Pathological perturbations of the brain are rarely confined to a single locus; instead, they often spread via axonal pathways to influence other regions. Patterns of such disease propagation are constrained by the extraordinarily complex, yet highly organized, topology of the underlying neural architecture; the so-called connectome. Thus, network organization fundamentally influences brain disease, and a connectomic approach grounded in network science is integral to understanding neuropathology. Here, we consider how brain-network topology shapes neural responses to damage, highlighting key maladaptive processes (such as diaschisis, transneuronal degeneration and dedifferentiation), and the resources (including degeneracy and reserve) and processes (such as compensation) that enable adaptation. We then show how knowledge of network topology allows us not only to describe pathological processes but also to generate predictive models of the spread and functional consequences of brain disease.

Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using highresolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and frontoparietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.network efficiency | dynamic connectivity | time-dependent network T he coordination of brain activity between disparate neural populations is a dynamic and context-dependent process (1-3). Although dynamic patterns of neural synchronization may be evident in time-dependent measures of functional connectivity (4, 5), the temporal stability of high-level topological properties is unknown. The topology of large-scale cortical activity-such as its efficient network layout (6), community structure (7), network hubs (8), rich-club organization (9, 10), and small worldness (11, 12)-may reflect fundamental aspects of cortical computation. Temporal fluctuations in these graph-theoretic measures may hence speak to adaptive properties of neuronal information processing.With international connectome mapping consortia such as the Human Connectome Project (HCP) (13) and the developing Human Connectome Project in full swing, resting-state functional magnetic resonance imaging (rsfMRI) data of unprecedented temporal resolution are now available to map the time-resolved properties of functional brain networks. Imaging the brain at rest reveals spontaneous low-frequency fluctuations in brain activity that are temporally correlated between functionally related regions (14-17). Interregional correlations are referred to as functional connections, and they collectively form a complex network (18). Functional brain networks are typically mapped in a timeaveraged sense, based on the assumption that functional connections remain relatively static (stationary)...

Estimates of functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) are sensitive to artefacts caused by in-scanner head motion. This susceptibility has motivated the development of numerous denoising methods designed to mitigate motion-related artefacts. Here, we compare popular retrospective rs-fMRI denoising methods, such as regression of head motion parameters and mean white matter (WM) and cerebrospinal fluid (CSF) (with and without expansion terms), aCompCor, volume censoring (e.g., scrubbing and spike regression), global signal regression and ICA-AROMA, combined into 19 different pipelines. These pipelines were evaluated across five different quality control benchmarks in four independent datasets associated with varying levels of motion. Pipelines were benchmarked by examining the residual relationship between in-scanner movement and functional connectivity after denoising; the effect of distance on this residual relationship; whole-brain differences in functional connectivity between high- and low-motion healthy controls (HC); the temporal degrees of freedom lost during denoising; and the test-retest reliability of functional connectivity estimates. We also compared the sensitivity of each pipeline to clinical differences in functional connectivity in independent samples of people with schizophrenia and obsessive-compulsive disorder. Our results indicate that (1) simple linear regression of regional fMRI time series against head motion parameters and WM/CSF signals (with or without expansion terms) is not sufficient to remove head motion artefacts; (2) aCompCor pipelines may only be viable in low-motion data; (3) volume censoring performs well at minimising motion-related artefact but a major benefit of this approach derives from the exclusion of high-motion individuals; (4) while not as effective as volume censoring, ICA-AROMA performed well across our benchmarks for relatively low cost in terms of data loss; (5) the addition of global signal regression improved the performance of nearly all pipelines on most benchmarks, but exacerbated the distance-dependence of correlations between motion and functional connectivity; and (6) group comparisons in functional connectivity between healthy controls and schizophrenia patients are highly dependent on preprocessing strategy. We offer some recommendations for best practice and outline simple analyses to facilitate transparent reporting of the degree to which a given set of findings may be affected by motion-related artefact.

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The human brain is a complex, interconnected network par excellence. Accurate and informative mapping of this human connectome has become a central goal of neuroscience. At the heart of this endeavor is the notion that brain connectivity can be abstracted to a graph of nodes, representing neural elements (e.g., neurons, brain regions), linked by edges, representing some measure of structural, functional or causal interaction between nodes. Such a representation brings connectomic data into the realm of graph theory, affording a rich repertoire of mathematical tools and concepts that can be used to characterize diverse anatomical and dynamical properties of brain networks. Although this approach has tremendous potential - and has seen rapid uptake in the neuroimaging community - it also has a number of pitfalls and unresolved challenges which can, if not approached with due caution, undermine the explanatory potential of the endeavor. We review these pitfalls, the prevailing solutions to overcome them, and the challenges at the forefront of the field.

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Analyses of functional interactions between large-scale brain networks have identified two broad systems that operate in apparent competition or antagonism with each other. One system, termed the default mode network (DMN), is thought to support internally oriented processing. The other system acts as a generic external attention system (EAS) and mediates attention to exogenous stimuli. Reports that the DMN and EAS show anticorrelated activity across a range of experimental paradigms suggest that competition between these systems supports adaptive behavior. Here, we used functional MRI to characterize functional interactions between the DMN and different EAS components during performance of a recollection task known to coactivate regions of both networks. Using methods to isolate task-related, context-dependent changes in functional connectivity between these systems, we show that increased cooperation between the DMN and a specific right-lateralized frontoparietal component of the EAS is associated with more rapid memory recollection. We also show that these cooperative dynamics are facilitated by a dynamic reconfiguration of the functional architecture of the DMN into core and transitional modules, with the latter serving to enhance integration with frontoparietal regions. In particular, the right posterior cingulate cortex may act as a critical information-processing hub that provokes these context-dependent reconfigurations from an intrinsic or default state of antagonism. Our findings highlight the dynamic, contextdependent nature of large-scale brain dynamics and shed light on their contribution to individual differences in behavior.complex | graph | modularity | rest | connectome I ncreasing evidence points to a fundamental distinction between two large-scale functional systems in the brain (1-4). One system, comprising regions of lateral prefrontal and parietal cortex, dorsal anterior cingulate, and anterior insula/frontoopercular regions, typically shows increased activation during performance of challenging cognitive tasks and has been implicated in attentional and cognitive control functions (5, 6). It may thus be generally referred to as an external attention system (EAS), but it has also been labeled the task-positive and extrinsic network (3, 4). The other system, often called the default mode network (DMN), is localized primarily to midline posterior and anterior cortical regions, the angular gyri, and medial and lateral temporal cortices (7,8). It often shows decreased activity during tasks requiring attention to external stimuli (9, 10) and increased activity during unconstrained thought, introspection, and self-related processing (7, 11). The apparent antagonism between these two systems is mirrored in their spontaneous dynamics, which are often strongly anticorrelated (2). These competitive interactions are thought to promote adaptive and efficient alternation between DMN-dominated introspective thought and EAS-mediated processing of external stimuli (1-4).Several lines of evidence support thi...

The processing of rewards and losses are crucial to everyday functioning. Considerable interest has been attached to investigating the anticipation and outcome phases of reward and loss processing, but results to date have been inconsistent. It is unclear if anticipation and outcome of a reward or loss recruit similar or distinct brain regions. In particular, while the striatum has widely been found to be active when anticipating a reward, whether it activates in response to the anticipation of losses as well remains ambiguous. Furthermore, concerning the orbitofrontal/ventromedial prefrontal regions, activation is often observed during reward receipt. However, it is unclear if this area is active during reward anticipation as well. We ran an Activation Likelihood Estimation meta‐analysis of 50 fMRI studies, which used the Monetary Incentive Delay Task (MIDT), to identify which brain regions are implicated in the anticipation of rewards, anticipation of losses, and the receipt of reward. Anticipating rewards and losses recruits overlapping areas including the striatum, insula, amygdala and thalamus, suggesting that a generalised neural system initiates motivational processes independent of valence. The orbitofrontal/ventromedial prefrontal regions were recruited only during the reward outcome, likely representing the value of the reward received. Our findings help to clarify the neural substrates of the different phases of reward and loss processing, and advance neurobiological models of these processes.