Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1–3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain–behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available—with a total sample size of around 50,000 individuals—to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain–phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.

Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations1,2, despite evidence for concentric functional zones3 and maps of complex actions4. Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action5 and physiological control6, arousal7, errors8 and pain9. This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions4 and connectivity to internal organs10 such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate–isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement.

There is a growing consensus that social cognition and behavior emerge from interactions across distributed regions of the "social brain". Researchers have traditionally focused their attention on functional response properties of these gray matter networks and neglected the vital role of white matter connections in establishing such networks and their functions. In this article, we conduct a comprehensive review of prior research on structural connectivity in social neuroscience and highlight the importance of this literature in clarifying brain mechanisms of social cognition. We pay particular attention to three key social processes: face processing, embodied cognition, and theory of mind, and their respective underlying neural networks. To fully identify and characterize the anatomical architecture of these networks, we further implement probabilistic tractography on a large sample of diffusion-weighted imaging data. The combination of an in-depth literature review and the empirical investigation gives us an unprecedented, well-defined landscape of white matter pathways underlying major social brain networks. Finally, we discuss current problems in the field, outline suggestions for best practice in diffusion-imaging data collection and analysis, and offer new directions for future research.

Social behavior is often shaped by the rich storehouse of biographical information that we hold for other people. In our daily life, we rapidly and flexibly retrieve a host of biographical details about individuals in our social network, which often guide our decisions as we navigate complex social interactions. Even abstract traits associated with an individual, such as their political affiliation, can cue a rich cascade of person-specific knowledge. Here, we asked whether the anterior temporal lobe (ATL) serves as a hub for a distributed neural circuit that represents person knowledge. Fifty participants across two studies learned biographical information about fictitious people in a 2-d training paradigm. On day 3, they retrieved this biographical information while undergoing an fMRI scan. A series of multivariate and connectivity analyses suggest that the ATL stores abstract person identity representations. Moreover, this region coordinates interactions with a distributed network to support the flexible retrieval of person attributes. Together, our results suggest that the ATL is a central hub for representing and retrieving person knowledge.person knowledge | anterior temporal lobe | person identity node | semantic memory | social neuroscience A s social creatures, it is essential that we develop a rich storehouse of knowledge about other members of our social network, such as who they are, how they look and sound, where they live, and what they do for a living. However, little is known about how and where such "person knowledge" is represented, stored, and retrieved in the brain. This inquiry is challenging because person knowledge is highly multimodal and multifaceted, being linked to both abstract features such as personality and social status as well as more concrete features such as eye color; in addition, familiar individuals are associated with detailed episodic and semantic memories (e.g., memories of shared experiences and biographic information) (1, 2). The neural circuit for person knowledge must therefore have the ability to combine multiple sources of information into an abstract representation accessible from multiplicative cues.An influential theory by Burton and Bruce (3) proposes that person recognition is achieved through a hierarchical process that begins with the activation of modality-specific recognition units that selectively respond to the presence of a known face, name, or voice. This information is then sent to an amodal person identity node (PIN) that integrates information from the modality-specific recognition units into a multimodal representation for that individual. Excitation of the PIN ultimately allows the retrieval of personspecific semantic information independently of stimulus modality (4, 5). A similar design is embedded in the "hub-and-spoke" theory of semantic knowledge, which proposes that different features of a concept (such as its color or taste) are distributed throughout the brain (the "spokes") and that a centralized "hub" integrates these features into a cohe...

Face processing supports our ability to recognize friend from foe, form tribes, and understand the emotional implications of changes in facial musculature. This skill relies on a distributed network of brain regions but how these regions interact is poorly understood. Here, we integrate anatomical and functional connectivity measurements with behavioral assays to create a global model of the face connectome. We dissect key features such as the network topology and fiber composition. We propose a neurocognitive model with three core streams, and face processing along these streams occurs in a parallel and reciprocal fashion. While long-range fiber paths are important, face network is dominated by short-range fibers. Last, we provide some evidence that the well-known right lateralization of face processing arises from imbalanced intra/interhemispheric connections. In sum, the face network relies on dynamic communication across highly structured fiber tracts, which enables coherent face processing that underpins behavior and cognition.