Keywords: fMRI Analysis, fMRI (resting state)

Motivation: Resting-state functional connectivity (RSFC) is widely used to predict behavioral traits in individuals.

Goal(s): A pervasive dilemma when collecting functional MRI data is whether to prioritize sample size or scan duration given fixed resources.

Approach: We systematically investigate the trade-off between sample size and scan time in the context of prediction accuracy and reliability of brain-behavior relationships using RSFC.

Results: Increasing sample size (with fixed scan time) or scan time (with fixed sample size) leads to similar accuracy. Reliability of brain-behavior association can only be improved with bigger sample sizes but not scan time. 

Impact: Our findings establish an empirically informed reference for calibrating scan times and sample sizes to maximize prediction of behavioral performance and reliability of brain-behavior associations when using resting-state functional connectivity.

Keywords: fMRI Analysis, fMRI (task based), speech, swallowing, fMRI analysis, dynamic imaging

Motivation: SimulScan enables the joint imaging of dynamic oropharyngeal movements during speech and swallowing along with their central control from functional MRI. 

Goal(s): The large, complex dataset requires memory-efficient analysis to find the uncover the underlying relationships between the dynamic and functional imaging data. 

Approach: Here we develop a memory-efficient implementation of partial least square (PLS) and apply it to a blocked tongue tapping task.

Results: The PLS method separates out correlated motions and brain function for different components of the task. 

Impact: SimulScan with PLS analysis can enable the visualization of central control of complex processes such as speech and swallowing. This approach will enable the in-depth study of healthy and disordered speech and swallowing mechanisms in age and disease. 

Keywords: fMRI Acquisition, fMRI, layer fMRI

Motivation: Although fMRI has achieved sub-millimeter spatial resolution especially with ultra-high field (≥7T) scanners, its spatial specificity has not kept pace.

Goal(s): This study aims to map and validate the influences of natural respiratory variations on fMRI signals and use it to improve laminar fMRI specificity.

Approach: We compare the influences of natural respiratory variations with the patterns induced by deep breath and breath hold tasks and probe their spatial correlation with vascular density.

Results: This respiratory variation revealed information can be used to remove macrovascular-dominated voxels, thereby enhance laminar fMRI specificity.

Impact: We highlight the significance of natural respiratory variations for improving laminar fMRI specificity. By understanding these variations and their link with vascular density, we can better identify and exclude macrovascular-dominated voxels, marking a notable advancement in high-resolution fMRI specificity.

Keywords: Functional Connectivity, High-Field MRI, fMRI, interneuron, neurovascular coupling

Motivation: SST neurons, 30% of cortical interneurons, are crucial in interpreting fMRI data and understanding neurovascular coupling within the cortex.

Goal(s): In this study we want to investigate the the SST-induced hemodynamic response  

Approach: we used several methods such as neural recording, BOLD-fMRI, and optical intrinsic signaling (OIS) with pharmacological applications.

Results: We observed SST neuron activation causing local neural inhibition, resulting in negative BOLD-fMRI at projection sites. Additionally, it triggered initial NO-induced fast vasodilation, followed by astrocyte-mediated slow vasodilation.

Impact: BOLD-fMRI reflects neural activity changes, yet certain interneurons induce hemodynamic responses without altering neural activity. Studying SST-induced responses is vital for interpreting fMRI.

Keywords: fMRI Analysis, Brain, cerebellum, high-field, motion correction

Motivation: The cerebellar cortex is organized in stripe-like clusters, similar to the neocortical layers/columns. The cerebellar anatomical complexity and lack of non-invasive methods makes their detection in humans challenging.

Goal(s): To determine if the human cerebellum shows stripe-like patterns as observed in animals.

Approach: We employed high-resolution, motion-corrected, RF-shimmed, 7T MRI to construct detailed cerebellocortical surfaces. We examined the presence of stripes across fMRI paradigms, their relationship to macrovasculature and variability. We additionally used immunohistochemistry for validation.

Results: We observed consistent stripe-like patterns in the human cerebellum. These patterns were not associated with macrovasculature and conformed with immunohistochemistry, indicating a neuronal origin.

Impact: Cerebellar stripes are a widely-known functional-organization feature but unreported in humans. Here, we combine motion-corrected, 7T-(f)MRI and immunohistochemistry to demonstrate stripe-like patterns in humans. This may provide a new paradigm for cerebellar function, akin to the discoveries in neocortical layers.

Keywords: fMRI Analysis, Velocity & Flow

Motivation: fMRI has been used to measure large scale cerebrospinal fluid (CSF) flow dynamics with high sensitivity and temporal resolution, however the measured signal is not quantitative.

Goal(s): Our goal is to develop a physics-based neural network framework for flow quantification directly from fMRI flow-enhanced signals.

Approach:  We designed a neural network that can use fMRI data as input to predict flow velocity. We then trained the model on a simulated dataset generated using a physics-based model.

Results: Validation on phantom and human data showed accurate predictions of flow velocity when using measured fMRI signals as input into the neural network.

Impact: Here, we significantly increase quantitative information obtainable from fMRI which will enable neuroimaging researchers studying fluid flow dynamics to take advantage of the high sensitivity and temporal resolution of fMRI to obtain flow signals that are physically interpretable.

Keywords: fMRI Acquisition, Blood vessels, brain, contrast mechanisms, flow, fMRI (task based), gray matter, high-field MRI, in silico, modelling, signal modeling

Motivation: The emerging fMRI method VASO provides improved neuronal specificity compared to BOLD, however the precise interpretation of its origins and principled means to optimize this sequence is not straightforward.

Goal(s): To use biophysical models to investigate the origins of the VASO signal and compare it with direct estimates of CBV.

Approach: We extend our 3D biophysical Vascular Anatomical Network framework to incorporate intravascular signals undergoing inversion recovery to model the VASO sequence.

Results: The VASO signal appears sensitive to slab thickness, and activation biases occur if the slab is too thin. Simulated profiles of VASO differ from measurements, possibly due to model simplifications.

Impact: Our new methodology enables biophysical simulations of fMRI based on inverting blood. Our findings may provide a deeper understanding of the hemodynamic origins of VASO and provide guidance for optimizing SS-SI VASO protocols to yield veridical representation of neural activity.

Keywords: fMRI Analysis, fMRI (resting state), High-Field MRI

Motivation: GRE-EPI, the most widely used sequence for BOLD fMRI, is highly biased towards large draining veins that follow the cortical curvature and influence the surrounding magnetic field in an orientation-dependent manner increasing with field strength.

Goal(s): This work aims to investigate large vein biases resulting in cortical orientation-dependent signal variations in GRE-EPI and bSSFP resting-state fMRI signals.

Approach: We compared 2D and 3D GRE-EPI with 3D bSSFP rs-fMRI signal fluctuations in their dependence on the cortical orientation to B₀ in five subjects at 9.4 Tesla.

Results: Unlike GRE-EPI, intra- and inter-subject comparisons revealed no dependence of bSSFP on the cortical orientation to B₀.

Impact: Fluctuations in the GRE-EPI signal are highly dependent on the cortical orientation and depth. This was not observed with bSSFP, demonstrating the potentially higher specificity of bSSFP for smaller veins, closer to brain activation at field strengths ≥ 7 Tesla.

Keywords: Task/Intervention Based fMRI, Data Analysis, activation mapping

Motivation: The nonlinear nature of neurovascular coupling and background brain activities that are unrelated to extrinsic stimuli in tasks could result in an inadequate mapping of the task-evoked brain activations with the conventional-GLM in fMRI.

Goal(s): The aim of this study is to propose a new approach to map task-evoked brain activation without the linear assumption.

Approach: we proposed a model-free approach to map task-evoked activation in the whole brain by measuring increases in BOLD signal synchronization within anatomical structures.

Results: Compared to the GLM approach, the model-free approach could detect regions of brain activations beyond the conventional-GLM's characterization, especially in the white matter.

Impact: The model-free approach detects task-evoked brain activations by measuring changes in BOLD signal synchronization within local anatomical structures, which is expected to serve as a standardized tool to measure neural activities with nonlinear hemodynamic responses.

Keywords: fMRI Analysis, fMRI (task based), Working Memory, Brain Functional Connectivity, Neuroreceptors

Motivation: Task-based fMRI studies highlighted the dorsolateral prefrontal cortex (dlPFC) involvement during working memory (WM) processes. However, BOLD fMRI indirectly estimates neural activity and lacks neuroreceptor specificity.

Goal(s): We investigated inhibitory and excitatory receptor density influence on functional connectivity (FC) during varying WM loads.

Approach: Using N-back fMRI tasks and Receptor-Enriched Analysis of Functional Connectivity by Targets (REACT), we assessed GABA-A and mGluR5 connectivity effects.

Results: We found decreased GABA-A- and increased mGluR5-enriched FC with increasing WM load in networks involving the dlPFC, in line with fMRI and single-voxel MRS studies. Therefore, REACT is a promising tool bridging whole-brain molecular organization and FC.

Impact: Our molecular-enriched fMRI analysis revealed how varying working memory load modulates functional connectivity related to the underlying neurotransmitters. This provides crucial information for a better understanding of the neural mechanisms underlying brain disorders like Alzheimer’s disease and Schizophrenia.

Keywords: fMRI Analysis, fMRI, cerebral circulation time

Motivation: Cerebral circulation time (CCT) is a metric that provides insight into cerebrovascular health. However, conventional CCT measurements typically require injection of contrast agents, or demonstrate high variability.

Goal(s): We propose an improved, contrast-free method to calculate CCT by cross-correlating fMRI signals from the putamen and sagittal sinus.  

Approach: n 16 healthy adult datasets (8 subjects, 2 sessions), we compared CCT estimates using the internal carotid artery (as proposed in the literature) or putamen as “arterial” references in breath-hold and resting-state data.

Results: The putamen ROI provides more reliable CCT estimates, consistent with values from bolus-tracking methods.

Impact: A modified analysis of fMRI data provides a robust method to measure cerebral circulation time on a single-subject level. This method may offer an accessible, contrast-free metric of cerebrovascular health for future application in patient populations. 

Keywords: Functional Connectivity, fMRI (resting state), novel contrast mechanisms, non-BOLD fMRI, diffusion fMRI, resting-state connectivity

Motivation: Unlike BOLD (neurovascular contrast), dfMRI offers neuromorphological contrast that can detect white matter (WM) activity and attenuates anti-correlations in functional connectivity (FC) analysis.

Goal(s): This work investigated resting-state gray and white matter connectivity and anti-correlations in BOLD and dfMRI, at 3T and 7T.

Approach: FC matrices and graph metrics were computed.

Results: Positive correlations were consistent among the contrasts whereas anti-correlations were attenuated with reduced hemodynamic contributions, suggesting a vascular origin to the latter. DfMRI FC displayed higher clustering than BOLD in WM. DfMRI provides unique insights into brain connectivity, particularly in WM, suggesting its value in enhancing our understanding of brain function.

Impact: In functional connectivity analysis, diffusion fMRI exhibits comparable positive correlations to BOLD but reduces anti-correlations, indicating a potential vascular origin for the latter. Additionally, it uncovers previously overlooked white matter connectivity, traditionally treated as a nuisance variable.

Keywords: Functional Connectivity, High-Field MRI, Neuro, Structural connectivity, Functional connectivity

Motivation: A definitive baseline connectome of brainstem nuclei is missing.

Goal(s): To improve brainstem hodology in living humans by using the similarity between functional and structural connectomes of brainstem nuclei as ground truth.

Approach: In healthy subjects, we mapped 58 Brainstem Navigator atlas labels to high spatial resolution functional and diffusion-weighted 7 Tesla MRI, and computed their functional and structural connectivity, the latter computed using three probabilistic tractography methods proposed in the literature (seed-, ACT-, ACT-SIFT-based), with 148 cortical and 21 subcortical areas.

Results: ACT-SIFT outperformed the other methods within the brainstem and the cortex by reducing large fiber bias. 

Impact: Comparison of structural and functional connectomes achieved with different methodology can improve the understanding and mapping of brainstem nuclei connections in living humans and establish a baseline connectome useful to evaluate a broad set of diseases including movement/sleep disorders.

Keywords: Functional Connectivity, Brain Connectivity, Dynamic Causal Modeling, BOLD, Neuroscience

Motivation: Task-driven BOLD signal nonlinearities in visuomotor areas have been reported both during execution and observation of tasks.

Goal(s): We aim to study how cerebral and cerebellar regions of a visuomotor network influence each other and drive nonlinear BOLD responses.

Approach: Dynamic Causal Modeling was used to estimate causal influences as effective connectivity to assess how the activity of each region modulated BOLD signal nonlinearities in a visuomotor task.

Results: Execution and observation networks showed the same fixed (0^th order) effective connectivity, while BOLD signal nonlinearities were modulated in the motor planning loop during execution only and were driven by the cerebellum.

Impact: Dynamic causal modeling elucidates the central role of the cerebellum as a forward controller in regulating input-driven modulation differentially in execution and observation. These mechanisms may be affected by pathologies and could have an important role in visuomotor disability.

Keywords: Task/Intervention Based fMRI, fMRI (resting state), fMRI(task based), functional connectivity, neuroscience

Motivation: Despite the enormous potential inherent in rsfMRI, the neural basis underlying rsfMRI connectivity remains unclear. 

Goal(s): We aim to dissect the role of the TRN inhibitory neural population in rsfMRI connectivity given its prominent role in maintaining/regulating thalamo-cortical oscillations.

Approach: We examined brain-wide activity and rsfMRI connectivity changes after optogenetically manipulating neural activity in rodent TRN. 

Results: We demonstrate that somatosensory-specific TRN inhibitory networks play a role in modulating rsfMRI connectivity of sensorimotor and default mode networks.

Impact: Present studies examining neural basis of rsfMRI have primarily focused on excitatory networks. Here, we investigated the role of a major inhibitory thalamic nucleus to advance our understanding of the contributions of inhibitory inputs in regulating brain-wide rsfMRI networks.

Keywords: Functional Connectivity, fMRI (task based), pattern separation; hippocampal-cortical connection; cortical-cortical connection

Motivation: Studies have extensively demonstrated roles of hippocampus and its subdivisions during pattern separation, but cortical involvement has not yet been elucidated. 

Goal(s): Our goal is to evaluate whole-brain functional connectivity changes during pattern separation.

Approach: We compared cortical-hippocampus and cortical-cortical FNCs during a total of 258 correct and incorrect lure discrimination trials, using high-resolution and high-quality 7T fMRI data. 

Results: Cortical-CA3DG FNCs and cortical-CA1 FNCs were significantly involved during pattern separation and completion, respectively. Around 83.35% cortical-cortical connections were with higher FNCs during lure discriminations, indicating their potential involvement during pattern separation. 

Impact: Besides hippocampus and its subdivisions, cortical regions and its connections to hippocampus might be extensively involved in pattern separation process.

Keywords: fMRI Analysis, fMRI (resting state), Connectivity gradient, Hippocampus, Development, Cognitive training

Motivation: The hippocampus-cortical connections have shown rapid developmental-changed nature during childhood and learning-adapted plasticity with skill acquirement.

Goal(s): However, little is known about the effect of development interacting with cognitive training on the hippocampal connectivity gradient during puberty.

Approach: Here we employed longitudinal dataset (191 scans from training/control groups: n = 43/45) which collected neuroimaging data of school-age children across 0/3/5-year abacus mental calculation (AMC) training stages to explore this question.

Results: By calculating connectivity gradient of hippocampus, we observed significantly development-induced gradient aggregation of hippocampus, and training promoted that effect, which were resulted from changes in functional connectivity between hippocampus with different cortices.

Impact: These findings provide novel insights into development and training effects on function specialization of hippocampus during puberty from a largescale perspective of connectivity gradient, which may be helpful for better understanding of functionally atypical trait of hippocampal disorder for clinicians.

Keywords: Functional Connectivity, Neonatal

Motivation: The topological organisation of RSNs can be studied with graph theory. While graph nodes can be defined using atlases in adults, infant atlases are not readily available. 

Goal(s): To create a framework to define nodes and edges for graph theory analyses of infant RSNs.

Approach: We resampled the original template voxel size and created evenly distributed nodes within RSNs.

Results: We present a mask comprising 605 evenly-spaced spheres to discretize neonatal RSNs. Graph theory demonstrated lower global and/or nodal efficiency in 4 networks in HEU neonates compared to HUU, indicating decreased information transmission throughout and regionally within affected networks.

Impact: The proposed method may enable more comprehensive analyses of the topological organisation of RSNs in infant cohorts. This will advance knowledge on how functional networks process and distribute information from birth.

Keywords: Elastography, Elastography

Motivation: The need for advanced imaging techniques to precisely localize neuronal activity, overcoming the limitations of fMRI in terms of temporal resolution and direct measurement of neural activation.

Goal(s): To determine how neural activity influences tissue stiffness by detecting viscoelastic changes associated with neuronal firing and hemodynamic responses.

Approach: Using real-time MR elastography techniques to simultaneously measure both BOLD activation and viscoelastic changes in the brain during visual stimulation at two different time scales.

Results: Distinct viscoelastic activation patterns strongly link neurovascular coupling and tissue stiffness. However, no rapid viscoelastic response related directly to the underlying neural activity was detected.

Impact: Functional real-time MR elastography is sensitive to biomechanical property changes associated with the hemodynamic response to brain stimulation providing a valuable tool to study possible effects that occur on a subsecond timescale.

Keywords: Functional Connectivity, Brain Connectivity, brain stimulation

Motivation: TMS has become an invaluable asset in both research and clinical environments. However, variability in individual responses to TMS is a persistent issue, which limits its broader adoption.

Goal(s): The integration of TMS with fMRI through interleaved paradigms is a promising strategy for gaining insights into the factors that underlie this response variability.

Approach: Adopting an interleaved TMS-fMRI approach we explored the different factors of stimulation dose, sex differences, and cognitive state.

Results: Interleaved TMS-fMRI revealed individual dose-response patterns. Inherent sex differences were found between men and women. Precise timing of TMS relative to cognitive state demonstrated differential effects on relevant brain regions.

Impact: The findings represent a critical step toward addressing the challenge of response variability. TMS-fMRI promises to be a valuable tool for not only understanding  factors that influence TMS response but also for potentially enhancing response rates in TMS applications.