_applied_  
_sciences_  
_Article_  
_Investigating the Impact of Local Manipulations on Spontaneous and Evoked Brain Complexity Indices: A Large-Scale Computational Model_  
**[PERSON] 1,2,*,+, [PERSON] 3,*,+,  
Recently, structural human brain networks, also known as 'connectomes' [8; 9], became available thanks to different initiatives like the Human Connectome Project and the UK Biobank Imaging project [10; 11; 12]. When such networks are endowed with mathematical models of local excitability in each node, they allow the development of computational models to simulate whole-brain dynamics [13; 14; 15]. These models provide a controlled and mechanistic framework to explore a broad spectrum of parameters that are potentially relevant for better understanding global brain properties such as those implicated in brain complexity [2; 16]. Most empirical and computational investigations have explored network complexity based on spontaneous neural activity [6]. Such an observational approach can be usefully complemented by a causal approach, whereby stimulations are used to assess the complexity of neuronal interactions from a causal, rather than correlational, perspective. Along these lines, the Perturbational Complexity Index (PCI) [17] empirically estimates the joint presence of differentiation and integration by quantifying the richness of the spatiotemporal patterns of cortical activity extracted from the electroencephalographic (EEG) response to a brief local perturbation with transcranial magnetic stimulation (TMS). PCI has provided a reliable index to assess the loss and recovery of consciousness in both physiological and pathological conditions [18]. Supported by a series of multiscale experiments in cell cultures [19; 20], cortical slices [21], animal models [22; 23; 24], and intracranial and extracranial human studies [17; 18; 25; 26; 27; 28; 29], this perturb-and-measure approach has also recently been applied in silico. This computational neuroscience implementation can further elucidate the mechanistic link between brain complexity and anatomic-physiological brain properties. Specifically, the relationship between brain complexity and structural connectivity [30; 31] as well as functional network properties [32] have been investigated. In addition, the modulations of brain complexity dependent on global neuromodulation [33] as well as local node properties [34] have been recently addressed. Crucially, it is also possible to link single node properties to large-scale network dynamics [35; 36] and explore the global effects of local manipulations, such as virtual lesions [37] and silencing [38]. Previous computational investigations have shown that large-scale spontaneous network dynamics change considerably upon local connectivity alterations (i.e., local node deletion within spatially defined regions) [39]. Along these lines, a strong reduction of brain-wide activity was observed particularly when simulating the regional inactivation of posterior areas [40]. These results are particularly relevant in view of a postulated critical role of posterior regions of the cortex [41; 42] in supporting complexity and consciousness. So far, however, computational studies relating local properties to global brain states have focused mostly on spontaneous activity [37] and have not explored the complexity of neuronal interaction from a causal perspective (i.e., using controlled model perturbations). Here, we exploited direct perturbations and investigated the large-scale impact of controlled local manipulations (i.e., local node silencing) on network activity and brain complexity in a whole-brain computational model. Specifically, using \"The Virtual Brain\" platform [13; 43], we extensively and systematically varied the topology of the stimulated and the silenced nodes and performed a direct comparison between metrics derived from observational (Lempel-Ziv complexity) [44] and perturbational approaches (PCI).