Keywords: Functional Connectivity, Multimodal

Motivation: Previous findings from fMRI and from structural microvascular observations link vascular activity or structural organisation with neuronal activity.

Goal(s): To verify whether macrovascular properties are related to functional (FC) and structural connectivity (SC), as well as function-structure coupling (SDI).

Approach: FC and SC node strength, SDI, and vascular density and distance were compared, using a venous atlas, the Human Connectome Project data, and one subject of the Natural Scene Dataset for which we obtained an arteries-dominated vascular map.

Results: Correlations between veins-dominated vascular properties, FC, and SDI, suggest a link between density and proximity of venous structures and connectivity strength of an area.

Impact: Cerebral angioarchitecture is often overlooked in integrated multimodal MRI modelling, but its properties can help understanding the nature of BOLD-fMRI-based “vascular” or “physiological” networks.

Keywords: Task/Intervention Based fMRI, fMRI, modeling

Motivation: Understanding the impact of cortical vascular architecture on the spatiotemporal features of hemodynamic responses. 

Goal(s): Automatic extraction of realistic cortical vasculature models from microscopy data, and simulation of hemodynamic changes across the extracted vascular network.

Approach: We present a pipeline that utilizes graph theory for extracting the vasculature from microscopy data, representing it as a vascular graph. Simulations were performed using the extracted vascular graphs by converting the connectivity matrix into a dynamic system modeled by RC circuits.

Results: We extracted two realistic vascular graphs and used them to mimic hemodynamic changes resulting from simulated arterial dilation.

Impact: Vascular graphs extracted by the developed pipeline could serve to simulate hemodynamic changes across the cortical vasculature. This provides a tool to enhance fMRI signal interpretation and provide valuable insights into the role of vascular dysfunction in cerebrovascular diseases.

Keywords: fMRI Analysis, fMRI (resting state)

Motivation: There has been evidence that macrovasculature may bias the analysis of resting-state functional connectivity (FC) and that such a bias may be difficult to remove.

Goal(s): This study intends to demonstrate such an effect can be predicted with a biophysical model.

Approach: We used a biophysical model with experimentally acquired vascular maps to simulate macrovascular effects on functional connectivity estimates from BOLD fMRI.

Results: Our results show that it is feasible to model the macrovascular BOLD contribution to FC through simulation. While both arteries and veins contribute, our model is more accurately captures effects of veins.

Impact: This study aims to demonstrate the feasibility of simulating the BOLD signal in a voxel containing macrovasculature using a biophysical model, which will enable correction of the macrovascular bias in resting-state and other types of fMRI.

Keywords: fMRI Analysis, fMRI (task based)

Motivation: Kernel Canonical Correlation Analysis (KCCA) is an efficient way to detect brain activation globally with less computational complexity. However, the current KCCA is limited to the linear kernel, and the performance for other more general types of kernels is not completely understood due to a lack of inverse mapping.

Goal(s): This study aims to expand the current KCCA method to arbitrary nonlinear kernels. 

Approach: Compute correlation vector r measures the importance of each voxel’s contributing to the signal in kernel space. 

Results: Our results suggest that nonlinear kernels, such as the Gaussian kernel, can increase the prediction robustness under voxel shuffling.

Impact: The method proposed in this abstract allows us to get the activation pattern from fMRI for any type of linear or nonlinear kernel mapping. 

Keywords: fMRI Analysis, fMRI

Motivation: Positive correlations between baseline cerebral blood flow (CBF) and BOLD-cerebrovascular reactivity (CVR), both between- and within-subjects were reported. However, there are no studies showing the correlation between CVR and other physiological parameters such as oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO₂).

Goal(s): In this study, these baseline parameters were measured using MRI, and the correlations between CVR and baseline CBF, OEF and CMRO₂ were investigated.

Approach: Arterial spin labeling and quantitative BOLD were performed to quantify CBF and OEF.

Results: Positive correlations between CBF and CVR, CMRO₂ and CVR were found. Negative correlation between OEF and CVR was found.

Impact: This study presents the correlation of CVR in healthy brain with baseline CBF, OEF, and CMRO₂. Positive correlations between CBF and CVR, CMRO₂ and CVR were found. Negative correlation between OEF and CVR was also found.

Keywords: fMRI Analysis, fMRI (task based), neurovascular coupling, negative BOLD response, neural suppression

Motivation: Many fMRI studies have observed a negative BOLD response (NBR) that is often associated with neural suppression. However, the temporal dynamics of the NBR remains unclear.

Goal(s): Here, we investigate temporal linearity of the NBR and characterize spatial variations of the BOLD negative hemodynamic response function (nHRF) along cortical surface. 

Approach: We examine temporal linearity of the NBR with a unilateral visual stimulus with various stimulus durations, then use the same stimulus with 2-s duration for spatial variation of nHRF.

Results: We found unique nHRF dynamics varying gradually along cortical surface, and non-linear behavior of the NBR with different stimulus duration. 

Impact: The unique dynamics of the NBR can confound linear analyses of event-related fMRI experiments. In addition, our results with shift-invariant experiments with different stimulus durations suggest that temporal linearity does not hold for the NBR.

Keywords: fMRI Analysis, fMRI, cerebral metabolism, calibrated fMRI, aging, sex

Motivation: Resting CMRO₂ is a marker of brain health that may inform typical and pathological brain aging. However, there is conflicting literature describing how CMRO₂ changes across the lifespan, which may be influenced by extraneous variables such as sex and end-tidal partial pressure of carbon dioxide (P_ETCO₂).

Goal(s): This study aimed to evaluate CMRO₂ changes across the lifespan, after considering these possible confounding variables.

Approach: Dual-calibrated BOLD fMRI quantified grey matter absolute CMRO₂ in 83 participants.

Results: Sex and age significantly predicted CMRO₂. Females had higher CMRO₂ than males, and CMRO₂ decreased with increasing age for females. 

Impact: Grey matter CMRO₂ decreases in normal healthy aging. Considering both sexes, the CMRO₂ decline rate was -0.88 per year, after accounting for P_ETCO₂ and sex. When males and females were analysed separately, females only showed a significant decline with age.

Keywords: fMRI Analysis, Data Analysis, Cortical eigenmode, GLM analysis

Motivation: In traditional fMRI experiments, the BOLD signal is influenced by the spatial distribution of veins, which is closely linked to the morphological characteristics of the cortex.

Goal(s): Cortical eigenmode decomposition represents a frequency-domain approach for analyzing brain structures, yielding a set of spatial bases for dissecting large-scale brain activities.

Approach: In this study, we introduced an eigenmode-based General Linear Model method to investigate the influence of spatial patterns on the activation of specific fMRI tasks.

Results: The results reveal a strong correlation in spatial distribution between the reconstructed z-map and the conventional activation map.

Impact: Quantitative cortical eigenmode analysis offers a frequency-domain perspective for integrating structural and functional neuroimages. Eigenmodes encode connectivity patterns within the cortical structure, offering a promising avenue for unveiling implicit connections across cortical surface through their application to brain activity analysis.

Keywords: fMRI Analysis, fMRI, multimodal imaging, physiology, caffeine

Motivation: Understanding caffeine's influence on the brain, CSF flow, and autonomic signals is essential. Recent research highlights the dynamic nature of these processes, creating a compelling need to investigate how caffeine impacts them.

Goal(s): This study aims to elucidate how caffeine, as a stimulant affecting sympathetic activity, affects brain activity, CSF flow, and autonomic signals.

Approach: Using an EEG-fMRI setup, we monitor individuals following caffeine intake to assess its effects on neural, CSF, and autonomic dynamics.

Results: The study's results transform our understanding of caffeine's impact, guiding scientists, clinicians, and patients to make right choices about caffeine consumption's effects on autonomic function, and health.

Impact: The results of this study can improve our understanding of caffeine's influence on neural and physiological processes, towards novel experiment design and analysis strategies.

Keywords: fMRI Analysis, fMRI, physiology, regression, task

Motivation: Understanding autonomic regulation during a cognitive task is vital for unraveling its neural mechanisms. This study delves into autonomic regression in fMRI, examining the roles of respiration, pupil size, and PPG amplitude during alertness.

Goal(s): Our goal is to enhance our understanding of the autonomic nervous system's response to alert conditions by analyzing fMRI data and modeling with various physiological signals.

Approach: Through the examination of respiration, pupil size, and PPG amplitude and fMRI, we aim to reveal patterns of autonomic regression.

Results: The results uncover significant associations between autonomic parameters and fMRI, shedding light on the neuro-physiological correlates of cognitive stress.

Impact: Our findings advance strategies for managing systemic variations during alert conditions, providing valuable insights for fMRI researchers. 

Keywords: fMRI Analysis, Brain, Cardiovascular, fMRI (resting state), Hypertension, Neurofluids

Motivation: Vascular factors, like blood pressure, play a role in brain disorders, but their pathological mechanisms are still unknown.

Goal(s): The study's objective was to examine the impact of blood pressure (systole/diastole) on the physiological pulsations assessed through ultrafast resting-state functional magnetic resonance imaging.

Approach: We quantified brain pulsation amplitudes with the ALFF method and employed multiple linear regression to model the influence of blood pressure metrics on very low-frequency, respiratory, and cardiovascular pulsations.

Results: Systolic blood pressure positively correlated with vasomotor pulsation amplitudes, while both systolic and diastolic pressures were positively correlated with cardiovascular pulsation amplitudes.

Impact: Precise knowledge regarding the influence of blood pressure on brain pulsations plays a pivotal role in future developments of treatments, particularly in conditions like Alzheimer's disease, where blood pressure is a significant risk factor affecting cerebral function.

Keywords: fMRI Analysis, fMRI (resting state), complexity

Motivation: BOLD-fMRI intrinsic functional connectivity has limited capability to assess the temporal dynamics of complex brain networks. The insufficient signal-to-noise ratio of 3T MRI might prevent the detection of subtle alterations.

Goal(s): Detecting resting-state complexity alterations in healthy subsamples using 7T MRI.

Approach: Multiscale entropy was computed for ten scales from 0.1 to 1 Hz. A whole-brain ANCOVA was conducted to assess entropy differences of the scales between 30 healthy adults with spider phobia and 45 without.

Results: Spider phobia showed decreased entropy in several fear-related brain regions in all scales except 1 Hz.

Impact: 7T fMRI detected reduced high-frequency resting-state multiscale entropy related to spider phobia, indicating worse local processing of fear and memory-related brain regions.

Keywords: fMRI Analysis, fMRI

Motivation: The BOLD response to acetazolamide offers robust estimation of cerebrovascular reactivity (CVR) with dynamic interrogation for maximal (CVR_max) augmentation. The lack of physiologic units limits use to relative CVR reductions versus normal hemispheres, confounding use for bilateral steno-occlusive disease (SOD).

Goal(s): Develop normal voxel search procedure for BOLD-CVR informed by DSC perfusion in SOD.

Approach: DSC in unilateral SOD patients undergoing BOLD-CVR were used to train random forest classifiers and identify voxels with CVR_max within 10% of ground-truth normal hemispheric CVR_max.

Results: Median percent-differences <8% from ground-truth were achieved, indicating robust performance for extension of BOLD-CVR to bilateral SOD.

Impact: We demonstrate the feasibility of a random forest classifier as a normal voxel search algorithm, in order to identify candidate voxels serving as an auto-normalization for CVR studies in settings of bilateral or indeterminate patters of steno-occlusive disease.

Keywords: fMRI Analysis, Visualization

Motivation: Graph-theoretical methods to analyze fMRI data can be powerful and flexible. Common to such methods is the construction of a graph adjacency matrix, which cannot be intuitively understood when visualized in itself. 

Goal(s): We aim to develop a framework to visualize adjacency matrices intuitively while retaining intuitive spatial localizability in relation to the brain. 

Approach: By stratify voxel-wise functional connectivity adjacency matrices through agglomerative clustering to form edge bundles, we 3D-render them with their end-point locations in a brain contour to ease localization.

Results: 3D rendered, color-coded, edge bundles and their end-points can be distinctly identified in relationship to the brain. 

Impact: Our visualization framework allows both scientist and clinicians to employ graph theoretical analysis methods on fMRI data in a intuitive manner while retaining spatial localizability in the brain.

Keywords: fMRI Analysis, fMRI

Motivation: Cerebrovascular reactivity (CVR) mapping can provide valuable information for the evaluation of lesion and neurovascular uncoupling in brain tumors.

Goal(s): To evaluate the robustness of data-driven CVR analysis in patients with brain tumors.

Approach: CVR MRI of brain tumor patients (n=18) was performed with a breath-holding task paradigm. CVR map was obtained using GLM with four regressors for comparison: (1) gray matter (GM), (2) GM with lesion removed, (3) whole brain (WB), and (4) WB with lesion removed. 

Results: Proper temporal and spatial filtering reduced the differences between the four regressors, resulting in similar CVR maps with highly correlated BOLD contrast.

Impact: The study demonstrated that with proper temporal processing, robust data-driven CVR analysis can be obtained in patients with brain tumors. 

Keywords: fMRI Analysis, Data Analysis, Multiscale Sample Entropy, Complexity

Motivation: Multiscale sample entropy (MSE) is a common complexity metric used in functional Magnetic Resonance Imaging (fMRI), yet it has not been applied to analyze the evolution across age groups.

Goal(s): The goal of this study is to analyze the evolution of fMRI complexity over the lifespan using MSE.

Approach: Resting-state fMRI data of 526 subjects age 6 – 85 years were analyzed with a linear mixed-effect model (LME) of MSE in different brain regions.

Results: Overall, a decrease in mean gray matter complexity was observed after puberty. LME model showed significant decrease in complexity with age in middle frontal and superior frontal gyrus.

Impact: The findings of this study shed light on the evolution of brain complexity with development and aging and may provide benchmark for detecting aberrant complexity in brain disorders.