Model specification and the reliability of fMRI results: implications for longitudinal neuroimaging studies in psychiatry.
ABSTRACT: Functional Magnetic Resonance Imagine (fMRI) is an important assessment tool in longitudinal studies of mental illness and its treatment. Understanding the psychometric properties of fMRI-based metrics, and the factors that influence them, will be critical for properly interpreting the results of these efforts. The current study examined whether the choice among alternative model specifications affects estimates of test-retest reliability in key emotion processing regions across a 6-month interval. Subjects (N?=?46) performed an emotional-faces paradigm during fMRI in which neutral faces dynamically morphed into one of four emotional faces. Median voxelwise intraclass correlation coefficients (mvICCs) were calculated to examine stability over time in regions showing task-related activity as well as in bilateral amygdala. Four modeling choices were evaluated: a default model that used the canonical hemodynamic response function (HRF), a flexible HRF model that included additional basis functions, a modified CompCor (mCompCor) model that added corrections for physiological noise in the global signal, and a final model that combined the flexible HRF and mCompCor models. Model residuals were examined to determine the degree to which each pipeline met modeling assumptions. Results indicated that the choice of modeling approaches impacts both the degree to which model assumptions are met and estimates of test-retest reliability. ICC estimates in the visual cortex increased from poor (mvICC?=?0.31) in the default pipeline to fair (mvICC?=?0.45) in the full alternative pipeline - an increase of 45%. In nearly all tests, the models with the fewest assumption violations generated the highest ICC estimates. Implications for longitudinal treatment studies that utilize fMRI are discussed.
Project description:The hemodynamic response function (HRF) describes the local response of brain vasculature to functional activation. Accurate HRF modeling enables the investigation of cerebral blood flow regulation and improves our ability to interpret fMRI results. Block designs have been used extensively as fMRI paradigms because detection power is maximized; however, block designs are not optimal for HRF parameter estimation. Here we assessed the utility of block design fMRI data for HRF modeling. The trueness (relative deviation), precision (relative uncertainty), and identifiability (goodness-of-fit) of different HRF models were examined and test-retest reproducibility of HRF parameter estimates was assessed using computer simulations and fMRI data from 82 healthy young adult twins acquired on two occasions 3 to 4 months apart. The effects of systematically varying attributes of the block design paradigm were also examined. In our comparison of five HRF models, the model comprising the sum of two gamma functions with six free parameters had greatest parameter accuracy and identifiability. Hemodynamic response function height and time to peak were highly reproducible between studies and width was moderately reproducible but the reproducibility of onset time was low. This study established the feasibility and test-retest reliability of estimating HRF parameters using data from block design fMRI studies.
Project description:INTRODUCTION:Functional MRI (fMRI) is commonly used to investigate the neural mechanisms underlying psychological processes and behavioral responses. However, to draw well-founded conclusions from fMRI studies, more research on the reliability of fMRI is needed. METHODS:We invited a sample of 41 female students to participate in two identical fMRI sessions, separated by 5 weeks on average. To investigate the potential effect of left-handedness on the stability of neural activity, we oversampled left-handed participants (N = 20). Inside the scanner, we presented photographs of familiar and unfamiliar children's faces preceded by neutral and threatening primes to the participants. We calculated intraclass correlations (ICCs) to investigate the test-retest reliability of peak activity in areas that showed significant activity during the first session (primary visual cortex, fusiform face area, inferior frontal gyrus, and superior temporal gyrus). In addition, we examined how many trials were needed to reliably measure the effects. RESULTS:Across all participants, only fusiform face area activity in response to faces showed good test-retest reliability (ICC = 0.71). All other test-retest reliabilities were low (0.01 ≤ ICC ≤ 0.35). Reliabilities varied only slightly with increasing numbers of trials, with no consistent increase in ICCs. Test-retest reliabilities for left-handed participants (0.28 ≤ ICC ≤0.66) were generally somewhat higher than for right-handed participants (-0.13 ≤ ICC ≤0.75), but not statistically significant. CONCLUSION:Our study shows good test-retest reliability for fusiform facer area activity in response to faces, but low test-retest reliability for other contrasts and areas.
Project description:Functional magnetic resonance imaging (fMRI), being an indirect measure of brain activity, is mathematically defined as a convolution of the unmeasured latent neural signal and the hemodynamic response function (HRF). The HRF is known to vary across the brain and across individuals, and it is modulated by neural as well as non-neural factors. Three parameters characterize the shape of the HRF, which is obtained by performing deconvolution on resting-state fMRI data: response height, time-to-peak and full-width at half-max. The data provided here, obtained from 47 healthy adults, contains these three HRF parameters at every voxel in the brain, as well as HRF parameters from the default-mode network (DMN). In addition, we have provided functional connectivity (FC) data from the same DMN regions, obtained for two cases: data with deconvolution (HRF variability minimized) and data with no deconvolution (HRF variability corrupted). This would enable researchers to compare regional changes in HRF with corresponding FC differences, to assess the impact of HRF variability on FC. Importantly, the data was obtained in a 7T MRI scanner. While most fMRI studies are conducted at lower field strengths, like 3T, ours is the first study to report HRF data obtained at 7T. FMRI data at ultra-high fields contains larger contributions from small vessels, consequently HRF variability is lower for small vessels at higher field strengths. This implies that findings made from this data would be more conservative than from data acquired at lower fields, such as 3T. Results obtained with this data and further interpretations are available in our recent research study (Rangaprakash et al., in press) . This is a valuable dataset for studying HRF variability in conjunction with FC, and for developing the HRF profile in healthy individuals, which would have direct implications for fMRI data analysis, especially resting-state connectivity modeling. This is the first public HRF data at 7T.
Project description:Prior developmental functional magnetic resonance imaging (fMRI) studies have demonstrated elevated activation patterns in the amygdala and prefrontal cortex (PFC) in response to viewing emotional faces. As adolescence is a time of substantial variability in mood and emotional responsiveness, the stability of activation patterns could be fluctuating over time. In the current study, 27 healthy adolescents (age: 12-19 years) were scanned three times over a period of six months (mean test-retest interval of three months; final samples N=27, N=22, N=18). At each session, participants performed the same emotional faces task. At first measurement the presentation of emotional faces resulted in heightened activation in bilateral amygdala, bilateral lateral PFC and visual areas including the fusiform face area. Average activation did not differ across test-sessions over time, indicating that at the group level activation patterns in this network do not vary significantly over time. However, using the Intraclass Correlation Coefficient (ICC), fMRI reliability demonstrated only fair reliability for PFC (ICC=0.41-0.59) and poor reliability for the amygdala (ICC<0.4). These findings suggest substantial variability of brain activity over time and may have implications for studies investigating the influence of treatment effects on changes in neural levels in adolescents with psychiatric disorders.
Project description:Functional MRI (fMRI) is an indirect measure of neural activity as a result of the convolution of the hemodynamic response function (HRF) and latent (unmeasured) neural activity. Recent studies have shown variability of HRF across brain regions (intra-subject spatial variability) and between subjects (inter-subject variability). Ignoring this HRF variability during data analysis could impair the reliability of such fMRI results. Using whole-brain resting-state fMRI (rs-fMRI), we employed hemodynamic deconvolution to estimate voxel-wise HRF. Studying the impact of mental disorders on HRF variability, we identified HRF aberrations in soldiers (N = 87) with posttraumatic stress disorder (PTSD) and mild-traumatic brain injury (mTBI) compared to combat controls. Certain subcortical and default-mode regions were found to have significant HRF aberrations in the clinical groups. These brain regions have been previously associated with neurochemical alterations in PTSD, which are known to impact the shape of the HRF. We followed-up these findings with seed-based functional connectivity (FC) analysis using regions-of-interest (ROIs) whose HRFs differed between the groups. We found that part of the connectivity group differences reported from traditional FC analysis (no deconvolution) were attributable to HRF variability. These findings raise the question of the degree of reliability of findings from conventional rs-fMRI studies (especially in psychiatric populations like PTSD and mTBI), which are corrupted by HRF variability. We also report and discus, for the first time, voxel-level HRF alterations in PTSD and mTBI. To the best of our knowledge, this is the first study to report evidence for the impact of HRF variability on connectivity group differences. Our work has implications for rs-fMRI connectivity studies. We encourage researchers to incorporate hemodynamic deconvolution during pre-processing to minimize the impact of HRF variability.
Project description:To date, only one study has examined test-retest reliability of resting state fMRI (R-fMRI) in children, none in clinical developing groups. Here, we assessed short-term test-retest reliability in a sample of 46 children (11-17.9 years) with attention-deficit/hyperactivity disorder (ADHD) and 57 typically developing children (TDC). Our primary test-retest reliability measure was the intraclass correlation coefficient (ICC), quantified for a range of R-fMRI metrics. We aimed to (1) survey reliability within and across diagnostic groups, and (2) compare voxel-wise ICC between groups. We found moderate-to-high ICC across all children and within groups, with higher-order functional networks showing greater ICC. Nearly all R-fMRI metrics exhibited significantly higher ICC in TDC than in children with ADHD for one or more regions. In particular, posterior cingulate and ventral precuneus exhibited group differences in ICC across multiple measures. In the context of overall moderate-to-high test-retest reliability in children, regional differences in ICC related to diagnostic groups likely reflect the underlying pathophysiology for ADHD. Our currently limited understanding of the factors contributing to inter- and intra-subject variability in ADHD underscores the need for large initiatives aimed at examining their impact on test-retest reliability in both clinical and developing populations.
Project description:Recent studies have demonstrated significant regional variability in the hemodynamic response function (HRF), highlighting the difficulty of correctly interpreting functional MRI (fMRI) data without proper modeling of the HRF. The focus of this study was to investigate the HRF variability within visual cortex. The HRF was estimated for a number of cortical visual areas by deconvolution of fMRI blood oxygenation level dependent (BOLD) responses to brief, large-field visual stimulation. Significant HRF variation was found across visual areas V1, V2, V3, V4, VO-1,2, V3AB, IPS-0,1,2,3, LO-1,2, and TO-1,2. Additionally, a subpopulation of voxels was identified that exhibited an impulse response waveform that was similar, but not identical, to an inverted version of the commonly described and modeled positive HRF. These voxels were found within the retinotopic confines of the stimulus and were intermixed with those showing positive responses. The spatial distribution and variability of these HRFs suggest a vascular origin for the inverted waveforms. We suggest that the polarity of the HRF is a separate factor that is independent of the suppressive or activating nature of the underlying neuronal activity. Correctly modeling the polarity of the HRF allows one to recover an estimate of the underlying neuronal activity rather than discard the responses from these voxels on the assumption that they are artifactual. We demonstrate this approach on phase-encoded retinotopic mapping data as an example of the benefits of accurately modeling the HRF during the analysis of fMRI data.
Project description:Modeling the Hemodynamic Response Function (HRF) is a critical step in fMRI studies of brain activity, and it is often desirable to estimate HRF parameters with physiological interpretability. A biophysically informed model of the HRF can be described by a non-linear time-invariant dynamic system. However, the identification of this dynamic system may leave much uncertainty on the exact values of the parameters. Moreover, the high noise levels in the data may hinder the model estimation task. In this context, the estimation of the HRF may be seen as a problem of model falsification or invalidation, where we are interested in distinguishing among a set of eligible models of dynamic systems. Here, we propose a systematic tool to determine the distinguishability among a set of physiologically plausible HRF models. The concept of absolutely input-distinguishable systems is introduced and applied to a biophysically informed HRF model, by exploiting the structure of the underlying non-linear dynamic system. A strategy to model uncertainty in the input time-delay and magnitude is developed and its impact on the distinguishability of two physiologically plausible HRF models is assessed, in terms of the maximum noise amplitude above which it is not possible to guarantee the falsification of one model in relation to another. Finally, a methodology is proposed for the choice of the input sequence, or experimental paradigm, that maximizes the distinguishability of the HRF models under investigation. The proposed approach may be used to evaluate the performance of HRF model estimation techniques from fMRI data.
Project description:The Balloon Analogue Risk Task (BART) provides a reliable and ecologically valid model for the assessment of individual risk-taking propensity and is frequently used in neuroimaging and developmental research. Although the test-retest reliability of risk-taking behavior during the BART is well established, the reliability of brain activation patterns in response to risk-taking during the BART remains elusive. In this study, we used functional magnetic resonance imaging (fMRI) and evaluated the test-retest reliability of brain responses in 34 healthy adults during a modified BART by calculating the intraclass correlation coefficients (ICC) and Dice's similarity coefficients (DSC). Analyses revealed that risk-induced brain activation patterns showed good test-retest reliability (median ICC ?= ?0.62) and moderate to high spatial consistency, while brain activation patterns associated with win or loss outcomes only had poor to fair reliability (median ICC ?= ?0.33 for win and 0.42 for loss). These findings have important implications for future utility of the BART in fMRI to examine brain responses to risk-taking and decision-making.
Project description:Graphical methods of radiotracer kinetic modeling in PET are ideal for parametric imaging and data quality assurance but can suffer from noise bias. This study compared the Logan and Multilinear Analysis-1 (MA1) graphical models to the standard one-tissue-compartment (1TC) model, including correction for partial-volume effects, in dynamic PET-CT studies of myocardial sympathetic innervation in the left ventricle (LV) using [11C]HED.Test and retest [11C]HED PET imaging (47?±?22 days apart) was performed in 18 subjects with heart failure symptoms. Myocardial tissue volume of distribution (VT) was estimated using Logan and MA1 graphical methods and compared to the 1TC standard model values using intraclass correlation (ICC) and Bland-Altman analysis of the non-parametric reproducibility coefficient (NPC).A modeling start-time of t*?= 5 min gave the best fit for both Logan and MA1 (R2?=?0.95) methods. Logan slightly underestimated VT relative to 1TC (p?=?0.002), whereas MA1 did not (p?=?0.96). Both the MA1 and Logan models exhibited good-to-excellent agreement with the 1TC (MA1-1TC ICC?=?0.96; Logan-1TC ICC?=?0.93) with no significant differences in NPC between the two comparisons (p?=?0.92). All methods exhibited good-to-excellent test-retest repeatability with no significant differences in NPC (p?=?0.57).Logan and MA1 models exhibited similar agreement and variability compared to the 1TC for modeling of [11C]HED kinetics. Using t*?=?5 min and partial-volume correction produced accurate estimates of VT as an index of myocardial sympathetic innervation.