Assessment of temporal complexity in functional MRI between rest and task conditions

Dynamic models of cortical activity, as measured by functional magnetic resonance imaging (fMRI), have recently brought out important insights into the organization of brain function. In terms of temporal complexity, these hemodynamic signals have been shown to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the properties and spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures (i.e., Hurst exponent versus multiscale entropy) and reported high similarity between them. Second, we investigated the influence of experimental paradigms and found high task-specific complexity. We considered four mental tasks in the HCP database for the analysis: Emotion, Working memory, Social, and Language. Third, we tailored a recently-proposed statistical framework that incorporates the structural connectome, to assess the spatial distribution of complexity measures. These results highlight brain regions including parts of the default mode network and cingulate cortex with significantly stronger complex behaviour than the rest of the brain, irrespective of task. In sum, temporal complexity measures of fMRI are reliable markers of the cognitive status.

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Multimodal neural correlates of cognitive control in the Human Connectome Project

10.1101/124123 ◽

2017 ◽

Author(s):

Dov B. Lerman-Sinkoff ◽

Jing Sui ◽

Srinivas Rachakonda ◽

Sridhar Kandala ◽

Vince D. Calhoun ◽

...

Keyword(s):

Cognitive Control ◽

Structural Mri ◽

Resting State Fmri ◽

Neural Correlates ◽

Brain Regions ◽

Control Performance ◽

Human Connectome Project ◽

Brain Structure And Function ◽

And Function ◽

And Task

AbstractCognitive control is a construct that refers to the set of functions that enable decisionmaking and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA+jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions hypothesized to be modulated by cognitive control signaling, such as visual cortex. Taken together, these results illustrate the potential utility of multi-modal analyses in identifying the neural correlates of cognitive control across different indicators of brain structure and function.

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Heritability and interindividual variability of regional structure-function coupling

Nature Communications ◽

10.1038/s41467-021-25184-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Zijin Gu ◽

Keith Wakefield Jamison ◽

Mert Rory Sabuncu ◽

Amy Kuceyeski

Keyword(s):

Functional Connectivity ◽

Structure Function ◽

Regional Differences ◽

Regional Scale ◽

Brain Regions ◽

Diffusion Weighted Mri ◽

Regional Structure ◽

Human Connectome Project ◽

Structural Connections ◽

The Relationship

AbstractWhite matter structural connections are likely to support flow of functional activation or functional connectivity. While the relationship between structural and functional connectivity profiles, here called SC-FC coupling, has been studied on a whole-brain, global level, few studies have investigated this relationship at a regional scale. Here we quantify regional SC-FC coupling in healthy young adults using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project and study how SC-FC coupling may be heritable and varies between individuals. We show that regional SC-FC coupling strength varies widely across brain regions, but was strongest in highly structurally connected visual and subcortical areas. We also show interindividual regional differences based on age, sex and composite cognitive scores, and that SC-FC coupling was highly heritable within certain networks. These results suggest regional structure-function coupling is an idiosyncratic feature of brain organisation that may be influenced by genetic factors.

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Slow Cortical Waves via Cyclicity

10.1101/2021.05.16.444387 ◽

2021 ◽

Author(s):

Ivan Abraham ◽

Bahar Shahsavarani ◽

Ben Zimmerman ◽

Fatima Husain ◽

yuliy baryshnikov

Keyword(s):

Time Series ◽

Dynamic Structure ◽

Resting State Fmri ◽

Brain Regions ◽

Fine Grained ◽

Human Connectome Project ◽

Fmri Time Series ◽

The Brain ◽

Cortical Waves ◽

Temporal Ordering

Fine-grained information about dynamic structure of cortical networks is crucial in unpacking brain function. Here,we introduced a novel analytical method to characterize the dynamic interaction between distant brain regions,based on cyclicity analysis, and applied it to data from the Human Connectome Project. Resting-state fMRI time series are aperiodic and, hence, lack a base frequency. Cyclicity analysis, which is time-reparametrization invariant, is effective in recovering dynamic temporal ordering of such time series along a circular trajectory without assuming any time scale. Our analysis detected the propagation of slow cortical waves across thebrain with consistent shifts in lead-lag relationships between specific brain regions. We also observed short bursts of strong temporal ordering that dominated overall lead-lag relationships between pairs of regions in the brain, which were modulated by tasks. Our results suggest the possible role played by slow waves of ordered information between brain regions that underlie emergent cognitive function.

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Spatial Patterns for Discriminative Estimation

10.1101/746891 ◽

2019 ◽

Author(s):

Alberto Llera ◽

Roselyne Chauvin ◽

Peter Mulders ◽

Jilly Naaijen ◽

Maarten Mennes ◽

...

Keyword(s):

Spatial Patterns ◽

Brain Connectivity ◽

Covariance Structure ◽

Brain Regions ◽

Linear Filters ◽

Cognitive Changes ◽

Experimental Conditions ◽

Cognitive States ◽

Human Connectome Project ◽

Perfect Discrimination

AbstractFunctional connectivity between brain regions is modulated by cognitive states or experimental conditions. A multivariate methodology that can capture fMRI connectivity maps in light of different experimental conditions would be of primary importance to learn about the specific roles of the different brain areas involved in the observed connectivity variations. Here we detail, adapt, optimize and evaluate a supervised dimensionality reduction model to fMRI timeseries. We demonstrate the strength of such an approach for fMRI data using data from the Human Connectome Project to show that the model provides close to perfect discrimination between different fMRI tasks at low dimensionality. The straightforward interpretability and relevance of the model results is demonstrated by the obtained linear filters relating to anatomical areas well known to be involved on each considered task, and its robustness by testing discriminatory generalization and spatial reproducibility with respect to the number of subjects and fMRI time-points acquired. We additionally suggest how such approach can provide a complementary view to traditional task fMRI analyses by looking at changes in the covariance structure as a substitute to changes in the mean signal. We conclude that the presented methodology provides a robust tool to investigate brain connectivity alterations across induced cognitive changes and has the potential to be used in pathological or pharmacological cohort studies. A publicly available toolbox is provided to facilitate the end use and further development of this methodology to extract Spatial Patterns for Discriminative Estimation (SP♠DE).

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Effective Degrees of Freedom of the Pearson’s Correlation Coefficient under Autocorrelation

10.1101/453795 ◽

2018 ◽

Cited By ~ 2

Author(s):

Soroosh Afyouni ◽

Stephen M. Smith ◽

Thomas E. Nichols

Keyword(s):

Time Series ◽

Correlation Coefficient ◽

Degrees Of Freedom ◽

Brain Regions ◽

Resting State Functional Connectivity ◽

Pearson’S Correlation ◽

Individual Level ◽

Human Connectome Project ◽

Pearson's Correlation ◽

The Impact

AbstractThe dependence between pairs of time series is commonly quantified by Pearson’s correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher’s transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors – before or after Fisher’s transformation – becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardized Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical “xDF” method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.

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Entropy and Complexity Analyses in Alzheimer’s Disease: An MEG Study

The Open Biomedical Engineering Journal ◽

10.2174/1874120701004010223 ◽

2010 ◽

Vol 4 (1) ◽

pp. 223-235 ◽

Cited By ~ 39

Author(s):

Carlos Gómez ◽

Roberto Hornero

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Fractal Dimension ◽

Background Activity ◽

Senile Plaques ◽

Approximate Entropy ◽

Brain Regions ◽

Brain Disorder ◽

Complexity Measures ◽

Control Subjects

Alzheimer’s disease (AD) is one of the most frequent disorders among elderly population and it is considered the main cause of dementia in western countries. This irreversible brain disorder is characterized by neural loss and the appearance of neurofibrillary tangles and senile plaques. The aim of the present study was the analysis of the magnetoencephalogram (MEG) background activity from AD patients and elderly control subjects. MEG recordings from 36 AD patients and 26 controls were analyzed by means of six entropy and complexity measures: Shannon spectral entropy (SSE), approximate entropy (ApEn), sample entropy (SampEn), Higuchi’s fractal dimension (HFD), Maragos and Sun’s fractal dimension (MSFD), and Lempel-Ziv complexity (LZC). SSE is an irregularity estimator in terms of the flatness of the spectrum, whereas ApEn and SampEn are embbeding entropies that quantify the signal regularity. The complexity measures HFD and MSFD were applied to MEG signals to estimate their fractal dimension. Finally, LZC measures the number of different substrings and the rate of their recurrence along the original time series. Our results show that MEG recordings are less complex and more regular in AD patients than in control subjects. Significant differences between both groups were found in several brain regions using all these methods, with the exception of MSFD (p-value < 0.05, Welch’s t-test with Bonferroni’s correction). Using receiver operating characteristic curves with a leave-one-out cross-validation procedure, the highest accuracy was achieved with SSE: 77.42%. We conclude that entropy and complexity analyses from MEG background activity could be useful to help in AD diagnosis.

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Automatic Segmentation of the Dorsal Claustrum in Humans Using in vivo High-Resolution MRI

Cerebral Cortex Communications ◽

10.1093/texcom/tgaa062 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Shai Berman ◽

Roey Schurr ◽

Gal Atlan ◽

Ami Citri ◽

Aviv A Mezer

Keyword(s):

High Resolution ◽

Automatic Segmentation ◽

Thin Sheet ◽

Brain Regions ◽

Reproducible Research ◽

Human Connectome Project ◽

High Resolution Mri ◽

Magnetic Resonance Imaging Mri ◽

And Function

Abstract The claustrum is a thin sheet of neurons enclosed by white matter and situated between the insula and the putamen. It is highly interconnected with sensory, frontal, and subcortical regions. The deep location of the claustrum, with its fine structure, has limited the degree to which it could be studied in vivo. Particularly in humans, identifying the claustrum using magnetic resonance imaging (MRI) is extremely challenging, even manually. Therefore, automatic segmentation of the claustrum is an invaluable step toward enabling extensive and reproducible research of the anatomy and function of the human claustrum. In this study, we developed an automatic algorithm for segmenting the human dorsal claustrum in vivo using high-resolution MRI. Using this algorithm, we segmented the dorsal claustrum bilaterally in 1068 subjects of the Human Connectome Project Young Adult dataset, a publicly available high-resolution MRI dataset. We found good agreement between the automatic and manual segmentations performed by 2 observers in 10 subjects. We demonstrate the use of the segmentation in analyzing the covariation of the dorsal claustrum with other brain regions, in terms of macro- and microstructure. We identified several covariance networks associated with the dorsal claustrum. We provide an online repository of 1068 bilateral dorsal claustrum segmentations.

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Sex Classification by Resting State Brain Connectivity

Cerebral Cortex ◽

10.1093/cercor/bhz129 ◽

2019 ◽

Vol 30 (2) ◽

pp. 824-835 ◽

Cited By ~ 12

Author(s):

Susanne Weis ◽

Kaustubh R Patil ◽

Felix Hoffstaedter ◽

Alessandra Nostro ◽

B T Thomas Yeo ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Brain Regions ◽

Brain Organization ◽

Functional Brain ◽

Clear Cut ◽

Human Connectome Project ◽

Machine Learning Approach ◽

Imaging Research ◽

Functional Brain Organization

Abstract A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants’ sex can be classified based on spatially specific resting state (RS) brain connectivity, using 2 samples from the Human Connectome Project (n1 = 434, n2 = 310) and 1 fully independent sample from the 1000BRAINS study (n = 941). The classifier, which was trained on 1 sample and tested on the other 2, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporoparietal regions, insula, and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone.

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