Global neural dynamics emerge from multi-scale brain structures, with nodes dynamically communicating to form transient ensembles that may represent neural information. Neural activity can be measured empirically at scales spanning proteins and subcellular domains to neuronal assemblies or whole-brain networks connected through tracts, but it has remained challenging to bridge knowledge between empirically tractable scales. Multi-scale models of brain function have begun to directly link the emergence of global brain dynamics in conscious and unconscious brain states with microscopic changes at the level of cells. In particular, adaptive exponential integrate-and-fire (AdEx) mean-field models representing statistical properties of local populations of neurons have been connected following human tractography data to represent multi-scale neural phenomena in simulations using The Virtual Brain (TVB). While mean-field models can be run on personal computers for short simulations, or in parallel on high-performance computing (HPC) architectures for longer simulations and parameter scans, the computational burden remains red heavy and vast areas of the parameter space remain unexplored. In this work, we report that our HPC framework, a modular set of methods used here to implement the TVB-AdEx model for the graphics processing unit (GPU) and analyze emergent dynamics, notably accelerates simulations and substantially reduces computational resource requirements. The framework preserves the stability and robustness of the TVB-AdEx model, thus facilitating a finer-resolution exploration of vast parameter spaces as well as longer simulations that were previously near impossible to perform. Comparing our GPU implementations of the TVB-AdEx framework with previous implementations using central processing units (CPUs), we first show correspondence of the resulting simulated time-series data from GPU and CPU instantiations. Next, the similarity of parameter combinations, giving rise to patterns of functional connectivity, between brain regions is demonstrated. By varying global coupling together with spike-frequency adaptation, we next replicate previous results indicating inter-dependence of these parameters in inducing transitions between dynamics associated with conscious and unconscious brain states. Upon further exploring parameter space, we report a nonlinear interplay between the spike-frequency adaptation and subthreshold adaptation, as well as previously unappreciated interactions between the global coupling, adaptation, and propagation velocity of action potentials along the human connectome. Given that simulation and analysis toolkits are made public as open-source packages, this framework serves as a template onto which other models can be easily scripted. Further, personalized data-sets can be used for for the creation of red virtual brain twins toward facilitating more precise approaches to the study of epilepsy, sleep, anesthesia, and disorders of consciousness. These results thus represent potentially impactful, publicly available methods for simulating and analyzing human brain states. Full article

Thanks to the availability of connectome data that map connectivity between multiple brain areas, it is now possible to build models of whole-brain activity. At the same time, advances in mean-field techniques have led to biologically based population models that integrate biophysical features such as membrane conductances or synaptic conductances. In this paper, we show that this approach can be used in brain-wide models of mice, macaques, and humans.We illustrate this approach by showing the transition from wakefulness to sleep, simulated using multi-scale models, in the three species. We compare the level of synchrony between the three species and find that the mouse brain displays a higher overall synchrony of slow waves compared to monkey and human brains. We show that these differences are due to the different delays in axonal signal propagation between regions associated with brain size differences between the species. We also make the program code—which provides a set of open-source tools for simulating large-scale activity in the cerebral cortex of mice, monkeys, and humans—publicly available. Full article

Brain complexity relies on the integrity of structural and functional brain networks, where specialized areas synergistically cooperate on a large scale. Local alterations within these areas can lead to widespread consequences, leading to a reduction in overall network complexity. Investigating the mechanisms governing this occurrence and exploring potential compensatory interventions is a pressing research focus. In this study, we employed a whole-brain in silico model to simulate the large-scale impact of local node alterations. These were assessed by network complexity metrics derived from both the model’s spontaneous activity (i.e., Lempel–Ziv complexity (LZc)) and its responses to simulated local perturbations (i.e., the Perturbational Complexity Index (PCI)). Compared to LZc, local node silencing of distinct brain regions induced large-scale alterations that were paralleled by a systematic drop of PCI. Specifically, while the intact model engaged in complex interactions closely resembling those obtained in empirical studies, it displayed reduced PCI values across all local manipulations. This approach also revealed the heterogeneous impact of different local manipulations on network alterations, emphasizing the importance of posterior hubs in sustaining brain complexity. This work marks an initial stride toward a comprehensive exploration of the mechanisms underlying the loss and recovery of brain complexity across different conditions. Full article

Pyramidal neurons and interneurons play critical roles in regulating the neuronal activities in the mammalian cortex, where they exhibit different firing patterns. Pyramidal neurons mainly exhibit regular-spiking firing patterns, while interneurons have fast-spiking firing patterns. Cortical neurons have distinct action potential onset dynamics, in which the evoked action potential is rapid and highly variable. However, it is still unclear how cortical regular-spiking and fast-spiking neurons discriminate between different types of stimuli by changing their action potential onset parameters. Thus, we used intracellular recordings of regular-spiking and fast-spiking neurons, taken from layer 2/3 in the somatosensory cortex of adult mice, to investigate changes in the action potential waveform in response to two distinct stimulation protocols: the conventional step-and-hold and frozen noise. The results show that the frozen noise stimulation paradigm evoked more rapid action potential with lower threshold potential in both neuron types. Nevertheless, the difference in the action potential rapidity in response to different stimuli was significant in regular-spiking pyramidal neurons while insignificant in fast-spiking interneurons. Furthermore, the threshold variation was significantly higher for regular-spiking neurons than for fast-spiking neurons. Our findings demonstrate that different types of cortical neurons exhibit various onset dynamics of the action potentials, implying that different mechanisms govern the initiation of action potentials across cortical neuron subtypes. Full article

Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses but also how long it takes to instantiate the network model in computer memory. On the hardware side, acceleration via highly parallel GPUs is being increasingly utilized. On the software side, code generation approaches ensure highly optimized code at the expense of repeated code regeneration and recompilation after modifications to the network model. Aiming for a greater flexibility with respect to iterative model changes, here we propose a new method for creating network connections interactively, dynamically, and directly in GPU memory through a set of commonly used high-level connection rules. We validate the simulation performance with both consumer and data center GPUs on two neuroscientifically relevant models: a cortical microcircuit of about 77,000 leaky-integrate-and-fire neuron models and 300 million static synapses, and a two-population network recurrently connected using a variety of connection rules. With our proposed ad hoc network instantiation, both network construction and simulation times are comparable or shorter than those obtained with other state-of-the-art simulation technologies while still meeting the flexibility demands of explorative network modeling. Full article

Obsessive–compulsive disorder (OCD) represents a frequent and highly disabling mental disorder. Past attempts to characterize different disease subgroups focused on the time of onset (late vs. early onset), presence of insight (poor insight), and post-infectious forms (pediatric acute-onset neuropsychiatric syndrome, PANS). Each subgroup may be associated with a differing impact on cognition, functioning, sleep quality, and treatment response profile. Certain lines of evidence suggest brain-derived neurotrophic factor (BDNF) levels may differ between individuals living with OCD as compared with controls, but there is a lack of evidence on the variation of BDNF levels in OCD subgroups. Lastly, the potential of assessing inflammatory states, electroencephalogram, and polysomnography to characterize these subtypes has been hardly explored. Estimates of drug-resistance rates indicate that 20% and up to 65% of affected adults and up to 35% of the pediatric population may not benefit from pharmacological treatments. At least part of the variability in treatment response could depend on the underlying biological heterogeneity. In the present project, we aim to increase the accuracy in characterizing the phenotypical and biological signature for the different OCD subtypes through clinical, cognitive, and sleep markers, along with other possible markers that may be biologically plausible. Full article