Jonathan R. Polimeni

YNIMG Journal 2022 Journal Article

SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI

• Qiyuan Tian
• Ziyu Li
• Qiuyun Fan
• Jonathan R. Polimeni
• Berkin Bilgic
• David H. Salat
• Susie Y. Huang

Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does not require additional high-SNR data for training. Specifically, SDnDTI divides multi-directional DTI data into many subsets of six DWI volumes and transforms DWIs from each subset to along the same diffusion-encoding directions through the diffusion tensor model, generating multiple repetitions of DWIs with identical image contrasts but different noise observations. SDnDTI removes noise by first denoising each repetition of DWIs using a deep 3-dimensional CNN with the average of all repetitions with higher SNR as the training target, following the same approach as normal supervised learning based denoising methods, and then averaging CNN-denoised images for achieving higher SNR. The denoising efficacy of SDnDTI is demonstrated in terms of the similarity of output images and resultant DTI metrics compared to the ground truth generated using substantially more DWI volumes on two datasets with different spatial resolutions, b-values and numbers of input DWI volumes provided by the Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI results preserve image sharpness and textural details and substantially improve upon those from the raw data. The results of SDnDTI are comparable to those from supervised learning-based denoising and outperform those from state-of-the-art conventional denoising algorithms including BM4D, AONLM and MPPCA. By leveraging domain knowledge of diffusion MRI physics, SDnDTI makes it easier to use CNN-based denoising methods in practice and has the potential to benefit a wider range of research and clinical applications that require accelerated DTI acquisition and high-quality DTI data for mapping of tissue microstructure, fiber tracts and structural connectivity in the living human brain.

YNIMG Journal 2022 Journal Article

Slice-direction geometric distortion evaluation and correction with reversed slice-select gradient acquisitions

• Anna I. Blazejewska
• Thomas Witzel
• Jesper L.R. Andersson
• Lawrence L. Wald
• Jonathan R. Polimeni

Accurate spatial alignment of MRI data acquired across multiple contrasts in the same subject is often crucial for data analysis and interpretation, but can be challenging in the presence of geometric distortions that differ between acquisitions. It is well known that single-shot echo-planar imaging (EPI) acquisitions suffer from distortion in the phase-encoding direction due to B0 field inhomogeneities arising from tissue magnetic susceptibility differences and other sources, however there can be distortion in other encoding directions as well in the presence of strong field inhomogeneities. High-resolution ultrahigh-field MRI typically uses low bandwidth in the slice-encoding direction to acquire thin slices and, when combined with the pronounced B0 inhomogeneities, is prone to an additional geometric distortion in the slice direction as well. Here we demonstrate the presence of this slice distortion in high-resolution 7T EPI acquired with a novel pulse sequence allowing for the reversal of the slice-encoding gradient polarity that enables the acquisition of pairs of images with equal magnitudes of distortion in the slice direction but with opposing polarities. We also show that the slice-direction distortion can be corrected using gradient reversal-based method applying the same software used for conventional corrections of phase-encoding direction distortion.

YNIMG Journal 2021 Journal Article

Ground-truth “resting-state” signal provides data-driven estimation and correction for scanner distortion of fMRI time-series dynamics

• Rajat Kumar
• Liang Tan
• Alan Kriegstein
• Andrew Lithen
• Jonathan R. Polimeni
• Lilianne R. Mujica-Parodi
• Helmut H. Strey

The fMRI community has made great strides in decoupling neuronal activity from other physiologically induced T2* changes, using sensors that provide a ground-truth with respect to cardiac, respiratory, and head movement dynamics. However, blood oxygenation level-dependent (BOLD) time-series dynamics are also confounded by scanner artifacts, in complex ways that can vary not only between scanners but even, for the same scanner, between sessions. Unfortunately, the lack of an equivalent ground truth for BOLD time-series has thus far stymied the development of reliable methods for identification and removal of scanner-induced noise, a problem that we have previously shown to severely impact detection sensitivity of resting-state brain networks. To address this problem, we first designed and built a phantom capable of providing dynamic signals equivalent to that of the resting-state brain. Using the dynamic phantom, we then compared the ground-truth time-series with its measured fMRI data. Using these, we introduce data-quality metrics: Standardized Signal-to-Noise Ratio (ST-SNR) and Dynamic Fidelity that, unlike currently used measures such as temporal SNR (tSNR), can be directly compared across scanners. Dynamic phantom data acquired from four “best-case” scenarios: high-performance scanners with MR-physicist-optimized acquisition protocols, still showed scanner instability/multiplicative noise contributions of about 6–18% of the total noise. We further measured strong non-linearity in the fMRI response for all scanners, ranging between 8–19% of total voxels. To correct scanner distortion of fMRI time-series dynamics at a single-subject level, we trained a convolutional neural network (CNN) on paired sets of measured vs. ground-truth data. The CNN learned the unique features of each session's noise, providing a customized temporal filter. Tests on dynamic phantom time-series showed a 4- to 7-fold increase in ST-SNR and about 40–70% increase in Dynamic Fidelity after denoising, with CNN denoising outperforming both the temporal bandpass filtering and denoising using Marchenko-Pastur principal component analysis. Critically, we observed that the CNN temporal denoising pushes ST-SNR to a regime where signal power is higher than that of noise (ST-SNR > 1). Denoising human-data with ground-truth-trained CNN, in turn, showed markedly increased detection sensitivity of resting-state networks. These were visible even at the level of the single-subject, as required for clinical applications of fMRI.

YNIMG Journal 2021 Journal Article

Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising

• Qiyuan Tian
• Natalia Zaretskaya
• Qiuyun Fan
• Chanon Ngamsombat
• Berkin Bilgic
• Jonathan R. Polimeni
• Susie Y. Huang

Automatic cerebral cortical surface reconstruction is a useful tool for cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T1-weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-mm isotropic resolution for improved accuracy of cortical surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of cortical surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the cortical surface reconstruction resulting from denoised single-repetition sub-millimeter T1-weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0. 6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ~0. 016, high whole-brain averaged peak signal-to-noise ratio of ~33. 5 dB and structural similarity index of ~0. 92, and minimal gray matter–white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray matter–white matter surface placement, gray matter–cerebrospinal fluid surface placement and cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm—sufficiently accurate for most applications. These discrepancies were approximately one third to half of those from 1-mm isotropic resolution data. The denoising performance was equivalent to averaging ~2. 5 repetitions of the data in terms of image similarity, and 1. 6–2. 2 repetitions in terms of the cortical surface placement accuracy. The scan-rescan variability of the cortical surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved cortical surface reconstruction at sub-millimeter resolution.

YNIMG Journal 2021 Journal Article

Investigating mechanisms of fast BOLD responses: The effects of stimulus intensity and of spatial heterogeneity of hemodynamics

• Jingyuan E. Chen
• Gary H. Glover
• Nina E. Fultz
• Bruce R. Rosen
• Jonathan R. Polimeni
• Laura D. Lewis

Recent studies have demonstrated that fast fMRI can track neural activity well above the temporal limit predicted by the canonical hemodynamic response model. While these findings are promising, the biophysical mechanisms underlying these fast fMRI phenomena remain underexplored. In this study, we discuss two aspects of the hemodynamic response, complementary to several existing hypotheses, that can accommodate faster fMRI dynamics beyond those predicted by the canonical model. First, we demonstrate, using both visual and somatosensory paradigms, that the timing and shape of hemodynamic response functions (HRFs) vary across graded levels of stimulus intensity-with lower-intensity stimulation eliciting faster and narrower HRFs. Second, we show that as the spatial resolution of fMRI increases, voxel-wise HRFs begin to deviate from the canonical model, with a considerable portion of voxels exhibiting faster temporal dynamics than predicted by the canonical HRF. Collectively, both stimulus/task intensity and image resolution can affect the sensitivity of fMRI to fast brain activity, which may partly explain recent observations of fast fMRI signals. It is further noteworthy that, while the present investigations focus on fast neural responses, our findings suggest that a revised hemodynamic model may benefit the many fMRI studies using paradigms with wide ranges of contrast levels (e.g., resting or naturalistic conditions) or with modern, high-resolution MR acquisitions.

YNIMG Journal 2021 Journal Article

Simultaneous pure T2 and varying T2′-weighted BOLD fMRI using Echo Planar Time-resolved Imaging for mapping cortical-depth dependent responses

• Fuyixue Wang
• Zijing Dong
• Lawrence L. Wald
• Jonathan R. Polimeni
• Kawin Setsompop

Spin-echo (SE) BOLD fMRI has high microvascular specificity, and thus provides a more reliable means to localize neural activity compared to conventional gradient-echo BOLD fMRI. However, the most common SE BOLD acquisition method, SE-EPI, is known to suffer from T2′ contrast contamination with undesirable draining vein bias. To address this, in this study, we extended a recently developed distortion/blurring-free multi-shot EPI technique, Echo-Planar Time-resolved Imaging (EPTI), to cortical-depth dependent SE-fMRI at 7T to test whether it could provide purer SE BOLD contrast with minimal T2′ contamination for improved neuronal specificity. From the same acquisition, the time-resolved feature of EPTI also provides a series of asymmetric SE (ASE) images with varying T2′ weightings, and enables extraction of data equivalent to conventional SE EPI with different echo train lengths (ETLs). This allows us to systematically examine how T2′-contribution affects different SE acquisition strategies using a single dataset. A low-rank spatiotemporal subspace reconstruction was implemented for the SE-EPTI acquisition, which incorporates corrections for both shot-to-shot phase variations and dynamic B0 drifts. SE-EPTI was used in a visual task fMRI experiment to demonstrate that i) the pure SE image provided by EPTI results in the highest microvascular specificity; ii) the ASE EPTI series, with a graded introduction of T2′ weightings at time points farther away from the pure SE, show a gradual sensitivity increase along with increasing draining vein bias; iii) the longer ETL seen in conventional SE EPI acquisitions will induce more draining vein bias. Consistent results were observed across multiple subjects, demonstrating the robustness of the proposed technique for SE-BOLD fMRI with high specificity.

YNIMG Journal 2020 Journal Article

DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning

• Qiyuan Tian
• Berkin Bilgic
• Qiuyun Fan
• Congyu Liao
• Chanon Ngamsombat
• Yuxin Hu
• Thomas Witzel
• Kawin Setsompop

Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and poses a critical barrier to performing high-quality DTI in routine clinical practice and large-scale research studies. In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning. DeepDTI maps the input non-diffusion-weighted (b ​= ​0) image and six DWI volumes sampled along optimized diffusion-encoding directions, along with T1-weighted and T2-weighted image volumes, to the residuals between the input and high-quality output b = 0 image and DWI volumes using a 10-layer three-dimensional convolutional neural network (CNN). The inputs and outputs of DeepDTI are uniquely formulated, which not only enables residual learning to boost CNN performance but also enables tensor fitting of resultant high-quality DWIs to generate orientational DTI metrics for tractography. The very deep CNN used by DeepDTI leverages the redundancy in local and non-local spatial information and across diffusion-encoding directions and image contrasts in the data. The performance of DeepDTI was systematically quantified in terms of the quality of the output images, DTI metrics, DTI-based tractography and tract-specific analysis results. We demonstrate rotationally-invariant and robust estimation of DTI metrics from DeepDTI that are comparable to those obtained with two b ​= ​0 images and 21 DWIs for the primary eigenvector derived from DTI and two b ​= ​0 images and 26–30 DWIs for various scalar metrics derived from DTI, achieving 3. 3–4. 6 × ​acceleration, and twice as good as those of a state-of-the-art denoising algorithm at the group level. The twenty major white-matter tracts can be accurately identified from the tractography of DeepDTI results. The mean distance between the core of the major white-matter tracts identified from DeepDTI results and those from the ground-truth results using 18 ​b ​= ​0 images and 90 DWIs measures around 1–1. 5 ​mm. DeepDTI leverages domain knowledge of diffusion MRI physics and power of deep learning to render DTI, DTI-based tractography, major white-matter tracts identification and tract-specific analysis more feasible for a wider range of neuroscientific and clinical studies.

YNIMG Journal 2020 Journal Article

Eye-selective fMRI activity in human primary visual cortex: Comparison between 3 ​T and 9.4 ​T, and effects across cortical depth

• Natalia Zaretskaya
• Jonas Bause
• Jonathan R. Polimeni
• Pablo R. Grassi
• Klaus Scheffler
• Andreas Bartels

The primary visual cortex of humans contains patches of neurons responding preferentially to stimulation of one eye (the ocular dominance columns). Multiple previous studies attempted to detect their activity using fMRI. The majority of these fMRI studies used magnetic field strengths of 4 ​T and higher. However, there have been reports of reliable eye-selective activations at 3 ​T as well. In this study we investigated the possibility of detecting eye-selective V1 activity using high-resolution GE-EPI fMRI at 3 ​T and sub-millimeter resolution fMRI at ultrahigh 9. 4 ​T magnetic field strengths with acquisition parameters optimized for each field strength. High-resolution fMRI at 9. 4 ​T also allowed us to examine the eye-selectivity responses across the cortical depth, which are expected to be strongest in the middle layers. We observed a substantial increase in the percentage of eye-selective voxels, as well as a doubling in run-to-run consistency of eye preference at ultrahigh field compared to 3 ​T. We also found that across cortical depth, eye selectivity increased towards the superficial layers, and that signal contrast increased while noise remained nearly constant towards the surface. The depth-resolved results are consistent with a distortion of spatial specificity of the GE-EPI signal by ascending venules and large draining veins on the cortical surface. The effects of larger vessels cause increasing signal amplitude, but also displacement of the maximum BOLD signal relative to neural activity. In summary, our results show that increase in spatial resolution, reduced partial volume effects, and improved sensitivity at 9. 4 ​T allow for better detection of eye-selective signals related to ocular dominance columns. However, although ultrahigh field yields higher sensitivity to the ocular dominance signal, GE-EPI still suffers from specificity issues, with a prominent signal contribution at shallow depths from larger cortical vessels.

YNIMG Journal 2020 Journal Article

Impact of prospective motion correction, distortion correction methods and large vein bias on the spatial accuracy of cortical laminar fMRI at 9.4 Tesla

• Jonas Bause
• Jonathan R. Polimeni
• Johannes Stelzer
• Myung-Ho In
• Philipp Ehses
• Pablo Kraemer-Fernandez
• Ali Aghaeifar
• Eric Lacosse

Functional imaging with sub-millimeter spatial resolution is a basic requirement for assessing functional MRI (fMRI) responses across different cortical depths and is used extensively in the emerging field of laminar fMRI. Such studies seek to investigate the detailed functional organization of the brain and may develop to a new powerful tool for human neuroscience. However, several studies have shown that measurement of laminar fMRI responses can be biased by the image acquisition and data processing strategies. In this work, measurements with three different gradient-echo EPI BOLD fMRI protocols with a voxel size down to 650 ​μm isotropic were performed at 9. 4 ​T. We estimated how prospective motion correction can help to improve spatial accuracy by reducing the number of spatial resampling steps in postprocessing. In addition, we demonstrate key requirements for accurate geometric distortion correction to ensure that distortion correction maps are properly aligned to the functional data and that strong variations of distortions near large veins can lead to signal overlays which cannot be corrected for during postprocessing. Furthermore, this study illustrates the spatial extent of bias induced by pial and other larger veins in laminar BOLD experiments. Since these issues under investigation affect studies performed with more conventional spatial resolutions, the methods applied in this work may also help to improve the understanding of the BOLD signal more broadly.

YNIMG Journal 2020 Journal Article

In vivo functional localization of the temporal monocular crescent representation in human primary visual cortex

• Shahin Nasr
• Cristen LaPierre
• Christopher E. Vaughn
• Thomas Witzel
• Jason P. Stockmann
• Jonathan R. Polimeni

The temporal monocular crescent (TMC) is the most peripheral portion of the visual field whose perception relies solely on input from the ipsilateral eye. According to a handful of post-mortem histological studies in humans and non-human primates, the TMC is represented visuotopically within the most anterior portion of the primary visual cortical area (V1). However, functional evidence of the TMC visuotopic representation in human visual cortex is rare, mostly due to the small size of the TMC representation (~6% of V1) and due to the technical challenges of stimulating the most peripheral portion of the visual field inside the MRI scanner. In this study, by taking advantage of custom-built MRI-compatible visual stimulation goggles with curved displays, we successfully stimulated the TMC region of the visual field in eight human subjects, half of them right-eye dominant, inside a 3 ​T MRI scanner. This enabled us to localize the representation of TMC, along with the blind spot representation (another visuotopic landmark in V1), in all volunteers, which match the expected spatial pattern based on prior anatomical studies. In all hemispheres, the TMC visuotopic representation was localized along the peripheral border of V1, within the most anterior portion of the calcarine sulcus, without any apparent extension into the second visual area (V2). We further demonstrate the reliability of this localization within/across experimental sessions, and consistency in the spatial location of TMC across individuals after accounting for inter-subject structural differences.

YNIMG Journal 2020 Journal Article

Resting-state “physiological networks”

• Jingyuan E. Chen
• Laura D. Lewis
• Catie Chang
• Qiyuan Tian
• Nina E. Fultz
• Ned A. Ohringer
• Bruce R. Rosen
• Jonathan R. Polimeni

Slow changes in systemic brain physiology can elicit large fluctuations in fMRI time series, which manifest as structured spatial patterns of temporal correlations between distant brain regions. Here, we investigated whether such “physiological networks”—sets of segregated brain regions that exhibit similar responses following slow changes in systemic physiology—resemble patterns associated with large-scale networks typically attributed to remotely synchronized neuronal activity. By analyzing a large group of subjects from the 3T Human Connectome Project (HCP) database, we demonstrate brain-wide and noticeably heterogenous dynamics tightly coupled to either respiratory variation or heart rate changes. We show, using synthesized data generated from physiological recordings across subjects, that these physiologically-coupled fluctuations alone can produce networks that strongly resemble previously reported resting-state networks, suggesting that, in some cases, the “physiological networks” seem to mimic the neuronal networks. Further, we show that such physiologically-relevant connectivity estimates appear to dominate the overall connectivity observations in multiple HCP subjects, and that this apparent “physiological connectivity” cannot be removed by the use of a single nuisance regressor for the entire brain (such as global signal regression) due to the clear regional heterogeneity of the physiologically-coupled responses. Our results challenge previous notions that physiological confounds are either localized to large veins or globally coherent across the cortex, therefore emphasizing the necessity to consider potential physiological contributions in fMRI-based functional connectivity studies. The rich spatiotemporal patterns carried by such “physiological” dynamics also suggest great potential for clinical biomarkers that are complementary to large-scale neuronal networks.

YNIMG Journal 2019 Journal Article

Dependence of resting-state fMRI fluctuation amplitudes on cerebral cortical orientation relative to the direction of B0 and anatomical axes

• Olivia Viessmann
• Klaus Scheffler
• Marta Bianciardi
• Lawrence L. Wald
• Jonathan R. Polimeni

Functional magnetic resonance imaging (fMRI) is now capable of sub-millimetre scale measurements over the entire human brain, however with such high resolutions each voxel is influenced by the local fine-scale details of the cerebral cortical vascular anatomy. The cortical vasculature is structured with the pial vessels lying tangentially along the grey matter surface, intracortical diving arterioles and ascending venules running perpendicularly to the surface, and a randomly oriented capillary network within the parenchyma. It is well-known that the amplitude of the blood-oxygenation level dependent (BOLD) signal emanating from a vessel depends on its orientation relative to the B 0 -field. Thus the vascular geometric hierarchy will impart an orientation dependence to the BOLD signal amplitudes and amplitude differences due to orientation differences constitute a bias for interpreting neuronal activity. Here, we demonstrate a clear effect of cortical orientation to B 0 in the resting-state BOLD-fMRI amplitude (quantified as the coefficient of temporal signal variation) for 1. 1 mm isotropic data at 7T and 2 mm isotropic at 3T. The maximum bias, i. e. the fluctuation amplitude difference between regions where cortex is perpendicular to vs. parallel to B 0, is about + 70 % at the pial surface at 7T and + 11 % at 3T. The B 0 orientation bias declines with cortical depth, becomes progressively smaller closer to the white matter surface, but then increases again to a local maximum within the white matter just beneath the cortical grey matter, suggesting a distinct tangential network of white matter vessels that also generate a BOLD orientation effect. We further found significant (negative) biases with the cortex orientation to the anterior-posterior anatomical axis of the head: a maximum negative bias of about − 30 % at the pial surface at 7T and about − 13 % at 3T. The amount of signal variance explained by the low frequency drift, motion and the respiratory cycle also showed a cortical orientation dependence; only the cardiac cycle induced signal variance was independent of cortical orientation, suggesting that the cardiac induced component of the image time-series fluctuations is not related to a significant change in susceptibility. Although these orientation effects represent a signal bias, and are likely to be a nuisance in high-resolution analyses, they may help characterize the vascular influences on candidate fMRI acquisitions and, thereby, may be exploited to improve the neuronal specificity of fMRI.

YNIMG Journal 2019 Journal Article

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

• Donald J. Hagler
• SeanN. Hatton
• M. Daniela Cornejo
• Carolina Makowski
• Damien A. Fair
• Anthony Steven Dick
• Matthew T. Sutherland
• B.J. Casey

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

YNIMG Journal 2019 Journal Article

Intracortical smoothing of small-voxel fMRI data can provide increased detection power without spatial resolution losses compared to conventional large-voxel fMRI data

• Anna I. Blazejewska
• Bruce Fischl
• Lawrence L. Wald
• Jonathan R. Polimeni

Continued improvement in MRI acquisition technology has made functional MRI (fMRI) with small isotropic voxel sizes down to 1 mm and below more commonly available. Although many conventional fMRI studies seek to investigate regional patterns of cortical activation for which conventional voxel sizes of 3 mm and larger provide sufficient spatial resolution, smaller voxels can help avoid contamination from adjacent white matter (WM) and cerebrospinal fluid (CSF), and thereby increase the specificity of fMRI to signal changes within the gray matter. Unfortunately, temporal signal-to-noise ratio (tSNR), a metric of fMRI sensitivity, is reduced in high-resolution acquisitions, which offsets the benefits of small voxels. Here we introduce a framework that combines small, isotropic fMRI voxels acquired at 7 T field strength with a novel anatomically-informed, surface mesh-navigated spatial smoothing that can provide both higher detection power and higher resolution than conventional voxel sizes. Our smoothing approach uses a family of intracortical surface meshes and allows for kernels of various shapes and sizes, including curved 3D kernels that adapt to and track the cortical folding pattern. Our goal is to restrict smoothing to the cortical gray matter ribbon and avoid noise contamination from CSF and signal dilution from WM via partial volume effects. We found that the intracortical kernel that maximizes tSNR does not maximize percent signal change (ΔS/S), and therefore the kernel configuration that optimizes detection power cannot be determined from tSNR considerations alone. However, several kernel configurations provided a favorable balance between boosting tSNR and ΔS/S, and allowed a 1. 1-mm isotropic fMRI acquisition to have higher performance after smoothing (in terms of both detection power and spatial resolution) compared to an unsmoothed 3. 0-mm isotropic fMRI acquisition. Overall, the results of this study support the strategy of acquiring voxels smaller than the cortical thickness, even for studies not requiring high spatial resolution, and smoothing them down within the cortical ribbon with a kernel of an appropriate shape to achieve the best performance—thus decoupling the choice of fMRI voxel size from the spatial resolution requirements of the particular study. The improvement of this new intracortical smoothing approach over conventional surface-based smoothing is expected to be modest for conventional resolutions, however the improvement is expected to increase with higher resolutions. This framework can also be applied to anatomically-informed intracortical smoothing of higher-resolution data (e. g. along columns and layers) in studies with prior information about the spatial structure of activation.

YNIMG Journal 2019 Journal Article

Laminar (f)MRI: A short history and future prospects

• David G. Norris
• Jonathan R. Polimeni

YNIMG Journal 2019 Journal Article

On the analysis of rapidly sampled fMRI data

• Jingyuan E. Chen
• Jonathan R. Polimeni
• Saskia Bollmann
• Gary H. Glover

Recent advances in parallel imaging and simultaneous multi-slice techniques have permitted whole-brain fMRI acquisitions at sub-second sampling intervals, without significantly sacrificing the spatial coverage and resolution. Apart from probing brain function at finer temporal scales, faster sampling rates may potentially lead to enhanced functional sensitivity, owing possibly to both cleaner neural representations (due to less aliased physiological noise) and additional statistical benefits (due to more degrees of freedom for a fixed scan duration). Accompanying these intriguing aspects of fast acquisitions, however, confusion has also arisen regarding (1) how to preprocess/analyze these fast fMRI data, and (2) what exactly is the extent of benefits with fast acquisitions, i. e. , how fast is fast enough for a specific research aim? The first question is motivated by the altered spectral distribution and noise characteristics at short sampling intervals, while the second question seeks to reconcile the complicated trade-offs between the functional contrast-to-noise ratio and the effective degrees of freedom. Although there have been recent efforts to empirically approach different aspects of these two questions, in this work we discuss, from a theoretical perspective accompanied by some illustrative, proof-of-concept experimental in vivo human fMRI data, a few considerations that are rarely mentioned, yet are important for both preprocessing and optimizing statistical inferences for studies that employ acquisitions with sub-second sampling intervals. Several summary recommendations include concerns regarding advisability of relying on low-pass filtering to de-noise physiological contributions, employment of statistical models with sufficient complexity to account for the substantially increased serial correlation, and cautions regarding using rapid sampling to enhance functional sensitivity given that different analysis models may associate with distinct trade-offs between contrast-to-noise ratios and the effective degrees of freedom. As an example, we demonstrate that as TR shortens, the intrinsic differences in how noise is accommodated in general linear models and Pearson correlation analyses (assuming Gaussian distributed stochastic signals and noise) can result in quite different outcomes, either gaining or losing statistical power.

YNIMG Journal 2018 Journal Article

Advantages of cortical surface reconstruction using submillimeter 7 T MEMPRAGE

• Natalia Zaretskaya
• Bruce Fischl
• Martin Reuter
• Ville Renvall
• Jonathan R. Polimeni

Recent advances in MR technology have enabled increased spatial resolution for routine functional and anatomical imaging, which has created demand for software tools that are able to process these data. The availability of high-resolution data also raises the question of whether higher resolution leads to substantial gains in accuracy of quantitative morphometric neuroimaging procedures, in particular the cortical surface reconstruction and cortical thickness estimation. In this study we adapted the FreeSurfer cortical surface reconstruction pipeline to process structural data at native submillimeter resolution. We then quantified the differences in surface placement between meshes generated from (0. 75 mm)3 isotropic resolution data acquired in 39 volunteers and the same data downsampled to the conventional 1 mm3 voxel size. We find that when processed at native resolution, cortex is estimated to be thinner in most areas, but thicker around the Cingulate and the Calcarine sulci as well as in the posterior bank of the Central sulcus. Thickness differences are driven by two kinds of effects. First, the gray–white surface is found closer to the white matter, especially in cortical areas with high myelin content, and thus low contrast, such as the Calcarine and the Central sulci, causing local increases in thickness estimates. Second, the gray–CSF surface is placed more interiorly, especially in the deep sulci, contributing to local decreases in thickness estimates. We suggest that both effects are due to reduced partial volume effects at higher spatial resolution. Submillimeter voxel sizes can therefore provide improved accuracy for measuring cortical thickness.

YNIMG Journal 2018 Journal Article

Analysis strategies for high-resolution UHF-fMRI data

• Jonathan R. Polimeni
• Ville Renvall
• Natalia Zaretskaya
• Bruce Fischl

Functional MRI (fMRI) benefits from both increased sensitivity and specificity with increasing magnetic field strength, making it a key application for Ultra-High Field (UHF) MRI scanners. Most UHF-fMRI studies utilize the dramatic increases in sensitivity and specificity to acquire high-resolution data reaching sub-millimeter scales, which enable new classes of experiments to probe the functional organization of the human brain. This review article surveys advanced data analysis strategies developed for high-resolution fMRI at UHF. These include strategies designed to mitigate distortion and artifacts associated with higher fields in ways that attempt to preserve spatial resolution of the fMRI data, as well as recently introduced analysis techniques that are enabled by these extremely high-resolution data. Particular focus is placed on anatomically-informed analyses, including cortical surface-based analysis, which are powerful techniques that can guide each step of the analysis from preprocessing to statistical analysis to interpretation and visualization. New intracortical analysis techniques for laminar and columnar fMRI are also reviewed and discussed. Prospects for single-subject individualized analyses are also presented and discussed. Altogether, there are both specific challenges and opportunities presented by UHF-fMRI, and the use of proper analysis strategies can help these valuable data reach their full potential.

YNIMG Journal 2018 Journal Article

Challenges and opportunities for brainstem neuroimaging with ultrahigh field MRI

• Roberta Sclocco
• Florian Beissner
• Marta Bianciardi
• Jonathan R. Polimeni
• Vitaly Napadow

The human brainstem plays a central role in connecting the cerebrum, the cerebellum and the spinal cord to one another, hosting relay nuclei for afferent and efferent signaling, and providing source nuclei for several neuromodulatory systems that impact central nervous system function. While the investigation of the brainstem with functional or structural magnetic resonance imaging has been hampered for years due to this brain structure's physiological and anatomical characteristics, the field has seen significant advances in recent years thanks to the broader adoption of ultrahigh-field (UHF) MRI scanning. In the present review, we focus on the advantages offered by UHF in the context of brainstem imaging, as well as the challenges posed by the investigation of this complex brain structure in terms of data acquisition and analysis. We also illustrate how UHF MRI can shed new light on the neuroanatomy and neurophysiology underlying different brainstem-based circuitries, such as the central autonomic network and neurotransmitter/neuromodulator systems, discuss existing and foreseeable clinical applications to better understand diseases such as chronic pain and Parkinson's disease, and explore promising future directions for further improvements in brainstem imaging using UHF MRI techniques.

YNIMG Journal 2018 Journal Article

Neuroimaging with ultra-high field MRI: Present and future

• Jonathan R. Polimeni
• Kâmil Uludağ

YNIMG Journal 2018 Journal Article

Relative latency and temporal variability of hemodynamic responses at the human primary visual cortex

• Fa-Hsuan Lin
• Jonathan R. Polimeni
• Jo-Fu Lotus Lin
• Kevin W.-K. Tsai
• Ying-Hua Chu
• Pu-Yeh Wu
• Yi-Tien Li
• Yi-Cheng Hsu

The blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signal is a robust surrogate for local neuronal activity. However, it has been shown to vary substantially across subjects, brain regions, and repetitive measurements. This variability represents a limit to the precision of the BOLD response and the ability to reliably discriminate brain hemodynamic responses elicited by external stimuli or behavior that are nearby in time. While the temporal variability of the BOLD signal at human visual cortex has been found in the range of a few hundreds of milliseconds, the spatial distributions of the average and standard deviation of this temporal variability have not been quantitatively characterized. Here we use fMRI measurements with a high sampling rate (10Hz) to map the latency, intra- and inter-subject variability of the evoked BOLD signal in human primary (V1) visual cortices using an event-related fMRI paradigm. The latency relative to the average BOLD signal evoked by 30 stimuli was estimated to be 0. 03±0. 20s. Within V1, the absolute value of the relative BOLD latency was found correlated to intra- and inter-subject temporal variability. After comparing these measures to retinotopic maps, we found that locations with V1 areas sensitive to smaller eccentricity have later responses and smaller inter-subject variabilities. These correlations were found from data with either short inter-stimulus interval (ISI; average 4s) or long ISI (average 30s). Maps of the relative latency as well as inter-/intra-subject variability were found visually asymmetric between hemispheres. Our results suggest that the latency and variability of regional BOLD signal measured with high spatiotemporal resolution may be used to detect regional differences in hemodynamics to inform fMRI studies. However, the physiological origins of timing index distributions and their hemispheric asymmetry remain to be investigated.

YNIMG Journal 2018 Journal Article

Stimulus-dependent hemodynamic response timing across the human subcortical-cortical visual pathway identified through high spatiotemporal resolution 7T fMRI

• Laura D. Lewis
• Kawin Setsompop
• Bruce R. Rosen
• Jonathan R. Polimeni

Recent developments in fMRI acquisition techniques now enable fast sampling with whole-brain coverage, suggesting fMRI can be used to track changes in neural activity at increasingly rapid timescales. When images are acquired at fast rates, the limiting factor for fMRI temporal resolution is the speed of the hemodynamic response. Given that HRFs may vary substantially in subcortical structures, characterizing the speed of subcortical hemodynamic responses, and how the hemodynamic response shape changes with stimulus duration (i. e. the hemodynamic nonlinearity), is needed for designing and interpreting fast fMRI studies of these regions. We studied the temporal properties and nonlinearities of the hemodynamic response function (HRF) across the human subcortical visual system, imaging superior colliculus (SC), lateral geniculate nucleus of the thalamus (LGN) and primary visual cortex (V1) with high spatiotemporal resolution 7 Tesla fMRI. By presenting stimuli of varying durations, we mapped the timing and nonlinearity of hemodynamic responses in these structures at high spatiotemporal resolution. We found that the hemodynamic response is consistently faster and narrower in subcortical structures than in cortex. However, the nonlinearity in LGN is similar to that in cortex, with shorter duration stimuli eliciting larger and faster responses than would have been predicted by a linear model. Using oscillatory visual stimuli, we tested the frequency response in LGN and found that its BOLD response tracked high-frequency (0. 5 Hz) oscillations. The LGN response magnitudes were comparable to V1, allowing oscillatory BOLD signals to be detected in LGN despite the small size of this structure. These results suggest that the increase in the speed and amplitude of the hemodynamic response when neural activity is brief may be the key physiological driver of fast fMRI signals, enabling detection of high-frequency oscillations with fMRI. We conclude that subcortical visual structures exhibit fast and nonlinear hemodynamic responses, and that these dynamics enable detection of fast BOLD signals even within small deep brain structures when imaging is performed at ultra-high field.

YNIMG Journal 2017 Journal Article

Functional density and edge maps: Characterizing functional architecture in individuals and improving cross-subject registration

• Tong Tong
• Iman Aganj
• Tian Ge
• Jonathan R. Polimeni
• Bruce Fischl

Population-level inferences and individual-level analyses are two important aspects in functional magnetic resonance imaging (fMRI) studies. Extracting reliable and informative features from fMRI data that capture biologically meaningful inter-subject variation is critical for aligning and comparing functional networks across subjects, and connecting the properties of functional brain organization with variations in behavior, cognition and genetics. In this study, we derive two new measures, which we term functional density map and edge map, and demonstrate their usefulness in characterizing the function of individual brains. Specifically, using data from the Human Connectome Project (HCP), we show that (1) both functional maps capture intrinsic properties of the functional connectivity pattern in individuals while exhibiting large variation across subjects; (2) functional maps derived from either resting-state or task-evoked fMRI can be used to accurately identify subjects from a population; and (3) cross-subject alignment using these functional maps considerably reduces functional variation and improves functional correspondence across subjects over state-of-the-art multimodal registration algorithms. Our results suggest that the proposed functional density and edge maps are promising features in characterizing the functional architecture in individuals and provide an alternative way to explore the functional variation across subjects.

YNIMG Journal 2017 Journal Article

HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging

• Qiuyun Fan
• Aapo Nummenmaa
• Jonathan R. Polimeni
• Thomas Witzel
• Susie Y. Huang
• Van J. Wedeen
• Bruce R. Rosen
• Lawrence L. Wald

The parameter selection for diffusion MRI experiments is dominated by the “k-q tradeoff” whereby the Signal to Noise Ratio (SNR) of the images is traded for either high spatial resolution (determined by the maximum k-value collected) or high diffusion sensitivity (effected by b-value or the q vector) but usually not both. Furthermore, different brain regions (such as gray matter and white matter) likely require different tradeoffs between these parameters due to the size of the structures to be visualized or the length-scale of the microstructure being probed. In this case, it might be advantageous to combine information from two scans – a scan with high q but low k (high angular resolution in diffusion but low spatial resolution in the image domain) to provide maximal information about white matter fiber crossing, and one low q but high k (low angular resolution but high spatial resolution) for probing the cortex. In this study, we propose a method, termed HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging, for acquiring and combining the information from these two complementary types of scan with the goal of studying diffusion in the cortex without compromising white matter fiber information. The white-gray boundary and pial surface obtained from anatomical scans are incorporated as prior information to guide the fusion. We study the complementary advantages of the fused datasets, and assess the quality of the HIBRID data compared to either alone.

YNIMG Journal 2017 Journal Article

Impacting the effect of fMRI noise through hardware and acquisition choices – Implications for controlling false positive rates

• Lawrence L. Wald
• Jonathan R. Polimeni

We review the components of time-series noise in fMRI experiments and the effect of image acquisition parameters on the noise. In addition to helping determine the total amount of signal and noise (and thus temporal SNR), the acquisition parameters have been shown to be critical in determining the ratio of thermal to physiological induced noise components in the time series. Although limited attention has been given to this latter metric, we show that it determines the degree of spatial correlations seen in the time-series noise. The spatially correlations of the physiological noise component are well known, but recent studies have shown that they can lead to a higher than expected false-positive rate in cluster-wise inference based on parametric statistical methods used by many researchers. Based on understanding the effect of acquisition parameters on the noise mixture, we propose several acquisition strategies that might be helpful reducing this elevated false-positive rate, such as moving to high spatial resolution or using highly-accelerated acquisitions where thermal sources dominate. We suggest that the spatial noise correlations at the root of the inflated false-positive rate problem can be limited with these strategies, and the well-behaved spatial auto-correlation functions (ACFs) assumed by the conventional statistical methods are retained if the high resolution data is smoothed to conventional resolutions.

YNIMG Journal 2017 Journal Article

Reduction of across-run variability of temporal SNR in accelerated EPI time-series data through FLEET-based robust autocalibration

• Anna I. Blazejewska
• Himanshu Bhat
• Lawrence L. Wald
• Jonathan R. Polimeni

Temporal signal-to-noise ratio (tSNR) is a key metric for assessing the ability to detect brain activation in fMRI data. A recent study has shown substantial variation of tSNR between multiple runs of accelerated EPI acquisitions reconstructed with the GRAPPA method using protocols commonly used for fMRI experiments. Across-run changes in the location of high-tSNR regions could lead to misinterpretation of the observed brain activation patterns, reduced sensitivity of the fMRI studies, and biased results. We compared conventional EPI autocalibration (ACS) methods with the recently-introduced FLEET ACS method, measuring their tSNR variability, as well as spatial overlap and displacement of high-tSNR clusters across runs in datasets acquired from human subjects at 7T and 3T. FLEET ACS reconstructed data had higher tSNR levels, as previously reported, as well as better temporal consistency and larger overlap of the high-tSNR clusters across runs compared with reconstructions using conventional multi-shot (ms) EPI ACS data. tSNR variability across two different runs of the same protocol using ms-EPI ACS data was about two times larger than for the protocol using FLEET ACS for acceleration factors (R) 2 and 3, and one and half times larger for R=4. The level of across-run tSNR consistency for data reconstructed with FLEET ACS was similar to within-run tSNR consistency. The displacement of high-tSNR clusters across two runs (inter-cluster distance) decreased from ∼8mm in the time-series reconstructed using conventional ms-EPI ACS data to ∼4mm for images reconstructed using FLEET ACS. However, the performance gap between conventional ms-EPI ACS and FLEET ACS narrowed with increasing parallel imaging acceleration factor. Overall, the FLEET ACS method provides a simple solution to the problem of varying tSNR across runs, and therefore helps ensure that an assumption of fMRI analysis—that tSNR is largely consistent across runs—is met for accelerated acquisitions.

YNIMG Journal 2016 Journal Article

Automatic cortical surface reconstruction of high-resolution T1 echo planar imaging data

• Ville Renvall
• Thomas Witzel
• Lawrence L. Wald
• Jonathan R. Polimeni

Echo planar imaging (EPI) is the method of choice for the majority of functional magnetic resonance imaging (fMRI), yet EPI is prone to geometric distortions and thus misaligns with conventional anatomical reference data. The poor geometric correspondence between functional and anatomical data can lead to severe misplacements and corruption of detected activation patterns. However, recent advances in imaging technology have provided EPI data with increasing quality and resolution. Here we present a framework for deriving cortical surface reconstructions directly from high-resolution EPI-based reference images that provide anatomical models exactly geometric distortion-matched to the functional data. Anatomical EPI data with 1mm isotropic voxel size were acquired using a fast multiple inversion recovery time EPI sequence (MI-EPI) at 7T, from which quantitative T 1 maps were calculated. Using these T 1 maps, volumetric data mimicking the tissue contrast of standard anatomical data were synthesized using the Bloch equations, and these T 1-weighted data were automatically processed using FreeSurfer. The spatial alignment between T 2 ⁎-weighted EPI data and the synthetic T 1-weighted anatomical MI-EPI-based images was improved compared to the conventional anatomical reference. In particular, the alignment near the regions vulnerable to distortion due to magnetic susceptibility differences was improved, and sampling of the adjacent tissue classes outside of the cortex was reduced when using cortical surface reconstructions derived directly from the MI-EPI reference. The MI-EPI method therefore produces high-quality anatomical data that can be automatically segmented with standard software, providing cortical surface reconstructions that are geometrically matched to the BOLD fMRI data.

YNIMG Journal 2016 Journal Article

Intracortical depth analyses of frequency-sensitive regions of human auditory cortex using 7T fMRI

• Jyrki Ahveninen
• Wei-Tang Chang
• Samantha Huang
• Boris Keil
• Norbert Kopco
• Stephanie Rossi
• Giorgio Bonmassar
• Thomas Witzel

Despite recent advances in auditory neuroscience, the exact functional organization of human auditory cortex (AC) has been difficult to investigate. Here, using reversals of tonotopic gradients as the test case, we examined whether human ACs can be more precisely mapped by avoiding signals caused by large draining vessels near the pial surface, which bias blood-oxygen level dependent (BOLD) signals away from the actual sites of neuronal activity. Using ultra-high field (7T) fMRI and cortical depth analysis techniques previously applied in visual cortices, we sampled 1mm isotropic voxels from different depths of AC during narrow-band sound stimulation with biologically relevant temporal patterns. At the group level, analyses that considered voxels from all cortical depths, but excluded those intersecting the pial surface, showed (a) the greatest statistical sensitivity in contrasts between activations to high vs. low frequency sounds and (b) the highest inter-subject consistency of phase-encoded continuous tonotopy mapping. Analyses based solely on voxels intersecting the pial surface produced the least consistent group results, even when compared to analyses based solely on voxels intersecting the white-matter surface where both signal strength and within-subject statistical power are weakest. However, no evidence was found for reduced within-subject reliability in analyses considering the pial voxels only. Our group results could, thus, reflect improved inter-subject correspondence of high and low frequency gradients after the signals from voxels near the pial surface are excluded. Using tonotopy analyses as the test case, our results demonstrate that when the major physiological and anatomical biases imparted by the vasculature are controlled, functional mapping of human ACs becomes more consistent from subject to subject than previously thought.

YNIMG Journal 2016 Journal Article

MGH–USC Human Connectome Project datasets with ultra-high b-value diffusion MRI

• Qiuyun Fan
• Thomas Witzel
• Aapo Nummenmaa
• Koene R.A. Van Dijk
• John D. Van Horn
• Michelle K. Drews
• Leah H. Somerville
• Margaret A. Sheridan

The MGH–USC CONNECTOM MRI scanner housed at the Massachusetts General Hospital (MGH) is a major hardware innovation of the Human Connectome Project (HCP). The 3T CONNECTOM scanner is capable of producing a magnetic field gradient of up to 300mT/m strength for in vivo human brain imaging, which greatly shortens the time spent on diffusion encoding, and decreases the signal loss due to T2 decay. To demonstrate the capability of the novel gradient system, data of healthy adult participants were acquired for this MGH–USC Adult Diffusion Dataset (N =35), minimally preprocessed, and shared through the Laboratory of Neuro Imaging Image Data Archive (LONI IDA) and the WU–Minn Connectome Database (ConnectomeDB). Another purpose of sharing the data is to facilitate methodological studies of diffusion MRI (dMRI) analyses utilizing high diffusion contrast, which perhaps is not easily feasible with standard MR gradient system. In addition, acquisition of the MGH–Harvard–USC Lifespan Dataset is currently underway to include 120 healthy participants ranging from 8 to 90 years old, which will also be shared through LONI IDA and ConnectomeDB. Here we describe the efforts of the MGH–USC HCP consortium in acquiring and sharing the ultra-high b-value diffusion MRI data and provide a report on data preprocessing and access. We conclude with a demonstration of the example data, along with results of standard diffusion analyses, including q-ball Orientation Distribution Function (ODF) reconstruction and tractography.

YNIMG Journal 2016 Journal Article

Rapid multi-orientation quantitative susceptibility mapping

• Berkin Bilgic
• Luke Xie
• Russell Dibb
• Christian Langkammer
• Aysegul Mutluay
• Huihui Ye
• Jonathan R. Polimeni
• Jean Augustinack

Three-dimensional gradient echo (GRE) is the main workhorse sequence used for susceptibility weighted imaging (SWI), quantitative susceptibility mapping (QSM), and susceptibility tensor imaging (STI). Achieving optimal phase signal-to-noise ratio requires late echo times, thus necessitating a long repetition time (TR). Combined with the large encoding burden of whole-brain coverage with high resolution, this leads to increased scan time. Further, the dipole kernel relating the tissue phase to the underlying susceptibility distribution undersamples the frequency content of the susceptibility map. Scans at multiple head orientations along with calculation of susceptibility through multi-orientation sampling (COSMOS) are one way to effectively mitigate this issue. Additionally, STI requires a minimum of 6 head orientations to solve for the independent tensor elements in each voxel. The requirements of high-resolution imaging with long TR at multiple orientations substantially lengthen the acquisition of COSMOS and STI. The goal of this work is to dramatically speed up susceptibility mapping at multiple head orientations. We demonstrate highly efficient acquisition using 3D-GRE with Wave-CAIPI and dramatically reduce the acquisition time of these protocols. Using R=15-fold acceleration with Wave-CAIPI permits acquisition per head orientation in 90s at 1. 1mm isotropic resolution, and 5: 35min at 0. 5mm isotropic resolution. Since Wave-CAIPI fully harnesses the 3D spatial encoding capability of receive arrays, the maximum g-factor noise amplification remains below 1. 30 at 3T and 1. 12 at 7T. This allows a 30-min exam for STI with 12 orientations, thus paving the way to its clinical application.

YNIMG Journal 2014 Journal Article

A study-specific fMRI normalization approach that operates directly on high resolution functional EPI data at 7Tesla

• Günther Grabner
• Benedikt A. Poser
• Kyoko Fujimoto
• Jonathan R. Polimeni
• Lawrence L. Wald
• Siegfried Trattnig
• Ivan Toni
• Markus Barth

Due to the availability of ultra-high field scanners and novel imaging methods, high resolution, whole brain functional MR imaging (fMRI) has become increasingly feasible. However, it is common to use extensive spatial smoothing to account for inter-subject anatomical variation when pooling over subjects. This reduces the spatial details of group level functional activation considerably, even when the original data was acquired with high resolution. In our study we used an accelerated 3D EPI sequence at 7Tesla to acquire whole brain fMRI data with an isotropic spatial resolution of 1. 1mm which shows clear gray/white matter contrast due to the stronger T1 weighting of 3D EPI. To benefit from the high spatial resolution on the group level, we develop a study specific, high resolution anatomical template which is facilitated by the good anatomical contrast that is present in the average functional EPI images. Different template generations with increasing accuracy were created by using a hierarchical linear and stepwise non-linear registration approach. As the template is based on the functional data themselves no additional co-registration step with the usual T1-weighted anatomical data is necessary which eliminates a potential source of misalignment. To test the improvement of functional localization and spatial details we performed a group level analysis of a finger tapping experiment in eight subjects. The most accurate template shows better spatial localization – such as a separation of somatosensory and motor areas and of single digit activation – compared to the simple linear registration. The number of activated voxels is increased by a factor of 1. 2, 2. 5, and 3. 1 for somatosensory, supplementary motor area, and dentate nucleus, respectively, for the functional contrast between left versus right hand. Similarly, the number of activated voxels is increased 1. 4- and 2. 4-fold for right little versus right index finger and left little versus left index finger, respectively. The Euclidian distance between the activation (center of gravity) of the respective fingers was found to be 13. 90mm using the most accurate template.

YNIMG Journal 2014 Journal Article

Corrigendum to “fMRI hemodynamics accurately reflects neuronal timing in the human brain measured by MEG” [Neuroimage 78 (2013) 372–384]

• Fa-Hsuan Lin
• Thomas Witzel
• Tommi Raij
• Jyrki Ahveninen
• Kevin Wen-Kai Tsai
• Ying-Hua Chu
• Wei-Tang Chang
• Aapo Nummenmaa

YNIMG Journal 2014 Journal Article

Dynamic and static contributions of the cerebrovasculature to the resting-state BOLD signal

• Sungho Tak
• Danny J.J. Wang
• Jonathan R. Polimeni
• Lirong Yan
• J. Jean Chen

Functional magnetic resonance imaging (fMRI) in the resting state, particularly fMRI based on the blood-oxygenation level-dependent (BOLD) signal, has been extensively used to measure functional connectivity in the brain. However, the mechanisms of vascular regulation that underlie the BOLD fluctuations during rest are still poorly understood. In this work, using dual-echo pseudo-continuous arterial spin labeling and MR angiography (MRA), we assess the spatio-temporal contribution of cerebral blood flow (CBF) to the resting-state BOLD signals and explore how the coupling of these signals is associated with regional vasculature. Using a general linear model analysis, we found that statistically significant coupling between resting-state BOLD and CBF fluctuations is highly variable across the brain, but the coupling is strongest within the major nodes of established resting-state networks, including the default-mode, visual, and task-positive networks. Moreover, by exploiting MRA-derived large vessel (macrovascular) volume fraction, we found that the degree of BOLD–CBF coupling significantly decreased as the ratio of large vessels to tissue volume increased. These findings suggest that the portion of resting-state BOLD fluctuations at the sites of medium-to-small vessels (more proximal to local neuronal activity) is more closely regulated by dynamic regulations in CBF, and that this CBF regulation decreases closer to large veins, which are more distal to neuronal activity.

YNIMG Journal 2014 Journal Article

Dynamic functional imaging of brain glucose utilization using fPET-FDG

• Marjorie Villien
• Hsiao-Ying Wey
• Joseph B. Mandeville
• Ciprian Catana
• Jonathan R. Polimeni
• Christin Y. Sander
• Nicole R. Zürcher
• Daniel B. Chonde

Glucose is the principal source of energy for the brain and yet the dynamic response of glucose utilization to changes in brain activity is still not fully understood. Positron emission tomography (PET) allows quantitative measurement of glucose metabolism using 2-[18F]-fluorodeoxyglucose (FDG). However, FDG PET in its current form provides an integral (or average) of glucose consumption over tens of minutes and lacks the temporal information to capture physiological alterations associated with changes in brain activity induced by tasks or drug challenges. Traditionally, changes in glucose utilization are inferred by comparing two separate scans, which significantly limits the utility of the method. We report a novel method to track changes in FDG metabolism dynamically, with higher temporal resolution than exists to date and within a single session. Using a constant infusion of FDG, we demonstrate that our technique (termed fPET-FDG) can be used in an analysis pipeline similar to fMRI to define within-session differential metabolic responses. We use visual stimulation to demonstrate the feasibility of this method. This new method has a great potential to be used in research protocols and clinical settings since fPET-FDG imaging can be performed with most PET scanners and data acquisition and analysis are straightforward. fPET-FDG is a highly complementary technique to MRI and provides a rich new way to observe functional changes in brain metabolism.

YNIMG Journal 2014 Journal Article

Quantitative comparison of cortical surface reconstructions from MP2RAGE and multi-echo MPRAGE data at 3 and 7 T

• Kyoko Fujimoto
• Jonathan R. Polimeni
• André J.W. van der Kouwe
• Martin Reuter
• Tobias Kober
• Thomas Benner
• Bruce Fischl
• Lawrence L. Wald

The Magnetization-Prepared 2 Rapid Acquisition Gradient Echo (MP2RAGE) method achieves spatially uniform contrast across the entire brain between gray matter and surrounding white matter tissue and cerebrospinal fluid by rapidly acquiring data at two points during an inversion recovery, and then combining the two volumes so as to cancel out sources of intensity and contrast bias, making it useful for neuroimaging studies at ultrahigh field strengths (≥7T). To quantify the effectiveness of the MP2RAGE method for quantitative morphometric neuroimaging, we performed tissue segmentation and cerebral cortical surface reconstruction of the MP2RAGE data and compared the results with those generated from conventional multi-echo MPRAGE (MEMPRAGE) data across a group of healthy subjects. To do so, we developed a preprocessing scheme for the MP2RAGE image data to allow for automatic cortical segmentation and surface reconstruction using FreeSurfer and analysis methods to compare the positioning of the surface meshes. Using image volumes with 1mm isotropic voxels we found a scan–rescan reproducibility of cortical thickness estimates to be 0. 15mm (or 6%) for the MEMPRAGE data and a slightly lower reproducibility of 0. 19mm (or 8%) for the MP2RAGE data. We also found that the thickness estimates were systematically smaller in the MP2RAGE data, and that both the interior and exterior cortical boundaries estimated from the MP2RAGE data were consistently positioned within the corresponding boundaries estimated from the MEMPRAGE data. Therefore several measureable differences exist in the appearance of cortical gray matter and its effect on automatic segmentation methods that must be considered when choosing an acquisition or segmentation method for studies requiring cortical surface reconstructions. We propose potential extensions to the MP2RAGE method that may help to reduce or eliminate these discrepancies.

YNIMG Journal 2013 Journal Article

Corrigendum to “Surface based analysis of diffusion orientation for identifying architectonic domains in the in vivo human cortex” [NeuroImage 69 (2013) 87–100]

• Jennifer A. McNab
• Jonathan R. Polimeni
• Ruopeng Wang
• Jean C. Augustinack
• Kyoko Fujimoto
• Allison Stevens
• Christina Triantafyllou
• Thomas Janssens

YNIMG Journal 2013 Journal Article

fMRI hemodynamics accurately reflects neuronal timing in the human brain measured by MEG

• Fa-Hsuan Lin
• Thomas Witzel
• Tommi Raij
• Jyrki Ahveninen
• Kevin Wen-Kai Tsai
• Yin-Hua Chu
• Wei-Tang Chang
• Aapo Nummenmaa

Neuronal activation sequence information is essential for understanding brain functions. Extracting such timing information from blood oxygenation level dependent (BOLD) fMRI is confounded by interregional neurovascular differences and poorly understood relations between BOLD and electrophysiological response delays. Here, we recorded whole-head BOLD fMRI at 100ms resolution and magnetoencephalography (MEG) during a visuomotor reaction-time task. Both methods detected the same activation sequence across five regions, from visual towards motor cortices, with linearly correlated interregional BOLD and MEG response delays. The smallest significant interregional BOLD delay was 100ms; all delays ≥400ms were significant. Switching the order of external events reversed the sequence of BOLD activations, indicating that interregional neurovascular differences did not confound the results. This may open new avenues for using fMRI to follow rapid activation sequences in the brain.

YNIMG Journal 2013 Journal Article

Surface based analysis of diffusion orientation for identifying architectonic domains in the in vivo human cortex

• Jennifer A. McNab
• Jonathan R. Polimeni
• Ruopeng Wang
• Jean C. Augustinack
• Kyoko Fujimoto
• Allison Stevens
• Thomas Janssens
• Reza Farivar

Diffusion tensor MRI is sensitive to the coherent structure of brain tissue and is commonly used to study large-scale white matter structure. Diffusion in gray matter is more isotropic, however, several groups have observed coherent patterns of diffusion anisotropy within the cerebral cortical gray matter. We extend the study of cortical diffusion anisotropy by relating it to the local coordinate system of the folded cerebral cortex. We use 1mm and sub-millimeter isotropic resolution diffusion imaging to perform a laminar analysis of the principal diffusion orientation, fractional anisotropy, mean diffusivity and partial volume effects. Data from 6 in vivo human subjects, a fixed human brain specimen and an anesthetized macaque were examined. Large regions of cortex show a radial diffusion orientation. In vivo human and macaque data displayed a sharp transition from radial to tangential diffusion orientation at the border between primary motor and somatosensory cortex, and some evidence of tangential diffusion in secondary somatosensory cortex and primary auditory cortex. Ex vivo diffusion imaging in a human tissue sample showed some tangential diffusion orientation in S1 but mostly radial diffusion orientations in both M1 and S1.

YNIMG Journal 2013 Journal Article

The minimal preprocessing pipelines for the Human Connectome Project

• Matthew F. Glasser
• Stamatios N. Sotiropoulos
• J. Anthony Wilson
• Timothy S. Coalson
• Bruce Fischl
• Jesper L. Andersson
• Junqian Xu
• Saad Jbabdi

The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines.

YNIMG Journal 2013 Journal Article

Whole-head rapid fMRI acquisition using echo-shifted magnetic resonance inverse imaging

• Wei-Tang Chang
• Aapo Nummenmaa
• Thomas Witzel
• Jyrki Ahveninen
• Samantha Huang
• Kevin Wen-Kai Tsai
• Ying-Hua Chu
• Jonathan R. Polimeni

The acquisition time of BOLD contrast functional MRI (fMRI) data with whole-brain coverage typically requires a sampling rate of one volume in 1–3s. Although the volumetric sampling time of a few seconds is adequate for measuring the sluggish hemodynamic response (HDR) to neuronal activation, faster sampling of fMRI might allow for monitoring of rapid physiological fluctuations and detection of subtle neuronal activation timing information embedded in BOLD signals. Previous studies utilizing a highly accelerated volumetric MR inverse imaging (InI) technique have provided a sampling rate of one volume per 100ms with 5mm spatial resolution. Here, we propose a novel modification of this technique, the echo-shifted InI, which allows TE to be longer than TR, to measure BOLD fMRI at an even faster sampling rate of one volume per 25ms with whole-brain coverage. Compared with conventional EPI, echo-shifted InI provided an 80-fold speedup with similar spatial resolution and less than 2-fold temporal SNR loss. The capability of echo-shifted InI to detect HDR timing differences was tested empirically. At the group level (n =6), echo-spaced InI was able to detect statistically significant HDR timing differences of as low as 50ms in visual stimulus presentation. At the level of individual subjects, significant differences in HDR timing were detected for 400ms stimulus-onset differences. Our results also show that the temporal resolution of 25ms is necessary for maintaining the temporal detecting capability at this level. With the capabilities of being able to distinguish the timing differences in the millisecond scale, echo-shifted InI could be a useful fMRI tool for obtaining temporal information at a time scale closer to that of neuronal dynamics.

YNIMG Journal 2011 Journal Article

Physiological noise and signal-to-noise ratio in fMRI with multi-channel array coils

• Christina Triantafyllou
• Jonathan R. Polimeni
• Lawrence L. Wald

Sensitivity in BOLD fMRI is characterized by the signal to noise ratio (SNR) of the time-series (tSNR), which contains fluctuations from thermal and physiological noise sources. Alteration of an acquisition parameter can affect the tSNR differently depending on the relative magnitude of the physiological and thermal noise, therefore knowledge of this ratio is essential for optimizing fMRI acquisitions. In this study, we compare image and time-series SNR from array coils at 3T with and without parallel imaging (GRAPPA) as a function of image resolution and acceleration. We use the “absolute unit” SNR method of Kellman and McVeigh to calculate the image SNR (SNR0) in a way that renders it comparable to tSNR, allowing determination of the thermal to physiological noise ratio, and the pseudo-multiple replica method to quantify the image noise alterations due to the GRAPPA reconstruction. The Kruger and Glover noise model, in which the physiological noise standard deviation is proportional to signal strength, was found to hold for the accelerated and non-accelerated array coil data. Thermal noise dominated the EPI time-series for medium to large voxel sizes for single-channel and 12-channel head coil configurations, but physiological noise dominated the 32-channel array acquisition even at 1mm×1mm×3mm resolution. At higher acceleration factors, image SNR is reduced and the time-series becomes increasingly thermal noise dominant. However, the tSNR reduction is smaller than the reduction in image SNR due to the presence of physiological noise.

YNIMG Journal 2010 Journal Article

Laminar analysis of 7T BOLD using an imposed spatial activation pattern in human V1

• Jonathan R. Polimeni
• Bruce Fischl
• Douglas N. Greve
• Lawrence L. Wald

With sufficient image encoding, high-resolution fMRI studies are limited by the biological point-spread of the hemodynamic signal. The extent of this spread is determined by the local vascular distribution and by the spatial specificity of blood flow regulation, as well as by measurement parameters that (i) alter the relative sensitivity of the acquisition to activation-induced hemodynamic changes and (ii) determine the image contrast as a function of vessel size. In particular, large draining vessels on the cortical surface are a major contributor to both the BOLD signal change and to the spatial bias of the BOLD activation away from the site of neuronal activity. In this work, we introduce a laminar surface-based analysis method and study the relationship between spatial localization and activation strength as a function of laminar depth by acquiring 1mm isotropic, single-shot EPI at 7T and sampling the BOLD signal exclusively from the superficial, middle, or deep cortical laminae. We show that highly-accelerated EPI can limit image distortions to the point where a boundary-based registration algorithm accurately aligns the EPI data to the surface reconstruction. The spatial spread of the BOLD response tangential to the cortical surface was analyzed as a function of cortical depth using our surface-based analysis. Although sampling near the pial surface provided the highest signal strength, it also introduced the most spatial error. Thus, avoiding surface laminae improved spatial localization by about 40% at a cost of 36% in z-statistic, implying that optimal spatial resolution in functional imaging of the cortex can be achieved using anatomically-informed spatial sampling to avoid large pial vessels.

YNIMG Journal 2010 Journal Article

Near-isometric flattening of brain surfaces

• Mukund Balasubramanian
• Jonathan R. Polimeni
• Eric L. Schwartz

Flattened representations of brain surfaces are often used to visualize and analyze spatial patterns of structural organization and functional activity. Here, we present a set of rigorous criteria and accompanying test cases with which to evaluate flattening algorithms that attempt to preserve shortest-path distances on the original surface. We also introduce a novel flattening algorithm that is the first to satisfy all of these criteria and demonstrate its ability to produce accurate flat maps of human and macaque visual cortex. Using this algorithm, we have recently obtained results showing a remarkable, unexpected degree of consistency in the shape and topographic structure of visual cortical areas within humans and macaques, as well as between these two species.

YNIMG Journal 2009 Journal Article

Locating the functional and anatomical boundaries of human primary visual cortex

• Oliver Hinds
• Jonathan R. Polimeni
• Niranjini Rajendran
• Mukund Balasubramanian
• Katrin Amunts
• Karl Zilles
• Eric L. Schwartz
• Bruce Fischl

The primary visual cortex (V1) can be delineated both functionally by its topographic map of the visual field and anatomically by its distinct pattern of laminar myelination. Although it is commonly assumed that the specialized anatomy V1 exhibits corresponds in location with functionally defined V1, demonstrating this in human has not been possible thus far due to the difficulty of determining the location of V1 both functionally and anatomically in the same individual. In this study we use MRI to measure the anatomical and functional V1 boundaries in the same individual and demonstrate close agreement between them. Functional V1 location was measured by parcellating occipital cortex of 10 living humans into visual cortical areas based on the topographic map of the visual field measured using functional MRI. Anatomical V1 location was estimated for these same subjects using a surface-based probabilistic atlas derived from high-resolution structural MRI of the stria of Gennari in 10 intact ex vivo human hemispheres. To ensure that the atlas prediction was correct, it was validated against V1 location measured using an observer-independent cortical parcellation based on the laminar pattern of cell density in serial brain sections from 10 separate individuals. The close agreement between the independent anatomically and functionally derived V1 boundaries indicates that the whole extent of V1 can be accurately predicted based on cortical surface reconstructions computed from structural MRI scans, eliminating the need for functional localizers of V1. In addition, that the primary cortical folds predict the location of functional V1 suggests that the mechanism giving rise to V1 location is tied to the development of the cortical folds.

YNIMG Journal 2009 Journal Article

Predicting the location of entorhinal cortex from MRI

• Bruce Fischl
• Allison A. Stevens
• Niranjini Rajendran
• B.T. Thomas Yeo
• Douglas N. Greve
• Koen Van Leemput
• Jonathan R. Polimeni
• Sita Kakunoori

Entorhinal cortex (EC) is a medial temporal lobe area critical to memory formation and spatial navigation that is among the earliest parts of the brain affected by Alzheimer's disease (AD). Accurate localization of EC would thus greatly facilitate early detection and diagnosis of AD. In this study, we used ultra-high resolution ex vivo MRI to directly visualize the architectonic features that define EC rostrocaudally and mediolaterally, then applied surface-based registration techniques to quantify the variability of EC with respect to cortical geometry, and made predictions of its location on in vivo scans. The results indicate that EC can be localized quite accurately based on cortical folding patterns, within 3 mm in vivo, a significant step forward in our ability to detect the earliest effects of AD when clinical intervention is most likely to be effective.

YNIMG Journal 2008 Journal Article

Accurate prediction of V1 location from cortical folds in a surface coordinate system

• Oliver P. Hinds
• Niranjini Rajendran
• Jonathan R. Polimeni
• Jean C. Augustinack
• Graham Wiggins
• Lawrence L. Wald
• H. Diana Rosas
• Andreas Potthast

Previous studies demonstrated substantial variability of the location of primary visual cortex (V1) in stereotaxic coordinates when linear volume-based registration is used to match volumetric image intensities [Amunts, K. , Malikovic, A. , Mohlberg, H. , Schormann, T. , and Zilles, K. (2000). Brodmann’s areas 17 and 18 brought into stereotaxic space—where and how variable? Neuroimage, 11(1): 66–84]. However, other qualitative reports of V1 location [Smith, G. (1904). The morphology of the occipital region of the cerebral hemisphere in man and the apes. Anatomischer Anzeiger, 24: 436–451; Stensaas, S. S. , Eddington, D. K. , and Dobelle, W. H. (1974). The topography and variability of the primary visual cortex in man. J Neurosurg, 40(6): 747–755; Rademacher, J. , Caviness, V. S. , Steinmetz, H. , and Galaburda, A. M. (1993). Topographical variation of the human primary cortices: implications for neuroimaging, brain mapping, and neurobiology. Cereb Cortex, 3(4): 313–329] suggested a consistent relationship between V1 and the surrounding cortical folds. Here, the relationship between folds and the location of V1 is quantified using surface-based analysis to generate a probabilistic atlas of human V1. High-resolution (about 200 μm) magnetic resonance imaging (MRI) at 7 T of ex vivo human cerebral hemispheres allowed identification of the full area via the stria of Gennari: a myeloarchitectonic feature specific to V1. Separate, whole-brain scans were acquired using MRI at 1. 5 T to allow segmentation and mesh reconstruction of the cortical gray matter. For each individual, V1 was manually identified in the high-resolution volume and projected onto the cortical surface. Surface-based intersubject registration [Fischl, B. , Sereno, M. I. , Tootell, R. B. , and Dale, A. M. (1999b). High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp, 8(4): 272–84] was performed to align the primary cortical folds of individual hemispheres to those of a reference template representing the average folding pattern. An atlas of V1 location was constructed by computing the probability of V1 inclusion for each cortical location in the template space. This probabilistic atlas of V1 exhibits low prediction error compared to previous V1 probabilistic atlases built in volumetric coordinates. The increased predictability observed under surface-based registration suggests that the location of V1 is more accurately predicted by the cortical folds than by the shape of the brain embedded in the volume of the skull. In addition, the high quality of this atlas provides direct evidence that surface-based intersubject registration methods are superior to volume-based methods at superimposing functional areas of cortex and therefore are better suited to support multisubject averaging for functional imaging experiments targeting the cerebral cortex.

YNIMG Journal 2008 Journal Article

Event-related single-shot volumetric functional magnetic resonance inverse imaging of visual processing

• Fa-Hsuan Lin
• Thomas Witzel
• Joseph B. Mandeville
• Jonathan R. Polimeni
• Thomas A. Zeffiro
• Douglas N. Greve
• Graham Wiggins
• Lawrence L. Wald

Developments in multi-channel radio-frequency (RF) coil array technology have enabled functional magnetic resonance imaging (fMRI) with higher degrees of spatial and temporal resolution. While modest improvement in temporal acceleration has been achieved by increasing the number of RF coils, the maximum attainable acceleration in parallel MRI acqisition is intrinsically limited only by the amount of independent spatial information in the combined array channels. Since the geometric configuration of a large-n MRI head coil array is similar to that used in EEG electrode or MEG SQUID sensor arrays, the source localization algorithms used in MEG or EEG source imaging can be extended to also process MRI coil array data, resulting in greatly improved temporal resolution by minimizing k-space traversal during signal acquisition. Using a novel approach, we acquire multi-channel MRI head coil array data and then apply inverse reconstruction methods to obtain volumetric fMRI estimates of blood oxygenation level dependent (BOLD) contrast at unprecedented whole-brain acquisition rates of 100 ms. We call this combination of techniques magnetic resonance Inverse Imaging (InI), a method that provides estimates of dynamic spatially-resolved signal change that can be used to construct statistical maps of task-related brain activity. We demonstrate the sensitivity and inter-subject reliability of volumetric InI using an event-related design to probe the hemodynamic signal modulations in primary visual cortex. Robust results from both single subject and group analyses demonstrate the sensitivity and feasibility of using volumetric InI in high temporal resolution investigations of human brain function.