Paradoxical lesions, plasticity and active inference

Abstract

Paradoxical lesions are secondary brain lesions that ameliorate functional deficits caused by the initial insult. This effect has been explained in several ways; particularly by the reduction of functional inhibition, or by increases in the excitatory-to-inhibitory synaptic balance within perilesional tissue. In this article, we simulate how and when a modification of the excitatory-inhibitory balance triggers the reversal of a functional deficit caused by a primary lesion. For this, we introduce in-silico lesions to an active inference model of auditory word repetition. The first in-silico lesion simulated damage to the extrinsic (between region) connectivity causing a functional deficit that did not fully resolve over 100 trials of a word repetition task. The second lesion was implemented in the intrinsic (within region) connectivity, compromising the model’s ability to rebalance excitatory-inhibitory connections during learning. We found that when the second lesion was mild, there was an increase in experience-dependent plasticity that enhanced performance relative to a single lesion. This paradoxical lesion effect disappeared when the second lesion was more severe because plasticity-related changes were disproportionately amplified in the intrinsic connectivity, relative to lesioned extrinsic connections. Finally, this framework was used to predict the physiological correlates of paradoxical lesions. This formal approach provides new insights into the computational and neurophysiological mechanisms that allow some patients to recover after large or multiple lesions.

Simulating lesion-dependent functional recovery mechanisms

“…In-silico lesions can be simulated by manipulating precision, where precision scores confidence (i.e., the inverse of uncertainty) in beliefs about the causes of sensations. As in our prior work (Sajid et al, 2020b;Sajid et al, 2020c), we manipulate precision, over model parameters () i A and () i B which results in different types of damage that can be linked to pathological lesions in the human brain.…”

“…To illustrate how particular lesions could trigger different recovery mechanisms, we extended our previous generative model (Sajid et al, 2020b;Sajid et al, 2020c) and active inference scheme for simulating word repetition (Ueno et al, 2011;Moritz-Gasser and Duffau, 2013;Nozari and Dell, 2013). The subject (i.e., model) hears a single spoken word and must repeat it.…”

“…Building on prior work (Sajid et al, 2020b;Sajid et al, 2020c), we used a simulated work repetition paradigm to measure degeneracy, redundancy, and task performance under four different levels of in-silico lesion severity. The key extension in our current model of word repetition, compared to our previous model, is that we introduced two distinct degenerate networks-premorbid and alternative-into a hierarchical model that also captured the neuromodulatory aspect of attention.…”

Simulating lesion-dependent functional recovery mechanisms

“…In-silico lesions can be simulated by manipulating precision, where precision scores confidence (i.e., the inverse of uncertainty) in beliefs about the causes of sensations. As in our prior work 26 , 27 , we manipulate precision, over model parameters and which results in different types of damage that can be linked to pathological lesions in the human brain.…”

“…Building on prior work 26 , 27 , we used a simulated work repetition paradigm to measure degeneracy, redundancy, and task performance under four different levels of in-silico lesion severity. The key extension in our current model of word repetition, compared to our previous model, is that we introduced two distinct degenerate networks—premorbid and alternative—into a hierarchical model that also captured the neuromodulatory aspect of attention.…”