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 Development of Functional Connectivity during Adolescence: A Longitudinal Study Using an Action–Observation Paradigm

2011 ◽  
Vol 23 (12) ◽  
pp. 3713-3724 ◽  
Author(s):  
Daniel J. Shaw ◽  
Marie-Helene Grosbras ◽  
Gabriel Leonard ◽  
G. Bruce Pike ◽  
Tomáš Paus
Keyword(s):  
Social Cognition ◽  
Functional Connectivity ◽  
Action Observation ◽  
Brain Regions ◽  
Female Adolescents ◽  
Emotional States ◽  
Social Emotional ◽  
Social Stimuli ◽  
The Social ◽  
Insight Into

Successful interpersonal interactions rely on an ability to read the emotional states of others and to modulate one's own behavior in response. The actions of others serve as valuable social stimuli in this respect, offering the observer an insight into the actor's emotional state. Social cognition continues to mature throughout adolescence. Here we assess longitudinally the development of functional connectivity during early adolescence within two neural networks implicated in social cognition: one network of brain regions consistently engaged during action observation and another one associated with mentalizing. Using fMRI, we reveal a greater recruitment of the social–emotional network during the observation of angry hand actions in male relative to female adolescents. These findings are discussed in terms of known sex differences in adolescent social behavior.

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 Causally informed activity flow models provide mechanistic insight into the emergence of cognitive processes from brain network interactions

2021 ◽  
Author(s):  
Ruben Sanchez-Romero ◽  
Takuya Ito ◽  
Ravi D. Mill ◽  
Stephen José Hanson ◽  
Michael W. Cole
Keyword(s):  
Functional Connectivity ◽  
Effective Connectivity ◽  
Pearson Correlation ◽  
Explanatory Power ◽  
Memory Task ◽  
Brain Regions ◽  
Brain Network ◽  
List Type ◽  
Flow Models ◽  
Insight Into

AbstractBrain activity flow models estimate the movement of task-evoked activity over brain connections to help explain the emergence of task-related functionality. Activity flow estimates have been shown to accurately predict task-evoked brain activations across a wide variety of brain regions and task conditions. However, these predictions have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. Starting from Pearson correlation (the current field standard), we progress from FC measures with poor to excellent causal grounding, demonstrating a continuum of causal validity using simulations and empirical fMRI data. Finally, we apply a causal FC method to a dorsolateral prefrontal cortex region, demonstrating causal network mechanisms contributing to its strong activation during a 2-back (relative to a 0-back) working memory task. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.Highlights-Activity flow models provide insight into how cognitive neural effects emerge from brain network interactions.-Functional connectivity methods grounded in causal principles facilitate mechanistic interpretations of task activity flow models.-Mechanistic activity flow models accurately predict task-evoked neural effects across a wide variety of brain regions and cognitive tasks.

scholarly journals Heterogeneity of EEG resting-state brain networks in absolute pitch

2020 ◽  
Author(s):  
Marielle Greber ◽  
Carina Klein ◽  
Simon Leipold ◽  
Silvano Sele ◽  
Lutz Jäncke
Keyword(s):  
Functional Connectivity ◽  
Auditory Cortex ◽  
Resting State ◽  
Large Scale ◽  
Absolute Pitch ◽  
Brain Regions ◽  
Brain Network ◽  
Higher Order ◽  
Neural Basis ◽  
Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

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 Parietal and hippocampal hyper-connectivity is associated with low math achievement in adolescence

2021 ◽  
Author(s):  
Roberto A. Abreu-Mendoza ◽  
Melanie Pincus ◽  
Yaira Chamorro ◽  
Dietsje Jolles ◽  
Esmeralda Matute ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Math Achievement ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Math Performance ◽  
Resting State Functional Connectivity ◽  
Neural Basis ◽  
Math Ability ◽  
Control Seed

Mathematical cognition requires coordinated activity across multiple brain regions, leading to the emergence of resting-state functional connectivity as a method for studying the neural basis of differences in mathematical achievement. Hyper-connectivity of the intraparietal sulcus (IPS), a key locus of mathematical and numerical processing, has been associated with poor mathematical skills in childhood, whereas greater connectivity has been related to better performance in adulthood. No studies to date have considered its role in adolescence. Further, hippocampal connectivity can predict mathematical learning, yet no studies have considered its contributions to contemporaneous measures of math achievement. Here, we used seed-based resting-state fMRI analyses to examine IPS and hippocampal intrinsic functional connectivity relations to math achievement in a group of 31 adolescents (mean age=16.42 years, range 15-17), whose math performance spanned the 1% to 99% percentile. After controlling for IQ, IPS connectivity was negatively related to math achievement, akin to findings in children. However, the specific temporooccipital regions, were more akin to the posterior loci implicated in adults. Hippocampal connectivity with frontal regions was also negatively correlated with concurrent math measures, which contrasts with results from learning studies. Finally, hyper-connectivity was not a global feature of low math performance, as connectivity of Heschl’s gyrus, a control seed not involved in math cognition, was not modulated by math performance. Together, our results point to adolescence as a transitional stage in which patterns found in childhood and adulthood can be observed; most notably, hyper-connectivity continues to be related to low math ability in this period.

scholarly journals Maladaptive Self-Focused Attention and Default Mode Network Connectivity: A Transdiagnostic Investigation Across Social Anxiety and Body Dysmorphic Disorders

2021 ◽  
Author(s):  
Angela Fang ◽  
Bengi Baran ◽  
Clare C Beatty ◽  
Jennifer Mosley ◽  
Jamie D Feusner ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Social Anxiety ◽  
Default Mode Network ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Focused Attention ◽  
Neural Basis ◽  
Default Mode ◽  
Body Dysmorphic Disorders ◽  
Primary Body

Abstract Maladaptive self-focused attention (SFA) is a bias toward internal thoughts, feelings, and physical states. Despite its role as a core maintaining factor of symptoms in cognitive theories of social anxiety and body dysmorphic disorders, studies have not examined its neural basis. In this study, we hypothesized that maladaptive SFA would be associated with hyperconnectivity in the default mode network (DMN) in self-focused patients with these disorders. Thirty patients with primary social anxiety disorder or primary body dysmorphic disorder, and 28 healthy individuals were eligible and scanned. Eligibility was determined by scoring greater than 1SD or below 1SD of the Public Self-Consciousness Scale normative mean, respectively, for each group. Seed-to-voxel functional connectivity was computed using a DMN posterior cingulate cortex (PCC) seed. There was no evidence of increased DMN functional connectivity in patients compared to controls. Patients (regardless of diagnosis) showed reduced functional connectivity of the PCC with several brain regions, including the bilateral superior parietal lobule (SPL), compared to controls, which was inversely correlated with maladaptive SFA but not associated with social anxiety, body dysmorphic, or depression severity, or rumination. Abnormal PCC-SPL connectivity may represent a transdiagnostic neural marker of SFA that reflects difficulty shifting between internal versus external attention.

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 Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Satoru Hiwa ◽  
Shogo Obuchi ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Working Memory ◽  
Functional Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Support Vector ◽  
Neural Basis ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Properties

Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.

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