How to Characterize the Function of a Brain Region
Authors: Sarah Genon, Andrew Reid, Robert Langner, Katrin Amunts, Simon B. Eickhoff
Journal: Trends in Cognitive Sciences (2018)

While it is largely accepted that the brain is topographically organized into distinct areas that are integrated into networks, the unique contribution of each area to behavior is yet to be elucidated. Diverse lines of research using different approaches have contributed to numerous behavioral associations for any brain area. Emerging databases of task-based activation data offer the possibility of characterizing the engagement of brain regions across a broad range of experimental behavioral conditions. New large samples of both imaging and phenotypical data provide an opportunity to complement the activation pattern by examining cross-subject associations between imaging-derived neurobiological markers and ecological behavioral characteristics. Many brain regions have been defined, but a comprehensive formalization of each region's function in relation to human behavior is still lacking. Current knowledge comes from various fields, which have diverse conceptions of 'functions'. We briefly review these fields and outline how the heterogeneity of associations could be harnessed to disclose the computational function of any region. Aggregating activation data from neuroimaging studies allows us to characterize the functional engagement of a region across a range of experimental conditions. Furthermore, large-sample data can disclose covariation between brain region features and ecological behavioral phenotyping. Combining these two approaches opens a new perspective to determine the behavioral associations of a brain region, and hence its function and broader role within large-scale functional networks. Many brain regions have been defined, but a comprehensive formalization of each region's function in relation to human behavior is still lacking. Current knowledge comes from various fields, which have diverse conceptions of 'functions'. We briefly review these fields and outline how the heterogeneity of associations could be harnessed to disclose the computational function of any region. Aggregating activation data from neuroimaging studies allows us to characterize the functional engagement of a region across a range of experimental conditions. Furthermore, large-sample data can disclose covariation between brain region features and ecological behavioral phenotyping. Combining these two approaches opens a new perspective to determine the behavioral associations of a brain region, and hence its function and broader role within large-scale functional networks. Ever 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 [1Amunts K. et al.Interoperable atlases of the human brain.Neuroimage. 2014; 99: 525-532Crossref PubMed Scopus (63) Google Scholar]. 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 [2Lorenz S. et al.Two new cytoarchitectonic areas on the human mid-fusiform gyrus.Cereb. Cortex. 2017; 27: 373-385PubMed Google Scholar, 3Toga A.W. et al.Towards multimodal atlases of the human brain.Nat. Rev. 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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. The 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') [6Gall, F.J. (1818) Anatomie et Physiologie du Systême Nerveux en Général, et du Cerveau en Particulier: Avec des Observations sur la Possibilité de Reconnoitre Plusieurs Dispositions Intellectuelles et Morales de l'Homme et des Animaux par la Configuration de Leurs Têtes, F. Schoell (in French)Google Scholar]. 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 [7Friston K.J. Functional and effective connectivity: a review.Brain Connect. 2011; 1: 13-36Crossref PubMed Scopus (2004) Google Scholar]). 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 [8Lichtheim L. On aphasia.Brain. 1885; 7: 433-484Crossref Google Scholar]. Following this view, the concept of disconnection syndromes refuted strict localizationism as a complete or sufficient account of cortical organization [7Friston K.J. Functional and effective connectivity: a review.Brain Connect. 2011; 1: 13-36Crossref PubMed Scopus (2004) Google Scholar]. 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 [7Friston K.J. Functional and effective connectivity: a review.Brain Connect. 2011; 1: 13-36Crossref PubMed Scopus (2004) Google Scholar, 9Tononi G. et al.A measure for brain complexity: relating functional segregation and integration in the nervous system.Proc. Natl. Acad. Sci. U. S. A. 1994; 91: 5033-5037Crossref PubMed Scopus (1064) Google Scholar, 10Fox P.T. Friston K.J. Distributed processing; distributed functions?.Neuroimage. 2012; 61: 407-426Crossref PubMed Scopus (69) Google Scholar]. 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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 [16Price C.J. Friston K.J. Functional ontologies for cognition: the systematic definition of structure and function.Cogn. Neuropsychol. 2005; 22: 262-275Crossref PubMed Scopus (184) Google Scholar]), 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. Considering 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 [17Friston K. The history of the future of the Bayesian brain.Neuroimage. 2012; 62: 1230-1233Crossref PubMed Scopus (120) Google Scholar], computing probabilities that minimize uncertainty [18Friston K. The free-energy principle: a unified brain theory?.Nat. Rev. Neurosci. 2010; 11: 127-138Crossref PubMed Scopus (3398) Google Scholar] and support decision making [19Beck J.M. et al.Probabilistic population codes for Bayesian decision making.Neuron. 2008; 60: 1142-1152Abstract Full Text Full Text PDF PubMed Scopus (438) Google Scholar]. This view has been successful, for instance, in explaining perceptual processes as integrative processing of probabilistic distributions [20Knill D.C. Pouget A. The Bayesian brain: the role of uncertainty in neural coding and computation.Trends Neurosci. 2004; 27: 712-719Abstract Full Text Full Text PDF PubMed Scopus (1386) Google Scholar]. It has also been adapted to explain higher aspects of cognition, such as human optimistic bias [21Sharot T. et al.How unrealistic optimism is maintained in the face of reality.Nat. Neurosci. 2011; 14: 1475-1479Crossref PubMed Scopus (348) Google Scholar] and, relatedly, the role of the lateral prefrontal cortex in updating beliefs [22Sharot T. et al.Selectively altering belief formation in the human brain.Proc. Natl. Acad. Sci. U. S. A. 2012; 109: 17058-17062Crossref PubMed Scopus (98) Google Scholar]. Importantly, 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'. In 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. For 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. This 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 [23Horner A.J. Doeller C.F. Plasticity of hippocampal memories in humans.Curr. Opin. 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Finally, it has been assumed that the hippocampus supports more complex behavioral constructs, such as creative thinking or flexible cognition [45Duff M.C. et al.Hippocampal amnesia disrupts creative thinking.Hippocampus. 2013; 23: 1143-1149Crossref PubMed Scopus (109) Google Scholar]. How 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 [46Driver J. et al.Introduction. Mental processes in the human brain.Philos. Trans. R. Soc. Lond. B Biol. Sci. 2007; 362: 757-760Crossref PubMed Scopus (8) Google Scholar]. 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.Box 1Matching Brain Organization and Cognitive OntologiesOne important issue in the study of brain–behavior relationships is whether currently available ontologies and taxonomies can be mapped onto the brain. That is, if we could capture the exact topographical organization of the brain, it seems unlikely that we would find concepts at the current level of description of behavior that could be mapped to the identified biological units. It seems likely that the behavioral structure as it is implemented (i.e., 'coded') in the brain does not fit with past and contemporary cognitive theories of behavioral processes.As reviewed in [16Price C.J. Friston K.J. Functional ontologies for cognition: the systematic definition of structure and function.Cogn. Neuropsychol. 2005; 22: 262-275Crossref PubMed Scopus (184) Google Scholar], cognitive scientists have traditionally formalized the components of behavioral function using behavioral studies of normal and neurologically impaired individuals. As further reviewed in [77Poldrack R.A. Mapping mental function to brain structure: how can cognitive neuroimaging succeed?.Perspect. Psychol. Sci. 2010; 5: 753-761Crossref PubMed Scopus (126) Google Scholar, 93Poldrack R.A. Yarkoni T. From brain maps to cognitive ontologies: informatics and the search for mental structure.Annu. Rev. Psychol. 2016; 67: 587-612Crossref PubMed Scopus (179) Google Scholar], the results of activation studies have challenged the classic models, as they have evidenced overlap between neural systems activated by tasks that share no known cognitive components. In agreement with a previous suggestion of systematically assigning labels that encompass the operations that each area performs [8Lichtheim L. On aphasia.Brain. 1885; 7: 433-484Crossref Google Scholar], we propose to build a systematic ontology with a bottom-up perspective (starting from the biological substrate: the brain).One challenge in the future will indeed be to integrate concepts and data from disparate brain science disciplines within a unified framework of brain biology [94Changeux J.-P. Climbing brain levels of organisation from genes to consciousness.Trends Cogn. Sci. 2017; 21: 168-181Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar]. Following up that perspective, building neurocognitive models (i.e., models combining cognitive and anatomical models) of normal functioning and pathology relies on the organization of the cognitive components into a single framework [16Price C.J. Friston K.J. Functional ontologies for cognition: the systematic definition of structure and function.Cogn. Neuropsychol. 2005; 22: 262-275Crossref PubMed Scopus (184) Google Scholar]. Such a theoretical position clearly implies that biological evidence should drive the revision of cognitive theories, provided that the new conceptualization derived from brain–behavior data has been robustly tested.In addition to these theoretical considerations, one should also consider the clinical utility as a 'quality marker' for psychological ontologies. That is, a behavioral/cognitive taxonomy that is in agreement with brain organization should, in principle, also help researchers to understand, diagnose, or classify neurocognitive pathologies. Thus, in the future, assigning operation-function to brain regions should go hand in hand with the evolution of formal cognitive taxonomies, which in turn should benefit the understanding of neurocognitive pathology. One important issue in the study of brain–behavior relationships is whether currently available ontologies and taxonomies can be mapped onto the brain. That is, if we could capture the exact topographical organization of the brain, it seems unlikely that we would find concepts at the current level of description of behavior that could be mapped to the identified biological units. It seems likely that the behavioral structure as it is implemented (i.e., 'coded') in the brain does not fit with past and contemporary cognitive theories of behavioral processes. As reviewed in [16Price C.J. Friston K.J. Functional ontologies for cognition: the systematic definition of structure and function.Cogn. Neuropsychol. 2005; 22: 262-275Crossref PubMed Scopus (184) Google Scholar], cognitive scientists have traditionally formalized the components of behavioral function using behavioral studies of normal and neurologically impaired individuals. As further reviewed in [77Poldrack R.A. Mapping mental function to brain structure: how can cognitive neuroimaging succeed?.Perspect. Psychol. Sci. 2010; 5: 753-761Crossref PubMed Scopus (126) Google Scholar, 93Poldrack R.A. Yarkoni T. From brain maps to cognitive ontologies: informatics and the search for mental structure.Annu. Rev. Psychol. 2016; 67: 587-612Crossref PubMed Scopus (179) Google Scholar], the results of activation studies have challenged the classic models, as they have evidenced overlap between neural systems activated by tasks that share no known cognitive components. In agreement with a previous suggestion of systematically assigning labels that encompass the operations that each area performs [8Lichtheim L. On aphasia.Brain. 1885; 7: 433-484Crossref Google Scholar], we propose to build a systematic ontology with a bottom-up perspective (starting from the biological substrate: the brain). One challenge in the future will indeed be to integrate concepts and data from disparate brain science disciplines within a unified framework of brain biology [94Changeux J.-P. Climbing brain levels of organisation from genes to consciousness.Trends Cogn. Sci. 2017; 21: 168-181Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar]. Following up that perspective, building neurocognitive models (i.e., models combining cognitive and anatomical models) of normal functioning and pathology relies on the organization of the cognitive components into a single framework [16Price C.J. Friston K.J. Functional ontologies for cognition: the systematic definition of structure and function.Cogn. Neuropsychol. 2005; 22: 262-275Crossref PubMed Scopus (184) Google Scholar]. Such a theoretical position clearly implies that biological evidence should drive the revision of cognitive theories, provided that the new conceptualization derived from brain–behavior data has been robustly tested. In addition to these theoretical considerations, one should also consider the clinical utility as a 'quality marker' for psychological ontologies. That is, a behavioral/cognitive taxonomy that is in agreement with brain organization should, in principle, also help researchers to understand, diagnose, or classify neurocognitive pathologies. Thus, in the future, assigning operation-function to brain regions should go hand in hand with the evolution of formal cognitive taxonomies, which in turn should benefit the understanding of neurocognitive pathology. One 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 [47Scoville W.B. Milner B. Loss of recent memory after bilateral hippocampal lesions.J. Neurol. Neurosurg. Psychiatry. 1957; 20: 11-21Crossref PubMed Scopus (4869) Google Scholar]. 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 [48Stangor C. Research Methods for the Behavioral Sciences. Nelson Education, 2014Google Scholar]. Furthermore, at the brain level, several issues arising from the spatially structured distribution of lesions [49Mah Y.H. et al.Human brain lesion-deficit inference remapped.Brain. 2014; 137: 2522-2531Crossref PubMed Scopus (221) Google Scholar] 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 (du