A computational model for cerebral cortical dysfunction in autism spectrum disorders

Abstract

Background: Perturbations to the microscopic level balance between synaptic excitation and inhibition and neuron organization in the cerebral cortex are suggested to underlie autism spectrum disorder (ASD) traits. The mechanism linking these perturbations to cognitive behaviors in ASD is unknown. This study strives to bridge this gap by generating clinically testable diagnostic and pharmacological predictions based on the effect of synaptic imbalance and neuron distribution on a computational local circuit model of the cerebral cortex.

Methods: We use a computational microscopic model of the cerebral cortex that incorporates N-methyl-D-aspartate and gamma-aminobutyric acid synaptic kinetics. We employ the model circuit during model tasks similar to visually guided and gap oculomotor saccade tasks and interpret qualitative model predictions of saccade hypometria and dysmetria. We consider the effects of varying the excitatory to inhibitory synaptic balance, neuron density, and neuron clustering in this model.

Results: An increase of synaptic excitation over synaptic inhibition results in increased hypometria and dysmetria. Similar effects by either reduced inhibition or increased excitation suggest that a variety of pharmacological compounds can be used for both screening and medical management. On the other hand, any change to the microscopic neuron anatomy that increases the effective maximum distance between excitatory neurons decreases hypometria but has no affect on dysmetria.

Conclusions: Perturbations to a computational model of a local cerebral cortical circuit can account for saccade hypometria and dysmetria reported in ASD studies. This approach may provide a direct link between cerebral cortical function and ASD behaviors.

Figure 1

(A) Schematic of the model. Model neurons are recurrently connected (double arrows) within and between the E neuron (circles) and I neuron (square) layers. Synaptic weights between neurons are governed by the “short” (j_short) and “long” (j_long) distances between neurons on the ring structure. During the saccade tasks, an external cue current is fed into the E neuron layer as depicted. (B) Example of the effective current distribution from E neuron x_i to neurons within the E layer that results from activating neighboring E and I neurons. (C) Example of neuron spatial distributions during clustering simulations. Black circles represent E neurons as in (A). The dashed line represents the maximum nearest-neighbor distance (d_far) between neurons. As clustering is increased, d_far is increased while the minimum nearest-neighbor distance (d_near) is decreased. E, excitatory; I, inhibitory.

Figure 2

Schematic of saccade tasks. Cue current location and duration are indicated by black boxes. These were 500 pA Gaussian cues centered at the indicated marks. Arrows represent the time interval at which saccade measurements are taken. (A) Constant cue task: cues are presented for 1.5 seconds. Hypometria and dysmetria are calculated from the average single-neuron spike rate over the 2.9- to 3.0-second interval. (B) Transient cue task: 200-millisecond transient cues are presented with intervals ranging from .5 to 6.5 seconds (6.5 seconds is the default condition). Hypometria and dysmetria are calculated from the average single-neuron spike rate over the 14.9- to 15.0-second interval regardless of the cue interval.

Figure 3

Synaptic balance and saccade performance measures. (A) Hypometria versus synaptic balance for changes in g_GABA (circle), τ_GABA (triangle), and g_NMDA (square). Linear regressions are plotted for g_GABA (solid line, r² = .9974), τ_GABA (dashed line, r² = .9993), and g_NMDA (solid line, r² = .998). (B) Correlation between hypometria and dysmetria for changes in g_GABA (r² = .9965). GABA, gamma-aminobutyric acid; NMDA, N-methyl-D-aspartate.

Figure 4

Hypometria versus cue interval with log-regressions are shown for low g_GABA (50% g_GABA, circle), medium g_GABA (70% g_GABA, square), and high g_GABA (90% g_GABA, triangle) simulations. GABA, gamma-aminobutyric acid.

Figure 5

Hypometria versus E neuron distribution for three g_GABA titrations as in Figure 4. (A) Hypometria versus nearest-neighbor distance for homogeneously distributed neurons. Linear regressions shown for low g_GABA (r² = .9946), medium g_GABA (r² = .9019), and high g_GABA (r² = .5378). Distance is set by adjusting the total number of neurons. (B) Hypometria versus maximum nearest-neighbor distance for clustered neuron systems. Linear regressions shown for low g_GABA (r² = .9959), medium g_GABA (r² = .9555), and high g_GABA (r² = .4839). Distances are set as in Figure 1C and the clustering equation in Supplement 1. E, excitatory; GABA, gamma-aminobutyric acid.

Figure 6

Saccade pathology (e.g., hypometria and dysmetria) is predicted to be a U-shaped function of synaptic balance with typically developed adults (T) at the bottom. Subjects with ASD (A) are hypothesized to be on the left branch. The black curve represents a task with poor resolution (e.g., a visually guided task). The gray curve is from a task with better resolution (e.g., a random gap, transient cue task). Case 1 has the weakest discrimination, which is improved by altering the task design, i.e., shifting to the gray curve (case 2). Further improved discrimination is achieved by using a GABA antagonist or NMDA agonist (case 3). A, subjects with ASD; ASD, autism spectrum disorder; GABA, gamma-aminobutyric acid; NMDA, N-methyl-D-aspartate; T, typically developed adults.