scholarly journals A dynamical systems framework to uncover the drivers of large-scale cortical activity

2019 ◽  
Author(s):  
Arian Ashourvan ◽  
Sérgio Pequito ◽  
Maxwell Bertolero ◽  
Jason Z. Kim ◽  
Danielle S. Bassett ◽  
...  
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Intrinsic Activity ◽  
Model Parameters ◽  
Sensory Stimuli ◽  
Human Connectome Project ◽  
External Stimuli ◽  
And Task

ABSTRACTA fundamental challenge in neuroscience is to uncover the principles governing complex interactions between the brain and its external environment. Over the past few decades, the development of functional neuroimaging techniques and tools from graph theory, network science, and computational neuroscience have markedly expanded opportunities to study the intrinsic organization of brain activity. However, many current computational models are fundamentally limited by little to no explicit assessment of the brain’s interactions with external stimuli. To address this limitation, we propose a simple scheme that jointly estimates the intrinsic organization of brain activity and extrinsic stimuli. Specifically, we adopt a linear dynamical model (intrinsic activity) under unknown exogenous inputs (e.g., sensory stimuli), and jointly estimate the model parameters and exogenous inputs. First, we demonstrate the utility of this scheme by accurately estimating unknown external stimuli in a synthetic example. Next, we examine brain activity at rest and task for 99 subjects from the Human Connectome Project, and find significant task-related changes in the identified system, and task-related increases in the estimated external inputs showing high similarity to known task regressors. Finally, through detailed examination of fluctuations in the spatial distribution of the oscillatory modes of the estimated system during the resting state, we find an apparent non-stationarity in the profile of modes that span several brain regions including the visual and the dorsal attention systems. The results suggest that these brain structures display a time-varying relationship, or alternatively, receive non-stationary exogenous inputs that can lead to apparent system non-stationarities. Together, our embodied model of brain activity provides an avenue to gain deeper insight into the relationship between cortical functional dynamics and their drivers.

scholarly journals External drivers of BOLD signal’s non-stationarity

2021 ◽  
Author(s):  
Arian Ashourvan ◽  
Sérgio Pequito ◽  
Maxwell Bertolero ◽  
Jason Z. Kim ◽  
Danielle S. Bassett ◽  
...  
Keyword(s):  
Computational Models ◽  
Linear Time ◽  
Brain Activity ◽  
External Input ◽  
Imaging Data ◽  
External Stimulation ◽  
Cortical Input ◽  
Linear Time Invariant ◽  
Human Connectome Project ◽  
And Task

AbstractA fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. Together, we provide evidence that this embodied brain activity model offers information about the structure and dimensionality of the BOLD signal’s external drivers and shines light on likely external sources contributing to the BOLD signal’s non-stationarity.

scholarly journals Quasi-Periodic Patterns of Intrinsic Brain Activity in Individuals and their Relationship to Global Signal

2017 ◽  
Author(s):  
Behnaz Yousefi ◽  
Jaemin Shin ◽  
Eric H. Schumacher ◽  
Shella D. Keilholz
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Primary Source ◽  
Intrinsic Activity ◽  
Periodic Patterns ◽  
Physiological Noise ◽  
Global Signal ◽  
Human Connectome Project ◽  
Large Scale Networks ◽  
Induced Signal

AbstractQuasiperiodic patterns (QPPs) as reported by Majeed et al., 2011 are prominent features of the brain’s intrinsic activity that involve important large-scale networks (default mode, DMN; task positive, TPN) and are likely to be major contributors to widely used measures of functional connectivity. We examined the variability of these patterns in 470 individuals from the Human Connectome Project resting state functional MRI dataset. The QPPs from individuals can be coarsely categorized into two types: one where strong anti-correlation between the DMN and TPN is present, and another where most areas are strongly correlated. QPP type could be predicted by an individual’s global signal, with lower global signal corresponding to QPPs with strong anti-correlation. After regression of global signal, all QPPs showed strong anti-correlation between DMN and TPN. QPP occurrence and type was similar between a subgroup of individuals with extremely low motion (or even high motion) and the rest of the sample, which shows that motion is not a major contributor to the QPPs. After regression of estimates of slow respiratory and cardiac induced signal fluctuations, more QPPs showed strong anti-correlation between DMN and TPN, an indication that while physiological noise influences the QPP type, it is not the primary source of the QPP itself. QPPs were more similar for the same subjects scanned on different days than for different subjects. These results provide the first assessment of the variability in individual QPPs and their relationship to physiological parameters.

scholarly journals Beyond the evoked/intrinsic neural process dichotomy

2018 ◽  
Vol 2 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Taylor Bolt ◽  
Michael L. Anderson ◽  
Lucina Q. Uddin
Keyword(s):  
Brain Function ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Activity Patterns ◽  
Intrinsic Activity ◽  
Spontaneous Brain Activity ◽  
Evoked Activity ◽  
Functional Brain Activity ◽  
Temporal Trajectory ◽  
And Task

Contemporary functional neuroimaging research has increasingly focused on characterization of intrinsic or “spontaneous” brain activity. Analysis of intrinsic activity is often contrasted with analysis of task-evoked activity that has traditionally been the focus of cognitive neuroscience. But does this evoked/intrinsic dichotomy adequately characterize human brain function? Based on empirical data demonstrating a close functional interdependence between intrinsic and task-evoked activity, we argue that the dichotomy between intrinsic and task-evoked activity as unobserved contributions to brain activity is artificial. We present an alternative picture of brain function in which the brain’s spatiotemporal dynamics do not consist of separable intrinsic and task-evoked components, but reflect the enaction of a system of mutual constraints to move the brain into and out of task-appropriate functional configurations. According to this alternative picture, cognitive neuroscientists are tasked with describing both the temporal trajectory of brain activity patterns across time, and the modulation of this trajectory by task states, without separating this process into intrinsic and task-evoked components. We argue that this alternative picture of brain function is best captured in a novel explanatory framework called enabling constraint. Overall, these insights call for a reconceptualization of functional brain activity, and should drive future methodological and empirical efforts.

scholarly journals Peripheral BDNF: a candidate biomarker of healthy neural activity during learning is disrupted in schizophrenia

2014 ◽  
Vol 45 (4) ◽  
pp. 841-854 ◽  
Author(s):  
A. J. Skilleter ◽  
C. S. Weickert ◽  
A. Vercammen ◽  
R. Lenroot ◽  
T. W. Weickert
Keyword(s):  
Functional Neuroimaging ◽  
Brain Activity ◽  
Healthy Adults ◽  
Enzyme Linked Immunosorbent Assay ◽  
Association Learning ◽  
Healthy Humans ◽  
Important Regulator ◽  
Learning Test ◽  
Parietal Cortices ◽  
And Task

Background.Brain-derived neurotrophic factor (BDNF) is an important regulator of synaptogenesis and synaptic plasticity underlying learning. However, a relationship between circulating BDNF levels and brain activity during learning has not been demonstrated in humans. Reduced brain BDNF levels are found in schizophrenia and functional neuroimaging studies of probabilistic association learning in schizophrenia have demonstrated reduced activity in a neural network that includes the prefrontal and parietal cortices and the caudate nucleus. We predicted that brain activity would correlate positively with peripheral BDNF levels during probabilistic association learning in healthy adults and that this relationship would be altered in schizophrenia.Method.Twenty-five healthy adults and 17 people with schizophrenia or schizo-affective disorder performed a probabilistic association learning test during functional magnetic resonance imaging (fMRI). Plasma BDNF levels were measured by enzyme-linked immunosorbent assay (ELISA).Results.We found a positive correlation between circulating plasma BDNF levels and brain activity in the parietal cortex in healthy adults. There was no relationship between plasma BDNF levels and task-related activity in the prefrontal, parietal or caudate regions in schizophrenia. A direct comparison of these relationships between groups revealed a significant diagnostic difference.Conclusions.This is the first study to show a relationship between peripheral BDNF levels and cortical activity during learning, suggesting that plasma BDNF levels may reflect learning-related brain activity in healthy humans. The lack of relationship between plasma BDNF and task-related brain activity in patients suggests that circulating blood BDNF may not be indicative of learning-dependent brain activity in schizophrenia.

scholarly journals Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain

2019 ◽  
Vol 5 (1) ◽  
pp. eaat7854 ◽  
Author(s):  
Peng Wang ◽  
Ru Kong ◽  
Xiaolu Kong ◽  
Raphaël Liégeois ◽  
Csaba Orban ◽  
...  
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Circuit Model ◽  
Model Parameters ◽  
Optimization Approach ◽  
Functional Magnetic Resonance ◽  
Cortical Hierarchy ◽  
Out Of Sample ◽  
External Stimuli ◽  
Recurrent Connections

We considered a large-scale dynamical circuit model of human cerebral cortex with region-specific microscale properties. The model was inverted using a stochastic optimization approach, yielding markedly better fit to new, out-of-sample resting functional magnetic resonance imaging (fMRI) data. Without assuming the existence of a hierarchy, the estimated model parameters revealed a large-scale cortical gradient. At one end, sensorimotor regions had strong recurrent connections and excitatory subcortical inputs, consistent with localized processing of external stimuli. At the opposing end, default network regions had weak recurrent connections and excitatory subcortical inputs, consistent with their role in internal thought. Furthermore, recurrent connection strength and subcortical inputs provided complementary information for differentiating the levels of the hierarchy, with only the former showing strong associations with other macroscale and microscale proxies of cortical hierarchies (meta-analysis of cognitive functions, principal resting fMRI gradient, myelin, and laminar-specific neuronal density). Overall, this study provides microscale insights into a macroscale cortical hierarchy in the dynamic resting brain.

MULTIPLE OSCILLATORY STATES IN MODELS OF COLLECTIVE NEURONAL DYNAMICS

2014 ◽  
Vol 24 (06) ◽  
pp. 1450020 ◽  
Author(s):  
STILIYAN KALITZIN ◽  
MARCUS KOPPERT ◽  
GEORGE PETKOV ◽  
FERNANDO LOPES DA SILVA
Keyword(s):  
Analytical Model ◽  
Large Scale ◽  
Computational Models ◽  
Phase Synchronization ◽  
Initial Conditions ◽  
State Transitions ◽  
Model Parameters ◽  
Reactive Control ◽  
Stable States ◽  
Optimal Method

In our previous studies, we showed that the both realistic and analytical computational models of neural dynamics can display multiple sustained states (attractors) for the same values of model parameters. Some of these states can represent normal activity while other, of oscillatory nature, may represent epileptic types of activity. We also showed that a simplified, analytical model can mimic this type of behavior and can be used instead of the realistic model for large scale simulations. The primary objective of the present work is to further explore the phenomenon of multiple stable states, co-existing in the same operational model, or phase space, in systems consisting of large number of interconnected basic units. As a second goal, we aim to specify the optimal method for state control of the system based on inducing state transitions using appropriate external stimulus. We use here interconnected model units that represent the behavior of neuronal populations as an effective dynamic system. The model unit is an analytical model (S. Kalitzin et al., Epilepsy Behav. 22 (2011) S102–S109) and does not correspond directly to realistic neuronal processes (excitatory–inhibitory synaptic interactions, action potential generation). For certain parameter choices however it displays bistable dynamics imitating the behavior of realistic neural mass models. To analyze the collective behavior of the system we applied phase synchronization analysis (PSA), principal component analysis (PCA) and stability analysis using Lyapunov exponent (LE) estimation. We obtained a large variety of stable states with different dynamic characteristics, oscillatory modes and phase relations between the units. These states can be initiated by appropriate initial conditions; transitions between them can be induced stochastically by fluctuating variables (noise) or by specific inputs. We propose a method for optimal reactive control, allowing forced transitions from one state (attractor) into another.

scholarly journals Subjective visual perception: from local processing to emergent phenomena of brain activity

2014 ◽  
Vol 369 (1641) ◽  
pp. 20130534 ◽  
Author(s):  
Theofanis I. Panagiotaropoulos ◽  
Vishal Kapoor ◽  
Nikos K. Logothetis
Keyword(s):  
Visual Perception ◽  
Visual Stimulation ◽  
Brain Activity ◽  
Sensory Information ◽  
Brain Regions ◽  
Intrinsic Activity ◽  
Subjective Perception ◽  
Conscious Perception ◽  
Perceptual Awareness ◽  
Sensory Stimuli

The combination of electrophysiological recordings with ambiguous visual stimulation made possible the detection of neurons that represent the content of subjective visual perception and perceptual suppression in multiple cortical and subcortical brain regions. These neuronal populations, commonly referred to as the neural correlates of consciousness , are more likely to be found in the temporal and prefrontal cortices as well as the pulvinar, indicating that the content of perceptual awareness is represented with higher fidelity in higher-order association areas of the cortical and thalamic hierarchy, reflecting the outcome of competitive interactions between conflicting sensory information resolved in earlier stages. However, despite the significant insights into conscious perception gained through monitoring the activities of single neurons and small, local populations, the immense functional complexity of the brain arising from correlations in the activity of its constituent parts suggests that local, microscopic activity could only partially reveal the mechanisms involved in perceptual awareness. Rather, the dynamics of functional connectivity patterns on a mesoscopic and macroscopic level could be critical for conscious perception. Understanding these emergent spatio-temporal patterns could be informative not only for the stability of subjective perception but also for spontaneous perceptual transitions suggested to depend either on the dynamics of antagonistic ensembles or on global intrinsic activity fluctuations that may act upon explicit neural representations of sensory stimuli and induce perceptual reorganization. Here, we review the most recent results from local activity recordings and discuss the potential role of effective, correlated interactions during perceptual awareness.

scholarly journals Virtual reality-based sensorimotor adaptation shapes subsequent spontaneous and naturalistic stimulus-driven brain activity

2021 ◽  
Author(s):  
Meytal Wilf ◽  
Celine Dupuis ◽  
Davide Nardo ◽  
Diana Huber ◽  
Sibilla Sander ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Large Scale ◽  
Right Hemisphere ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Visual Response ◽  
Spatial Shift ◽  
Before And After ◽  
Behavioural Tests ◽  
The Right

Our everyday life summons numerous novel sensorimotor experiences, to which our brain needs to adapt in order to function properly. However, tracking plasticity of naturalistic behaviour and associated brain modulations is challenging. Here we tackled this question implementing a prism adaptation training in virtual reality (VRPA) in combination with functional neuroimaging. Three groups of healthy participants (N=45) underwent VRPA (with a spatial shift either to the left/right side, or with no shift), and performed fMRI sessions before and after training. To capture modulations in free-flowing, task-free brain activity, the fMRI sessions included resting state and free viewing of naturalistic videos. We found significant decreases in spontaneous functional connectivity between large-scale cortical networks, namely attentional and default mode/fronto-parietal networks, only for adaptation groups. Additionally, VRPA was found to bias visual representations of naturalistic videos, as following rightward adaptation, we found upregulation of visual response in an area in the parieto-occipital sulcus (POS) in the right hemisphere. Notably, the extent of POS upregulation correlated with the size of the VRPA induced after-effect measured in behavioural tests. This study demonstrates that a brief VRPA exposure is able to change large-scale cortical connectivity and correspondingly bias the representation of naturalistic sensory inputs.

scholarly journals Quantifying differences between passive and task-evoked intrinsic functional connectivity in a large-scale brain simulation

2018 ◽  
Author(s):  
Antonio Ulloa ◽  
Barry Horwitz
Keyword(s):  
Computational Model ◽  
Resting State ◽  
Short Term Memory ◽  
Brain Activity ◽  
Brain Regions ◽  
Intrinsic Activity ◽  
Task Execution ◽  
Short Term ◽  
Visual Short Term Memory ◽  
And Task

AbstractEstablishing a connection between intrinsic and task-evoked brain activity is critical because it would provide a way to map task-related brain regions in patients unable to comply with such tasks. A crucial question within this realm is to what extent the execution of a cognitive task affects the intrinsic activity of brain regions not involved in the task. Computational models can be useful to answer this question because they allow us to distinguish task from non-task neural elements while giving us the effects of task execution on non-task regions of interest at the neuroimaging level. The quantification of those effects in a computational model would represent a step towards elucidating the intrinsic versus task-evoked connection. Here we used computational modeling and graph theoretical metrics to quantify changes in intrinsic functional brain connectivity due to task execution. We used our Large-Scale Neural Modeling framework to embed a computational model of visual short-term memory into an empirically derived connectome. We simulated a neuroimaging study consisting of ten subjects performing passive fixation (PF), passive viewing (PV) and delay match-to-sample (DMS) tasks. We used the simulated BOLD fMRI time-series to calculate functional connectivity (FC) matrices and used those matrices to compute several graph theoretical measures. After determining that the simulated graph theoretical measures were largely consistent with experiments, we were able to quantify the differences between the graph metrics of the PF condition and those of the PV and DMS conditions. Thus, we show that we can use graph theoretical methods applied to simulated brain networks to aid in the quantification of changes in intrinsic brain functional connectivity during task execution. Our results represent a step towards establishing a connection between intrinsic and task-related brain activity.Author SummaryStudies of resting-state conditions are popular in neuroimaging. Participants in resting-state studies are instructed to fixate on a neutral image or to close their eyes. This type of study has advantages over traditional task-based studies, including its ability to allow participation of those with difficulties performing tasks. Further, a resting-state neuroimaging study reveals intrinsic activity of participants’ brains. However, task-related brain activity may change this intrinsic activity, much as a stone thrown in a lake causes ripples on the water’s surface. Can we measure those activity changes? To answer that question, we merged a computational model of visual short-term memory (task regions) with an anatomical model incorporating major connections between brain regions (non-task regions). In a computational model, unlike real data, we know how different regions are connected and which regions are doing the task. First, we simulated neuronal and neuroimaging activity of both task and non-task regions during three conditions: passive fixation (baseline), passive viewing, and visual short-term memory. Then, applying graph theory to the simulated neuroimaging of non-task regions, we computed differences between the baseline and the other conditions. Our results show that we can measure changes in non-task regions due to brain activity changes in task-related regions.

Large-scale Computational Models of Ongoing Brain Activity

2017 ◽  
pp. 425-437 ◽  
Author(s):  
Tristan T. Nakagawa ◽  
Mohit H. Adhikari ◽  
Gustavo Deco
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Brain Activity
Sign in / Sign up

Export Citation Format

Share Document