Temporal Signatures of Criticality in Human Cortical Excitability as Probed by Early Somatosensory Responses

Abstract

Brain responses vary considerably from moment to moment, even to identical sensory stimuli. This has been attributed to changes in instantaneous neuronal states determining the system's excitability. Yet the spatiotemporal organization of these dynamics remains poorly understood. Here we test whether variability in stimulus-evoked activity can be interpreted within the framework of criticality, which postulates dynamics of neural systems to be tuned toward the phase transition between stability and instability as is reflected in scale-free fluctuations in spontaneous neural activity. Using a novel noninvasive approach in 33 male human participants, we tracked instantaneous cortical excitability by inferring the magnitude of excitatory postsynaptic currents from the N20 component of the somatosensory evoked potential. Fluctuations of cortical excitability demonstrated long-range temporal dependencies decaying according to a power law across trials, a hallmark of systems at critical states. As these dynamics covaried with changes in prestimulus oscillatory activity in the alpha band (8-13 Hz), we establish a mechanistic link between ongoing and evoked activity through cortical excitability and argue that the co-emergence of common temporal power laws may indeed originate from neural networks poised close to a critical state. In contrast, no signatures of criticality were found in subcortical or peripheral nerve activity. Thus, criticality may represent a parsimonious organizing principle of variability in stimulus-related brain processes on a cortical level, possibly reflecting a delicate equilibrium between robustness and flexibility of neural responses to external stimuli.

Neural excitability and sensory input determine intensity perception with opposing directions in initial cortical responses

“…This variant of CCA extracts a number of spatially distinct components based on a pattern matching between average SEP and single trials. Similar to Stephani et al, 2020 , a prominent CCA component was identified in all subjects, which showed a clear peak at around 20 ms post-stimulus ( Figure 1b ) and displayed the pattern of the typical N20 tangential dipole ( Figure 1c ). Furthermore, neuronal sources of this CCA component were primarily located in the anterior wall of the post-central gyrus (Brodmann area 3b) in the primary somatosensory cortex ( Figure 1d and e ).…”

“…Single-trial activity of the early SEP was extracted using a variant of canonical correlation analysis (CCA; Scheer et al, 2013 ; Waterstraat et al, 2015 ), as previously reported for a similar paradigm examining the fluctuation of single-trial SEPs in response to stimuli with constant intensity ( Stephani et al, 2020 ). This variant of CCA extracts a number of spatially distinct components based on a pattern matching between average SEP and single trials.…”

“…However, when keeping the sensory input constant, the amplitude of this early part of the SEP only depends on the excitability of the involved, well-defined neuronal population in the primary somatosensory cortex, and therefore represents an excellent instantaneous probe thereof. Notably, amplitude fluctuations of the N20 component have recently been found to relate to pre-stimulus alpha activity both at a given instance and through their long-term temporal dynamics, which suggests that both measures reflect a common modulating factor, that is, cortical excitability ( Stephani et al, 2020 ).…”

Somatosensory evoked potentials that index lateral inhibition are modulated according to the mode of perceptual processing: comparing or combining multi-digit tactile motion

“…In addition, inter-finger suppression has not been found to affect earlier components such as N20 (Forss et al, 1995). However, a recent study showed that important trial-by-trial variability dynamics occur as early as 20 ms after tactile stimulus onset (Stephani et al, 2020). Our 30 Hz low-pass filter did not allow us to assess very early components.…”

Self-Organization Toward Criticality by Synaptic Plasticity

“…Overall, we have observed in experiments, how developing networks self-organize to a critical state [18][19][20], how states may change from wakefulness to deep sleep [21][22][23][24][25], under drugs [26] or in a disease like epilepsy [27][28][29][30]. Criticality was mainly investigated in in vivo neural activity during the resting state dynamics [31][32][33][34], but there are also some studies during task-induced changes and in presence of external stimuli [35][36][37][38][39]. These results show how criticality and the deviations thereof can be harnessed for computation, but can also reflect cases where selforganization fails.…”