Contra networks

Olaf Sporns, Nature Neuroscience 17:652-60 (2014)

"Contributions and challenges for network models in cognitive neuroscience"

This paper might be one of the most stimulating I have read recently - mainly because I agree with very little of it! In summarry he reviews studies that have anaylsed the connections between brain areas (histological, diffusion tensor, imaging and functional connectivity) using network models. Network theory is, in my eyes, just a posh way of getting numbers that characterise a particular way of connecting nodes together. Sporns offers to take a critical look at some of the advantages and limitations of network models in cognitive neuroscience. Unsurprisingly he has a very positive take. I challenge you to read it with a straight face.

Comments

  1. The field of network analysis is eating its own tail. They beg the question that network analysis tells us anything new. Typical hollow and meaningless conclusions are:
    • Network models offer a theoretical framework that encompasses both local and global processes and thus resolves the long standing conflict between localised and distributed processing.
    • By addressing how connectivity mediates both segregation and integration, network approaches not only reconcile these seemingly opposing perspectives, they also sugget that their coexistence is fundamental for brain function.
    • Integration in large-scale brain networks is supported by a set of specialised brain regions that transiently orchestrate interactions between functional modules
    • Integrative processes have a distinct anatomical substrate
    • There are robust relationships between network architecture and functional specialization
    Basically, in each case, the premises depend upon the conclusion. If you start by delineating modules, calculating connectivity, and proximity measures, these conclusions say nothing more than "I am using this method". They explain nothing.

    Just as an example, let's take the first one - that networks might allow us to resolve the global-local processing problem. The argument appears to be like this:

    • Premise 1: global network meausures capture important characteristics related to overall signaling capacity. (Note that this is itself a rather fanciful comment for functional networks -- even if I accept the doctrine of networks! In real computer networks, signalling capacity is the rate of transmission. This depends mainly on how much the data is compressed at encoding, and how fast the channel can change its signal. In the brain analogy, it might depend on refractory periods, but more importantly on the common language spoken by the two nodes. The network topology can play a part, but perhaps a minor one that is important only if we hold the language fixed -- i.e. encoding and representation use the same scheme across the brain.)
    • Premise 2: most RSNs are associated with specific behavioural or cognitive domains... network communities corresponding to specific domains were found to be associated with distinct functional fingerprints
    • Conclusion: we have found numbers characterising networks at local and global scales, which breaks the dilemma of whether computation is done at local or global scales.
    Hopefully you can see that all the arguments above are of this form: "I can measure new numbers meaning X - so the brain works by X"

    In the same vein, functional networks that show changes in such global/local parameters say nothing more than: "global/local correlations change over time, so this solves dilemmas we had about global/local processing".
    Incidentally, if you believe in these functional changes over time, they invalidate any conclusions you have made about structural networks!

  2. Is it surprising that functional connectivity parallels structural connectivity? Could it be any other way?
  3. Quantitative tools are overrated. Yes, they allow applications of quantitative measures of network topology. But to what end? We don't yet have even a qualitative theory of how the cognition is done. Perhaps we should start working on that?
  4. Claims that
    • it allows identification of structural network elements that are specialised for carrying out integrative function.; and
    • quantitative approaches provide information on regional roles in neural processing within and across communities.
    I.e. Betweenness (Centrality, Degree) are markers of a node having integrative function. Why? couldn't we just be looking at a relay station? The thalamus might be a relay station (I don't think it is, of course) yet still be at the centre of network topology - with no integrative function whatsoever.
  5. Areas which are highly functionally connected are unlikely to be computing anything interesting. In fact, if the activity of two areas is highly correlated, it suggests that they represent information in similar ways, and thus no real computation has been performed. If the frontoparietal module correlates with visual and tactile networks, that means that it encodes the two types of information (visual and tactile) in parallel. I.e. frontoparietal activity is a linear combination of visual and tactile information, and thus no computation has been performed, only superposition.

    In fact, shouldn't computation be defined as occurring just when areas are structurally connected but no functional correlation is seen?

  6. what are the fundamental rules and principles of network science? They are either tautologous (trivially true) or not based on the kind of information processing the brain does. No conclusion is going to be relevant to the neural basis of cognition if you neglect:
    1. the direction of connections,
    2. inhibitiory vs excitatory,
    3. preservation / distortion / manipulation of representations,
    4. cortical lamination...
    Even if you add these "instatiation properties" to network theory, what does the network theory itself add?
  7. In general, areas that are nearby each other in the brain are more likely to connect together. So, surely it is more interesting to consider the cases in which that rule is broken, i.e. where the greatest deviation from standard white-matter-distance occurs?
    In these situations, connectivity might be informative, because there must be a reason behind connections that are are not predictable from anatomical location,
  8. Do you really believe that the kinds of processes that generate human thought, understanding, belief, reasoning etc. can be even coarsely described in terms of the topology of the network? I'd say certainly not, at least with anything resembling the kind of network we're talking about today.
On the other hand, in favour of networks,
  1. MEG and connectivity might help understand short term plasticity.
  2. It is still possible that network statistics of a "node" might help understand why lesions to some brain areas cause more symptoms than others. One might argue that a node with high centrality would cause more deficits than a node with low centrality. But in drawing this conclusion, you make certain silent assumptions. In particular, you are suggesting that lesions that fail to disconnect two regions, because there are other (indirect) routes between the two regions, then this redundancy of connectivity allows "information flow" "around" the lesion. First, it is not clear that connections in a network represent information flow, even if they were directional. Functionally derived networks, in particular, notoriously connect nodes which are simply driven by a common source. Second, it seems incoherent to suppose that nodes are performing computations, if you then speak of a damaged node being replaced by a chain of nodes that effectively "conduct around" it. Third, if we bite the bullet and suggest that some connections really do pass right through some nodes, e.g. if white matter and grey matter were not fully distinguished (as is likely to be the case in the striatum), then it is entirely unsuprising that a lesion would affect distant areas - this is just a glamorisation of the age-old phenomenon of diaschisis, and needs no network statistics to explain it.
  3. ?
Somewhere, the vague and heterogeneous metaphors of electrical conduction, internet servers, ethernet hubs, logic gates, connectionist synaptic weights and telephone switchboards have become muddled and confused, to the point where teasing out meaningful conclusions seems futile.

Bad words

Many words are misused because people fail to distinguish between the two meanings of the word. Usually one meaning is technical, and the other metaphorical.

  • Coherent:

    1. The phase relation of two oscillations at the same frequency are fixed over time.
    2. a set of propositions that do not contradict.
    Any talk about "representations being coherent" is doomed to carry no meaning until we understand how propositions are expressed at a cellular level.

  • Integration:

    1. Infinitessimal summation over space or time.
    2. To unite several components into a single whole.
    Without an understanding of what it means to put two meaningful propositions together, i.e. in terms of physiology, any talk of "integration of information" will fail to refer to anything.

  • Circuit:

    1. A circuit is formed when a voltage (energy gradient) is applied across a conductor to generate a current. No information is transferred. Generally a loop.
    2. "circuit board" or "integrated circuit" - a piece of hardware that has inputs and outputs, a power source, and a specification that indicates precisely what outputs will occur when any combination of inputs is provided -- i.e. a well defined computation is performed. Generally not loop shaped. Generally does not form a comlete circuit until it is powered up.

  • Network:

    1. Structure with many connected nodes e.g. a fishing net;
    2. Computer system capable of transferring information.
    The former network does not transmit anything from one node to another, nor does it perform any computations. The key property of the latter "network" is actually the protocol used to coordinate the movement of information. As long as the protocol is right, the topology is generally straightforward. Just because computer networks also happen to be structural networks, this does not mean that any structural network is capable of transferring information.