What do anatomical properties of the brain reveal about the ontology of human cognitive abilities? Daniel Kristanto1, Xinyang Liu2,3, Werner Sommer4, Andrea Hildebrandt2,5*#, Changsong Zhou1,6*# 1: Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong 2: Department of Psychology, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany 3: Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, 200062 Shanghai, China 4: Department of Psychology, Humboldt University at Berlin, Berlin, Germany, Department of Psychology, Zhejiang Normal University, Jin Hua, China 5: Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany 6: Department of Physics, Zhejiang University, 310000 Hangzhou, China *Corresponding authors. E-mail addresses: [email protected] (A. Hildebrandt), [email protected] (C. Zhou). # Senior authors Abstract Over the past decades, the neuropsychological science community has endeavored to determine the number and nature of distinguishable human cognitive abilities. Based on covariance structure analyses of inter-individual performance differences in multiple cognitive tasks, the ability structure has been substantiated with sufficient consensus. However, there remains a crucial open question that must be answered to develop unified theoretical views and translations toward neuropsychological applications: Is the cognitive ability structure ascertained at the behavioral level similarly reflected in the anatomical and functional properties of the brain? In the current study, we explored the cognitive ability structure derived from positive and negative networks 1 reflected by the brain’s anatomical properties (thickness, myelination, curvature, and sulcus depth) that were found to be associated with performance in 15 cognitive tasks. The derived neurometric ontological structure was contrasted with the entities of psychometric ontology. Overall, we observed that the brain-derived ontological structures are partly consistent with each other, but also show interesting differences that complement the psychometric ontology. Moreover, we discovered that brain areas associated with the inferred abilities are segregated, with little or no overlap between the abilities. Nevertheless, they are also integrated as they are densely connected by white matter projections with an average connection density higher than the brain connectome. The consistency and differences between psychometric and neurometric ontologies are crucial for theory building, diagnostics, and neuropsychological therapy, which highlights the need for their simultaneous and complementary consideration. Keywords: Cognitive ontology, individual differences, neuroanatomical properties of the brain, brain networks, segregation and integration Introduction Broadly accepted cognitive ontologies are crucial for unified neuro-psychological theories, cognitive diagnostics and mental health therapy. Over the past decades, research on human cognition and intelligence, aimed at describing, measuring, and classifying abilities and unveiling their ontological structure, has mainly been based on inter-individual covariations of performance across multiple tasks. Since the synthesis model known as Cattell–Horn–Carroll (CHC) theory of cognitive abilities was compiled and disseminated (1), there has been a widespread consensus on the psychometric ontology of intelligence, notwithstanding some caveats (2). According to the CHC model, stratum I comprises individual differences in a large number of cognitive tasks that require maximal performance in terms of speed or/and accuracy. Stratum II incorporates broad latent (not directly observable but task-derived) abilities, e.g., fluid reasoning (Gf), comprehension knowledge (Gc, also called crystallized intelligence), short-term memory (Gsm), long-term storage and retrieval (Glr), and cognitive processing speed (Gs). These broad, domain-specific abilities are all positively associated with each other. The CHC model accounts for this positive manifold by assuming a general factor of intelligence (g) at stratum III. A somewhat separate literature on ontological entities of human intelligence concerns executive functions (EF). Most suggestions 2 about the ontological entities of EF derived from psychometric tasks encompass working memory, shifting, and inhibition (e.g., (3)). Cognitive neuroscience is a much younger discipline than the psychometrics of intelligence and investigates among others the anatomical properties and neural mechanisms underlying the ontological entities of human cognition. Early studies focused on stratum I, aiming at understanding the neuroanatomical basis and neurofunctional mechanisms underpinning the mastery of specific cognitive tasks. Following this research direction, several studies adopted connectome-based predictive modeling (4, 5), a method to map brain properties and performance scores on specific cognitive tasks (6–9). As a further step toward generalization, latent cognitive abilities in stratum II of the CHC model were mapped to brain structure and activity (10–17). Such studies are highly valuable for generating neuroanatomical and neurofunctional explanations of the psychometric ontological entities of cognition beyond specific tasks. However, the quest for brain-derived ontological entities of cognition has recently gained prominence, such that the psychometric entities are not be taken as granted but rather explored in a bottom-up fashion, i.e., from the brain to psychological constructs (18). The cognitive neuroscience community has thus far not sufficiently appreciated the psychometric desiderata—expressed already by Cronbach and Meehl (1955)—of not conflating psychological constructs with their operational measures (19). That is, a task is only a task and ontological entities of cognition must be determined by analysis beyond single tasks. Consequently, modern cognitive neuroscience approaches to derive ontological entities of cognition must use multiple tasks to explore distinct indicators of anatomical and functional properties of the brain to support the existence of stratum II ontological entities of cognition (the top-down approach), and also to mine the brain to derive ontological entities that do not take psychometric entities for granted (the bottom-up approach, see (20)). The availability of large-scale, multivariate cognitive neuroscience databases enables such an alternative bottom-up approach, which involves the exploration of potential ontological entities of human cognition on the basis of brain properties (21). For example, Bolt and colleagues investigated which cognitive entities are revealed by brain activation patterns observed when performing a series of cognitive tasks (22). However, they did not systematically discuss the consistency and disparity between their brain-derived ontology and those established in 3 psychometrics. We argue that such direct comparisons are necessary to better substantiate the ontology of human cognition, which should accommodate both the covariance structures of task performance and the brain networks producing these abilities (20, 23). The present study contributes to the establishment of a formal ontology for psychology, cognitive neuroscience, and their applied disciplines by adopting a bottom-up approach (see (20)). We explored the cognitive ontology derived from the brain’s anatomical properties, including thickness, myelination, curvature, and sulcus depth, which are associated with performance in 15 psychometric tasks. Moreover, we went beyond existing approaches by comparing the derived cognitive ontologies with the psychometric ontology revealed by the same tasks. Specifically, we investigated the extent to which brain-derived ontologies as reflected by shared brain areas across the 15 cognitive tasks (stratum I) are consistent with or distinct to the established psychometric ontologies at stratum II of the CHC model. Next, as suggested in the literature (20), we expected that the ability-related brain areas would be segregated, to support differentiation between the inferred cognitive ontologies. Furthermore, we investigated how the ability-related brain areas are coupled via the underlying network connectivity to form the positive manifold underlying the general intelligence that is captured at stratum III of the CHC model. Results Our overall methodological framework is illustrated in Fig. 1. We first identified task- related brain areas based on linear associations between brain properties (thickness, myelination, curvature, and sulcus depth) and task performances collected by the Washington University and University of Minnesota Consortium’s (WU-Minn) Human Connectome Project (HCP; see Materials, Table 1, and SI Appendix Table S1 for the task descriptions and further details). Next, we quantified the overlap between task-related brain areas with these different anatomical properties, by using an Intersect Over Union (IOU) index to create IOU matrices. These matrices were then subjected to exploratory factor analysis (EFA) to obtain a brain-derived cognitive ontology. This ontology was descriptively compared with the psychometric ontology derived from covariances across the same