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Large-Scale Brain Networks in Cognition: Emerging Principles Vinod Menon, Phd

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Large-Scale Brain Networks in Cognition: Emerging Principles Vinod Menon, Phd Large-Scale Brain Networks in Cognition: Emerging Principles Vinod Menon, PhD Department of Psychiatry and Behavioral Sciences Department of Neurology and Neurological Sciences Program in Neuroscience Stanford University Medical School Stanford, California © 2010 Menon Large-Scale Brain Networks in Cognition: Emerging Principles 45 A Network Perspective 5. A working memory–executive function network NOTES on Cognition anchored in prefrontal and inferior parietal cortices. Functional brain imaging has focused primarily The nodes of these core networks have been inferred on localization of function, revealing activation from the results of fMRI studies, during tasks that in specific brain regions during the performance of manipulate one or more of these cognitive functions. particular cognitive tasks. It is becoming increasingly A full characterization of core functional brain apparent that cognitive neuroscience needs to go networks, however, will require additional studies beyond this mapping of complex cognitive and to validate the nodes of these networks using other psychological constructs onto individual brain areas criteria, to measure their edges, and to determine (Fuster, 2006). As a result, a network paradigm is whether other core networks exist. becoming increasingly useful for understanding the neural underpinnings of cognition (Bressler and In recent years, diffusion tensor imaging (DTI) Menon, 2010). Furthermore, a consensus is emerging and resting state fMRI have emerged as novel tools that the key to understanding the functions of any for characterizing structural and functional brain specific brain region lies in understanding how its networks. They are able to do so independently of connectivity differs from the pattern of connections cognitive domains, experimental manipulations, and in other functionally related brain areas (Passingham behavior. Recent work in systems neuroscience has et al., 2002). In recent years, neuroscientists’ characterized several major brain networks that are interests have shifted towards developing a deeper identifiable in both the resting brain (Damoiseaux et understanding of how intrinsic brain architecture al., 2006; Seeley et al., 2007b) and the active brain influences cognitive and affective information (Toro et al., 2008). Importantly, major functional processing (Greicius et al., 2003; Fox and Raichle, brain networks (and their composite subnetworks) 2007; Dosenbach et al., 2008). show close correspondence in independent analyses of resting and task-related connectivity patterns In the sections that follow, we briefly review (Smith et al., 2009), suggesting that functional emerging methods for characterizing and identifying networks coupled at rest are also systematically major neurocognitive networks in the human brain. engaged during cognition. The analysis of resting We then provide two specific examples of how such state functional connectivity, using both model- networks can provide fundamental new insights into based and model-free approaches, has proved to be the brain bases of fundamental cognitive processes. a useful technique for investigating functionally The first example focuses on the surprisingly crucial coupled networks in the human brain. Although the role of the insular cortex in salience, attention, and method relies on analysis of low-frequency signals cognitive control. The second example demonstrates in fMRI data, electrophysiological studies point to a how intrinsic functional and structural connectivity reliable neurophysiological basis for these signals (He of the parietal cortex can inform and constrain et al., 2008; Nir et al., 2008). information processing models across multiple cognitive domains. The analysis of resting state fMRI allows us to discover the organization and connectivity of several Identifying Major Cognitive major brain networks that cannot be easily captured Networks with the help of other techniques. Conceptualizing A formal characterization of core brain networks— the brain as comprising multiple distinct, interacting anatomically distinct, large-scale brain systems networks provides a systematic framework for having distinct cognitive functions—was first understanding fundamental aspects of human enunciated by Mesulam (1990). In this view, the brain function. human brain contains at least five major core functional networks: Independent component analysis (ICA) has turned 1. A spatial attention network anchored in posterior out to be an important method for identifying parietal cortex (PPC) and frontal eye fields; intrinsic connectivity networks (ICNs) from 2. A language network anchored in Wernicke’s and resting state fMRI data (Damoiseaux et al., 2006; Broca’s areas; Seeley et al., 2007a). ICA has been used to identify 3. An explicit memory network anchored in the ICNs involved in executive control, episodic hippocampal–entorhinal complex and inferior memory, autobiographical memory, self-related parietal cortex; processing, and detection of salient events. ICA 4. A face-object recognition network anchored in has also revealed a sensorimotor ICN anchored midtemporal and temporopolar cortices; and in bilateral somatosensory and motor cortices; a © 2010 Menon 46 NOTES visuospatial attention network anchored in intraparietal sulci and frontal eye fields; a higher-order visual network anchored in lateral occipital and inferior temporal cortices; and a lower-order visual network anchored in the striate and extrastriate cortex (Damoiseaux et al., 2006). This technique has also allowed intrinsic (Fig. 1) as well as task-related (Fig. 2) fMRI activation patterns to be used for the identification of distinct functionally coupled systems. These systems include a central-executive network (CEN) anchored in dorsolateral prefrontal cortex (DLPFC) and PPC, and a salience network (SN) anchored in anterior insula (AI) and anterior cingulate cortex (ACC) (Seeley et al., 2007a; Sridharan et al., 2008). Figure 1. Two core neurocognitive networks identified using intrinsic physiological coupling in resting state fMRI data. The SN (shown in red) is These prominent networks can be readily important for monitoring the saliency of external inputs and internal brain identified across a wide range of cognitive events, and the CEN (shown in blue) is engaged in higher-order cognitive tasks, and their responses increase and and attentional control. The SN is anchored in AI and ACC and features extensive connectivity with subcortical and limbic structures involved in decrease proportionately with task reward and motivation. The CEN links the dorsolateral prefrontal and pos- demands. The CEN and SN typically terior parietal cortices, and has subcortical coupling that is distinct from show increases in activation, whereas that of the SN. Seeley et al. (2007), their Fig. 2, reprinted with permission. the default-mode network (DMN) shows antTHAL, anterior thalamus; dACC, dorsal anterior cingulate cortex; dCN, decreases in activation (Raichle et al., dorsal caudate nucleus; dmTHAL, dorsomedial thalamus; FI, fronto-insular cortex; HT, hypothalamus; PAG, periaqueductal gray; Put, putamen; SLEA, 2001; Greicius et al., 2003; Greicius and sublenticular extended amygdala; SN/VTA, substantia nigra/ventral teg- Menon, 2004). CEN nodes that show mental area; TP, temporal pole. strong intrinsic functional coupling also show strong coactivation during cognitively challenging tasks. In particular, the CEN is critical for actively maintaining and manipulating information in working memory, and for judgment and decision- making in the context of goal-directed behavior (Miller and Cohen, 2001; Petrides, 2005; Muller and Knight, 2006; Koechlin and Summerfield, 2007). The DMN includes the medial temporal lobes and the angular gyrus (AG), in addition to the posterior cingulate cortex (PCC) and the ventromedial prefrontal cortex (VMPFC). These areas perform a Figure 2. Three major functional networks in the human brain. Task-relat- variety of functions: The PCC is activated ed activation patterns in the CEN and SN, and deactivation patterns in the during tasks that involve autobiographical DMN, during an auditory event segmentation task. Activation and deacti- memory and self-referential processes vation patterns can be decomposed into distinct subpatterns. A, Analysis (Buckner and Carroll, 2007); the VMPFC with the general linear model (GLM) revealed regional activations (Left) in the right anterior insula (rAI) and ACC (blue circles); DLPFC and PPC (green is associated with social cognitive processes circles) and deactivations (Right) in the VMPFC and PCC. B, ICA provided related to self and others (Amodio and converging evidence for spatially distinct networks. From left to right: SN Frith, 2006); the medial temporal lobe is (rAI and ACC), CEN (rDLPFC and rPPC), and DMN (VMPFC and PCC). Srid- engaged in episodic and autobiographical haran et al. (2008), their Fig. 1, reprinted with permission. memory (Cabeza et al., 2004); and the © 2010 Menon Large-Scale Brain Networks in Cognition: Emerging Principles 47 AG is implicated in semantic processing (Binder Within the framework of a network model, the NOTES et al., 2009). The DMN thus collectively comprises disparate functions ascribed to the insula can be an integrated system for autobiographical, self- conceptualized by a few basic mechanisms: monitoring, and social cognitive functions, even 1. Bottom-up detection of salient events; though a unique task-based function cannot be 2. Switching between other large-scale networks
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