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Research ArticleNew Research, Novel Tools and Methods

Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain

Andreas Spiegler, Enrique C. A. Hansen, Christophe Bernard, Anthony R. McIntosh and Viktor K. Jirsa
eNeuro 21 September 2016, 3 (5) ENEURO.0068-16.2016; DOI: https://doi.org/10.1523/ENEURO.0068-16.2016
Andreas Spiegler
1Institut de la Santé et de la Recherche Médical, Institut de Neurosciences des Systèmes UMR_S 1106, Aix Marseille Univ., INSERM, INS, Inst. Neurosci. Syst., 13005 Marseille, France
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Enrique C. A. Hansen
1Institut de la Santé et de la Recherche Médical, Institut de Neurosciences des Systèmes UMR_S 1106, Aix Marseille Univ., INSERM, INS, Inst. Neurosci. Syst., 13005 Marseille, France
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Christophe Bernard
1Institut de la Santé et de la Recherche Médical, Institut de Neurosciences des Systèmes UMR_S 1106, Aix Marseille Univ., INSERM, INS, Inst. Neurosci. Syst., 13005 Marseille, France
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Anthony R. McIntosh
2Rotman Research Institute of Baycrest Center, University of Toronto, Toronto, Ontario M6A 2E1, Canada
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Viktor K. Jirsa
1Institut de la Santé et de la Recherche Médical, Institut de Neurosciences des Systèmes UMR_S 1106, Aix Marseille Univ., INSERM, INS, Inst. Neurosci. Syst., 13005 Marseille, France
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  • Figure 1.
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    Figure 1.

    Structure of the large-scale brain model. a, The large-scale brain model is composed of the geometry of the brain of 116 subcortical areas and the two cerebral hemispheres. b, There are 37 cortical areas, each containing between 29 and 683 nodes (dots in a), for a total of 8,192 nodes per hemisphere. c, Homogeneous and heterogeneous SC. Heterogeneous SC corresponds to white matter tracts connecting brain areas over long distances. Homogeneous SC corresponds to gray matter fibers, with short-range connections within a given area, but also enabling some communication over short distances between neighboring areas. Although Area 2 is not connected to Areas 1 and 3 via the white matter, it is weakly linked to both areas via a set of short-range SC. d, Homogeneous SC matrix for the 16,384 nodes. The synaptic weights are color coded. The diagonal describes in warm colors the strong SC of adjacent nodes. SC decreases with distance, which is shown in cold colors. SC of nearby nodes are scattered (e.g., blue dots) in d because each cerebral hemisphere is described by a surface, which makes it impossible to cluster nodes locally along both axes. Note the absence on interhemispheric short-range SC. e, Heterogeneous SC for the 190 (74 cortical plus 116 subcortical) areas for weights (left) and time delays (right). Within one hemisphere, the 58 subcortical areas mostly project to the 37 cortical areas. Some connections between subcortical areas can also be seen. The 37 cortical areas project heavily to both cortical and subcortical areas. Some interhemispheric connections can also been seen. Note also the presence of large time delays.

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    Figure 2.

    The large-scale brain model works near criticality. a, Each node in the model is parameterized by γ to operate intrinsically at the same distance from the critical point if unconnected. A node shows zero activity or oscillation (∼42 Hz) in response to stimulation (red crosses). The activity at each node is described by two time-dependent variables, ψ1(t) and ψ2(t). The closer a node operates to the critical point, the larger and the longer lasting is the oscillation (compare γ1 and, γ2). When the critical point is reached, the node intrinsically performs a rhythm of constant magnitude. The model, however, is set so that the critical point is never exceeded. b, Principles of activity spreading after stimulation. The damped oscillation generated in the stimulated node (1) is sent via its efferent connections to its target node (2), triggering there, in turn, a damped oscillation with weaker amplitude and faster decay, which then propagates to the next node. Activity ψ1 ( j ) (t) of node (j) is scaled by cij and transmitted to node (i) via homogeneous and heterogeneous connections (SCs), delayed by τij in the latter case. In such a chain, activity would decay fast. c, In the large-scale brain model, multiple activity re-entry points can be found. At any time point, the dynamics of a node is influenced by all incoming activity. The response of the node to stimulation (1) is relayed to linked nodes (2–4), which may be fed back to 1 via 4 and may allow the induced activity to dissipate on a much longer time scale. The network response thus depends upon the SC and allows the network to operate near criticality. d, Activation of dynamically responsive networks. Activity after stimulating a node (1 or 2) in a series connection decays fast (as in b). However, activity may circulate and thus decays slower in a feedback network (4–5). Such remaining activity after the initial stimulation decay reveals the so-called dynamically responsive networks.

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    Figure 3.

    Dissipation after stimulation. a, Response of area PFCcl to the activation of three different regions PMCdl, CCp, and PCm (abbreviations are given in Table 1). Note that the amplitude, decay, and phase of the response depend upon the stimulated area. The main determinants of the response pattern are the connections, the synaptic weights, and the time delays. The envelope of the time series is computed (black, gray, and green lines for the three stimulation sites). b, Spatiotemporal activation following stimulation of three different regions. At a given time point, we extract the amplitude of the envelope for the 16,500 nodes (the 16,384 cortical nodes and the 116 subcortical ones), which we normalize to 1. The color scale thus indicates the contribution of a given region to the overall activity. The dissipation of activity after stimulating two distant brain areas, PMCdl and CCp (located far from one another: PMCdl in the lateral surface, CCp in the medial surface) leads to similar topographical patterns (for t > 640 ms). In contrast, a distinct pattern appears when stimulating PCm, which is adjacent to CCp. c, Extraction of the main activated propagation subnetworks. We use the stimulation of PMCdl as an example. We calculate the covariance among the 16,500 time series (the 16,384 cortical nodes and the 116 subcortical ones) for a time window centered at 750 ms and then perform a PCA to extract the subnetworks capturing >99% of the activity. Three different networks are thus dynamically responsive when PMCdl is stimulated.

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    Figure 4.

    Comparison between dynamically responsive networks to stimulation (top rows) and the experimentally observed RS networks (bottom rows) for the lateral and medial surface of the brain. a–h, Default mode, visual, auditory-phonological, somatomotor, memory, ventral-stream, dorsal attention, and working memory. We used 20% to 80% for the ratio of heterogeneous/homogeneous SC and a range of 10 mm for the homogeneous SC. The white to red scale gives the relative contribution of areas to the responsive networks (top rows) and the RS networks (bottom rows). The stimulation sites are given in Table 2 and Figure 7. Note that the bottom rows are activity masks for the 74 cortical areas constituting the RS networks, where activity is not localized within areas and uniformly color coded (see Materials and Methods). The top rows show the vector field Ψ (x, t) on the mesh of 16,384 cortical nodes and thus localized activity.

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    Figure 5.

    Repertoire of dynamically responsive networks. a, The number of networks responsive to cerebral stimulation depends on the spatial range of the homogeneous SC and the ratio of homogeneous SC to heterogeneous SC. b, Similar to a for the number of effective cerebral stimulation sites leading to different networks.

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    Figure 6.

    Influence of the structure on the RS-like networks. The pattern of each stimulation-responsive network (from Fig. 5) that best explains an experimentally observed RS network (rows) is correlated with the underlying heterogeneous SC using seven graph-theoretic measures (columns). Incoming, outgoing, or all connected ties to an area can be measured in terms of number (i.e., in-, out-, total-degree) or in terms of strength (i.e., in-, out-, total-strength). The clustering coefficient measures the degree to which areas in a graph tend to cluster together. BC indicates a matching with warmer colors, where comparisons marked with a star are statistically significant. Note that correlations may be high but not significant using a permutation test. The in-degree of the heterogeneous SC can be related to the two memory networks and the attention network. The activation of the other RS networks emerges in a way that is not predicted by the network metrics.

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    Figure 7.

    RS-like networks triggered by stimulation. a, b, Cortical stimulations in a, and subcortical stimulations in b lead to dynamically responsive networks correlating significantly with RS networks for a ratio of 20% to 80% of the heterogeneous/homogeneous SC and a range of 10 mm of the homogeneous SC. BC = [0, 1] indicates a matching with higher values. The eigenvectors, EV (1–3 in descending order of eigenvalues and captured variance), indicate the responsive networks to an effective stimulation matching with RS networks. Abbreviations are listed in Table 1. Note that the sites triggering a particular pattern can be scattered over the cerebral hemispheres (e.g., for the two memory networks and the somatomotor network).

Tables

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    Table 1:

    Abbreviations of brain areas

    A1Primary auditory cortex (57,74)CldCapsule of the nucleus lateralis dorsalis
    A2Secondary auditory cortex (33,64)CnMdNucleus centrum medianum thalami
    AmygAmygdala (151,135)CsNucleus centralis superior thalami
    CCaGyrus cinguli anterior (54,49)CslNucleus centralis superior lateralis thalami
    CCpGyrus cinguli posterior (167,179)GLNucleus geniculatus lateralis thalami
    CCrGyrus cinguli retrosplenialis (68,67)GMNucleus geniculatus medialis thalami
    CCsGyrus cinguli subgenualis (29,42)GMpcNucleus geniculatus medialis thalami, pars parvocellularis
    FEFFrontal eye field (104,161)ILIntralaminar nuclei of the thalamus
    GGustatory cortex (52,42)LDLaterodorsal nucleus (thalamus)
    HCHippocampal cortex (75,54)LiNucleus limitans thalami
    IaAnterior insula (48,71)LPNucleus lateralis posterior thalami
    IpPosterior insula (82,111)MDNucleus medialis dorsalis thalami
    M1Primary motor area (463,460)MDdcNucleus medialis dorsalis thalami, pars densocellularis
    PCiInferior parietal cortex (454,371)MDmcNucleus medialis dorsalis thalami, pars magnocellularis
    PCipCortex of the intraparietal sulcus (355,486)MDmfNucleus medialis dorsalis thalami, pars multiformis
    PCmMedial parietal cortex (196,241)MDpcNucleus medialis dorsalis thalami, pars parvocellularis
    PCsSuperior parietal cortex (199,177)MLMidline nuclei of the thalamus
    PFCclCentrolateral prefrontal cortex (328,227)PaNucleus paraventricularis thalami
    PFCdlDorsolateral prefrontal cortex (248,216)PacNucleus paraventricularis caudalis thalami
    PFCdmDorsomedial prefrontal cortex (211,270)PcnNucleus paracentralis thalami
    PFCmMedial prefrontal cortex (61,68)PfNucleus parafascicularis thalami
    PFCorbOrbital prefrontal cortex (310,265)PTNucleus parataenialis thalami
    PFCpolPole of prefrontal cortex (279,279)PulNucleus pulvinaris thalami
    PFCvlVentrolateral prefrontal cortex (380,479)Pul.iNucleus pulvinaris inferior thalami
    PHCParahippocampal cortex (267,212)lPul.lNucleus pulvinaris lateralis thalami
    PMCdlDorsolateral premotor cortex (108,138)Pul.mNucleus pulvinaris medialis thalami
    PMCmMedial premotor cortex (149,68)Pul.oNucleus pulvinaris oralis thalami
    PMCvlVentrolateral premotor cortex (126,138)RNucleus reticularis thalami
    S1Primary somatosensory cortex (487,420)ReNucleus reuniens thalami
    S2Secondary somatosensory cortex (107,116)SGNucleus suprageniculatus thalami
    TCcCentral temporal cortex (436,422)Teg.aNucleus tegmentalis anterior
    TCiInferior temporal cortex (390,306)VAventral anterior nucleus (thalamus)
    TCpolPole of temporal cortex (91,101)VAmcNucleus ventralis anterior thalami, pars magnocellularis
    TCsSuperior temporal cortex (306,352)VApcNucleus ventralis anterior thalami, pars parvocellularis
    TCvVentral temporal cortex (260,317)VLventral lateral nucleus (thalamus)
    V1Visual area 1 (147,180)VLcNucleus ventralis lateralis thalami, pars caudalis
    V2Secondary visual cortex (683,663)VLmNucleus ventralis lateralis thalami, pars medialis
    VLoNucleus ventralis lateralis thalami, pars oralis
    ADNucleus anterior dorsalis thalamiVLpsNucleus ventralis lateralis thalami, pars postrema
    AMNucleus anterior medialis thalamiVPNucleus ventralis posterior
    ANAnterior nuclei of the thalamusVPINucleus ventralis posterior inferior thalami
    AVNucleus anterior ventralis thalamiVPLAentral posterior lateral nucleus (thalamus)
    CaudNucleus caudatusVPLcNucleus ventralis posterior lateralis thalami, pars caudalis
    CdcNucleus centralis densocellularis thalamiVPLoNucleus ventralis posterior lateralis thalami, pars oralis
    CifNucleus centralis inferior thalamiVPMNucleus ventralis posterior medialis thalami
    CimNucleus centralis intermedialis thalamiVPMpcNucleus ventralis posterior medialis, pars parvocellularis
    ClNucleus centralis lateralis thalamiXArea X (thalamus)
    ClauClaustrumClcNucleus centralis latocellularis thalami
    • Number of nodes per cortical areas in brackets (left, right).

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    Table 2:

    The stimulation sites corresponding to the dynamically responsive network that best match a particular RS network

    Resting-state networkStimulation condition
    Cortex (excluding subcortex)Subcortex (excluding cortex)Cortex and subcortex
    Default modePFCm (0.8337)AD (0.8420)AD (0.8506)
    VisualCCs (0.6455)GL (0.6953)GL (0.7510)
    Auditory-phonologicalTCs (0.7147)GMPC (0.6630)TCs (0.7147)
    Somato-motorM1 (0.8153)MDDC (0.8199)M1 (0.8153)
    MemoryV2 (0.8646)MDDC (0.8454)V2 (0.8646)
    Ventral streamCCa (0.7845)ML, AN, SG (0.8122)CCa (0.7845)
    Dorsal attentionM1 (0.7039)R, VA, X (0.7097)AD (0.7631)
    Working memoryCCs (0.8006)PAC, Cdc (0.8204)GL (0.8069)
    • All responsive networks of a parameter configuration were compared to the eight experimentally known RS networks. A permutation test was performed to test the significance of each comparison. The multiple comparisons were corrected using the Bonferroni–Holm correction. For the comparison, the dynamically responsive networks were differentiated into: cortically, subcortically responsive networks, and the union of all responsive networks irrespective of the stimulation site. For each of these three groups separately, the parameterization was found to show the best accordance of stimulation responsive networks with the entire set of RS networks. The optimal parameterization is the ratio of 20% to 80% for the heterogeneous/homogeneous SC and a range of 10 mm for the homogeneous SC for all groups, except the range is with 17 mm different for the group of responsive networks to subcortical stimulation. Note the presence of cortical and subcortical sites in the last column, which has higher matching values on average over the eight RS networks compared with the other groups. The value in parenthesis is the matching coefficient (it varies between 0 and 1). Abbreviations are listed in Table 1.

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Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain
Andreas Spiegler, Enrique C. A. Hansen, Christophe Bernard, Anthony R. McIntosh, Viktor K. Jirsa
eNeuro 21 September 2016, 3 (5) ENEURO.0068-16.2016; DOI: 10.1523/ENEURO.0068-16.2016

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Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain
Andreas Spiegler, Enrique C. A. Hansen, Christophe Bernard, Anthony R. McIntosh, Viktor K. Jirsa
eNeuro 21 September 2016, 3 (5) ENEURO.0068-16.2016; DOI: 10.1523/ENEURO.0068-16.2016
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Keywords

  • connectivity
  • connectome
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