Stimulus Feature-Specific Information Flow Along the Columnar Cortical Microcircuit Revealed by Multivariate Laminar Spiking Analysis

Abstract

Most of the mammalian neocortex is comprised of a highly similar anatomical structure, consisting of a granular cell layer between superficial and deep layers. Even so, different cortical areas process different information. Taken together, this suggests that cortex features a canonical functional microcircuit that supports region-specific information processing. For example, the primate primary visual cortex (V1) combines the two eyes' signals, extracts stimulus orientation, and integrates contextual information such as visual stimulation history. These processes co-occur during the same laminar stimulation sequence that is triggered by the onset of visual stimuli. Yet, we still know little regarding the laminar processing differences that are specific to each of these types of stimulus information. Univariate analysis techniques have provided great insight by examining one electrode at a time or by studying average responses across multiple electrodes. Here we focus on multivariate statistics to examine response patterns across electrodes instead. Specifically, we applied multivariate pattern analysis (MVPA) to linear multielectrode array recordings of laminar spiking responses to decode information regarding the eye-of-origin, stimulus orientation, and stimulus repetition. MVPA differs from conventional univariate approaches in that it examines patterns of neural activity across simultaneously recorded electrode sites. We were curious whether this added dimensionality could reveal neural processes on the population level that are challenging to detect when measuring brain activity without the context of neighboring recording sites. We found that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1. Conversely, orientation information was transient and equally pronounced along all layers. More importantly, using time-resolved MVPA, we were able to evaluate laminar response properties beyond those yielded by univariate analyses. Specifically, we performed a time generalization analysis by training a classifier at one point of the neural response and testing its performance throughout the remaining period of stimulation. Using this technique, we demonstrate repeating (reverberating) patterns of neural activity that have not previously been observed using standard univariate approaches.

Laminar microcircuitry of visual cortex producing attention-associated electric fields

“…Population spiking was measured and analyzed to supplement primary LFP and CSD results. Multiunit activity was derived through well-documented means (Legatt et al 1980) and has been demonstrated to be effective across multiple brain areas (Logothetis et al 2001;Roelfsema et al 2004;Self et al 2013;Shapcott et al 2016;Tovar et al 2020;Westerberg et al, 2020a;Xing et al 2009). The broadband neural signal was lowpass filtered at 3 kHz, highpass filtered at 300 Hz, full-wave rectified, and lastly, lowpass filtered at 150 Hz.…”

“…Fortunately, macaque monkeys produce homologues of the attention-associated EEG signals (N2pc: Woodman et al 2007;Cohen et al, 2009;Purcell et al, 2013;Pd: Cosman et al, 2018). Laminar neurophysiological measurements (Schroeder et al, 1998;Maier et al, 2010;Buffalo et al, 2011;Hansen et al, 2011;Self et al, 2013;Godlove et al, 2014;Engel et al, 2016;Klein et al, 2016;Hembrook-Short et al, 2017;Nandy et al, 2017;Trautmann et al, 2019;Westerberg et al, 2019;Tovar et al, 2020;Ferro et al, 2021) and EEG (Schmid et al, 2006;Woodman et al, 2007;Sandhaeger et al, 2019) are well established in macaques. However, despite many studies linking intra-and extracortical signals (Schroeder and Givre, 1992;Whittingstall and Logothetis, 2009;Musall et al, 2014;Snyder and Smith, 2015), to date, little is known about the laminar origins of ERPs in primates.…”

Feedforward attentional selection in sensory cortex

“…Detection of multiunit activity was achieved through previously described means (Legatt et al, 1980). This method has proved useful across brain areas and research groups (Logothetis et al, 2001; Self et al, 2013; Shapcott et al, 2016; Supèr and Roelfsema, 2005; Teeuwen et al, 2021; Tovar et al, 2020; Westerberg et al, 2020). Briefly, broadband neural activation was filtered between 0.5-5 kHz, the predominate range of spiking activity.…”

Resolving the mesoscopic missing link: Biophysical modeling of EEG from cortical columns in primates

“…As such, these considerations can generalize to model organisms more closely matching human cognition, such as the macaque monkey. Laminar recordings with CSD (Nicholson and Freeman, 1975;Pettersen et al, 2006) have already been used to elucidate the organization of multiple cortical areas in macaques (Bastos et al, 2020;Buffalo et al, 2011;Engel et al, 2016;Ferro et al, 2021;Godlove et al, 2014;Hansen and Dragoi, 2011;Hembrook-Short et al, 2017;Kajikawa et al, 2017;Klein et al, 2016;Maier et al, 2011Maier et al, , 2010Mehta et al, 2000;Nandy et al, 2017;Ninomiya et al, 2015;Schroeder et al, 1998;Self et al, 2013;Tovar et al, 2020;Trautmann et al, 2019;van Kerkoerle et al, 2017;Westerberg et al, , 2019 and their respective contributions to various EEG signals (Givre et al, 1994;Sajad et al, 2019;Westerberg et al, 2022). Despite this progress, the biophysical relationship between the mesoscopic laminar LFP/CSD and the microscopic cellular sources has not been evaluated.…”