Feed-forward information and zero-lag synchronization in the sensory thalamocortical circuit are modulated during stimulus perception

Abstract

The direction of functional information flow in the sensory thalamocortical circuit may play a role in stimulus perception, but, surprisingly, this process is poorly understood. We addressed this problem by evaluating a directional information measure between simultaneously recorded neurons from somatosensory thalamus (ventral posterolateral nucleus, VPL) and somatosensory cortex (S1) sharing the same cutaneous receptive field while monkeys judged the presence or absence of a tactile stimulus. During stimulus presence, feed-forward information (VPL → S1) increased as a function of the stimulus amplitude, while pure feed-back information (S1 → VPL) was unaffected. In parallel, zero-lag interaction emerged with increasing stimulus amplitude, reflecting externally driven thalamocortical synchronization during stimulus processing. Furthermore, VPL → S1 information decreased during error trials. Also, VPL → S1 and zero-lag interaction decreased when monkeys were not required to report the stimulus presence. These findings provide evidence that both the direction of information flow and the instant synchronization in the sensory thalamocortical circuit play a role in stimulus perception.

Inferring neural information flow from spiking data

“…MT: Middle temporal area; LIP: Lateral intraparietal area; FEF: Frontal eye fields; A1: Primary auditory area; PFC: Prefontal cortex area; VPL: Ventral posterolateral nucleus of the thalamus; S1: Primary somatosensory area; PMd: Premotor cortex; M1: Primary motor area. Method Simultaneous dataset Online data/software Year Cross-correlation [13] V1 and V2 area neurons from anesthesized macaque monkeys during visual stimulation doi.org/10.6080/K0B27SHN 2015 GLM [23] In-vitro ganglion cells from macaque monkeys during visual stimulation github.com/pillowlab/neuroGLM 2008 GLM [24] MT and LIP area neurons from macaque monkeys performing a visual task github.com/jcbyts/mtlipglm 2017 GLM [25] Rat hippocampus during exploration of an open square field github.com/NII-Kobayashi/GLMCC 2019 GLM [26] LIP and FEF area neurons from macaque monkeys performing a visual task doi.org/10.5061/dryad.gb5mkkwk7 2020 GC [72] A1 and PFC area neurons from ferrets performing an auditory task github.com/Arsha89/AGC_Analysis 2018 GC-DI [56] VPL and S1 area neurons from macaque monkeys performing a somatosensory task github.com/AdTau/DI-Inference 2019 Dim. reduction [94] PMd and M1 area neurons from macaque monkeys performing a motor task github.com/ripple-neuro 2014 Dim.…”

“…For instance, multiple trials are needed to evaluate dependencies between the information message and neural spike trains [82] because the former is usually variable only across trials. On the other hand, spike-train interactions should be validated at a single-trial level due to its possible variable statistics during repeated trials [56] .…”

“…Critically, in neuroscience studies, one may argue that the use of temporal samples (instead of trials) may compromise the inference of the exact times at which spike-train interactions occur. However, over the last years, a few works have shown that interaction times can also be revealed in this framework via delayed versions of the original measures [55] and ad hoc statistical tests (e.g., see Supplementary information in [56] ). Finally, the statistical power of model-free GC-derived measures can be assessed by performing nonparametric significance testing of the estimated quantities using methods such as permutation tests [57] .…”

“…In addition, a variant of the DI rate estimator introduced in [29] was proposed in [63] , which showed an accurate estimation of the conduction delays between neurons in different brain areas during motor tasks performed by rodents and nonhuman primates. On the other hand, time-delayed versions of the CTW-based estimator were elaborated in [56] , [64] to infer task-driven directional interactions between the thalamus and the somatosensory area 1 (S1) in monkeys performing a tactile detection task [56] , and across cortical somatosensory, premotor and motor areas in monkeys performing a tactile dissemination task [64] . Finally, an extension of the CTW algorithm for non-necessarily finite-order Markov processes [65] was used to estimate the DI rate between neural spike trains from the buccal ganglion of Aplysia [66] .…”

“…Surprisingly enough, the effect of stimulus on estimated neural interactions has been to date largely neglected or uniquely considered as a source of covariation [21] . However, there is a growing consensus that the relationship between the stimulus (or any internal variable) and the estimated interactions is a necessary condition to support the information flow interpretation [64] , [56] , [83] , [84] , [82] . For instance, the use of GC-derived measures to analyze single-trial and time-varying neural interactions in monkeys performing perceptual decision-making tasks have showed the modulatory effect of stimulus information and internal percepts into inter-area interactions [64] , [56] .…”