T HESIS ’ FRAMEWORK
1.2 From neural networks to cognition
Recent convergence of computational science and neuroscience has opened new perspectives in understanding brain function in all its complexity. Park and Friston13 recently published a review of structural and functional neural networks and the actual state of knowledge in modelling cognition. This theme is of major importance to map the human brain14 and integrate functional connectivity to its structural neural
components. Relying on computational neuroscience, topological network analysis and biophysics of neural connection, it has now become possible to present a theoretical model of topological hierarchical modular networks explaining how diverse functions can emerge from underlying local apparent static neuronal networks. These advances are a giant leap in our ability to ground psychological theories at a neural level.15
Instead of breaking the brain down into segregated functional specialised regions, it is also possible to consider cognition as the result of global integration of multiple local integrators. Indeed, the structural organisation of the brain is both modular and hierarchical (Figure 2). Local integration is organised within a restraint neural network called a module (short-range connection), itself being constituted of submodules, whereas global integration leading to higher cognition relies on longer-range connections between modules. Each module has its own specific computational objective that can be accessed by more than one network. Therefore, the same structural network can serve more than one function. This theoretical approach of the brain, inspired by graph theory,16 is largely supported by advances in connectivity studies17 and has been shown to be reliable in identifying structural brain connectivity.18 defined in humans by relying on nodes, edges, modules and hubs.
The second section discusses limitation of the structure-function connectivity convergence, before presenting a theoretical model of canonical neuronal circuits capable of integrating these divergences.
1.2.1 Nodes and edges
Brain areas are delimited by underlying structural and functional properties that are defined through architectonics, inter-areal connectivity, physiological characteristics and topological organisation.19 In humans, neuroimaging can be used to help define brain regions and recognise patterns of connectivity called nodes. Two main methods are used:
clustering
transition in connectivity.
Clustering defines nodes by building spheres around “hot spots” from large cumulated collections of task-evoked functional magnetic resonance imaging (fMRI)
Figure 2: Multiscale
PART 1 – Thesis’ framework
studies. Transition in connectivity delimits nodes by identifying boundaries revealed by abrupt changes in patterns of connectivity, mainly using resting-state functional connectivity magnetic resonance imaging (rs-fcMRI), transcranial magnetic stimulation (TMS) and diffusion tensor imaging (DTI) tractography.20
Once these nodes have been delimited, the difficulty is to correctly model the way they are connected one to another (i.e. define edges). Three methods are concurrently used to define edges: structural, functional and effective connectivity.
Structural connectivity using DTI has largely improved our mapping of large long-distance connections between brain regions.21 However, contrarily to tracing methods, DTI cannot identify short or intrinsic connections. Functional connectivity using fMRI, rs-fcMRI, electroencephalography (EEG) and magnetoencephalography (MEG) analyses the coherence between signals from different regions and creates edges of different strength between them. This approach relies on statistical dependency that can be confounded by indirect pathways. Therefore, edges identified through functional connectivity might not be anatomically connected and need to rely on structural connectivity to assure physiological plausibility. Both functional and structural connectivity cannot define the direction of the signals between nodes. To achieve this, a third method called effective connectivity is used. This approach consists of constructing a neuronal integration model and testing and adjusting it to fit at best-observed responses (fMRI, EEG, TMS).
1.2.2 Modules and hubs
Other than respecting physiological principles, neural network modelling assumes the system is organised to optimise information passing, robustness, adaptability and resilience – and all this for differing functionalities within the same structure. To achieve this, the structural architecture of the brain seems to favour multiple, short to average length paths between clustered node pairs forming modules. Modules are then connected one to another through sparse, weak extrinsic connections (Figure 3).
Some nodes (rich-club hubs) are therefore heavily connected to other nodes within the same module (provincial hubs), whereas others connect the module to other modules (connector hubs).22
Figure 3: Network attributes. A) Local networks called nodes are connected to each other by edges. B) Some nodes show a high degree of connectivity to other nodes where others show a lower degree. C) Nodes have important connections in close range clusters called modules. Image from van der Heuvel (2013).23
1.2.3 Structure-function convergences and divergences
Even if rs-fcMRI provides more information on short-range intra-cortical interconnectivity, the presence of intrinsic connectivity networks (ICNs) during resting state reveals similar network modules to those found during task-related fMRI studies (salience network, dorsal attention network, executive control network, sensorimotor visual and auditory networks).23 Furthermore, both these approaches reveal a modular hierarchical organisation of brain networks consistent with brain anatomy, thereby supporting structural-functional convergeance.24 Functional networks, however, are task dependant. Long-range network architectures are therefore believed to organise themselves through rich-club hubs, depending upon demands. This means that for any one given structural connectivity pattern, many overlaying functional connectivity patterns can co-exist.
1.2.4 The underlying mechanisms of predictive coding
Within each local network or node, neurones are organised within cortical layers and integrate excitatory and inhibitory populations of cells. Neuronal activity (excitatory postsynaptic potential, inhibitory postsynaptic potential, spiking, inter-layer synchronicity) within a single cortical column can be modelled using canonical circuits of intrinsic connections.
In predictive coding, the Bayesian brain hypothesis has us assume the brain needs to construct a realistic model of our ever-changing environment. It then updates and optimises this model, using sensorial inputs. In other words, the central neural system can be seen as a computational inference machine that is designed to actively predict and provide a meaning to sensorial inputs.15 Neuronal networks then form a probabilistic generative model of incoming sensorial signals. In this context, Friston relies on the free-energy principle to provide a predictive coding model that explains how edge strength between modules can be modulated.25
By integrating intrinsic excitatory and inhibitory connectivity and extrinsic bottom-up (upwards) or top-down (downwards) connectivity to canonical microcircuits, it is possible to build a modular hierarchical neural network capable of optimising efficiency by minimising the error between the prediction and the sensorial input at each hierarchical level of processing (Figure 4).25, 26 In this system, oscillation and inter-network communication can be perceived as modulators of error.
Figure 4: Modular hierarchical (expectations). Triangles represent superficial (grey) and deep (black)
PART 1 – Thesis’ framework
1.2.5 Future perspectives for predictive coding
The Bayesian brain and the free-energy principle25 provide a theoretical framework capable of coherently modelling neural networks on higher cognitive functions. Even if it is believed to be the most advanced model in the field, it nevertheless remains a theoretical model that does not yet dispose of enough solid proof at a physiological level, to be recognised as an existing mechanism.
We can nevertheless admit that this modular hierarchical model is an important advance upon common linear models of neural networks. This model also reveals how important it is to constantly update existing models with new acquired knowledge on neural network architecture and its functional integration. New tracing methods,27 improvements in causal models for connectivity using EEG and MEG (Grangier causality or dynamic causal modelling)28, 29 and higher resolution in functional imaging30 are bound to provide precious insights in underlying mechanisms of cognition. In this context, computational modelling and effective connectivity will undeniably have an important role and serve to bridge existing gaps between structure and function.
1.3 Ageing and driving difficulties