Multi-Task Batteries for Precision Functional Mapping
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eLife Assessment
This important study presents a new approach to individualised functional brain mapping using multi-task batteries, and introduces a toolbox to support its implementation. Convincing evidence from simulations and empirical analyses across multiple datasets demonstrates clear advantages over traditional single-task approaches and provides practical guidance on task selection. The extent to which these improvements translate into more accurate recovery of individual-specific functional boundaries beyond well-characterised systems remains to be established.
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Abstract
Functional brain mapping is an important tool to understand the organization of the human brain, both at the group level, but also to an increasing degree at the level of the individual. There are currently two main approaches to do so. Resting-state fMRI relies on inter-regional correlations of random fluctuations of the signal. In contrast, task-based localizers typically use a single-contrast between a task of interest and a matched control task to identify the location of a functional region in an individual brain. In this paper, we propose and evaluate a third approach: the use of multi-task batteries for both localization of a single functional region and parcellation of multiple functional regions. We show that multi-task localizers produce more consistent estimation of a single functional region across subjects than the single-contrast approach using the same amount of fMRI data. Furthermore, we demonstrate that the multi-task approach is sensitive to true inter-individual differences in region size, and does not suffer the same influence of signal-to-noise ratio that biases the single-contrast localizer. We then address the question of how to select tasks for the battery, and present a data-driven strategy that optimizes the characterization of a brain structure of interest. We show that such batteries outperform randomly selected batteries both for building individual parcellations as well as individual connectivity models. Finally, we demonstrate that an interspersed design - where all tasks are presented in each imaging run - yields more reliable results than splitting the tasks across different runs. We present an open source toolbox for the implementation of multi-task batteries, along with a library containing group-averaged activity patterns that can be used to optimize battery selection for different brain structures of interest.
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eLife Assessment
This important study presents a new approach to individualised functional brain mapping using multi-task batteries, and introduces a toolbox to support its implementation. Convincing evidence from simulations and empirical analyses across multiple datasets demonstrates clear advantages over traditional single-task approaches and provides practical guidance on task selection. The extent to which these improvements translate into more accurate recovery of individual-specific functional boundaries beyond well-characterised systems remains to be established.
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Reviewer #1 (Public review):
Summary:
In this well-written and well-presented manuscript, Arafat and colleagues describe the proper use and advantages of multi-task batteries to understand the organization of the human brain. The authors present both simulation and empirical results suggesting a substantial advantage in using many short tasks vs a single localizer in identifying specific task-engaged regions. The natural question arises as to which tasks should be used within the battery, and how they should be organized. The authors address this question by demonstrating a data-driven strategy for task selection that outperforms random selection, and they further demonstrate the advantages of highly interspersed tasks over a more typical one-task-per-run strategy.
Strengths:
In general, I find this work highly compelling. The topic …
Reviewer #1 (Public review):
Summary:
In this well-written and well-presented manuscript, Arafat and colleagues describe the proper use and advantages of multi-task batteries to understand the organization of the human brain. The authors present both simulation and empirical results suggesting a substantial advantage in using many short tasks vs a single localizer in identifying specific task-engaged regions. The natural question arises as to which tasks should be used within the battery, and how they should be organized. The authors address this question by demonstrating a data-driven strategy for task selection that outperforms random selection, and they further demonstrate the advantages of highly interspersed tasks over a more typical one-task-per-run strategy.
Strengths:
In general, I find this work highly compelling. The topic itself should be of high interest to the majority of researchers conducting human functional neuroimaging studies. The manuscript itself is comprehensive and sound. The analyses and data are truly excellent, with only a few, relatively minor issues that can be improved. The authors do an exceptional job of laying out the motivation and logic for almost every analysis and conclusion in the manuscript.
I want to point specifically to the potential impact of this work. While the claims made here are very appropriately constrained to the conclusions that can be drawn from the actual analyses, their impact is potentially far-reaching. By the end of this manuscript, we are left with a set of ideas that in effect overturns 2-3 decades of received knowledge about how functional neuroimaging tasks should be designed to optimally understand the organization of the human brain.
Thanks to this paper, I personally will be rethinking how I design all of my fMRI studies in the future after reading this work. The authors are to be commended for this excellent contribution to the literature.
Weaknesses:
I struggled to understand the motivation and logic of the "connectivity modeling" section of the analyses.
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Reviewer #2 (Public review):
Summary:
This paper presents theoretical and empirical insights into the use of multi-task batteries for precision functional brain mapping and offers practical guidelines for optimal task design. Specifically, the authors evaluate differences between single-contrast and multi-task localizers, explore data-driven strategies for battery selection, such as minimizing collinearity, and compare grouped and interspersed stimulus-presentation designs. Through a combination of simulations and analyses of empirical fMRI data, the study provides a systematic set of recommendations for improving the reliability and specificity of individualized functional mapping.
Strengths:
Traditional functional mapping has long relied on single-contrast localizers or resting-state fMRI. However, there is growing recognition that …
Reviewer #2 (Public review):
Summary:
This paper presents theoretical and empirical insights into the use of multi-task batteries for precision functional brain mapping and offers practical guidelines for optimal task design. Specifically, the authors evaluate differences between single-contrast and multi-task localizers, explore data-driven strategies for battery selection, such as minimizing collinearity, and compare grouped and interspersed stimulus-presentation designs. Through a combination of simulations and analyses of empirical fMRI data, the study provides a systematic set of recommendations for improving the reliability and specificity of individualized functional mapping.
Strengths:
Traditional functional mapping has long relied on single-contrast localizers or resting-state fMRI. However, there is growing recognition that diverse batteries of general tasks can yield more detailed functional maps with higher signal-to-noise ratios (SNRs). This manuscript systematically evaluates these advantages using both simulations and empirical data. The contribution is timely and provides the community with not only a theoretical justification for multi-task designs but also practical tools, in the form of the MultiTaskBattery toolbox, for implementing them.
Weaknesses:
Although the results are robust, they are largely consistent with existing expectations in the field, and the conceptual novelty or "surprise" factor is therefore somewhat limited. Nevertheless, synthesizing these findings into a coherent set of design recommendations provides significant value to researchers.
Additionally, there appears to be a slight mismatch between the content of the manuscript and its designated article type. Although the manuscript was submitted as a "Tools and Resources" article, its extensive empirical analyses and theoretical evaluation make it read more like a "Research Article." I defer this categorization to the Editor's judgment.
Finally, the authors use inter-subject overlap as a primary metric for validating the accuracy of functional mapping (Figure 3). However, given that genuine inter-individual variability in brain organization is a central premise of precision mapping, greater overlap across subjects may not necessarily indicate more accurate individual-level localization. A more detailed analysis or discussion of how to distinguish measurement noise from genuine individual differences would make the paper more comprehensive and strengthen its overall contribution.
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Reviewer #3 (Public review):
Summary:
This study introduces a principled framework for optimizing multi-task batteries for individualized functional brain mapping. Through simulations and empirical validation, the authors show that selecting tasks to maximize differences in regional response profiles can substantially improve the identification of functional brain regions. The work represents a valuable methodological advance for precision functional mapping, although some assumptions underlying the broader applicability of the framework would benefit from further discussion.
Strengths:
The manuscript addresses an important methodological challenge in precision functional mapping using a rigorous combination of theoretical analyses, simulations, and empirical validation. The framework is practical and well supported by open-source …
Reviewer #3 (Public review):
Summary:
This study introduces a principled framework for optimizing multi-task batteries for individualized functional brain mapping. Through simulations and empirical validation, the authors show that selecting tasks to maximize differences in regional response profiles can substantially improve the identification of functional brain regions. The work represents a valuable methodological advance for precision functional mapping, although some assumptions underlying the broader applicability of the framework would benefit from further discussion.
Strengths:
The manuscript addresses an important methodological challenge in precision functional mapping using a rigorous combination of theoretical analyses, simulations, and empirical validation. The framework is practical and well supported by open-source software and a publicly available task library, making it readily accessible for adoption and further development by the research community. The manuscript is well written, logically structured, and clearly presents both the methodological framework and its practical implementation.
Weaknesses:
(1) The abstract and introduction emphasize the application of the framework to individualized brain parcellation. While the presented analyses convincingly demonstrate improved prediction of held-out task responses using atlas-guided parcel assignments, they do not directly validate whether the optimized task batteries improve the estimation of an individual's true functional boundaries. The empirical validation relies on atlas-defined parcel identities as the reference standard, yet substantial inter-individual variability in the location and extent of functional regions - particularly within association cortex - has been well documented. Consequently, improved recovery of atlas-defined parcel labels does not necessarily imply more accurate recovery of an individual's functional organization. It would therefore be valuable to clarify this distinction in the abstract and discussion and to discuss how inter-individual variability may influence the interpretation and generalizability of the parcellation analyses.
(2) The framework assumes that informative task batteries can be designed to distinguish neighboring functional regions. While this is compelling for well-characterized systems with distinct functional response profiles, it is less clear how the approach generalizes to finer-scale subdivisions within association cortex (e.g., subnetworks), where neighboring regions may exhibit highly similar task-response profiles and their functional roles remain incompletely understood. In these settings, the relevant functional dimensions may not yet be known, making it difficult to design optimized task batteries a priori. It would therefore be valuable for the authors to discuss how the framework could be extended to such cases.
(3) More generally, the framework assumes that the sampled task space adequately captures the functional dimensions that differentiate cortical regions. However, particularly within the association cortex, neighboring regions may exhibit similar task-response profiles while differing in the information they represent, their interactions with other regions, the computations they perform, or their cortical layer-specific response patterns. In such cases, the dimensions that best distinguish cortical organization may not be fully reflected in task-response profiles alone, but instead become apparent through complementary approaches such as representational analyses, task-evoked or resting-state connectivity, computational modelling, or laminar response profiles. It would therefore be valuable to discuss how the proposed framework relates to these complementary perspectives.
(4) Many task batteries inherently contain tasks that vary substantially in cognitive demand. Given that task difficulty is itself a major organizational axis in association cortex, it would be helpful for the authors to discuss how the optimization framework accounts for this. Specifically, could differences in task difficulty drive regional differentiation, even when tasks probe similar underlying cognitive processes? If so, how does the framework distinguish between organizational differences arising from a common demand axis and those reflecting more specific functional specializations?
(5) The Discussion places the proposed framework in the broader context of precision functional mapping and refers readers to a companion paper demonstrating advantages over resting-state ("inside-out") approaches. Given that these comparisons motivate several of the broader recommendations made in the Discussion, it would be helpful to provide a brief summary of the main findings of the companion paper here. This would allow readers to better understand the basis for these conclusions without relying on a separate manuscript.
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