Using High Spatial Resolution to Improve BOLD fMRI Detection at 3T

For different functional magnetic resonance imaging experiments using blood oxygenation level-dependent (BOLD) contrast, the acquisition of T ₂*-weighted scans at a high spatial resolution may be advantageous in terms of time-course signal-to-noise ratio and of BOLD sensitivity when the regions are prone to susceptibility artifacts. In this study, we explore this solution by examining how spatial resolution influences activations elicited when appetizing food pictures are viewed. Twenty subjects were imaged at 3 T with two different voxel volumes, 3.4 μl and 27 μl. Despite the diminution of brain coverage, we found that high-resolution acquisition led to a better detection of activations. Though known to suffer to different degrees from susceptibility artifacts, the activations detected by high spatial resolution were notably consistent with those reported in published activation likelihood estimation meta-analyses, corresponding to taste-responsive regions. Furthermore, these regions were found activated bilaterally, in contrast with previous findings. Both the reduction of partial volume effect, which improves BOLD contrast, and the mitigation of susceptibility artifact, which boosts the signal to noise ratio in certain regions, explained the better detection noted with high resolution. The present study provides further evidences that high spatial resolution is a valuable solution for human BOLD fMRI, especially for studying food-related stimuli.     

Investigating the Effects of Antipsychotics and Schizotypy on the N400 Using Event-Related Potentials and Semantic Categorization

Within the field of cognitive neuroscience, functional magnetic resonance imaging (fMRI) is a popular method of visualizing brain function. This is in part because of its excellent spatial resolution, which allows researchers to identify brain areas associated with specific cognitive processes. However, in the quest to localize brain functions, it is relevant to note that many cognitive, sensory, and motor processes have temporal distinctions that are imperative to capture, an aspect that is left unfulfilled by fMRI’s suboptimal temporal resolution. To better understand cognitive processes, it is thus advantageous to utilize event-related potential (ERP) recording as a method of gathering information about the brain. Some of its advantages include its fantastic temporal resolution, which gives researchers the ability to follow the activity of the brain down to the millisecond. It also directly indexes both excitatory and inhibitory post-synaptic potentials by which most brain computations are performed. This sits in contrast to fMRI, which captures an index of metabolic activity. Further, the non-invasive ERP method does not require a contrast condition: raw ERPs can be examined for just one experimental condition, a distinction from fMRI where control conditions must be subtracted from the experimental condition, leading to uncertainty in associating observations with experimental or contrast conditions. While it is limited by its poor spatial and subcortical activity resolution, ERP recordings’ utility, relative cost-effectiveness, and associated advantages offer strong rationale for its use in cognitive neuroscience to track rapid temporal changes in neural activity. In an effort to foster increase in its use as a research imaging method, and to ensure proper and accurate data collection, the present article will outline – in the framework of a paradigm using semantic categorization to examine the effects of antipsychotics and schizotypy on the N400 – the procedure and key aspects associated with ERP data acquisition.     

Insights into new techniques for high resolution functional MRI

Non-invasive functional magnetic resonance imaging (fMRI) has opened a unique window into human and animal brain function, with a spatial resolution of a few millimeters and a temporal resolution of a few seconds. To further improve the current technical limitations of fMRI, various post-processing and data acquisition schemes were developed. Improved fMRI methods include variations of a conventional fMRI technique, mapping a single physiological parameter such as cerebral blood flow or cerebral blood volume, and direct mapping of neural activity. Advances in fMRI techniques allow scientists to map submillimeter columnar and laminar functional structures and to detect tens of millisecond neural activity in certain specific tasks.     

Time-dependent spatial specificity of high-resolution fMRI: insights into mesoscopic neurovascular coupling

High-resolution functional magnetic resonance imaging (fMRI) is becoming increasingly popular because of the growing availability of ultra-high magnetic fields which are capable of improving sensitivity and spatial resolution. However, it is debatable whether increased spatial resolutions for haemodynamic-based techniques, like fMRI, can accurately detect the true location of neuronal activity. We have addressed this issue in functional columns and layers of animals with haemoglobin-based optical imaging and different fMRI contrasts, such as blood oxygenation level-dependent, cerebral blood flow and cerebral blood volume fMRI. In this review, we describe empirical evidence primarily from our own studies on how well these fMRI signals are spatially specific to the neuronally active site and discuss insights into neurovascular coupling at the mesoscale.This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity’.     

High-resolution functional magnetic resonance imaging of the animal brain

To fully understand brain function, one must look beyond the level of a single neuron. By elucidating the spatial properties of the columnar and laminar functional architectures, information regarding the neural processing in the brain can be gained. To map these fine functional structures noninvasively and repeatedly, functional magnetic resonance imaging (fMRI) can be employed. In this article the basic principles of fMRI are introduced, including specific hardware requirements and the equipment necessary for animal magnetic resonance research. Since fMRI measures a change in secondary hemodynamic responses induced by neural activity, it is critical to understand the principles and potential pitfalls of fMRI techniques. Thus, the underlying physics of conventional blood oxygenation, cerebral blood flow, and cerebral blood volume-based fMRI techniques are extensively discussed. Tissue-specific signal change is close to the site of neural activity, while signals from large vessels can be distant from the actual active site. Thus, methods to minimize large vessel contributions and to maximize tissue signals are described. The fundamental limitation of fMRI spatial resolution is the intrinsic hemodynamic response. Based on our high-resolution fMRI studies, the hemodynamic response is regulated at submillimeter functional domains and thus spatial resolution can be achieved to an order of 100 microm. Since hemodynamic responses are sluggish, it is difficult to obtain very high temporal resolution. By using an approach with multiple experiments with different stimulus conditions, temporal resolution can be improved on the order of 100 ms. With current fMRI technologies, submillimeter columnar- and laminar-specific specific functional images can be obtained from animal brains.     

Functional magnetic resonance imaging of awake behaving macaques

In recent years, more and more laboratories have developed functional Magnetic Resonance Imaging (fMRI) for awake non-human primates. This research is essential to provide a link between non-invasive hemodynamic signals recorded in the human brain and the vast body of knowledge gained from invasive electrophysiological studies in monkeys. Given that their brain structure is so closely related to that of humans and that monkeys can be trained to perform complicated behavioral tasks, results obtained with monkey fMRI and electrophysiology can be compared to fMRI results obtained in humans, and provide information crucial to a better understanding of the mechanisms by which different cortical areas perform their functions in the human brain. However, despite that the first publications on fMRI in awake behaving macaques appeared ～10 years ago (Logothetis et al. (1999) [1], Stefanacci et al. (1998) [2], Dubowitz et al. (1998) [3]), relatively few laboratories perform such experiments routinely, a sign of the significant technical difficulties that must be overcome. The higher spatial resolution required because of the animal’s smaller brain results in poorer signal-to-noise ratios than in human fMRI, which is further compounded by problems due to animal motion. Here, we discuss the specific challenges and benefits of fMRI in the awake monkey and review the methodologies and strategies for scanning behaving macaques.     

Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data

The neural patterns recorded during a neuroscientific experiment reflect complex interactions between many brain regions, each comprising millions of neurons. However, the measurements themselves are typically abstracted from that underlying structure. For example, functional magnetic resonance imaging (fMRI) datasets comprise a time series of three-dimensional images, where each voxel in an image (roughly) reflects the activity of the brain structure(s)–located at the corresponding point in space–at the time the image was collected. FMRI data often exhibit strong spatial correlations, whereby nearby voxels behave similarly over time as the underlying brain structure modulates its activity. Here we develop topographic factor analysis (TFA), a technique that exploits spatial correlations in fMRI data to recover the underlying structure that the images reflect. Specifically, TFA casts each brain image as a weighted sum of spatial functions. The parameters of those spatial functions, which may be learned by applying TFA to an fMRI dataset, reveal the locations and sizes of the brain structures activated while the data were collected, as well as the interactions between those structures.     

Development a flexible light‐sheet fluorescence microscope for high‐speed 3D imaging of calcium dynamics and 3D imaging of cellular microstructure

We report a flexible light‐sheet fluorescence microscope (LSFM) designed for studying dynamic events in cardiac tissue at high speed in 3D and the correlation of these events to cell microstructure. The system employs two illumination‐detection modes: the first uses angle‐dithering of a Gaussian light sheet combined with remote refocusing of the detection plane for video‐rate volumetric imaging; the second combines digitally‐scanned light‐sheet illumination with an axially‐swept light‐sheet waist and stage‐scanned acquisition for improved axial resolution compared to the first mode. We present a characterisation of the spatial resolution of the system in both modes. The first illumination‐detection mode achieves dual spectral‐channel imaging at 25 volumes per second with 1024 × 200 × 50 voxel volumes and is demonstrated by time‐lapse imaging of calcium dynamics in a live cardiomyocyte. The second illumination‐detection mode is demonstrated through the acquisition of a higher spatial resolution structural map of the t‐tubule network in a fixed cardiomyocyte cell.     

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

Functional magnetic resonance imaging (fMRI) is currently the standard method of evaluating brain function in the field of Cognitive Neuroscience, in part because fMRI data acquisition and analysis techniques are readily available. Because fMRI has excellent spatial resolution but poor temporal resolution, this method can only be used to identify the spatial location of brain activity associated with a given cognitive process (and reveals virtually nothing about the time course of brain activity). By contrast, event-related potential (ERP) recording, a method that is used much less frequently than fMRI, has excellent temporal resolution and thus can track rapid temporal modulations in neural activity. Unfortunately, ERPs are under utilized in Cognitive Neuroscience because data acquisition techniques are not readily available and low density ERP recording has poor spatial resolution. In an effort to foster the increased use of ERPs in Cognitive Neuroscience, the present article details key techniques involved in high density ERP data acquisition. Critically, high density ERPs offer the promise of excellent temporal resolution and good spatial resolution (or excellent spatial resolution if coupled with fMRI), which is necessary to capture the spatial-temporal dynamics of human brain function.Download video file.^{(101M, mp4)}     

Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging

Echo planar imaging (EPI) is an MRI technique of particular value to neuroscience, with its use for virtually all functional MRI (fMRI) and diffusion imaging of fiber connections in the human brain. EPI generates a single 2D image in a fraction of a second; however, it requires 2-3 seconds to acquire multi-slice whole brain coverage for fMRI and even longer for diffusion imaging. Here we report on a large reduction in EPI whole brain scan time at 3 and 7 Tesla, without significantly sacrificing spatial resolution, and while gaining functional sensitivity. The multiplexed-EPI (M-EPI) pulse sequence combines two forms of multiplexing: temporal multiplexing (m) utilizing simultaneous echo refocused (SIR) EPI and spatial multiplexing (n) with multibanded RF pulses (MB) to achieve m×n images in an EPI echo train instead of the normal single image. This resulted in an unprecedented reduction in EPI scan time for whole brain fMRI performed at 3 Tesla, permitting TRs of 400 ms and 800 ms compared to a more conventional 2.5 sec TR, and 2-4 times reductions in scan time for HARDI imaging of neuronal fibertracks. The simultaneous SE refocusing of SIR imaging at 7 Tesla advantageously reduced SAR by using fewer RF refocusing pulses and by shifting fat signal out of the image plane so that fat suppression pulses were not required. In preliminary studies of resting state functional networks identified through independent component analysis, the 6-fold higher sampling rate increased the peak functional sensitivity by 60%. The novel M-EPI pulse sequence resulted in a significantly increased temporal resolution for whole brain fMRI, and as such, this new methodology can be used for studying non-stationarity in networks and generally for expanding and enriching the functional information.     

Functional magnetic resonance imaging as a tool for investigating human cortical motor function.

Non-invasive functional magnetic resonance imaging (fMRI) mapping techniques sensitive to the local changes of blood flow, blood volume, and blood oxygenation which accompany neuronal activation have been widely used over the last few years to investigate the functional organization of human cortical motor systems, and specifically of the primary motor cortex. Validation studies have demonstrated a good correspondence between quantitative and topographic aspects of data acquired by fMRI and positron emission tomography. The spatial and temporal resolution affordable by fMRI has allowed to achieve new important information on the distributed representation of hand movements in multiple functional modules, and on the intensity and spatial extent of neural activation in the contralateral and ipsilateral primary motor cortex in relation to parametric and nonparametric aspects of movement and to the degree of handedness. Neural populations with different functional characteristics have been identified in anatomically defined regions, and the temporal aspects of the activation during voluntary movement tracked in different components of the motor system. Finally, this technique has proved useful to deepen our understanding of the neural basis of motor imagery, demonstrating increased activity in the primary motor cortex during mental representation of sequential finger movements.     

Ultra high-resolution fMRI in monkeys with implanted RF coils

Spatiotemporally resolved functional MRI (fMRI) in animals can reveal how wide-spread neural networks are organized and accompanying electrophysiological recordings can show how small neural assemblies contribute to this organization. Here we present a novel technique that yields high-resolution structural and functional images of the monkey brain with small, tissue-compatible, intraosteally implantable radiofrequency coils. Voxel sizes as small as 0.0113 microl with high signal-to-noise and contrast-to-noise ratios were obtained, revealing both structural and functional cortical architecture in great detail. Up to a certain point, contrast sensitivity increased with decreasing voxel size, probably because of the decreased partial volume effects. Spatial specificity was demonstrated by the lamina-specific activation in experiments comparing responses to moving and flickering stimuli. The implications of this technique for combined fMRI/electrophysiology experiments and its limitations in terms of spatial coverage are discussed.     

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator''s coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes.     

Exploiting Complexity Information for Brain Activation Detection

We present a complexity-based approach for the analysis of fMRI time series, in which sample entropy (SampEn) is introduced as a quantification of the voxel complexity. Under this hypothesis the voxel complexity could be modulated in pertinent cognitive tasks, and it changes through experimental paradigms. We calculate the complexity of sequential fMRI data for each voxel in two distinct experimental paradigms and use a nonparametric statistical strategy, the Wilcoxon signed rank test, to evaluate the difference in complexity between them. The results are compared with the well known general linear model based Statistical Parametric Mapping package (SPM12), where a decided difference has been observed. This is because SampEn method detects brain complexity changes in two experiments of different conditions and the data-driven method SampEn evaluates just the complexity of specific sequential fMRI data. Also, the larger and smaller SampEn values correspond to different meanings, and the neutral-blank design produces higher predictability than threat-neutral. Complexity information can be considered as a complementary method to the existing fMRI analysis strategies, and it may help improving the understanding of human brain functions from a different perspective.     

Modeling Inter‐Subject Variability in fMRI Activation Location: A Bayesian Hierarchical Spatial Model

Summary The aim of this article is to develop a spatial model for multi‐subject fMRI data. There has been extensive work on univariate modeling of each voxel for single and multi‐subject data, some work on spatial modeling of single‐subject data, and some recent work on spatial modeling of multi‐subject data. However, there has been no work on spatial models that explicitly account for inter‐subject variability in activation locations. In this article, we use the idea of activation centers and model the inter‐subject variability in activation locations directly. Our model is specified in a Bayesian hierarchical framework which allows us to draw inferences at all levels: the population level, the individual level, and the voxel level. We use Gaussian mixtures for the probability that an individual has a particular activation. This helps answer an important question that is not addressed by any of the previous methods: What proportion of subjects had a significant activity in a given region. Our approach incorporates the unknown number of mixture components into the model as a parameter whose posterior distribution is estimated by reversible jump Markov chain Monte Carlo. We demonstrate our method with a fMRI study of resolving proactive interference and show dramatically better precision of localization with our method relative to the standard mass‐univariate method. Although we are motivated by fMRI data, this model could easily be modified to handle other types of imaging data.     

Denoising functional magnetic resonance imaging time-series using anisotropic spatial averaging

We propose a novel iterative scheme for adaptive smoothing of functional MR images. The method estimates a signal model at every voxel in the time-series, which is subsequently used in determining the weights of the smoothing kernel. The method does not require any information about the test hypothesis and is well-suited as a preprocessing step for both hypothesis-driven and data-driven analysis techniques. We demonstrate the performance of the proposed method by applying it to preprocess both simulated and real fMRI data. The method is found to effectively suppress the noise while preserving the shapes of the active brain regions.     

Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain

Functional magnetic resonance data acquired in a task-absent condition (“resting state”) require new data analysis techniques that do not depend on an activation model. In this work, we introduce an alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are themselves central within the network. Google''s PageRank algorithm is a variant of eigenvector centrality. Thus far, other centrality measures - in particular “betweenness centrality” - have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements. Eigenvector centrality is computationally much more efficient than betweenness centrality and does not require thresholding of similarity values so that it can be applied to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality. Eigenvector centrality can be used on a variety of different similarity metrics. Here, we present applications based on linear correlations and on spectral coherences between fMRI times series. This latter approach allows us to draw conclusions of connectivity patterns in different spectral bands. We apply this method to fMRI data in task-absent conditions where subjects were in states of hunger or satiety. We show that eigenvector centrality is modulated by the state that the subjects were in. Our analyses demonstrate that eigenvector centrality is a computationally efficient tool for capturing intrinsic neural architecture on a voxel-wise level.     

Human ocular dominance columns as revealed by high-field functional magnetic resonance imaging.

We mapped ocular dominance columns (ODCs) in normal human subjects using high-field (4 T) functional magnetic resonance imaging (fMRI) with a segmented echo planar imaging technique and an in-plane resolution of 0.47 x 0.47 mm(2). The differential responses to left or right eye stimulation could be reliably resolved in anatomically well-defined sections of V1. The orientation and width ( approximately 1 mm) of mapped ODC stripes conformed to those previously revealed in postmortem brains stained with cytochrome oxidase. In addition, we showed that mapped ODC patterns could be largely reproduced in different experiments conducted within the same experimental session or over different sessions. Our results demonstrate that high-field fMRI can be used for studying the functions of human brains at columnar spatial resolution.