In humans, the parts of the brain involved in vision are organized into distinct regions that are arranged into a hierarchy. Each of these regions contains neurons that are specialized for a particular role, such as responding to shape, color or motion. To actually ‘see’ an object, these different regions must communicate with each other and exchange information via connections between lower and higher levels of the hierarchy. However, it remains unclear how these connections work.

A brain region called the primary visual cortex is the lowest level of the visual cortical hierarchy as it is the first area to receive information from the eye. This region then passes information to higher regions in the hierarchy including the frontal eye fields (FEF), which help to control visual attention and eye movements. In turn, the FEF is thought to provide ‘feedback’ to the primary visual cortex.

Cocchi et al. examined how the FEF and primary visual cortex communicate with the rest of the brain by temporarily inhibiting the activity of these regions in human volunteers. The experiments show that inhibiting the primary visual cortex increased communication between this region and higher level visual areas. On the other hand, inhibiting the FEF reduced communication between this region and lower visual areas.

Computer simulations revealed that inhibiting particular brain regions alters communication between visual regions by changing the timing of local neural activity. In the simulations, inhibiting the primary visual cortex slows down neural activity in that region, leading to better communication with higher regions, which already operate on slower timescales. By contrast, inhibition of the FEF reduces its influence on lower visual regions by increasing the difference in timescales of neural activity between these regions.

The next step is to determine whether similar mechanisms regulate changes in the activity of neural networks outside of the visual system.

The primate visual system is a hierarchical network of feedforward and feedback connections that supports visual perception, object recognition and selective attention (Gilbert, 2013). At the earliest level of the visual hierarchy, the primary visual cortex receives input from the lateral geniculate nucleus and propagates this information to higher association areas including the posterior parietal cortex, ventral temporal cortex and frontal eye fields (FEF) (Hubel and Wiesel, 1962; Felleman and Van Essen, 1991; Van Essen and Maunsell, 1983; Maunsell and van Essen, 1983). During active perception, neural signals originating in these later cortical areas provide top-down modulation of activity in early visual areas, depending on factors such as expectation and context (Gilbert, 2013; Felleman and Van Essen, 1991; Moore and Armstrong, 2003; Bastos et al., 2015).

Functional interactions between early sensory regions and later association areas have been investigated in humans using task-based functional magnetic resonance imaging (fMRI) (Bressler et al., 2008; Vossel et al., 2012). This work has demonstrated that task demands play an important role in determining the nature of the interactions between early and late visual cortex (Gilbert, 2013). For example, studies combining fMRI with transcranial magnetic stimulation (TMS) during performance of visual tasks have shown that stimulation of the FEF can modulate neural activity in several visual areas, including the primary visual cortex (area V1) (Bressler et al., 2008; Ruff et al., 2006, 2008). Increments in the strength of excitatory TMS over the right FEF have been shown to increase neural activity in early retinotopic cortex representing the peripheral visual field, and to reduce activity in central visual-field representations (Ruff et al., 2006). This neurophysiological effect confers a perceptual advantage for detection of stimuli in the visual periphery relative to the fovea (Ruff et al., 2006).

Despite evidence for dynamic bidirectional interactions between FEF and early visual cortex during task-related visual processing, numerous findings from resting-state fMRI (rsfMRI) studies, in both human and non-human primates, suggest there is little or no functional coupling between these regions in the absence of active task demands (i.e., in the resting-state) (Vincent et al., 2007; Belcher et al., 2013; Damoiseaux et al., 2006; Yeo et al., 2011; Power et al., 2011; Mantini et al., 2013; Gordon et al., 2016). At rest, FEF belongs to extensive networks of fronto-parietal regions (Yeo et al., 2011; Power et al., 2011), whereas the early visual cortex, including area V1, belongs to a primary visual network encompassing occipital and inferior parietal regions (Yeo et al., 2011; Power et al., 2011; Gordon et al., 2016). Despite this apparent functional segregation between visual networks encompassing early visual cortex and FEF at rest, the nature of any latent interactions between them remains unknown. Likewise, the neural principles facilitating the emergence of integration between segregated regions at opposing ends of the visual cortical hierarchy remain unclear.

Here we combined rsfMRI and non-invasive brain stimulation to examine the causal influence of perturbations of local neural activity within early and late visual areas – specifically areas V1/V2 and FEF – in the absence of visual task demands. Across two separate imaging sessions, we employed continuous theta-burst TMS (Huang et al., 2005) to inhibit intrinsic neural activity either within the right occipital pole or within the right FEF. We recorded resting-state brain activity immediately before and after TMS, and examined the influence of this perturbation on functional and effective connectivity between the targeted regions and the rest of the brain. We also employed computational modelling to provide candidate mechanisms for expected changes in interactions between cortical regions following local stimulation. Based on recent empirical (Hasson et al., 2008; Murray et al., 2014; Bassett et al., 2013; Lerner et al., 2011; Honey et al., 2012; Gauthier et al., 2012) and computational (Gollo et al., 2015; Chaudhuri et al., 2015) work we tested the hypothesis that a temporal hierarchy of timescales, recapitulating the hierarchical organization of neuronal receptive fields, can explain the effects of local stimulation on inter-regional connectivity. According to the model, activity in higher regions such as FEF fluctuates at a slower temporal scale than activity in early sensory regions such as V1/V2 (Murray et al., 2014; Honey et al., 2012; Chaudhuri et al., 2015). We simulated the effects of local inhibition of early and late visual areas to test whether the proposed temporal gradient from sensory areas (fast) to association areas (slow) matched the observed changes in connectivity following local cortical perturbations in human participants. Our experimental and modelling results suggest that local inhibition of early visual cortex reduces the discrepancy in endogenous synchronization between lower and higher levels of the visual cortical hierarchy, whereas inhibition of FEF increases the discrepancy. Our work provides novel insights into the neural mechanisms that underlie the effects of local inhibition on large-scale brain dynamics.

Twenty-three healthy adult volunteers participated in two combined TMS-rsfMRI experiments, conducted on separate days. The number of participants was decided based on previous work adopting a similar experimental design (Cocchi et al., 2015; Eldaief et al., 2011). A schematic representation of the complete experimental protocol is presented in Figure 1 (details in the Materials and methods). After exclusion of data from two participants due to excessive head motion or medication, we analyzed resting-state data from 21 of the participants (11 female mean age 27.7, S.D. ± 5.2 years) acquired on a 3T Siemens Trio scanner. Immediately following an initial rsfMRI session, continuous (inhibitory) theta-burst TMS (Huang et al., 2005) was applied over one of two different cortical regions of interest (ROIs). A repeat rsfMRI scan was acquired from each participant immediately following TMS stimulation. The anatomical locations of the two TMS targets were: (1) Right early visual cortex – the posterior aspect of the medial occipital lobe (MNI centroid x = 25, y = −92, z = −9), corresponding with areas V1/V2; and (2) Right FEF (MNI centroid x = 31 y = −2, z = 47) – a brain hub within the frontal cortex (see Materials and methods for target region identification). The exact locations of the stimulation sites for each participant are shown in Figure 1—figure supplement 1. During TMS, participants closed their eyes and were seated to ensure a relaxed muscle state. This procedure ensured that local stimulation was delivered with participants in a state of rest and in the absence of structured visual input. The order of stimulation of the two cortical sites was counterbalanced across participants.

Figure 1 with 1 supplement see all

Download asset Open asset

Intrinsic functional connectivity of right early visual cortex and frontal eye fields

To characterize the intrinsic connectivity profiles of the two seed regions-of-interest (ROIs; 7.5 mm radius) at baseline, we conducted seed-to-whole brain functional connectivity analyses of the resting-state data acquired prior to focal stimulation. These analyses quantified the correlation between the blood-oxygen-level dependent (BOLD) signal timecourse extracted from the seed ROIs and the timecourses extracted from all other brain voxels (see Materials and methods). The ROIs were centered on coordinates within the right occipital pole and the right FEF, as specified above.

As shown in Figure 2, activity within the right occipital seed (V1/V2) was positively correlated with activity in other nodes of the visual network, including the lingual gyri, fusiform gyri, the lateral occipital cortex, and the cuneus bilaterally (Yeo et al., 2011; Power et al., 2011; Gordon et al., 2016) (p<0.05 familywise error corrected at cluster level (FWE), Figure 2a – red). By contrast, activity in the same occipital seed was negatively correlated (i.e., anticorrelated) with activity in bilateral FEF, the left supramarginal gyrus, the left inferior post-central gyrus, and the left posterior insula (Figure 2a – blue, p<0.05 FWE). These results confirm and extend previous findings by showing that in the absence of active visual task demands, interactions between early visual cortex and the right FEF can be anticorrelated (Yeo et al., 2011; Power et al., 2011; Gordon et al., 2016; Ekstrom et al., 2008).

Figure 2 with 2 supplements see all

Download asset Open asset

Neural activity in the right FEF seed was positively correlated with activity in bilateral supplementary motor areas, medial dorsal cingulate, bilateral dorsolateral prefrontal cortex, bilateral superior parietal cortex, and extrastriate visual areas including bilateral precuneus, middle occipital gyri, and inferior temporal gyri (Figure 2b – red, p<0.05 FWE). Activity within the right FEF seed was also negatively correlated with activity in default mode regions encompassing the medial prefrontal cortex, posterior cingulate cortex, angular gyrus and bilateral medial temporal gyri (Power et al., 2011) (Figure 2b – blue, p<0.05 FWE). These findings are consistent with those of previous neuroimaging studies of intrinsic functional connectivity profiles of human FEF (Yeo et al., 2011; Hutchison et al., 2012), and related findings in macaque (Vincent et al., 2007; Hutchison et al., 2012). Specifically, our results support the idea that the FEF is a functional brain hub, with widespread connections to a variety of resting-state networks including the fronto-parietal and default-mode systems (Power et al., 2013; Fornito et al., 2016; van den Heuvel, 2013a, 2013b).

Note that the aforementioned baseline effects were replicated using different sized TMS-seed regions (see Figure 2—figure supplement 1), baseline data (Figure 2—figure supplement 2) and preprocessing procedures (including global signal regression; see Materials and methods).

Effects of local inhibitory TMS on functional connectivity

Having established baseline patterns of whole-brain connectivity for the two seed regions at rest, we next examined the influence of local inhibitory TMS on this network activity. To this end, we compared patterns of functional connectivity before and immediately after application of TMS over the right occipital pole and FEF target sites (see Materials and methods).

Inhibitory TMS of right visual cortex (V1/V2) resulted in the emergence of positive correlations between this region and bilateral FEF (Figure 3—figure supplement 1 and Supplementary file 2). TMS of the visual cortex also resulted in the emergence/increase of positive correlations between V1/V2 and extrastriate visual regions including the lingual gyri, the lateral occipital cortex and the parietal cortex (p<0.05 FWE; Figure 3a – red, details in Supplementary files 1 and 2). These results were replicated when we adjusted the radius of the seed regions to 10 mm and 15 mm, and across different data preprocessing pipelines (Figure 3—figure supplement 2 and Materials and methods). On the other hand, inhibitory TMS of right FEF resulted in a reduction in positive correlations between the targeted FEF region and visual areas encompassing the bilateral fusiform and occipital gyri (Figure 3b – blue, details in Supplementary files 1 and 2). Overall, these effects were replicated using TMS-seed regions of 10 mm and 15 mm radius (see Figure 3—figure supplement 2) and using different preprocessing procedures (including global signal regression; see Materials and methods). Note that the individual sites of stimulation could not be unequivocally linked with specific effects on functional connectivity (Figure 3—figure supplement 3).

Figure 3 with 5 supplements see all

Download asset Open asset

We also assessed changes in functional connectivity pre- and post-TMS (for both V1/V2 and FEF sessions) using a control region ipsilateral to the TMS stimulation site but outside the networks of interest (i.e., the inferior portion of the right motor cortex, x = 51, y = −10, z = 18). These analyses revealed no significant changes in functional connectivity between the pre- and post-TMS scans, for both V1/V2 and FEF sessions.

Computational modelling

Results from functional and effective connectivity analyses suggest that V1/V2 stimulation (Figure 3, Figure 3—figure supplement 5) increased feedforward interactions between this cortical area and higher visual cortical areas. Conversely, reduced positive correlations following FEF stimulation were driven by a reduction in feedback connectivity (i.e., a reduced influence of FEF on V1/V2). Based on evidence from empirical and computational work (Hasson et al., 2008; Murray et al., 2014; Lerner et al., 2011; Honey et al., 2012; Gauthier et al., 2012; Gollo et al., 2015), we hypothesized that these opposing effects of inhibitory TMS on widespread connectivity might be accounted for by the different timescales at which sensory brain regions and network hubs fluctuate in their levels of activity. To test this hypothesis, we used the Kuramoto model of coupled heterogeneous oscillators, constrained by knowledge of whole brain anatomical connectivity and then adjusted to maximally match the acquired resting-state data at baseline (see Materials and methods for details). The effect of inhibitory TMS was simulated by slowing down (by a variable amount) the intrinsic frequency of the target region. This decision was motivated by the observation that changes in connectivity induced by inhibitory TMS were related to a reduction in the power of the local BOLD signal. Such an energy decrease is embodied in our oscillatory model by a slowdown in the intrinsic oscillatory frequency. The model simulates the dynamics of the whole brain at a relatively high resolution (513 uniformly sized cortical and sub-cortical regions). We focused on the differences in functional connectivity between the two regions targeted by TMS (V1/V2 and FEF) and the rest of the brain. Simulation results showed that virtual inhibition of right V1/V2 within the model increased the positive correlations between this region and the rest of the brain (red in Figure 4a), consistent with the group effects we observed following actual TMS over this region in our participant group. This increase in connectivity was robust across a broad range of parameters (i.e., slowing of intrinsic frequencies, omega, Figure 4a). Importantly, we found that the simulated effect of local V1/V2 inhibition was due mainly to a significant increase in connectivity between this node and other nodes encompassing occipito-temporal and frontal areas of the right hemisphere (Figure 4b). Conversely, simulated inhibition of FEF, again by slowing its intrinsic frequency, was associated with the reduction of correlations in this region’s connectivity with the rest of the brain (blue in Figure 4a and Figure 4—figure supplement 1). This significant effect involved right frontal areas surrounding FEF, and occipito-temporal cortices (Figure 4c). Overall, the effects observed within the model were consistent with the effects observed following actual TMS of V1/V2 and FEF in the experimental participants.

Figure 4 with 4 supplements see all

Download asset Open asset

To test whether the opposing changes in functional connectivity revealed by the simulations were driven by non-specific effects of local stimulation, we performed several additional simulations. First, we assessed whether inhibition (delta ω of 0.001) of cortical regions not relevant for our hypotheses – in this instance areas in the vicinity of the right postcentral gyrus and left superior temporal gyrus – could also produce effects on functional connectivity similar to those observed after simulated inhibition of V1/V2 or FEF. Second, we tested whether inhibition of these two control regions would cause specific changes in functional connectivity between early visual cortex and FEF. Simulated inhibition of either control region yielded a markedly different change in connectivity than that observed following simulated inhibition of V1/V2 or FEF (see Figure 4—figure supplement 2). Moreover, connectivity between V1/V2 and FEF was not affected by simulated inhibition of either of the two control regions.

We examined dynamic reconfigurations of intrinsic interactions between cortical areas at lower and higher ends of the visual hierarchy in humans following local stimulation with TMS. We applied inhibitory theta-burst stimulation via TMS to one of two regions – early visual cortex and FEF – within the right hemisphere, and examined changes in functional and effective connectivity in the resting-state. Local inhibitory TMS of these regions generated opposing patterns of connectivity between early and late areas within the cortical hierarchy. These distinct effects are consistent with an intrinsic timescale-based cortical hierarchy (Murray et al., 2014; Bassett et al., 2013; Lerner et al., 2011; Honey et al., 2012; Gauthier et al., 2012; Gollo et al., 2015; Chaudhuri et al., 2015), with early visual areas (V1/V2) exhibiting shorter timescales than those of higher areas (such as FEF). By incorporating these timescale effects, the computational model provides supporting evidence for opposing changes in diffuse connectivity following focal inhibitory TMS in the human brain. This finding highlights that inter-regional effects of focal TMS can be predicted by the hierarchical organization of timescales across the cerebral cortex.

At baseline, prior to inhibitory stimulation, neural activity in early visual cortex was anticorrelated with activity in the FEF bilaterally. Previous neuroimaging work in humans and monkey has suggested little or no functional coupling between FEF and V1/V2 in the resting state (Vincent et al., 2007; Belcher et al., 2013; Damoiseaux et al., 2006; Yeo et al., 2011; Power et al., 2011; Mantini et al., 2013; Gordon et al., 2016). For instance, areas V1 and V2 lie within a primary visual network that does not include FEF (Yeo et al., 2011). Our results support previous work by showing a functional segregation between early and late areas within the visual cortical hierarchy at rest. This finding is consistent with the notion that task-induced changes in sensory visual areas may drive the emergence of functional integration between levels of the visual cortical hierarchy (Ekstrom et al., 2008; Roelfsema, 2006).

A novel finding of our study is that inhibition of early visual cortex resulted in the emergence of positive correlations between this region and FEF, along with other extrastriate and parietal visual areas. Previous studies have investigated intrinsic reconfigurations of large-scale neural systems following focal perturbations of peripheral sensorimotor regions (e.g., Cocchi et al., 2015; O'Shea et al., 2007; Grefkes and Fink, 2011). In line with our results, fMRI studies have shown that local perturbation of primary motor areas can lead to recruitment of related areas (O'Shea et al., 2007; Grefkes and Fink, 2011; Cocchi et al., 2015). For example, inhibitory TMS of the primary motor cortex in healthy adults has recently been shown to increase the strength of resting-state functional connectivity within the sensorimotor system (Cocchi et al., 2015). Our finding that unilateral inhibition of early visual cortex increases the positive coupling with FEF and other visual areas adds to a growing literature suggesting that local perturbations of neural activity in peripheral nodes recruit functionally related brain regions (Cocchi et al., 2015; O'Shea et al., 2007; Grefkes and Fink, 2011; Bestmann et al., 2010). Specifically, our computational modelling suggests that the effect of inhibitory TMS of early visual cortex might be due to slowing of intrinsic timescales within V1/V2, so that they become closer to the slow intrinsic fluctuations of higher cortical regions such as the parietal cortex and FEF (Murray et al., 2014; Honey et al., 2012; Gollo et al., 2015; Chaudhuri et al., 2015).

In contrast to the increased positive coupling apparent after inhibition of early visual cortex, an identical inhibitory TMS protocol delivered over the right FEF led to decoupling between the target region and bilateral early visual cortex. Results from the DCM analysis suggest that such functional decoupling was related to a significant decrease in feedback signals from FEF to early visual cortex. In keeping with the neural mechanism proposed to explain increased connectivity following the inhibition of V1/V2, results from our computational model suggest that the observed reduction in feedback modulation from FEF to V1/V2 can be explained by a further slowing of local FEF dynamics by TMS. The observed changes in connectivity following inhibitory TMS may therefore be explained in terms of a reduction in synchronization discrepancy following local stimulation of V1/V2 and an increase in synchronization discrepancy following stimulation of FEF. Within this broad context, preliminary analyses suggest that there are a host of topographical specificities and nuances that accompany virtual simulations. Further work is needed to address the specificity of our model and the source of possible discrepancies between virtual and empirical findings.

Our findings also reveal a relationship between the magnitude of local signal change and remote modulations in intrinsic functional connectivity. We found that TMS-induced changes in local BOLD signal amplitude at the stimulation site were related to remote modulations in functional connectivity. This suggests that variations in ongoing low-frequency (<0.1 Hz) fluctuations may be a marker of TMS-induced modulation of widespread cortical connectivity.

The dynamic integration of information between sensory and association regions of the cortex is essential for normal brain function. Here we combined functional brain imaging, neural stimulation and computational modelling to elucidate the neural mechanisms that support the emergence and dissolution of interactions between cortical regions within the human visual system following local perturbations in neural activity. Our results suggest that the selective effect of local inhibitory TMS on diffuse patterns of connectivity can be accounted for by an intrinsic hierarchical ordering of cortical timescales (Murray et al., 2014; Honey et al., 2012; Gollo et al., 2015).

The study was approved by The University of Queensland Human Research Ethics Committee. Written informed consent was obtained for all participants.

1. 1. Bassett DS
   2. Wymbs NF
   3. Rombach MP
   4. Porter MA
   5. Mucha PJ
   6. Grafton ST

   (2013) Task-based core-periphery organization of human brain dynamics

   PLoS Computational Biology 9:e1003171.

   https://doi.org/10.1371/journal.pcbi.1003171

   • Google Scholar
2. 1. Bastos AM
   2. Vezoli J
   3. Bosman CA
   4. Schoffelen JM
   5. Oostenveld R
   6. Dowdall JR
   7. De Weerd P
   8. Kennedy H
   9. Fries P

   (2015) Visual areas exert feedforward and feedback influences through distinct frequency channels

   Neuron 85:390–401.

   https://doi.org/10.1016/j.neuron.2014.12.018

   • Google Scholar
3. 1. Behzadi Y
   2. Restom K
   3. Liau J
   4. Liu TT

   (2007) A component based noise correction method (CompCor) for BOLD and perfusion based fMRI

   NeuroImage 37:90–101.

   https://doi.org/10.1016/j.neuroimage.2007.04.042

   • Google Scholar
4. 1. Belcher AM
   2. Yen CC
   3. Stepp H
   4. Gu H
   5. Lu H
   6. Yang Y
   7. Silva AC
   8. Stein EA

   (2013) Large-scale brain networks in the awake, truly resting marmoset monkey

   Journal of Neuroscience 33:16796–16804.

   https://doi.org/10.1523/JNEUROSCI.3146-13.2013

   • Google Scholar
5. 1. Bestmann S
   2. Swayne O
   3. Blankenburg F
   4. Ruff CC
   5. Teo J
   6. Weiskopf N
   7. Driver J
   8. Rothwell JC
   9. Ward NS

   (2010) The role of contralesional dorsal premotor cortex after stroke as studied with concurrent TMS-fMRI

   Journal of Neuroscience 30:11926–11937.

   https://doi.org/10.1523/JNEUROSCI.5642-09.2010

   • Google Scholar
6. 1. Bhowmik D
   2. Shanahan M

   (2013) Metastability and inter-band frequency modulation in networks of oscillating spiking neuron populations

   PLoS One 8:e62234.

   https://doi.org/10.1371/journal.pone.0062234

   • Google Scholar
7. 1. Breakspear M
   2. Heitmann S
   3. Daffertshofer A

   (2010) Generative models of cortical oscillations: neurobiological implications of the kuramoto model

   Frontiers in Human Neuroscience 4:190.

   https://doi.org/10.3389/fnhum.2010.00190

   • Google Scholar
8. 1. Bressler SL
   2. Tang W
   3. Sylvester CM
   4. Shulman GL
   5. Corbetta M

   (2008) Top-down control of human visual cortex by frontal and parietal cortex in anticipatory visual spatial attention

   Journal of Neuroscience 28:10056–10061.

   https://doi.org/10.1523/JNEUROSCI.1776-08.2008

   • Google Scholar
9. 1. Chao-Gan Y
   2. Yu-Feng Z

   (2010) DPARSF: A MATLAB toolbox for "Pipeline" data analysis of resting-state fMRI

   Frontiers in Systems Neuroscience 4:13.

   https://doi.org/10.3389/fnsys.2010.00013

   • Google Scholar
10. 1. Chaudhuri R
    2. Knoblauch K
    3. Gariel MA
    4. Kennedy H
    5. Wang XJ

    (2015) A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex

    Neuron 88:419–431.

    https://doi.org/10.1016/j.neuron.2015.09.008

    • Google Scholar
11. 1. Cocchi L
    2. Sale MV
    3. Lord A
    4. Zalesky A
    5. Breakspear M
    6. Mattingley JB

    (2015) Dissociable effects of local inhibitory and excitatory theta-burst stimulation on large-scale brain dynamics

    Journal of Neurophysiology 113:3375–3385.

    https://doi.org/10.1152/jn.00850.2014

    • Google Scholar
12. 1. Damoiseaux JS
    2. Rombouts SARB
    3. Barkhof F
    4. Scheltens P
    5. Stam CJ
    6. Smith SM
    7. Beckmann CF

    (2006) Consistent resting-state networks across healthy subjects

    PNAS 103:13848–13853.

    https://doi.org/10.1073/pnas.0601417103

    • Google Scholar
13. 1. Daunizeau J
    2. Stephan KE
    3. Friston KJ

    (2012) Stochastic dynamic causal modelling of fMRI data: should we care about neural noise?

    NeuroImage 62:464–481.

    https://doi.org/10.1016/j.neuroimage.2012.04.061

    • Google Scholar
14. 1. Ekstrom LB
    2. Roelfsema PR
    3. Arsenault JT
    4. Bonmassar G
    5. Vanduffel W

    (2008) Bottom-up dependent gating of frontal signals in early visual cortex

    Science 321:414–417.

    https://doi.org/10.1126/science.1153276

    • Google Scholar
15. 1. Eldaief MC
    2. Halko MA
    3. Buckner RL
    4. Pascual-Leone A

    (2011) Transcranial magnetic stimulation modulates the brain's intrinsic activity in a frequency-dependent manner

    PNAS 108:21229–21234.

    https://doi.org/10.1073/pnas.1113103109

    • Google Scholar
16. 1. Felleman DJ
    2. Van Essen DC

    (1991) Distributed hierarchical processing in the primate cerebral cortex

    Cerebral Cortex 1:1–47.

    https://doi.org/10.1093/cercor/1.1.1

    • Google Scholar
17. Book

    1. Fornito A
    2. Zalesky A

    (2016)

    Bullmore E. Centrality and hubs

    In: Fornito A, editors. Fundamentals of Brain Network Analysis. 1. London: Academic Press. pp. 137–161..

    • Google Scholar
18. 1. Friston KJ
    2. Harrison L
    3. Penny W

    (2003) Dynamic causal modelling

    NeuroImage 19:1273–1302.

    https://doi.org/10.1016/S1053-8119(03)00202-7

    • Google Scholar
19. 1. Friston KJ
    2. Kahan J
    3. Biswal B
    4. Razi A

    (2014) A DCM for resting state fMRI

    NeuroImage 94:396–407.

    https://doi.org/10.1016/j.neuroimage.2013.12.009

    • Google Scholar
20. 1. Gauthier B
    2. Eger E
    3. Hesselmann G
    4. Giraud AL
    5. Kleinschmidt A

    (2012) Temporal tuning properties along the human ventral visual stream

    Journal of Neuroscience 32:14433–14441.

    https://doi.org/10.1523/JNEUROSCI.2467-12.2012

    • Google Scholar
21. 1. Gilbert CD
    2. Li W

    (2013) Top-down influences on visual processing

    Nature Reviews Neuroscience 14:350–363.

    https://doi.org/10.1038/nrn3476

    • Google Scholar
22. 1. Gollo LL
    2. Zalesky A
    3. Hutchison RM
    4. van den Heuvel M
    5. Breakspear M

    (2015) Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations

    Philosophical Transactions of the Royal Society B: Biological Sciences 370:20140165.

    https://doi.org/10.1098/rstb.2014.0165

    • Google Scholar
23. 1. Gordon EM
    2. Laumann TO
    3. Adeyemo B
    4. Huckins JF
    5. Kelley WM
    6. Petersen SE

    (2016) Generation and Evaluation of a cortical area parcellation from resting-state correlations

    Cerebral Cortex 26:288–303.

    https://doi.org/10.1093/cercor/bhu239

    • Google Scholar
24. 1. Grefkes C
    2. Fink GR

    (2011) Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches

    Brain 134:1264–1276.

    https://doi.org/10.1093/brain/awr033

    • Google Scholar
25. 1. Hamada M
    2. Murase N
    3. Hasan A
    4. Balaratnam M
    5. Rothwell JC

    (2013) The role of interneuron networks in driving human motor cortical plasticity

    Cerebral Cortex 23:1593–1605.

    https://doi.org/10.1093/cercor/bhs147

    • Google Scholar
26. 1. Hasson U
    2. Yang E
    3. Vallines I
    4. Heeger DJ
    5. Rubin N

    (2008) A hierarchy of temporal receptive windows in human cortex

    Journal of Neuroscience 28:2539–2550.

    https://doi.org/10.1523/JNEUROSCI.5487-07.2008

    • Google Scholar
27. 1. Heinen K
    2. Feredoes E
    3. Weiskopf N
    4. Ruff CC
    5. Driver J

    (2014) Direct evidence for attention-dependent influences of the frontal eye-fields on feature-responsive visual cortex

    Cerebral Cortex 24:2815–2821.

    https://doi.org/10.1093/cercor/bht157

    • Google Scholar
28. 1. Hellyer PJ
    2. Scott G
    3. Shanahan M
    4. Sharp DJ
    5. Leech R

    (2015) Cognitive flexibility through metastable neural dynamics Is disrupted by damage to the structural connectome

    Journal of Neuroscience 35:9050–9063.

    https://doi.org/10.1523/JNEUROSCI.4648-14.2015

    • Google Scholar
29. 1. Honey CJ
    2. Thesen T
    3. Donner TH
    4. Silbert LJ
    5. Carlson CE
    6. Devinsky O
    7. Doyle WK
    8. Rubin N
    9. Heeger DJ
    10. Hasson U

    (2012) Slow cortical dynamics and the accumulation of information over long timescales

    Neuron 76:423–434.

    https://doi.org/10.1016/j.neuron.2012.08.011

    • Google Scholar
30. 1. Huang YZ
    2. Edwards MJ
    3. Rounis E
    4. Bhatia KP
    5. Rothwell JC

    (2005) Theta burst stimulation of the human motor cortex

    Neuron 45:201–206.

    https://doi.org/10.1016/j.neuron.2004.12.033

    • Google Scholar
31. 1. Hubel DH
    2. Wiesel TN

    (1962) Receptive fields, binocular interaction and functional architecture in the cat's visual cortex

    The Journal of Physiology 160:106–154.

    https://doi.org/10.1113/jphysiol.1962.sp006837

    • Google Scholar
32. 1. Hutchison RM
    2. Gallivan JP
    3. Culham JC
    4. Gati JS
    5. Menon RS
    6. Everling S

    (2012) Functional connectivity of the frontal eye fields in humans and macaque monkeys investigated with resting-state fMRI

    Journal of Neurophysiology 107:2463–2474.

    https://doi.org/10.1152/jn.00891.2011

    • Google Scholar
33. 1. Kammer T
    2. Beck S
    3. Erb M
    4. Grodd W

    (2001) The influence of current direction on phosphene thresholds evoked by transcranial magnetic stimulation

    Clinical Neurophysiology 112:2015–2021.

    https://doi.org/10.1016/S1388-2457(01)00673-3

    • Google Scholar
34. Book

    1. Kuramoto Y

    (1984) Chemical Oscillations, Waves and Turbulence

    New York: Springer Verlag..

    https://doi.org/10.1007/978-3-642-69689-3

    • Google Scholar
35. 1. Lerner Y
    2. Honey CJ
    3. Silbert LJ
    4. Hasson U

    (2011) Topographic mapping of a hierarchy of temporal receptive windows using a narrated story

    Journal of Neuroscience 31:2906–2915.

    https://doi.org/10.1523/JNEUROSCI.3684-10.2011

    • Google Scholar
36. 1. Mantini D
    2. Corbetta M
    3. Romani GL
    4. Orban GA
    5. Vanduffel W

    (2013) Evolutionarily novel functional networks in the human brain?

    Journal of Neuroscience 33:3259–3275.

    https://doi.org/10.1523/JNEUROSCI.4392-12.2013

    • Google Scholar
37. 1. Maunsell JH
    2. van Essen DC

    (1983)

    The connections of the middle temporal visual area (MT) and their relationship to a cortical hierarchy in the macaque monkey

    Journal of Neuroscience 3:2563–2586.

    • Google Scholar
38. 1. Moore T
    2. Armstrong KM

    (2003) Selective gating of visual signals by microstimulation of frontal cortex

    Nature 421:370–373.

    https://doi.org/10.1038/nature01341

    • Google Scholar
39. 1. Murray JD
    2. Bernacchia A
    3. Freedman DJ
    4. Romo R
    5. Wallis JD
    6. Cai X
    7. Padoa-Schioppa C
    8. Pasternak T
    9. Seo H
    10. Lee D
    11. Wang XJ

    (2014) A hierarchy of intrinsic timescales across primate cortex

    Nature Neuroscience 17:1661–1663.

    https://doi.org/10.1038/nn.3862

    • Google Scholar
40. 1. Nyffeler T
    2. Wurtz P
    3. Lüscher HR
    4. Hess CW
    5. Senn W
    6. Pflugshaupt T
    7. von Wartburg R
    8. Lüthi M
    9. Müri RM

    (2006) Extending lifetime of plastic changes in the human brain

    European Journal of Neuroscience 24:2961–2966.

    https://doi.org/10.1111/j.1460-9568.2006.05154.x

    • Google Scholar
41. 1. O'Shea J
    2. Johansen-Berg H
    3. Trief D
    4. Göbel S
    5. Rushworth MF

    (2007) Functionally specific reorganization in human premotor cortex

    Neuron 54:479–490.

    https://doi.org/10.1016/j.neuron.2007.04.021

    • Google Scholar
42. 1. Power JD
    2. Barnes KA
    3. Snyder AZ
    4. Schlaggar BL
    5. Petersen SE

    (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

    NeuroImage 59:2142–2154.

    https://doi.org/10.1016/j.neuroimage.2011.10.018

    • Google Scholar
43. 1. Power JD
    2. Cohen AL
    3. Nelson SM
    4. Wig GS
    5. Barnes KA
    6. Church JA
    7. Vogel AC
    8. Laumann TO
    9. Miezin FM
    10. Schlaggar BL
    11. Petersen SE

    (2011) Functional network organization of the human brain

    Neuron 72:665–678.

    https://doi.org/10.1016/j.neuron.2011.09.006

    • Google Scholar
44. 1. Power JD
    2. Mitra A
    3. Laumann TO
    4. Snyder AZ
    5. Schlaggar BL
    6. Petersen SE

    (2014) Methods to detect, characterize, and remove motion artifact in resting state fMRI

    NeuroImage 84:320–341.

    https://doi.org/10.1016/j.neuroimage.2013.08.048

    • Google Scholar
45. 1. Power JD
    2. Schlaggar BL
    3. Lessov-Schlaggar CN
    4. Petersen SE

    (2013) Evidence for hubs in human functional brain networks

    Neuron 79:798–813.

    https://doi.org/10.1016/j.neuron.2013.07.035

    • Google Scholar
46. 1. Roberts JA
    2. Perry A
    3. Lord AR
    4. Roberts G
    5. Mitchell PB
    6. Smith RE
    7. Calamante F
    8. Breakspear M

    (2016) The contribution of geometry to the human connectome

    NeuroImage 124:379–393.

    https://doi.org/10.1016/j.neuroimage.2015.09.009

    • Google Scholar
47. 1. Roelfsema PR

    (2006) Cortical algorithms for perceptual grouping

    Annual Review of Neuroscience 29:203–227.

    https://doi.org/10.1146/annurev.neuro.29.051605.112939

    • Google Scholar
48. 1. Ruff CC
    2. Bestmann S
    3. Blankenburg F
    4. Bjoertomt O
    5. Josephs O
    6. Weiskopf N
    7. Deichmann R
    8. Driver J

    (2008) Distinct causal influences of parietal versus frontal areas on human visual cortex: evidence from concurrent TMS-fMRI

    Cerebral Cortex 18:817–827.

    https://doi.org/10.1093/cercor/bhm128

    • Google Scholar
49. 1. Ruff CC
    2. Blankenburg F
    3. Bjoertomt O
    4. Bestmann S
    5. Freeman E
    6. Haynes JD
    7. Rees G
    8. Josephs O
    9. Deichmann R
    10. Driver J

    (2006) Concurrent TMS-fMRI and psychophysics reveal frontal influences on human retinotopic visual cortex

    Current Biology 16:1479–1488.

    https://doi.org/10.1016/j.cub.2006.06.057

    • Google Scholar
50. 1. Schmidt R
    2. LaFleur KJ
    3. de Reus MA
    4. van den Berg LH
    5. van den Heuvel MP

    (2015) Kuramoto model simulation of neural hubs and dynamic synchrony in the human cerebral connectome

    BMC Neuroscience 16:54.

    https://doi.org/10.1186/s12868-015-0193-z

    • Google Scholar
51. 1. Stephan KE
    2. Penny WD
    3. Moran RJ
    4. den Ouden HE
    5. Daunizeau J
    6. Friston KJ

    (2010) Ten simple rules for dynamic causal modeling

    NeuroImage 49:3099–3109.

    https://doi.org/10.1016/j.neuroimage.2009.11.015

    • Google Scholar
52. 1. Tagliazucchi E
    2. Laufs H

    (2014) Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep

    Neuron 82:695–708.

    https://doi.org/10.1016/j.neuron.2014.03.020

    • Google Scholar
53. 1. Thiebaut de Schotten M
    2. Urbanski M
    3. Valabregue R
    4. Bayle DJ
    5. Volle E

    (2014) Subdivision of the occipital lobes: an anatomical and functional MRI connectivity study

    Cortex 56:121–137.

    https://doi.org/10.1016/j.cortex.2012.12.007

    • Google Scholar
54. 1. van den Heuvel MP
    2. Sporns O

    (2013a) An anatomical substrate for integration among functional networks in human cortex

    Journal of Neuroscience 33:14489–14500.

    https://doi.org/10.1523/JNEUROSCI.2128-13.2013

    • Google Scholar
55. 1. van den Heuvel MP
    2. Sporns O

    (2013b) Network hubs in the human brain

    Trends in Cognitive Sciences 17:683–696.

    https://doi.org/10.1016/j.tics.2013.09.012

    • Google Scholar
56. 1. Van Dijk KR
    2. Hedden T
    3. Venkataraman A
    4. Evans KC
    5. Lazar SW
    6. Buckner RL

    (2010) Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization

    Journal of Neurophysiology 103:297–321.

    https://doi.org/10.1152/jn.00783.2009

    • Google Scholar
57. 1. Van Essen DC
    2. Maunsell JHR

    (1983) Hierarchical organization and functional streams in the visual cortex

    Trends in Neurosciences 6:370–375.

    https://doi.org/10.1016/0166-2236(83)90167-4

    • Google Scholar
58. 1. Vincent JL
    2. Patel GH
    3. Fox MD
    4. Snyder AZ
    5. Baker JT
    6. Van Essen DC
    7. Zempel JM
    8. Snyder LH
    9. Corbetta M
    10. Raichle ME

    (2007) Intrinsic functional architecture in the anaesthetized monkey brain

    Nature 447:83–86.

    https://doi.org/10.1038/nature05758

    • Google Scholar
59. 1. Vossel S
    2. Weidner R
    3. Driver J
    4. Friston KJ
    5. Fink GR

    (2012) Deconstructing the architecture of dorsal and ventral attention systems with dynamic causal modeling

    Journal of Neuroscience 32:10637–10648.

    https://doi.org/10.1523/JNEUROSCI.0414-12.2012

    • Google Scholar
60. 1. Woźniak-Kwaśniewska A
    2. Szekely D
    3. Aussedat P
    4. Bougerol T
    5. David O

    (2014) Changes of oscillatory brain activity induced by repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex in healthy subjects

    NeuroImage 88:91–99.

    https://doi.org/10.1016/j.neuroimage.2013.11.029

    • Google Scholar
61. 1. Yang H
    2. Long XY
    3. Yang Y
    4. Yan H
    5. Zhu CZ
    6. Zhou XP
    7. Zang YF
    8. Gong QY

    (2007) Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI

    NeuroImage 36:144–152.

    https://doi.org/10.1016/j.neuroimage.2007.01.054

    • Google Scholar
62. 1. Yeo BT
    2. Krienen FM
    3. Sepulcre J
    4. Sabuncu MR
    5. Lashkari D
    6. Hollinshead M
    7. Roffman JL
    8. Smoller JW
    9. Zöllei L
    10. Polimeni JR
    11. Fischl B
    12. Liu H
    13. Buckner RL

    (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity

    Journal of Neurophysiology 106:1125–1165.

    https://doi.org/10.1152/jn.00338.2011

    • Google Scholar
63. 1. Zalesky A
    2. Fornito A
    3. Harding IH
    4. Cocchi L
    5. Yücel M
    6. Pantelis C
    7. Bullmore ET

    (2010) Whole-brain anatomical networks: does the choice of nodes matter?

    NeuroImage 50:970–983.

    https://doi.org/10.1016/j.neuroimage.2009.12.027

    • Google Scholar

Author details

1. Luca Cocchi

   1. Queensland Brain Institute, The University of Queensland, Brisbane, Australia
   2. QIMR Berghofer Medical Research Institute, Brisbane, Australia

   Contribution

   LC, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   Luca.Cocchi@qimrberghofer.edu.au

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-3651-2676

2. Martin V Sale

   Queensland Brain Institute, The University of Queensland, Brisbane, Australia

   Contribution

   MVS, Conception and design, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Leonardo L Gollo

   QIMR Berghofer Medical Research Institute, Brisbane, Australia

   Contribution

   LLG, Conceived (with LC and MB) and designed the simulations and performed the simulations and analyzed results, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Peter T Bell

   Queensland Brain Institute, The University of Queensland, Brisbane, Australia

   Contribution

   PTB, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Vinh T Nguyen

   QIMR Berghofer Medical Research Institute, Brisbane, Australia

   Contribution

   VTN, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Andrew Zalesky

   Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Australia

   Contribution

   AZ, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Michael Breakspear

   1. QIMR Berghofer Medical Research Institute, Brisbane, Australia
   2. Metro North Mental Health Service, Brisbane, Australia

   Contribution

   MB, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Jason B Mattingley

   1. Queensland Brain Institute, The University of Queensland, Brisbane, Australia
   2. School of Psychology, The University of Queensland, Brisbane, Australia

   Contribution

   JBM, Conception and design, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-0929-9216

• 3,228

  views

• 504

  downloads

• 90

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.