
    36. Fliss TP
    37. Frensley C
    38. Gates SN
    39. Glattfelder KJ
    40. Halverson KR
    41. Hart MR
    42. Hohmann JG
    43. Howell MP
    44. Jeung DP
    45. Johnson RA
    46. Karr PT
    47. Kawal R
    48. Kidney JM
    49. Knapik RH
    50. Kuan CL
    51. Lake JH
    52. Laramee AR
    53. Larsen KD
    54. Lau C
    55. Lemon TA
    56. Liang AJ
    57. Liu Y
    58. Luong LT
    59. Michaels J
    60. Morgan JJ
    61. Morgan RJ
    62. Mortrud MT
    63. Mosqueda NF
    64. Ng LL
    65. Ng R
    66. Orta GJ
    67. Overly CC
    68. Pak TH
    69. Parry SE
    70. Pathak SD
    71. Pearson OC
    72. Puchalski RB
    73. Riley ZL
    74. Rockett HR
    75. Rowland SA
    76. Royall JJ
    77. Ruiz MJ
    78. Sarno NR
    79. Schaffnit K
    80. Shapovalova NV
    81. Sivisay T
    82. Slaughterbeck CR
    83. Smith SC
    84. Smith KA
    85. Smith BI
    86. Sodt AJ
    87. Stewart NN
    88. Stumpf K-R
    89. Sunkin SM
    90. Sutram M
    91. Tam A
    92. Teemer CD
    93. Thaller C
    94. Thompson CL
    95. Varnam LR
    96. Visel A
    97. Whitlock RM
    98. Wohnoutka PE
    99. Wolkey CK
    100. Wong VY
    101. Wood M
    102. Yaylaoglu MB
    103. Young RC
    104. Youngstrom BL
    105. Yuan XF
    106. Zhang B
    107. Zwingman TA
    108. Jones AR

    (2007) Genome-wide atlas of gene expression in the adult mouse brain

    Nature 445:168–176.

    https://doi.org/10.1038/nature05453

    • PubMed
    • Google Scholar
61. 1. Li M
    2. Santpere G
    3. Imamura Kawasawa Y
    4. Evgrafov OV
    5. Gulden FO
    6. Pochareddy S
    7. Sunkin SM
    8. Li Z
    9. Shin Y
    10. Zhu Y
    11. Sousa AMM
    12. Werling DM
    13. Kitchen RR
    14. Kang HJ
    15. Pletikos M
    16. Choi J
    17. Muchnik S
    18. Xu X
    19. Wang D
    20. Lorente-Galdos B
    21. Liu S
    22. Giusti-Rodríguez P
    23. Won H
    24. de Leeuw CA
    25. Pardiñas AF
    26. Hu M
    27. Jin F
    28. Li Y
    29. Owen MJ
    30. O’Donovan MC
    31. Walters JTR
    32. Posthuma D
    33. Reimers MA
    34. Levitt P
    35. Weinberger DR
    36. Hyde TM
    37. Kleinman JE
    38. Geschwind DH
    39. Hawrylycz MJ
    40. State MW
    41. Sanders SJ
    42. Sullivan PF
    43. Gerstein MB
    44. Lein ES
    45. Knowles JA
    46. Sestan N
    47. BrainSpan Consortium
    48. PsychENCODE Consortium
    49. PsychENCODE Developmental Subgroup

    (2018) Integrative functional genomic analysis of human brain development and neuropsychiatric risks

    Science 362:eaat7615.

    https://doi.org/10.1126/science.aat7615

    • PubMed
    • Google Scholar
62. 1. Liu J
    2. Xia M
    3. Wang X
    4. Liao X
    5. He Y

    (2020) The spatial organization of the chronnectome associates with cortical hierarchy and transcriptional profiles in the human brain

    NeuroImage 222:117296.

    https://doi.org/10.1016/j.neuroimage.2020.117296

    • PubMed
    • Google Scholar
63. 1. Maier-Hein KH
    2. Neher PF
    3. Houde JC
    4. Côté MA
    5. Garyfallidis E
    6. Zhong J
    7. Chamberland M
    8. Yeh FC
    9. Lin YC
    10. Ji Q
    11. Reddick WE
    12. Glass JO
    13. Chen DQ
    14. Feng Y
    15. Gao C
    16. Wu Y
    17. Ma J
    18. He R
    19. Li Q
    20. Westin CF
    21. Deslauriers-Gauthier S
    22. González JOO
    23. Paquette M
    24. St-Jean S
    25. Girard G
    26. Rheault F
    27. Sidhu J
    28. Tax CMW
    29. Guo F
    30. Mesri HY
    31. Dávid S
    32. Froeling M
    33. Heemskerk AM
    34. Leemans A
    35. Boré A
    36. Pinsard B
    37. Bedetti C
    38. Desrosiers M
    39. Brambati S
    40. Doyon J
    41. Sarica A
    42. Vasta R
    43. Cerasa A
    44. Quattrone A
    45. Yeatman J
    46. Khan AR
    47. Hodges W
    48. Alexander S
    49. Romascano D
    50. Barakovic M
    51. Auría A
    52. Esteban O
    53. Lemkaddem A
    54. Thiran JP
    55. Cetingul HE
    56. Odry BL
    57. Mailhe B
    58. Nadar MS
    59. Pizzagalli F
    60. Prasad G
    61. Villalon-Reina JE
    62. Galvis J
    63. Thompson PM
    64. Requejo FDS
    65. Laguna PL
    66. Lacerda LM
    67. Barrett R
    68. Dell’Acqua F
    69. Catani M
    70. Petit L
    71. Caruyer E
    72. Daducci A
    73. Dyrby TB
    74. Holland-Letz T
    75. Hilgetag CC
    76. Stieltjes B
    77. Descoteaux M

    (2017) The challenge of mapping the human connectome based on diffusion tractography

    Nature Communications 8:1349.

    https://doi.org/10.1038/s41467-017-01285-x

    • PubMed
    • Google Scholar
64. 1. Markello RD
    2. Misic B

    (2021) Comparing spatial null models for brain maps

    NeuroImage 236:118052.

    https://doi.org/10.1016/j.neuroimage.2021.118052

    • PubMed
    • Google Scholar
65. Software

    1. Markello R

    (2021a) markello_transcriptome, version swh:1:rev:3abbc85596a5baacd93e5e9e56c906c9dbb080f3

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:ed4b1a9e5eb2449f1d9f5bb65c51477aa8c350dc;origin=https://github.com/netneurolab/markello_transcriptome;visit=swh:1:snp:4f5eeca5d011970f437459b46fbf885ac1554644;anchor=swh:1:rev:3abbc85596a5baacd93e5e9e56c906c9dbb080f3
66. Software

    1. Markello R

    (2021b) abagen, version swh:1:rev:2aeab5bd0f147fa76b488645e148a1c18095378d

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:24ed1ac6001e876742bf4c8317902313926be07c;origin=https://github.com/rmarkello/abagen;visit=swh:1:snp:7d534f07cc7c0a549243db17dc6de7d2ede98383;anchor=swh:1:rev:2aeab5bd0f147fa76b488645e148a1c18095378d
67. Data

    1. Markello R
    2. Shafiei G
    3. Zheng YQ
    4. Mišić B

    (authors) (2021c) Rmarkello/Abagen

    Zenodo.

    https://doi.org/10.5281/zenodo.3451463
68. Preprint

    1. Martins D
    2. Dipasquale O
    3. Veronese M
    4. Turkheimer FE
    5. Loggia M
    6. McMahon S
    7. Williams SC

    (2021) Transcriptional and Cellular Signatures of Cortical Morphometric Similarity Remodelling in Chronic Pain

    bioRxiv.

    https://doi.org/10.1101/2021.03.24.436777

    • Google Scholar
69. 1. McColgan P
    2. Gregory S
    3. Seunarine KK
    4. Razi A
    5. Papoutsi M
    6. Johnson E
    7. Durr A
    8. Roos RAC
    9. Leavitt BR
    10. Holmans P
    11. Scahill RI
    12. Clark CA
    13. Rees G
    14. Tabrizi SJ
    15. Track-On HD Investigators

    (2018) Brain Regions Showing White Matter Loss in Huntington’s Disease Are Enriched for Synaptic and Metabolic Genes

    Biological Psychiatry 83:456–465.

    https://doi.org/10.1016/j.biopsych.2017.10.019

    • PubMed
    • Google Scholar
70. Conference

    1. McKinney W

    (2010) Data Structures for Statistical Computing in Python

    Proceedings of the 9th Python in Science Conference.

    https://doi.org/10.25080/Majora-92bf1922-00a

    • Google Scholar
71. 1. Messé A

    (2020) Parcellation influence on the connectivity-based structure-function relationship in the human brain

    Human Brain Mapping 41:1167–1180.

    https://doi.org/10.1002/hbm.24866

    • PubMed
    • Google Scholar
72. 1. Miller JA
    2. Ding S-L
    3. Sunkin SM
    4. Smith KA
    5. Ng L
    6. Szafer A
    7. Ebbert A
    8. Riley ZL
    9. Royall JJ
    10. Aiona K
    11. Arnold JM
    12. Bennet C
    13. Bertagnolli D
    14. Brouner K
    15. Butler S
    16. Caldejon S
    17. Carey A
    18. Cuhaciyan C
    19. Dalley RA
    20. Dee N
    21. Dolbeare TA
    22. Facer BAC
    23. Feng D
    24. Fliss TP
    25. Gee G
    26. Goldy J
    27. Gourley L
    28. Gregor BW
    29. Gu G
    30. Howard RE
    31. Jochim JM
    32. Kuan CL
    33. Lau C
    34. Lee C-K
    35. Lee F
    36. Lemon TA
    37. Lesnar P
    38. McMurray B
    39. Mastan N
    40. Mosqueda N
    41. Naluai-Cecchini T
    42. Ngo N-K
    43. Nyhus J
    44. Oldre A
    45. Olson E
    46. Parente J
    47. Parker PD
    48. Parry SE
    49. Stevens A
    50. Pletikos M
    51. Reding M
    52. Roll K
    53. Sandman D
    54. Sarreal M
    55. Shapouri S
    56. Shapovalova NV
    57. Shen EH
    58. Sjoquist N
    59. Slaughterbeck CR
    60. Smith M
    61. Sodt AJ
    62. Williams D
    63. Zöllei L
    64. Fischl B
    65. Gerstein MB
    66. Geschwind DH
    67. Glass IA
    68. Hawrylycz MJ
    69. Hevner RF
    70. Huang H
    71. Jones AR
    72. Knowles JA
    73. Levitt P
    74. Phillips JW
    75. Sestan N
    76. Wohnoutka P
    77. Dang C
    78. Bernard A
    79. Hohmann JG
    80. Lein ES

    (2014) Transcriptional landscape of the prenatal human brain

    Nature 508:199–206.

    https://doi.org/10.1038/nature13185

    • PubMed
    • Google Scholar
73. 1. Mišić B
    2. Fatima Z
    3. Askren MK
    4. Buschkuehl M
    5. Churchill N
    6. Cimprich B
    7. Deldin PJ
    8. Jaeggi S
    9. Jung M
    10. Korostil M
    11. Kross E
    12. Krpan KM
    13. Peltier S
    14. Reuter-Lorenz PA
    15. Strother SC
    16. Jonides J
    17. McIntosh AR
    18. Berman MG

    (2014) The functional connectivity landscape of the human brain

    PLOS ONE 9:e111007.

    https://doi.org/10.1371/journal.pone.0111007

    • PubMed
    • Google Scholar
74. 1. Morgan SE
    2. Seidlitz J
    3. Whitaker KJ
    4. Romero-Garcia R
    5. Clifton NE
    6. Scarpazza C
    7. van Amelsvoort T
    8. Marcelis M
    9. van Os J
    10. Donohoe G
    11. Mothersill D
    12. Corvin A
    13. Pocklington A
    14. Raznahan A
    15. McGuire P
    16. Vértes PE
    17. Bullmore ET

    (2019) Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes

    PNAS 116:9604–9609.

    https://doi.org/10.1073/pnas.1820754116

    • PubMed
    • Google Scholar
75. 1. Negi SK
    2. Guda C

    (2017) Global gene expression profiling of healthy human brain and its application in studying neurological disorders

    Scientific Reports 7:897.

    https://doi.org/10.1038/s41598-017-00952-9

    • PubMed
    • Google Scholar
76. 1. Nørgaard M
    2. Beliveau V
    3. Ganz M
    4. Svarer C
    5. Pinborg LH
    6. Keller SH
    7. Jensen PS
    8. Greve DN
    9. Knudsen GM

    (2021) A high-resolution in vivo atlas of the human brain’s benzodiazepine binding site of GABAA receptors

    NeuroImage 232:117878.

    https://doi.org/10.1016/j.neuroimage.2021.117878

    • PubMed
    • Google Scholar
77. 1. Oldham MC
    2. Konopka G
    3. Iwamoto K
    4. Langfelder P
    5. Kato T
    6. Horvath S
    7. Geschwind DH

    (2008) Functional organization of the transcriptome in human brain

    Nature Neuroscience 11:1271–1282.

    https://doi.org/10.1038/nn.2207

    • Google Scholar
78. 1. Oldham S
    2. Arnatkevic Iūtė A
    3. Smith RE
    4. Tiego J
    5. Bellgrove MA
    6. Fornito A

    (2020) The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics

    NeuroImage 222:117252.

    https://doi.org/10.1016/j.neuroimage.2020.117252

    • PubMed
    • Google Scholar
79. Book

    1. Oliphant TE

    (2006)

    A Guide to NumPy

    Trelgol Publishing USA.

    • Google Scholar
80. Preprint

    1. Park B -y
    2. Park H
    3. Morys F
    4. Kim M
    5. Byeon K
    6. Lee H
    7. Kim SH
    8. Valk S
    9. Dagher A
    10. Bernhardt B

    (2020) Body Mass Variations Relate to Fractionated Functional Brain Hierarchies

    bioRxiv.

    https://doi.org/10.1101/2020.08.07.241794

    • Google Scholar
81. 1. Park BY
    2. Bethlehem RA
    3. Paquola C
    4. Larivière S
    5. Rodríguez-Cruces R
    6. Vos de Wael R
    7. Neuroscience in Psychiatry Network (NSPN) Consortium
    8. Bullmore ET
    9. Bernhardt BC

    (2021) An expanding manifold in transmodal regions characterizes adolescent reconfiguration of structural connectome organization

    eLife 10:e64694.

    https://doi.org/10.7554/eLife.64694

    • PubMed
    • Google Scholar
82. 1. Parkes L
    2. Fulcher BD
    3. Yücel M
    4. Fornito A

    (2017) Transcriptional signatures of connectomic subregions of the human striatum

    Genes, Brain, and Behavior 16:647–663.

    https://doi.org/10.1111/gbb.12386

    • PubMed
    • Google Scholar
83. 1. Parkes L
    2. Fulcher B
    3. Yücel M
    4. Fornito A

    (2018) An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI

    NeuroImage 171:415–436.

    https://doi.org/10.1016/j.neuroimage.2017.12.073

    • PubMed
    • Google Scholar
84. 1. Pedregosa F
    2. Varoquaux G
    3. Gramfort A
    4. Michel V
    5. Thirion B
    6. Grisel O
    7. Blondel M
    8. Prettenhofer P
    9. Weiss R
    10. Dubourg V

    (2011)

    Scikit-learn: Machine learning in Python

    Journal of Machine Learning Research 12:2825–2830.

    • Google Scholar
85. 1. Perez F
    2. Granger BE

    (2007) IPython: A System for Interactive Scientific Computing

    Computing in Science & Engineering 9:21–29.

    https://doi.org/10.1109/MCSE.2007.53

    • Google Scholar
86. 1. Preller KH
    2. Burt JB
    3. Ji JL
    4. Schleifer CH
    5. Adkinson BD
    6. Stämpfli P
    7. Seifritz E
    8. Repovs G
    9. Krystal JH
    10. Murray JD
    11. Vollenweider FX
    12. Anticevic A

    (2018) Changes in global and thalamic brain connectivity in LSD-induced altered states of consciousness are attributable to the 5-HT2A receptor

    eLife 7:e35082.

    https://doi.org/10.7554/eLife.35082

    • PubMed
    • Google Scholar
87. 1. Richiardi J
    2. Altmann A
    3. Milazzo AC
    4. Chang C
    5. Chakravarty MM
    6. Banaschewski T
    7. Barker GJ
    8. Bokde ALW
    9. Bromberg U
    10. Büchel C
    11. Conrod P
    12. Fauth-Bühler M
    13. Flor H
    14. Frouin V
    15. Gallinat J
    16. Garavan H
    17. Gowland P
    18. Heinz A
    19. Lemaître H
    20. Mann KF
    21. Martinot JL
    22. Nees F
    23. Paus T
    24. Pausova Z
    25. Rietschel M
    26. Robbins TW
    27. Smolka MN
    28. Spanagel R
    29. Ströhle A
    30. Schumann G
    31. Hawrylycz M
    32. Poline JB
    33. Greicius MD
    34. IMAGEN consortium

    (2015) BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks

    Science 348:1241–1244.

    https://doi.org/10.1126/science.1255905

    • PubMed
    • Google Scholar
88. Preprint

    1. Richiardi J
    2. Altmann A
    3. Greicius M

    (2017) Distance Is Not Everything in Imaging Genomics of Functional Networks: Reply to a Commentary on Correlated Gene Expression Supports Synchronous Activity in Brain Networks

    bioRxiv.

    https://doi.org/10.1101/132746

    • Google Scholar
89. 1. Rittman T
    2. Rubinov M
    3. Vértes PE
    4. Patel AX
    5. Ginestet CE
    6. Ghosh BCP
    7. Barker RA
    8. Spillantini MG
    9. Bullmore ET
    10. Rowe JB

    (2016) Regional expression of the MAPT gene is associated with loss of hubs in brain networks and cognitive impairment in Parkinson disease and progressive supranuclear palsy

    Neurobiology of Aging 48:153–160.

    https://doi.org/10.1016/j.neurobiolaging.2016.09.001

    • PubMed
    • Google Scholar
90. Software

    1. Rittman T
    2. Rittman M
    3. Azevedo T

    (2017) Maybrain software package

    RittmanResearch.

    https://github.com/RittmanResearch/maybrain
91. 1. Rizzo G
    2. Veronese M
    3. Expert P
    4. Turkheimer FE
    5. Bertoldo A

    (2016) MENGA: A New Comprehensive Tool for the Integration of Neuroimaging Data and the Allen Human Brain Transcriptome Atlas

    PLOS ONE 11:e0148744.

    https://doi.org/10.1371/journal.pone.0148744

    • PubMed
    • Google Scholar
92. 1. Roberts JA
    2. Perry A
    3. Lord AR
    4. Roberts G
    5. Mitchell PB
    6. Smith RE
    7. Calamante F
    8. Breakspear M

    (2016) The contribution of geometry to the human connectome

    NeuroImage 124:379–393.

    https://doi.org/10.1016/j.neuroimage.2015.09.009

    • PubMed
    • Google Scholar
93. 1. Romero-Garcia R
    2. Whitaker KJ
    3. Váša F
    4. Seidlitz J
    5. Shinn M
    6. Fonagy P
    7. Dolan RJ
    8. Jones PB
    9. Goodyer IM
    10. NSPN Consortium
    11. Bullmore ET
    12. Vértes PE

    (2018) Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex

    NeuroImage 171:256–267.

    https://doi.org/10.1016/j.neuroimage.2017.12.060

    • PubMed
    • Google Scholar
94. 1. Romme IAC
    2. de Reus MA
    3. Ophoff RA
    4. Kahn RS
    5. van den Heuvel MP

    (2017) Connectome Disconnectivity and Cortical Gene Expression in Patients With Schizophrenia

    Biological Psychiatry 81:495–502.

    https://doi.org/10.1016/j.biopsych.2016.07.012

    • PubMed
    • Google Scholar
95. 1. Rousseeuw PJ

    (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis

    Journal of Computational and Applied Mathematics 20:53–65.

    https://doi.org/10.1016/0377-0427(87)90125-7

    • Google Scholar
96. 1. Schilling KG
    2. Nath V
    3. Hansen C
    4. Parvathaneni P
    5. Blaber J
    6. Gao Y
    7. Neher P
    8. Aydogan DB
    9. Shi Y
    10. Ocampo-Pineda M
    11. Schiavi S
    12. Daducci A
    13. Girard G
    14. Barakovic M
    15. Rafael-Patino J
    16. Romascano D
    17. Rensonnet G
    18. Pizzolato M
    19. Bates A
    20. Fischi E
    21. Thiran J-P
    22. Canales-Rodríguez EJ
    23. Huang C
    24. Zhu H
    25. Zhong L
    26. Cabeen R
    27. Toga AW
    28. Rheault F
    29. Theaud G
    30. Houde J-C
    31. Sidhu J
    32. Chamberland M
    33. Westin C-F
    34. Dyrby TB
    35. Verma R
    36. Rathi Y
    37. Irfanoglu MO
    38. Thomas C
    39. Pierpaoli C
    40. Descoteaux M
    41. Anderson AW
    42. Landman BA

    (2019) Limits to anatomical accuracy of diffusion tractography using modern approaches

    NeuroImage 185:1–11.

    https://doi.org/10.1016/j.neuroimage.2018.10.029

    • PubMed
    • Google Scholar
97. 1. Seidlitz J
    2. Váša F
    3. Shinn M
    4. Romero-Garcia R
    5. Whitaker KJ
    6. Vértes PE
    7. Wagstyl K
    8. Kirkpatrick Reardon P
    9. Clasen L
    10. Liu S
    11. Messinger A
    12. Leopold DA
    13. Fonagy P
    14. Dolan RJ
    15. Jones PB
    16. Goodyer IM
    17. NSPN Consortium
    18. Raznahan A
    19. Bullmore ET

    (2018) Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation

    Neuron 97:231–247.

    https://doi.org/10.1016/j.neuron.2017.11.039

    • PubMed
    • Google Scholar
98. 1. Seidlitz J
    2. Nadig A
    3. Liu S
    4. Bethlehem RAI
    5. Vértes PE
    6. Morgan SE
    7. Váša F
    8. Romero-Garcia R
    9. Lalonde FM
    10. Clasen LS
    11. Blumenthal JD
    12. Paquola C
    13. Bernhardt B
    14. Wagstyl K
    15. Polioudakis D
    16. de la Torre-Ubieta L
    17. Geschwind DH
    18. Han JC
    19. Lee NR
    20. Murphy DG
    21. Bullmore ET
    22. Raznahan A

    (2020) Author Correction: Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders

    Nature Communications 11:5936.

    https://doi.org/10.1038/s41467-020-19362-z

    • PubMed
    • Google Scholar
99. 1. Sepulcre J
    2. Grothe MJ
    3. d’Oleire Uquillas F
    4. Ortiz-Terán L
    5. Diez I
    6. Yang H-S
    7. Jacobs HIL
    8. Hanseeuw BJ
    9. Li Q
    10. El-Fakhri G
    11. Sperling RA
    12. Johnson KA

    (2018) Neurogenetic contributions to amyloid beta and tau spreading in the human cortex

    Nature Medicine 24:1910–1918.

    https://doi.org/10.1038/s41591-018-0206-4

    • PubMed
    • Google Scholar
100. 1. Shafiei G
     2. Markello RD
     3. Vos de Wael R
     4. Bernhardt BC
     5. Fulcher BD
     6. Misic B

     (2020) Topographic gradients of intrinsic dynamics across neocortex

     eLife 9:e62116.

     https://doi.org/10.7554/eLife.62116

     • PubMed
     • Google Scholar
101. Preprint

     1. Shafiei G
     2. Bazinet V
     3. Dadar M

     (2021) Global Network Structure and Local Transcriptomic Vulnerability Shape Atrophy in Sporadic and Genetic Behavioral Variant Frontotemporal Dementia

     bioRxiv.

     https://doi.org/10.1101/2021.08.24.457538

     • Google Scholar
102. 1. Shin J
     2. French L
     3. Xu T
     4. Leonard G
     5. Perron M
     6. Pike GB
     7. Richer L
     8. Veillette S
     9. Pausova Z
     10. Paus T

     (2018) Cell-Specific Gene-Expression Profiles and Cortical Thickness in the Human Brain

     Cerebral Cortex 28:3267–3277.

     https://doi.org/10.1093/cercor/bhx197

     • PubMed
     • Google Scholar
103. 1. Shine JM
     2. Breakspear M
     3. Bell PT
     4. Ehgoetz Martens KA
     5. Shine R
     6. Koyejo O
     7. Sporns O
     8. Poldrack RA

     (2019) Human cognition involves the dynamic integration of neural activity and neuromodulatory systems

     Nature Neuroscience 22:289–296.

     https://doi.org/10.1038/s41593-018-0312-0

     • PubMed
     • Google Scholar
104. 1. Simmons JP
     2. Nelson LD
     3. Simonsohn U

     (2011) False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant

     Psychological Science 22:1359–1366.

     https://doi.org/10.1177/0956797611417632

     • PubMed
     • Google Scholar
105. 1. Sousa AMM
     2. Zhu Y
     3. Raghanti MA
     4. Kitchen RR
     5. Onorati M
     6. Tebbenkamp ATN
     7. Stutz B
     8. Meyer KA
     9. Li M
     10. Kawasawa YI
     11. Liu F
     12. Perez RG
     13. Mele M
     14. Carvalho T
     15. Skarica M
     16. Gulden FO
     17. Pletikos M
     18. Shibata A
     19. Stephenson AR
     20. Edler MK
     21. Ely JJ
     22. Elsworth JD
     23. Horvath TL
     24. Hof PR
     25. Hyde TM
     26. Kleinman JE
     27. Weinberger DR
     28. Reimers M
     29. Lifton RP
     30. Mane SM
     31. Noonan JP
     32. State MW
     33. Lein ES
     34. Knowles JA
     35. Marques-Bonet T
     36. Sherwood CC
     37. Gerstein MB
     38. Sestan N

     (2017) Molecular and cellular reorganization of neural circuits in the human lineage

     Science 358:1027–1032.

     https://doi.org/10.1126/science.aan3456

     • PubMed
     • Google Scholar
106. 1. Steegen S
     2. Tuerlinckx F
     3. Gelman A
     4. Vanpaemel W

     (2016) Increasing Transparency Through a Multiverse Analysis

     Perspectives on Psychological Science 11:702–712.

     https://doi.org/10.1177/1745691616658637

     • PubMed
     • Google Scholar
107. 1. Thirion B
     2. Varoquaux G
     3. Dohmatob E
     4. Poline JB

     (2014) Which fMRI clustering gives good brain parcellations?

     Frontiers in Neuroscience 8:167.

     https://doi.org/10.3389/fnins.2014.00167

     • PubMed
     • Google Scholar
108. 1. Thompson WH
     2. Wright J
     3. Bissett PG
     4. Poldrack RA

     (2020) Dataset decay and the problem of sequential analyses on open datasets

     eLife 9:e53498.

     https://doi.org/10.7554/eLife.53498

     • PubMed
     • Google Scholar
109. Preprint

     1. Valk SL
     2. Kanske P
     3. Park B -y
     4. Hong SJ
     5. Boeckler-Raettig A
     6. Trautwein FM
     7. Bernhardt BC
     8. Singer T

     (2021) Functional Network Plasticity of the Human Social Brain

     bioRxiv.

     https://doi.org/10.1101/2020.11.11.377895

     • Google Scholar
110. 1. van der Walt S
     2. Colbert SC
     3. Varoquaux G

     (2011) The NumPy Array: A Structure for Efficient Numerical Computation

     Computing in Science & Engineering 13:22–30.

     https://doi.org/10.1109/MCSE.2011.37

     • Google Scholar
111. 1. Van Essen DC
     2. Smith SM
     3. Barch DM
     4. Behrens TEJ
     5. Yacoub E
     6. Ugurbil K
     7. WU-Minn HCP Consortium

     (2013) The WU-Minn Human Connectome Project: an overview

     NeuroImage 80:62–79.

     https://doi.org/10.1016/j.neuroimage.2013.05.041

     • PubMed
     • Google Scholar
112. 1. Vértes PE
     2. Rittman T
     3. Whitaker KJ
     4. Romero-Garcia R
     5. Váša F
     6. Kitzbichler MG
     7. Wagstyl K
     8. Fonagy P
     9. Dolan RJ
     10. Jones PB
     11. Goodyer IM
     12. NSPN Consortium
     13. Bullmore ET

     (2016) Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks

     Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 371:20150362.

     https://doi.org/10.1098/rstb.2015.0362

     • PubMed
     • Google Scholar
113. 1. Virtanen P
     2. Gommers R
     3. Oliphant TE
     4. Haberland M
     5. Reddy T
     6. Cournapeau D
     7. Burovski E
     8. Peterson P
     9. Weckesser W
     10. Bright J
     11. van der Walt SJ
     12. Brett M
     13. Wilson J
     14. Millman KJ
     15. Mayorov N
     16. Nelson ARJ
     17. Jones E
     18. Kern R
     19. Larson E
     20. Carey CJ
     21. Polat İ
     22. Feng Y
     23. Moore EW
     24. VanderPlas J
     25. Laxalde D
     26. Perktold J
     27. Cimrman R
     28. Henriksen I
     29. Quintero EA
     30. Harris CR
     31. Archibald AM
     32. Ribeiro AH
     33. Pedregosa F
     34. van Mulbregt P
     35. SciPy 1.0 Contributors
     36. Vijaykumar A
     37. Bardelli AP
     38. Rothberg A
     39. Hilboll A
     40. Kloeckner A
     41. Scopatz A
     42. Lee A
     43. Rokem A
     44. Woods CN
     45. Fulton C
     46. Masson C
     47. Häggström C
     48. Fitzgerald C
     49. Nicholson DA
     50. Hagen DR
     51. Pasechnik DV
     52. Olivetti E
     53. Martin E
     54. Wieser E
     55. Silva F
     56. Lenders F
     57. Wilhelm F
     58. Young G
     59. Price GA
     60. Ingold GL
     61. Allen GE
     62. Lee GR
     63. Audren H
     64. Probst I
     65. Dietrich JP
     66. Silterra J
     67. Webber JT
     68. Slavič J
     69. Nothman J
     70. Buchner J
     71. Kulick J
     72. Schönberger JL
     73. de Miranda Cardoso JV
     74. Reimer J
     75. Harrington J
     76. Rodríguez JLC
     77. Nunez-Iglesias J
     78. Kuczynski J
     79. Tritz K
     80. Thoma M
     81. Newville M
     82. Kümmerer M
     83. Bolingbroke M
     84. Tartre M
     85. Pak M
     86. Smith NJ
     87. Nowaczyk N
     88. Shebanov N
     89. Pavlyk O
     90. Brodtkorb PA
     91. Lee P
     92. McGibbon RT
     93. Feldbauer R
     94. Lewis S
     95. Tygier S
     96. Sievert S
     97. Vigna S
     98. Peterson S
     99. More S
     100. Pudlik T
     101. Oshima T
     102. Pingel TJ
     103. Robitaille TP
     104. Spura T
     105. Jones TR
     106. Cera T
     107. Leslie T
     108. Zito T
     109. Krauss T
     110. Upadhyay U
     111. Halchenko YO
     112. Vázquez-Baeza Y

     (2020) SciPy 1.0: fundamental algorithms for scientific computing in Python

     Nature Methods 17:261–272.

     https://doi.org/10.1038/s41592-019-0686-2

     • PubMed
     • Google Scholar
114. 1. Vogel JW
     2. Iturria-Medina Y
     3. Strandberg OT
     4. Smith R
     5. Levitis E
     6. Evans AC
     7. Hansson O
     8. Alzheimer’s Disease Neuroimaging Initiative
     9. Swedish BioFinder Study

     (2020) Spread of pathological tau proteins through communicating neurons in human Alzheimer’s disease

     Nature Communications 11:2612.

     https://doi.org/10.1038/s41467-020-15701-2

     • PubMed
     • Google Scholar
115. 1. Wang D
     2. Liu S
     3. Warrell J
     4. Won H
     5. Shi X
     6. Navarro FCP
     7. Clarke D
     8. Gu M
     9. Emani P
     10. Yang YT
     11. Xu M
     12. Gandal MJ
     13. Lou S
     14. Zhang J
     15. Park JJ
     16. Yan C
     17. Rhie SK
     18. Manakongtreecheep K
     19. Zhou H
     20. Nathan A
     21. Peters M
     22. Mattei E
     23. Fitzgerald D
     24. Brunetti T
     25. Moore J
     26. Jiang Y
     27. Girdhar K
     28. Hoffman GE
     29. Kalayci S
     30. Gümüş ZH
     31. Crawford GE
     32. PsychENCODE Consortium
     33. Roussos P
     34. Akbarian S
     35. Jaffe AE
     36. White KP
     37. Weng Z
     38. Sestan N
     39. Geschwind DH
     40. Knowles JA
     41. Gerstein MB

     (2018) Comprehensive functional genomic resource and integrative model for the human brain

     Science 362:eaat8464.

     https://doi.org/10.1126/science.aat8464

     • PubMed
     • Google Scholar
116. Data

     1. Waskom M
     2. Botvinnik O
     3. OḰane ­D
     4. Hobson P
     5. Ostblom J
     6. Lukauskas S
     7. Gemperline DC
     8. Augspurger T
     9. Halchenko Y
     10. Cole JB

     (authors) (2018) Mwaskom/Seaborn

     Zenodo.

     https://doi.org/10.5281/zenodo.592845
117. Data

     1. Waskom M
     2. Larson E
     3. Brodbeck C
     4. Gramfort A
     5. Burns S
     6. Luessi M
     7. Weidemann CT
     8. Bitzer S
     9. Markiewicz C
     10. LaPlante R
     11. Halchenko Y
     12. Engemann DA
     13. van Vliet M
     14. Ghosh S
     15. Klein N
     16. Piantoni G
     17. Brett M
     18. Gwilliams L
     19. King JR
     20. Liu D

     (authors) (2020) nipy/pysurfer

     Zenodo.

     https://doi.org/10.5281/zenodo.592515
118. 1. Whitaker KJ
     2. Vértes PE
     3. Romero-Garcia R
     4. Váša F
     5. Moutoussis M
     6. Prabhu G
     7. Weiskopf N
     8. Callaghan MF
     9. Wagstyl K
     10. Rittman T
     11. Tait R
     12. Ooi C
     13. Suckling J
     14. Inkster B
     15. Fonagy P
     16. Dolan RJ
     17. Jones PB
     18. Goodyer IM
     19. NSPN Consortium
     20. Bullmore ET

     (2016) Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome

     PNAS 113:9105–9110.

     https://doi.org/10.1073/pnas.1601745113

     • PubMed
     • Google Scholar
119. 1. Yao Z
     2. van Velthoven CTJ
     3. Nguyen TN
     4. Goldy J
     5. Sedeno-Cortes AE
     6. Baftizadeh F
     7. Bertagnolli D
     8. Casper T
     9. Chiang M
     10. Crichton K
     11. Ding S-L
     12. Fong O
     13. Garren E
     14. Glandon A
     15. Gouwens NW
     16. Gray J
     17. Graybuck LT
     18. Hawrylycz MJ
     19. Hirschstein D
     20. Kroll M
     21. Lathia K
     22. Lee C
     23. Levi B
     24. McMillen D
     25. Mok S
     26. Pham T
     27. Ren Q
     28. Rimorin C
     29. Shapovalova N
     30. Sulc J
     31. Sunkin SM
     32. Tieu M
     33. Torkelson A
     34. Tung H
     35. Ward K
     36. Dee N
     37. Smith KA
     38. Tasic B
     39. Zeng H

     (2021) A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation

     Cell 184:3222–3241.

     https://doi.org/10.1016/j.cell.2021.04.021

     • PubMed
     • Google Scholar
120. Preprint

     1. Zhao K
     2. Zheng Q
     3. Che T
     4. Martin D
     5. Li Q
     6. Ding Y
     7. Zheng Y
     8. Liu Y
     9. Li S

     (2020) Regional Radiomics Similarity Networks (R2SN) in the Human Brain: Reproducibility, Small-World and Biological Basis

     bioRxiv.

     https://doi.org/10.1101/2020.12.09.418509

     • Google Scholar
121. 1. Zheng Y-Q
     2. Zhang Y
     3. Yau Y
     4. Zeighami Y
     5. Larcher K
     6. Misic B
     7. Dagher A
     8. Kennedy H

     (2019) Local vulnerability and global connectivity jointly shape neurodegenerative disease propagation

     PLOS Biology 17:e3000495.

     https://doi.org/10.1371/journal.pbio.3000495

     • Google Scholar

Author details

1. Ross D Markello

   McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing

   For correspondence

   ross.markello@mail.mcgill.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1057-1336

2. Aurina Arnatkeviciute

   School of Psychological Sciences & Monash Biomedical Imaging, Monash University, Clayton, Australia

   Contribution

   Conceptualization, Data curation, Software, Writing – review and editing

   Competing interests

   No competing interests declared

3. Jean-Baptiste Poline

   McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9794-749X

4. Ben D Fulcher

   School of Physics, University of Sydney, Sydney, Australia

   Contribution

   Conceptualization, Software, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3003-4055

5. Alex Fornito

   School of Psychological Sciences & Monash Biomedical Imaging, Monash University, Clayton, Australia

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

6. Bratislav Misic

   McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada

   Contribution

   Conceptualization, Project administration, Supervision, Visualization, Writing – original draft, Writing – review and editing

   For correspondence

   bratislav.misic@mcgill.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0307-2862

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