Biology subjects

Bandettini, P.

Publications and source records attributed to Bandettini, P..

6 recordsLinked to original sources

bioRxiv2025

Blood volume sensitive laminar fMRI with VASO in human hippocampus: Capabilities and biophysical challenges at clinical 7T scanners

Sub-millimeter resolution functional magnetic resonance imaging (fMRI) at ultra-high field ([≥] 7T) has offered an unprecedented opportunity to probe mesoscopic computations at a columnar or laminar level. However, its application has been primarily restricted to the neocortex. Inferior brain regions, particularly the hippocampus (HC), are challenging targets for laminar fMRI. Recent developments in acquisition methods have shown the feasibility of laminar recordings in the HC using gradient-echo blood oxygenation level-dependent (BOLD) contrast. Nonetheless, the spatial specificity of the BOLD signal is compromised by the draining veins bias. Cerebral blood volume (CBV)-sensitive sequences including vascular space occupancy (VASO) have emerged as a promising augmentation tool to capture the vein-free laminar activity. Yet, its feasibility in the HC is unclear and challenged by methodological constraints. Here, we optimized VASO to mitigate the macrovasculature contribution in HC. By evaluating a series of advanced acquisition strategies tailored to HC, we obtained improved VASO signal quality with minimal artifacts. The optimized protocol was further validated with an autobiographical memory task. Our findings show that combining the high detection power of gradient-echo BOLD with the vein-free VASO contrast allows for differentiation between neural activity-related BOLD signals and those biased by draining veins. These results demonstrate the feasibility of submillimeter VASO acquired with conventional 7T scanners in the HC to map the circuit-level mechanisms of memory retrieval across HC subfields, laying a foundation to investigate the microcircuitry of HC-driven complex cognitive functions and their alterations in neurodegeneration.

neuroscience↗

bioRxiv2024

T1234: A distortion-matched structural scan solution to misregistration of high resolution fMRI data

PurposeHigh-resolution fMRI at 7T is challenged by suboptimal alignment quality between functional data and structural scans. This study aims to develop a rapid acquisition method that provides distortion-matched, artifact-mitigated structural reference data. MethodsWe introduce an efficient sequence protocol termed T1234, which offers adjustable distortions. This approach involves a T1-weighted 2-inversion 3D-EPI sequence with four spatial encoding directions optimized for high-resolution fMRI. A forward Bloch model was used for T1 quantification and protocol optimization. Twenty participants were scanned at 7T using both structural and functional protocols to evaluate the utility of T1234. ResultsResults from two protocols are presented. A fast distortion-free protocol reliably produced whole-brain segmentations at 0.8mm isotropic resolution within 3:00-3:40 minutes. It demonstrates robustness across sessions, participants, and three different 7T SIEMENS scanners. For a protocol with geometric distortions that matched functional data, T1234 facilitates layer-specific fMRI signal analysis with enhanced laminar precision. ConclusionThis structural mapping approach enables precise registration with fMRI data. T1234 has been successfully implemented, validated, and tested, and is now available to users at our center and at over 50 centers worldwide.

neuroscience↗

bioRxiv2024

Fuzzy ripple artifact in high resolution fMRI: identification, cause, and mitigation

PurposeHigh resolution fMRI is a rapidly growing research field focused on capturing functional signal changes across cortical layers. However, the data acquisition is limited by low spatial frequency EPI artifacts; termed here as Fuzzy Ripples. These artifacts limit the practical applicability of acquisition protocols with higher spatial resolution, faster acquisition speed, and they challenge imaging in lower brain areas. MethodsWe characterize Fuzzy Ripple artifacts across commonly used sequences and distinguish them from conventional EPI Nyquist ghosts, off-resonance effects, and GRAPPA artifacts. To investigate their origin, we employ dual polarity readouts. ResultsOur findings indicate that Fuzzy Ripples are primarily caused by readout-specific imperfections in k-space trajectories, which can be exacerbated by inductive coupling between third-order shims and readout gradients. We also find that these artifacts can be mitigated through complex-valued averaging of dual polarity EPI or by disconnecting the third-order shim coils. ConclusionThe proposed mitigation strategies allow overcoming current limitations in layer-fMRI protocols: O_LIAchieving resolutions beyond 0.8mm is feasible, and even at 3T, we achieved 0.53mm voxel functional connectivity mapping. C_LIO_LISub-millimeter sampling acceleration can be increased to allow sub-second TRs and laminar whole brain protocols with up to GRAPPA 8. C_LIO_LISub-millimeter fMRI is achievable in lower brain areas, including the cerebellum. C_LI

neuroscience↗

bioRxiv2023

Commentary on Pang et al. (2023) Nature

Pang et al. (2023) present novel analyses demonstrating that brain dynamics can be understood as resulting from the excitation of geometric modes, derived from the shape of the brain. Notably, they demonstrate that linear combinations of geometric modes can reconstruct patterns of fMRI data more accurately, and with fewer dimensions, than comparable connectivity-derived modes. Equipped with these results, and underpinned by neural field theory, the authors contend that the geometry of the cortical surface provides a more parsimonious explanation of brain activity than structural brain connectivity. This claim runs counter to prevailing theories of information flow in the brain, which emphasize the role of long-distance axonal projections and fasciculated white matter in relaying signals between cortical regions (Honey et al. 2009; Deco et al. 2011; Seguin et al., 2023). While we acknowledge that cortical geometry plays an important role in shaping human brain function, we feel that the presented work falls short of establishing that the brains geometry is "a more fundamental constraint on dynamics than complex interregional connectivity" (Pang et al. 2023). Here, we provide 1) a brief critique of the papers framing and 2) evidence showing that their methodology lacks specificity to the brains orientation and shape. Ultimately, we recognize that the geometric mode approach is a powerful representational framework for brain dynamics analysis, but we also believe that there are key caveats to consider alongside the claims made in the manuscript.

neuroscience↗

bioRxiv2022

Improved whole-brain multivariate hemodynamic deconvolution for multi-echo fMRI with stability selection

Conventionally, analysis of functional MRI (fMRI) data relies on available information about the experimental paradigm to establish hypothesized models of brain activity. However, this information can be inaccurate, incomplete or unavailable in multiple scenarios such as resting-state, naturalistic paradigms or clinical conditions. In these cases, blind estimates of neuronal-related activity can be obtained with paradigm-free analysis methods such as hemodynamic deconvolution. Yet, current formulations of the hemodynamic deconvolution problem have three important limitations: 1) their efficacy strongly depends on the appropriate selection of regularization parameters, 2) being univariate, they do not take advantage of the information present across the brain, and 3) they do not provide any measure of statistical certainty associated with each detected event. Here we propose a novel approach that addresses all these limitations. Specifically, we introduce MvME-SPFM (multivariate multi-echo sparse paradigm free mapping), a novel hemodynamic deconvolution algorithm that operates at the whole brain level and adds spatial information via a mixed-norm regularization term over all voxels. Additionally, MvME-SPFM employs a stability selection procedure that removes the need to select regularization parameters and also lets us obtain an estimate of the true probability of having a neuronal-related BOLD event at each voxel and time-point based on the area under the curve (AUC) of the stability paths. Besides, the formulation is tailored for multi-echo fMRI acquisitions, which allows us to better isolate fluctuations of BOLD origin on the basis of their linear dependence with Echo Time (TE) and to assign physiologically interpretable units (i.e., changes in the apparent transverse relaxation [Formula]) to the resulting deconvolved events. We demonstrate that this algorithm outperforms existing state-of-the-art deconvolution approaches, and shows higher spatial and temporal agreement with the activation maps and BOLD signals obtained with a standard model-based linear regression approach, even at the level of individual neuronal events. Consequently, the proposed algorithm provides more reliable estimates of neuronal-related activity, here in terms of [Formula], for the study of the dynamics of brain activity when no information about the timings of the BOLD events is available. This algorithm will be made publicly available as part of the splora Python package.

neuroscience↗

bioRxiv2019

Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest

Brain functional connectivity (FC) changes have been measured across seconds using fMRI. This is true for both rest and task scenarios. Moreover, it is well accepted that task engagement alters FC, and that dynamic estimates of FC during and before task events can help predict their nature and performance. Yet, when it comes to dynamic FC (dFC) during rest, there is no consensus about its origin or significance. Some argue that rest dFC reflects fluctuations in on-going cognition, or is a manifestation of intrinsic brain maintenance mechanisms, which could have predictive clinical value. Conversely, others have concluded that rest dFC is mostly the result of sampling variability, head motion or fluctuating sleep states. Here, we present novel analyses suggesting that rest dFC is influenced by short periods of distinct mental processing, and that the cognitive nature of such mental processes can be inferred blindly from the data. As such, several different behaviorally relevant whole-brain FC configurations may occur during a single rest scan even when subjects were continuously awake and displayed minimal motion. In addition, using low dimensional embeddings as visualization aids, we show how FC states--commonly used to summarize and interpret resting dFC--can accurately and robustly reveal periods of externally imposed tasks; however, they may be less effective in capturing periods of distinct cognition during rest.

neuroscience↗