Focal Brain Lesions to Critical Locations Cause Widespread Disruption of the Modular Organization of the Brain

247

228

Hubs are network components that hold positions of high importance for network function. Previous research has identified hubs in human brain networks derived from neuroimaging data; however, there is little consensus on the localization of such hubs. Moreover, direct evidence regarding the role of various proposed hubs in network function (e.g., cognition) is scarce. Regions of the default mode network (DMN) have been frequently identified as "cortical hubs" of brain networks. On theoretical grounds, we have argued against some of the methods used to identify these hubs and have advocated alternative approaches that identify different regions of cortex as hubs. Our framework predicts that our proposed hub locations may play influential roles in multiple aspects of cognition, and, in contrast, that hubs identified via other methods (including salient regions in the DMN) might not exert such broad influence. Here we used a neuropsychological approach to directly test these predictions by studying long-term cognitive and behavioral outcomes in 30 patients, 19 with focal lesions to six "target" hubs identified by our approaches (high system density and participation coefficient) and 11 with focal lesions to two "control" hubs (high degree centrality). In support of our predictions, we found that damage to target locations produced severe and widespread cognitive deficits, whereas damage to control locations produced more circumscribed deficits. These findings support our interpretation of how neuroimaging-derived network measures relate to cognition and augment classic neuroanatomically based predictions about cognitive and behavioral outcomes after focal brain injury.functional connectivity | neuropsychology | fMRI | brain hubs T he careful description of circumscribed cognitive and behavioral deficits following localized brain damage has provided much of our knowledge of the functional geography of the brain. In some cases, however, relatively small, circumscribed lesions seem to have broader effects than would be predicted from their size and location. Historically, these effects sometimes have been attributed to diaschisis (i.e., effects at a distance) owing to connections between affected and unaffected brain regions. The potential importance of interactivity among brain regions is supported by recent research (1-3); for example, He et al. (4) found that visuospatial inattention after right inferior parietal lesions was best explained by the effects of those lesions on more superior parietal activity.In the broadest sense, interactive explanations of brain function can be thought of as reflecting the organization of the brain as a large-scale network. The advent of large-scale network descriptions of brain structure and function extends the possibility of richer and broader explanations of unusually severe cognitive and behavioral consequences that sometimes follow circumscribed lesions. Some large-scale network studies have focused on "hubs," a term from network science that indicates potential points of impo...

Section: Significancesupporting

confidence: 76%

Network measures predict neuropsychological outcome after brain injury

Warren

Power²,

Bruss

et al. 2014

247

228

“…We chose Newman's modularity (SI Text, section 8) to quantify such changes of modular integrity at the whole-brain level, because this metric provides a summary measure of within-module connectivity strength in relation to connectivity strength to other modules. In previous studies, modularity during the task-free resting state has been reported as a whole brain-level predictor for (offline) behavioral efficiency (24) and shown to describe modular network integrity in brain lesion patients (25). These observations of a role of modularity in interindividual variability further motivate investigation of this measure in intraindividual variability.…”

Section: Resultsmentioning

confidence: 86%

Ongoing dynamics in large-scale functional connectivity predict perception

Sadaghiani

Poline

Kleinschmidt

et al. 2015

253

220

Most brain activity occurs in an ongoing manner not directly locked to external events or stimuli. Regional ongoing activity fluctuates in unison with some brain regions but not others, and the degree of long-range coupling is called functional connectivity, often measured with correlation. Strength and spatial distributions of functional connectivity dynamically change in an ongoing manner over seconds to minutes, even when the external environment is held constant. Direct evidence for any behavioral relevance of these continuous large-scale dynamics has been limited. Here, we investigated whether ongoing changes in baseline functional connectivity correlate with perception. In a continuous auditory detection task, participants perceived the target sound in roughly one-half of the trials. Very long (22-40 s) interstimulus intervals permitted investigation of baseline connectivity unaffected by preceding evoked responses. Using multivariate classification, we observed that functional connectivity before the target predicted whether it was heard or missed. Using graph theoretical measures, we characterized the difference in functional connectivity between states that lead to hits vs. misses. Before misses compared with hits and task-free rest, connectivity showed reduced modularity, a measure of integrity of modular network structure. This effect was strongest in the default mode and visual networks and caused by both reduced within-network connectivity and enhanced across-network connections before misses. The relation of behavior to prestimulus connectivity was dissociable from that of prestimulus activity amplitudes. In conclusion, moment to moment dynamic changes in baseline functional connectivity may shape subsequent behavioral performance. A highly modular network structure seems beneficial to perceptual efficiency.functional connectivity | brain networks | dynamics | graph theory | classification T he brain is highly active in a continuous manner, and much of neural activity is not directly locked to external events or stimuli. This continuous brain activity is spatiotemporally organized into a functional connectivity architecture that comprises several large-scale networks. Large-scale networks span different cerebral lobes and include subcortical structures (1). The regions comprised in such networks commonly coactivate together in response to task demands (2), but they also show correlated and spontaneous activity fluctuations when no changes occur in the external environment. The functional connectivity architecture ensuing from these activity cofluctuations largely persists across all mental states, including various tasks, resting wakefulness, and sleep, albeit showing some degree of modulation across these states (3, 4).Strength and spatial distributions of functional connectivity within this architecture are not, however, stationary across time. At the spatial level of large-scale networks, functional connectivity shows prominent changes over the range of seconds to minutes (5). These so-called in...

“…For example, in patients with focal brain lesions, damage to connector nodes, but not local nodes, decreases the modularity of the global network structure (88). Moreover, transcranial magnetic stimulation to connector regions in two different modules (fronto-parietal and cingulate-opercular) increased global connectivity (likely decreasing modularity) (89).…”

Section: Discussionmentioning

confidence: 99%

The modular and integrative functional architecture of the human brain

Bertolero

Yeo

D’Esposito

2015

535

478

Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules' processing, and what the precise role of connector nodes is for between-module communication remains underspecified. Here, we use a network model of the brain derived from resting-state functional MRI (rs-fMRI) data and investigate the modular functional architecture of the human brain by analyzing activity at different types of nodes in the network across 9,208 experiments of 77 cognitive tasks in the BrainMap database. Using an authortopic model of cognitive functions, we find a strong spatial correspondence between the cognitive functions and the network's modules, suggesting that each module performs a discrete cognitive function. Crucially, activity at local nodes within the modules does not increase in tasks that require more cognitive functions, demonstrating the autonomy of modules' functions. However, connector nodes do exhibit increased activity when more cognitive functions are engaged in a task. Moreover, connector nodes are located where brain activity is associated with many different cognitive functions. Connector nodes potentially play a role in betweenmodule communication that maintains the modular function of the brain. Together, these findings provide a network account of the brain's modular yet integrated implementation of cognitive functions. modularity | hubs | cognition | graph theory | network