Abstract:The blood-oxygen-level-dependent (BOLD) signal in functional MRI (fMRI) measures neuronal activation indirectly. Previous studies have found aperiodic, systemic low-frequency oscillations (sLFOs, ~0.1Hz) in BOLD signals from resting-state (RS) fMRI, which reflects the non-neuronal cerebral perfusion information. In this study, we investigated the possibility of extracting vascular information from the sLFOs in RS BOLD fMRI, which could provide complementary information to the neuronal activations. Two features… Show more
“…The model is based on the hypothesis that the cumulative effects of vessel dilations and contractions (pulsation, vasomotion) will exert force on the walls of ventricles (in which there are no blood vessels), especially the lateral ventricles, forcing CSF in and out of the fourth ventricle at the bottom (see Figure 1). In this model, the global mean of fMRI signals (GMS) is used as a surrogate signal to indirectly assess the cumulative effects of vessel dilations and contractions in the brain 20, 25, 26 . Notably, several previous studies had used this method for blood tracking 27–30 .…”
Cerebral spinal fluid (CSF) plays an important role in the clearance of metabolic waste products from the brain, yet the driving forces of CSF flow are not fully understood. It is commonly believed that CSF flow is facilitated by the blood vessel wall movements (i.e., hemodynamic oscillations) in the brain. A coherent pattern of low frequency hemodynamic oscillations and CSF flow was found recently during non-rapid eye movement sleep (NREM) sleep via functional MRI. However, questions remain regarding 1) the explanation of coupling between hemodynamic oscillations and CSF flow using fMRI signals; 2) the existence of the coupling during wakefulness; 3) the direction of CSF flow. In this resting state fMRI study, we proposed a mechanical model to explain the coupling between hemodynamics and CSF flow through the lens of fMRI. We found that the observed delays between these two signals match those predicted by the model. Moreover, by conducting separated fMRI scans of the brain and neck, we confirmed the low frequency CSF flow at the fourth ventricle is bidirectional. Our finding also demonstrates that CSF flow is facilitated by hemodynamic oscillations mainly in the low frequency range, even when the individual is awake.
“…The model is based on the hypothesis that the cumulative effects of vessel dilations and contractions (pulsation, vasomotion) will exert force on the walls of ventricles (in which there are no blood vessels), especially the lateral ventricles, forcing CSF in and out of the fourth ventricle at the bottom (see Figure 1). In this model, the global mean of fMRI signals (GMS) is used as a surrogate signal to indirectly assess the cumulative effects of vessel dilations and contractions in the brain 20, 25, 26 . Notably, several previous studies had used this method for blood tracking 27–30 .…”
Cerebral spinal fluid (CSF) plays an important role in the clearance of metabolic waste products from the brain, yet the driving forces of CSF flow are not fully understood. It is commonly believed that CSF flow is facilitated by the blood vessel wall movements (i.e., hemodynamic oscillations) in the brain. A coherent pattern of low frequency hemodynamic oscillations and CSF flow was found recently during non-rapid eye movement sleep (NREM) sleep via functional MRI. However, questions remain regarding 1) the explanation of coupling between hemodynamic oscillations and CSF flow using fMRI signals; 2) the existence of the coupling during wakefulness; 3) the direction of CSF flow. In this resting state fMRI study, we proposed a mechanical model to explain the coupling between hemodynamics and CSF flow through the lens of fMRI. We found that the observed delays between these two signals match those predicted by the model. Moreover, by conducting separated fMRI scans of the brain and neck, we confirmed the low frequency CSF flow at the fourth ventricle is bidirectional. Our finding also demonstrates that CSF flow is facilitated by hemodynamic oscillations mainly in the low frequency range, even when the individual is awake.
“…3 a) and intra-individual differences can also be examined, such as the influence of caffeine on functional connectivity (Poldrack et al 2015 ) (Fig. 3 b) and BOLD signal variability (Yang et al 2018 ). Fig.…”
We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility–both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.
“…On the other hand, with task-based fMRI, within-scanner or out-of-scanner measures can screen patients to ensure that they are truly performing the task and are suitable for the study. Moreover, there may be many other factors that affect the participant's current state and their BOLD signal, such as medications (Pereira-Sanchez et al 2021), caffeination state (Yang et al 2019;Magalhães et al 2021), and mental exhaustion (Esposito et al 2014). Although many of these variables can be recorded during data collection, estimating their effects on the BOLD signal and functional connectivity will require much larger sample sizes than are typically observed in fMRI studies.…”
Section: Black-box Attacks Reflect Lack Of Control Over Functional Connectivity Measuresmentioning
Functional connectome-based predictive models continue to grow in popularity and predictive performance. As these models become more widely used, researchers have begun to question the idea of bias in the models, which is a crucial component of ethics in artificial intelligence. However, we show that model trustworthiness is a more important but vastly overlooked component of the ethics of functional connectome-based predictive models. In this work, we define “trust” as robustness to adversarial attacks, or data alterations designed to trick a model. We show that typical implementations of connectome-based models are untrustworthy and can easily be manipulated through adversarial attacks. We use classification of self-reported biological sex in three datasets (Adolescent Brain Cognitive Development Study, Human Connectome Project, and Philadelphia Neurodevelopmental Cohort) and for three types of predictive models (support vector machine (SVM), logistic regression, kernel SVM) as a benchmark to show that many forms of adversarial attacks are effective against connectome-based models. The attacks include changing the prediction by altering the data at test time, real-world changes at the time of scanning, and improving performance by injecting a pattern into the data. Despite drastic changes in prediction performance after adversarial attacks, the corrupted connectomes appear nearly identical to the original ones and perform similarly in downstream analyses. These findings demonstrate a need to evaluate the trustworthiness and ethics of connectome-based models before we can apply them broadly, as well as a need to develop methods that are robust to a wide range of adversarial attacks.
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