Biology subjects

Astrand, E.

Publications and source records attributed to Astrand, E..

7 recordsLinked to original sources

bioRxiv2026

Successful single-session neural self-regulation through neurofeedback varies between features

Neurofeedback (NFB) and Brain-Computer Interface (BCI) research seldom present within-session individual learning dynamics. This is even though a large proportion of NFB and BCI users cannot learn the neural self-regulation required to control the feedback. Understanding the time course and learning dynamics between subjects will enable us to design more effective NFB and BCI protocols that promote the learning of neural self-regulation. In this study, we aimed to analyze individual learning trajectories of self-regulation of four different cortical rhythms, in terms of both frequency and spatial selectivity. Twenty healthy subjects performed four sessions of NFB training, each session with feedback reflecting a different cortical rhythm as measured with an electroencephalogram. We specifically tested frontal midline (fm) Theta, occipital Alpha, unilateral centrotemporal sensorimotor rhythms (SMR), and central Beta. We show that all subjects were able to self-regulate at least two of these features, however, with varied specificity in the spatial and frequency domains. Unexpectedly, we show that none of the subjects succeeded in regulating fm Theta. Using a clustering approach, we identified two different learning dynamics among the learners across features: a linear increase/decrease and a non-linear plateau-like trajectory. This is the first NFB study employing an intra-subject cross-over experimental design, enabling the direct comparison of neural self-regulation between multiple features. Our results provide important insights into the "non-learner" problem, showing that it is not a feature-universal personal trait. We further show feature-specific spatial and frequency selectivity of neural self-regulation, providing important considerations for future NFB protocols.

neuroscience↗

bioRxiv2023

Distinct neural states encode task identity in frontal eye field and interact with its core spatial properties

The prefrontal cortex (PFC) plays a key role in selecting, maintaining and representing sensory information, and in its integration with our current internal goals and expectations, implementing such cognitive processes as executive functions, attention, decision making and working memory. Performing computation over all of these functional cognitive processes and dynamically shifting from one to the other based on task demands requires a complex functional organization and a high degree of coding flexibility. The PFC cells show a non-linear mixed selectivity characterized by specific tuning for multiple task- and behaviour related parameters. This non-linear mixed selectivity thought to allow for a high-dimensional representation of information. Here, we asked if the PFC is mainly involved in specific task-parameters representation, or if it additionally holds a higher order representation for task-identity. We thus trained two macaques to perform three different tasks: a memory guided saccade task and two detection tasks involving different attention mechanisms. Multi-unit activity was recorded in the frontal eye field, bilaterally, while monkeys performed these three tasks in a same session. Using demixed Principal Component Analysis, we found a two-dimensional neural state that characterized each of these tasks. The lower dimensional representation of the activity recorded during the performance of the two attentional tasks were more similar to each other than to the memory-guided saccade task. Furthermore, we report that task and spatial information are non-linearly mixed, a signature of a high-dimensional neural representation. Overall, this indicates that PFC encodes task identity information and flexibly adjusts its sensory processes as a function of the specific ongoing task. Significance StatementOne long lasting question in cognitive neuroscience is whether PFC mainly represents the different parameters that are needed to perform any given task, or if it additionally holds a higher order representation for task-identity. We recorded from the macaque frontal eye fields while monkeys performed a memory guided saccade task and two detection tasks involving different attention mechanisms. We found a task-identity neural state in which the two attentional tasks were represented similarly to each other but differently from the memory-guided saccade task. We also report that task and spatial information are non-linearly mixed, a signature of a high-dimensional neuronal representation. Overall, this indicates that PFC encodes task identity information and flexibly adjusts its sensory processes as a function of the specific ongoing task.

neuroscience↗

bioRxiv2022

The cost of multiplexing: PFC integrates multiple sources of information in non-orthogonal components accounting for behavioral variability

The frontal eye field (FEF) is a cortical area classically associated with spatial attention, perception, and oculomotor functions. FEF exhibits complex response properties through mixed selectivity neurons, allowing a high dimensional representation of the information. However, recent studies have shown that FEF encodes information in a low-dimensional regime hence limiting the coding capacity of the neural population. How the FEF encodes multiple sources of information with such limited encoding capacity remains elusive. To address this question, we trained two macaques to perform a visual attention task while we recorded FEF neuronal activity using multi-contact electrodes. FEF neurons encoded task- (time in the trial; CTOA) and behaviour- (reaction time, RT; focus of attention, TA) related parameters prior to the target onset. We found a clear modulation of the RT and TA as a function of the CTOA. Using dPCA, we characterized the functional relationship between neural populations associated with each parameter and investigated how this functional relationship predicts behaviour. We found that CTOA variability was associated with two different components the activation of which was correlated with the TA and the RT, respectively. These CTOA-related components were non-orthogonal with the RT and TA-related components, respectively. These results suggest that, when different sources of information are implemented during task performance, they show a very precise geometrical configuration in non-orthogonal components, which allows a high capacity of information coding at a cost of modulating both the capacity of the monkey to use attention information and its responsiveness toward external stimuli.

neuroscience↗

bioRxiv2020

Selection and suppression of visual information in the macaque prefrontal cortex

In everyday life, we are continuously struggling at focusing on our current goals while at the same time avoiding distractions. Attention is the neuro-cognitive process devoted to the selection of behaviorally relevant sensory information while at the same time preventing distraction by irrelevant information. Visual selection can be implemented by both long-term (learning-based spatial prioritization) and short term (dynamic spatial attention) mechanisms. On the other hand, distraction can be prevented proactively, by strategically prioritizing task-relevant information at the expense of irrelevant information, or reactively, by actively suppressing the processing of distractors. The distinctive neuronal signature of each of these four processes is largely unknown. Likewise, how selection and suppression mechanisms interact to drive perception has never been explored neither at the behavioral nor at the neuronal level. Here, we apply machine-learning decoding methods to prefrontal cortical (PFC) activity to monitor dynamic spatial attention with an unprecedented spatial and temporal resolution. This leads to several novel observations. We first identify independent behavioral and neuronal signatures for learning-based attention prioritization and dynamic attentional selection. Second, we identify distinct behavioral and neuronal signatures for proactive and reactive suppression mechanisms. We find that while distracting task-relevant information is suppressed proactively, task-irrelevant information is suppressed reactively. Critically, we show that distractor suppression, whether proactive or reactive, strongly depends on both learning-based attention prioritization and dynamic attentional selection. Overall, we thus provide a unified neuro-cognitive framework describing how the prefrontal cortex implements spatial selection and distractor suppression in order to flexibly optimize behavior in dynamic environments.

neuroscience↗

bioRxiv2019

Rhythmic variations in prefrontal inter-neuronal correlations, their underlying mechanisms and their behavioral correlates

Functional neuronal correlations between pairs of neurons are thought to play an important role in neuronal information processing and optimal neuronal computations during attention, perception, decision-making and learning. These noise correlations are often assumed to be stable in time. However, recent studies suggest that cognitive processes are rhythmic, this rhythmicity accounting for variations in overt behavioral performance. Whether this rhythmicity coincides with variations in shared noise variability is unknown. Here, we perform simultaneous recordings from the macaque frontal eye fields, while animals are engaged in a spatial memory task. We report that noise correlations in prefrontal cortex fluctuate rhythmically in the high alpha (10-16Hz) and beta (20-30Hz) frequency ranges. Importantly, these rhythmic modulations in shared neuronal variability account for dynamic changes in overt behavioral performance. They also coincide with increased spike-LFP phase coupling in these specific frequency ranges, the spatial profile of which vary between superficial and deep cortical layers. Finally, we demonstrate, using an artificial neuronal model, that rhythmic variations in noise correlation oscillations parsimoniously arise from long range (LFP) and local spike-LFP phase coupling mechanisms. Thus a significant portion of noise correlation fluctuations can be attributed to long-range global network rhythmicity.

neuroscience↗

bioRxiv2019

Interneuronal correlations dynamically adjust to task demands at multiple time-scales

Functional neuronal correlations between pairs of neurons are thought to play an important role in neuronal information processing and optimal neuronal computations during attention, perception, decision-making and learning. Here, we report dynamic changes in prefrontal neuronal noise correlations at multiple time-scales, as a function of task contingencies. Specifically, we record neuronal activity from the macaque frontal eye fields, a cortical region at the source of spatial attention top-down control, while the animals are engaged in tasks of varying cognitive demands. First, we show that noise correlations decrease as cognitive engagement and task demands increase, both across tasks and within-trials. Second, we demonstrate, for the first time, rhythmic modulations of noise correlations in the alpha and beta frequency ranges that account both for overt behavioral performance and for layer specific modulations in spike-field coherence. All this taken together demonstrates a strong functional role of noise correlations in cognitive flexibility.

neuroscience↗

bioRxiv2018

Neuronal population correlates of target selection and distractor filtering

Frontal Eye Field (FEF) single-cell neuronal activity discriminates between relevant and irrelevant visual stimuli and its magnitude has been shown to predict conscious perception. How this is reflected at the population level in terms of spatial codes is unknown. We recorded neuronal population activity in the FEF while monkeys were performing a forced choice cued detection task with identical target and distractor stimuli. Using machine learning techniques, we quantified information about the spatial estimate of targets and distracters in the FEF population activity and we analyzed how these relate to the report of perception. We found that the FEF population activity provides a precise estimate of the spatial location of perception. This estimate doesnt necessarily match the actual physical world. Importantly, the closer this prefrontal population estimate is to the veridical spatial information, the higher the probability that the stimulus was reported as perceived. This was observed both when the reported stimulus was a target (i.e. correct detection trials) or a distractor (i.e. false alarm trials). Overall, we thus show that how and what we perceive of our environments depends on the precision with which this environment is coded by prefrontal neuronal populations.

neuroscience↗