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The default mode network (DMN) is a set of widely distributed brain regions in the parietal, temporal and frontal cortex. These regions often show reductions in activity during attention-demanding tasks but increase their activity across multiple forms of complex cognition, many of which are linked to memory or abstract thought. Within the cortex, the DMN has been shown to be located in regions furthest away from those contributing to sensory and motor systems. Here, we consider how our knowledge of the topographic characteristics of the DMN can be leveraged to better understand how this network contributes to cognition and behaviour.Regions of the default mode network (DMN) are distributed across the brain and show patterns of activity that have linked them to various different functional domains. In this Perspective, Smallwood and colleagues consider how an examination of the topographic characteristics of the DMN can shed light on its contribution to cognition.

The categorisation of long-term memory into semantic and episodic systems has been an influential catalyst for research on human memory organisation. However, the impact of variable cognitive control demands on this classical distinction remains to be elucidated. Across two independent experiments, here we directly compare neural processes for the controlled versus automatic retrieval of semantic and episodic memory. In a multi-session functional magnetic resonance imaging experiment, we first identify a common cluster of cortical activity centred on the left inferior frontal gyrus and anterior insular cortex for the retrieval of both weakly-associated semantic and weakly-encoded episodic memory traces. In an independent large-scale individual difference study, we further reveal a common neural circuitry in which reduced functional interaction between the identified cluster and ventromedial prefrontal cortex, a default mode network hub, is linked to better performance across both memory types. Our results provide evidence for shared neural processes supporting the controlled retrieval of information from functionally distinct long-term memory systems. Making sense of the world around us often requires flexible access to information from both semantic and episodic memory systems. Here, the authors show that controlled retrieval from functionally distinct long-term memory stores is supported by shared neural processes in the human brain.

Mind wandering seems to be a prototypical feature of attention-deficit/hyperactivity disorder (ADHD). However, an important emerging distinction of mind-wandering types hinges on whether a given episode of mind wandering reflects a failure of executive control (spontaneous mind wandering) or the engagement of controlled processes for internal processing (deliberate mind wandering). Here we distinguish between spontaneous and deliberate mind wandering and test the hypothesis that symptoms of ADHD are associated with the former but not the latter. We assessed ADHD symptomatology and everyday levels of deliberate and spontaneous mind wandering in two large non-clinical samples ( Ns = 1,354). In addition, to provide converging evidence, we examined rates of deliberate and spontaneous mind wandering in a clinically diagnosed ADHD sample. Results provide clear evidence that spontaneous, but not deliberate, mind wandering is a central feature of ADHD symptomatology at both the clinical and non-clinical level. We discuss the implications of these results for understanding both ADHD and mind wandering.

Although many different accounts of the functions of the default mode network (DMN) have been proposed, few can adequately account for the spectrum of different cognitive functions that utilize this network. The current study used functional magnetic resonance imaging (fMRI) to explore the hypothesis that the role of the DMN in higher order cognition is to allow cognition to be shaped by information from stored representations rather than information in the immediate environment. Using a novel task paradigm, we observed increased BOLD activity in regions of the medial prefrontal cortex and posterior cingulate cortex when individuals made decisions on the location of shapes from the prior trial and decreased BOLD activity when individuals made decisions on the location of shapes on the current trial. These data are inconsistent with views of the DMN as a task-negative system or one that is sensitive only to stimuli with strong personal or emotional ties. Instead the involvement of the DMN when people make decisions about where a shape was, rather than where it is now, supports the hypothesis that the core hubs of the DMN allow cognition to be guided by information other than the immediate perceptual input. We propose that a variety of different forms of higher order thought (such as imagining the future or considering the perspective of another person) engage the DMN because these more complex introspective forms of higher order thought all depend on the capacity for cognition to be shaped by representations that are not present in the external environment.

Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface—and are precisely equidistant—from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.

The analysis of brain function in resting-state network (RSN) models, ascertained through the functional connectivity pattern of resting-state functional magnetic resonance imaging (rs-fMRI), is sufficiently powerful for studying large-scale functional integration of the brain. However, in RSN-based research, the network architecture has been regarded as the same through different frequency bands. Thus, here, we aimed to examined whether the network architecture changes with frequency. The blood oxygen level-dependent (BOLD) signal was decomposed into four frequency bands—ranging from 0.007 to 0.438 Hz—and the clustering algorithm was applied to each of them. The best clustering number was selected for each frequency band based on the overlap ratio with task activation maps. The results demonstrated that resting-state BOLD signals exhibited frequency-specific network architecture; that is, the networks finely subdivided in the lower frequency bands were integrated into fewer networks in higher frequency bands rather than reconfigured, and the default mode network and networks related to perception had sufficiently strong architecture to survive in an environment with a lower signal-to-noise ratio. These findings provide a novel framework to enable improved understanding of brain function through the multiband frequency analysis of ultra-slow rs-fMRI data.

The 21st century marks the emergence of “big data” with a rapid increase in the availability of datasets with multiple measurements. In neuroscience, brain-imaging datasets are more commonly accompanied by dozens or hundreds of phenotypic subject descriptors on the behavioral, neural, and genomic level. The complexity of such “big data” repositories offer new opportunities and pose new challenges for systems neuroscience. Canonical correlation analysis (CCA) is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities. Importantly, CCA is well suited to describing relationships across multiple sets of data, such as in recently available big biomedical datasets. Our primer discusses the rationale, promises, and pitfalls of CCA. •Introduction to the feature of canonical correlation analysis and its applications in combining two or more domains of data, such as behavioural and neuroimaging measures.•The utility of different variations the pros/cons of CCA.•Tips on application of CCA on rich phenotype datasets such as UK Biobank and HCP.

Lysergic acid diethylamide (LSD) and psilocybin are serotonergic psychedelic compounds with potential in the treatment of mental health disorders. Past neuroimaging investigations have revealed that both compounds can elicit significant changes to whole-brain functional organization and dynamics. A recent proposal linked past findings into a unified model and hypothesized reduced whole-brain hierarchical organization as a key mechanism underlying the psychedelic state, but this has yet to be directly tested. We applied a non-linear dimensionality reduction technique previously used to map hierarchical connectivity gradients to assess cortical organization in the LSD and psilocybin state from two previously published pharmacological resting-state fMRI datasets (N = 15 and 9, respectively). Results supported our primary hypothesis: The principal gradient of cortical connectivity, describing a hierarchy from unimodal to transmodal cortex, was significantly flattened under both drugs relative to their respective placebo conditions. Between-condition contrasts revealed that this was driven by a reduction of functional differentiation at both hierarchical extremes – default and frontoparietal networks at the upper end, and somatomotor at the lower. Gradient-based connectivity mapping indicated that this was underpinned by a disruption of modular unimodal connectivity and increased unimodal-transmodal crosstalk. Results involving the second and third gradient, which, respectively represent axes of sensory and executive differentiation, also showed significant alterations across both drugs. These findings provide support for a recent mechanistic model of the psychedelic state relevant to therapeutic applications of psychedelics. More fundamentally, we provide the first evidence that macroscale connectivity gradients are sensitive to an acute pharmacological manipulation, supporting a role for psychedelics as scientific tools to perturb cortical functional organization.

Human cognition flexibly guides decision-making in familiar and novel situations. Although these decisions are often treated as dichotomous, in reality, situations are neither completely familiar, nor entirely new. Contemporary accounts of brain organization suggest that neural function is organized along a connectivity gradient from unimodal regions of sensorimotor cortex, through executive regions to transmodal default mode network. We examined whether this graded view of neural organization helps to explain how decision-making changes across situations that vary in their alignment with long-term knowledge. We used a semantic judgment task, which parametrically varied the global semantic similarity of items within a feature matching task to create a ‘task gradient’, from conceptual combinations that were highly overlapping in long-term memory to trials that only shared the goal-relevant feature. We found the brain’s response to the task gradient varied systematically along the connectivity gradient, with the strongest response in default mode network when the probe and target items were highly overlapping conceptually. This graded functional change was seen in multiple brain regions and within individual brains, and was not readily explained by task difficulty. Moreover, the gradient captured the spatial layout of networks involved in semantic processing, providing an organizational principle for controlled semantic cognition across the cortex. In this way, the cortex is organized to support semantic decision-making in both highly familiar and less familiar situations. •Neural response to semantic similarity varied along principal gradient connectivity.•Default network showed strongest response when input overlapped with long-term memory.•This graded functional change was seen in multiple brain regions.•This graded functional change was not readily explained by task difficulty.•The gradient captured the spatial layout of networks involved in semantic processing.