scholarly journals Structural differences between healthy subjects and patients with schizophrenia and schizoaffective disorder: A graph and control theoretical perspective

2021 ◽  
Author(s):  
Cristiana Dimulescu ◽  
Serdar Gareayaghi ◽  
Fabian Kamp ◽  
Sophie Fromm ◽  
Klaus Obermayer ◽  
...  
Keyword(s):  
Schizoaffective Disorder ◽  
Large Scale ◽  
Brain Activity ◽  
Theoretical Perspective ◽  
Structural Mri ◽  
Input Energy ◽  
Strong Reduction ◽  
Patient Groups ◽  
And Control ◽  
The Brain

AbstractThe coordinated, dynamical interactions of large-scale networks give rise to cognitive function. Recent advances in network neuroscience have suggested that the anatomical organization of such networks puts a fundamental constraint on the dynamical landscape of the brain. Consequently, changes in large-scale brain activity have been hypothesized to underlie many neurological and psychiatric disorders. Specifically, evidence has emerged that large-scale dysconnectivity might play a crucial role in the pathophysiology of schizophrenia. To investigate potential differences in graph and control theoretic measures between patients with schizophrenia (SCZ), patients with schizoaffective disorder (SCZaff) and matched healthy controls (HC), we use structural MRI data. More specifically, we first calculate seven graph measures of integration, segregation, centrality and resilience and test for group differences. Second, we extend our analysis beyond these traditional measures and employ a simplified noise-free linear discrete-time and time-invariant network model to calculate two complementary measures of controllability. Average controllability identifies brain areas that can guide brain activity into different, easily reachable states with little input energy. Modal controllability on the other hand, characterizes regions that can push the brain into difficult-to-reach states, i.e. states that require substantial input energy. We identified differences in standard network and controllability measures for both patient groups compared to HCs. Specifically, we found a strong reduction of betweenness centrality for both patient groups and a strong reduction in average controllability for the SCZ group again in comparison to the HC group. Our findings of network level deficits might help to explain the many cognitive deficits associated with these disorders.

scholarly journals Structural Differences Between Healthy Subjects and Patients With Schizophrenia or Schizoaffective Disorder: A Graph and Control Theoretical Perspective

2021 ◽  
Vol 12 ◽  
Author(s):  
Cristiana Dimulescu ◽  
Serdar Gareayaghi ◽  
Fabian Kamp ◽  
Sophie Fromm ◽  
Klaus Obermayer ◽  
...  
Keyword(s):  
Schizoaffective Disorder ◽  
Large Scale ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Theoretical Perspective ◽  
Input Energy ◽  
Strong Reduction ◽  
Patient Groups ◽  
And Control ◽  
The Brain

The coordinated dynamic interactions of large-scale brain circuits and networks have been associated with cognitive functions and behavior. Recent advances in network neuroscience have suggested that the anatomical organization of such networks puts fundamental constraints on the dynamical landscape of brain activity, i.e., the different states, or patterns of regional activation, and transition between states the brain can display. Specifically, it has been shown that densely connected, central regions control the transition between states that are “easily” reachable (in terms of expended energy), whereas weakly connected areas control transitions to states that are hard-to-reach. Changes in large-scale brain activity have been hypothesized to underlie many neurological and psychiatric disorders. Evidence has emerged that large-scale dysconnectivity might play a crucial role in the pathophysiology of schizophrenia, especially regarding cognitive symptoms. Therefore, an analysis of graph and control theoretic measures of large-scale brain connectivity in patients offers to give insight into the emergence of cognitive disturbances in the disorder. To investigate these potential differences between patients with schizophrenia (SCZ), patients with schizoaffective disorder (SCZaff) and matched healthy controls (HC), we used structural MRI data to assess the microstructural organization of white matter. We first calculate seven graph measures of integration, segregation, centrality and resilience and test for group differences. Second, we extend our analysis beyond these traditional measures and employ a simplified noise-free linear discrete-time and time-invariant network model to calculate two complementary measures of controllability. Average controllability, which identifies brain areas that can guide brain activity into different, easily reachable states with little input energy and modal controllability, which characterizes regions that can push the brain into difficult-to-reach states, i.e., states that require substantial input energy. We identified differences in standard network and controllability measures for both patient groups compared to HCs. We found a strong reduction of betweenness centrality for both patient groups and a strong reduction in average controllability for the SCZ group again in comparison to the HC group. Our findings of network level deficits might help to explain the many cognitive deficits associated with these disorders.

Individual T1-weighted/T2-weighted ratio brain networks: Small-worldness, hubs and modular organization

2018 ◽  
Vol 29 (05) ◽  
pp. 1840007
Author(s):  
Huijun Wu ◽  
Hao Wang ◽  
Linyuan Lü
Keyword(s):  
Large Scale ◽  
Fluid Intelligence ◽  
Brain Networks ◽  
Structural Mri ◽  
Modular Organization ◽  
Enormous Amount ◽  
Positive Correlations ◽  
Law Degree ◽  
Network Metrics ◽  
The Brain

Applying network science to investigate the complex systems has become a hot topic. In neuroscience, understanding the architectures of complex brain networks was a vital issue. An enormous amount of evidence had supported the brain was cost/efficiency trade-off with small-worldness, hubness and modular organization through the functional MRI and structural MRI investigations. However, the T1-weighted/T2-weighted (T1w/T2w) ratio brain networks were mostly unexplored. Here, we utilized a KL divergence-based method to construct large-scale individual T1w/T2w ratio brain networks and investigated the underlying topological attributes of these networks. Our results supported that the T1w/T2w ratio brain networks were comprised of small-worldness, an exponentially truncated power–law degree distribution, frontal-parietal hubs and modular organization. Besides, there were significant positive correlations between the network metrics and fluid intelligence. Thus, the T1w/T2w ratio brain networks open a new avenue to understand the human brain and are a necessary supplement for future MRI studies.

scholarly journals Go with the FLOW: visualizing spatiotemporal dynamics in optical widefield calcium imaging

2021 ◽  
Vol 18 (181) ◽  
pp. 20210523
Author(s):  
Nathaniel J. Linden ◽  
Dennis R. Tabuena ◽  
Nicholas A. Steinmetz ◽  
William J. Moody ◽  
Steven L. Brunton ◽  
...  
Keyword(s):  
Coherent Structures ◽  
Calcium Imaging ◽  
Large Scale ◽  
Vector Fields ◽  
Brain Activity ◽  
Time Varying ◽  
Finite Time Lyapunov Exponent ◽  
Synthetic Datasets ◽  
The Brain ◽  
Analyse Time

Widefield calcium imaging has recently emerged as a powerful experimental technique to record coordinated large-scale brain activity. These measurements present a unique opportunity to characterize spatiotemporally coherent structures that underlie neural activity across many regions of the brain. In this work, we leverage analytic techniques from fluid dynamics to develop a visualization framework that highlights features of flow across the cortex, mapping wavefronts that may be correlated with behavioural events. First, we transform the time series of widefield calcium images into time-varying vector fields using optic flow. Next, we extract concise diagrams summarizing the dynamics, which we refer to as FLOW (flow lines in optical widefield imaging) portraits . These FLOW portraits provide an intuitive map of dynamic calcium activity, including regions of initiation and termination, as well as the direction and extent of activity spread. To extract these structures, we use the finite-time Lyapunov exponent technique developed to analyse time-varying manifolds in unsteady fluids. Importantly, our approach captures coherent structures that are poorly represented by traditional modal decomposition techniques. We demonstrate the application of FLOW portraits on three simple synthetic datasets and two widefield calcium imaging datasets, including cortical waves in the developing mouse and spontaneous cortical activity in an adult mouse.

scholarly journals ARM Controller and EEG based Drowsiness Tracking and Controlling during Driving

2018 ◽  
Vol 6 (3) ◽  
pp. 127
Author(s):  
B. Naresh ◽  
S. Rambabu ◽  
D. Khalandar Basha
Keyword(s):  
Brain Activity ◽  
Traffic Signals ◽  
Electrical Signal ◽  
Emotional States ◽  
Brain Wave ◽  
Computer Interfaces ◽  
Medium Level ◽  
And Control ◽  
To Receive ◽  
The Brain

<span>This paper discussed about EEG-Based Drowsiness Tracking during Distracted Driving based on Brain computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity commands through controller device in real time. With these signals from brain in mat lab signals spectrum analyzed and estimates driver concentration and meditation conditions. If there is any nearest vehicles to this vehicle a voice alert given to driver for alert. And driver going to sleep gives voice alert for driver using voice chip. And give the information about traffic signal indication using RFID. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human feelings, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) is used to receive the raw data from brain wave sensor and it is used to extract and process the signal using Mat lab platform. The nearest vehicles information is information is taken through ultrasonic sensors and gives voice alert. And traffic signals condition is detected through RF technology.</span>

scholarly journals Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

2020 ◽  
Author(s):  
Paul Triebkorn ◽  
Joelle Zimmermann ◽  
Leon Stefanovski ◽  
Dipanjan Roy ◽  
Ana Solodkin ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Network ◽  
Model Parameters ◽  
Network Modelling ◽  
Neural Populations ◽  
Global Coupling ◽  
The Brain

AbstractUsing The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters: conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain.Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.

scholarly journals Mapping Pavlovian Conditioning Effects on the Brain: Blocking, Contiguity, and Excitatory Effects

2001 ◽  
Vol 86 (2) ◽  
pp. 809-823 ◽  
Author(s):  
Dirk Jones ◽  
F. Gonzalez-Lima
Keyword(s):  
Pavlovian Conditioning ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Blocking Effect ◽  
Anterior Cingulate ◽  
Caudate Putamen ◽  
Fdg Uptake ◽  
The Brain ◽  
Previous Learning

Pavlovian conditioning effects on the brain were investigated by mapping rat brain activity with fluorodeoxyglucose (FDG) autoradiography. The goal was to map the effects of the same tone after blocking or eliciting a conditioned emotional response (CER). In the tone-blocked group, previous learning about a light blocked a CER to the tone. In the tone-excitor group, the same pairings of tone with shock US resulted in a CER to the tone in the absence of previous learning about the light. A third group showed no CER after pseudorandom presentations of these stimuli. Brain systems involved in the various associative effects of Pavlovian conditioning were identified, and their functional significance was interpreted in light of previous FDG studies. Three conditioning effects were mapped: 1) blocking effects: FDG uptake was lower in medial prefrontal cortex and higher in spinal trigeminal and cuneate nuclei in the tone-blocked group relative to the tone-excitor group. 2) Contiguity effects: relative to pseudorandom controls, similar FDG uptake increases in the tone-blocked and -excitor groups were found in auditory regions (inferior colliculus and cortex), hippocampus (CA1), cerebellum, caudate putamen, and solitary nucleus. Contiguity effects may be due to tone-shock pairings common to the tone-blocked and -excitor groups rather than their different CER. And 3) excitatory effects: FDG uptake increases limited to the tone-excitor group occurred in a circuit linked to the CER, including insular and anterior cingulate cortex, vertical diagonal band nucleus, anterior hypothalamus, and caudoventral caudate putamen. This study provided the first large-scale map of brain regions underlying the Kamin blocking effect on conditioning. In particular, the results suggest that suppression of prefrontal activity and activation of unconditioned stimulus pathways are important neural substrates of the Kamin blocking effect.

Application of the Hilbert_Huang Transform and Wavelet Transform in the EEG Processing

2013 ◽  
Vol 756-759 ◽  
pp. 1753-1757
Author(s):  
Gui Xin Zhang ◽  
Ping Dong Wu ◽  
Man Ling Huang
Keyword(s):  
Wavelet Transform ◽  
Brain Activity ◽  
Eeg Signal ◽  
Hilbert Huang Transform ◽  
Signal Processing Method ◽  
Communication Method ◽  
Machine Interface ◽  
And Control ◽  
The Brain ◽  
Eeg Signal Processing

Brain-Machine Interface (BMI) could make people control machine through EEG which is produced by the brain activity, and it provide a new communication method between human and machine. The research for BMI will extend the ability of communication and control the environment and machine. The key point of the BMI is how to abstract and distinguish different EEG characters. Therefore, EEG signal processing method is the emphasis of BMI. Wavelet Transform and Hilbert-Huang Transform are used to analyze the EEG signal in this paper. The results indicate that these two methods could abstract the main characters of the EEG, but the Hilbert-Huang Transform could express the distributing status in the time and frequency aspect of the EEG more accurately, because it produces the self-adaptive basis according the data, and obtain the local and instantaneous frequency of the EEG.

Non-Linear Mechanisms in the Brain

1998 ◽  
Vol 53 (7-8) ◽  
pp. 677-685 ◽  
Author(s):  
Gottfried Mayer-Kress
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
External Noise ◽  
Neuronal System ◽  
Neuron Models ◽  
General Role ◽  
Non Linear ◽  
Spatio Temporal ◽  
The Brain

Abstract Non-linear dynamical models of brain activity can describe the spontaneous emergence of large-scale coherent structures both in a temporal and spatial domain. We discuss a number of discrete time dynamical neuron models that illustrate some of the mechanisms involved. Of special interest is the phenomenon of spatio-temporal stochastic resonance in which co­herent structures emerge as a result of the interaction of the neuronal system with external noise at a given level punitive data. We then discuss the general role of stochastic noise in brain dynamics and how similar concepts can be studied in the context of networks of con­nected brains on the Internet.

scholarly journals The Role of Interhemispheric Interactions in Cortical Plasticity

2021 ◽  
Vol 15 ◽  
Author(s):  
Jan Antoni Jablonka ◽  
Robert Binkowski ◽  
Marcin Kazmierczak ◽  
Maria Sadowska ◽  
Władysław Sredniawa ◽  
...  
Keyword(s):  
Cortical Plasticity ◽  
Brain Activity ◽  
Direct Proof ◽  
Contralateral Hemisphere ◽  
Dependent Plasticity ◽  
Plastic Changes ◽  
The Right ◽  
And Control ◽  
Cortical Map ◽  
The Brain

Despite the fact that there is a growing awareness to the callosal connections between hemispheres the two hemispheres of the brain are commonly treated as independent structures when peripheral or cortical manipulations are applied to one of them. The contralateral hemisphere is often used as a within-animal control of plastic changes induced onto the other side of the brain. This ensures uniform conditions for producing experimental and control data, but it may overlook possible interhemispheric interactions. In this paper we provide, for the first time, direct proof that cortical, experience-dependent plasticity is not a unilateral, independent process. We mapped metabolic brain activity in rats with 2-[14C] deoxyglucose (2DG) following experience-dependent plasticity induction after a month of unilateral (left), partial whiskers deprivation (only row B was left). This resulted in ∼45% widening of the cortical sensory representation of the spared whiskers in the right, contralateral barrel field (BF). We show that the width of 2DG visualized representation is less than 20% when only contralateral stimulation of the spared row of whiskers is applied in immobilized animals. This means that cortical map remodeling, which is induced by experience-dependent plasticity mechanisms, depends partially on the contralateral hemisphere. The response, which is observed by 2DG brain mapping in the partially deprived BF after standard synchronous bilateral whiskers stimulation, is therefore the outcome of at least two separately activated plasticity mechanisms. A focus on the integrated nature of cortical plasticity, which is the outcome of the emergent interactions between deprived and non-deprived areas in both hemispheres may have important implications for learning and rehabilitation. There is also a clear implication that there is nothing like “control hemisphere” since any plastic changes in one hemisphere have to have influence on functioning of the opposite one.

scholarly journals Oscillations, criticality and responsiveness in complex brain networks

2019 ◽  
Author(s):  
MinKyung Kim ◽  
UnCheol Lee
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Conscious State ◽  
Brain Network ◽  
State Dependent ◽  
Model Study ◽  
Brain States ◽  
Specific Stimulation ◽  
The Brain ◽  
Oscillation Properties

AbstractBrain networks during unconscious states resulting from sleep, anesthesia, or traumatic injuries are associated with a limited capacity for complex responses to stimulation. Even during the conscious resting state, responsiveness to stimulus is highly dependent on spontaneous brain activities. Many empirical findings have been suggested that the brain responsiveness is determined mainly by the ongoing brain activity when a stimulus is given. However, there has been no systematic study exploring how such various brain activities with high or low synchronization, amplitude, and phase response to stimuli. In this model study, we simulated large-scale brain network dynamics in three brain states (below, near, and above the critical state) and investigated a relationship between ongoing oscillation properties and a stimulus decomposing the brain activity into fundamental oscillation properties (instantaneous global synchronization, amplitude, and phase). We identified specific stimulation conditions that produce varying levels of brain responsiveness. When a single pulsatile stimulus was applied to globally desynchronized low amplitude of oscillation, the network generated a large response. By contrast, when a stimulus was applied to specific phases of oscillation that were globally synchronized with high amplitude activity, the response was inhibited. This study proposes the oscillatory conditions to induce specific stimulation outcomes in the brain that can be systematically derived from networked oscillator properties, and reveals the presence of state-dependent temporal windows for optimal brain stimulation. The identified relationship will help advance understanding of the small/large responsiveness of the brain in different states of consciousness and suggest state-dependent methods to modulate responsiveness.Author SummaryA responsiveness of the brain network to external stimulus is different across brain states such as wakefulness, sleep, anesthesia, and traumatic injuries. It has been shown that responsiveness of the brain during conscious state also varies due to the diverse transient states of the brain characterized by different global and local oscillation properties. In this computational model study using large-scale brain network, we hypothesized that the brain responsiveness is determined by the interactions of networked oscillators when a stimulus is applied to the brain. We examined relationships between responsiveness of the brain network, global synchronization levels, and instantaneous oscillation properties such as amplitude and phase in different brain states. We found specific stimulation conditions of the brain that produce large or small levels of responsiveness. The identified relationship suggests the existence of temporal windows that periodically inhibit sensory information processing during conscious state and develops state-dependent methods to modulate brain responsiveness considering dynamically changed functional brain network.

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