scholarly journals A new model for simultaneous dimensionality reduction and time-varying functional connectivity estimation

2021 ◽  
Vol 17 (4) ◽  
pp. e1008580
Author(s):  
Diego Vidaurre
Keyword(s):  
Functional Connectivity ◽  
Dimensionality Reduction ◽  
Information Transfer ◽  
Brain Regions ◽  
Single Step ◽  
Fast Time ◽  
Time Varying ◽  
Time Resolved ◽  
Neuronal Communication ◽  
Common Strategy

An important question in neuroscience is whether or not we can interpret spontaneous variations in the pattern of correlation between brain areas, which we refer to as functional connectivity or FC, as an index of dynamic neuronal communication in fMRI. That is, can we measure time-varying FC reliably? And, if so, can FC reflect information transfer between brain regions at relatively fast-time scales? Answering these questions in practice requires dealing with the statistical challenge of having high-dimensional data and a comparatively lower number of time points or volumes. A common strategy is to use PCA to reduce the dimensionality of the data, and then apply some model, such as the hidden Markov model (HMM) or a mixture model of Gaussian distributions, to find a set of distinct FC patterns or states. The distinct spatial properties of these FC states together with the time-resolved switching between them offer a flexible description of time-varying FC. In this work, I show that in this context PCA can suffer from systematic biases and loss of sensitivity for the purposes of finding time-varying FC. To get around these issues, I propose a novel variety of the HMM, named HMM-PCA, where the states are themselves PCA decompositions. Since PCA is based on the data covariance, the state-specific PCA decompositions reflect distinct patterns of FC. I show, theoretically and empirically, that fusing dimensionality reduction and time-varying FC estimation in one single step can avoid these problems and outperform alternative approaches, facilitating the quantification of transient communication in the brain.

scholarly journals The statistical challenge of finding spontaneous changes in functional connectivity in high-dimensional fMRI data

2020 ◽  
Author(s):  
Diego Vidaurre
Keyword(s):  
Functional Connectivity ◽  
Information Transfer ◽  
Brain Regions ◽  
Single Step ◽  
High Dimensional ◽  
Fast Time ◽  
Time Varying ◽  
Time Resolved ◽  
Neuronal Communication ◽  
Bayesian Mixture

An important question in neuroscience is whether or not we can interpret spontaneous variations in the pattern of correlation between brain areas, which we refer to as functional connectivity or FC , as an index of dynamic neuronal communication in fMRI . That is, can we measure time-varying FC reliably? And, if so, can FC reflect information transfer between brain regions at relatively fast-time scales? Answering these questions in practice requires dealing with the statistical challenge of having high-dimensional data and a comparatively lower number of time points or volumes. A common strategy is to use PCA to reduce the dimensionality of the data, and then apply some model, such as the hidden Markov model (HMM) or a Bayesian mixture of distributions, to find a set of distinct FC patterns or states. The distinct spatial properties of these FC states together with the time resolved switching between them offer a flexible description of time-varying FC . In this work, we show that in this context PCA can suffer from systematic biases and loss of sensitivity for the purposes of finding time-varying FC . To get around these issues, we propose a novel variety of the HMM, the HMM- PCA , where the states are themselves PCA decompositions. Since PCA is based on the data covariance, the state-specific PCA decompositions reflect distinct patterns of FC . We show, theoretically and empirically, that fusing dimensionality reduction and time-varying FC estimation in one single step can avoid these problems and outperform alternative approaches, eventually facilitating the quantification of transient communication in the brain.

scholarly journals Topological Segmentation of Time-Varying Functional Connectivity Highlights the Role of Preferred Cortical Circuits

2020 ◽  
Author(s):  
Jacob Billings ◽  
Manish Saggar ◽  
Shella Keilholz ◽  
Giovanni Petri
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Persistent Homology ◽  
Brain Regions ◽  
Topological Data Analysis ◽  
Time Varying ◽  
Imaging Data ◽  
Experimental Conditions ◽  
Common Time ◽  
Brain Imaging Data

Functional connectivity (FC) and its time-varying analogue (TVFC) leverage brain imaging data to interpret brain function as patterns of coordinating activity among brain regions. While many questions remain regarding the organizing principles through which brain function emerges from multi-regional interactions, advances in the mathematics of Topological Data Analysis (TDA) may provide new insights into the brain’s spontaneous self-organization. One tool from TDA, “persistent homology”, observes the occurrence and the persistence of n-dimensional holes presented in the metric space over a dataset. The occurrence of n-dimensional holes within the TVFC point cloud may denote conserved and preferred routes of information flow among brain regions. In the present study, we compare the use of persistence homology versus more traditional TVFC metrics at the task of segmenting brain states that differ across a common time-series of experimental conditions. We find that the structures identified by persistence homology more accurately segment the stimuli, more accurately segment volunteer performance during experimentally defined tasks, and generalize better across volunteers. Finally, we present empirical and theoretical observations that interpret brain function as a topological space defined by cyclic and interlinked motifs among distributed brain regions, especially, the attention networks.

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

scholarly journals Linear Input as a Fundamental Motif of Brain Connectivity Organization

2019 ◽  
Author(s):  
Jonathan F. O’Rawe ◽  
Hoi-Chung Leung
Keyword(s):  
Functional Connectivity ◽  
Information Transfer ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Fundamental Aspect ◽  
Biased Sampling ◽  
Whole Brain ◽  
3 Dimensional ◽  
Face Area ◽  
Learning Techniques

AbstractDescribing the pattern of region-to-region functional connectivity is an important step towards understanding information transfer and transformation between brain regions. Although fMRI data are limited in spatial resolution, recent advances in technology afford more precise mapping. Here, we extended previous methods, connective field mapping, to 3 dimensions to provide a more concise estimate of the organization and potential information transformation from one region to another. We first replicated previous work with the 3 dimensional model by showing that the topology of functional connectivity between early visual regions maintained along their eccentricity axis or the anterior-posterior dimension. We then examined higher order visual regions (e,g, fusiform face area) and showed that their pattern of connectivity, the convergence and biased sampling, seem to contribute to some of their core receptive field properties. We further demonstrated that linearity of input is a fundamental aspect of functional connectivity of the whole brain, with higher linearity between regions within a network than across networks; that is, high connective linearity was evident between early visual areas, and between prefrontal areas, but less evident between them. By decomposing the whole brain linearity matrix with manifold learning techniques, we found that the principle mode of the linearity maps onto decompositions in both functional connectivity and genetic expression reported in previous studies. The current work provides evidence supporting that linearity of input is likely a fundamental motif of functional connectivity between regions for information processing across the brain, with high linearity preserving the integrity of information from one region to another within a network.

scholarly journals In-phase and in-antiphase connectivity in EEG

2021 ◽  
Author(s):  
Christian O'Reilly ◽  
John D Lewis ◽  
Rebecca J Theilmann ◽  
Mayada Elsabbagh ◽  
Jeanne Townsend
Keyword(s):  
Functional Connectivity ◽  
Local Field ◽  
Significant Proportion ◽  
Brain Regions ◽  
Field Potentials ◽  
Volume Conduction ◽  
Confounding Effect ◽  
Neuronal Communication ◽  
Neural Communication ◽  
Eeg Connectivity

Zero-lag synchrony is generally discarded from functional connectivity studies to eliminate the confounding effect of volume conduction. Demonstrating genuine and significant unlagged synchronization between distant brain regions would indicate that most electroencephalography (EEG) connectivity studies neglect an important mechanism for neuronal communication. We previously demonstrated that local field potentials recorded intracranially tend to synchronize with no lag between homotopic brain regions. This synchrony occurs most frequently in antiphase, potentially supporting corpus callosal inhibition and interhemispheric rivalry. We are now extending our investigation to EEG. By comparing the coherency in a recorded and a surrogate dataset, we confirm the presence of a significant proportion of genuine zero-lag synchrony unlikely to be due to volume conduction or to recording reference artifacts. These results stress the necessity for integrating zero-lag synchrony in our understanding of neural communication and for disentangling volume conduction and zero-lag synchrony when estimating EEG sources and their functional connectivity.

The Functional Language Network

2017 ◽  
Author(s):  
Angela D. Friederici ◽  
Noam Chomsky
Keyword(s):  
Functional Connectivity ◽  
Information Transfer ◽  
Inferior Frontal Gyrus ◽  
Brain Regions ◽  
Oscillatory Activity ◽  
Neural Basis ◽  
Functional Neural Network ◽  
Ventral Pathway ◽  
Open Issue ◽  
Language Network

How information content is encoded and decoded in the sending and receiving brain areas is still an open issue. A possible though speculative view is that encoding and decoding requires similarity at the neuronal level in the encoding and decoding regions. This chapter discusses the functional neural network of language. It first describes the language network at the neurotransmitter level and then discusses the available data at the level of functional connectivity and oscillatory activity. Section 1 looks at the neural basis of information transfer, namely at the neurotransmitters which are crucially involved in the transmission of information from one neuron to the next. Section 2 uses functional connectivity analyses to provide information about how different brain regions work together. They allow us to make statements about which regions work together, and moreover, about the direction of the information flow between these. Section 3 models the language circuit as a a dynamic temporo-frontal network with initial input-driven information processed bottom-up from the auditory cortex to the frontal cortex along the ventral pathway, with semantic information reaching the anterior inferior frontal gyrus, and syntactic information reaching the posterior inferior frontal gyrus.

scholarly journals Behavioural relevance of spontaneous, transient brain network interactions in fMRI

2019 ◽  
Author(s):  
D. Vidaurre ◽  
A. Llera ◽  
S.M. Smith ◽  
M.W. Woolrich
Keyword(s):  
Functional Connectivity ◽  
Network Architecture ◽  
Brain Networks ◽  
Structural Data ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Human Connectome Project ◽  
Brain Data ◽  
Structural Brain

AbstractHow spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods on Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.Significance statementComplex cognition is dynamic and emerges from the interaction between multiple areas across the whole brain, i.e. from brain networks. Hence, the utility of functional MRI to investigate brain activity depends on how well it can capture time-varying network interactions. Here, we develop methods to predict behavioural traits of individuals from either time-varying functional connectivity, time-averaged functional connectivity, or structural brain data. We use these to show that the time-varying nature of functional brain networks in fMRI can be reliably measured and can explain aspects of behaviour not captured by structural data or time-averaged functional connectivity. These results provide important insights to the question of how the brain represents information and how these representations can be measured with fMRI.

scholarly journals Frontoparietal Activity Interacts With Task-Evoked Changes in Functional Connectivity

2018 ◽  
Vol 29 (2) ◽  
pp. 802-813 ◽  
Author(s):  
Kai Hwang ◽  
James M Shine ◽  
Mark D’Esposito
Keyword(s):  
Functional Connectivity ◽  
Activity Patterns ◽  
Relevant Information ◽  
Brain Regions ◽  
Task Demands ◽  
Neural Systems ◽  
Time Varying ◽  
Fmri Study ◽  
Tracking Time ◽  
Candidate Regions

Abstract Flexible interactions between brain regions enable neural systems to adaptively transfer and process information. However, the neural substrates that regulate adaptive communications between brain regions are understudied. In this human fMRI study, we investigated this issue by tracking time-varying, task-evoked changes in functional connectivity between localized occipitotemporal regions while participants performed different tasks on the same visually presented stimuli. We found that functional connectivity between ventral temporal and the primary visual regions selectively increased during the processing of task-relevant information. Further, additional task demands selectively strengthen these targeted connectivity patterns. To identify candidate regions that contribute to this increase in inter-regional coupling, we regressed the task-specific time-varying connectivity strength between primary visual and occipitotemporal regions against voxel-wise activity patterns elsewhere in the brain. This allowed us to identify a set of frontal and parietal regions whose activity increased as a function of task-evoked functional connectivity. These results suggest that frontoparietal regions may provide top-down biasing signals to influence task-specific interactions between brain regions.

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

2021 ◽  
Vol 17 (4) ◽  
pp. e1008129
Author(s):  
Aref Pariz ◽  
Ingo Fischer ◽  
Alireza Valizadeh ◽  
Claudio Mirasso
Keyword(s):  
Functional Connectivity ◽  
Information Exchange ◽  
Information Transfer ◽  
Phase Response Curve ◽  
Signal Transmission ◽  
Brain Networks ◽  
Brain Regions ◽  
Transmission Delays ◽  
Neural Populations

Brain networks exhibit very variable and dynamical functional connectivity and flexible configurations of information exchange despite their overall fixed structure. 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 of the connection delay and the detuning between the natural frequencies of neural populations 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 could be determined 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.

scholarly journals Questions and controversies in the study of time-varying functional connectivity in resting fMRI

2020 ◽  
Vol 4 (1) ◽  
pp. 30-69 ◽  
Author(s):  
Daniel J. Lurie ◽  
Daniel Kessler ◽  
Danielle S. Bassett ◽  
Richard F. Betzel ◽  
Michael Breakspear ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Careful Analysis ◽  
Research Area ◽  
Time Varying ◽  
Time Resolved ◽  
Open Questions ◽  
Wide Range ◽  
Neuroimaging Data ◽  
Core Concepts ◽  
New Research

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as “dynamic” or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.

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