scholarly journals The dynamics of human cognition: increasing global integration coupled with decreasing segregation found using iEEG

2016 ◽  
Author(s):  
Josephine Cruzat ◽  
Gustavo Deco ◽  
Adrià Tauste ◽  
Alessandro Principe ◽  
Albert Costa ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Processing ◽  
Cognitive Task ◽  
Brain Networks ◽  
Brain Regions ◽  
Gamma Band ◽  
Human Cognition ◽  
Global Integration ◽  
Large Brain ◽  
Underlying Network

AbstractCognitive processing requires the ability to flexibly integrate and process information across large brain networks. More information is needed on how brain networks dynamically reorganize to allow such broad communication across many different brain regions in order to integrate the necessary information. Here, we use intracranial EEG to record neural activity from 12 epileptic patients while they perform three cognitive tasks in order to study how the functional connectivity changes to facilitate communication across the underlying network spanning many different brain regions. At the topological level, this facilitation is characterized by measures of integration and segregation. Across all patients, we found significant increases in integration and decreases in segregation during cognitive processing, especially in the gamma band (50-90 Hz). Accordingly, we also found significantly higher level of global synchronization and functional connectivity during the execution of the cognitive task, again particularly in the gamma band. More importantly, we demonstrate here for the first time that the modulations at the level of functional connectivity facilitating communication across the network were not caused by changes in the level of the underlying oscillations but caused by a rearrangement of the mutual synchronisation between the different nodes as proposed by the “Communication Through Coherence” Theory.

scholarly journals Effects of long-term cocaine self-administration on brain resting-state functional connectivity in nonhuman primates

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Stephen J. Kohut ◽  
Dionyssios Mintzopoulos ◽  
Brian D. Kangas ◽  
Hannah Shields ◽  
Kelly Brown ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Processing ◽  
Resting State ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Resting State Functional Connectivity ◽  
Self Administration ◽  
Chronic Cocaine

AbstractLong-term cocaine use is associated with a variety of neural and behavioral deficits that impact daily function. This study was conducted to examine the effects of chronic cocaine self-administration on resting-state functional connectivity of the dorsal anterior cingulate (dACC) and putamen—two brain regions involved in cognitive function and motoric behavior—identified in a whole brain analysis. Six adult male squirrel monkeys self-administered cocaine (0.32 mg/kg/inj) over 140 sessions. Six additional monkeys that had not received any drug treatment for ~1.5 years served as drug-free controls. Resting-state fMRI imaging sessions at 9.4 Tesla were conducted under isoflurane anesthesia. Functional connectivity maps were derived using seed regions placed in the left dACC or putamen. Results show that cocaine maintained robust self-administration with an average total intake of 367 mg/kg (range: 299–424 mg/kg). In the cocaine group, functional connectivity between the dACC seed and regions primarily involved in motoric behavior was weaker, whereas connectivity between the dACC seed and areas implicated in reward and cognitive processing was stronger. In the putamen seed, weaker widespread connectivity was found between the putamen and other motor regions as well as with prefrontal areas that regulate higher-order executive function; stronger connectivity was found with reward-related regions. dACC connectivity was associated with total cocaine intake. These data indicate that functional connectivity between regions involved in motor, reward, and cognitive processing differed between subjects with recent histories of cocaine self-administration and controls; in dACC, connectivity appears to be related to cumulative cocaine dosage during chronic exposure.

Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity

2020 ◽  
Vol 30 (09) ◽  
pp. 2050047
Author(s):  
Lubin Wang ◽  
Xianbin Li ◽  
Yuyang Zhu ◽  
Bei Lin ◽  
Qijing Bo ◽  
...  
Keyword(s):  
High Risk ◽  
Functional Connectivity ◽  
Symptom Severity ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Support Vector ◽  
Healthy Controls ◽  
Intrinsic Functional Connectivity ◽  
Ultra High Risk

Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.

scholarly journals Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

2021 ◽  
Author(s):  
Etienne Combrisson ◽  
Michele Allegra ◽  
Ruggero Basanisi ◽  
Robin A. A. Ince ◽  
Bruno L Giordano ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Scientific Practice ◽  
Local Level ◽  
Random Effect ◽  
Brain Networks ◽  
Brain Regions ◽  
Group Level ◽  
Starting Point ◽  
Neurophysiological Data ◽  
Statistical Approaches

The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuity in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present a open-source Python toolbox called Frites that includes all of the methods used here, from functional connectivity estimations to the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.

scholarly journals Centralized and distributed cognitive task processing in the human connectome

2019 ◽  
Vol 3 (2) ◽  
pp. 455-474 ◽  
Author(s):  
Enrico Amico ◽  
Alex Arenas ◽  
Joaquín Goñi
Keyword(s):  
Cognitive Processing ◽  
Resting State ◽  
Large Scale ◽  
Cognitive Task ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Cognitive Changes ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
Jensen Shannon Divergence

A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectivity (FC). A systematic approach to measure pairwise functional distance at different brain states is lacking. This would provide a straightforward way to quantify differences in cognitive processing across tasks; also, it would help in relating these differences in task-based FCs to the underlying structural network. Here we propose a framework, based on the concept of Jensen-Shannon divergence, to map the task-rest connectivity distance between tasks and resting-state FC. We show how this information theoretical measure allows for quantifying connectivity changes in distributed and centralized processing in functional networks. We study resting state and seven tasks from the Human Connectome Project dataset to obtain the most distant links across tasks. We investigate how these changes are associated with different functional brain networks, and use the proposed measure to infer changes in the information-processing regimes. Furthermore, we show how the FC distance from resting state is shaped by structural connectivity, and to what extent this relationship depends on the task. This framework provides a well-grounded mathematical quantification of connectivity changes associated with cognitive processing in large-scale brain networks.

scholarly journals Intrinsic Architecture Underlying the Relations among the Default, Dorsal Attention, and Frontoparietal Control Networks of the Human Brain

2013 ◽  
Vol 25 (1) ◽  
pp. 74-86 ◽  
Author(s):  
R. Nathan Spreng ◽  
Jorge Sepulcre ◽  
Gary R. Turner ◽  
W. Dale Stevens ◽  
Daniel L. Schacter
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Dynamic Balance ◽  
Brain Regions ◽  
Human Cognition ◽  
Control Network ◽  
Resting State Functional Connectivity ◽  
Attention Networks ◽  
Large Scale Networks ◽  
Frontoparietal Control Network

Human cognition is increasingly characterized as an emergent property of interactions among distributed, functionally specialized brain networks. We recently demonstrated that the antagonistic “default” and “dorsal attention” networks—subserving internally and externally directed cognition, respectively—are modulated by a third “frontoparietal control” network that flexibly couples with either network depending on task domain. However, little is known about the intrinsic functional architecture underlying this relationship. We used graph theory to analyze network properties of intrinsic functional connectivity within and between these three large-scale networks. Task-based activation from three independent studies were used to identify reliable brain regions (“nodes”) of each network. We then examined pairwise connections (“edges”) between nodes, as defined by resting-state functional connectivity MRI. Importantly, we used a novel bootstrap resampling procedure to determine the reliability of graph edges. Furthermore, we examined both full and partial correlations. As predicted, there was a higher degree of integration within each network than between networks. Critically, whereas the default and dorsal attention networks shared little positive connectivity with one another, the frontoparietal control network showed a high degree of between-network interconnectivity with each of these networks. Furthermore, we identified nodes within the frontoparietal control network of three different types—default-aligned, dorsal attention-aligned, and dual-aligned—that we propose play dissociable roles in mediating internetwork communication. The results provide evidence consistent with the idea that the frontoparietal control network plays a pivotal gate-keeping role in goal-directed cognition, mediating the dynamic balance between default and dorsal attention networks.

scholarly journals Network Dynamics Theory of Human Intelligence

2016 ◽  
Author(s):  
Aki Nikolaidis ◽  
Aron K. Barbey
Keyword(s):  
Functional Connectivity ◽  
Scientific Discovery ◽  
Brain Networks ◽  
Network Dynamics ◽  
Brain Regions ◽  
Biological Characteristics ◽  
Human Intelligence ◽  
Neural Basis ◽  
Developmental Neuroscience ◽  
Insight Into

AbstractScientific discovery and insight into the biological foundations of human intelligence have advanced considerably with progress in neuroimaging. Neuroimaging methods allow for not only an exploration of what biological characteristics underlie intelligence and creativity, but also a detailed assessment of how these biological characteristics emerge through child and adolescent development. In the past 10 years, functional connectivity, a metric of coherence in activation across brain regions, has been used extensively to probe cognitive function; however more recently neuroscientists have begun to investigate the dynamics of these functional connectivity patterns, revealing important insight into these networks as a result. In the present article, we expand current theories on the neural basis of human intelligence by developing a framework that integrates both how short-term dynamic fluctuations in brain networks and long-term development of brain networks over time contribute to intelligence and creativity. Applying this framework, we propose testable hypotheses regarding the neural and developmental correlates of intelligence. We review important topics in both network neuroscience and developmental neuroscience, and we consolidate these insights into a Network Dynamics Theory of human intelligence.

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 New graph-theoretical-multimodal approach using temporal and structural correlations reveal disruption in the thalamo-cortical network in patients with schizophrenia

2018 ◽  
Author(s):  
Paolo Finotteli ◽  
Caroline Garcia Forlim ◽  
Paolo Dulio ◽  
Leonie Klock ◽  
Alessia Pini ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cortical Network ◽  
Future Research ◽  
Connectivity Matrix

Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive and behavioral symptoms in this disorder. When building brain networks, functional and structural networks are typically modelled independently: functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks. In this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural and topological information of MRI measurements (structural and resting state imaging) to patients with schizophrenia (N=35) and matched healthy individuals (N=41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out. By using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole brain connectivity including functional, structural and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting state fMRI, and can be applied to EEG, MEG etc.

scholarly journals Analysis of Brain Functional Connectivity Neural Circuits in Children With Autism Based on Persistent Homology

2021 ◽  
Vol 15 ◽  
Author(s):  
Di Liang ◽  
Shengxiang Xia ◽  
Xianfu Zhang ◽  
Weiwei Zhang
Keyword(s):  
Functional Connectivity ◽  
Neural Circuits ◽  
Persistent Homology ◽  
Brain Networks ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Unknown Etiology ◽  
Control Group ◽  
Threshold Values ◽  
The Right

Autism spectrum disorder (ASD) is a complex neuropsychiatric disorder with a complex and unknown etiology. Statistics demonstrate that the number of people diagnosed with ASD is increasing in countries around the world. Currently, although many neuroimaging studies indicate that ASD is characterized by abnormal functional connectivity (FC) patterns within brain networks rather than local functional or structural abnormalities, the FC characteristics of ASD are still poorly understood. In this study, a Vietoris-Rips (VR) complex filtration model of the brain functional network was established by using resting-state functional magnetic resonance imaging (fMRI) data of children aged 6–13 years old [including 54 ASD patients and 52 typical development (TD) controls] from the Autism Brain Imaging Data Exchange (ABIDE) public database. VR complex filtration barcodes are calculated by using persistent homology to describe the changes in the FC neural circuits of brain networks. The number of FC neural circuits with different length ranges at different threshold values is calculated by using the barcodes, the different brain regions participating in FC neural circuits are discussed, and the connectivity characteristics of brain FC neural circuits in the two groups are compared and analyzed. Our results show that the number of FC neural circuits with lengths of 8–12 is significantly decreased in the ASD group compared with the TD control group at threshold values of 0.7, 0.8 and 0.9, and there is no significant difference in the number of FC neural circuits with lengths of 4–7 and 13–16 and lengths 16. When the thresholds are 0.7, 0.8, and 0.9, the number of FC neural circuits in some brain regions, such as the right orbital part of the superior frontal gyrus, the left supplementary motor area, the left hippocampus, and the right caudate nucleus, involved in the study is significantly decreased in the ASD group compared with the TD control group. The results of this study indicate that there are significant differences in the FC neural circuits of brain networks in the ASD group compared with the TD control group.

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.

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