Time-variant Analysis of Fast-fMRI and Dynamic Contrast Agent MRI Sequences as Examples of 4-dimensional Image Analysis

2006 ◽  
Vol 45 (06) ◽  
pp. 643-650 ◽  
Author(s):  
L. Leistritz ◽  
W. Hesse ◽  
T. Wüstenberg ◽  
C. Fitzek ◽  
J. R. Reichenbach ◽  
...  
Keyword(s):  
Image Analysis ◽  
Contrast Agent ◽  
Information Transfer ◽  
Primary Motor Cortex ◽  
Brain Areas ◽  
Significant Information ◽  
Dynamic Neural Networks ◽  
Directed Information ◽  
Mri Sequences ◽  
Perfusion Maps

Summary Objectives: Image sequences with time-varying information content need appropriate analysis strategies. The exploration of directed information transfer (interactions) between neuronal assemblies is one of the most important aims of current functional MRI (fMRI) analysis. Additionally, we examined perfusion maps in dynamic contrast agent MRI sequences of stroke patients. In this investigation, the focus centers on distinguishing between brain areas with normal and reduced perfusion on the basis of the dynamics of contrast agent inflow and washout. Methods: Fast fMRI sequences were analyzed with time-variant Granger causality (tvGC). The tvGC is based on a time-variant autoregressive model and is used for the quantification of the directed information transfer between activated brain areas. Generalized Dynamic Neural Networks (GDNN) with time-variant weights were applied on dynamic contrast agent MRI sequences as a nonlinear operator in order to enhance differences in the signal courses of pixels of normal and injured tissues. Results: A simple motor task (self-paced finger tapping) is used in an fMRI design to investigate directed interactions between defined brain areas. A significant information transfer can be determined for the direction primary motor cortex to supplementary motor area during a short time period of about five seconds after stimulus. The analysis of dynamic contrast agent MRI sequences demonstrates that the trained GDNN enables a reliable tissue classification. Three classes are of interest: normal tissue, tissue at risk for death, and dead tissue. Conclusions: The time-variant multivariate analysis of directed information transfer derived from fMRI sequences and the computation of perfusion maps by GDNN demonstrate that dynamic analysis methods are essential tools for 4D image analysis.

scholarly journals Local Anesthesia at ST36 to Reveal Responding Brain Areas todeqi

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Ling-min Jin ◽  
Cai-juan Qin ◽  
Lei Lan ◽  
Jin-bo Sun ◽  
Fang Zeng ◽  
...  
Keyword(s):  
Local Anesthesia ◽  
Primary Motor Cortex ◽  
Superior Temporal Gyrus ◽  
Brain Regions ◽  
Manual Acupuncture ◽  
Brain Areas ◽  
Potential Approach ◽  
Brain Responses ◽  
Set Up ◽  
Significant Activation

Background.Development of non-deqicontrol is still a challenge. This study aims to set up a potential approach to non-deqicontrol by using lidocaine anesthesia at ST36.Methods.Forty healthy volunteers were recruited and they received two fMRI scans. One was accompanied with manual acupuncture at ST36 (DQ group), and another was associated with both local anesthesia and manual acupuncture at the same acupoint (LA group).Results.Comparing to DQ group, more than 90 percentdeqisensations were reduced by local anesthesia in LA group. The mainly activated regions in DQ group were bilateral IFG, S1, primary motor cortex, IPL, thalamus, insula, claustrum, cingulate gyrus, putamen, superior temporal gyrus, and cerebellum. Surprisingly only cerebellum showed significant activation in LA group. Compared to the two groups, bilateral S1, insula, ipsilateral IFG, IPL, claustrum, and contralateral ACC were remarkably activated.Conclusions.Local anesthesia at ST36 is able to block most of thedeqifeelings and inhibit brain responses todeqi, which would be developed into a potential approach for non-deqicontrol. Bilateral S1, insula, ipsilateral IFG, IPL, claustrum, and contralateral ACC might be the key brain regions responding todeqi.

scholarly journals Local field potentials allow accurate decoding of muscle activity

2012 ◽  
Vol 108 (1) ◽  
pp. 18-24 ◽  
Author(s):  
Robert D. Flint ◽  
Christian Ethier ◽  
Emily R. Oby ◽  
Lee E. Miller ◽  
Marc W. Slutzky
Keyword(s):  
Upper Limb ◽  
Local Field ◽  
Muscle Activity ◽  
Primary Motor Cortex ◽  
Target Location ◽  
Local Field Potentials ◽  
Field Potentials ◽  
Reach To Grasp ◽  
Significant Information ◽  
Complex Movement

Local field potentials (LFPs) in primary motor cortex include significant information about reach target location and upper limb movement kinematics. Some evidence suggests that they may be a more robust, longer-lasting signal than action potentials (spikes). Here we assess whether LFPs can also be used to decode upper limb muscle activity, a complex movement-related signal. We record electromyograms from both proximal and distal upper limb muscles from monkeys performing a variety of reach-to-grasp and isometric wrist force tasks. We show that LFPs can be used to decode activity from both proximal and distal muscles with performance rivaling that of spikes. Thus, motor cortical LFPs include information about more aspects of movement than has been previously demonstrated. This provides further evidence suggesting that LFPs could provide a highly informative, long-lasting signal source for neural prostheses.

scholarly journals Coding of digit displacement by cell spiking and network oscillations in the monkey sensorimotor cortex

2012 ◽  
Vol 108 (12) ◽  
pp. 3342-3352 ◽  
Author(s):  
Claire L. Witham ◽  
Stuart N. Baker
Keyword(s):  
Firing Rate ◽  
Primary Motor Cortex ◽  
Field Potential ◽  
Oscillatory Activity ◽  
Significant Information ◽  
Information Theoretic ◽  
Sensory State ◽  
Somatosensory Cortices ◽  
Finger Flexion ◽  
Local Field Potential Lfp

β-Band oscillations occur in motor and somatosensory cortices and muscle activity. Oscillations appear most strongly after movements, suggesting that they may represent or probe the limb's final sensory state. We tested this idea by training two macaque monkeys to perform a finger flexion to one of four displacements, which was then held for 2 s without visual feedback of absolute displacement. Local field potential (LFP) and single unit spiking were recorded from the rostral and caudal primary motor cortex and parietal areas 3a, 3b, 2, and 5. Information theoretic analysis determined how well unit firing rate or the power of LFP oscillations coded finger displacement. All areas encoded significant information about finger displacement after the movement into target, both in β-band (∼20 Hz) oscillatory activity and unit firing rate. On average, the information carried by unit firing was greater (0.07 bits) and peaked earlier (0.73 s after peak velocity) than that by LFP β-oscillations (0.05 bits and 0.95 s). However, there was considerable heterogeneity among units: some cells did not encode maximal information until midway through the holding phase. In 30% of cells, information in rate lagged information in LFP oscillations recorded at the same site. Finger displacement may be represented in the cortex in multiple ways. Coding the digit configuration immediately after a movement probably relies on nonoscillatory feedback, or efference copy. With increasing delay after movement cessation, oscillatory processing may also play a part.

scholarly journals Contrast Agent Specific Imaging Modes for the Ultrasonic Assessment of Parenchymal Cerebral Echo Contrast Enhancement

2000 ◽  
Vol 20 (12) ◽  
pp. 1709-1716 ◽  
Author(s):  
Thomas Postert ◽  
Patricia Hoppe ◽  
Jens Federlein ◽  
Sebastian Helbeck ◽  
Helmut Ermert ◽  
...  
Keyword(s):  
White Matter ◽  
Contrast Agent ◽  
Contrast Enhancement ◽  
Second Harmonic ◽  
Harmonic Imaging ◽  
Transcranial Ultrasound ◽  
Brain Areas ◽  
Lentiform Nucleus ◽  
Ultrasonic Assessment ◽  
First Time

Previous work has demonstrated that cerebral echo contrast enhancement can be assessed by means of transcranial ultrasound using transient response second harmonic imaging (HI). The current study was designed to explore possible advantages of two new contrast agent specific imaging modes, contrast burst imaging (CBI) and time variance imaging (TVI), that are based on the detection of destruction or splitting of microbubbles caused by ultrasound in comparison with contrast harmonic imaging (CHI), which is a broadband phase-inversion—based implementation of HI. Nine healthy individuals with adequate acoustic temporal bone windows were included in the study. Contrast harmonic imaging, CBI, and TVI examinations were performed in an axial diencephalic plane of section after an intravenous bolus injection of 4 g galactose-based microbubble suspension in a concentration of 400 mg/mL. Using time-intensity curves, peak intensities and times-to peak-intensity (TPIs) were calculated off-line in anterior and posterior parts of the thalamus, in the region of the lentiform nucleus, and in the white matter. The potential of the different techniques to visualize cerebral contrast enhancement in different brain areas was compared. All techniques produced accurate cerebral contrast enhancement in the majority of investigated brain areas. Contrast harmonic imaging visualized signal increase in 28 of 36 regions of interest (ROIs). In comparison, TVI and CBI examinations were successful in 32 and 35 investigations, respectively. In CHI examinations, contrast enhancement was most difficult to visualize in posterior parts of the thalamus (6 of 9) and the lentiform nucleus (6 of 9). In TVI examinations, anterior parts of the thalamus showed signal increase in only 6 of 9 examinations. For all investigated imaging modes, PIs and TPIs in different ROIs did not differ significantly, except that TVI demonstrated significantly higher PIs in the lentiform nucleus as compared with the thalamus and the white matter ( P < 0.05). The current study demonstrates for the first time that CBI and TVI represent new ultrasonic tools that allow noninvasive assessment of focal cerebral contrast enhancement and that CBI and TVI improve diagnostic sensitivity as compared with CHI.

scholarly journals Local Cortical Activity of Distant Brain Areas can Phase-Lock to the Respiratory Rhythm in the Freely Behaving Rat

2018 ◽  
Author(s):  
Daniel Rojas-Líbano ◽  
Jonathan Wimmer del Solar ◽  
Marcelo Aguilar ◽  
Rodrigo Montefusco-Siegmund ◽  
Pedro E. Maldonado
Keyword(s):  
Primary Motor Cortex ◽  
Dorsal Hippocampus ◽  
Respiratory Rhythm ◽  
Brain Regions ◽  
Local Processing ◽  
Potential Candidate ◽  
Global Integration ◽  
Brain Areas ◽  
Phase Lock ◽  
Freely Behaving

ABSTRACTAn important unresolved question about neural processing is the mechanism by which distant brain areas coordinate their activities and relate their local processing to global neural events. A potential candidate for the local-global integration are slow rhythms such as respiration, which is also linked to sensory exploration. In this article, we asked if there are modulations of local cortical processing which are time-locked to (peripheral) sensory-motor exploratory rhythms. We studied rats freely behaving on an elevated platform where they would display exploratory and rest behaviors. Concurrent with behavior, we monitored orofacial sampling rhythms (whisking and sniffing) and local field potentials (LFP) from olfactory bulb, dorsal hippocampus, primary motor cortex, primary somatosensory cortex and primary visual cortex. We defined exploration as simultaneous whisking and sniffing above 5 Hz and found that this activity peaked at about 8 Hz. We considered rest as the absence of whisking and sniffing, and in this case, mean respiration occurred at about 3 Hz. We found a consistent shift across all areas toward these rhythm peaks accompanying behavioral state changes. We also found, across areas, that LFP gamma (70-100 Hz) amplitude could phase-lock to the animal’s respiratory rhythm, a finding indicative of respiration-locked changes in local processing. The respiratory rhythm, although occurring at the same frequencies of hippocampal theta, was not spectrally coherent with it, implying a different oscillator. Our results are consistent with the notion of respiration as a binder or integrator of activity between distant brain regions.

scholarly journals The Banach-Tarski Paradox Dictates Snyergistic Routes for Scale-Free Neurodynamics

2020 ◽  
Author(s):  
Arturo Tozzi ◽  
James F. Peters
Keyword(s):  
Information Transfer ◽  
Neural Dynamics ◽  
Free Oscillations ◽  
Scale Invariant ◽  
Brain Areas ◽  
Scale Free ◽  
Cortical Oscillations ◽  
The Central Nervous System ◽  
Self Similar ◽  
Physical Changes

Neuroscientists are able to detect physical changes in information entropy in available neurodata. However, the information paradigm is inadequate to fully describe nervous dynamics and mental activities such as perception. This paper provides an effort to build explanations to neural dynamics alternative to thermodynamic and information accounts. We recall the Banach&ndash;Tarski paradox (BTP), which informally states that, when pieces of a ball are moved and rotated without changing their shape, a synergy between two balls of the same volume is achieved instead of the original one. We show how and why BTP might display this physical and biological synergy meaningfully, making it possible to tackle nervous activities. The anatomical and functional structure of the central nervous system&rsquo;s nodes and edges allows to perform a sequence of moves inside the connectome that doubles the amount of available cortical oscillations. In particular, a BTP-based mechanism permits scale-invariant nervous oscillations to amplify and propagate towards far apart brain areas. Paraphrasing the BPT&rsquo;s definition, we could state that: when a few components of a self-similar nervous oscillation are moved and rotated throughout the cortical connectome, two self-similar oscillations are achieved instead of the original one. Furthermore, based on topological structures, we illustrate how, counterintuitively, the amplification of scale-free oscillations does not require information transfer.

Signal Informatics as an Advanced Integrative Concept in the Framework of Medical Informatics

2009 ◽  
Vol 48 (01) ◽  
pp. 18-28 ◽  
Author(s):  
M. Ungureanu ◽  
C. Ligges ◽  
D. Hemmelmann ◽  
T. Wüstenberg ◽  
J. Reichenbach ◽  
...  
Keyword(s):  
Signal Processing ◽  
Granger Causality ◽  
Medical Informatics ◽  
Information Transfer ◽  
Event Related Potentials ◽  
Brain Regions ◽  
Related Potentials ◽  
Directed Information ◽  
Integrative Concept ◽  
Processing Steps

Summary Objectives: The main objective is to show current topics and future trends in the field of medical signal processing which are derived from current research concepts. Signal processing as an integrative concept within the scope of medical informatics is demonstrated. Methods: For all examples time-variant multivariate autoregressive models were used. Based on this modeling, the concept of Granger causality in terms of the time-variant Granger causality index and the time-variant partial directed coherence was realized to investigate directed information transfer between different brain regions. Results: Signal informatics encompasses several diverse domains including: processing steps, methodologies, levels and subject fields, and applications. Five trends can be recognized and in order to illustrate these trends, three analysis strategies derived from current neuroscientific studies are presented. These examples comprise high-dimensional fMRI and EEG data. In the first example, the quantification of time-variant-directed information transfer between activated brain regions on the basis of fast-fMRI data is introduced and discussed. The second example deals with the investigation of differences in word processing between dyslexic and normal reading children. Different dynamic neural networks of the directed information transfer are identified on the basis of event-related potentials. The third example shows time-variant cortical connectivity networks derived from a source model. Conclusions: These examples strongly emphasize the integrative nature of signal informatics, encompassing processing steps, methodologies, levels and subject fields, and applications.

scholarly journals Prediction of Subjective Affective State From Brain Activations

2009 ◽  
Vol 101 (3) ◽  
pp. 1294-1308 ◽  
Author(s):  
Edmund T. Rolls ◽  
Fabian Grabenhorst ◽  
Leonardo Franco
Keyword(s):  
Single Neuron ◽  
Affective State ◽  
Small Populations ◽  
Functional Magnetic Resonance ◽  
Brain Areas ◽  
Significant Information ◽  
Information Encoding ◽  
Information Theoretic ◽  
Neuroimaging Data ◽  
The Brain

Decoding and information theoretic techniques were used to analyze the predictions that can be made from functional magnetic resonance neuroimaging data on individual trials. The subjective pleasantness produced by warm and cold applied to the hand could be predicted on single trials with typically in the range 60–80% correct from the activations of groups of voxels in the orbitofrontal and medial prefrontal cortex and pregenual cingulate cortex, and the information available was typically in the range 0.1–0.2 (with a maximum of 0.6) bits. The prediction was typically a little better with multiple voxels than with one voxel, and the information increased sublinearly with the number of voxels up to typically seven voxels. Thus the information from different voxels was not independent, and there was considerable redundancy across voxels. This redundancy was present even when the voxels were from different brain areas. The pairwise stimulus-dependent correlations between voxels, reflecting higher-order interactions, did not encode significant information. For comparison, the activity of a single neuron in the orbitofrontal cortex can predict with 90% correct and encode 0.5 bits of information about whether an affectively positive or negative visual stimulus has been shown, and the information encoded by small numbers of neurons is typically independent. In contrast, the activation of a 3 × 3 × 3-mm voxel reflects the activity of ∼0.8 million neurons or their synaptic inputs and is not part of the information encoding used by the brain, thus providing a relatively poor readout of information compared with that available from small populations of neurons.

scholarly journals Transmission delays and frequency detuning can regulate information flow between brain regions

2020 ◽  
Author(s):  
Aref Pariz ◽  
Ingo Fischer ◽  
Alireza Valizadeh ◽  
Claudio Mirasso
Keyword(s):  
Functional Connectivity ◽  
Information Transfer ◽  
Effective Connectivity ◽  
Signal Transmission ◽  
Brain Networks ◽  
Frequency Detuning ◽  
Brain Oscillations ◽  
Brain Areas ◽  
Neural Populations

AbstractBrain networks exhibit very variable and dynamical functional connectivity and flexible configurations of information exchange despite their overall fixed structure (connectome). Brain oscillations are hypothesized to underlie time-dependent functional connectivity by periodically changing the excitability of neural populations. In this paper, we investigate the role that the connection delay and the frequency detuning between different neural populations play in the transmission of signals. Based on numerical simulations and analytical arguments, we show that the amount of information transfer between two oscillating neural populations can be determined solely by their connection delay and the mismatch in their oscillation frequencies. Our results highlight the role of the collective phase response curve of the oscillating neural populations for the efficacy of signal transmission and the quality of the information transfer in brain networks.Author summaryCollective dynamics in brain networks is characterized by a coordinated activity of their constituent neurons that lead to brain oscillations. Many evidences highlight the role that brain oscillations play in signal transmission, the control of the effective communication between brain areas and the integration of information processed by different specialized regions. Oscillations periodically modulate the excitability of neurons and determine the response those areas receiving the signals. Based on the communication trough coherence (CTC) theory, the adjustment of the phase difference between local oscillations of connected areas can specify the timing of exchanged signals and therefore, the efficacy of the communication channels. An important factor is the delay in the transmission of signals from one region to another that affects the phase difference and timing, and consequently the impact of the signals. Despite this delay plays an essential role in CTC theory, its role has been mostly overlooked in previous studies. In this manuscript, we concentrate on the role that the connection delay and the oscillation frequency of the populations play in the signal transmission, and consequently in the effective connectivity, between two brain areas. Through extensive numerical simulations, as well as analytical results with reduced models, we show that these parameters have two essential impacts on the effective connectivity of the neural networks: First, that the populations advancing in phase to others do not necessarily play the role of the information source; and second, that the amount and direction of information transfer dependents on the oscillation frequency of the populations.

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