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

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

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

10.1101/2021.02.19.431983 ◽

2021 ◽

Author(s):

Bruce C. Hansen ◽

Michelle R. Greene ◽

David J. Field

Keyword(s):

Temporal Resolution ◽

Visual Information ◽

Neural Code ◽

Visual Code ◽

Neural Signals ◽

Intelligent Behavior ◽

Spatiotemporal Visualization ◽

State Space Theory ◽

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.

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

10.1101/464487 ◽

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 ◽

Wide Field ◽

Spatiotemporal Resolution ◽

Macro Scale ◽

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.

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

Brain Sciences ◽

10.3390/brainsci9060144 ◽

2019 ◽

Vol 9 (6) ◽

pp. 144 ◽

Cited By ~ 1

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 ◽

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.

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

10.1101/2020.05.01.071662 ◽

2020 ◽

Cited By ~ 1

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 ◽

Positron Emission ◽

Metabolic Connectivity ◽

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.

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

Frontiers in Neural Circuits ◽

10.3389/fncir.2021.608655 ◽

2021 ◽

Vol 15 ◽

Author(s):

Abolfazl Ziaeemehr ◽

Alireza Valizadeh

Keyword(s):

Functional Connectivity ◽

Temporal Resolution ◽

Computational Study ◽

Brain Regions ◽

Kuramoto Model ◽

Functional Networks ◽

Frequency Bands ◽

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.

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

10.1101/2020.09.10.291591 ◽

2020 ◽

Author(s):

Abolfazl Ziaeemehr ◽

Alireza Valizadeh

Keyword(s):

Functional Connectivity ◽

Temporal Resolution ◽

Computational Study ◽

Brain Regions ◽

Kuramoto Model ◽

Functional Networks ◽

Frequency Bands ◽

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.

The Perceptual Neural Trace of Memorable Unseen Scenes

10.1101/414052 ◽

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 ◽

Neural Signal ◽

Subjective Awareness ◽

Behavioral Studies ◽

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.

Metabolic and Hemodynamic Resting-State Connectivity of the Human Brain: A High-Temporal Resolution Simultaneous BOLD-fMRI and FDG-fPET Multimodality Study

Cerebral Cortex ◽

10.1093/cercor/bhaa393 ◽

2021 ◽

Author(s):

Sharna D Jamadar ◽

Phillip G D Ward ◽

Emma X Liang ◽

Edwina R Orchard ◽

Zhaolin Chen ◽

...

Keyword(s):

Positron Emission Tomography ◽

Human Brain ◽

Temporal Resolution ◽

Fdg Pet ◽

Brain Connectivity ◽

Emission Tomography ◽

Positron Emission ◽

Metabolic Connectivity ◽

Abstract Simultaneous [18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging (FDG-PET/fMRI) provides the capacity to image 2 sources of energetic dynamics in the brain—glucose metabolism and the hemodynamic response. fMRI connectivity has been enormously useful for characterizing interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease, but FDG-sPET cannot be used to estimate subject-level measures of “connectivity,” only across-subject “covariance.” Here, we applied high-temporal resolution constant infusion functional positron emission tomography (fPET) to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterized by frontoparietal 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 FDG-sPET metabolic covariance, indicating that metabolic brain connectivity is a nonergodic 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 multimodality biomarkers of neurological diseases.

Modelling Analysis of Electric Vehicle Penetration Scenario using Dynamic Optimal Power Generation Mix Model with High Temporal Resolution

IEEJ Transactions on Power and Energy ◽

10.1541/ieejpes.135.61 ◽

2015 ◽

Vol 135 (1) ◽

pp. 61-70 ◽

Cited By ~ 2

Author(s):

Ryoichi Komiyama ◽

Yasumasa Fujii

Keyword(s):

Power Generation ◽

Electric Vehicle ◽

Temporal Resolution ◽

Optimal Power ◽

Modelling Analysis

Multidetector-row Computed Tomography in the Assessment of Coronary Artery Disease – New Techniques and Insights

European Cardiology Review ◽

10.15420/ecr.2010.6.2.43 ◽

2010 ◽

Vol 6 (2) ◽

pp. 43 ◽

Cited By ~ 1

Author(s):

Andreas H Mahnken ◽

Keyword(s):

Computed Tomography ◽

Coronary Artery Disease ◽

Coronary Artery ◽

Data Acquisition ◽

Temporal Resolution ◽

Multidetector Row ◽

Ct Scanners ◽

Artery Disease ◽

Cardiac Mdct

Over the last decade, cardiac computed tomography (CT) technology has experienced revolutionary changes and gained broad clinical acceptance in the work-up of patients suffering from coronary artery disease (CAD). Since cardiac multidetector-row CT (MDCT) was introduced in 1998, acquisition time, number of detector rows and spatial and temporal resolution have improved tremendously. Current developments in cardiac CT are focusing on low-dose cardiac scanning at ultra-high temporal resolution. Technically, there are two major approaches to achieving these goals: rapid data acquisition using dual-source CT scanners with high temporal resolution or volumetric data acquisition with 256/320-slice CT scanners. While each approach has specific advantages and disadvantages, both technologies foster the extension of cardiac MDCT beyond morphological imaging towards the functional assessment of CAD. This article examines current trends in the development of cardiac MDCT.