scholarly journals A Bayesian method for reducing bias in neural representational similarity analysis

2016 ◽  
Author(s):  
Ming Bo Cai ◽  
Nicolas W. Schuck ◽  
Jonathan Pillow ◽  
Yael Niv
Keyword(s):  
Neural Activity ◽  
Cognitive Task ◽  
Activity Patterns ◽  
Covariance Structure ◽  
Similarity Matrix ◽  
Imaging Data ◽  
Signal To Noise ◽  
Experimental Conditions ◽  
Sensory Stimuli ◽  
Neural Imaging

Abstract1In neuroscience, the similarity matrix of neural activity patterns in response to different sensory stimuli or under different cognitive states reflects the structure of neural representational space. Existing methods derive point estimations of neural activity patterns from noisy neural imaging data, and the similarity is calculated from these point estimations. We show that this approach translates structured noise from estimated patterns into spurious bias structure in the resulting similarity matrix, which is especially severe when signal-to-noise ratio is low and experimental conditions cannot be fully randomized in a cognitive task. We propose an alternative Bayesian framework for computing representational similarity in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data, and directly estimate this covariance structure from imaging data while marginalizing over the unknown activity patterns. Converting the estimated covariance structure into a correlation matrix offers an unbiased estimate of neural representational similarity. Our method can also simultaneously estimate a signal-to-noise map that informs where the learned representational structure is supported more strongly, and the learned covariance matrix can be used as a structured prior to constrain Bayesian estimation of neural activity patterns.

scholarly journals Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias

2018 ◽  
Author(s):  
Ming Bo Cai ◽  
Nicolas W. Schuck ◽  
Jonathan W. Pillow ◽  
Yael Niv
Keyword(s):  
Neural Activity ◽  
Signal To Noise Ratio ◽  
Activity Patterns ◽  
Covariance Structure ◽  
Neural Population ◽  
Similarity Analysis ◽  
Imaging Data ◽  
Signal To Noise ◽  
Representational Similarity Analysis ◽  
Noise Ratio

AbstractThe activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degree of similarity between these neural activity patterns in response to different events is used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum a posteriori estimation of neural activity patterns, which can be further used for fMRI decoding. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).Author summaryWe show the severity of the bias introduced when performing representational similarity analysis (RSA) based on neural activity pattern estimated within imaging runs. Our Bayesian RSA method significantly reduces the bias and can learn a shared representational structure across multiple participants. We also demonstrate its extension as a new multi-class decoding tool.

scholarly journals Classification of Spatiotemporal Neural Activity Patterns in Brain Imaging Data

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Min Song ◽  
Minseok Kang ◽  
Hyeonsu Lee ◽  
Yong Jeong ◽  
Se-Bum Paik
Keyword(s):  
Brain Imaging ◽  
Neural Activity ◽  
Activity Patterns ◽  
Imaging Data ◽  
Brain Imaging Data

scholarly journals NOSA, an analytical toolbox for multicellular optical electrophysiology

2020 ◽  
Author(s):  
Sebastian Oltmanns ◽  
Frauke Sophie Abben ◽  
Anatoli Ender ◽  
Sophie Aimon ◽  
Richard Kovacs ◽  
...  
Keyword(s):  
Electrical Activity ◽  
Activity Patterns ◽  
Neuronal Firing ◽  
Specific Cell ◽  
Full Potential ◽  
Imaging Data ◽  
Signal To Noise ◽  
Voltage Imaging ◽  
Movement Correction ◽  
Cross Correlation Analysis

AbstractUnderstanding how neural networks generate activity patterns and communicate with each other requires monitoring the electrical activity from many neurons simultaneously. Perfectly suited tools for addressing this challenge are genetically encoded voltage indicators (GEVIs) because they can be targeted to specific cell types and optically report the electrical activity of individual, or populations of neurons. However, analyzing and interpreting the data from voltage imaging experiments is challenging because high recording speeds and properties of current GEVIs yield only low signal-to-noise ratios, making it necessary to apply specific analytical tools. Here, we present NOSA (Neuro-Optical Signal Analysis), a novel open source software designed for analyzing voltage imaging data and identifying temporal interactions between electrical activity patterns of different origin.In this manuscript we explain the challenges that arise during voltage imaging experiments and provide hands-on analytical solutions. We demonstrate how NOSA’s baseline fitting, filtering algorithms and movement correction can compensate for shifts in baseline fluorescence and extract electrical patterns from low signal-to-noise recordings. Moreover, NOSA contains powerful features to identify oscillatory frequencies in electrical patterns and extract neuronal firing characteristics. NOSA is the first open-access software to provide an option for analyzing simultaneously recorded optical and electrical data derived from patch-clamp or other electrode-based recordings. To identify temporal relations between electrical activity patterns we implemented different options to perform cross correlation analysis, demonstrating their utility during voltage imaging in Drosophila and mice. All features combined, NOSA will facilitate the first steps into using GEVIs and help to realize their full potential for revealing cell-type specific connectivity and functional interactions. If you would like to test NOSA, please send an email to the lead contact.

Heart Rate Variability, Homeostasis, and Brain Function

2012 ◽  
Vol 26 (4) ◽  
pp. 178-203 ◽  
Author(s):  
Francesco Riganello ◽  
Sergio Garbarino ◽  
Walter G. Sannita
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Communication Disorders ◽  
Affective States ◽  
Experimental Conditions ◽  
Cardiovascular Research ◽  
Sensory Stimuli ◽  
Additional Information ◽  
Brain Injured

Measures of heart rate variability (HRV) are major indices of the sympathovagal balance in cardiovascular research. These measures are thought to reflect complex patterns of brain activation as well and HRV is now emerging as a descriptor thought to provide information on the nervous system organization of homeostatic responses in accordance with the situational requirements. Current models of integration equate HRV to the affective states as parallel outputs of the central autonomic network, with HRV reflecting its organization of affective, physiological, “cognitive,” and behavioral elements into a homeostatic response. Clinical application is in the study of patients with psychiatric disorders, traumatic brain injury, impaired emotion-specific processing, personality, and communication disorders. HRV responses to highly emotional sensory inputs have been identified in subjects in vegetative state and in healthy or brain injured subjects processing complex sensory stimuli. In this respect, HRV measurements can provide additional information on the brain functional setup in the severely brain damaged and would provide researchers with a suitable approach in the absence of conscious behavior or whenever complex experimental conditions and data collection are impracticable, as it is the case, for example, in intensive care units.

scholarly journals The relationship between hippocampal-dependent task performance and hippocampal grey matter myelination and iron content

2021 ◽  
Vol 5 ◽  
pp. 239821282110119
Author(s):  
Ian A. Clark ◽  
Martina F. Callaghan ◽  
Nikolaus Weiskopf ◽  
Eleanor A. Maguire
Keyword(s):  
Individual Differences ◽  
Autobiographical Memory ◽  
Task Performance ◽  
Iron Content ◽  
Grey Matter ◽  
Cognitive Task ◽  
Spatial Navigation ◽  
Imaging Data ◽  
Future Thinking

Individual differences in scene imagination, autobiographical memory recall, future thinking and spatial navigation have long been linked with hippocampal structure in healthy people, although evidence for such relationships is, in fact, mixed. Extant studies have predominantly concentrated on hippocampal volume. However, it is now possible to use quantitative neuroimaging techniques to model different properties of tissue microstructure in vivo such as myelination and iron. Previous work has linked such measures with cognitive task performance, particularly in older adults. Here we investigated whether performance on scene imagination, autobiographical memory, future thinking and spatial navigation tasks was associated with hippocampal grey matter myelination or iron content in young, healthy adult participants. Magnetic resonance imaging data were collected using a multi-parameter mapping protocol (0.8 mm isotropic voxels) from a large sample of 217 people with widely-varying cognitive task scores. We found little evidence that hippocampal grey matter myelination or iron content were related to task performance. This was the case using different analysis methods (voxel-based quantification, partial correlations), when whole brain, hippocampal regions of interest, and posterior:anterior hippocampal ratios were examined, and across different participant sub-groups (divided by gender and task performance). Variations in hippocampal grey matter myelin and iron levels may not, therefore, help to explain individual differences in performance on hippocampal-dependent tasks, at least in young, healthy individuals.

scholarly journals Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1723
Author(s):  
Anne K. Schütz ◽  
Verena Schöler  ◽  
E. Tobias Krause  ◽  
Mareike Fischer  ◽  
Thomas Müller  ◽  
...  
Keyword(s):  
Animal Welfare ◽  
Early Recognition ◽  
Activity Patterns ◽  
Automatic Detection ◽  
Animal Husbandry ◽  
Video Data ◽  
Activity Level ◽  
Red Foxes ◽  
Experimental Conditions ◽  
Animal Activity

Animal activity is an indicator for its welfare and manual observation is time and cost intensive. To this end, automatic detection and monitoring of live captive animals is of major importance for assessing animal activity, and, thereby, allowing for early recognition of changes indicative for diseases and animal welfare issues. We demonstrate that machine learning methods can provide a gap-less monitoring of red foxes in an experimental lab-setting, including a classification into activity patterns. Therefore, bounding boxes are used to measure fox movements, and, thus, the activity level of the animals. We use computer vision, being a non-invasive method for the automatic monitoring of foxes. More specifically, we train the existing algorithm ‘you only look once’ version 4 (YOLOv4) to detect foxes, and the trained classifier is applied to video data of an experiment involving foxes. As we show, computer evaluation outperforms other evaluation methods. Application of automatic detection of foxes can be used for detecting different movement patterns. These, in turn, can be used for animal behavioral analysis and, thus, animal welfare monitoring. Once established for a specific animal species, such systems could be used for animal monitoring in real-time under experimental conditions, or other areas of animal husbandry.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

scholarly journals Statistical assessment of the stability of neural movement representations

2011 ◽  
Vol 106 (2) ◽  
pp. 764-774 ◽  
Author(s):  
Ian H. Stevenson ◽  
Anil Cherian ◽  
Brian M. London ◽  
Nicholas A. Sachs ◽  
Eric Lindberg ◽  
...  
Keyword(s):  
Primary Motor Cortex ◽  
Tuning Curve ◽  
Modulation Depth ◽  
Measurement Noise ◽  
Neural Representation ◽  
Experimental Conditions ◽  
Sensory Stimuli ◽  
Tuning Curves ◽  
Dynamic Tuning ◽  
Preferred Direction

In systems neuroscience, neural activity that represents movements or sensory stimuli is often characterized by spatial tuning curves that may change in response to training, attention, altered mechanics, or the passage of time. A vital step in determining whether tuning curves change is accounting for estimation uncertainty due to measurement noise. In this study, we address the issue of tuning curve stability using methods that take uncertainty directly into account. We analyze data recorded from neurons in primary motor cortex using chronically implanted, multielectrode arrays in four monkeys performing center-out reaching. With the use of simulations, we demonstrate that under typical experimental conditions, the effect of neuronal noise on estimated preferred direction can be quite large and is affected by both the amount of data and the modulation depth of the neurons. In experimental data, we find that after taking uncertainty into account using bootstrapping techniques, the majority of neurons appears to be very stable on a timescale of minutes to hours. Lastly, we introduce adaptive filtering methods to explicitly model dynamic tuning curves. In contrast to several previous findings suggesting that tuning curves may be in constant flux, we conclude that the neural representation of limb movement is, on average, quite stable and that impressions to the contrary may be largely the result of measurement noise.

scholarly journals Recurrent circuitry is required to stabilize piriform cortex odor representations across brain states

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Kevin A Bolding ◽  
Shivathmihai Nagappan ◽  
Bao-Xia Han ◽  
Fan Wang ◽  
Kevin M Franks
Keyword(s):  
Neural Activity ◽  
Neural Circuits ◽  
Activity Patterns ◽  
Piriform Cortex ◽  
Theoretical Studies ◽  
Pattern Completion ◽  
Attractor Networks ◽  
Attractor Dynamics ◽  
Brain States ◽  
Associative Function

Pattern completion, or the ability to retrieve stable neural activity patterns from noisy or partial cues, is a fundamental feature of memory. Theoretical studies indicate that recurrently connected auto-associative or discrete attractor networks can perform this process. Although pattern completion and attractor dynamics have been observed in various recurrent neural circuits, the role recurrent circuitry plays in implementing these processes remains unclear. In recordings from head-fixed mice, we found that odor responses in olfactory bulb degrade under ketamine/xylazine anesthesia while responses immediately downstream, in piriform cortex, remain robust. Recurrent connections are required to stabilize cortical odor representations across states. Moreover, piriform odor representations exhibit attractor dynamics, both within and across trials, and these are also abolished when recurrent circuitry is eliminated. Here, we present converging evidence that recurrently-connected piriform populations stabilize sensory representations in response to degraded inputs, consistent with an auto-associative function for piriform cortex supported by recurrent circuitry.

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