Resting State Dynamic Functional Connectivity in Neurodegenerative Conditions: A Review of Magnetic Resonance Imaging Findings

Filippi, Massimo; Spinelli, Edoardo Gioele; Cividini, Camilla; Agosta, Federica

doi:10.3389/fnins.2019.00657

Cited by 91 publications

(80 citation statements)

References 78 publications

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“…Additionally, the presently used modalities could also be refined and alternative representations could be considered. For instance, different methods for quantifying brain structure (Pipitone et al, 2014) or brain function (Rahim et al, 2019), and adding data on structural asymmetry (Wachinger et al, 2016) or dynamic functional connectivity (Filippi et al, 2019) could provide improved predictive performance. Furthermore, the influence of MR data quality on accuracy should be assessed in future studies.…”

Section: Discussionmentioning

confidence: 99%

Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging

Liem

Dadi

Engemann

et al. 2020

Preprint

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Cognitive decline occurs in healthy and pathological aging, and both may be preceded by subtle changes in the brain — offering a basis for cognitive predictions. Previous work has largely focused on predicting a diagnostic label from structural brain imaging. Our study broadens the scope of applications to cognitive decline in healthy aging by predicting future decline as a continuous trajectory, rather than a diagnostic label. Furthermore, since brain structure as well as function changes in aging, it is reasonable to expect predictive gains when using multiple brain imaging modalities. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Non-brain data (including demographics and clinical and neuropsychological scores) were combined with structural and functional connectivity MRI data from the OASIS-3 project (N = 662; age = 46 – 96y). The combined input data was entered into cross-validated multi-target random forest models to predict future cognitive decline (measured by the Clinical Dementia Rating and the Mini-Mental State Examination), on average 5.8y into the future. The analysis was preregistered and all analysis code is publicly available. We found that combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42) and that cognitive performance, daily functioning, and subcortical volume drove the performance of our model. In contrast, including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective cognitive, pharmacological, and behavioral interventions to be tailored to the individual.

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Section: Discussionmentioning

confidence: 99%

Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging

Liem

Dadi

Engemann

et al. 2020

Preprint

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“…Addressing structural integrity with non-invasive magnetic resonance imaging (MRI) provides quantitative and correlative measures of tissue integrity. It robustly detects neuroinflammation and neurodegeneration as seen in multiple sclerosis (MS; Filippi et al, 2019). However, it is not clear how microstructural integrity drives the entire network behavior and how it is related to histopathology and behavior.…”

Section: Introductionmentioning

confidence: 99%

Myelination- and immune mediated MR-based brain network correlates

Cerina

Gallus

Koirala³

et al. 2020

Preprint

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Background: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS), characterized by inflammatory and neurodegenerative processes. Despite demyelination being a hallmark of the disease, how it relates to neurodegeneration has still not been completely unraveled, and research is still ongoing into how these processes can be tracked non-invasively. Magnetic resonance imaging (MRI) derived brain network characteristics, which closely mirror disease processes and relate to functional impairment, recently became important variables for characterizing immune-mediated neurodegeneration, however their histopathological basis remains unclear. Methods: In order to determine the MRI-derived correlates of myelin dynamics and to test if brain network characteristics derived from diffusion tensor imaging reflect microstructural tissue reorganization, we took advantage of the cuprizone model of general demyelination in mice and performed longitudinal histological and imaging analyses with behavioral tests. By introducing cuprizone into the diet, we induced targeted and consistent demyelination of oligodendrocytes, over a period of 5 weeks. Subsequent myelin synthesis was enabled by reintroduction of normal food. Results: Using specific immune-histological markers, we demonstrated that two weeks of cuprizone diet induced a 52% reduction of myelin content in the corpus callosum (CC) and a 35% reduction in the neocortex. An extended cuprizone diet increased myelin loss in the CC, while remyelination commenced in the neocortex. These histologically determined dynamics were reflected by MRI measurements from diffusion tensor imaging. Demyelination was associated with decreased fractional anisotropy (FA) values and increased modularity and clustering at the network level. MRI-derived modularization of the brain network and FA reduction in key anatomical regions, including the hippocampus, thalamus and analyzed cortical areas, were closely related to impaired memory function and anxiety-like behavior. Conclusion: Network specific remyelination, shown by histology and MRI metrics, determined amelioration of functional performance and neuropsychiatric symptoms. Taken together, we illustrate the histological basis for the MRI-driven network responses to demyelination, where increased modularity leads to evolving damage and abnormal behavior in MS. Quantitative information about in vivo myelination processes is mirrored by diffusion based imaging of microstructural integrity and network characteristics.

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“…In the past, many studies made the simplifying assumption that the correlation or functional connection between different regions of the brain is static [3]. However, dynamic functional connectivity (dFC) more accurately reflects non-stationary brain activity and is, therefore, receiving increased attention [4,5]. A common approach to find the dFC is to use a sliding window, the length of which determines the final number of time points, of the BOLD time series data to determine repeated states by using a clustering algorithm [6].…”

Section: Introductionmentioning

confidence: 99%

A Study of Brain Neuronal and Functional Complexities Estimated Using Multiscale Entropy in Healthy Young Adults

Menon

Krishnamurthy

2019

Entropy

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Brain complexity estimated using sample entropy and multiscale entropy (MSE) has recently gained much attention to compare brain function between diseased or neurologically impaired groups and healthy control groups. Using resting-state functional magnetic resonance imaging (rfMRI) blood oxygen-level dependent (BOLD) signals in a large cohort (n = 967) of healthy young adults, the present study maps neuronal and functional complexities estimated by using MSE of BOLD signals and BOLD phase coherence connectivity, respectively, at various levels of the brain’s organization. The functional complexity explores patterns in a higher dimension than neuronal complexity and may better discern changes in brain functioning. The leave-one-subject-out cross-validation method is used to predict fluid intelligence using neuronal and functional complexity MSE values as features. While a wide range of scales was selected with neuronal complexity, only the first three scales were selected with functional complexity. Fewer scales are advantageous as they preclude the need for long BOLD signals to calculate good estimates of MSE. The presented results corroborate with previous findings and provide a baseline for other studies exploring the use of MSE to examine changes in brain function related to aging, diseases, and clinical disorders.

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Resting State Dynamic Functional Connectivity in Neurodegenerative Conditions: A Review of Magnetic Resonance Imaging Findings

Cited by 91 publications

References 78 publications

Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging

Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging

Myelination- and immune mediated MR-based brain network correlates

A Study of Brain Neuronal and Functional Complexities Estimated Using Multiscale Entropy in Healthy Young Adults

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