Caveats and nuances of model-based and model-free representational connectivity analysis

2021 ◽  
Author(s):  
Hamid Karimi-Rouzbahani ◽  
Alexandra Woolgar ◽  
Richard N Henson ◽  
Hamed Nili
Keyword(s):  
Brain Connectivity ◽  
Activity Patterns ◽  
Brain Regions ◽  
Specific Aspect ◽  
Activation Patterns ◽  
New Approach ◽  
Model Based ◽  
Model Free ◽  
Shared Information ◽  
The Relationship

Brain connectivity analyses have conventionally relied on statistical relationship between one-dimensional summaries of activation in different brain areas. However, summarising activation patterns within each area to a single dimension ignores the potential statistical dependencies between their multi-dimensional activity patterns. Representational Connectivity Analyses (RCA) is a method that quantifies the relationship between multi-dimensional patterns of activity without reducing the dimensionality of the data. We consider two variants of RCA. In model-free RCA, the goal is to quantify the shared information for two brain regions. In model-based RCA, one tests whether two regions have shared information about a specific aspect of the stimuli/task, as defined by a model. However, this is a new approach and the potential caveats of model-free and model-based RCA are still understudied. We first explain how model-based RCA detects connectivity through the lens of models, and then present three scenarios where model-based and model-free RCA give discrepant results. These conflicting results complicate the interpretation of functional connectivity. We highlight the challenges in three scenarios: complex intermediate models, common patterns across regions and transformation of representational structure across brain regions. The paper is accompanied by scripts that reproduce the results. In each case, we suggest potential ways to mitigate the difficulties caused by inconsistent results. The results of this study shed light on some understudied aspects of RCA, and allow researchers to use the method more effectively.

scholarly journals Category representations in the brain are both discretely localized and widely distributed

2018 ◽  
Vol 119 (6) ◽  
pp. 2256-2264 ◽  
Author(s):  
Zarrar Shehzad ◽  
Gregory McCarthy
Keyword(s):  
Functional Mri ◽  
Brain Connectivity ◽  
Strong Support ◽  
Brain Regions ◽  
Distributed Representations ◽  
Domain Specific ◽  
Category Information ◽  
Shared Information ◽  
Unique Domain ◽  
The Brain

Whether category information is discretely localized or represented widely in the brain remains a contentious issue. Initial functional MRI studies supported the localizationist perspective that category information is represented in discrete brain regions. More recent fMRI studies using machine learning pattern classification techniques provide evidence for widespread distributed representations. However, these latter studies have not typically accounted for shared information. Here, we find strong support for distributed representations when brain regions are considered separately. However, localized representations are revealed by using analytical methods that separate unique from shared information among brain regions. The distributed nature of shared information and the localized nature of unique information suggest that brain connectivity may encourage spreading of information but category-specific computations are carried out in distinct domain-specific regions. NEW & NOTEWORTHY Whether visual category information is localized in unique domain-specific brain regions or distributed in many domain-general brain regions is hotly contested. We resolve this debate by using multivariate analyses to parse functional MRI signals from different brain regions into unique and shared variance. Our findings support elements of both models and show information is initially localized and then shared among other regions leading to distributed representations being observed.

scholarly journals Prefrontal solution to the bias-variance tradeoff during reinforcement learning

2020 ◽  
Author(s):  
Dongjae Kim ◽  
Jaeseung Jeong ◽  
Sang Wan Lee
Keyword(s):  
Adaptive Control ◽  
Reinforcement Learning ◽  
Prediction Error ◽  
Brain Regions ◽  
Decision Task ◽  
Prediction Errors ◽  
Model Based ◽  
Model Free ◽  
Bias Variance ◽  
The Brain

AbstractThe goal of learning is to maximize future rewards by minimizing prediction errors. Evidence have shown that the brain achieves this by combining model-based and model-free learning. However, the prediction error minimization is challenged by a bias-variance tradeoff, which imposes constraints on each strategy’s performance. We provide new theoretical insight into how this tradeoff can be resolved through the adaptive control of model-based and model-free learning. The theory predicts the baseline correction for prediction error reduces the lower bound of the bias–variance error by factoring out irreducible noise. Using a Markov decision task with context changes, we showed behavioral evidence of adaptive control. Model-based behavioral analyses show that the prediction error baseline signals context changes to improve adaptability. Critically, the neural results support this view, demonstrating multiplexed representations of prediction error baseline within the ventrolateral and ventromedial prefrontal cortex, key brain regions known to guide model-based and model-free learning.One sentence summaryA theoretical, behavioral, computational, and neural account of how the brain resolves the bias-variance tradeoff during reinforcement learning is described.

scholarly journals The relationship between spatial configuration and functional connectivity of brain regions

2017 ◽  
Author(s):  
Janine D. Bijsterbosch ◽  
Mark W. Woolrich ◽  
Matthew F. Glasser ◽  
Emma C. Robinson ◽  
Christian F. Beckmann ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spatial Configuration ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Spatial Arrangement ◽  
Brain Regions ◽  
Imaging Data ◽  
Functional Regions ◽  
Cortical Regions ◽  
The Relationship

AbstractBrain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behavior. For example, studies have used "functional connectivity fingerprints" to characterise individuals' brain activity. Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modelling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behaviour and lifestyle. We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits.

scholarly journals The relationship between spatial configuration and functional connectivity of brain regions

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Janine Diane Bijsterbosch ◽  
Mark W Woolrich ◽  
Matthew F Glasser ◽  
Emma C Robinson ◽  
Christian F Beckmann ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spatial Configuration ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Spatial Arrangement ◽  
Brain Regions ◽  
Imaging Data ◽  
Functional Regions ◽  
Cortical Regions ◽  
The Relationship

Brain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behaviour. For example, studies have used ‘functional connectivity fingerprints’ to characterise individuals' brain activity. Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modelling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behaviour and lifestyle. We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits.

scholarly journals The relationship between spatial configuration and functional connectivity of brain regions revisited

2019 ◽  
Author(s):  
Janine D. Bijsterbosch ◽  
Christian F. Beckmann ◽  
Mark W. Woolrich ◽  
Stephen M. Smith ◽  
Samuel J. Harrison
Keyword(s):  
Functional Connectivity ◽  
Network Structure ◽  
Resting State ◽  
Spatial Configuration ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Spatial Effects ◽  
Functional Brain ◽  
Dual Regression ◽  
The Relationship

AbstractIn our previous paper (Bijsterbosch et al., 2018), we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.Impact statementWe show that functional connectivity network matrices as estimated from resting state functional MRI are biased by spatially overlapping network structure.

scholarly journals The relationship between dynamic functional network connectivity and spatial orientation in healthy young adults

2021 ◽  
Author(s):  
Mohammad S. E. Sendi ◽  
Charles A. Ellis ◽  
Robyn L. Miller ◽  
David H. Salat ◽  
Vince D. Calhoun
Keyword(s):  
Young Adults ◽  
Spatial Orientation ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Functional Network ◽  
Sensorimotor Network ◽  
Functional Network Connectivity ◽  
The Relationship ◽  
Dynamic Functional Network Connectivity

ABSTRACTSpatial orientation is essential to interacting with a physical environment, and better understanding it could contribute to a better understanding of a variety of diseases and disorders that are characterized by deficits in spatial orientation. Many previous studies have focused on the relationship between spatial orientation and individual brain regions, though in recent years studies have begun to examine spatial orientation from a network perspective. This study analyzes dynamic functional network connectivity (dFNC) values extracted from over 800 resting-state fMRI recordings of healthy young adults (age 22-37 years) and applies unsupervised machine learning methods to identify neural brain states that occur across all subjects. We estimated the occupancy rate (OCR) for each subject, which was proportional to the amount of time that they spent in each state, and investigated the link between the OCR and spatial orientation and the state-specific FNC values and spatial orientation controlling for age and sex. Our findings showed that the amount of time subjects spent in a state characterized by increased connectivity within and between visual, auditory, and sensorimotor networks and within the default mode network while at rest corresponded to their performance on tests of spatial orientation. We also found that increased sensorimotor network connectivity in two of the identified states negatively correlated with decreased spatial orientation, further highlighting the relationship between the sensorimotor network and spatial orientation. This study provides insight into how the temporal properties of the functional brain connectivity within and between key brain networks may influence spatial orientation.

scholarly journals The relationship between spatial configuration and functional connectivity of brain regions revisited

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Janine Diane Bijsterbosch ◽  
Christian F Beckmann ◽  
Mark W Woolrich ◽  
Stephen M Smith ◽  
Samuel J Harrison
Keyword(s):  
Functional Connectivity ◽  
Spatial Configuration ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Spatial Effects ◽  
Functional Brain ◽  
Spatial Overlap ◽  
Exact Location ◽  
Dual Regression ◽  
The Relationship

Previously we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity (Bijsterbosch et al., 2018). Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.

scholarly journals Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal

2021 ◽  
Vol 12 (2) ◽  
pp. 269-280
Author(s):  
Neda Sanjari ◽  
◽  
Ahmad Shalbaf ◽  
Reza Shalbaf ◽  
Jamie Sleigh ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Effective Connectivity ◽  
Doa Estimation ◽  
Brain Regions ◽  
Transfer Entropy ◽  
Effect Site ◽  
Anesthetic Drugs ◽  
Key Factor ◽  
The Relationship ◽  
Better Than

Introduction: Ensuring an adequate Depth of Anesthesia (DOA) during surgery is essential for anesthesiologists. Since the effect of anesthetic drugs is on the central nervous system, brain signals such as Electroencephalogram (EEG) can be used for DOA estimation. Anesthesia can interfere among brain regions, so the relationship among different areas can be a key factor in the anesthetic process. Methods: In this paper, by combining the Wiener causality concept and the conditional mutual information, a nonlinear effective connectivity measure called Transfer Entropy (TE) is presented to describe the relationship between EEG signals at frontal and temporal regions from eight volunteers in three anesthetic states (awake, unconscious and recovery). This index is also compared with Granger causality and partial directional coherence methods as common effective connectivity indexes. Results: Based on a statistical analysis of the probability predictive value and Kruskal-Wallis statistical method, TE can effectively fallow the effect-site concentration of propofol and distinguish the anesthetic states well, and perform better than the other effective connectivity indexes. This index is also better than Bispectral Index (BIS) as commercial DOA monitor because of the faster response and higher correlation with the drug concentration effect-site, less irregularity in the unconscious state and better ability to distinguish three states of anesthestesia. Conclusion: TE index is a confident indicator for designing a new monitoring system of the two EEG channels for DOA estimation.

scholarly journals Individual differences in dopamine function underlying the balance between model-based and model-free control

2019 ◽  
Author(s):  
Ying Lee ◽  
Lorenz Deserno ◽  
Nils B. Kroemer ◽  
Shakoor Pooseh ◽  
Liane Oehme ◽  
...  
Keyword(s):  
Individual Differences ◽  
Control Individual ◽  
General Notion ◽  
Sequential Decision ◽  
Model Based ◽  
Model Free ◽  
Positron Emission ◽  
Model Free Control ◽  
Induced Changes ◽  
The Relationship

AbstractReinforcement learning involves a balance between model-free (MF) and model-based (MB) systems. Recent studies suggest that individuals with either pharmacologically enhanced levels of dopamine (DA) or higher baseline levels of DA exhibit more MB control. However, it remains unknown whether such pharmacological effects depend on baseline DA.Here, we investigated whether effects of L-DOPA on the balance of MB/MF control depend on ventral striatal baseline DA. Sixty participants had two functional magnetic resonance imaging (fMRI) scans while performing a two-stage sequential decision-making task under 150 mg L-DOPA or placebo (counterbalanced), followed by a 4-hour 18F-DOPA positron emission tomography (PET) scan (on a separate occasion).We found an interaction between baseline DA levels and L-DOPA induced changes in MB control. Individuals with higher baseline DA levels showed a greater L-DOPA induced enhancement in MB control. Surprisingly, we found a corresponding drug-by-baseline DA interaction on MF, but not MB learning signals in the ventromedial prefrontal cortex. We did not find a significant interaction between baseline DA levels and L-DOPA effects on MF control or MB/MF balance.In sum, our findings point to a baseline dependency of L-DOPA effects on differential aspects of MB and MF control. Individual differences in DA washout may be an important moderator of L-DOPA effects. Overall, our findings complement the general notion where higher DA levels is related to a greater reliance on MB control. Although the relationship between phasic DA firing and MF learning is conventionally assumed in the animal literature, the relationship between DA and MF control is not as straightforward and requires further clarification.

scholarly journals Modulation of narrowband and broadband gamma connectivity in retinal degeneration mice according to electrical stimulation pulse width

2021 ◽  
Vol 27 (4) ◽  
pp. 148-155
Author(s):  
Stephen Kugbere Agadagba ◽  
Leanne Lai Hang Chan
Keyword(s):  
Electrical Stimulation ◽  
Retinal Degeneration ◽  
Pulse Width ◽  
Brain Connectivity ◽  
Gamma Oscillations ◽  
Brain Regions ◽  
Systematic Investigation ◽  
Stimulation Pulse ◽  
Connectivity Indices ◽  
The Relationship

Brain connectivity involves the structural, functional and effective communication between neurons across brain regions and is expressed in neuronal oscillations. Previous research has reported the evidence of two types of gamma oscillations namely the broadband gamma (30 Hz - 90 Hz) and narrowband gamma (55 Hz - 70 Hz) oscillations which have been implicated in excitatory and inhibitory network transmission. There is presently no systematic investigation of the relationship between electrical stimulation pulse width and narrow or broadband gamma oscillations in visual-deficient mice. In the current study, we set out to bridge this gap in knowledge by exploring the modulation of brain connectivity indices in broadband gamma and narrowband gamma oscillations in response to varying electrical stimulation pulse width in retinal degeneration (rd) mice. The results revealed that a low pulse width (0.5 ms/phase) strongly enhances coherence and directional connectivity of broadband and narrowband gamma oscillations in contra visual cortex and contra prefrontal cortex of rd mice. This study serves a crucial role in the design and utilisation of visual prostheses by contributing to the understanding of information transmission between different brain regions under retinal electrical stimulation in visual-deficit population.

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