New paper on Autism

S. Vattikuti and C.C. Chow, ‘A computational model for cerebral cortical dysfunction in Autism Spectrum Disorders’, Biol Psychiatry 67:672-6798 (2010).  PMID: 19880095

PDF available here.

Shashaank Vattikuti was a medical student and wanted to do a rotation in my lab.    He had done some pediatric rotations and was frustrated at the lack of treatments for autistic children.    He thought that  a better biophysical understanding of the neural activity that caused autism was necessary to make progress.  The conventional wisdom is that autism is due to some problem in global connectivity in the brain.  This makes sense because neural imaging data seems to show that different regions of the brain seem to be less functionally connected in autistics.   However, Shashaank thought that the deficit was probably a local microscopic one and that the global perturbations were due to the brain’s attempt to compensate for these deficits.

He found papers that showed that cortical structures called minicolums (on the hundred micron scale) were denser (closer together)  in autistics.  This immediately set off a flag in my head because I had previously shown for spiking neurons  (e.g. Chow and Coombes) that localized persistent activity (often called a bump) was more stable if the neuron density increased.   Bumps in networks of spiking neurons tended to wander around for small numbers but stabilized as the density increased.   Shashaank also found genetic and physiological evidence that the synaptic balance is tilted towards an excess of excitation in autistics.

The real breakthrough in making this more than an academic exercise was that Shashaank also found a simple behavioral task to model, where subjects visually fixate on a point for a certain amount of time and then shift their gaze to a target when instructed.  Most people will undershoot and this is called hypometria and make errors called dysmetria.  The data was mixed but it seemed like autistics had more hypometria and dysmetria.  The way we implemented this visually guided task in the model was to consider a one dimensional network of excitatory and inhibitory neurons.  The parameters were tuned so that when a stimulus was applied at a given position a bump of firing neurons would form.  The fixation point was represented by a stimulus applied to a location in the network.  We then stimulated at another location to indicate the saccade target and tracked how the bump moved.  We found that when there was an excess of excitation, hypometria and dysmetria both increased.  However, when the minicolumn structure was perturbed, only hypometria increased.

Before Shashaank ran the simulations, I really wasn’t sure what would happen.  Given more excitation, it is plausible that the bump would move more quickly and hence reduce hypometria with increased excitation.  Instead, the excessive excitation makes bumps more persistent and stable so it is harder to move them once they are established.   Hence, our  result hinges on there being prior neural activity that must be moved in a saccade task.    This is consistent  with autistics having more difficulty switching mental tasks.  Our  hypothesis is that the underlying source of autistic symptoms arise from excessive local persistent activity.  This excessive persistence is also why the effective connectivity between brain regions is reduced because each region is less responsive to external inputs.    It also suggests that restoring synaptic balance with medication may alleviate some of these symptoms.  We’re currently trying to devise ways to validate our hypothesis.

References:

Casanova MF, van Kooten IA, Switala AE, van Engeland H, Heinsen H, Steinbusch HW, et al. (2006): Minicolumnar abnormalities in autism. Acta Neuropathol 112:287–303.

C.C. Chow and S. Coombes, ‘Existence and Wandering of bumps in a spiking neural network model’. SIAM Journal on Applied Dynamical Systems 5, 552-574 (2006) [PDF]