Interpreting and Utilising Intersubject Variability in Brain Function
Authors: Mohamed L. Seghier, Cathy J. Price
Journal: Trends in Cognitive Sciences (2018)

A wealth of scientifically and clinically relevant information is hidden, and potentially invalidated, when data are averaged across subjects. There is growing interest in using neuroimaging to explain differences in human abilities and disabilities. Progress in this endeavour requires us to treat intersubject variability as data rather than noise. Our plastic and noisy brains intrinsically change the parameterisation of each individual's brain, providing a rich opportunity to understand differences in brain function. Normal variability can be used to decode different neural pathways that can sustain the same task (degeneracy). This is of paramount importance for understanding why patients have variable outcomes after damage to seemingly similar brain regions. We consider between-subject variance in brain function as data rather than noise. We describe variability as a natural output of a noisy plastic system (the brain) where each subject embodies a particular parameterisation of that system. In this context, variability becomes an opportunity to: (i) better characterise typical versus atypical brain functions; (ii) reveal the different cognitive strategies and processing networks that can sustain similar tasks; and (iii) predict recovery capacity after brain damage by taking into account both damaged and spared processing pathways. This has many ramifications for understanding individual learning preferences and explaining the wide differences in human abilities and disabilities. Understanding variability boosts the translational potential of neuroimaging findings, in particular in clinical and educational neuroscience. We consider between-subject variance in brain function as data rather than noise. We describe variability as a natural output of a noisy plastic system (the brain) where each subject embodies a particular parameterisation of that system. In this context, variability becomes an opportunity to: (i) better characterise typical versus atypical brain functions; (ii) reveal the different cognitive strategies and processing networks that can sustain similar tasks; and (iii) predict recovery capacity after brain damage by taking into account both damaged and spared processing pathways. This has many ramifications for understanding individual learning preferences and explaining the wide differences in human abilities and disabilities. Understanding variability boosts the translational potential of neuroimaging findings, in particular in clinical and educational neuroscience. No two human brains are identical, with variability in brain anatomy and function emerging from how each individual brain is genetically built and shaped by its intimate interaction with the environment. Most functional imaging studies discount this variability to establish how subjects typically execute a given task, with the assumption that brain activations are spatially and temporally similar across subjects. Methods for measuring intersubject variability have mainly focused on developing models of normal brain function (i.e., norms) that allow abnormality to be quantified in patient populations. This quantification of norms relies on a reductionist framework that aims to collapse the data across the subject dimension and focus on the significant common (i.e., overlapping) or mean effects (Box 1). Such over-reliance on aggregate statistics (see Glossary) in the pursuit of the norm can invalidate some of the conclusions drawn from group level analyses.Box 1Variability along the Sixth DimensionA typical functional neuroimaging experiment (e.g., fMRI) requires the analysis of multidimensional data. Each experiment can be defined as a 6D dataset (Figure I): (i–iii) the three space dimensions, (iv) time, (v) an experimentally manipulated 'task', and (vi) 'subject', where the same experiment is repeated in multiple subjects. Other extra dimensions may include (vii) 'session' for longitudinal or repeated studies, and (viii) 'group' when subjects are drawn from different healthy or clinical populations. The focus here is about the sixth dimension 'subject'.In group studies, the information along the sixth dimension is typically treated as a 'nuisance' and is deliberately 'compressed' or 'reduced' to make group inferences. This approach hides other sources of variability. Different facets of variability can manifest in different dimensions, including (i) intertrial variability between events and items within the same run that might be linked to changes in strategy or learning [105Coste C.P. et al.Ongoing brain activity fluctuations directly account for intertrial and indirectly for intersubject variability in Stroop task performance.Cereb. Cortex. 2011; 21: 2612-2629Crossref PubMed Scopus (0) Google Scholar]; (ii) inter-regional variability (or spatial variability) in the neurovascular coupling or BOLD sensitivity [106Conner C.R. et al.Variability of the relationship between electrophysiology and BOLD-fMRI across cortical regions in humans.J. Neurosci. 2011; 31: 12855-12865Crossref PubMed Scopus (84) Google Scholar]; (iii) intrasubject (or intersession) variability related to the reliability and reproducibility of fMRI findings, in longitudinal or test–retest studies [91Bennett C.M. Miller M.B. How reliable are the results from functional magnetic resonance imaging?.Ann. N. Y. Acad. Sci. 2010; 1191Crossref PubMed Scopus (247) Google Scholar]; (iv) interindividual variability, also known as intersubject, between-subject, or across-subject variability (focus of the current review); (v) intersite variability between different scanning environments, which is sometimes a concern for large databases that include scans from different laboratories [107Brown G.G. et al.Multisite reliability of cognitive BOLD data.Neuroimage. 2011; 54: 2163-2175Crossref PubMed Scopus (44) Google Scholar]; and (vi) variability in methodology: related to contextual or situational factors, for instance, differences in experimental design, acquisition sequences, and analysis methods [108Carp J. The secret lives of experiments: methods reporting in the fMRI literature.Neuroimage. 2012; 63: 289-300Crossref PubMed Scopus (160) Google Scholar].We argue here that functional variability between subjects reflects the behaviour of the brain under parameterisation that is specific to the individual. Accordingly, the more two subjects differ in their parameterisation, the more their brain function will differ. The main structural and physiological parameters (measurable at the mesoscopic or macroscopic level) that govern such individual-specific parameterisation are: grey matter density [43Josse G. et al.Predicting language lateralization from gray matter.J. Neurosci. 2009; 29: 13516-13523Crossref PubMed Scopus (0) Google Scholar]; cortical thickness [53Saggar M. et al.Estimating individual contribution from group-based structural correlation networks.Neuroimage. 2015; 120: 274-284Crossref PubMed Scopus (9) Google Scholar]; morphological anatomy [109Juch H. et al.Anatomical variability of the lateral frontal lobe surface: implication for intersubject variability in language neuroimaging.Neuroimage. 2005; 24: 504-514Crossref PubMed Scopus (0) Google Scholar, 110Cachia A. et al.How interindividual differences in brain anatomy shape reading accuracy.Brain Struct. Funct. 2018; 223: 701-712Crossref PubMed Scopus (0) Google Scholar]; cortical layers [111Deshpande G. et al.Tissue specificity of nonlinear dynamics in baseline fMRI.Magn. Reson. Med. 2006; 55: 626-632Crossref PubMed Scopus (0) Google Scholar]; white matter circuitry (tracts and pathways) [1Miller M.B. et al.Individual differences in cognitive style and strategy predict similarities in the patterns of brain activity between individuals.Neuroimage. 2012; 59: 83-93Crossref PubMed Scopus (35) Google Scholar, 47Golestani A.M. et al.Constrained by our connections: white matter's key role in interindividual variability in visual working memory capacity.J. Neurosci. 2014; 34: 14913-14918Crossref PubMed Scopus (9) Google Scholar]; myelination [44Hunt B.A. et al.Relationships between cortical myeloarchitecture and electrophysiological networks.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: 13510-13515Crossref PubMed Scopus (10) Google Scholar]; callosal topography [112Josse G. et al.Explaining function with anatomy: language lateralization and corpus callosum size.J. Neurosci. 2008; 28: 14132-14139Crossref PubMed Scopus (0) Google Scholar] that influences the degree of functional lateralisation across subjects; functional connectivity [113Xu T. et al.Assessing variations in areal organization for the intrinsic brain: from fingerprints to reliability.Cereb. Cortex. 2016; 26: 4192-4211Crossref PubMed Scopus (14) Google Scholar] and its association with variability in task-related brain activity [114Wig G.S. et al.Medial temporal lobe BOLD activity at rest predicts individual differences in memory ability in healthy young adults.Proc. Natl. Acad. Sci. U. S. A. 2008; 105: 18555-18560Crossref PubMed Scopus (38) Google Scholar, 115Stevens A.A. et al.Functional brain network modularity captures inter- and intra-individual variation in working memory capacity.PLoS One. 2012; 7e30468Crossref PubMed Scopus (97) Google Scholar, 116Mennes M. et al.Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity.Neuroimage. 2010; 50: 1690-1701Crossref PubMed Scopus (217) Google Scholar] and its divergence from structural connectivity [45Park H.J. Friston K. Structural and functional brain networks: from connections to cognition.Science. 2013; 342: 1238411Crossref PubMed Scopus (545) Google Scholar]; brain oscillations and rhythms [13Basile L.F. et al.Individual topographic variability is inherent to cortical physiology but task-related differences may be noise.PLoS One. 2015; 10e0128343Crossref PubMed Scopus (1) Google Scholar, 117Başar E. et al.Gamma, alpha, delta, and theta oscillations govern cognitive processes.Int. J. Psychophysiol. 2001; 39: 241-248Crossref PubMed Scopus (577) Google Scholar, 118Haegens S. et al.Inter- and intra-individual variability in alpha peak frequency.Neuroimage. 2014; 92CGoogle Scholar], metabolism [119Wang G.J. et al.Intersubject variability of brain glucose metabolic measurements in young normal males.J. Nucl. Med. 1994; 35: 1457-1466PubMed Google Scholar] and vasculature [120van der Zwan A. et al.A quantitative investigation of the variability of the major cerebral arterial territories.Stroke. 1993; 24: 1951-1959Crossref PubMed Google Scholar]; and neurotransmitters and hormones [121Shafir T. et al.Postmenopausal hormone use impact on emotion processing circuitry.Behav. Brain Res. 2012; 226: 147-153Crossref PubMed Scopus (0) Google Scholar, 122van den Brink R.L. et al.Catecholaminergic neuromodulation shapes intrinsic MRI functional connectivity in the human brain.J. Neurosci. 2016; 36: 7865-7876Crossref PubMed Google Scholar]. A typical functional neuroimaging experiment (e.g., fMRI) requires the analysis of multidimensional data. Each experiment can be defined as a 6D dataset (Figure I): (i–iii) the three space dimensions, (iv) time, (v) an experimentally manipulated 'task', and (vi) 'subject', where the same experiment is repeated in multiple subjects. Other extra dimensions may include (vii) 'session' for longitudinal or repeated studies, and (viii) 'group' when subjects are drawn from different healthy or clinical populations. The focus here is about the sixth dimension 'subject'. In group studies, the information along the sixth dimension is typically treated as a 'nuisance' and is deliberately 'compressed' or 'reduced' to make group inferences. This approach hides other sources of variability. Different facets of variability can manifest in different dimensions, including (i) intertrial variability between events and items within the same run that might be linked to changes in strategy or learning [105Coste C.P. et al.Ongoing brain activity fluctuations directly account for intertrial and indirectly for intersubject variability in Stroop task performance.Cereb. Cortex. 2011; 21: 2612-2629Crossref PubMed Scopus (0) Google Scholar]; (ii) inter-regional variability (or spatial variability) in the neurovascular coupling or BOLD sensitivity [106Conner C.R. et al.Variability of the relationship between electrophysiology and BOLD-fMRI across cortical regions in humans.J. Neurosci. 2011; 31: 12855-12865Crossref PubMed Scopus (84) Google Scholar]; (iii) intrasubject (or intersession) variability related to the reliability and reproducibility of fMRI findings, in longitudinal or test–retest studies [91Bennett C.M. Miller M.B. How reliable are the results from functional magnetic resonance imaging?.Ann. N. Y. Acad. Sci. 2010; 1191Crossref PubMed Scopus (247) Google Scholar]; (iv) interindividual variability, also known as intersubject, between-subject, or across-subject variability (focus of the current review); (v) intersite variability between different scanning environments, which is sometimes a concern for large databases that include scans from different laboratories [107Brown G.G. et al.Multisite reliability of cognitive BOLD data.Neuroimage. 2011; 54: 2163-2175Crossref PubMed Scopus (44) Google Scholar]; and (vi) variability in methodology: related to contextual or situational factors, for instance, differences in experimental design, acquisition sequences, and analysis methods [108Carp J. The secret lives of experiments: methods reporting in the fMRI literature.Neuroimage. 2012; 63: 289-300Crossref PubMed Scopus (160) Google Scholar]. We argue here that functional variability between subjects reflects the behaviour of the brain under parameterisation that is specific to the individual. Accordingly, the more two subjects differ in their parameterisation, the more their brain function will differ. The main structural and physiological parameters (measurable at the mesoscopic or macroscopic level) that govern such individual-specific parameterisation are: grey matter density [43Josse G. et al.Predicting language lateralization from gray matter.J. Neurosci. 2009; 29: 13516-13523Crossref PubMed Scopus (0) Google Scholar]; cortical thickness [53Saggar M. et al.Estimating individual contribution from group-based structural correlation networks.Neuroimage. 2015; 120: 274-284Crossref PubMed Scopus (9) Google Scholar]; morphological anatomy [109Juch H. et al.Anatomical variability of the lateral frontal lobe surface: implication for intersubject variability in language neuroimaging.Neuroimage. 2005; 24: 504-514Crossref PubMed Scopus (0) Google Scholar, 110Cachia A. et al.How interindividual differences in brain anatomy shape reading accuracy.Brain Struct. Funct. 2018; 223: 701-712Crossref PubMed Scopus (0) Google Scholar]; cortical layers [111Deshpande G. et al.Tissue specificity of nonlinear dynamics in baseline fMRI.Magn. Reson. Med. 2006; 55: 626-632Crossref PubMed Scopus (0) Google Scholar]; white matter circuitry (tracts and pathways) [1Miller M.B. et al.Individual differences in cognitive style and strategy predict similarities in the patterns of brain activity between individuals.Neuroimage. 2012; 59: 83-93Crossref PubMed Scopus (35) Google Scholar, 47Golestani A.M. et al.Constrained by our connections: white matter's key role in interindividual variability in visual working memory capacity.J. Neurosci. 2014; 34: 14913-14918Crossref PubMed Scopus (9) Google Scholar]; myelination [44Hunt B.A. et al.Relationships between cortical myeloarchitecture and electrophysiological networks.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: 13510-13515Crossref PubMed Scopus (10) Google Scholar]; callosal topography [112Josse G. et al.Explaining function with anatomy: language lateralization and corpus callosum size.J. Neurosci. 2008; 28: 14132-14139Crossref PubMed Scopus (0) Google Scholar] that influences the degree of functional lateralisation across subjects; functional connectivity [113Xu T. et al.Assessing variations in areal organization for the intrinsic brain: from fingerprints to reliability.Cereb. Cortex. 2016; 26: 4192-4211Crossref PubMed Scopus (14) Google Scholar] and its association with variability in task-related brain activity [114Wig G.S. et al.Medial temporal lobe BOLD activity at rest predicts individual differences in memory ability in healthy young adults.Proc. Natl. Acad. Sci. U. S. A. 2008; 105: 18555-18560Crossref PubMed Scopus (38) Google Scholar, 115Stevens A.A. et al.Functional brain network modularity captures inter- and intra-individual variation in working memory capacity.PLoS One. 2012; 7e30468Crossref PubMed Scopus (97) Google Scholar, 116Mennes M. et al.Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity.Neuroimage. 2010; 50: 1690-1701Crossref PubMed Scopus (217) Google Scholar] and its divergence from structural connectivity [45Park H.J. Friston K. Structural and functional brain networks: from connections to cognition.Science. 2013; 342: 1238411Crossref PubMed Scopus (545) Google Scholar]; brain oscillations and rhythms [13Basile L.F. et al.Individual topographic variability is inherent to cortical physiology but task-related differences may be noise.PLoS One. 2015; 10e0128343Crossref PubMed Scopus (1) Google Scholar, 117Başar E. et al.Gamma, alpha, delta, and theta oscillations govern cognitive processes.Int. J. Psychophysiol. 2001; 39: 241-248Crossref PubMed Scopus (577) Google Scholar, 118Haegens S. et al.Inter- and intra-individual variability in alpha peak frequency.Neuroimage. 2014; 92CGoogle Scholar], metabolism [119Wang G.J. et al.Intersubject variability of brain glucose metabolic measurements in young normal males.J. Nucl. Med. 1994; 35: 1457-1466PubMed Google Scholar] and vasculature [120van der Zwan A. et al.A quantitative investigation of the variability of the major cerebral arterial territories.Stroke. 1993; 24: 1951-1959Crossref PubMed Google Scholar]; and neurotransmitters and hormones [121Shafir T. et al.Postmenopausal hormone use impact on emotion processing circuitry.Behav. Brain Res. 2012; 226: 147-153Crossref PubMed Scopus (0) Google Scholar, 122van den Brink R.L. et al.Catecholaminergic neuromodulation shapes intrinsic MRI functional connectivity in the human brain.J. Neurosci. 2016; 36: 7865-7876Crossref PubMed Google Scholar]. The search for the mean group effect (i.e., central tendency), typically defined as the ultimate representative subject, implicitly treats variability that cannot be explained by any experimental manipulation as a nuisance, noise, or measurement error. This ignores many relevant sources of intersubject variability, including the use of different cognitive strategies for the same task [1Miller M.B. et al.Individual differences in cognitive style and strategy predict similarities in the patterns of brain activity between individuals.Neuroimage. 2012; 59: 83-93Crossref PubMed Scopus (35) Google Scholar, 2Seghier M.L. et al.Inter-subject variability in the use of two different neuronal networks for reading aloud familiar words.Neuroimage. 2008; 42: 1226-1236Crossref PubMed Scopus (0) Google Scholar, 3Iaria G. et al.Cognitive strategies dependent on the hippocampus and caudate nucleus in human navigation: variability and change with practice.J. Neurosci. 2003; 23: 5945-5952Crossref PubMed Google Scholar] (Box 2), differences in learning or subjective judgment [4Sanfratello L. et al.Same task, different strategies: how brain networks can be influenced by memory strategy.Hum. 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This is in part because the characterisation of intersubject variability requires a large number of observations from a large number of individuals, but also because of the challenge of developing methodologies for analysing, interpreting, and using between-subject variance [12Stelzer J. et al.Deficient approaches to human neuroimaging.Front. Hum. Neurosci. 2014; 8: 462Crossref PubMed Scopus (27) Google Scholar, 20Dubois J. Adolphs R. Building a science of individual differences from fMRI.Trends Cogn. Sci. 2016; 20: 425-443Abstract Full Text Full Text PDF PubMed Scopus (107) Google Scholar].Box 2Intersubject Variability in Cognitive StrategyIn typical multisubject neuroimaging studies, tasks are assumed to be performed in the same way or using a single strategy [123Hedge C. et al.The reliability paradox: why robust cognitive tasks do not produce reliable individual differences.Behav. Res. Methods. 2018; (Published online July 19, 2017)https://doi.org/10.3758/s13428-017-0935-1Crossref Scopus (80) Google Scholar]. However, many tasks are unconstrained, allowing subjects to adopt their own strategy. Figure I intuitively visualises the problem of averaging individual activation maps that differ in their features [124Thirion B. et al.Analysis of a large fMRI cohort: statistical and methodological issues for group analyses.Neuroimage. 2007; 35: 105-120Crossref PubMed Scopus (333) Google Scholar], when subjects adopt different strategies. The resulting average in this toy example depicts a hybrid image that differs to that encoded in each of the original images. This average image contains many false negatives (where features vary between images) and false positives (where feature combinations create new features).A hypothetical example is a task that requires subjects to hold and manipulate many numbers when performing a serial addition of successive integers. Practically, inside the scanner, the subject is shown an Arabic digit (e.g., 8) and his/her task is to verbally say the exact sum of all numbers from 1 up to the presented digit (sum of 1 to 8). If researchers have a limited knowledge of the many ways (or strategies) by which the task can be executed, they will assume that all subjects will do the task in exactly in the same way. However, there are at least three known cognitive strategies to execute this task as illustrated in Figure II.Obviously, each strategy involves specific cognitive processes, with distinct activation patterns. This yields high between-subject variability and weak effects when the analysis sums over strategy. In contrast, paying attention to the individual pattern may help to (i) understand that different strategies do exist, and (ii) potentially predict the strategy that the individual was using. For tasks in which the strategies are known a priori (e.g., reading), researchers can use clever experimental manipulations to push the participant (implicitly or explicitly) towards a particular strategy. For other tasks, strategies can be inferred by looking at structure or patterns in the across-subject dimension of the group data.Perhaps more importantly, the different types of variability in brain parameters and cognitive strategies are intimately connected. For example, slow changes in structural parameters such as grey matter (density and volume), white matter connectivity, and vasculature underpin the faster, more dynamic changes in endogenous functional connectivity and brain rhythms, which in turn influence task-related brain activity and behaviour [114Wig G.S. et al.Medial temporal lobe BOLD activity at rest predicts individual differences in memory ability in healthy young adults.Proc. Natl. Acad. Sci. U. S. A. 2008; 105: 18555-18560Crossref PubMed Scopus (38) Google Scholar, 115Stevens A.A. et al.Functional brain network modularity captures inter- and intra-individual variation in working memory capacity.PLoS One. 2012; 7e30468Crossref PubMed Scopus (97) Google Scholar, 116Mennes M. et al.Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity.Neuroimage. 2010; 50: 1690-1701Crossref PubMed Scopus (217) Google Scholar, 125Tavor I. et al.Task-free MRI predicts individual differences in brain activity during task performance.Science. 2016; 352: 216-220Crossref PubMed Scopus (170) Google Scholar, 126Barnes K.A. et al.Individual differences in intrinsic brain connectivity predict decision strategy.J. 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Neurosci. 2013; 33: 19499-19503Crossref PubMed Scopus (165) Google Scholar, 48Kohno M. et al.A neural network that links brain function, white-matter structure and risky behavior.Neuroimage. 2017; 149: 15-22Crossref PubMed Scopus (1) Google Scholar].Figure IICumulative Sums of Integers via Three Possible Strategies.View Large Image Figure ViewerDownload Hi-res image Download (PPT) In typical multisubject neuroimaging studies, tasks are assumed to be performed in the same way or using a single strategy [123Hedge C. et al.The reliability paradox: why robust cognitive tasks do not produce reliable individual differences.Behav. Res. Methods. 2018; (Published online July 19, 2017)https://doi.org/10.3758/s13428-017-0935-1Crossref Scopus (80) Google Scholar]. However, many tasks are unconstrained, allowing subjects to adopt their own strategy. Figure I intuitively visualises the problem of averaging individual activation maps that differ in their features [124Thirion B. et al.Analysis of a large fMRI cohort: statistical and methodological issues for group analyses.Neuroimage. 2007; 35: 105-120Crossref PubMed Scopus (333) Google Scholar], when subjects adopt different strategies. The resulting average in this toy example depicts a hybrid image that differs to that encoded in each of the original images. This average image contains many false negatives (where features vary between images) and false positives (where feature combinations create new features). A hypothetical example is a task that requires subjects to hold and manipulate many numbers when performing a serial addition of successive integers. Practically, inside the scanner, the subject is shown an Arabic digit (e.g., 8) and his/her task is to verbally say the exact sum of all numb