Studying Brain Organization via Spontaneous fMRI Signal

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Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the timevarying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker.resting-state fMRI | spontaneous fluctuations | arousal | electrophysiology D uring both active task engagement and rest, the human brain exhibits fluctuations in neural activity that can be readily measured using functional MRI (fMRI). In recent years, examining the spatiotemporal organization of these fluctuations has generated novel insight into the functional architecture of the human brain and its changes with development and disease (1). A prominent approach for mapping this architecture is to study interregional correlations in the fMRI signal fluctuations, which, even during rest, appear to be indicative of networks supporting specific functions. However, despite the promise and rapidly increasing application of this technique in the endeavor of brain connectomics (2-4), its sensitivity and specificity are compromised by unexplained variability arising from multiple neural and nonneural sources (e.g., refs. 5-11). As a result, the interpretation of resting-state fMRI data and the efficacy of these data as a biomarker rely critically on understanding and accounting for such sources of variability (11-13).Changes in arousal, mediated by an interaction between the ascending arousal system and the neocortex, may strongly modulate neuronal activity in much of the brain (14-18). Indeed, changes in vigilance and arousal (hereafter described jointly as "arousal"), which can be especially prevalent during the passive and uncontrolled resting state, result in fMRI signal variability that may confound the extraction of functional networks (5, 19). For example, the amplitude and extent of correlations in restingstate fMRI data vary with EEG-and behaviorally defined indicators of drowsiness and light sleep (20)(21)(22)(23)(24)(25) and are altered by sleep deprivation (26-28) and caffeine-induced changes in arousal state (29). Distinct patterns of functional connectivity across multiple networks have been associated with distinct EEG-defined sleep stages (30, 31) with sufficient reliabilit...

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confidence: 99%

Tracking brain arousal fluctuations with fMRI

Chang

Leopold

Schölvinck

et al. 2016

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299

“…R esting-state functional connectivity (FC) (1), which measures the temporal correlation of spontaneous hemodynamic signals (HemoS), is widely accepted as a core noninvasive tool to infer the functional network organization of brains in humans and other animals (2)(3)(4)(5)(6). Recent studies further suggest that HemoS-based FC (Hemo-FC) is sensitive enough to detect network-level functional changes due to behavioral training (7), wakefulness levels (8), and psychiatric diseases (9)(10)(11)(12).…”

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confidence: 99%

Transient neuronal coactivations embedded in globally propagating waves underlie resting-state functional connectivity

Matsui

Murakami

Ohki

2016

177

210

Resting-state functional connectivity (FC), which measures the correlation of spontaneous hemodynamic signals (HemoS) between brain areas, is widely used to study brain networks noninvasively. It is commonly assumed that spatial patterns of HemoSbased FC (Hemo-FC) reflect large-scale dynamics of underlying neuronal activity. To date, studies of spontaneous neuronal activity cataloged heterogeneous types of events ranging from waves of activity spanning the entire neocortex to flash-like activations of a set of anatomically connected cortical areas. However, it remains unclear how these various types of large-scale dynamics are interrelated. More importantly, whether each type of large-scale dynamics contributes to Hemo-FC has not been explored. Here, we addressed these questions by simultaneously monitoring neuronal calcium signals (CaS) and HemoS in the entire neocortex of mice at high spatiotemporal resolution. We found a significant relationship between two seemingly different types of large-scale spontaneous neuronal activity-namely, global waves propagating across the neocortex and transient coactivations among cortical areas sharing high FC. Different sets of cortical areas, sharing high FC within each set, were coactivated at different timings of the propagating global waves, suggesting that spatial information of cortical network characterized by FC was embedded in the phase of the global waves. Furthermore, we confirmed that such transient coactivations in CaS were indeed converted into spatially similar coactivations in HemoS and were necessary to sustain the spatial structure of Hemo-FC. These results explain how global waves of spontaneous neuronal activity propagating across large-scale cortical network contribute to Hemo-FC in the resting state.brain network | spontaneous activity | Ca imaging | optical imaging | fMRI

“…An alternate strategy is to leverage spontaneous variability in neural activity. Neural responses to repeated presentations of sensory stimuli, or in the absence of stimulation and explicit task demands, exhibit variability that is attributed to ongoing spontaneous activity (10)(11)(12)(13). Traditional analyses consider spontaneous activity to be noise, but there is increasing evidence that shared spontaneous variability is a signature of functional organization (14).…”

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confidence: 99%

Distributed representation of context by intrinsic subnetworks in prefrontal cortex

Waskom

Wagner

2017

Human prefrontal cortex supports goal-directed behavior by representing abstract information about task context. The organizational basis of these context representations, and of representations underlying other higher-order processes, is unknown. Here, we use multivariate decoding and analyses of spontaneous correlations to show that context representations are distributed across subnetworks within prefrontal cortex. Examining targeted prefrontal regions, we found that pairs of voxels with similar context preferences exhibited spontaneous correlations that were approximately twice as large as those between pairs with opposite context preferences. This subnetwork organization was stable across task-engaged and resting states, suggesting that abstract context representations are constrained by an intrinsic functional architecture. These results reveal a principle of fine-scaled functional organization in association cortex.fMRI | resting-state | rule | cognitive control | functional organization T he cerebral cortex exhibits functional organization at multiple spatial scales. At a coarse scale, the cortex is parcellated into functional areas (1, 2) that coordinate as networks through longrange connections (3-5). These areas represent and compute information with functional circuitry that is organized more finely. Some fine-scaled, intrinsic principles have been described in detail, particularly for sensory cortex (6, 7). In contrast, the subregional organization of prefrontal cortex (PFC), which gives rise to higherorder processes, including attention, decision-making, and goaldirected action (8, 9), remains largely uncharacterized. Whether PFC representations are encoded within an equipotential system or constrained by an intrinsic functional architecture is currently unknown.Sensory cortex can be mapped by parametrically varying stimulus attributes and measuring changes in the neural response, but complex and dynamic response properties limit the effectiveness of this approach for mapping PFC and other association regions. An alternate strategy is to leverage spontaneous variability in neural activity. Neural responses to repeated presentations of sensory stimuli, or in the absence of stimulation and explicit task demands, exhibit variability that is attributed to ongoing spontaneous activity (10-13). Traditional analyses consider spontaneous activity to be noise, but there is increasing evidence that shared spontaneous variability is a signature of functional organization (14). Analyses of spontaneous correlations have been used to identify multiple large-scale functional networks (4,5,15) and boundaries between functional areas (2, 16-18) in human and nonhuman primate association cortex. The spontaneous correlation structure also mirrors established fine-scaled principles of functional organization in visual cortex, such as preferences for retinotopic position (19) and stimulus orientation (20). We therefore leveraged spontaneous activity to examine the finescaled functional organization of human PFC.We focus...