Ubiquitous Neocortical Decoding of Tactile Input Patterns

Local field potential sharp waves with diversified impact on cortical neuronal encoding of haptic input

Cortical sensory processing is greatly impacted by internally generated activity. But controlling for that activity is difficult since the thalamocortical network is a high-dimensional system with rapid state changes. Therefore, to unwind the cortical computational architecture there is a need for physiological 'landmarks' that can be used as frames of reference for computational state. Here we use a waveshape transform method to identify conspicuous local field potential sharp waves (LFP-SPWs) in the somatosensory cortex (S1). LFP-SPW events triggered short-lasting but massive neuronal activation in all recorded neurons with a subset of neurons initiating their activation up to 20 ms before the LFP-SPW onset. In contrast, LFP-SPWs differentially impacted the neuronal spike responses to ensuing tactile inputs, depressing the tactile responses in some neurons and enhancing them in others. When LFP-SPWs coactivated with more distant cortical surface (ECoG)-SPWs, suggesting an involvement of these SPWs in global cortical signaling, the impact of the LFP-SPW on the neuronal tactile response could change substantially, including inverting its impact to the opposite. These cortical SPWs shared many signal fingerprint characteristics as reported for hippocampal SPWs and may be a biomarker for a particular type of state change that is possibly shared byboth hippocampus and neocortex.

ECoG activity distribution patterns detects global cortical responses following weak tactile inputs

Many studies have suggested that the neocortex operates as a global network of functionally interconnected neurons, indicating that any sensory input could shift activity distributions across the whole brain. A tool assessing the activity distribution across cortical regions with high temporal resolution could then potentially detect subtle changes that may pass unnoticed in regionalized analyses. We used eight-channel, distributed electrocorticogram (ECoG) recordings to analyze changes in global activity distribution caused by single pulse electrical stimulations of the paw. We analyzed the temporally evolving patterns of the activity distributions using principal component analysis (PCA). We found that the localized tactile stimulation caused clearly measurable changes in global ECoG activity distribution. These changes in signal activity distribution patterns were detectable across a small number of ECoG channels, even when excluding the somatosensory cortex, suggesting that the method has high sensitivity, potentially making it applicable to human electroencephalography (EEG) for detection of pathological changes.

Differential encoding of temporally evolving color patterns across nearby V1 neurons

Whereas studies of the V1 cortex have focused mainly on neural line orientation preference, color inputs are also known to have a strong presence among these neurons. Individual neurons typically respond to multiple colors and nearby neurons have different combinations of preferred color inputs. However, the computations performed by V1 neurons on such color inputs have not been extensively studied. Here we aimed to address this issue by studying how different V1 neurons encode different combinations of inputs composed of four basic colors. We quantified the decoding accuracy of individual neurons from multi-electrode array recordings, comparing multiple individual neurons located within 2 mm along the vertical axis of the V1 cortex of the anesthetized rat. We found essentially all V1 neurons to be good at decoding spatiotemporal patterns of color inputs and they did so by encoding them in different ways. Quantitative analysis showed that even adjacent neurons encoded the specific input patterns differently, suggesting a local cortical circuitry organization which tends to diversify rather than unify the neuronal responses to each given input. Using different pairs of monocolor inputs, we also found that V1 neocortical neurons had a diversified and rich color opponency across the four colors, which was somewhat surprising given the fact that rodent retina express only two different types of opsins. We propose that the processing of color inputs in V1 cortex is extensively composed of multiple independent circuitry components that reflect abstract functionalities resident in the internal cortical processing rather than the raw sensory information per se.

Hippocampal output profoundly impacts the interpretation of tactile input patterns in SI cortical neurons

Due to continuous state variations in neocortical circuits, individual somatosensory cortex (SI) neurons in vivo display a variety of intracellular responses to the exact same spatiotemporal tactile input pattern. To manipulate the internal cortical state, we here used brief electrical stimulation of the output region of the hippocampus, which preceded the delivery of specific tactile afferent input patterns to digit 2 of the anesthetized rat. We find that hippocampal output had a diversified, remarkably strong impact on the intracellular response types displayed by each neuron in the primary SI to each given tactile input pattern. Qualitatively, this impact was comparable to that previously described for cortical output, which was surprising given the widely assumed specific roles of the hippocampus, such as in cortical memory formation. The findings show that hippocampal output can profoundly impact the state-dependent interpretation of tactile inputs and hence influence perception, potentially with affective and semantic components.

Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior

One of the major challenges in system neurosciences consists in developing techniques for estimating the cognitive information content in brain activity. This has an enormous potential in different domains spanning from clinical applications, cognitive enhancement to a better understanding of the neural bases of cognition. In this context, the inclusion of machine learning techniques to decode different aspects of human cognition and behavior and its use to develop brain-computer interfaces for applications in neuroprosthetics has supported a genuine revolution in the field. However, while these approaches have been shown quite successful for the study of the motor and sensory functions, success is still far from being reached when it comes to covert cognitive functions such as attention, motivation and decision making. While improvement in this field of BCIs is growing fast, a new research focus has emerged from the development of strategies for decoding neural activity. In this review, we aim at exploring how the advanced in decoding of brain activity is becoming a major neuroscience tool moving forward our understanding of brain functions, providing a robust theoretical framework to test predictions on the relationship between brain activity and cognition and behavior.

Beyond body maps: Information content of specific body parts is distributed across the somatosensory homunculus

The homunculus in primary somatosensory cortex (S1) is famous for its body part selectivity, but this dominant feature may eclipse other representational features, e.g., information content, also relevant for S1 organization. Using multivariate fMRI analysis, we ask whether body part information content can be identified in S1 beyond its primary region. Throughout S1, we identify significant representational dissimilarities between body parts but also subparts in distant non-primary regions (e.g., between the hand and the lips in the foot region and between different face parts in the foot region). Two movements performed by one body part (e.g., the hand) could also be dissociated well beyond its primary region (e.g., in the foot and face regions), even within Brodmann area 3b. Our results demonstrate that information content is more distributed across S1 than selectivity maps suggest. This finding reveals underlying information contents in S1 that could be harnessed for rehabilitation and brain-machine interfaces.

Remote cortical perturbation dynamically changes the network solutions to given tactile inputs in neocortical neurons

The neocortex has a globally encompassing network structure, which for each given input constrains the possible combinations of neuronal activations across it. Hence, its network contains solutions. But in addition, the cortex has an ever-changing multidimensional internal state, causing each given input to result in a wide range of specific neuronal activations. Here we use intracellular recordings in somatosensory cortex (SI) neurons of anesthetized rats to show that remote, subthreshold intracortical electrical perturbation can impact such constraints on the responses to a set of spatiotemporal tactile input patterns. Whereas each given input pattern normally induces a wide set of preferred response states, when combined with cortical perturbation response states that did not otherwise occur were induced and consequently made other response states less likely. The findings indicate that the physiological network structure can dynamically change as the state of any given cortical region changes, thereby enabling a rich, multifactorial, perceptual capability.

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Tactile sensory input patterns evoke multi-structure cortical neuron responses

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Multi-structure responses are shown to be impacted by remote cortical regions

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Highly dynamic neuron responses reflects global cortical information integration

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Perception hence depends on globally distributed activity at the time of input

Widely Different Correlation Patterns Between Pairs of Adjacent Thalamic Neurons In vivo

We have previously reported different spike firing correlation patterns among pairs of adjacent pyramidal neurons within the same layer of S1 cortex in vivo, which was argued to suggest that acquired synaptic weight modifications would tend to differentiate adjacent cortical neurons despite them having access to near-identical afferent inputs. Here we made simultaneous single-electrode loose patch-clamp recordings from 14 pairs of adjacent neurons in the lateral thalamus of the ketamine-xylazine anesthetized rat in vivo to study the correlation patterns in their spike firing. As the synapses on thalamic neurons are dominated by a high number of low weight cortical inputs, which would be expected to be shared for two adjacent neurons, and as far as thalamic neurons have homogenous membrane physiology and spike generation, they would be expected to have overall similar spike firing and therefore also correlation patterns. However, we find that across a variety of thalamic nuclei the correlation patterns between pairs of adjacent thalamic neurons vary widely. The findings suggest that the connectivity and cellular physiology of the thalamocortical circuitry, in contrast to what would be expected from a straightforward interpretation of corticothalamic maps and uniform intrinsic cellular neurophysiology, has been shaped by learning to the extent that each pair of thalamic neuron has a unique relationship in their spike firing activity.

Multi-structure Cortical States Deduced From Intracellular Representations of Fixed Tactile Input Patterns

The brain has a never-ending internal activity, whose spatiotemporal evolution interacts with external inputs to constrain their impact on brain activity and thereby how we perceive them. We used reproducible touch-related spatiotemporal sensory inputs and recorded intracellularly from rat (Sprague-Dawley, male) neocortical neurons to characterize this interaction. The synaptic responses, or the summed input of the networks connected to the neuron, varied greatly to repeated presentations of the same tactile input pattern delivered to the tip of digit 2. Surprisingly, however, these responses tended to sort into a set of specific time-evolving response types, unique for each neuron. Further, using a set of eight such tactile input patterns, we found each neuron to exhibit a set of specific response types for each input provided. Response types were not determined by the global cortical state, but instead likely depended on the time-varying state of the specific subnetworks connected to each neuron. The fact that some types of responses recurred indicates that the cortical network had a non-continuous landscape of solutions for these tactile inputs. Therefore, our data suggest that sensory inputs combine with the internal dynamics of the brain networks, thereby causing them to fall into one of the multiple possible perceptual attractor states. The neuron-specific instantiations of response types we observed suggest that the subnetworks connected to each neuron represent different components of those attractor states. Our results indicate that the impact of cortical internal states on external inputs is substantially more richly resolvable than previously shown.

Widespread Decoding of Tactile Input Patterns Among Thalamic Neurons

Whereas, there is data to support that cuneothalamic projections predominantly reach a topographically confined volume of the rat thalamus, the ventroposterior lateral (VPL) nucleus, recent findings show that cortical neurons that process tactile inputs are widely distributed across the neocortex. Since cortical neurons project back to the thalamus, the latter observation would suggest that thalamic neurons could contain information about tactile inputs, in principle regardless of where in the thalamus they are located. Here we use a previously introduced electrotactile interface for producing sets of highly reproducible tactile afferent spatiotemporal activation patterns from the tip of digit 2 and record neurons throughout widespread parts of the thalamus of the anesthetized rat. We find that a majority of thalamic neurons, regardless of location, respond to single pulse tactile inputs and generate spike responses to such tactile stimulation patterns that can be used to identify which of the inputs that was provided, at above-chance decoding performance levels. Thalamic neurons with short response latency times, compatible with a direct tactile afferent input via the cuneate nucleus, were typically among the best decoders. Thalamic neurons with longer response latency times as a rule were also found to be able to decode the digit 2 inputs, though typically at a lower decoding performance than the thalamic neurons with presumed direct cuneate inputs. These findings provide support for that tactile information arising from any specific skin area is widely available in the thalamocortical circuitry.

Concurrent affective and linguistic prosody with the same emotional valence elicits a late positive ERP response

Change in linguistic prosody generates a mismatch negativity response (MMN), indicating neural representation of linguistic prosody, while change in affective prosody generates a positive response (P3a), reflecting its motivational salience. However, the neural response to concurrent affective and linguistic prosody is unknown. The present paper investigates the integration of these two prosodic features in the brain by examining the neural response to separate and concurrent processing by electroencephalography (EEG). A spoken pair of Swedish words—[ˈfɑ́ːsɛn] phase and [ˈfɑ̀ːsɛn] damn—that differed in emotional semantics due to linguistic prosody was presented to 16 subjects in an angry and neutral affective prosody using a passive auditory oddball paradigm. Acoustically matched pseudowords—[ˈvɑ́ːsɛm] and [ˈvɑ̀ːsɛm]—were used as controls. Following the constructionist concept of emotions, accentuating the conceptualization of emotions based on language, it was hypothesized that concurrent affective and linguistic prosody with the same valence—angry [ˈfɑ̀ːsɛn] damn—would elicit a unique late EEG signature, reflecting the temporal integration of affective voice with emotional semantics of prosodic origin. In accordance, linguistic prosody elicited an MMN at 300–350 ms, and affective prosody evoked a P3a at 350–400 ms, irrespective of semantics. Beyond these responses, concurrent affective and linguistic prosody evoked a late positive component (LPC) at 820–870 ms in frontal areas, indicating the conceptualization of affective prosody based on linguistic prosody. This study provides evidence that the brain does not only distinguish between these two functions of prosody but also integrates them based on language and experience.

Focal neocortical lesions impair distant neuronal information processing

KEY POINTS

Parts of the fields of neuroscience and neurology consider the neocortex to be a functionally parcelled structure. Viewed through such a conceptual filter, there are multiple clinical observations after localized stroke lesions that seem paradoxical. We tested the effect that localized stroke-like lesions have on neuronal information processing in a part of the neocortex that is distant to the lesion using animal experiments. We find that the distant lesion degrades the quality of neuronal information processing of tactile input patterns in primary somatosensory cortex. The findings suggest that even the processing of primary sensory information depends on an intact neocortical network, with the implication that all neocortical processing may rely on widespread interactions across large parts of the cortex.

ABSTRACT

Recent clinical studies report a surprisingly weak relationship between the location of cortical brain lesions and the resulting functional deficits. From a neuroscience point of view, such findings raise questions as to what extent functional localization applies in the neocortex and to what extent the functions of different regions depend on the integrity of others. Here we provide an in-depth analysis of the changes in the function of the neocortical neuronal networks after distant focal stroke-like lesions in the anaesthetized rat. Using a recently introduced high resolution analysis of neuronal information processing, consisting of pre-set spatiotemporal patterns of tactile afferent activation against which the neuronal decoding performance can be quantified, we found that stroke-like lesions in distant parts of the cortex significantly degraded the decoding performance of individual neocortical neurons in the primary somatosensory cortex (decoding performance decreased from 30.9% to 24.2% for n = 22 neurons, Wilcoxon signed rank test, P = 0.028). This degrading effect was not due to changes in the firing frequency of the neuron (Wilcoxon signed rank test, P = 0.499) and was stronger the higher the decoding performance of the neuron, indicating a specific impact on the information processing capacity in the cortex. These findings suggest that even primary sensory processing depends on widely distributed cortical networks and could explain observations of focal stroke lesions affecting a large range of functions.