How to Characterize the Function of a Brain Region

1Ever since humans have scientifically investigated the mind, understanding how it is organized at the level of its biological substrate (i.e., the brain) has remained challenging. For over a century, great progress has been made in mapping the human brain (based on various characteristics), leading to a rapidly expanding number of parcellation schemes and atlases detailing the organization of cortical areas and modules. Several studies have demonstrated that the structural segregation of the cerebral cortex into different areas (distinguishable based on their biological properties, such as molecular, cellular, or fiber architecture) is closely related to its functional segregation and, in turn, its organization into functional networks.

2Current conceptualizations of brain function as a Bayesian machine, in which brain areas are seen as connected and relatively specialized computational units, are in contrast with the actual available knowledge about functional specialization. Studies over the past century show that the understanding of brain–behavior relationships has been an interdisciplinary endeavor, resulting in rich and heterogeneous patterns of behavioral functions for many brain regions. After reviewing the most common approaches that have contributed to this endeavor, we propose that assessing the relative functional specialization of brain regions requires a critical change in viewpoint, wherein the a priori defined construct is the brain region and the unknowns are the behavioral functions associated with it. In that perspective, recent advances in data aggregation offer novel opportunities for a systematic characterization of brain regions across a range of behavioral conditions and phenotypical features. Such an integrative approach could bring us to a pivotal stage in the history of brain mapping and cognitive neuroscience, in which we lift the conceptual fog that has clouded structure–function relationships in the brain, and focus on future formal conceptualizations of functional segregation and integration.

3The first theories regarding functional specialization of brain areas (which later led to the concept of functional segregation) had already been proposed in the early 19th century by Gall (whose view was later referred to as ‘phrenology’). However, many ‘functions’ that were associated with certain parts of the outer skull would not be considered as functions from a modern point of view (with the exception of language). The following decades were enlivened by debates on localizationism versus connectionism (for a detailed review, see). The pioneering work performed by Paul Broca and Carl Wernicke in the 19th century evidenced specific behavioral impairment following focal brain lesions, but, at the same time, it was also realized that the attribution of a specific function to a cortical area is related to its anatomical connectivity with distant brain regions. This was illustrated by Wernicke, who introduced the first network view for language comprehension and production.

4Following this view, the concept of disconnection syndromes refuted strict localizationism as a complete or sufficient account of cortical organization. Accordingly, the human brain mapping field currently relies on the assumption that the brain is governed by two fundamental principles of functional organization: segregation and integration. The former refers to the fact that the cerebral cortex is not a homogeneous entity but can be subdivided into regionally distinct modules (cortical areas or subcortical nuclei), based on functional and structural properties. The latter emphasizes that no brain region is by itself sufficient to perform a particular cognitive, sensory, or motor function. Rather, all mental capacities rely on a dynamic interplay and exchange of information between different regions.

5Importantly, these principles (functional segregation and integration) do not contradict each other, since integration can be conceptualized as interaction between relatively specialized regions, each subserving a distinct process. Accordingly, each area can perform a limited range of functions, but the concrete behavioral output depends on which inputs have been processed (from afferent connectivity) and which signal is sent to which other areas (based on efferent connectivity). In this ‘intrinsic’ and ‘connectivity’-based functional specialization of brain areas (developed in), these latter can be conceptualized as relatively specialized computational units, the observed behavioral effects of which depend on the coactivity (and thus information sent and received) of other areas.

6Considering brain areas as computational units raises the question of the mechanism of the computation, or basically the question of ‘what does the brain do and how?’ A relatively well-acknowledged view addressing this question is that the brain works as a Bayesian machine, computing probabilities that minimize uncertainty and support decision making. This view has been successful, for instance, in explaining perceptual processes as integrative processing of probabilistic distributions. It has also been adapted to explain higher aspects of cognition, such as human optimistic bias and, relatedly, the role of the lateral prefrontal cortex in updating beliefs.

7Importantly, this ‘Bayesian brain’ view entails an important shift in the conceptualization of ‘functions’. Traditionally, assigning functions to brain regions has mainly been based on conceptualizations of functions from many different disciplines that are interested in the study of the mind and behavior. Here we use the term ‘behavioral function’ to refer to these primarily psychology-related constructs. ‘Episodic memory’, ‘working memory’, ‘motor preparation’, ‘visual attention’, ‘memory consolidation’, ‘speech production’, ‘perspective taking’, and ‘emotional regulation’ are a few examples of these behavioral functions. However, the Bayesian brain hypothesis entails a different conceptualization of the functional specialization of brain regions. Specifically, ‘function’ refers to a computational operation performed by a given region, which contributes to the observed behavioral output. To incorporate this viewpoint, we use the term ‘operation-function’.

8In the following sections, we first illustrate the heterogeneity of behavioral functions that have been assigned to brain regions, according to previous approaches; these approaches are then reviewed. We then consider how functional specialization from such conceptualizations might be used in conjunction with recent advances in data aggregation methods to search for the core operation-function of brain regions.

9For any brain region, we can think of many different behavioral functions, based on the perspective from which we consider this brain region. In practice, most of these behavioral functions can somehow be related to one another and seem to comprise a core computational function (i.e., an operation-function) that grounds all behavioral associations but remains latent and is not directly observed. In other words, our current knowledge of the functional specialization of a given brain region can be conceptualized as a polyhedron with its many sides (i.e., many behavioral functions), the sum of which can only be appreciated by investigation from many different perspectives, but whose core center remains intangible.

10This conceptual polyhedron can be illustrated by one of the most studied parts of the brain, the hippocampus. It has been associated with different memory functions, such as episodic, autobiographical, explicit, contextual, or associative memory, and also with several ‘processes’, including declarative or incremental learning, recollection, encoding, retention, consolidation, novelty detection, binding, comparator, mismatch detection, pattern separation, and inferential processes. Furthermore, the hippocampus has been associated with particular ‘behavioral domains’ and ‘tasks’ such as spatial navigation, spatial discrimination, scene imagination, prospection, and allocentric representation. Finally, it has been assumed that the hippocampus supports more complex behavioral constructs, such as creative thinking or flexible cognition.

11How has such a ‘functional polyhedron’ been created? Actually, the hippocampus, like many brain regions, is a complex structure that can be engaged in many different behavioral functions, according to the context and to its interaction with other brain regions. Accordingly, different fields have used different approaches and conceptual frameworks to infer brain–behavior relationships, capturing one of its many behavioral associations. That is, ascribing behavioral functions to a brain region has been a multidisciplinary endeavor, resulting in multiple ontologies (Box 1) and different levels of description of mental functions, ranging from high-level behavioral descriptions to individual tasks and isolated processes hypothesized by cognitive models. Generally, when these constructs have been related to the brain, the discipline has shaped the ontologies, and the inferential approach has driven the level of description, producing heterogeneous conceptual associations for any brain region. In the following sections, we review the inferential approaches used in those different disciplines, and their concepts, advantages, and drawbacks.

12One of the first approaches linking brain and behavior was the lesion-deficit approach. Following the pioneering work of Broca and Wernicke in the 19th century, one of the most famous examples in the 20th century was the study of patient H.M. by Brenda Milner and colleagues. This patient had medial temporal lobe resection (mainly the hippocampus), which resulted in severe anterograde memory deficits. This led to the inference that the hippocampus plays a crucial role in the acquisition of new memories. As exemplified in this famous study, a ‘behavioral function’ is thus inferred from an observed ‘dysfunction’ (here anterograde amnesia) following restricted brain damage or loss. The main strength of this approach is the causal nature of the relationship between the two studied variables (brain and behavior). That is, observing a dysfunction following damage to a specific brain region allows one to infer a crucial role of this region in the respective behavioral function within the intact brain. This strength goes with the epistemological (see Glossary) limitation of being only quasi-experimental, as an experimental approach supporting causality implies the ability to demonstrate that alternative explanations have been eliminated. Specifically, in the lesion-dysfunction approach, the effect of prelesion factors (e.g., subclinical strokes and/or cognitive impairment) cannot be ruled out. In other words, the behavioral deficit (i.e., the effect on the dependent variable) could (partly) be driven by factors other than the lesion per se, thereby threatening internal validity. Furthermore, at the brain level, several issues arising from the spatially structured distribution of lesions and the influence of neuroplasticity (functional and structural adaptation to damage) can limit the inferential power of lesion-deficit mapping (Box 2). Despite these limitations, this approach has shaped many of the most pre-eminent assumptions about functional specialization, and is still considered a benchmark (due to its causal mechanism) against which findings obtained from other approaches are discussed.

13A more recent, experimental approach, mirroring the lesion-deficit approach but applied to healthy participants, is the study of behavioral consequences of virtual lesions created with brain stimulation techniques. In particular, brain activity can be locally impaired with transcranial magnetic stimulation (TMS), transcranial direct current stimulation, or transcranial alternating current stimulation. Likewise, the opposite effect (i.e., facilitation by increasing cortical excitability to enhance behavioral performance) is also possible, depending on the protocol. These approaches test cause–effect relationships by experimentally manipulating local brain activity and examining its effects on behavior. While representing a powerful experimental approach, internal validity can also be limited by the influence of individual cortical geometry and the relative lack of focality, as well as by the limited range of regions that can be targeted (Box 2). At the behavioral level, in contrast to the lesion-deficit approach, stimulation approaches do not tap into everyday behavior in natural settings. For the sake of experimental control and constraints of the laboratory setting needed for stimulus delivery, behavioral functions are usually operationalized from cognitive models (e.g., ‘memory recall’), and inference is made on isolated behavioral parameters such as reaction time. Thus, compared with the lesion-deficit approach, experimental brain stimulation can offer higher internal validity, but has limited ecological validity.

14In recent decades, neuroimaging techniques such as positron emission tomography (PET) and fMRI have produced a rapid growth in the study of brain–behavior relationships, by revealing localizations of brain activity changes induced by mental operations. fMRI quickly became preferred over PET for mapping task-evoked activity because it has a better spatial and temporal resolution; and can hence localize activity changes during specific mental events, such as successful memory encoding. With this technological progress, cognitive psychologists gained a new tool to test and refine cognitive models and theories. In this particular framework, the activation approach can be considered as experimental because it allows the researcher to freely manipulate an independent variable (behavioral condition) and observe its effect on the dependent variable (brain activation). For example, fMRI can provide support for dual-process cognitive models (such as recollection versus familiarity) by demonstrating that the two processes evoke distinct patterns of activity. However, addressing such questions with fMRI requires well-controlled designs, using two conditions which differ only in respect to the target processes (i.e., a ‘pure insertion’; cf.). For example, isolating ‘recollection’ in an fMRI scanner could be operationalized by contrasting recognition of intact word pairs (which engage both recollection and familiarity) with recognition of recombined word pairs (driven only by familiarity). Hence, mental functions are studied using very precise and therefore restricted experimental implementations inferred from cognitive theories. Consequently, as in stimulation studies, the mental operations a participant engages in are conceptually distant from everyday functions (such as the vivid recollection of a recent meeting), therefore limiting ecological validity. With regard to internal validity, the assumption of pure insertion (the assumption that extra processes can be inserted purely, without changing existing processes or eliciting new processes), upon which the interpretation of activation relies, has been questioned (cf.). For example, the internal validity of an fMRI study can be questioned by the fact that epiphenomena of the experimental setting (such as higher attentional demands in a task than in a control condition) cannot easily be dissociated from task-specific effects.

15Historically, lesion-deficit approaches (be they neuropsychological or stimulation based) were generally considered to be important with respect to the study of functional specialization of brain regions, because observing a relationship between a focal lesion and a behavioral deficit suggests that this region is necessary for performance. Hence, the lesion approach was long considered the gold standard for identifying the necessity of a brain region for a given function. By contrast, the activation approach was considered the optimal approach for identifying which brain areas were sufficient for a given behavioral function. Accordingly, it was initially hoped that a combination of lesion-deficit mapping and activation approaches would identify the necessary and sufficient brain regions for a given behavioral task. However, this view had to be revised subject to the degeneracy principle (i.e., the fact that one unique behavioral output or outcome can be achieved by different neurocognitive systems). This degeneracy theory resulted in a conceptual mourning in the human brain mapping field, as it was now considered that ‘there may be no necessary and sufficient brain area’ for any behavioral function.

16In behavioral science, an alternative to the experimental approach for probing associations between variables in natural conditions is to examine the relationships among naturally occurring variations in different variables in a correlative manner. In the study of brain–behavior relationships, the correlational approach can be used to relate neurobiological and behavioral characteristics through their covariation across individuals. Using MRI, this is most commonly performed by testing the correlation between brain morphology (such as local gray-matter volume or cortical thickness) and behavioral measures across a group of individuals. Structural brain–behavior correlations include studies on age, gender, and genetic differences (e.g.), studies on cognitive abilities and other psychological features derived from tests or questionnaires (e.g., personality traits), as well as studies aiming to identify morphological correlates of specific clinical symptoms measured with clinical ratings scales. This approach thus allows a conception of ‘behavioral function’, comprising complex phenotypes such as skills or clinical symptoms that are evident in everyday life. For example, episodic memory is frequently probed with the California Verbal Learning Test, which has been inspired by the real-life situation of learning a grocery list, hence probing progressive acquisition and consolidation of information in memory (e.g.,). Contrasting stimulation and activation studies, but mirroring the lesion-deficit approach, ‘behavioral function’ is thus not an abstract, experimentally controlled process, but a more ecological quantification of everyday cognition. Nevertheless, the inferential power (i.e., internal validity) of the correlation approach is undermined by a lack of experimental control, which implies that possible alternative processes and strategies may yield the same or similar behavioral outcomes. The internal validity is further reduced by the consideration that behavioral measurements could be relatively noisy proxies of the latent construct(s) they aim to target. This concern is especially noteworthy for scores based on subjective reports, the reliability of which is frequently questioned. Moreover, neurobiological features like local volume or cortical thickness estimates are influenced by numerous possible factors, which may show complex relationships with the covariate of interest. This, in turn, may lead to spurious brain–behavior associations (cf. discussion).

17In summary, associations between behavioral functions and brain regions have been studied by different research fields, which have different concepts of behavioral functions and use different inference approaches. Beyond technical strengths and weaknesses, the potential of these different approaches to associate particular behavioral functions with particular brain regions has been discussed with respect to ecological validity and internal validity (two epistemological qualities considered in behavioral sciences). With all their different strengths, these approaches have together contributed to assign multiple behavioral functions, corresponding to different levels of description, to brain regions.

18Although the complementarity of the different approaches can be seen as offering richness in behavioral associations for brain regions, the original goal of individual studies was typically to focus on a behavioral function and map it to a brain region, rather than elucidating the exact function of a given region. That is, the a priori defined construct was a mental operation, and the object of inference was the brain region that was related to it. We would contend that this modus operandi has only a very limited capacity to answer the initial question: ‘What does any part of the brain do?’ In particular, any inference about the role of any brain region that is derived using this modus operandi is complicated by the principle of degeneracy.

19For example, investigation into the association between the hippocampus and associative memory retrieval can be obscured by the fact that recalling an association of two items can be performed either by retrieving a unitized item integrating both components, or by retrieving the two associated items; with the second cognitive strategy being more likely to recruit the hippocampus. Accordingly, regardless of the approach used, finding a role for the hippocampus in the behavioral outcome depends on the neurocognitive aspects that the behavioral paradigm or measure mostly captures, and the neurocognitive system that the individual(s) recruit (different participants can recruit different neurocognitive systems). Consequently, identifying a role of the hippocampus in associative retrieval could require several studies, covering a very heterogeneous range of behavioral paradigms or measures and performed across different population samples.

20In conclusion, assessing the relative functional specialization of brain regions critically requires a change in viewpoint, where the a priori defined construct is the brain region and the unknowns are the behavioral functions associated with it. This implies screening a vast range of potential behavioral associations for a given brain region, and examining which of these are associated with the region of interest in an unbiased, statistically testable manner that accommodates the aforementioned complementarity of different approaches with respect to behavioral aspects. Recent advances in both data availability and statistical methods have provided very promising avenues for such region-of-interest-based approach.

21An overview of the behavioral functions engaging a given brain region could be achieved by scanning a group of individuals for a large range of behavioral conditions that target different mental functions. This approach has recently been undertaken with 12 participants as part of the Individual Brain Charting project.i With its ongoing development of specific decoding tools, this and related projects could significantly contribute to our understanding of the behavioral engagement of brain regions, providing rich and heterogeneous patterns of region–behavior associations at the individual level. Nevertheless, ensuring that patterns of association go beyond the idiosyncrasies of the specific experimental designs and participants will require data integration across many independent studies. Thus, a ‘subject level’ functional polyhedron that capitalizes on aggregation of experiments within-subject could complement findings from across-studies data aggregation.

22Integration across studies has now become possible due to two initiatives compiling the results of published activation studies: BrainMap and Neurosynth. Although differing in their approach to data extraction and labeling (Box 2), both contain the coordinates of local activation maxima as reported by many thousands of neuroimaging papers, along with descriptions of the behavioral conditions that yielded the respective activity patterns (e.g., ‘saccades’). By applying statistical tests accounting for the base rate of activation for a given region and the base rate of each behavioral condition in the database, the consistency of particular behavioral associations across thousands of studies can be analyzed for any region of interest. Such approaches could be seen as ‘functional behavioral profiling’ (‘functional’ referring to the use of activation data).

23Using such an approach, it has recently been demonstrated that the anterior insula is engaged in a very wide range of fMRI tasks, suggesting a generic functional role, such as task engagement maintenance; that could account for all the more specific mental processes that have previously been discussed for this region. As illustrated in this example, the patterns of associations across a wide range of tasks can foster new hypotheses, approximating as much as possible the core role of the region (and thus its operation-function), beyond the behavioral ontology of the original studies or the database. In a recent study, screening the range of studies activating the left rostral dorsal premotor cortex (PMd) revealed that this subregion was activated whenever the task required abstraction from the actual spatial (e.g., scene imagination), temporal (e.g., explicit memory), or mental frame (e.g., deception) (, Figure 1). This observation was only possible after integrating activations across different tasks and behavioral domains, and we can speculate that this ‘abstraction’ property actually reflects the use of sequential processing (spatial or temporal) in the PMd for various types of predictions beyond the current framework, in line with the Bayesian brain hypothesis.

24Although databases of activation data have existed for many years, systematic ‘functional behavioral profiling’ using these databases is still in its infancy. While this approach shows great potential for disclosing wide patterns of associations, many statistical considerations have to be taken into account. This difficulty has been illustrated recently by a vigorous debate over the functional specialization of the anterior cingulate cortex (ACC), based on conflicting conclusions derived by two groups of authors. Such discussions highlight the need to critically investigate the inferential approaches that rely on different statistical considerations, when aiming to comprehensively characterize the pattern of associations for a given brain region. Somewhat relatedly, the use of activation databases for behavioral profiling of brain regions has, to date, focused on single databases while, as discussed in Box 3, BrainMap and Neurosynth show complementary limitations and advantages, suggesting that their combination could provide a more comprehensive profiling and better overview than the previous focus on either of them in isolation. Finally, while a quantitative summary of activation data may thus disclose patterns across tasks from different research fields, it does not allow for disentangling whether the engagement of the brain region plays a crucial role in task performance or whether it is just an epiphenomenon related to experimental implementation (such as more intense visual fixation or cognitive engagement). This highlights the potential benefit of a correlational approach using more naturalistic tasks to complement our view on behavioral associations for a given brain region. The potential outcomes and limitations of such an approach are discussed in the next section.

25An emerging ethos of data sharing has promoted open access to a growing number of large datasets of neuroimaging and phenotypical data. The Human Connectome Project (HCP,), the 1000Brains study, and the UK Biobank are instances of such initiatives. They provide multimodal brain imaging, information on history and current life-style, questionnaire scores, and a substantial range of standard neuropsychological measures that address several cognitive dimensions, such as working memory, executive functions, and verbal learning. Brain characteristics measured with MRI in large-scale population data show a natural covariance with cognitive phenotypes (Figure 2A,), which allows a standard correlation approach for identification of specific brain regions that are related to behavioral dimensions of a priori interest (such as conscientiousness, Figure 2B). It would also allow evaluation of a specific association between a region of interest and a priori selected behavioral variables (such as hippocampus volume and memory performance, Figure 2C) within a hypothesis-driven framework.

26Supporting the validity of such an approach to capture brain–behavior relationships, measures tapping into similar aspects of behavior tend to show correlation in the same brain region (such as immediate recall and delayed recall in a list-learning task, or extraversion and conscientiousness in the assessment of personality). Such relationships open the perspective of an exploratory approach searching for significant associations between brain measurements in an a priori selected region of interest and a wide range of psychometric variables. That is, capitalizing on the hypothesis that neurobiological features such as gray-matter volume and cortical thickness covary locally with behavioral characteristics, the behavioral functions in which a given brain region potentially play a relative role could be inferred from its structural brain–behavior correlation across the range of phenotypical variables. As this approach is built on a very distinct conceptualization of mental functions through the assessment of complex, ecologically more valid tasks than the specific contrasts offered by the activation approach, it should reveal complementary patterns of behavioral association for any given region. Thus, ultimately, for a given brain region, the pattern of behavioral associations revealed by this structural correlation approach should be integrated with the pattern of behavioral associations derived from activation data, to offer a multiconceptual and multimodal pattern of behavioral associations for any brain region. This hybrid approach would, in turn, help to develop new hypotheses on the operation-function of any region.

27The correlation approach and, more generally, any data-driven approach using big datasets in which researchers just ‘let the data speak’ have their own limitations, as the neuroimaging and psychometric data may contain substantial noise, with confounding factors partly driving the observed effect. Ultimately, the function of any brain region should be considered within an integrative approach, including not only patterns revealed by local properties, but also interactions with other brain regions. In other words, data-driven approaches that adopt a functional segregation view and are applied to aggregated observations, offer a great opportunity for exploratory work and discovery science, but any resulting ‘operation-function’ hypothesis should be integrated into a functional model, tested with a hypothesis-based approach. As discussed previously, each approach has its own technical and scientific strengths and limitations, suggesting that a comprehensive evaluation of a given hypothesis should capitalize on a combination of different approaches, rather than focus exclusively on any one of them (see Outstanding Questions). Technical advances can now offer better experimental control, such as by combining electroencephalography (EEG) with focal brain stimulation. Furthermore, Bayesian-based methodological frameworks, such as dynamic causal modeling, have been successful in recent years in the statistical testing of neurocognitive models, and can now even be extended to combined EEG–fMRI paradigms. Altogether, these technical and methodological developments hold great promise for testing models of operation-functions computed by different brain areas in interaction.

28As previously discussed, in humans, assigning behavioral functions to neuronal populations using a noninvasive neuroimaging approach is restricted by the spatial resolution and precision of these techniques. In particular, individual differences in neuroanatomy can result in mixed functional activation patterns when data from several participants are aggregated in fMRI studies. Several improved approaches for areal alignment across participants in MRI data have been proposed, allowing further examination at the individual level of the structure–function relationships that are suggested by large-scale activation data aggregation (Box 4). Nevertheless, the spatial scale of local functional specialization remains limited by the intrinsic spatial resolution of MRI. That is, activation/structure–behavior covariance within a predefined area could represent a mixture of spatially proximate but functionally independent units, whose separation cannot be resolved by MRI. One consequence of this intrinsic spatial resolution is that inference approaches can only assign a ‘supraordinal’ function to a given brain area, summarizing the different functions performed by different neuronal subpopulations contained within a voxel. Invasive human studies and animal models could further test this hypothesis and help to refine our knowledge of the functional specialization of particular cell populations, such as place cells in the hippocampus (see e.g., Stachenfeld et al.), and thus complement functional specialization patterns derived from other approaches.

29Despite centuries of study of brain–behavior relationships, a clear formalization of the function of many brain regions, accounting for the engagement of the region in different behavioral functions, is lacking. Recent progress in data aggregation methods has opened a wide avenue for a systematic, multiconceptual characterization of behavioral associations for any brain region. On the one hand, previous decades of fMRI and PET activation experiments have provided a wealth of results that can be used to shift the perspective toward searching for the range of behavioral associations of brain regions using a quantitative approach. On the other hand, large-scale population datasets open new possibilities for a complementary approach based on covariance between neurobiological and behavioral features. This hybrid behavioral profiling could leverage the myriad of behavioral aspects of any brain region, hence progressively unveiling the nature of the function of brain regions and networks.