Autism is a neurodevelopmental disorder that manifests as a heterogeneous set of social, cognitive, motor, and perceptual symptoms. This system-wide pervasiveness suggests that, rather than narrowly impacting individual systems such as affection or vision, autism may broadly alter neural computation. Here, we propose that alterations in nonlinear, canonical computations occurring throughout the brain may underlie the behavioral characteristics of autism. One such computation, called divisive normalization, balances a neuron's net excitation with inhibition reflecting the overall activity of the neuronal population. Through neural network simulations, we investigate how alterations in divisive normalization may give rise to autism symptomatology. Our findings show that a reduction in the amount of inhibition that occurs through divisive normalization can account for perceptual consequences of autism, consistent with the hypothesis of an increased ratio of neural excitation to inhibition (E/I) in the disorder. These results thus establish a bridge between an E/I imbalance and behavioral data on autism that is currently absent. Interestingly, our findings implicate the context-dependent, neuronal milieu as a key factor in autism symptomatology, with autism reflecting a less "social" neuronal population. Through a broader discussion of perceptual data, we further examine how altered divisive normalization may contribute to a wide array of the disorder's behavioral consequences. These analyses show how a computational framework can provide insights into the neural basis of autism and facilitate the generation of falsifiable hypotheses. A computational perspective on autism may help resolve debates within the field and aid in identifying physiological pathways to target in the treatment of the disorder.A utism is a neurodevelopmental disorder that is dramatically increasing in prevalence (Fig. 1). Recent data place the number of children being diagnosed with autism in the United States at 1 in 68, more than doubling in the last decade (1-4). The disorder is highly pervasive, affecting individuals at cognitive, motor, and perceptual levels. It is furthermore a "spectrum disorder," with symptoms that manifest in varying degrees across individuals. This heterogeneity presents significant challenges to establishing a comprehensive characterization of the disorder.Research investigating the genetic and molecular basis of autism implicates over 100 genes (5), many of which are involved in synaptic development and function (6-8). As such, one prominent hypothesis is that autism arises from a neurophysiological excitation-to-inhibition (E/I) imbalance (9, 10). However, the connection between an E/I imbalance and the behavioral characteristics of the disorder remains unclear. Considering the pervasive nature of autism, and the covariance of loosely related symptoms (11-14), one possibility is that an E/I imbalance widely affects neural computation, in turn giving rise to the broad behavioral symptoms recognized as autism.Here,...

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Lesion‐behavior mapping (LBM) provides a statistical map of the association between voxel‐wise brain damage and individual differences in behavior. To understand whether two behaviors are mediated by damage to distinct regions, researchers often compare LBM weight outputs by either the Overlap method or the Correlation method. However, these methods lack statistical criteria to determine whether two LBM are distinct versus the same and are disconnected from a major goal of LBMs: predicting behavior from brain damage. Without such criteria, researchers may draw conclusions from numeric differences between LBMs that are irrelevant to predicting behavior. We developed and validated a predictive validity comparison method (PVC) that establishes a statistical criterion for comparing two LBMs using predictive accuracy: two LBMs are distinct if and only if they provide unique predictive power for the behaviors being assessed. We applied PVC to two lesion‐behavior stroke data sets, demonstrating its utility for determining when behaviors arise from the same versus different lesion patterns. Using region‐of‐interest‐based simulations derived from proportion damage from a large data set (n = 131), PVC accurately detected when behaviors were mediated by different regions (high sensitivity) versus the same region (high specificity). Both the Overlap method and Correlation method performed poorly on the simulated data. By objectively determining whether two behavioral deficits can be explained by single versus distinct patterns of brain damage, PVC provides a critical advance in establishing the brain bases of behavior. We have developed and released a GUI‐driven web app to encourage widespread adoption.

Lesion-behavior mapping (LBM) provides a statistical map of the association between voxel-wise brain damage and individual differences in behavior. To understand whether two behaviors are mediated by damage to distinct brain regions, researchers often compare LBM beta weight outputs by either the overlap method (emphasizing differences) or by the correlation method (emphasizing similarity). However, both methods lack a principled way to determine LBM distinctness and are disconnected from a major goal of LBMs, that of predicting behavior from brain damage. Without such criteria, researchers may draw conclusions from numeric differences between LBMs that are irrelevant to predicting behavior from brain damage. Here, we developed and validated a Predictive Validity Comparison method (PVC) that establishes such a criterion by directly comparing two LBMs based on the difference in predictive accuracy: two LBMs are significantly different if they provide unique predictive power for the two behaviors assessed. Applying the PVC to two lesion-behavior stroke data sets (Ding et al., 2020; Pustina et al., 2018) demonstrated the utility of a statistical criterion for determining when individual differences across behaviors result from the same vs. different lesion patterns. Using region-of-interest based simulations derived from proportion damage in Brodmann regions from a large group of participants (n=131), PVC detected when two behaviors were mediated by different regions (high sensitivity) and determined when two behaviors were mediated by the same region (high specificity). By objectively determining whether two behavioral deficits can be explained by a single vs. distinct patterns of brain damage, the PVC provides a critical advance in establishing the brain bases of behavior. We have released a GUI-driven, open-access web app of this method to encourage widespread adoption.