Dynamic Activity Patterns Research Articles

Identification and Classification of Functional Modules in the Brain

Event Abstract Back to Event Identification and Classification of Functional Modules in the Brain Jane Wang1*, Luis Amaral2 and James Booth1 1 Northwestern University, United States 2 Northwestern University, HHMI, United States The complexity of the human brain is surpassed only by the large amounts of data being acquired through modern brain mapping techniques. This deluge of data necessitates novel tools of analysis which take into account the vast interdependencies and relationships within and between anatomical regions. Functional connectivity analysis visualizes dynamic patterns of activity acquired through functional magnetic resonance imaging (fMRI) as networks of temporal correlation and takes advantage of multivariate statistical methods to extract information not available with univariate linear modeling. Time series analysis involving coherence and other spectral methods have also been applied to characterize networks of interactions between brain regions (Muller 2001, Sun 2004). At the same time, the mathematical tools of complex network theory can be employed in analyzing these interactions to reveal either local or global topological characteristics thought to be indicative of modes of function or information processing. We examine changes in language processing over the course of development in children (age 9 to age 15) participating in phonological judgment (rhyming) tasks of visually presented words (Cao 2007, Bitan 2008). We are able to recover and characterize functional modules at multiple scales without the presupposition of previously defined anatomical regions using a combination of multivariate time series analysis techniques as well as graph theoretical measures such as shortest path length, clustering coefficient, and unsupervised community detection. Importantly, our results show that task performance can be predicted by network-level measures at both a global and a local (single link) scale. References Bitan, T., Cheon, J., Lu, D., Burman D. D., & Booth, J. R. (2008) Developmental increase in top-down and bottom-up processing in a phonological task: An effective connectivity fMRI study. Journal of Cognitive Neuroscience, 21 (6): 1135-1145. Cao, F., Bitan, T., & Booth, J. R. (2007) Effective brain connectivity in children with reading difficulties during phonological processing. Brain and Language, 107: 91-101. Muller, K., Lohmann, G., Bosch, V., & von Cramon, D. Y. (2001). On multivariate spectral analysis of fMRI time series. NeuroImage, 14: 347-356. Sun, F. T., Lee, M. M., & D’Esposito, M. (2004) Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. NeuroImage, 21: 647-658. Figure 1 Keywords: General neuroinformatics, Neuroimaging Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011. Presentation Type: Poster Presentation Topic: General neuroinformatics Citation: Wang J, Amaral L and Booth J (2011). Identification and Classification of Functional Modules in the Brain. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00053 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Oct 2011; Published Online: 19 Oct 2011. * Correspondence: Dr. Jane Wang, Northwestern University, Evanston, United States, janewang@northwestern.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Jane Wang Luis Amaral James Booth Google Jane Wang Luis Amaral James Booth Google Scholar Jane Wang Luis Amaral James Booth PubMed Jane Wang Luis Amaral James Booth Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Qualitative Analysis of Dynamic Activity Patterns in Neural Networks

Qualitative Analysis of Dynamic Activity Patterns in Neural Networks

Principal network analysis: identification of subnetworks representing major dynamics using gene expression data

Abstract Motivation: Systems biology attempts to describe complex systems behaviors in terms of dynamic operations of biological networks. However, there is lack of tools that can effectively decode complex network dynamics over multiple conditions. Results: We present principal network analysis (PNA) that can automatically capture major dynamic activation patterns over multiple conditions and then generate protein and metabolic subnetworks for the captured patterns. We first demonstrated the utility of this method by applying it to a synthetic dataset. The results showed that PNA correctly captured the subnetworks representing dynamics in the data. We further applied PNA to two time-course gene expression profiles collected from (i) MCF7 cells after treatments of HRG at multiple doses and (ii) brain samples of four strains of mice infected with two prion strains. The resulting subnetworks and their interactions revealed network dynamics associated with HRG dose-dependent regulation of cell proliferation and differentiation and early PrPSc accumulation during prion infection. Availability: The web-based software is available at: http://sbm.postech.ac.kr/pna. Contact: dhhwang@postech.ac.kr; seungjin@postech.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online.

Patterned activity within the local cortical architecture

The cerebral cortex is a vastly complex structure consisting of multiple distinct populations of neurons residing in functionally specialized cortical compartments. A fundamental goal in systems neuroscience is to understand the interactions among cortical neurons and their relationship to behavior. It is hypothesized that dynamic activity patterns, such as oscillations in global neuronal activity, could span large, heterogeneous populations of cortical neurons in such a manner as to bind together the activity of otherwise disparate cortical networks. Little is known about the mechanisms by which such global oscillatory patterns entrain cortical networks, or the contribution of such activity patterns to cortical function. An important step toward elucidating the role of such patterned activity in cortical information processing is understanding these interactions at the local circuit level. Here, we highlight recent findings that provide insight into how dynamic activity patterns affect specific neuronal populations and circuits.

Development of biomechanical measures to assist in the prediction of early progression in idiopathic scoliosis

Idiopathic scoliosis (IS) is a class of paediatric spinal deformities that has historically posed a significant challenge to the orthopaedic and scientific community. Our understanding of the underlying mechanisms associated with the early progression to advanced stages that require surgical intervention is limited. Current clinical models of assessment provide limited insight into the biomechanical and neuromuscular factors that contribute to the alignment and stability of the spine. There is an emerging recognition that new insight into the factors that affect spinal stability and the progression of a spinal deformity may be gained by expanding current observational techniques beyond the clinic. Technological innovations and advancements in analytical techniques have provided a unique opportunity to develop measurements of neuromuscular function and spinal stability. The overall aim of our present work is to develop new biomechanical measures that will provide insight into the factors that contribute to the early progression of IS. The first specific aim of this pilot project is to further develop a technique that will provide a localized estimate of the asymmetric positioning of the Centre of Mass (COM) in relation to the underlying skeletal structures of the pelvis (sacrum) and spine (apex of spinal curvature). The second specific aim is to determine the association between these measures with persistent asymmetric posture and muscle activation patterns during the performance of dynamic activities that are typical of every day life. To achieve these objectives we are currently developing a mixed methods approach that includes the coupling of a radiological, biomechanical and ambulatory monitoring assessment. This includes a 3D assessment of the spine utilizing EOS to characterize the spinal deformity, and a dynamic assessment that is focused on characterizing the dynamic patterns of muscle activity and movement. This latter component of the project involves an in laboratory biomechanical assessment with a multiple camera Vicon and 16 channel EMG system, and an out of lab ambulatory monitoring assessment. This initial study will provide the foundation to conduct future work focused on identifying new biomechanical factors that are positively associated with the early progression of IS.

Sequential micro-PET based brain imaging in the unilateral labyrithectomy rat model: insights to compensatory brain plasticity

Objectives: Characterization of dynamic brain activation patterns in vestibular damage and compensation.

Saccade trajectories evoked by sequential and colliding stimulation of the monkey superior colliculus

Using microstimulation we employed an explicit experimental control of activity in the superior colliculus at two sites within the motor map. We compared saccade metrics and dynamics evoked at each site independently with those caused by sequential presentation and collisions of the two stimulation trains. Essentially, we forced controlled spatio-temporal patterns of activity into the saccade control circuit with various timing relationships from known sites within the collicular motor map, thus revealing the spatio-temporal transformation from superior colliculus to eye movement dynamics under experimentally controlled conditions. We extend prior findings about decreasing time intervals between sequential presentations of stimulations to include mid-flight combinations and dynamic modifications of trajectory. We explore how asynchronous collisions between two movements systematically engage a normalization mechanism of movement metrics, and demonstrate how dynamic patterns of activity across the SC motor map can create mid-flight curvature of movement through the post-collicular dynamics of a displacement controller. The explicit control addresses feasibility for systems control models and provides benchmark data for experimental verification of model mechanisms.

A GIS-based time-geographic approach of studying individual activities and interactions in a hybrid physical–virtual space

Modern information and communication technologies (ICT) are changing human activity and travel patterns that could have significant implications to our everyday lives and the human organization of space. Time geography, which examines human activities under various constraints in a space–time context, provides a useful framework to analyze the complex spatio-temporal relationships among activities and interactions taking place in both physical and virtual spaces. However, virtual activities and interactions conducted via ICT have characteristics that cannot be properly represented and analyzed under the classical time-geographic framework. This paper extends classical time-geographic concepts to accommodate the needs of representing and analyzing all activities and interactions in a hybrid physical–virtual space. In addition, this paper presents a space–time geographic information system (GIS) design that is capable of organizing complex activity and interaction data as spatio-temporal processes in an integrated space–time environment. This space–time GIS design offers a useful analytical environment for researchers to study increasingly dynamic human activity and travel patterns in today’s society and their implications toward changing travel demand patterns from both spatial and temporal perspectives.

Decoding dynamic patterns of neural activity using a ‘biologically plausible’ fixed set of weights

Event Abstract Back to Event Decoding dynamic patterns of neural activity using a ‘biologically plausible’ fixed set of weights Recent work using population decoding methods has shown that visual information in the inferior temporal and prefrontal cortex of macaque monkeys is contained in changing patterns of activity across a population of neurons. These changing patterns are due to many neurons having short time windows of selectivity relative to the length of a trial (Meyers et al. 2008). Optimally decoding these changing patterns of activity over short time periods (e.g., 150ms time periods) using a linear decoder requires that different weights are used at different time periods (Meyers 2008, Nikolic 2007). If these weights are interpreted as synaptic strengths (as is commonly assumed in neural network interpretations of decoding algorithms) then this creates a biologically unlikely model whereby the synaptic connections between neurons change within the timecourse of a single trial, consistently across trials of a given type, in a way that is time locked to the stimulus onset. In this work, we examine how decoding accuracy is affected by using a more biologically realistic ‘fixed’ set of weights, and by using different temporal binning schemes. We show that decoding accuracy does not decrease when longer time bins (up to ~600 ms) are used for decoding. Furthermore, when decoding over these relatively long time intervals, adding separate weights for different sub-bins only marginally increases the decoding accuracy compared to using one fixed set of weights. This suggests that including firing activity outside of many of the neuron’s time windows of selectivity does not drastically decrease the information that can be decoded within that time window. Additionally, we examine the consequence of using a fixed set of weights based on using the average firing rate over the whole trial, and find that the results at most time points are only moderately lower than when we use dynamically changing weights. Thus, a high degree of accuracy can still be achieved using a set of weights that is fixed over the course of a trial. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Decoding dynamic patterns of neural activity using a ‘biologically plausible’ fixed set of weights. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.354 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 11 Feb 2009; Published Online: 11 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

A Hidden Markov, Multivariate Autoregressive (HMM-mAR) Network Framework for Analysis of Surface EMG (sEMG) Data

As the primary noninvasive means to assess muscle activation, the surface electromyogram (sEMG) is of central importance for the study of motor behavior in both clinical and biomedical applications. However, multivariate sEMG analysis is complicated by the fact that data recorded during dynamic contractions are inherently nonstationary. To model this nonstationarity and to determine the dynamic muscle activity patterns during reaching movements, we propose combining hidden Markov models (HMMs) and multivariate autoregressive (mAR) models into a joint HMM-mAR framework. We further propose constructing muscle networks statistically by performing a second level, group analysis on the subject-specific models. Network structural features are subsequently investigated as input features for the purpose of classification. The proposed approach was applied to real sEMG recordings collected from healthy and stroke subjects during reaching movements. When examining group muscle networks, we note that specific muscle connection patterns were selectively recruited during reaching movements and were differentially recruited after stroke compared to healthy subjects. As the analysis was performed on the raw data, the amplitude and the underlying ldquocarrier datardquo of sEMG signals, we notice that the HMM-mAR model fits the amplitude data well, but not the raw or carrier data. The proposed sEMG analysis framework represents a fundamental departure from existing methods where only the amplitude is typically analyzed or the mAR coefficients are directly used for classification. As the method may provide additional insights into motor control, it appears a promising approach warranting further study.

Dynamic population coding of category information in inferior temporal and prefrontal cortex.

Most electrophysiology studies analyze the activity of each neuron separately. While such studies have given much insight into properties of the visual system, they have also potentially overlooked important aspects of information coded in changing patterns of activity that are distributed over larger populations of neurons. In this work, we apply a population decoding method to better estimate what information is available in neuronal ensembles and how this information is coded in dynamic patterns of neural activity in data recorded from inferior temporal cortex (ITC) and prefrontal cortex (PFC) as macaque monkeys engaged in a delayed match-to-category task. Analyses of activity patterns in ITC and PFC revealed that both areas contain "abstract" category information (i.e., category information that is not directly correlated with properties of the stimuli); however, in general, PFC has more task-relevant information, and ITC has more detailed visual information. Analyses examining how information coded in these areas show that almost all category information is available in a small fraction of the neurons in the population. Most remarkably, our results also show that category information is coded by a nonstationary pattern of activity that changes over the course of a trial with individual neurons containing information on much shorter time scales than the population as a whole.

Computational mechanisms of mechanosensory processing in the cricket

Crickets and many other orthopteran insects face the challenge of gathering sensory information from the environment from a set of multi-modal sensory organs and transforming these stimuli into patterns of neural activity that can encode behaviorally relevant stimuli. The cercal mechanosensory system transduces low frequency air movements near the animal's body and is involved in many behaviors including escape from predators, orientation with respect to gravity, flight steering, aggression and mating behaviors. Three populations of neurons are sensitive to both the direction and dynamics of air currents: an array of mechanoreceptor-coupled sensory neurons, identified local interneurons and identified projection interneurons. The sensory neurons form a functional map of air current direction within the central nervous system that represents the direction of air currents as three-dimensional spatio-temporal activity patterns. These dynamic activity patterns provide excitatory input to interneurons whose sensitivity and spiking output depend on the location of the neuronal arbors within the sensory map and the biophysical and electronic properties of the cell structure. Sets of bilaterally symmetric interneurons can encode the direction of an air current stimulus by their ensemble activity patterns, functioning much like a Cartesian coordinate system. These interneurons are capable of responding to specific dynamic stimuli with precise temporal patterns of action potentials that may encode these stimuli using temporal encoding schemes. Thus, a relatively simple mechanosensory system employs a variety of complex computational mechanisms to provide the animal with relevant information about its environment.

Neurobiology of depression: an integrated view of key findings

AimsThe objectives of the present review were to summarise the key findings from the clinical literature regarding the neurobiology of major depressive disorder (MDD) and their implications for maximising treatment outcomes. Several neuroanatomical structures in the prefrontal and limbic areas of the brain are involved in affective regulation. In patients with MDD, alterations in the dynamic patterns of activity among these structures have profound implications for the pathogenesis of this illness.DiscussionThe present work reviews the evidence for the progressive nature of MDD along with associated changes in neuroanatomical structure and function, especially for the hippocampus. The role of glucocorticoids, inflammatory cytokines and brain-derived growth factors are discussed as mediators of these pathological alterations. From this integrated model, the role of antidepressant therapy in restoring normative processes is examined along with additional treatment guidelines.ConclusionMajor depressive disorder is an illness with significant neurobiological consequences involving structural, functional and molecular alterations in several areas of the brain. Antidepressant pharmacotherapy is associated with restoration of the underlying physiology. Clinicians are advised to intervene with MDD using an early, comprehensive treatment approach that has remission as the goal.

The structure of consciousness

Subjective awareness may depend on neural networks in the brain supporting complex wiring schemes and dynamic patterns of activity.

Dynamic magnetic resonance inverse imaging of human brain function

MRI is widely used for noninvasive hemodynamic-based functional brain imaging. In traditional spatial encoding, however, gradient switching limits the temporal resolution, which makes it difficult to unambiguously identify possible fast nonhemodynamic changes. In this paper we propose a novel reconstruction approach, called dynamic inverse imaging (InI), that is capable of providing millisecond temporal resolution when highly parallel detection is used. To achieve an order-of-magnitude speedup in generating time-resolved contrast estimates and dynamic statistical parametric maps (dSPMs), the spatial information is derived from an array of detectors rather than by time-consuming gradient-encoding methods. The InI approach was inspired by electroencephalography (EEG) and magnetoencephalography (MEG) source localization techniques. Dynamic MR InI was evaluated by means of numerical simulations. InI was also applied to measure BOLD hemodynamic time curves at 20-ms temporal resolution in a visual stimulation experiment using a 90-channel head array. InI is expected to improve the time resolution of MRI and provide increased flexibility in the trade-off between spatial and temporal resolution for studies of dynamic activation patterns in the human brain.

Event Related Potentials and fMRI in Neuropsychopharmacology

Event related potentials (ERP) are important clinical and research instruments in neuropsychiatry, particularly due to their strategic role for the investigation of brain function. These techniques are often underutilized in the evaluation of neurological and psychiatric disorders, but nevertheless they can be most useful and highly effective in the diagnostic workup of a wide range of neuropsychiatric disorders as well as in monitoring the course of the disorders and the prediction of treatment responses. ERP are noninvasive instruments that directly reflect cortical neuronal activity. Cortical neuronal dysfunction plays a major role in variable neuropsychiatric disorders, and a change in cortical activity under medication might reflect treatment response and could be useful for monitoring drug effects. ERP are the only methods with a sufficiently high time resolution for the analysis of the dynamic patterns of neuronal brain activity, e.g., synchronization and desynchronization, oscillations, coherence, gamma band activity, latency of event related activity, etc., which are crucial for a deeper understanding of functional (neurophysiological) correlates of cognitive, emotional and behavioral disturbances in neuropsychiatric patients. Methodological advances have further improved and strengthened the position of ERP concerning research and clinical application. The usefulness and applicability of ERP in determining and monitoring clinico-pharmacological effects will be summarized mainly by focussing on the auditory evoked P300 and the N1/P2 component of auditory evoked potentials. Owing to important recent developments in the field of brain functional diagnostics the combination of neurophysiological techniques and functional magnetic resonance imaging (fMRI) will be included.

Neural Basis Subserving the Detection of Postural Instability: An fMRI Study

Human upright posture is a product of a complex dynamic system that relies on integration of input from multimodal sensory sources. Extensive research has explored the role of visual, vestibular, and somatosensory systems in the control of upright posture. However, the role of higher cognitive function in a participant's assessment of postural stability has been less studied. In previous research, we showed specific neural activation patterns in EEG associated with recognition of unstable postures in young healthy participants. Similar EEG patterns have been recently observed in regulation of posture equilibrium in dynamic stances. This article evaluates participants' postural stability in dynamic stances and neural activation patterns underlying visual recognition of unstable postures using event-related functional MRI (fMRI). Our results show that the "stable" participants were successful in recognition of unstable postures of a computer-animated body model and experienced egocentric motion. Successful recognition of unstable postures in these participants induces activation of distinct areas of the brain including bilateral parietal cortex, anterior cingulate cortex, and bilateral cerebellum. In addition, significant activation is observed in basal ganglia (caudate nucleus and putamen) but only during perception of animated postures. Our findings suggest the existence of modality-specific distributed activation of brain areas responsible for detection of postural instability.

Tumor Necrosis Factor-α Activates the Human Prolactin Gene Promoter via Nuclear Factor-κB Signaling

Pituitary function has been shown to be regulated by an increasing number of intrapituitary factors, including cytokines. Here we show that the important cytokine TNF-alpha activates prolactin gene transcription in pituitary GH3 cells stably expressing luciferase under control of 5 kb of the human prolactin promoter. Similar regulation of the endogenous rat prolactin gene by TNF-alpha in GH3 cells was confirmed using real-time PCR. Luminescence microscopy revealed heterogeneous dynamic response patterns of promoter activity in individual cells. In GH3 cells treated with TNF-alpha, Western blot analysis showed rapid inhibitory protein kappaB (IkappaBalpha) degradation and phosphorylation of p65. Confocal microscopy of cells expressing fluorescence-labeled p65 and IkappaBalpha fusion proteins showed transient cytoplasmic-nuclear translocation and subsequent oscillations in p65 localization and confirmed IkappaBalpha degradation. This was associated with increased nuclear factor kappaB (NF-kappaB)-mediated transcription from an NF-kappaB-responsive luciferase reporter construct. Disruption of NF-kappaB signaling by expression of dominant-negative variants of IkappaB kinases or truncated IkappaBalpha abolished TNF-alpha activation of the prolactin promoter, suggesting that this effect was mediated by NF-kappaB. TNF-alpha signaling was found to interact with other endocrine signals to regulate prolactin gene expression and is likely to be a major paracrine modulator of lactotroph function.

Visualization and characterization of dynamic patterns of flow, growth and activity of biofilms growing in porous media

Using a mesoscale porous media flat plate reactor we utilized a naturally bioluminescent biofilm (V. fischeri) and dye studies to obtain valuable information on the interactions between biofilms and reactive flow in porous media. The growth and development of the V. fischeri biofilm in a porous media geometry was studied using digital time lapse images of the bioluminescent signal given off by the developing biofilm. The effect of biofilm development on porous media hydrodynamics was examined using dye tracer studies and image analysis. The natural bioluminescence of the V. fischeri allowed real-time, in-situ study of biofilm development in porous media, without destruction of the biofilm. Dye studies and image analysis enabled the study of effects of biofilm accumulation on porous media hydraulics, with comparisons to plug flow and completely mixed systems with varying degrees of biofilm accumulation. The real-time nature of the study permitted us to visualize dynamic flow channel formation within the biofilm/porous media system. In addition, the sensitivity of the V. fischeri biofilm to dissolved oxygen allowed us to capture real-time images of reactive transport within the system. This work is the first meso-scale visualization of the interactions between biofilm and flow in porous media.

Imaging the dynamics of the auditory steady-state evoked response

This study used magnetoencephalography (MEG) to examine the dynamic patterns of neural activity underlying the auditory steady-state response. We examined the continuous time-series of responses to a 32-Hz amplitude modulation. Fluctuations in the amplitude of the evoked response were found to be mediated by non-linear interactions with oscillatory processes both at the same source, in the alpha and beta frequency bands, and in the opposite hemisphere.