C05
Cortical travelling waves of neural activity may have a role in shaping cognitive function. Theory and computational modelling predict that the strength and pattern of the incoming connectivity may direct travelling waves to alter cognitive performance. Here, we will probe experimentally and computationally the relationship between cortical waves at different spatial and temporal scales, neural response dynamics and cognitive performance, using neurophysiological recording and direct brain interventions in the perceptual decision-making network of primate V5/MT and its connections. Understanding how local wave patterns relate to global, inter-areal waves will open up new insights into the control of cognitive function through neural resource allocation.
Principal Investigators
Prof. Dr. Kristine Krug
Prof. Dr. med. Petra Ritter
Co-Workers
Amy Addlesee
Prof. Dr. Andrew Parker
Dr. Sascha Ziegler
Brain networks for perceptual decision-making
Perceptual decision-making is the process by which sensory information is integrated and evaluated to generate a behavioural choice. Local activity of neurons in the extrastriate visual, parietal cortex and prefrontal cortex contribute to perceptual decisions in primates (Gold, Shadlen 2007; Krug et al. 2013). Cognitive processes such as attention, working memory or reward can also modulate decision-making performance; it is affected by ageing or psychological disorders (Takagaki, Krug 2020). As neural mechanism, altered modulation of local activity in visual circuits by prefrontal and posterior parietal cortex has been implicated. To test this hypothesis, we will directly study the interactions between the visual cortex and more distal areas and their impact on local resource allocation in perceptual decision-making.
Cortical waves of activity
The cerebral neocortex exhibits several distinctive properties and functional states that exhibit wavelike or oscillatory physiological processes. These temporally rhythmic events change between sleeping and waking states, under shifts of attention, and, in the most specific case, provide a basis for encoding of spatial environment by neurons in the hippocampal archicortex. Furthermore, pathological changes in these events are linked to the emergence of pathological brain states, such as epilepsy and migraine, in which the cortical response is dominated by rhythmic activity. Emerging research from our collaborator John Reynolds’ lab shows a link between these wavelike signals and cognitive task performance (Davis et al. 2020), but there remain fundamental, open questions concerning neural computation in the presence of physiological wavelike events. Cortical travelling waves are present in most parts of the brain across various frequency bands and spatial scales (Muller et al. 2018). Memory processes (Muller et al. 2016; Zhang et al. 2018; Bhattacharya et al. 2022), visual perception (Davis et al. 2020; Han et al. 2008; Lozano- Soldevilla & VanRullen 2019; Muller et al. 2014), motor planning and execution (Rubino et al. 2006; Takahashi et al. 2011), among many other functions are thought to be supported by travelling waves. Recent studies have proposed that cortical travelling waves take centre stage in brain activity (Bolt et al. 2022; Raut et al. 2021) but may have been obscured by traditional analysis techniques (Muller et al. 2018; Alexander et al. 2015). While experimental observations of cortical travelling waves are abundant, our understanding of their underlying mechanisms lags behind.
Wave-like dynamics in decision-making
Visual Area V5/MT at the heart of the decision-making network represents perceptual decision signals for 3D and motion figures in macaque monkeys (Krug 2020; Wasmuht et al. 2019; Krug et al. 2013). In the marmoset, travelling waves in V5/MT affect perceptual performance (Davis et al. 2020). With the critical cortical stages of this circuit causally linked to perceptual decision performance, this is the ideal circuitry and task to investigate how in primates, intra-areal travelling waves are shaped by inter-areal travelling waves and how both control perceptual performance. The dynamics and frequency of these travelling waves are thought to coordinate the flow of information through the network and thus constitute a potential candidate for pathological changes with a decrease in performance, either because local connectivity or function within one area is disrupted or between areas.
Large scale brain network modeling
Using neuroimaging coupled with FUS allows us to modulate and measure the functional connectivity between brain regions. On its own, this is not sufficient to address the complexity of brain circuits. Factors such as the functional and structural specificity of brain circuits generate multiple computing possibilities. Recent studies use mathematical tools such as the virtual brain (VBT) to build biologically plausible computational models of brain network dynamics in humans (Ritter et al. 2013) and macaques (Shen et al. 2019). The models provide a mechanistic understanding of how distributed brain regions interact to process neural information. Using our own connectivity data, we build VBT models by reproducing observed data, test our hypotheses and generate new predictions. The predictions will in turn be tested through neurophysiological and behavioural data and thus provide a quantitative understanding of decision-making network dynamics and the consequences of their disruption.
Gold JI, Shadlen MN. 2007. The neural basis of decision making. Annu Rev Neurosci 30:535-74.
Krug K, Cicmil N, Parker AJ, Cumming BG. 2013. Causal interference with neuronal signals in V5/MT influences perceptual judgments about a stereo-motion task. Current Biology 23:1454-9.
K Krug (2020) Coding perceptual decisions: from single units to emergent signaling properties in cortical circuits. Annual Review of Vision Science 6:387-409.
Ritter P, Schirner M, McIntosh AR, Jirsa VK. 2013. The virtual brain integrates computational modeling and multimodal neuroimaging. Brain Connect 3:121-45.
Shen K, Bezgin G, Schirner M, Ritter P, Everling S, McIntosh AR. 2019. A macaque connectome for large-scale network simulations in TheVirtualBrain. Scientific Data 6:123.
Takagaki K, Krug K. 2020. The effects of reward and social context on visual processing for perceptual decision-making. Current Opinion in Physiology 16, 109-117.
Verhagen L, Gallea C, Folloni D, Constans C, Jensen D, Ahnine H, Roumazeilles L, Santin M, Ahmed B, Lehericy S, Klein-Flügge M, Krug K, Mars RB, Rushworth MF, Pouget P, Aubry JF, Sallet J. 2019. Offline impact of transcranial focused ultrasound on cortical activation in primates. eLIFE 8:e40541.
Goals and prospects
A biologically plausible, quantitative model of cognitive brain mechanisms in non-human primates is essential for understanding the basic neural mechanisms of our own cognitive functions. The homology between macaque and human allows a direct transposition of neural architecture and computational models. The combination of approaches including high resolution neuroimaging, brain stimulation, neurophysiology and behaviour will provide a detailed, multi-level understanding of the neural mechanisms that shape decision-making. Specifically, integrative models of brain function should provide a comprehensive understanding of the structural and functional organisation of local and long-range brain networks and their interactions. Psychological disorders such as autism or schizophrenia have been associated with altered functional connectivity of specific neuronal networks. Therefore, the neurophysiological validation of FUS stimulation protocol represents a promising prospect toward the development of non-invasive therapeutic interventions and treatments in human to improve and restore human cognitive performances.