Data and model considerations for estimating time-varying functional connectivity in fMRI

Ahrends, Christine; Stevner, Angus; Pervaiz, Usama; Ml, Kringelbach; Vuust, Peter; Woolrich, Mark W.; Vidaurre, Diego

doi:10.1016/j.neuroimage.2022.119026

Cited by 18 publications

(12 citation statements)

References 51 publications

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“…AROMA ) resulted in changes of temporal properties of several iCAPs (such as temporal durations) which are closer to zero (i.e. absent) for a large part of the sample even if characterized by a low motion level, as previously stated 40 . Besides, if we look at populations with inherited large motion levels, implications are even worse.…”

Section: Discussionsupporting

confidence: 60%

Head motion correction shapes functional network estimates: evidence from healthy and Parkinson’s disease cohorts

Saviola

Tambalo

Cabalo

et al. 2022

Preprint

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An open discussion in studies of intrinsic brain functional connectivity is the mitigation of head motion-related artifacts, particularly in the presence of peculiar symptomatology such as in Parkinson's disease (PD). Previous studies show that Independent Component Analysis (ICA) denoising improves the reproducibility of functional connectivity findings by detecting sources of non-neural signals. However, there is still no consensus about which pre-processing pipeline should be applied in natural high motion populations such as PD, particularly in relation to novel functional network descriptions derived from dynamic connectivity analyses. In this study, we investigated how different pre-processing pipelines affect intrinsic brain connectivity metrics, both static and dynamic, derived from a group of young healthy controls (HC) and a group of PD participants. A total of 20 HC and 20 PD subjects participated in this 3 T MRI study. Resting-state functional MRI images were used to test the effects of the pre-processing pipeline of static (sFC) and temporal-varying functional connectivity (dFC) estimations. Both MRI datasets were pre-processed using three different workflows differing in the motion correction approach: (i) standard motion realignment (mc); (ii) motion outlier detection and deweighting based on image intensity change estimations (DVARS) and (iii) ICA-based noise removal using reference noise features (AROMA). Furthermore, the PD dataset was also processed with a fourth method by applying an ICA-based denoising (FIX), previously trained on the HC group. sFC analysis was performed using Group ICA, by temporally concatenating different pre-processing types in pairs of different runs. Two types of dFC analyses were considered: innovation-driven co-activation patterns (iCAPs) and co-activation patterns (CAPs). CAPs allow dFC estimations that do not require the deconvolution of the hemodynamic response function and its derivative, thus potentially being less sensitive to head-motion related noise. We found that regardless of substantial head motion differences in the two groups, sFC results were consistent across denoising strategies. Conversely, dFC was extremely sensitive to denoising strategies, particularly for the PD group with the transient-based dFC analyses. Indeed, the use of the peak-based dFC framework enables the detection of time-varying networks but in a way that is highly dependent on the motion correction pipeline. In conclusion, we show that dynamic functional network representations are highly sensitive to both head motion and to fMRI denoising methods. These findings stress the importance of considering and reporting these experimental aspects to help with the reproducibility and interpretation of different studies. Future work is needed to further investigate transient-based dFC strategies that are more robust to head motion.

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Section: Discussionsupporting

confidence: 60%

Head motion correction shapes functional network estimates: evidence from healthy and Parkinson’s disease cohorts

Saviola

Tambalo

Cabalo

et al. 2022

Preprint

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“…Using this approach, a data-driven functional parcellation with 50 parcels was estimated, where all voxels are weighted according to their activity in each parcel, resulting in a weighted, overlapping parcellation. While other parcellations are available for the resting-state fMRI HCP dataset, we chose this parcellation because dynamic changes in FC can be better detected in this parcellation compared to functional or anatomical parcellations or more fine-grained parcellations (Ahrends et al, 2022). Timecourses were extracted using dual regression (Beckmann et al, 2009), where group-level components are regressed onto each subject’s fMRI data to obtain subject-specific versions of the parcels and their timecourses.…”

Section: Methodsmentioning

confidence: 99%

Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel

Ahrends

Vidaurre

2023

Preprint

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How specifically brain activity unfolds across time, namely the nature of brain dynamics, can sometimes be more predictive of behavioural and cognitive subject traits than both brain structure and summary measures of brain activity that average across time. Brain dynamics can be described by models of varying complexity, but what is the best way to use these models of brain dynamics for characterising subject differences and predicting individual traits is unclear. While most studies aiming at predicting subjects’ traits focus on having accurate predictions, for many practical applications, it is critical for the predictions not just to be accurate but also reliable. Kernel methods are a robust and computationally efficient way of expressing differences in brain dynamics between subjects for the sake of predicting individual traits, such as clinical or psychological variables. Using the Hidden Markov model (HMM) as a model of brain dynamics, we here propose the use of the Fisher kernel, a mathematically principled approach to predict phenotypes from subject-specific HMMs. The Fisher kernel is computed in a way that preserves the mathematical structure of the HMM. This results in benefits in terms of both accuracy and reliability when compared with kernels that ignore the structure of the underlying model of brain dynamics.

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“…We chose the 25 IC parcellation as it covers the major functional networks. Additionally, coarser parcellations are more reliable for estimating FC dynamics due to the smaller number of free parameters per state (Ahrends et al, 2022). For each participant, this resulted in 25 timeseries, composed of 4,800 time points across four scanning sessions (with 1,200 time points in each session) of approximately 15 minutes each.…”

Section: Methodsmentioning

confidence: 99%

“…We refer to this as hyperparameter selection variability. By varying the hyperparameters, we can discover distinct patterns of FC, for example across different time scales (Ahrends et al, 2022). In our study, we focused on varying two HMM hyperparameters: the number of states ( K ), and the prior probability of remaining in the same state (δ), which is parametrised by the prior Dirichlet distribution concentration parameter of the corresponding prior distribution 2 (Masaracchia et al, 2023), which effectively influences the time scale of the estimate.…”

Section: Methodsmentioning

confidence: 99%

“…When fixing the hyperparameters, we chose δ = 10, the default setting in the HMM-MAR toolbox, and K = 6, commonly used values in recent literature (Alonso & Vidaurre, 2023; Quinn et al, 2018; Vidaurre et al, 2018). The choice of states represents a compromise between having a higher number of states, which can increase the chances of certain states being present in only a subset of subjects (Ahrends et al, 2023), and a lower number of states, which can increase the chance of assigning entire sessions to a single state (i.e., model stasis) (Ahrends et al, 2022). Nevertheless, the specific choice of hyperparameters is less critical to our study, as our focus when fixing the hyperparameter is to investigate run-to-run variability which could be done with an alternative configuration.…”

Section: Methodsmentioning

confidence: 99%

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Stacking models of brain dynamics improves prediction of subject traits in fMRI

Griffin,

Ahrends,

Alfaro-Almagro

et al. 2023

Preprint

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Beyond structural and time-averaged functional connectivity brain measures, the way brain activity dynamically unfolds can add important information when investigating individual cognitive traits. One approach to leveraging this information is to extract features from models of brain network dynamics to predict individual traits. However, there are two potential sources of variation in the models’ estimation which will in turn affect the predictions: first, in certain cases, the estimation variability due to different initialisations or choice of inference method; and second, the variability induced by the choice of the model hyperparameters that determine the complexity of the model. Rather than merely being statistical noise, this variability may be useful in providing complementary information that can be leveraged to improve prediction accuracy. We propose stacking, a prediction-driven approach for model selection, to leverage this variability. Specifically, we combine predictions from multiple models of brain dynamics to generate predictions that are accurate and robust across multiple cognitive traits. We demonstrate the approach using the Hidden Markov Model, a probabilistic generative model of brain network dynamics. We show that stacking can significantly improve the prediction of subject-specific phenotypes, which is crucial for the clinical translation of findings.

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Data and model considerations for estimating time-varying functional connectivity in fMRI

Cited by 18 publications

References 51 publications

Head motion correction shapes functional network estimates: evidence from healthy and Parkinson’s disease cohorts

Head motion correction shapes functional network estimates: evidence from healthy and Parkinson’s disease cohorts

Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel

Stacking models of brain dynamics improves prediction of subject traits in fMRI

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