scholarly journals Dynamic Electrode-to-Image (DETI) Mapping Reveals the Human Brain’s Spatiotemporal Code of Visual Information

2021 ◽  
Author(s):  
Bruce C. Hansen ◽  
Michelle R. Greene ◽  
David J. Field
Keyword(s):  
Temporal Resolution ◽  
Visual Information ◽  
Neural Code ◽  
High Temporal Resolution ◽  
Visual Code ◽  
Neural Signals ◽  
Intelligent Behavior ◽  
Spatiotemporal Visualization ◽  
State Space Theory ◽  
The Brain

AbstractA chief goal of systems neuroscience is to understand how the brain encodes information in our visual environments. Understanding that neural code is crucial to explaining how visual content is transformed via subsequent semantic representations to enable intelligent behavior. Although the visual code is not static, this reality is often obscured in voxel-wise encoding models of BOLD signals due to fMRI’s poor temporal resolution. We leveraged the high temporal resolution of EEG to develop an encoding technique based in state-space theory. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. This technique offers a spatiotemporal visualization of the evolution of the neural code of visual information thought impossible to obtain from EEG and promises to provide insight into how visual meaning is developed through dynamic feedforward and recurrent processes.

scholarly journals Dynamic Electrode-to-Image (DETI) mapping reveals the human brain’s spatiotemporal code of visual information

2021 ◽  
Vol 17 (9) ◽  
pp. e1009456
Author(s):  
Bruce C. Hansen ◽  
Michelle R. Greene ◽  
David J. Field
Keyword(s):  
Temporal Resolution ◽  
Visual Information ◽  
Mapping Technique ◽  
High Temporal Resolution ◽  
Visual Code ◽  
Neural Signals ◽  
Future Studies ◽  
Spatial Frequencies ◽  
High Level ◽  
Neuroimaging Techniques

A number of neuroimaging techniques have been employed to understand how visual information is transformed along the visual pathway. Although each technique has spatial and temporal limitations, they can each provide important insights into the visual code. While the BOLD signal of fMRI can be quite informative, the visual code is not static and this can be obscured by fMRI’s poor temporal resolution. In this study, we leveraged the high temporal resolution of EEG to develop an encoding technique based on the distribution of responses generated by a population of real-world scenes. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. Our analyses of the mapping results revealed that scenes undergo a series of nonuniform transformations that prioritize different spatial frequencies at different regions of scenes over time. This mapping technique offers a potential avenue for future studies to explore how dynamic feedforward and recurrent processes inform and refine high-level representations of our visual world.

scholarly journals Ultrasound functional neuroimaging reveals propagation of task-related brain activity in behaving primates

2018 ◽  
Author(s):  
Alexandre Dizeux ◽  
Marc Gesnik ◽  
Harry Ahnine ◽  
Kevin Blaize ◽  
Fabrice Arcizet ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Anterior Cingulate ◽  
Global Network ◽  
High Temporal Resolution ◽  
Wide Field ◽  
Spatiotemporal Resolution ◽  
Macro Scale ◽  
The Brain

ABSTRACTIn recent decades, neuroimaging has played an invaluable role in improving the fundamental understanding of the brain. At the macro scale, neuroimaging modalities such as MRI, EEG, and MEG, exploit a wide field of view to explore the brain as a global network of interacting regions. However, this comes at the price of either limited spatiotemporal resolution or limited sensitivity. At the micro scale, electrophysiology is used to explore the dynamic aspects of neuronal activity with a very high temporal resolution. However, this modality requires a statistical averaging of several tens of single task responses. A large-scale neuroimaging modality of sufficient spatial and temporal resolution and sensitivity to study brain region activation dynamically would open new territories of possibility in neuroscienceWe show that neurofunctional ultrasound imaging (fUS) is both able to assess brain activation during single cognitive tasks within superficial and deeper areas of the frontal cortex areas, and image the directional propagation of information within and between these regions. Equipped with an fUS device, two macaque rhesus monkeys were instructed before a stimulus appeared to rest (fixation) or to look towards (saccade) or away (antisaccade) from a stimulus. Our results identified an abrupt transient change in activity for all acquisitions in the supplementary eye field (SEF) when the animals were required to change a rule regarding the task cued by a stimulus. Simultaneous imaging in the anterior cingulate cortex and SEF revealed a time delay in the directional functional connectivity of 0.27 ± 0.07 s and 0.9 ± 0.2 s for animals S and Y, respectively. These results provide initial evidence that recording cerebral hemodynamics over large brain areas at a high spatiotemporal resolution and sensitivity with neurofunctional ultrasound can reveal instantaneous monitoring of endogenous brain signals and behavior.

scholarly journals Uncovering Dynamic Brain Reconfiguration in MEG Working Memory n-Back Task Using Topological Data Analysis

2019 ◽  
Vol 9 (6) ◽  
pp. 144 ◽  
Author(s):  
Ali Nabi Duman ◽  
Ahmet Emin Tatar ◽  
Harun Pirim
Keyword(s):  
Working Memory ◽  
Data Analysis ◽  
Response Time ◽  
Temporal Resolution ◽  
Generative Models ◽  
Topological Data Analysis ◽  
High Temporal Resolution ◽  
The Brain ◽  
Back Task ◽  
Topological Data

The increasing availability of high temporal resolution neuroimaging data has increased the efforts to understand the dynamics of neural functions. Until recently, there are few studies on generative models supporting classification and prediction of neural systems compared to the description of the architecture. However, the requirement of collapsing data spatially and temporally in the state-of-the art methods to analyze functional magnetic resonance imaging (fMRI), electroencephalogram (EEG) and magnetoencephalography (MEG) data cause loss of important information. In this study, we addressed this issue using a topological data analysis (TDA) method, called Mapper, which visualizes evolving patterns of brain activity as a mathematical graph. Accordingly, we analyzed preprocessed MEG data of 83 subjects from Human Connectome Project (HCP) collected during working memory n-back task. We examined variation in the dynamics of the brain states with the Mapper graphs, and to determine how this variation relates to measures such as response time and performance. The application of the Mapper method to MEG data detected a novel neuroimaging marker that explained the performance of the participants along with the ground truth of response time. In addition, TDA enabled us to distinguish two task-positive brain activations during 0-back and 2-back tasks, which is hard to detect with the other pipelines that require collapsing the data in the spatial and temporal domain. Further, the Mapper graphs of the individuals also revealed one large group in the middle of the stimulus detecting the high engagement in the brain with fine temporal resolution, which could contribute to increase spatiotemporal resolution by merging different imaging modalities. Hence, our work provides another evidence to the effectiveness of the TDA methods for extracting subtle dynamic properties of high temporal resolution MEG data without the temporal and spatial collapse.

scholarly journals The temporal resolution of neural codes: does response latency have a unique role?

2002 ◽  
Vol 357 (1424) ◽  
pp. 987-1001 ◽  
Author(s):  
M. W. Oram ◽  
D. Xiao ◽  
B. Dritschel ◽  
K. R. Payne
Keyword(s):  
Temporal Resolution ◽  
Response Latency ◽  
Reaction Times ◽  
Gain Control ◽  
Limited Range ◽  
Stimulus Onset ◽  
Neural Code ◽  
Neural Signals ◽  
Response Measures ◽  
Onset Response

This article reviews the nature of the neural code in non-human primate cortex and assesses the potential for neurons to carry two or more signals simultaneously. Neurophysiological recordings from visual and motor systems indicate that the evidence for a role for precisely timed spikes relative to other spike times ( ca . 1–10 ms resolution) is inconclusive. This indicates that the visual system does not carry a signal that identifies whether the responses were elicited when the stimulus was attended or not. Simulations show that the absence of such a signal reduces, but does not eliminate, the increased discrimination between stimuli that are attended compared with when the stimuli are unattended. The increased accuracy asymptotes with increased gain control, indicating limited benefit from increasing attention. The absence of a signal identifying the attentional state under which stimuli were viewed can produce the greatest discrimination between attended and unattended stimuli. Furthermore, the greatest reduction in discrimination errors occurs for a limited range of gain control, again indicating that attention effects are limited. By contrast to precisely timed patterns of spikes where the timing is relative to other spikes, response latency provides a fine temporal resolution signal ( ca . 10 ms resolution) that carries information that is unavailable from coarse temporal response measures. Changes in response latency and changes in response magnitude can give rise to different predictions for the patterns of reaction times. The predictions are verified, and it is shown that the standard method for distinguishing executive and slave processes is only valid if the representations of interest, as evidenced by the neural code, are known. Overall, the data indicate that the signalling evident in neural signals is restricted to the spike count and the precise times of spikes relative to stimulus onset (response latency). These coding issues have implications for our understanding of cognitive models of attention and the roles of executive and slave systems.

scholarly journals Metabolic and haemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study

2020 ◽  
Author(s):  
Sharna D Jamadar ◽  
Phillip GD Ward ◽  
Emma Xingwen Liang ◽  
Edwina R Orchard ◽  
Zhaolin Chen ◽  
...  
Keyword(s):  
Human Brain ◽  
Temporal Resolution ◽  
Fdg Pet ◽  
Neurological Diseases ◽  
Brain Connectivity ◽  
Constant Infusion ◽  
High Temporal Resolution ◽  
Positron Emission ◽  
Metabolic Connectivity ◽  
The Brain

AbstractSimultaneous FDG-PET/fMRI ([18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging) provides the capacity to image two sources of energetic dynamics in the brain – glucose metabolism and haemodynamic response. Functional fMRI connectivity has been enormously useful for characterising interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease; but static FDG-PET cannot be used to estimate subjectlevel measures of connectivity, only across-subject covariance. Here, we applied high-temporal resolution constant infusion fPET to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterised by fronto-parietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with static FDG-PET metabolic covariance, indicating that metabolic brain connectivity is a non-ergodic process whereby individual brain connectivity cannot be inferred from group level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain, and open up the opportunity for novel fundamental studies of human brain connectivity as well as multi-modality biomarkers of neurological diseases.

scholarly journals Frequency-Resolved Functional Connectivity: Role of Delay and the Strength of Connections

2021 ◽  
Vol 15 ◽  
Author(s):  
Abolfazl Ziaeemehr ◽  
Alireza Valizadeh
Keyword(s):  
Functional Connectivity ◽  
Temporal Resolution ◽  
Computational Study ◽  
Brain Regions ◽  
Kuramoto Model ◽  
Functional Networks ◽  
High Temporal Resolution ◽  
Frequency Bands ◽  
The Brain

The brain functional network extracted from the BOLD signals reveals the correlated activity of the different brain regions, which is hypothesized to underlie the integration of the information across functionally specialized areas. Functional networks are not static and change over time and in different brain states, enabling the nervous system to engage and disengage different local areas in specific tasks on demand. Due to the low temporal resolution, however, BOLD signals do not allow the exploration of spectral properties of the brain dynamics over different frequency bands which are known to be important in cognitive processes. Recent studies using imaging tools with a high temporal resolution has made it possible to explore the correlation between the regions at multiple frequency bands. These studies introduce the frequency as a new dimension over which the functional networks change, enabling brain networks to transmit multiplex of information at any time. In this computational study, we explore the functional connectivity at different frequency ranges and highlight the role of the distance between the nodes in their correlation. We run the generalized Kuramoto model with delayed interactions on top of the brain's connectome and show that how the transmission delay and the strength of the connections, affect the correlation between the pair of nodes over different frequency bands.

scholarly journals Frequency-resolved functional connectivity: Role of delay and the strength of connections

2020 ◽  
Author(s):  
Abolfazl Ziaeemehr ◽  
Alireza Valizadeh
Keyword(s):  
Functional Connectivity ◽  
Temporal Resolution ◽  
Computational Study ◽  
Brain Regions ◽  
Kuramoto Model ◽  
Functional Networks ◽  
High Temporal Resolution ◽  
Frequency Bands ◽  
The Brain

AbstractThe brain functional network extracted from the BOLD signals reveals the correlated activity of the different brain regions, which is hypothesized to underlie the integration of the information across functionally specialized areas. Functional networks are not static and change over time and in different brain states, enabling the nervous system to engage and disengage different local areas in specific tasks on demand. Due to the low temporal resolution, however, BOLD signals do not allow the exploration of spectral properties of the brain dynamics over different frequency bands which are known to be important in cognitive processes. Recent studies using imaging tools with a high temporal resolution has made it possible to explore the correlation between the regions at multiple frequency bands. These studies introduce the frequency as a new dimension over which the functional networks change, enabling brain networks to transmit multiplex of information at any time. In this computational study, we explore the functional connectivity at different frequency ranges and highlight the role of the distance between the nodes in their correlation. We run the generalized Kuramoto model with delayed interactions on top of the brain’s connectome and show that how the transmission delay and the strength of the connections, affect the correlation between the pair of nodes over different frequency bands.

scholarly journals A miniaturized push–pull-perfusion probe for few-second sampling of neurotransmitters in the mouse brain

Lab on a Chip ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1332-1343 ◽  
Author(s):  
Floris T. G. van den Brink ◽  
Thas Phisonkunkasem ◽  
Ashish Asthana ◽  
Johan G. Bomer ◽  
Arn M. J. M. van den Maagdenberg ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Temporal Resolution ◽  
High Temporal Resolution ◽  
The Brain

Measuring neurotransmitters in the brain of living animals is a challenging task, especially when detailed information at high temporal resolution is required.

What Can Neuromorphic Event-Driven Precise Timing Add to Spike-Based Pattern Recognition?

2015 ◽  
Vol 27 (3) ◽  
pp. 561-593 ◽  
Author(s):  
Himanshu Akolkar ◽  
Cedric Meyer ◽  
Xavier Clady ◽  
Olivier Marre ◽  
Chiara Bartolozzi ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Temporal Resolution ◽  
Visual Processing ◽  
Visual Information ◽  
Spike Timing ◽  
High Temporal Resolution ◽  
Visual Sensors ◽  
Practical Applications ◽  
Temporal Precision ◽  
Event Based

This letter introduces a study to precisely measure what an increase in spike timing precision can add to spike-driven pattern recognition algorithms. The concept of generating spikes from images by converting gray levels into spike timings is currently at the basis of almost every spike-based modeling of biological visual systems. The use of images naturally leads to generating incorrect artificial and redundant spike timings and, more important, also contradicts biological findings indicating that visual processing is massively parallel, asynchronous with high temporal resolution. A new concept for acquiring visual information through pixel-individual asynchronous level-crossing sampling has been proposed in a recent generation of asynchronous neuromorphic visual sensors. Unlike conventional cameras, these sensors acquire data not at fixed points in time for the entire array but at fixed amplitude changes of their input, resulting optimally sparse in space and time—pixel individually and precisely timed only if new, (previously unknown) information is available (event based). This letter uses the high temporal resolution spiking output of neuromorphic event-based visual sensors to show that lowering time precision degrades performance on several recognition tasks specifically when reaching the conventional range of machine vision acquisition frequencies (30–60 Hz). The use of information theory to characterize separability between classes for each temporal resolution shows that high temporal acquisition provides up to 70% more information that conventional spikes generated from frame-based acquisition as used in standard artificial vision, thus drastically increasing the separability between classes of objects. Experiments on real data show that the amount of information loss is correlated with temporal precision. Our information-theoretic study highlights the potentials of neuromorphic asynchronous visual sensors for both practical applications and theoretical investigations. Moreover, it suggests that representing visual information as a precise sequence of spike times as reported in the retina offers considerable advantages for neuro-inspired visual computations.

scholarly journals The Perceptual Neural Trace of Memorable Unseen Scenes

2018 ◽  
Author(s):  
Yalda Mohsenzadeh ◽  
Caitlin Mullin ◽  
Aude Oliva ◽  
Dimitrios Pantazis
Keyword(s):  
Time Scales ◽  
Temporal Resolution ◽  
Rapid Serial Visual Presentation ◽  
Visual Processing ◽  
Visual Presentation ◽  
High Temporal Resolution ◽  
Neural Signal ◽  
Subjective Awareness ◽  
Behavioral Studies ◽  
The Brain

ABSTRACTSome scenes are more memorable than others: they cement in minds with consistencies across observers and time scales. While memory mechanisms are traditionally associated with the end stages of perception, recent behavioral studies suggest that the features driving these memorability effects are extracted early on, and in an automatic fashion. This raises the question: is the neural signal of memorability detectable during early perceptual encoding phases of visual processing? Using the high temporal resolution of magnetoencephalography (MEG), during a rapid serial visual presentation (RSVP) task, we traced the neural temporal signature of memorability across the brain. We found an early and prolonged memorability related signal recruiting a network of regions in both dorsal and ventral streams, detected outside of the constraints of subjective awareness. This enhanced encoding could be the key to successful storage and recognition.

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