When I was much, much younger, I thought it would not be possible to map the brain so many cells, so many connections, and so little structure. I was very wrong. There is structure. Every year there seems to be more of the structure identified.
There is a paper out by Wedeen etal (see citation below) that traces the nerve fibers of the brain using diffusion fMRI and shows they form a 3 dimensional grid. A common structure seems to be two ribbons on parallel fibers that cross each other at 90 degrees. Thus every fiber in the one ribbon will come extremely close to every fiber in the other ribbon. Think of a small piece of a simple weave cloth. One length-wise thread will lie against every width-wise thread as it goes from the top to the bottom of the cloth. Every pair of threads – one warp and one weft will meet no pairing will be missed. A third set of fibers can pass through at right angles to the first two.
Many areas of the cortical white matter were studied and the organized orthographic crossings were always found. All pathways had a sheet structure. The structure resembles and is continuous with grids in other brain regions like the basal ganglia and the brain stem.
The structure was found in rhesus, owl monkey, marmoset, the pro-simian galago, and human subjects.
Strong homology of deep cerebral grid structure was found across all species studied. These included the grid systems of the callosum, sagittal stratum, and supra-Sylvian pathways, as well as the crossing of the fornix and anterior commissure in all species studied In the rhesus monkey, central and sub- cortical grid structures, including those of the major frontal sulci (principal, arcuate, central), fit together continuously like a jigsaw puzzle. Thus, we hypothesize that the complex connectivity of the cerebral mantle represents a continuous elaboration of the simpler core.
From a embyonic developmental viewpoint of control by chemical gradients, it is not a complex structure.
This structure has a natural interpretation. By the Frobenius theorem, any three families of curves in 3D mutually cross in sheets if and only if they represent the gradients of three corresponding scalar functions. Accordingly, we hypothesize that the pathways of the brain follow a base-plan established by the three chemotactic gradients of early embryogenesis. Thus, the pathways of the mature brain presents an image of these three primordial gradients, plastically deformed by development. … Grid structure should restrict and simplify axonal path-finding compared with models that allow less constrained and less correlated connectivity within and between cerebral areas. If grid structure guides connectivity similar to the lane markers in a highway, then navigation would be reduced from a general 3D problem to a far simpler question of when to exit.
We can see how this sort of structure could start with more or less everything connected to everything and then during later fetal development and early childhood destroying the un-wanted connections. We can also see how this would be a useful system for plasticity (creating and modifying connections) without having to grow many new axons.
this structure supports incremental modification of connectivity by geometric modification within broad continuous families of parallel paths. Thus, the grid organization of cerebral pathways may represent a default connectivity, on which adaptation of structure and function can both occur incrementally in evolution and development, plasticity, and function.
A review by Zamora-López etal (see citation below) looks at the nature of networks in the brain, looking at how the brain can be both local and global at the same time.
A prominent problem is that a collection of specialized functions alone cannot give rise to a coherent perception of the reality. For that, different parts of the brain need to communicate and their information needs to be combined. Physiological recordings with multiple electrodes have revealed that distant neurons can synchronize, and neuro-imaging studies have extensively reported the co-activation of distant brain regions under different experimental conditions. These observations have set the foundations for novel approaches to understand the brain: that networks of segregated but interacting processes govern neural dynamics on top of the processing of the specialized regions. At the hidden ground of those functional and dynamic observations lies the fact that the neurons in a nervous system form a vast network with a mixture of both local and long-range connections.
The authors look at the nature of the brain’s networks, mainly by measurements of various network parameters in cats.
First, these networks are densely connected. Although far from an all-to-all connected system, approximately 30% of all possible links are present. As a consequence, cortical areas are all at very few processing steps of each other. Second, the path of information transmission between two cortical areas is not unique, but there exists many alternative routes through which information can flow. These two observations support the notion that the cerebral cortex is a highly interactive information processing system, and is dynamically flexible. Although cortical regions might specialize in the processing of a particular function, they do not operate independently, but in strong influence of each other. The fact that information can flow through different alternative paths significantly enhances the richness and complexity of the processing capabilities of the system with a limited number of resources. If the system were provided with mechanisms to selectively activate or inactivate paths of communication, the range of dynamical states it can host increases significantly. In the brain, inhibitory connections may very well be responsible for such switching dynamics and permit that the resources used by each process self-organize by mutual and transitory competition.
Sensory processing begins in very local groups in parallel with one another.
However, in order to generate a coherent perception of the reality, the brain needs to combine (integrate) this multisensory information at some place and during some time. For this to happen, the paths of information need to converge. Whether integration occurs in specialized and localized regions of the brain, resembling information processing of sensory features by specialized regions, or it happens as a consequence of distributed but coordinated processing in multiple areas is still a subject of debate. During the last decades, multi-electrode recordings have demonstrated that distant regions of the brain undergo transient states of correlated activity as the consequence of behavioral responses to sensory stimuli and cognitive tasks in non-human primates. Current neuroimaging techniques permit to observe the whole brain at work, revealing the occurrence of patterns of correlated activity between distributed cortical areas. From an anatomical point of view, it has been argued that the functional capacity of the nervous system to balance between segregation (specialization) and integration is facilitated by its structural organization.
They describe the architecture of cortico-cortical networks:
(i) cortical areas are at very short processing paths of each other (small-world property), (ii) there are many processing paths between two cortical areas, (iii) they are organized into a few modules, and (iv) they contain few highly connected areas, hubs, which (v) form a rich-club at the top of the network hierarchy. Although
regions exist which are specialized in a particular function, they do not operate independently. They work under the constant influence of each other. The modular and hierarchical architecture of the cortical networks represent the physical substrate that permits the brain to simultaneously process information of different modalities (parallel processing) and to integrate that information toward the generation of a coherent, global representation of the reality.
The small-world, rich-club, heirarchical network model answers many questions as well as mathematically fitting much of the data.
On the one hand, both physiological experiments and the study of patients with localized brain lesions have long evidenced that brain and cortical regions specialize in particular functions. On the other hand, during the last two decades, micro-electrode recordings at multiple sites and neuroimaging studies, have shown that distant regions of the brain undergo transient states of correlated activity. Based in these observations, a networked perspective has started to dominate in which brain activity is regarded as functional networks which rapidly emerge and dissolve, governed by coordination dynamics according to the sensory stimulation and the ongoing activity. Several models have been proposed that high-level functions are represented by distributed, interactive, and overlapping networks of neurons, which transcend any of the traditional subdivisions of the cortex by structural (cyto-architecture) or functional criteria.
Now, studying the anatomical connectivity of cortical and neural networks we find that, indeed, the nervous system is organized such that both approaches coexist. While different parts of the system specialize in performing particular functions, brain function is to be understood as emerging from the collective working of its constituents without a single coordinating center. The modular organization of the neural connectivity supports the specialization of different parts, and the highly interconnected hubs are responsible for the integration and/or coordination.
I have a reservation on both these papers. They do not deal with the connections with the thalamus, cerebellum, basal ganglia and other important partners of the cortex. But ‘walk before we run’; they are a great start to a more realistic understanding.
Wedeen VJ, Rosene DL, Wang R, Dai G, Mortazavi F, Hagmann P, Kaas JH, & Tseng WY (2012). The geometric structure of the brain fiber pathways. Science (New York, N.Y.), 335 (6076), 1628-34 PMID: 22461612
G. Zamora-Lopez, C. Zhou, & J. Kurths (2011). Exploring brain function from anatomical connectivity Fronteirs in Neuroscience, 5 DOI: 10.3389/fnins.2011.00083