Spacetime concordance in the primate cortex

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Spacetime concordance (STC) meets this need through an adaptive and robust framework optimized for high-speed analysis. At the core of STC, the Regional Functional Affinity (RFA) metric quantifies functional diversity and uniformity in primate connectomes using wakeful fMRI. This data-driven optimization achieves considerably faster processing, becoming broadly relevant to comparative neuroscience applications. We validated this approach using large-scale datasets from the Human Connectome Project (HCP) (N = 1,003) and the NIH Marmoset Brain Mapping Project (N = 26). Results demonstrate striking correspondence between parcellation boundaries of HCP atlas and functional heterogeneity, with boundary lines consistently aligning with regions of low RFA values. In humans, higher-order association networks exhibited lower RFA values indicating functional diversity, while primary sensorimotor networks displayed higher RFA values reflecting uniformity. Cross-species analysis revealed evolutionary conservation of this organizational principle alongside species-specific adaptations. The RFA metric successfully bridges discrete parcellation schemes and continuous models of brain function, offering new insights into primate brain organization and evolution. Introduction Functional parcellation of the human brain refers to the practice of dividing the brain into distinct regions based on specific functional properties, often observed using fMRI or other neuroimaging techniques [ 1 ]. Although these functional divisions are widely used and have proven useful in understanding brain organization, there are debates about whether they are ‘real’ in the sense of being absolute or biologically fixed [ 2 – 4 ]. In the current body of research [ 5 ], functional parcellations can be seen as representative of brain organization rather than rigid universal boundaries. First, brain regions are not necessarily partitioned exactly in the same way in every individual. Functional characteristics can vary according to genetic [ 6 , 7 ], developmental and experiential factors (e.g. age, education, or disease) [ 8 , 9 ], suggesting that functional parcellation is not a hard and fast rule, but a useful approximation. Second, the functional organization of the brain can change depending on context, task demands, and cognitive state [ 10 ]. Resting-state networks can look different from those engaged during a specific task, and even task-based parcellations can change depending on the cognitive function being assessed, implying more flexible than rigidly defined zones. Third, the precision of parcellation methods, whether based on task-based, resting-state, or naturalistic fMRI, can vary. Different analytical strategies can lead to different definitions of network regions [ 11 , 12 ]. There is no universally agreed-upon methodology, and this contributes to uncertainty about the true nature of functional boundaries. These considerations underscore the need for more flexible, continuous representations of brain networks that move beyond rigid boundaries and better capture complex dynamics and functional organization. Here, we propose a nonlinear, noise-adaptive, vertex-wise metric of spacetime dynamics, Regional Functional Affinity (RFA), to quantify the degree of regional boundaries by characterizing the functional diversity or uniformity in the primate connectomes using wakeful fMRI. This functional brain mapping method in primates was validated with fMRI measurements in resting states (rfMRI) of humans and marmosets, obtained from large-scale open datasets: the Human Connectome Project (HCP) [ 13 ] and the National Institute of Health (NIH) datasets [ 14 ], respectively. Results The RFA is defined as Kendall’s coefficient of concordance (KCC) [ 15 ] between the functional connectivity profiles of the entire brain (FC) of a node or vertex and its neighbors ( Figure 1 ). Lower RFA values reflect functional diversity within a region, whereas higher values indicate functional uniformity. Download figure Open in new tab Fig. 1. Schematic diagram of the regional functional affinity calculation. The spatial distribution of the RFA values across the human cortex is presented in Figure 2 , overlaid with the refined parcellation boundaries of the HCP 180-region atlas [ 16 ]. We observed a striking correspondence between parcellation boundaries and functional heterogeneity as measured by RFA, with boundary lines consistently aligned with regions exhibiting lower RFA values. Consistent with the findings of Gordon et al. [ 17 ], our RFA-based analysis successfully delineates the motor cortex into functionally distinct subregions, with boundary lines effectively separating motor effector and inter-effector regions. However, the visual cortex presents a notable exception, where the RFA metric shows less distinct patterns and the parcellation boundaries do not align as clearly with functional transitions. Download figure Open in new tab Fig. 2. Spacetime concordance in the human cortex. To further characterize the functional spacetime concordance in the human cortex, we extracted mean RFA values from all vertices within each of the 200 parcellation units [ 18 ] assigned to 15 canonical cortical networks defined in DU15NET [ 19 ] for each hemisphere. These values were plotted along a spiral trajectory in ascending order, as shown in the upper panel of Figure 3 . The analysis revealed that regions with lower RFA values (positioned inside the spiral) were predominantly located in higher-order association networks, including the salience and parietal memory network (SAL/PMN), default-mode network (DMN-B), frontoparietal network (FPN-A), and language network (LANG). This pattern indicates a high functional diversity within these networks, consistent with their roles in complex cognitive processes. In contrast, primary sensorimotor and sensory networks, including somatomotor networks (SMOT-A and SMOT-B) and visual peripheral networks (VIS-P), displayed higher RFA values (positioned outside the spiral), reflecting the functional uniformity characteristic of these fundamental processing areas. A particularly important observation emerged within the motor cortex, where regions sharing the same network assignment (indicated by identical color coding) displayed a wide range of RFA values. Some regions exhibited low RFA values that indicate high functional diversity, while others demonstrated high RFA values that reflect functional uniformity. Download figure Open in new tab Fig. 3. Spatial concordance in the primate cortex: An organization of local function from uniformity to diversity across large-scale networks and parcellations. The functional spacetime concordance analysis was extended to the marmoset cortex using a similar approach, extracting mean RFA values from all vertices within each of the 96 parcellation units assigned to fifteen canonical cortical networks defined by Tian et al. (2022) [ 20 ]. As shown in the lower panel of Figure 3 , in marmosets, regions with lower RFA values (inside the spiral) were located primarily in the parahippocampus and temporal pole network (phTP), frontal pole network (FPO) and orbital frontal network (orbFT). In particular, high-level visual network 3 (High-VN3), which corresponds to areas related to language in humans, also showed relatively low RFA values. Higher RFA values were predominantly found in networks associated with primary cortical areas, particularly lateral and medial visual networks (VIS-l and VIS-m). Interestingly, the frontoparietal-like network (FPN) in marmosets also showed relatively high RFA values, suggesting a more uniform functional organization compared to the human FPN. Discussion Functional parcellations represent pragmatic representations of how the brain operates at the system level, providing valuable information for understanding brain function [ 1 ]. Although they are real in the sense that they capture meaningful organizational principles, they are not absolute fixed regions. Brain organization is likely more fluid, with regions and networks dynamically interacting, and parcellations reflecting approximations rather than strict anatomical or functional boundaries [ 21 ]. However, current brain parcellation methods typically regard brain parcellation as rigid boundaries and often face common critiques, including inconsistency between studies, oversimplification, task dependence, and neglect of individual differences [ 12 , 22 – 24 ]. In this study, we propose RFA, a metric for assessing the regional concordance of FC profiles that captures spatiotemporal brain concordance, to provide a more precise and dynamic characterization of functional parcellations. The striking correspondence between the parcellation boundaries of the HCP atlas and regions of low RFA values indicates that functional boundaries naturally emerge at locations where regional functional uniformity transitions to diversity. This pattern validates RFA as an effective metric for characterizing functional organization and accurately mapping complex functional architecture. The visual cortex presents a notable exception, where RFA shows less distinct patterns and does not align well with HCP atlas boundaries. This inconsistency might be due to the multimodal nature of the HCP parcellation scheme, which incorporates not only FC but also structural features such as cortical thickness and myelination patterns [ 16 ]. This suggests that the HCP atlas is not a pure functional brain parcellation, but rather a composite of various brain modalities. Multiple neuroanatomical measures (FC, cortical thickness, myelination) may provide redundant information, limiting the unique contribution of functional measures. Comparative analysis between human and marmoset cortex reveals both evolutionary conservation and species-specific adaptations in functional organization. The general principle that primary sensory areas exhibit greater functional uniformity while association areas display greater diversity appears to be conserved between primate species, suggesting fundamental organizational constraints in mammalian brain evolution [ 25 ]. This conservation likely reflects selective pressure for efficient sensory processing that is critical for survival across species. However, important species differences emerged that reflect distinct evolutionary pressures and cognitive specializations. The relatively high RFA values in marmoset FPN contrast with the functional diversity observed in human FPN, potentially reflecting the expanded executive control capabilities that characterize human cognition [ 26 ]. This difference may reflect the evolutionary expansion and increased complexity of executive control networks in humans. In contrast, the greater functional diversity in the marmoset motor regions may reflect the increased demands for motor control of the marmoset locomotion and arboreal lifestyle [ 27 ]. The specialized motor requirements for navigating complex three-dimensional environments necessitate more functionally diverse motor control networks compared to human bipedal locomotion. Most interestingly, High-VN3, the regions corresponding to areas of human language [ 28 ], showed distinct functional characteristics in marmosets. These regions, responsible for face recognition, vocalization processing, and social cognition in marmosets [ 29 , 30 ], exhibited low RFA values indicating functional diversity. This finding provides valuable information on the evolutionary trajectory of language-related brain regions and their precursor functions in nonhuman primates. Although assigned to a single functional network, it is heterogeneity within the motor cortex. The wide range of RFA values within the motor cortex reflects the varied computational demands of different motor functions, from simple reflexive movements to complex skilled behaviors requiring extensive cortical coordination [ 31 , 32 ]. This variability challenges the traditional view of the motor cortex as a homogeneous functional unit and supports more nuanced models of motor control that incorporate multiple specialized subregions. This finding suggests that current parcellation schemes may oversimplify the functional organization of motor regions and supports the need for more continuous, spacetime concordance representations of brain function rather than discrete boundaries. The successful delineation of motor effector and intereffector regions [ 17 ] using RFA demonstrates the utility of RFA to identify functionally meaningful subdivisions within traditionally defined networks. In summary, the RFA metric provides a novel lens for understanding brain functional organization that bridges the gap between discrete parcellation schemes and continuous models of brain function. By revealing the spacetime concordance of functional coherence across the primate cortex, RFA offers new insights into the evolution of brain networks. Our findings support a more nuanced view of brain organization that emphasizes functional transitions rather than rigid boundaries, with important implications for both basic neuroscience and clinical applications. The successful cross-species validation of RFA in humans and marmosets establishes its utility for translational research and provides a foundation for future comparative studies of primate brain evolution and function. This framework represents a significant step toward reconciling the apparent contradiction between discrete parcellation schemes and the continuous nature of brain organization, offering new tools to understand brain function in health and disease. Methods Wakeful rfMRI data were obtained in human and marmoset from two sources respectively: the HCP (N = 1,003) [ 13 ] and the NIH Marmoset Brain Mapping Project (N = 26) [ 14 , 20 ]. To effectively reduce the influence of random noise on rfMRI and improve the signal-to-noise ratio, we employed MELODIC’s Incremental Group-PCA (MIGP) method [ 33 ] to concatenate the preprocessed time series of each vertex between all participants for subsequent group-level analyzes. Specifically, for each vertex, we first computed its whole brain FC profile by calculating the Pearson correlation coefficients between its time series and all other time series in the brain. The resulting correlation values were then transformed using Fisher’s r-to-z transformation to ensure a normal distribution for subsequent statistical analysis. After that, KCC was applied to assess the consistency of whole-brain FC (that is, spacetime) profiles between each target vertex and its neighboring vertices (6 neighbors for neighbor size of 1 and 18 neighbors for neighbor size of 2). The consistency value obtained through this computation represents the RFA of each vertex ( Figure 1 ). Data availability The wakeful rfMRI human data are available from the database of the Human Connectome Project (HCP) ( https://db.humanconnectome.org ). which is supported by the NIH Blueprint for Neuroscience Research 1U54MH091657 (principal investigators: David Van Essen and Kamil Ugurbil) and the McDonnell Center for Systems Neuroscience at Washington University. The wakeful rfMRI marmoset data are available from the Chinese Color Nest Data Community (CCNDC: https://ccndc.scidb.cn/en ) at Science Data Bank ( https://doi.org/10.57760/sciencedb.07943 or https://cstr.cn/31253.11.sciencedb.07943 ). Code availability All codes developed for RFA are available as part of the Connectome Computation System (CCS: https://github.com/zuoxinian/CCS ) via GitHub. Acknowledgments This work has been supported by the scientific and technological innovation 2030 - the major project of the Brain Science and Brain-Inspired Intelligence Technology (2021ZD0200500) and the Interdisciplinary Brain Database for In-vivo Population Imaging (ID-BRAIN) at the National Basic Science Data Center. Footnotes https://ccndc.scidb.cn/en References [1]. ↵ Eickhoff , S. B. , Yeo , B. T. T. & Genon , S. Imaging-based parcellations of the human brain . Nat. Rev. Neurosci . 11 , 672 – 686 ( 2018 ). OpenUrl [2]. ↵ Moghimi , P. et al. 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