Scalable Semisupervised Functional Neurocartography Reveals Canonical Neurons in Behavioral Networks

2016 ◽  
Vol 28 (8) ◽  
pp. 1453-1497 ◽  
Author(s):  
E. Paxon Frady ◽  
Ashish Kapoor ◽  
Eric Horvitz ◽  
William B. Kristan Jr.
Keyword(s):  
Neural Activity ◽  
Large Scale ◽  
Feature Space ◽  
Brain Regions ◽  
Machine Learning Techniques ◽  
Intersubject Variability ◽  
High Dimensions ◽  
Spatial And Temporal Scales ◽  
Canonical Neurons ◽  
The Rich

Large-scale data collection efforts to map the brain are underway at multiple spatial and temporal scales, but all face fundamental problems posed by high-dimensional data and intersubject variability. Even seemingly simple problems, such as identifying a neuron/brain region across animals/subjects, become exponentially more difficult in high dimensions, such as recognizing dozens of neurons/brain regions simultaneously. We present a framework and tools for functional neurocartography—the large-scale mapping of neural activity during behavioral states. Using a voltage-sensitive dye (VSD), we imaged the multifunctional responses of hundreds of leech neurons during several behaviors to identify and functionally map homologous neurons. We extracted simple features from each of these behaviors and combined them with anatomical features to create a rich medium-dimensional feature space. This enabled us to use machine learning techniques and visualizations to characterize and account for intersubject variability, piece together a canonical atlas of neural activity, and identify two behavioral networks. We identified 39 neurons (18 pairs, 3 unpaired) as part of a canonical swim network and 17 neurons (8 pairs, 1 unpaired) involved in a partially overlapping preparatory network. All neurons in the preparatory network rapidly depolarized at the onsets of each behavior, suggesting that it is part of a dedicated rapid-response network. This network is likely mediated by the S cell, and we referenced VSD recordings to an activity atlas to identify multiple cells of interest simultaneously in real time for further experiments. We targeted and electrophysiologically verified several neurons in the swim network and further showed that the S cell is presynaptic to multiple neurons in the preparatory network. This study illustrates the basic framework to map neural activity in high dimensions with large-scale recordings and how to extract the rich information necessary to perform analyses in light of intersubject variability.

scholarly journals Intrinsic coupling modes reveal the functional architecture of cortico-tectal networks

2015 ◽  
Author(s):  
Iain Stitt ◽  
Edgar Galindo-Leon ◽  
Florian Pieper ◽  
Gerhard Engler ◽  
Eva Fiedler ◽  
...  
Keyword(s):  
Neural Activity ◽  
High Frequency ◽  
Large Scale ◽  
Sensory Stimulation ◽  
Correlation Structure ◽  
Spatial And Temporal Scales ◽  
Cortical Oscillations ◽  
High Frequency Oscillations ◽  
Rich Information ◽  
The Rich

In the absence of sensory stimulation or motor output, the brain exhibits complex spatiotemporal patterns of intrinsically generated neural activity. However, little is known about how such patterns of activity are correlated between cortical and subcortical brain areas. Here, we investigate the large-scale correlation structure of ongoing cortical and superior colliculus (SC) activity across multiple spatial and temporal scales. Cortico-tectal interaction was characterized by correlated fluctuations in the amplitude of delta, spindle, low gamma and high frequency oscillations (> 100 Hz). Of these identified coupling modes, topographical patterns of high frequency coupling were the most consistent with anatomical connectivity, and reflected synchronized spiking in cortico-tectal networks. Ongoing high frequency cortico-tectal coupling was temporally governed by the phase of slow cortical oscillations. Collectively, our findings show that cortico-tectal networks can be resolved through the correlation structure of ongoing neural activity, and demonstrate the rich information conveyed by high frequency electrocorticographic signals.

scholarly journals Large-scale functional coactivation patterns reflect the structural connectivity of the medial prefrontal cortex

2020 ◽  
Author(s):  
Dale T Tovar ◽  
Robert S Chavez
Keyword(s):  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Large Scale ◽  
Spatial Scales ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Machine Learning Techniques ◽  
Affective Processing ◽  
Affective Neuroscience ◽  
Psychological Phenomena

Abstract The medial prefrontal cortex (MPFC) is among the most consistently implicated brain regions in social and affective neuroscience. Yet, this region is also highly functionally heterogeneous across many domains and has diverse patterns of connectivity. The extent to which the communication of functional networks in this area is facilitated by its underlying structural connectivity fingerprint is critical for understanding how psychological phenomena are represented within this region. In the current study, we combined diffusion magnetic resonance imaging and probabilistic tractography with large-scale meta-analysis to investigate the degree to which the functional co-activation patterns of the MPFC is reflected in its underlying structural connectivity. Using unsupervised machine learning techniques, we compared parcellations between the two modalities and found congruence between parcellations at multiple spatial scales. Additionally, using connectivity and coactivation similarity analyses, we found high correspondence in voxel-to-voxel similarity between each modality across most, but not all, subregions of the MPFC. These results provide evidence that meta-analytic functional coactivation patterns are meaningfully constrained by underlying neuroanatomical connectivity and provide convergent evidence of distinct subregions within the MPFC involved in affective processing and social cognition.

scholarly journals Large- and multi-scale networks in the rodent brain during novelty exploration

2020 ◽  
Author(s):  
Michael X Cohen ◽  
Bernhard Englitz ◽  
Arthur S C França
Keyword(s):  
Prefrontal Cortex ◽  
Parietal Cortex ◽  
Local Field ◽  
Neural Activity ◽  
Large Scale ◽  
Field Potential ◽  
Brain Regions ◽  
Data Availability ◽  
Data Matrix ◽  
Temporal Scales

AbstractNeural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, due to limited data availability and to the difficulty of analyzing rich multivariate datasets. Here we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (~4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration.Significance statementNeural activity is synchronized over space, time, and frequency. To characterize the dynamics of large-scale networks spanning multiple brain regions, we recorded data from the prefrontal cortex, parietal cortex, and hippocampus in awake behaving mice, and pooled data from spiking activity and local field potentials into one data matrix. Frequency-specific multivariate decomposition methods revealed a cornucopia of neural networks defined by coherent spatiotemporal patterns over time. These findings reveal a rich, dynamic, and multivariate landscape of large-scale neural activity patterns during foraging behavior.

scholarly journals Large-scale cellular-resolution imaging of neural activity in freely behaving mice

2021 ◽  
Author(s):  
D.P. Leman ◽  
I.A. Chen ◽  
K.A. Bolding ◽  
J. Tai ◽  
L.K. Wilmerding ◽  
...  
Keyword(s):  
Neural Activity ◽  
Large Scale ◽  
First Generation ◽  
Brain Regions ◽  
Field Of View ◽  
Cellular Resolution ◽  
Neural Populations ◽  
Resolution Imaging ◽  
Freely Behaving ◽  
Freely Moving

AbstractMiniaturized microscopes for head-mounted fluorescence imaging are powerful tools for visualizing neural activity during naturalistic behaviors, but the restricted field of view of first-generation ‘miniscopes’ limits the size of neural populations accessible for imaging. Here we describe a novel miniaturized mesoscope offering cellular-resolution imaging over areas spanning several millimeters in freely moving mice. This system enables comprehensive visualization of activity across entire brain regions or interactions across areas.

scholarly journals A spatiotemporal complexity architecture of human brain activity

2021 ◽  
Author(s):  
Stephan Krohn ◽  
Nina von Schwanenflug ◽  
Leonhard Waschke ◽  
Amy Romanello ◽  
Martin Gell ◽  
...  
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Connections ◽  
Signal Complexity ◽  
The Brain

The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity — a functional "complexome" that gives rise to the network organization of the human brain.

scholarly journals A Probabilistic, Distributed, Recursive Mechanism for Decision-making in the Brain

2016 ◽  
Author(s):  
Javier A. Caballero ◽  
Mark D. Humphries ◽  
Kevin N. Gurney
Keyword(s):  
Decision Making ◽  
Basal Ganglia ◽  
Neural Activity ◽  
Large Scale ◽  
Single Equation ◽  
Brain Regions ◽  
Compulsive Gambling ◽  
Choice Behaviour ◽  
Usable Information ◽  
The Brain

AbstractDecision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT). Using it to simulate the random-dot-motion task, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT. Its architecture maps to the recurrent cortico-basal-ganglia-thalamo-cortical loops, whose components are all implicated in decision-making. We show that the dynamics of its mapped computations match those of neural activity in the sensorimotor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus. This also predicts which aspects of neural dynamics are and are not part of inference. Our single-equation algorithm is probabilistic, distributed, recursive, and parallel. Its success at capturing anatomy, behaviour, and electrophysiology suggests that the mechanism implemented by the brain has these same characteristics.Author SummaryDecision-making is central to cognition. Abnormally-formed decisions characterize disorders like over-eating, Parkinson’s and Huntington’s diseases, OCD, addiction, and compulsive gambling. Yet, a unified account of decisionmaking has, hitherto, remained elusive. Here we show the essential composition of the brain’s decision mechanism by matching experimental data from monkeys making decisions, to the knowable function of a novel statistical inference algorithm. Our algorithm maps onto the large-scale architecture of decision circuits in the primate brain, replicating the monkeys’ choice behaviour and the dynamics of the neural activity that accompany it. Validated in this way, our algorithm establishes a basic framework for understanding the mechanistic ingredients of decisionmaking in the brain, and thereby, a basic platform for understanding how pathologies arise from abnormal function.

scholarly journals Identifying signal and noise structure in neural population activity with Gaussian process factor models

2020 ◽  
Author(s):  
Stephen L. Keeley ◽  
Mikio C. Aoi ◽  
Yiyi Yu ◽  
Spencer L. Smith ◽  
Jonathan W. Pillow
Keyword(s):  
Gaussian Process ◽  
Neural Activity ◽  
Latent Variable ◽  
Large Scale ◽  
Brain Regions ◽  
Factor Models ◽  
Latent Structure ◽  
Neural Population ◽  
Process Factor ◽  
Repeated Stimulus

AbstractNeural datasets often contain measurements of neural activity across multiple trials of a repeated stimulus or behavior. An important problem in the analysis of such datasets is to characterize systematic aspects of neural activity that carry information about the repeated stimulus or behavior of interest, which can be considered “signal”, and to separate them from the trial-to-trial fluctuations in activity that are not time-locked to the stimulus, which for purposes of such analyses can be considered “noise”. Gaussian Process factor models provide a powerful tool for identifying shared structure in high-dimensional neural data. However, they have not yet been adapted to the problem of characterizing signal and noise in multi-trial datasets. Here we address this shortcoming by proposing “signal-noise” Poisson-spiking Gaussian Process Factor Analysis (SNP-GPFA), a flexible latent variable model that resolves signal and noise latent structure in neural population spiking activity. To learn the parameters of our model, we introduce a Fourier-domain black box variational inference method that quickly identifies smooth latent structure. The resulting model reliably uncovers latent signal and trial-to-trial noise-related fluctuations in large-scale recordings. We use this model to show that predominantly, noise fluctuations perturb neural activity within a subspace orthogonal to signal activity, suggesting that trial-by-trial noise does not interfere with signal representations. Finally, we extend the model to capture statistical dependencies across brain regions in multi-region data. We show that in mouse visual cortex, models with shared noise across brain regions out-perform models with independent per-region noise.

scholarly journals Breathing modulates cortico-hippocampal dynamics during offline states

2021 ◽  
Author(s):  
Nikolaos Karalis ◽  
Anton Sirota
Keyword(s):  
Information Flow ◽  
Neural Activity ◽  
Information Transfer ◽  
Large Scale ◽  
Brain Regions ◽  
State Transitions ◽  
Population Activity ◽  
Brain Rhythm ◽  
Regional Population ◽  
Brain Circuit

Abstract Network dynamics have been proposed as a mechanistic substrate for the information transfer across cortical and hippocampal circuits. During sleep and offline states, synchronous reactivation across these regions underlies the consolidation of memories. However, little is known about the mechanisms that synchronize and coordinate these processes across widespread brain regions. Here we address the hypothesis that breathing acts as an oscillatory pacemaker, persistently coupling distributed brain circuit dynamics. Using large-scale recordings from seven cortical and subcortical brain regions in quiescent and sleeping mice, we identified a novel global mechanism, termed respiratory corollary discharge, that co-modulates neural activity across these circuits. Analysis of inter-regional population activity and optogenetic perturbations revealed that breathing rhythm couples hippocampal sharp-wave ripples and cortical DOWN/UP state transitions by jointly modulating excitability in these circuits. These results highlight breathing, a perennial brain rhythm, as an oscillatory scaffold for the functional coordination of the limbic circuit, supporting the segregation and integration of information flow across neuronal networks during offline states.

scholarly journals Long-range projections coordinate distributed brain-wide neural activity with a specific spatiotemporal profile

2016 ◽  
Vol 113 (51) ◽  
pp. E8306-E8315 ◽  
Author(s):  
Alex T. L. Leong ◽  
Russell W. Chan ◽  
Patrick P. Gao ◽  
Ying-Shing Chan ◽  
Kevin K. Tsia ◽  
...  
Keyword(s):  
Long Range ◽  
Neural Activity ◽  
Visual Processing ◽  
Large Scale ◽  
Functional Integration ◽  
Low Frequency ◽  
Brain Regions ◽  
Blood Oxygenation ◽  
Neural Basis ◽  
Excitatory Neurons

One challenge in contemporary neuroscience is to achieve an integrated understanding of the large-scale brain-wide interactions, particularly the spatiotemporal patterns of neural activity that give rise to functions and behavior. At present, little is known about the spatiotemporal properties of long-range neuronal networks. We examined brain-wide neural activity patterns elicited by stimulating ventral posteromedial (VPM) thalamo-cortical excitatory neurons through combined optogenetic stimulation and functional MRI (fMRI). We detected robust optogenetically evoked fMRI activation bilaterally in primary visual, somatosensory, and auditory cortices at low (1 Hz) but not high frequencies (5–40 Hz). Subsequent electrophysiological recordings indicated interactions over long temporal windows across thalamo-cortical, cortico-cortical, and interhemispheric callosal projections at low frequencies. We further observed enhanced visually evoked fMRI activation during and after VPM stimulation in the superior colliculus, indicating that visual processing was subcortically modulated by low-frequency activity originating from VPM. Stimulating posteromedial complex thalamo-cortical excitatory neurons also evoked brain-wide blood-oxygenation-level–dependent activation, although with a distinct spatiotemporal profile. Our results directly demonstrate that low-frequency activity governs large-scale, brain-wide connectivity and interactions through long-range excitatory projections to coordinate the functional integration of remote brain regions. This low-frequency phenomenon contributes to the neural basis of long-range functional connectivity as measured by resting-state fMRI.

scholarly journals Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning

2022 ◽  
Vol 12 ◽  
Author(s):  
Barnaby E. Walker ◽  
Allan Tucker ◽  
Nicky Nicolson
Keyword(s):  
Large Scale ◽  
New Technologies ◽  
Representation Learning ◽  
Natural World ◽  
Machine Learning Techniques ◽  
Herbarium Specimens ◽  
Learning Techniques ◽  
Rich Information ◽  
The Rich ◽  
Computational Resources

The mobilization of large-scale datasets of specimen images and metadata through herbarium digitization provide a rich environment for the application and development of machine learning techniques. However, limited access to computational resources and uneven progress in digitization, especially for small herbaria, still present barriers to the wide adoption of these new technologies. Using deep learning to extract representations of herbarium specimens useful for a wide variety of applications, so-called “representation learning,” could help remove these barriers. Despite its recent popularity for camera trap and natural world images, representation learning is not yet as popular for herbarium specimen images. We investigated the potential of representation learning with specimen images by building three neural networks using a publicly available dataset of over 2 million specimen images spanning multiple continents and institutions. We compared the extracted representations and tested their performance in application tasks relevant to research carried out with herbarium specimens. We found a triplet network, a type of neural network that learns distances between images, produced representations that transferred the best across all applications investigated. Our results demonstrate that it is possible to learn representations of specimen images useful in different applications, and we identify some further steps that we believe are necessary for representation learning to harness the rich information held in the worlds’ herbaria.

Sign in / Sign up

Export Citation Format

Share Document