Dynamic Cognitive States Predict Individual Variability in Behavior and Modulate with EEG Functional Connectivity during Working Memory

Short-term fluctuations in strategy, attention, or motivation can cause large variability in cognitive performance across task trials. Typically, this variability is treated as noise when analyzing the relationships among behavior, neural activity, and experimentally structured task rules and stimuli. These relationships are thought to remain consistent over repeatedly administered identical task conditions (e.g. trial types and stimuli) while the variability is assumed to be random and to cancel out when averaged across trials and individuals. We propose that the variability carries important information regarding a participant's internal cognitive states, and could provide insights into both intra- and inter-individual differences in performance and its neural bases. However, these states are difficult to quantify, as they are not directly measurable. Therefore, we use a mathematical, state-space modeling framework to estimate internal cognitive states from measured behavioral data to predict each participant's reaction time fluctuations. We can quantify each participant's sensitivity to different factors (e.g. previous performance or distractions) that were predicted to affect cognitive states, and thus become sources of variability. By including a participant's states in the behavioral model, we improved model performance by a factor of 10, over a model with only experimental task parameters. We show how the participant-specific states reflect neural activity by identifying EEG functional connectivity features that modulate with each state. Overall, this approach could better quantify and characterize both individual and population behavioral differences across time, which could improve understanding of the neural mechanisms underlying the interactions among cognitive, strategic and motivational processes affecting behavior.

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Bayesian Decoder Models with a Discriminative Observation Process

10.1101/2020.07.11.198564 ◽

2020 ◽

Author(s):

Mohammad R. Rezaei ◽

Alex E. Hadjinicolaou ◽

Sydney S. Cash ◽

Uri T. Eden ◽

Ali Yousefi

Keyword(s):

State Space ◽

Neural Activity ◽

Model Development ◽

High Dimensional ◽

Modeling Framework ◽

Movement Trajectories ◽

State Process ◽

Observation Process ◽

Space Modeling ◽

Low Dimensional

AbstractThe Bayesian state-space neural encoder-decoder modeling framework is an established solution to reveal how changes in brain dynamics encode physiological covariates like movement or cognition. Although the framework is increasingly being applied to progress the field of neuroscience, its application to modeling high-dimensional neural data continues to be a challenge. Here, we propose a novel solution that avoids the complexity of encoder models that characterize high-dimensional data as a function of the underlying state processes. We build a discriminative model to estimate state processes as a function of current and previous observations of neural activity. We then develop the filter and parameter estimation solutions for this new class of state-space modeling framework called the “direct decoder” model. We apply the model to decode movement trajectories of a rat in a W-shaped maze from the ensemble spiking activity of place cells and achieve comparable performance to modern decoding solutions, without needing an encoding step in the model development. We further demonstrate how a dynamical auto-encoder can be built using the direct decoder model; here, the underlying state process links the high-dimensional neural activity to the behavioral readout. The dynamical auto-encoder can optimally estimate the low-dimensional dynamical manifold which represents the relationship between brain and behavior.

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Individual differences in haemoglobin concentration influence BOLD fMRI functional connectivity and its correlation with cognition

10.1101/835660 ◽

2019 ◽

Cited By ~ 4

Author(s):

Phillip G. D. Ward ◽

Edwina R. Orchard ◽

Stuart Oldham ◽

Aurina Arnatkevičiūtė ◽

Francesco Sforazzini ◽

...

Keyword(s):

Individual Differences ◽

Functional Connectivity ◽

Neural Activity ◽

Haemoglobin Concentration ◽

Individual Variability ◽

Group Differences ◽

Bold Signal ◽

Confounding Variable ◽

Men And Women ◽

Bold Fmri

AbstractResting-state connectivity measures the temporal coherence of the spontaneous neural activity of spatially distinct regions, and is commonly measured using BOLD-fMRI. The BOLD response follows neuronal activity, when changes in the relative concentration of oxygenated and deoxygenated haemoglobin cause fluctuations in the MRI T2* signal. Since the BOLD signal detects changes in relative concentrations of oxy/deoxy-haemoglobin, individual differences in haemoglobin levels may influence the BOLD signal-to-noise ratio in a manner independent of the degree of neural activity. In this study, we examined whether group differences in haemoglobin may confound measures of functional connectivity. We investigated whether relationships between measures of functional connectivity and cognitive performance could be influenced by individual variability in haemoglobin. Finally, we mapped the neuroanatomical distribution of the influence of haemoglobin on functional connectivity to determine where group differences in functional connectivity are manifest.In a cohort of 518 healthy elderly subjects (259 men) each sex group was median split into two groups with high and low haemoglobin concentration. Significant differences were obtained in functional connectivity between the high and low haemoglobin groups for both men and women (Cohen’s d 0.17 and 0.03 for men and women respectively). The haemoglobin connectome in males showed a widespread systematic increase in functional connectivity correlational scores, whilst the female connectome showed predominantly parietal and subcortical increases and temporo-parietal decreases. Despite the haemoglobin groups having no differences in cognitive measures, significant differences in the linear relationships between cognitive performance and functional connectivity were obtained for all 5 cognitive tests in males, and 4 out of 5 tests in females.Our findings confirm that individual variability in haemoglobin levels that give rise to group differences are an important confounding variable in BOLD-fMRI-based studies of functional connectivity. Controlling for haemoglobin variability as a potentially confounding variable is crucial to ensure the reproducibility of human brain connectome studies, especially in studies that compare groups of individuals, compare sexes, or examine connectivity-cognition relationships.HighlightsIndividual differences in haemoglobin significantly impact measures of functional connectivity in the elderly.Significant differences in connectivity-cognition relationships are shown between groups separated by haemoglobin values without accompanying cognitive differences.The influence of haemoglobin on functional connectivity differs between men and women.

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Influence of single nucleotide polymorphisms on thrombin generation in factor V Leiden heterozygotes

Thrombosis and Haemostasis ◽

10.1160/th13-05-0360 ◽

2014 ◽

Vol 111 (03) ◽

pp. 438-446 ◽

Cited By ~ 8

Author(s):

Olivier Segers ◽

Paolo Simioni ◽

Daniela Tormene ◽

Elisabetta Castoldi

Keyword(s):

Thrombin Generation ◽

Factor V Leiden ◽

Individual Variability ◽

Intermediate Phenotype ◽

Individual Risk ◽

Factor V ◽

Nucleotide Polymorphisms ◽

Factor X ◽

Large Variability ◽

Fv Leiden

SummaryCarriership of the factor V (FV) Leiden mutation increases the risk of venous thromboembolism (VTE) ~4-fold, but the individual risk of each FV Leiden carrier depends on several co-inherited risk and protective factors. Under the hypothesis that thrombin generation might serve as an intermediate phenotype to identify genetic modulators of VTE risk, we enrolled 188 FV Leiden heterozygotes (11 with VTE) and determined the following parameters: thrombin generation in the absence and presence of activated protein C (APC); plasma levels of prothrombin, factor X, antithrombin, protein S and tissue factor pathway inhibitor; and the genotypes of 24 SNPs located in the genes encoding these coagulation factors and inhibitors. Multiple regression analysis was subsequently applied to identify the (genetic) determinants of thrombin generation. The endogenous thrombin potential (ETP) showed a striking inter-individual variability among different FV Leiden carriers and, especially when measured in the presence of APC, correlated with VTE risk. Several SNPs in the F2 (rs1799963, rs3136516), F10 (rs693335), SERPINC1 (rs2227589), PROS1 (Heerlen polymorphism) and TFPI (rs5940) genes significantly affected the ETPAPC and/or the ETP+APC in FV Leiden carriers. Most of these SNPs have shown an association with VTE risk in conventional epidemiological studies, suggesting that the genetic dissection of thrombin generation leads to the detection of clinically relevant SNPs. In conclusion, we have identified several SNPs that modulate thrombin generation in FV Leiden heterozygotes. These SNPs may help explain the large variability in VTE risk observed among different FV Leiden carriers.

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The effect of epoch length on estimated EEG functional connectivity and brain network organisation

Journal of Neural Engineering ◽

10.1088/1741-2560/13/3/036015 ◽

2016 ◽

Vol 13 (3) ◽

pp. 036015 ◽

Cited By ~ 87

Author(s):

Matteo Fraschini ◽

Matteo Demuru ◽

Alessandra Crobe ◽

Francesco Marrosu ◽

Cornelis J Stam ◽

...

Keyword(s):

Functional Connectivity ◽

Brain Network ◽

Epoch Length ◽

Eeg Functional Connectivity ◽

Network Organisation

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Modification of EEG functional connectivity and EEG power spectra in overweight and obese patients with food addiction: An eLORETA study

Brain Imaging and Behavior ◽

10.1007/s11682-014-9324-x ◽

2014 ◽

Vol 9 (4) ◽

pp. 703-716 ◽

Cited By ~ 27

Author(s):

Claudio Imperatori ◽

Mariantonietta Fabbricatore ◽

Marco Innamorati ◽

Benedetto Farina ◽

Maria Isabella Quintiliani ◽

...

Keyword(s):

Functional Connectivity ◽

Food Addiction ◽

Power Spectra ◽

Obese Patients ◽

Eeg Power Spectra ◽

Eeg Power ◽

Eeg Functional Connectivity

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EEG Functional Connectivity Predicts Continuous Fatigue Levels During Underload Task

10.1109/aiea53260.2021.00075 ◽

2021 ◽

Author(s):

He-Xuan Zhu

Keyword(s):

Functional Connectivity ◽

Eeg Functional Connectivity

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Development and Emergence of Individual Variability in the Functional Connectivity Architecture of the Preterm Human Brain

Cerebral Cortex ◽

10.1093/cercor/bhy302 ◽

2018 ◽

Vol 29 (10) ◽

pp. 4208-4222 ◽

Cited By ~ 7

Author(s):

Yuehua Xu ◽

Miao Cao ◽

Xuhong Liao ◽

Mingrui Xia ◽

Xindi Wang ◽

...

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Network Architecture ◽

Brain Networks ◽

Individual Variability ◽

Distance Constraint ◽

Behavioral Differences ◽

Sensorimotor Network ◽

Middle Range ◽

Brain Functional Connectivity

Abstract Individual variability in human brain networks underlies individual differences in cognition and behaviors. However, researchers have not conclusively determined when individual variability patterns of the brain networks emerge and how they develop in the early phase. Here, we employed resting-state functional MRI data and whole-brain functional connectivity analyses in 40 neonates aged around 31–42 postmenstrual weeks to characterize the spatial distribution and development modes of individual variability in the functional network architecture. We observed lower individual variability in primary sensorimotor and visual areas and higher variability in association regions at the third trimester, and these patterns are generally similar to those of adult brains. Different functional systems showed dramatic differences in the development of individual variability, with significant decreases in the sensorimotor network; decreasing trends in the visual, subcortical, and dorsal and ventral attention networks, and limited change in the default mode, frontoparietal and limbic networks. The patterns of individual variability were negatively correlated with the short- to middle-range connection strength/number and this distance constraint was significantly strengthened throughout development. Our findings highlight the development and emergence of individual variability in the functional architecture of the prenatal brain, which may lay network foundations for individual behavioral differences later in life.

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