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:10.1101/2021.07.28.454017

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…While this parcellation was shown to maximize the cross modularity index between the functional and structural data, future work may also consider other brain parcellations to elucidate the robustness of our results by studying if age related changes in SC can also explain the differences high-order functional interactions in whole brain models. Analogously, some variations in the MRI preprocessing pipeline could also affects our results [ 67 ], as previous works have shown that affect pairwise FC studies [ 68 ].…”

Section: Discussionmentioning

confidence: 98%

High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model

et al. 2022

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The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brain’s connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the differences in high-order functional interactions between age groups can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modeling more complex forms of pathological ageing or cognitive deficits.

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Section: Discussionmentioning

confidence: 98%

High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model

et al. 2022

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“…As a data reduction step for the fMRI HMM analysis (Ahrends et al, 2022;Vidaurre, Abeysuriya, et al, 2018;Vidaurre et al, 2017), HMM training. Only ICs that had time courses in the lower frequencies and did not spatially overlap with known artefacts (motion, vascular, physiological, susceptibility) or non-grey matter regions (white matter, CSF) were used in the subsequent analysis, resulting in 13 components being retained (see Figure 1).…”

Section: Fmri Spatial Icamentioning

confidence: 99%

Brain state dynamics differ between eyes open and eyes closed rest

Ingram,

Mayhew,

Bagshaw

2024

Human Brain Mapping

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The human brain exhibits spatio‐temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography‐functional magnetic resonance imaging (EEG‐fMRI) eyes open (EO) and eyes closed (EC) resting‐state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG‐defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window‐based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting‐state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha‐BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in‐depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

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High-order functional interactions in ageing explained via alterations in the connectome in a whole-brain model

Gatica

Rosas

Mediano

et al. 2021

Preprint

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The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brain's connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the age differences in high-order functional interactions can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modelling more complex forms of pathological ageing or cognitive deficits.

show abstract

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

Cited by 3 publications

References 49 publications

High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model

High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model

Brain state dynamics differ between eyes open and eyes closed rest

High-order functional interactions in ageing explained via alterations in the connectome in a whole-brain model

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