Rasmus M. Birn

YNICL Journal 2018 Journal Article

Association of acute depressive symptoms and functional connectivity of emotional processing regions following sport-related concussion

• William T. McCuddy
• Lezlie Y. España
• Lindsay D. Nelson
• Rasmus M. Birn
• Andrew R. Mayer
• Timothy B. Meier

Acute mood disturbance following sport-related concussion is common and is known to adversely affect post-concussion symptoms and recovery. The physiological underpinnings of depressive symptoms following concussion, however, are relatively understudied. We hypothesized that functional connectivity of the emotional processing network would be altered in concussed athletes and associated with the severity of depressive symptoms following concussion. Forty-three concussed collegiate athletes were assessed at approximately one day (N = 34), one week (N = 34), and one month post-concussion (N = 30). Fifty-one healthy contact-sport athletes served as controls and completed a single visit. The Hamilton Rating Scale for Depression (HAM-D) was used to measure depressive symptoms. Resting state fMRI data was collected on a 3 T scanner (TR = 2 s) and functional connectivity was calculated in a meta-analytically derived network of regions associated with emotional processing. Concussed athletes had elevated depressive symptoms across the first month post-concussion relative to control athletes, but showed partial recovery by one month relative to more acute visits (ps < 0. 05). Concussed athletes had significantly different connectivity in regions associated with emotional processing at one month post-concussion relative to one day post-concussion (p = 0. 002) and relative to controls (p = 0. 003), with higher connectivity between default mode and attention regions being common across analyses. Additionally, depressive symptoms in concussed athletes at one day (p = 0. 003) and one week post-concussion (p = 7 × 10−8) were inversely correlated with connectivity between attention (e. g. , right anterior insula) and default mode regions (e. g. , medial prefrontal cortex). Finally, the relationships with HAM-D scores were not driven by a general increase in somatic complaints captured by the HAM-D, but were strongly associated with mood-specific HAM-D items. These results suggest that connectivity of emotional processing regions is associated with acute mood disturbance following sport-related concussion. Increased connectivity between attention and default mode regions may reflect compensatory mechanisms.

YNIMG Journal 2018 Journal Article

Bayesian convolutional neural network based MRI brain extraction on nonhuman primates

• Gengyan Zhao
• Fang Liu
• Jonathan A. Oler
• Mary E. Meyerand
• Ned H. Kalin
• Rasmus M. Birn

Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory on nonhuman primates, which are a necessary part of neuroscience research. To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected three-dimensional (3D) conditional random field (CRF). The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine. As a probabilistic network, it is not only able to perform accurate high-resolution pixel-wise brain segmentation, but also capable of measuring the model uncertainty by Monte Carlo sampling with dropout in the testing stage. Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume. The proposed method was evaluated with a manually brain-extracted dataset comprising T1w images of 100 nonhuman primates. Our method outperforms six popular publicly available brain extraction packages and three well-established deep learning based methods with a mean Dice coefficient of 0. 985 and a mean average symmetric surface distance of 0. 220 mm. A better performance against all the compared methods was verified by statistical tests (all p-values < 10−4, two-sided, Bonferroni corrected). The maximum uncertainty of the model on nonhuman primate brain extraction has a mean value of 0. 116 across all the 100 subjects. The behavior of the uncertainty was also studied, which shows the uncertainty increases as the training set size decreases, the number of inconsistent labels in the training set increases, or the inconsistency between the training set and the testing set increases.

YNIMG Journal 2017 Journal Article

An improved model of motion-related signal changes in fMRI

• Rémi Patriat
• Richard C. Reynolds
• Rasmus M. Birn

Head motion is a significant source of noise in the estimation of functional connectivity from resting-state functional MRI (rs-fMRI). Current strategies to reduce this noise include image realignment, censoring time points corrupted by motion, and including motion realignment parameters and their derivatives as additional nuisance regressors in the general linear model. However, this nuisance regression approach assumes that the motion-induced signal changes are linearly related to the estimated realignment parameters, which is not always the case. In this study we develop an improved model of motion-related signal changes, where nuisance regressors are formed by first rotating and translating a single brain volume according to the estimated motion, re-registering the data, and then performing a principal components analysis (PCA) on the resultant time series of both moved and re-registered data. We show that these “Motion Simulated (MotSim)” regressors account for significantly greater fraction of variance, result in higher temporal signal-to-noise, and lead to functional connectivity estimates that are less affected by motion compared to the most common current approach of using the realignment parameters and their derivatives as nuisance regressors. This improvement should lead to more accurate estimates of functional connectivity, particularly in populations where motion is prevalent, such as patients and young children.

YNICL Journal 2014 Journal Article

Individual classification of children with epilepsy using support vector machine with multiple indices of diffusion tensor imaging

• Ishmael Amarreh
• Mary E. Meyerand
• Carl Stafstrom
• Bruce P. Hermann
• Rasmus M. Birn

INTRODUCTION: Support vector machines (SVM) have recently been demonstrated to be useful for voxel-based MR image classification. In the present study we sought to evaluate whether this method is feasible in the classification of childhood epilepsy intractability based on diffusion tensor imaging (DTI), with adequate accuracy. We applied SVM in conjunction DTI indices of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). DTI studies have reported white matter abnormalities in childhood-onset epilepsy, but the mechanisms underlying these abnormalities are not well understood. The aim of this study was to examine the relationship between epileptic seizures and cerebral white matter abnormalities identified by DTI in children with active compared to remitted epilepsy utilizing an automated and unsupervised classification method. METHODS: The DTI data were tensor-derived indices including FA, MD, AD and RD in 49 participants including 20 children with epilepsy 5-6 years after seizure onset as compared to healthy controls. To determine whether there was normalization of white matter diffusion behavior following cessation of seizures and treatment, the epilepsy subjects were grouped into those with active versus remitted epilepsy. Group comparisons were previously made examining FA, MD and RD via whole-brain tract-based spatial statistics (TBSS). The SVM analysis was undertaken with the WEKA software package with 10-fold cross validation. Weighted sensitivity, specificity and accuracy were measured for all the DTI indices for two classifications: (1) controls vs. all children with epilepsy and (2) controls vs. children with remitted epilepsy vs. children with active epilepsy. RESULTS: Using TBSS, significant differences were identified between controls and all children with epilepsy, between controls and children with active epilepsy, and also between the active and remitted epilepsy groups. There were no significant differences between the remitted epilepsy and controls on any DTI measure. In the SVM analysis, the best predictor between controls and all children with epilepsy was MD, with a sensitivity of 90-100% and a specificity between 96.6 and 100%. For the three-way classification, the best results were for FA with 100% sensitivity and specificity. CONCLUSION: DTI-based SVM classification appears promising for distinguishing children with active epilepsy from either those with remitted epilepsy or controls, and the question that arises is whether it will prove useful as a prognostic index of seizure remission. While SVM can correctly identify children with active epilepsy from other groups' diagnosis, further research is needed to determine the efficacy of SVM as a prognostic tool in longitudinal clinical studies.

YNIMG Journal 2014 Journal Article

The influence of spatial resolution and smoothing on the detectability of resting-state and task fMRI

• Erin K. Molloy
• Mary E. Meyerand
• Rasmus M. Birn

Functional MRI blood oxygen level-dependent (BOLD) signal changes can be subtle, motivating the use of imaging parameters and processing strategies that maximize the temporal signal-to-noise ratio (tSNR) and thus the detection power of neuronal activity-induced fluctuations. Previous studies have shown that acquiring data at higher spatial resolutions results in greater percent BOLD signal changes, and furthermore that spatially smoothing higher resolution fMRI data improves tSNR beyond that of data originally acquired at a lower resolution. However, higher resolution images come at the cost of increased acquisition time, and the number of image volumes also influences detectability. The goal of our study is to determine how the detection power of neuronally induced BOLD fluctuations acquired at higher spatial resolutions and then spatially smoothed compares to data acquired at the lower resolutions with the same imaging duration. The number of time points acquired during a given amount of imaging time is a practical consideration given the limited ability of certain populations to lie still in the MRI scanner. We compare acquisitions at three different in-plane spatial resolutions (3. 50×3. 50mm2, 2. 33×2. 33mm2, 1. 75×1. 75mm2) in terms of their tSNR, contrast-to-noise ratio, and the power to detect both task-related activation and resting-state functional connectivity. The impact of SENSE acceleration, which speeds up acquisition time increasing the number of images collected, is also evaluated. Our results show that after spatially smoothing the data to the same intrinsic resolution, lower resolution acquisitions have a slightly higher detection power of task-activation in some, but not all, brain areas. There were no significant differences in functional connectivity as a function of resolution after smoothing. Similarly, the reduced tSNR of fMRI data acquired with a SENSE factor of 2 is offset by the greater number of images acquired, resulting in few significant differences in detection power of either functional activation or connectivity after spatial smoothing.

YNIMG Journal 2013 Journal Article

Resting-state fMRI confounds and cleanup

• Kevin Murphy
• Rasmus M. Birn
• Peter A. Bandettini

The goal of resting-state functional magnetic resonance imaging (fMRI) is to investigate the brain's functional connections by using the temporal similarity between blood oxygenation level dependent (BOLD) signals in different regions of the brain “at rest” as an indicator of synchronous neural activity. Since this measure relies on the temporal correlation of fMRI signal changes between different parts of the brain, any non-neural activity-related process that affects the signals will influence the measure of functional connectivity, yielding spurious results. To understand the sources of these resting-state fMRI confounds, this article describes the origins of the BOLD signal in terms of MR physics and cerebral physiology. Potential confounds arising from motion, cardiac and respiratory cycles, arterial CO2 concentration, blood pressure/cerebral autoregulation, and vasomotion are discussed. Two classes of techniques to remove confounds from resting-state BOLD time series are reviewed: 1) those utilising external recordings of physiology and 2) data-based cleanup methods that only use the resting-state fMRI data itself. Further methods that remove noise from functional connectivity measures at a group level are also discussed. For successful interpretation of resting-state fMRI comparisons and results, noise cleanup is an often over-looked but essential step in the analysis pipeline.

YNIMG Journal 2013 Journal Article

The effect of resting condition on resting-state fMRI reliability and consistency: A comparison between resting with eyes open, closed, and fixated

• Rémi Patriat
• Erin K. Molloy
• Timothy B. Meier
• Gregory R. Kirk
• Veena A. Nair
• Mary E. Meyerand
• Vivek Prabhakaran
• Rasmus M. Birn

Resting-state fMRI (rs-fMRI) has been demonstrated to have moderate to high reliability and produces consistent patterns of connectivity across a wide variety of subjects, sites, and scanners. However, there is no one agreed upon method to acquire rs-fMRI data. Some sites instruct their subjects, or patients, to lie still with their eyes closed, while other sites instruct their subjects to keep their eyes open or even fixating on a cross during scanning. Several studies have compared those three resting conditions based on connectivity strength. In our study, we assess differences in metrics of test–retest reliability (using an intraclass correlation coefficient), and consistency of the rank-order of connections within a subject and the ranks of subjects for a particular connection from one session to another (using Kendall's W tests). Twenty-five healthy subjects were scanned at three different time points for each resting condition, twice the same day and another time two to three months later. Resting-state functional connectivity measures were evaluated in motor, visual, auditory, attention, and default-mode networks, and compared between the different resting conditions. Of the networks examined, only the auditory network resulted in significantly higher connectivity in the eyes closed condition compared to the other two conditions. No significant between-condition differences in connectivity strength were found in default mode, attention, visual, and motor networks. Overall, the differences in reliability and consistency between different resting conditions were relatively small in effect size but results were found to be significant. Across all within-network connections, and within default-mode, attention, and auditory networks statistically significant greater reliability was found when the subjects were lying with their eyes fixated on a cross. In contrast, primary visual network connectivity was most reliable when subjects had their eyes open (and not fixating on a cross).

YNIMG Journal 2013 Journal Article

The effect of scan length on the reliability of resting-state fMRI connectivity estimates

• Rasmus M. Birn
• Erin K. Molloy
• Rémi Patriat
• Taurean Parker
• Timothy B. Meier
• Gregory R. Kirk
• Veena A. Nair
• M. Elizabeth Meyerand

There has been an increasing use of functional magnetic resonance imaging (fMRI) by the neuroscience community to examine differences in functional connectivity between normal control groups and populations of interest. Understanding the reliability of these functional connections is essential to the study of neurological development and degenerate neuropathological conditions. To date, most research assessing the reliability with which resting-state functional connectivity characterizes the brain's functional networks has been on scans between 3 and 11min in length. In our present study, we examine the test–retest reliability and similarity of resting-state functional connectivity for scans ranging in length from 3 to 27min as well as for time series acquired during the same length of time but excluding half the time points via sampling every second image. Our results show that reliability and similarity can be greatly improved by increasing the scan lengths from 5min up to 13min, and that both the increase in the number of volumes as well as the increase in the length of time over which these volumes was acquired drove this increase in reliability. This improvement in reliability due to scan length is much greater for scans acquired during the same session. Gains in intersession reliability began to diminish after 9–12min, while improvements in intrasession reliability plateaued around 12–16min. Consequently, new techniques that improve reliability across sessions will be important for the interpretation of longitudinal fMRI studies.

YNIMG Journal 2012 Journal Article

Evidence for coordinated functional activity within the extended amygdala of non-human and human primates

• Jonathan A. Oler
• Rasmus M. Birn
• Rémi Patriat
• Andrew S. Fox
• Steven E. Shelton
• Cory A. Burghy
• Diane E. Stodola
• Marilyn J. Essex

Neuroanatomists posit that the central nucleus of the amygdala (Ce) and bed nucleus of the stria terminalis (BST) comprise two major nodes of a macrostructural forebrain entity termed the extended amygdala. The extended amygdala is thought to play a critical role in adaptive motivational behavior and is implicated in the pathophysiology of maladaptive fear and anxiety. Resting functional connectivity of the Ce was examined in 107 young anesthetized rhesus monkeys and 105 young humans using standard resting-state functional magnetic resonance imaging (fMRI) methods to assess temporal correlations across the brain. The data expand the neuroanatomical concept of the extended amygdala by finding, in both species, highly significant functional coupling between the Ce and the BST. These results support the use of in vivo functional imaging methods in nonhuman and human primates to probe the functional anatomy of major brain networks such as the extended amygdala.

YNIMG Journal 2012 Journal Article

Support vector machine classification and characterization of age-related reorganization of functional brain networks

• Timothy B. Meier
• Alok S. Desphande
• Svyatoslav Vergun
• Veena A. Nair
• Jie Song
• Bharat B. Biswal
• Mary E. Meyerand
• Rasmus M. Birn

Most of what is known about the reorganization of functional brain networks that accompanies normal aging is based on neuroimaging studies in which participants perform specific tasks. In these studies, reorganization is defined by the differences in task activation between young and old adults. However, task activation differences could be the result of differences in task performance, strategy, or motivation, and not necessarily reflect reorganization. Resting-state fMRI provides a method of investigating functional brain networks without such confounds. Here, a support vector machine (SVM) classifier was used in an attempt to differentiate older adults from younger adults based on their resting-state functional connectivity. In addition, the information used by the SVM was investigated to see what functional connections best differentiated younger adult brains from older adult brains. Three separate resting-state scans from 26 younger adults (18–35yrs) and 26 older adults (55–85) were obtained from the International Consortium for Brain Mapping (ICBM) dataset made publically available in the 1000 Functional Connectomes project www. nitrc. org/projects/fcon_1000. 100 seed-regions from four functional networks with 5mm3 radius were defined based on a recent study using machine learning classifiers on adolescent brains. Time-series for every seed-region were averaged and three matrices of z-transformed correlation coefficients were created for each subject corresponding to each individual's three resting-state scans. SVM was then applied using leave-one-out cross-validation. The SVM classifier was 84% accurate in classifying older and younger adult brains. The majority of the connections used by the classifier to distinguish subjects by age came from seed-regions belonging to the sensorimotor and cingulo-opercular networks. These results suggest that age-related decreases in positive correlations within the cingulo-opercular and default networks, and decreases in negative correlations between the default and sensorimotor networks, are the distinguishing characteristics of age-related reorganization.

YNIMG Journal 2012 Journal Article

The role of physiological noise in resting-state functional connectivity

• Rasmus M. Birn

Functional connectivity between different brain regions can be estimated from MRI data by computing the temporal correlation of low frequency (<0. 1Hz) fluctuations in the MRI signal. These correlated fluctuations occur even when the subject is “at rest” (not asked to perform any particular task) and result from spontaneous neuronal activity synchronized within multiple distinct networks of brain regions. This estimate of connectivity, however, can be influenced by physiological noise, such as cardiac and respiratory fluctuations. This brief review looks at the effect of physiological noise on estimates of resting-state functional connectivity, discusses ways to remove physiological noise, and provides a personal recollection of the early developments in these approaches. This review also discusses the importance of physiological noise correction and provides a summary of evidence demonstrating that functional connectivity does have a neuronal underpinning and cannot purely be the result of physiological noise.

YNIMG Journal 2010 Journal Article

Neural systems supporting lexical search guided by letter and semantic category cues: A self-paced overt response fMRI study of verbal fluency

• Rasmus M. Birn
• Lauren Kenworthy
• Laura Case
• Rachel Caravella
• Tyler B. Jones
• Peter A. Bandettini
• Alex Martin

Verbal fluency tasks have been widely used to evaluate language and executive control processes in the human brain. FMRI studies of verbal fluency, however, have used either silent word generation (which provides no behavioral measure) or cued generation of single words in order to contend with speech-related motion artifacts. In this study, we use a recently developed paradigm design to investigate the neural correlates of verbal fluency during overt, free recall, word generation so that performance and brain activity could be evaluated under conditions that more closely mirror standard behavioral test demands. We investigated verbal fluency to both letter and category cues in order to evaluate differential involvement of specific frontal and temporal lobe sites as a function of retrieval cue type, as suggested by previous neuropsychological and neuroimaging investigations. In addition, we incorporated both a task switching manipulation and an automatic speech condition in order to modulate the demand placed on executive functions. We found greater activation in the left hemisphere during category and letter fluency tasks, and greater right hemisphere activation during automatic speech. We also found that letter and category fluency tasks were associated with differential involvement of specific regions of the frontal and temporal lobes. These findings provide converging evidence that letter and category fluency performance is dependent on partially distinct neural circuitry. They also provide strong evidence that verbal fluency can be successfully evaluated in the MR environment using overt, self-paced, responses.

YNIMG Journal 2010 Journal Article

Sources of group differences in functional connectivity: An investigation applied to autism spectrum disorder

• Tyler B. Jones
• Peter A. Bandettini
• Lauren Kenworthy
• Laura K. Case
• Shawn C. Milleville
• Alex Martin
• Rasmus M. Birn

An increasing number of fMRI studies are using the correlation of low-frequency fluctuations between brain regions, believed to reflect synchronized variations in neuronal activity, to infer “functional connectivity”. In studies of autism spectrum disorder (ASD), decreases in this measure of connectivity have been found by focusing on the response to task modulation, by using only the rest periods, or by analyzing purely resting-state data. This difference in connectivity, however, could result from a number of different mechanisms — differences in noise, task-related fluctuations, task performance, or spontaneous neuronal activity. In this study, we investigate the difference in functional connectivity between adolescents with high-functioning ASD and typically developing control subjects by examining the residual fluctuations occurring on top of the fMRI response to an overt verbal fluency task. We find decreased correlations of these residuals (a decreased “connectivity”) in ASD subjects. Furthermore, we find that this decrease was not due to task-related effects, block-to-block variations in task performance, or increased noise, and the difference was greatest when primarily rest periods are considered. These findings suggest that the estimate of disrupted functional connectivity in ASD is likely driven by differences in task-unrelated neuronal fluctuations.

YNIMG Journal 2009 Journal Article

fMRI in the presence of task-correlated breathing variations

• Rasmus M. Birn
• Kevin Murphy
• Daniel A. Handwerker
• Peter A. Bandettini

Variations in the subject's heart rate and breathing pattern have been shown to result in significant fMRI signal changes, mediated in part by non-neuronal physiological mechanisms such as global changes in levels of arterial CO2. When these physiological changes are correlated with a task, as may happen in response to emotional stimuli or tasks that change levels of arousal, a concern arises that non-neuronal physiologically-induced signal changes may be misinterpreted as reflecting task-related neuronal activation. The purpose of this study is to provide information that can help in determining whether task activation maps are influenced by task-correlated physiological noise, particularly task-correlated breathing changes. We also compare different strategies to reduce the influence of physiological noise. Two paradigms are investigated — 1) a lexical decision task where some subjects showed task-related breathing changes, and 2) a task where subjects were instructed to hold their breath during the presentation of contrast-reversing checkerboard, an extreme case of task-correlated physiological noise. Consistent with previous literature, we find that MRI signal changes correlated with variations in breathing depth and rate have a characteristic spatial and temporal profile that is different from the typical activation-induced BOLD response. The delineation of activation in the presence of task correlated breathing changes was improved either by independent component analysis, or by including specific nuisance regressors in a regression analysis. The difference in the spatial and temporal characteristics of physiological-induced and neuronal-induced fluctuations exploited by these strategies suggests that activation can be studied even in the presence of task-correlated physiological changes.

YNIMG Journal 2009 Journal Article

The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?

• Kevin Murphy
• Rasmus M. Birn
• Daniel A. Handwerker
• Tyler B. Jones
• Peter A. Bandettini

Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step.

YNIMG Journal 2008 Journal Article

Integration of motion correction and physiological noise regression in fMRI

• Tyler B. Jones
• Peter A. Bandettini
• Rasmus M. Birn

Physiological fluctuations resulting from the heart beat and respiration are a dominant source of noise in fMRI, particularly at high field strengths. Commonly used physiological noise correction techniques, such as RETROspective Image CORrection (RETROICOR), rely critically on the timing of the image acquisition relative to the heart beat, but do not account for the effects of subject motion. Such motion affects the fluctuation amplitude, yet volume registration can distort the timing information. In this study, we aimed to systematically determine the optimal order of volume registration, slice-time correction and RETROICOR in their traditional forms. In addition, we evaluate the sensitivity of RETROICOR to timing errors introduced by the slice acquisition, and we develop a new method of accounting for timing errors introduced by volume registration into physiological correction (motion-modified RETROICOR). Both simulation and resting data indicate that the temporal standard deviation is reduced most by performing volume registration before RETROICOR and slice-time correction after RETROCIOR. While simulations indicate that physiological noise correction with regressors constructed on a slice-by-slice basis more accurately modeled physiological noise compared to using the same regressors for the entire volume, the difference between these regression techniques in subject data was minimal. The motion-modified RETROICOR showed marked improvement in simulations with varying amounts of subject motion, reducing the temporal standard deviation by up to 36% over the traditional RETROICOR. Though to a lesser degree than in simulation, the motion-modified RETROICOR performed better in nearly every voxel in the brain in both high- and low-resolution subject data.

YNIMG Journal 2008 Journal Article

The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration

• Rasmus M. Birn
• Monica A. Smith
• Tyler B. Jones
• Peter A. Bandettini

Changes in the subject’s breathing rate or depth, such as a breath-hold challenge, can cause significant MRI signal changes. However, the response function that best models breath-holding-induced signal changes, as well as those resulting from a wider range of breathing variations including those occurring during rest, has not yet been determined. Respiration related signal changes appear to be slower than neuronally induced BOLD signal changes and are not modeled accurately using the typical hemodynamic response functions used in fMRI. In this study, we derive a new response function to model the average MRI signal changes induced by variations in the respiration volume (breath-to-breath changes in the respiration depth and rate). This was done by averaging the response to a series of single deep breaths performed once every 40 s amongst otherwise constant breathing. The new “respiration response function” consists of an early overshoot followed by a later undershoot (peaking at approximately 16 s), and accurately models the MRI signal changes resulting from breath-holding as well as cued depth and rate changes.

YNIMG Journal 2006 Journal Article

Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI

• Rasmus M. Birn
• Jason B. Diamond
• Monica A. Smith
• Peter A. Bandettini

Subtle changes in a subject's breathing rate or depth, which occur naturally during rest at low frequencies (<0. 1 Hz), have been shown to be significantly correlated with fMRI signal changes throughout gray matter and near large vessels. The goal of this study was to investigate the impact of these low-frequency respiration variations on both task activation fMRI studies and resting-state functional connectivity analysis. Unlike MR signal changes correlated with the breathing motion (∼0. 3 Hz), BOLD signal changes correlated with across-breath variations in respiratory volume (∼0. 03 Hz) appear localized to blood vessels and regions with high blood volume, such as gray matter, similar to changes seen in response to a breath-hold challenge. In addition, the respiration-variation-induced signal changes were found to coincide with many of the areas identified as part of the ‘default mode’ network, a set of brain regions hypothesized to be more active at rest. Regions could therefore be classified as being part of a resting network based on their similar respiration-induced changes rather than their synchronized neuronal activity. Monitoring and removing these respiration variations led to a significant improvement in the identification of task-related activation and deactivation and only slight differences in regions correlated with the posterior cingulate at rest. Regressing out global signal changes or cueing the subject to breathe at a constant rate and depth resulted in an improved spatial overlap between deactivations and resting-state correlations among areas that showed deactivation.

YNIMG Journal 2005 Journal Article

The effect of stimulus duty cycle and “off” duration on BOLD response linearity

• Rasmus M. Birn
• Peter A. Bandettini

An ongoing question in functional MRI is precisely how measured signal changes relate to neuronal activity. While this question has been probed using animal models and electrophysiologic measures of neuronal activity, it has also been probed by examining, in humans, the spatial location, magnitude, and temporal dynamics of signal changes to well understood stimuli. With regard to dynamics, several earlier studies have revealed a larger than expected response to brief stimuli, hypothesized to result from nonlinearities in either the hemodynamics or the neuronal activity. In this study, we investigate the linearity of the increase in blood oxygenation level dependent (BOLD) contrast as a function of stimulus duty cycle, as well as the linearity of the decrease in BOLD as a function stimulus “off” duration. These findings not only shed further light on the mechanisms behind BOLD contrast but also give practical information as to what to keep in mind when performing and interpreting event related fMRI experiments. These experiments demonstrated: a) the BOLD signal decrease, on stimulus cessation, was smaller than predicted by a linear system—opposite to what has been reported in the literature associated with a signal increase, and b) the deconvolved event-related BOLD signal is highly dependent on duty cycle (the fraction of time activated vs. non-activated), Several potential mechanisms explaining these dynamics are discussed and modeled. We find that the experimental results are most consistent with a nonlinear neuronal response, but do not rule out significant effects of nonlinear hemodynamic factors, in particular the nonlinear relationship between oxygen extraction fraction and blood flow.

YNIMG Journal 2004 Journal Article

Experimental designs and processing strategies for fMRI studies involving overt verbal responses

• Rasmus M. Birn
• Robert W. Cox
• Peter A. Bandettini

Event-related paradigms have been used increasingly in the past few years for the localization of function in tasks involving overt speech. These designs exploit the differences in the temporal characteristics between the rapid motion-induced and the slower hemodynamic signal changes. The optimization of these designs and the best way to analyze the acquired data has not yet been fully explored. The purpose of this study is to investigate various design and analysis strategies for maximizing the detection of function while minimizing task-induced motion artifacts. Both event-related and blocked paradigms can be specifically designed to meet these goals. Various event-related and blocked designs were compared both in simulation and in experiments involving overt word reading in their ability to detect function and to avoid speech-induced motion artifact. A blocked design with task and control durations of 10 s and an event-related design with a minimum stimulus duration (SD) of 5 s and an average interstimulus interval (ISI) of 10 s were found to optimally detect blood oxygenation level-dependent signal changes without significant motion artifact. Ignoring images acquired during the speech can help recover function in areas particularly affected by motion but substantially reduces the detection power in other regions. Using the stimulus timing as an additional regressor to model the motion offers little benefit in practice due to the variability of the motion-induced signal change.

YNIMG Journal 2002 Journal Article

Detection versus Estimation in Event-Related fMRI: Choosing the Optimal Stimulus Timing

• Rasmus M. Birn
• Robert W. Cox
• Peter A. Bandettini

With the advent of event-related paradigms in functional MRI, there has been interest in finding the optimal stimulus timing, especially when the interstimulus interval is varied during the imaging run. Previous works have proposed stimulus timings to optimize either the estimation of the impulse response function (IRF) or the detection of signal changes. The purpose of this paper is to clarify that estimation and detection are fundamentally different goals and to determine the optimal stimulus timing and distribution with respect to both the accuracy of estimating the IRF and the power of detection assuming a particular hemodynamic model. Simulated stimulus distributions are varied systematically, from traditional blocked designs to rapidly varying event related designs. These simulations indicate that estimation of the hemodynamic impulse response function is optimized when stimuli are frequently alternated between task and control states, with shorter interstimulus intervals and stimulus durations, whereas the detection of activated areas is optimized by blocked designs. The stimulus timing for a given experiment should therefore be generated with the required detectability and estimation accuracy.

YNIMG Journal 2001 Journal Article

Estimated BOLD impulse response depends on stimulus ON/OFF ratio

• Rasmus M. Birn
• Peter A. Bandettini

YNIMG Journal 2001 Journal Article

Spatial Heterogeneity of the Nonlinear Dynamics in the FMRI BOLD Response

• Rasmus M. Birn
• Ziad S. Saad
• Peter A. Bandettini

Recent studies of blood oxygenation level dependent (BOLD) signal responses averaged over a region of interest have demonstrated that the response is nonlinear with respect to stimulus duration. Specifically, shorter duration stimuli produce signal changes larger than expected from a linear system. The focus of this study is to characterize the spatial heterogeneity of this nonlinear effect. A series of MR images of the visual and motor cortexes were acquired during visual stimulation and finger tapping, respectively, at five different stimulus durations (SD). The nonlinearity was assessed by fitting ideal linear responses to the responses at each SD. This amplitude, which is constant for different SD in a linear system, was normalized by the amplitude of the response to a blocked design, thus describing the amount by which the stimulus is larger than predicted from a linear extrapolation of the response to the long duration stimulus. The amplitude of the BOLD response showed a nonlinear behavior that varied considerably and consistently over space, ranging from almost linear to 10 times larger than a linear prediction at short SD. In the motor cortex different nonlinear behavior was found in the primary and supplementary motor cortexes.