Effect of temporal resolution and serial autocorrelations in event-related functional MRI

Iterative Data-adaptive Autoregressive (IDAR) whitening procedure for long and short TR fMRI

Introduction

Functional magnetic resonance imaging (fMRI) has become a fundamental tool for studying brain function. However, the presence of serial correlations in fMRI data complicates data analysis, violates the statistical assumptions of analyses methods, and can lead to incorrect conclusions in fMRI studies.

Methods

In this paper, we show that conventional whitening procedures designed for data with longer repetition times (TRs) (>2 s) are inadequate for the increasing use of short-TR fMRI data. Furthermore, we comprehensively investigate the shortcomings of existing whitening methods and introduce an iterative whitening approach named "IDAR" (Iterative Data-adaptive Autoregressive model) to address these shortcomings. IDAR employs high-order autoregressive (AR) models with flexible and data-driven orders, offering the capability to model complex serial correlation structures in both short-TR and long-TR fMRI datasets.

Results

Conventional whitening methods, such as AR(1), ARMA(1,1), and higher-order AR, were effective in reducing serial correlation in long-TR data but were largely ineffective in even reducing serial correlation in short-TR data. In contrast, IDAR significantly outperformed conventional methods in addressing serial correlation, power, and Type-I error for both long-TR and especially short-TR data. However, IDAR could not simultaneously address residual correlations and inflated Type-I error effectively.

Discussion

This study highlights the urgent need to address the problem of serial correlation in short-TR (< 1 s) fMRI data, which are increasingly used in the field. Although IDAR can address this issue for a wide range of applications and datasets, the complexity of short-TR data necessitates continued exploration and innovative approaches. These efforts are essential to simultaneously reduce serial correlations and control Type-I error rates without compromising analytical power.

Unraveling Alzheimer's Disease: Investigating Dynamic Functional Connectivity in the Default Mode Network through DCC-GARCH Modeling

Alzheimer's disease (AD) has a prolonged latent phase. Sensitive biomarkers of amyloid beta ( A β ), in the absence of clinical symptoms, offer opportunities for early detection and identification of patients at risk. Current A β biomarkers, such as CSF and PET biomarkers, are effective but face practical limitations due to high cost and limited availability. Recent blood plasma biomarkers, though accessible, still incur high costs and lack physiological significance in the Alzheimer's process. This study explores the potential of brain functional connectivity (FC) alterations associated with AD pathology as a non-invasive avenue for A β detection. While current stationary FC measurements lack sensitivity at the single-subject level, our investigation focuses on dynamic FC using resting-state functional MRI (rs-fMRI) and introduces the Generalized Auto-Regressive Conditional Heteroscedastic Dynamic Conditional Correlation (DCC-GARCH) model. Our findings demonstrate the superior sensitivity of DCC-GARCH to CSF A β status, and offer key insights into dynamic functional connectivity analysis in AD.

Shorter TR combined with finer atlas positively modulate topological organization of brain network: A resting state fMRI study

BACKGROUND

The use of shorter TR and finer atlases in rs-fMRI can provide greater detail on brain function and anatomy. However, there is limited understanding of the effect of this combination on brain network properties.

METHODS

A study was conducted with 20 healthy young volunteers who underwent rs-fMRI scans with both shorter (0.5s) and long (2s) TR. Two atlases with different degrees of granularity (90 vs 200 regions) were used to extract rs-fMRI signals. Several network metrics, including small-worldness, Cp, Lp, Eloc, and Eg, were calculated. Two-factor ANOVA and two-sample t-tests were conducted for both the single spectrum and five sub-frequency bands.

RESULTS

The network constructed using the combination of shorter TR and finer atlas showed significant enhancements in Cp, Eloc, and Eg, as well as reductions in Lp and γ in both the single spectrum and subspectrum (p < 0.05, Bonferroni correction). Network properties in the 0.082-0.1 Hz frequency range were weaker than those in the 0.01-0.082 Hz range.

CONCLUSION

Our findings suggest that the use of shorter TR and finer atlas can positively affect the topological characteristics of brain networks. These insights can inform the development of brain network construction methods.

Neural evidence for a separation of semantic and phonological control processes

There remain major doubts about the nature and domain specificity of inhibitory control processes, both within and between cognitive domains. This study examined inhibitory processes within the language domain, by contrasting semantic versus phonological inhibitory control. In an fMRI experiment, elderly participants performed phonological and semantic inhibitory control tasks involving resistance to highly or weakly interfering stimuli. In the semantic domain, inhibitory control effects, contrasting high vs. low interference control levels, were observed at univariate and multivariate levels in all fronto-parieto-temporal region-of-interests. In the phonological domain, inhibitory control effects were observed only at multivariate levels, and were restricted to the pars triangularis of the bilateral inferior frontal gyrus and to the left middle temporal gyrus. Critically, no reliable multivariate cross-domain prediction of neural patterns associated with inhibitory control was observed. This study supports a functional dissociation of the neural substrates associated with inhibitory control for phonological vs. semantic domains.

Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response

Fast fMRI enables the detection of neural dynamics over timescales of hundreds of milliseconds, suggesting it may provide a new avenue for studying subsecond neural processes in the human brain. The magnitudes of these fast fMRI dynamics are far greater than predicted by canonical models of the hemodynamic response. Several studies have established nonlinear properties of the hemodynamic response that have significant implications for fast fMRI. We first review nonlinear properties of the hemodynamic response function that may underlie fast fMRI signals. We then illustrate the breakdown of canonical hemodynamic response models in the context of fast neural dynamics. We will then argue that the canonical hemodynamic response function is not likely to reflect the BOLD response to neuronal activity driven by sparse or naturalistic stimuli or perhaps to spontaneous neuronal fluctuations in the resting state. These properties suggest that fast fMRI is capable of tracking surprisingly fast neuronal dynamics, and we discuss the neuroscientific questions that could be addressed using this approach.

Contribution of the multi-echo approach in accelerated functional magnetic resonance imaging multiband acquisition

We wanted to verify the effect of combining multi‐echo (ME) functional magnetic resonance imaging (fMRI) with slice acceleration in simultaneous multi‐slice acquisition. The aim was to shed light on the benefits of multiple echoes for various acquisition settings, especially for levels of slice acceleration and flip angle. Whole‐brain ME fMRI data were obtained from 26 healthy volunteers (using three echoes; seven runs with slice acceleration 1, 4, 6, and 8; and two different flip angles for each of the first three acceleration factors) and processed as single‐echo (SE) data and ME data based on optimal combinations weighted by the contrast‐to‐noise ratio. Global metrics (temporal signal‐to‐noise ratio, signal‐to‐noise separation, number of active voxels, etc.) and local characteristics in regions of interest were used to evaluate SE and ME data. ME results outperformed SE results in all runs; the differences became more apparent for higher acceleration, where a significant decrease in data quality is observed. ME fMRI can improve the observed data quality metrics over SE fMRI for a wide range of accelerated fMRI acquisitions.

Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI

Functional magnetic resonance imaging (fMRI), a non-invasive and widely used human neuroimaging method, is most known for its spatial precision. However, there is a growing interest in its temporal sensitivity. This is despite the temporal blurring of neuronal events by the blood oxygen level dependent (BOLD) signal, the peak of which lags neuronal firing by 4-6 seconds. Given this, the goal of this review is to answer a seemingly simple question - "What are the benefits of increased temporal sampling for fMRI?". To answer this, we have combined fMRI data collected at multiple temporal scales, from 323 to 1000 milliseconds, with a review of both historical and contemporary temporal literature. After a brief discussion of technological developments that have rekindled interest in temporal research, we next consider the potential statistical and methodological benefits. Most importantly, we explore how fast fMRI can uncover previously unobserved neuro-temporal dynamics - effects that are entirely missed when sampling at conventional 1 to 2 second rates. With the intrinsic link between space and time in fMRI, this temporal renaissance also delivers improvements in spatial precision. Far from producing only statistical gains, the array of benefits suggest that the continued temporal work is worth the effort.

Hybrid auditory fMRI: In pursuit of increasing data acquisition while decreasing the impact of scanner noise

BACKGROUND

Two challenges in auditory fMRI include the loud scanner noise during sound presentation and slow data acquisition. Here, we introduce a new auditory imaging protocol, termed "hybrid", that alleviates these obstacles.

NEW METHOD

We designed a within-subject experiment (N = 14) wherein language-driven activity was measured by hybrid, interleaved silent (ISSS), and continuous multiband acquisition. To determine the advantage of noise attenuation during sound presentation, hybrid was compared to multiband. To identify the benefits of increased temporal resolution, hybrid was compared to ISSS. Data were evaluated by whole-brain univariate general linear modeling (GLM) and multivariate pattern analysis (MVPA).

RESULTS

Comparison with existing methods: CONCLUSIONS: Our data revealed that hybrid imaging restored neural activity in the canonical language network that was absent due to the loud noise or slow sampling in the conventional imaging protocols. With its noise-attenuated sound presentation windows and increased acquisition speed, the hybrid protocol is well-suited for auditory fMRI research tracking neural activity pertaining to fast, time-varying acoustic events.

An empirical investigation of the benefit of increasing the temporal resolution of task-evoked fMRI data with multi-band imaging

Objective

There is a tendency for reducing TR in MRI experiments with multi-band imaging. We empirically investigate its benefit for the group-level statistical outcome in task-evoked fMRI.

Methods

Three visual fMRI data sets were collected from 17 healthy adult participants. Multi-band acquisition helped vary the TR (2000/1000/410 ms, respectively). Because these data sets capture different temporal aspects of the haemodynamic response (HRF), we tested several HRF models. We computed a composite descriptive statistic, H, from β’s of each first-level model fit and carried it to the group-level analysis. The number of activated voxels and the t value of the group-level analysis as well as a goodness-of-fit measure were used as surrogate markers of data quality for comparison.

Results

Increasing the temporal sampling rate did not provide a universal improvement in the group-level statistical outcome. Rather, both the voxel-wise and ROI-averaged group-level results varied widely with anatomical location, choice of HRF and the setting of the TR. Correspondingly, the goodness-of-fit of HRFs became worse with increasing the sampling frequency.

Conclusion

Rather than universally increasing the temporal sampling rate in cognitive fMRI experiments, these results advocate the performance of a pilot study for the specific ROIs of interest to identify the appropriate temporal sampling rate for the acquisition and the correspondingly suitable HRF for the analysis of the data.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10334-021-00918-z.

Does higher sampling rate (multiband + SENSE) improve group statistics - An example from social neuroscience block design at 3T

Multiband (MB) or Simultaneous multi-slice (SMS) acquisition schemes allow the acquisition of MRI signals from more than one spatial coordinate at a time. Commercial availability has brought this technique within the reach of many neuroscientists and psychologists. Most early evaluation of the performance of MB acquisition employed resting state fMRI or the most basic tasks. In this study, we tested whether the advantages of using MB acquisition schemes generalize to group analyses using a cognitive task more representative of typical cognitive neuroscience applications. Twenty-three subjects were scanned on a Philips 3 ​T scanner using five sequences, up to eight-fold acceleration with MB-factors 1 to 4, SENSE factors up to 2 and corresponding TRs of 2.45s down to 0.63s, while they viewed (i) movie blocks showing complex actions with hand object interactions and (ii) control movie blocks without hand object interaction. Data were processed using a widely used analysis pipeline implemented in SPM12 including the unified segmentation and canonical HRF modelling. Using random effects group-level, voxel-wise analysis we found that all sequences were able to detect the basic action observation network known to be recruited by our task. The highest t-values were found for sequences with MB4 acceleration. For the MB1 sequence, a 50% bigger voxel volume was needed to reach comparable t-statistics. The group-level t-values for resting state networks (RSNs) were also highest for MB4 sequences. Here the MB1 sequence with larger voxel size did not perform comparable to the MB4 sequence. Altogether, we can thus recommend the use of MB4 (and SENSE 1.5 or 2) on a Philips scanner when aiming to perform group-level analyses using cognitive block design fMRI tasks and voxel sizes in the range of cortical thickness (e.g. 2.7 ​mm isotropic). While results will not be dramatically changed by the use of multiband, our results suggest that MB will bring a moderate but significant benefit.

Improved autoregressive model for correction of noise serial correlation in fast fMRI

PURPOSE

In rapidly acquired functional MRI (fast fMRI) data, the noise serial correlations (SC) can produce problematically overestimated T-statistics which lead to invalid statistical inferences. This study aims to evaluate and improve the accuracy of high-order autoregressive model (AR(p), where p is the model order) based prewhitening method in the SC correction.

METHODS

Fast fMRI images were acquired at rest (null data) using a multiband simultaneous multi-slice echo planar imaging pulse sequence with repetition time (TR) = 300 and 500 ms. The SC effect in the fast fMRI data was corrected using the prewhitening method based on two AR(p) models: (1) the conventional model (fixed AR(p)) which preselects a constant p for all the image voxels; (2) an improved model (AR_AICc ) that employs the corrected Akaike information criterion voxel-wise to automatically select the model orders for each voxel. To evaluate accuracy of SC correction, false positive characteristics were measured by assuming the presence of block and event-related tasks in the null data without image smoothing. The performance of prewhitening was also examined in smoothed images by adding pseudo task fMRI signals into the null data and comparing the detected to simulated activations (ground truth).

RESULTS

The measured false positive characteristics agreed well with the theoretical curve when using the AR_AICc , and the activation maps in the smoothed data matched the ground truth. The AR_AICc showed improved performance than the fixed AR(p) method.

CONCLUSION

The AR_AICc can effectively remove noise SC, and accurate statistical analysis results can be obtained with the AR_AICc correction in fast fMRI.

Comparison of SMS-EPI and 3D-EPI at 7T in an fMRI localizer study with matched spatiotemporal resolution and homogenized excitation profiles

The simultaneous multi-slice EPI (SMS-EPI, a.k.a. MB-EPI) sequence has met immense popularity recently in functional neuroimaging. A still less common alternative is the use of 3D-EPI, which offers similar acceleration capabilities. The aim of this work was to compare the SMS-EPI and the 3D-EPI sequences in terms of sampling strategies for the detection of task-evoked activations at 7T using detection theory. To this end, the spatial and temporal resolutions of the sequences were matched (1.6 mm isotropic resolution, TR = 1200 ms) and their excitation profiles were homogenized by means of calibration-free parallel-transmission (Universal Pulses). We used a fast-event “localizer” paradigm of 5:20 min in order to probe sensorimotor functions (visual, auditory and motor tasks) as well as higher level functions (language comprehension, mental calculation), where results from a previous large-scale study at 3T (N = 81) served as ground-truth reference for the brain areas implicated in each cognitive function. In the current study, ten subjects were scanned while their activation maps were generated for each cognitive function with the GLM analysis. The SMS-EPI and 3D-EPI sequences were compared in terms of raw tSNR, t-score testing for the mean signal, activation strength and accuracy of the robust sensorimotor functions. To this end, the sensitivity and specificity of these contrasts were computed by comparing their activation maps to the reference brain areas obtained in the 3T study. Estimated flip angle distributions in the brain reported a normalized root mean square deviation from the target value below 10% for both sequences. The analysis of the t-score testing for the mean signal revealed temporal noise correlations, suggesting the use of this metric instead of the traditional tSNR for testing fMRI sequences. The SMS-EPI and 3D-EPI thereby yielded similar performance from a detection theory perspective.

Real-time presurgical resting-state fMRI in patients with brain tumors: Quality control and comparison with task-fMRI and intraoperative mapping

Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional imaging approach, which may complement or replace task-based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real-time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)-echo planar imaging (EPI) with repetition time: 400 ms. Moving-averaged sliding-window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting-state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement-related false-positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting-state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task-activation in motor cortex, Broca's, and Wernicke's areas was 5-10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real-time high-speed rsfMRI for presurgical mapping of eloquent cortex with real-time data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.

Model-based physiological noise removal in fast fMRI

Recent improvements in the speed and sensitivity of fMRI acquisition techniques suggest that fast fMRI can be used to detect and precisely localize sub-second neural dynamics. This enhanced temporal resolution has enormous potential for neuroscientists. However, physiological noise poses a major challenge for the analysis of fast fMRI data. Physiological noise scales with sensitivity, and its autocorrelation structure is altered in rapidly sampled data, suggesting that new approaches are needed for physiological noise removal in fast fMRI. Existing strategies either rely on external physiological recordings, which can be noisy or difficult to collect, or employ data-driven approaches which make assumptions that may not hold true in fast fMRI. We created a statistical model of harmonic regression with autoregressive noise (HRAN) to estimate and remove cardiac and respiratory noise from the fMRI signal directly. This technique exploits the fact that cardiac and respiratory noise signals are fully sampled (rather than aliasing) when imaging at fast rates, allowing us to track and model physiology over time without requiring external physiological measurements. We then created a joint model of neural hemodynamics, and physiological and autocorrelated noise to more accurately remove noise. We first verified that HRAN accurately estimates cardiac and respiratory dynamics and that our model demonstrates goodness-of-fit in fast fMRI data. In task-driven data, we then demonstrated that HRAN is able to remove physiological noise while leaving the neural signal intact, thereby increasing detection of task-driven voxels. Finally, we established that in both simulations and fast fMRI data HRAN is able to improve statistical inferences as compared with gold-standard physiological noise removal techniques. In conclusion, we created a tool that harnesses the novel information in fast fMRI to remove physiological noise, enabling broader use of the technology to study human brain function.

Language beyond the language system: Dorsal visuospatial pathways support processing of demonstratives and spatial language during naturalistic fast fMRI

Spatial demonstratives are powerful linguistic tools used to establish joint attention. Identifying the meaning of semantically underspecified expressions like "this one" hinges on the integration of linguistic and visual cues, attentional orienting and pragmatic inference. This synergy between language and extralinguistic cognition is pivotal to language comprehension in general, but especially prominent in demonstratives. In this study, we aimed to elucidate which neural architectures enable this intertwining between language and extralinguistic cognition using a naturalistic fMRI paradigm. In our experiment, 28 participants listened to a specially crafted dialogical narrative with a controlled number of spatial demonstratives. A fast multiband-EPI acquisition sequence (TR = 388 m s) combined with finite impulse response (FIR) modelling of the hemodynamic response was used to capture signal changes at word-level resolution. We found that spatial demonstratives bilaterally engage a network of parietal areas, including the supramarginal gyrus, the angular gyrus, and precuneus, implicated in information integration and visuospatial processing. Moreover, demonstratives recruit frontal regions, including the right FEF, implicated in attentional orienting and reference frames shifts. Finally, using multivariate similarity analyses, we provide evidence for a general involvement of the dorsal ("where") stream in the processing of spatial expressions, as opposed to ventral pathways encoding object semantics. Overall, our results suggest that language processing relies on a distributed architecture, recruiting neural resources for perception, attention, and extra-linguistic aspects of cognition in a dynamic and context-dependent fashion.

Reliability of Magnetoencephalography and High-Density Electroencephalography Resting-State Functional Connectivity Metrics

Resting-state connectivity, for example, based on magnetoencephalography (MEG) or electroencephalography (EEG), is a widely used method for characterizing brain networks and a promising imaging biomarker. However, there is no established standard as to which method, modality, and analysis variant is preferable and there is only limited knowledge on the reproducibility, an important prerequisite for clinical application. We conducted an MEG-/high-density (hd)-EEG-study on 22 young healthy adults, who were measured twice in a scan/rescan design after 7 ± 2 days. Reliability of resting-state (15 min, eyes-closed) connectivity in source space was calculated via intraclass correlation coefficient (ICC) in classical frequency bands (delta-gamma). We investigated the reliability of two commonly used connectivity metrics, namely the imaginary part of coherency and the weighted phase-lag index and the influence of frequency band, vigilance, and the number of trials. We found a strong increase of reliability with more trials and relatively mild effects of vigilance. Reliability was excellent in the alpha band for MEG, as well as hd-EEG (ICC >0.85); in the theta band, reliability was good for MEG and poor for EEG. Other frequency bands showed lower reliability, with delta band being the worst. Furthermore, we investigated the spatial reliability of resting-state connectivity in a vertex-based approach, which reached fair to good reliability (ICC up to 0.67) with 5 min of data. Our results indicate that excellent reliability of global connectivity is achievable in alpha band, and vertex-based connectivity was still fair to good. Moreover, electrophysiological resting-state studies could benefit from more data than used previously. MEG and hd-EEG were similar in their overall performance but showed frequency band-specific differences.

Dynamic-flip-angle ECG-gating with nuisance signal regression improves resting-state BOLD functional connectivity mapping by reducing cardiogenic noise

PURPOSE

To investigate an ECG-gated dynamic-flip-angle BOLD sequence with improved robustness against cardiogenic noise in resting-state fMRI.

METHODS

ECG-gating minimizes the cardiogenic noise but introduces T₁ -dependent signal variation, which is minimized by combination of a dynamic-flip-angle technique and retrospective nuisance signal regression (NSR) using signals of white matter, CSF, and global average. The technique was studied with simulations in a wide range of T₁ and B₁ fields and phantom imaging with pre-programmed TR variations. Resting-state fMRI of 20 healthy subjects was acquired with non-gated BOLD (NG), ECG-gated constant-flip-angle BOLD (GCFA), ECG-gated BOLD with retrospective T₁ -correction (GRC), and ECG-gated dynamic-flip-angle BOLD (GDFA), all processed by the same NSR method. GDFA was compared to alternative methods over temporal SNR (tSNR), seed-based connectivity, and whole-brain voxelwise connectivity based on intrinsic connectivity distribution (ICD). A previous large-cohort data set (N = 100) was used as a connectivity gold standard.

RESULTS

Simulations and phantom imaging show substantial reduction of the T₁ -dependent signal variation with GDFA alone, and further reduction with NSR. The resting-state study shows improved tSNR in the basal brain, comparing GDFA to NG, after both processed with NSR. Furthermore, GDFA significantly improved subcortical-subcortical and cortical-subcortical connectivity for several representative seeds and significantly improved ICD in the brainstem, thalamus, striatum, and prefrontal cortex, compared to the other 3 approaches.

CONCLUSION

GDFA with NSR improves mapping of the resting-state functional connectivity of the basal-brain regions by reducing cardiogenic noise.

Properties of face localizer activations and their application in functional magnetic resonance imaging (fMRI) fingerprinting

Functional localizers are particularly prevalent in functional magnetic resonance imaging (fMRI) studies concerning face processing. In this study, we extend the knowledge on face localizers regarding four important aspects: First, activation differences in occipital and fusiform face areas (OFA/FFA) and amygdala are characterized by increased activation while precuneus and medial prefrontal cortex show decreased deactivation to faces versus control stimuli. The face-selective posterior superior temporal sulcus is a hybrid area exhibiting increased activation within its inferior and decreased deactivation within its superior part. Second, the employed control stimuli can impact on whether a region is classified in group analyses as face-selective or not. We specifically investigated this for recently described cytoarchitectonic subregions of the fusiform cortex (FG-2/FG-4). Averaged activity across voxels in FG-4 was stronger for faces than objects, houses, or landscapes. In FG-2, averaged activity was only significantly stronger in comparison with landscapes, but small peaks within this area were detected for comparison versus objects and houses. Third, reproducibility of individual peak activations is excellent for right FFA and quite good for right OFA, whereas within all other areas it was too low to provide valid information on time-invariant individual peaks. Finally, the fine-grained spatial activation patterns in right OFA and FFA are both time-invariant within each individual and sufficiently different between individuals to enable identification of individual participants with near-perfect precision (fMRI fingerprinting).

On the analysis of rapidly sampled fMRI data

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.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series

In a recent study Eklund et al. have shown that cluster-wise family-wise error (FWE) rate-corrected inferences made in parametric statistical method-based functional magnetic resonance imaging (fMRI) studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACFs) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggest otherwise. Hence, the residuals from general linear model (GLM)-based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF. Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residual time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts, which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE-corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad hoc stochastic colored noise models. Furthermore, the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

A circular echo planar sequence for fast volumetric fMRI

PURPOSE

To demonstrate a circular EPI (CEPI) sequence as well as a generalized EPI reconstruction for fast fMRI with parallel imaging acceleration.

METHODS

The CEPI acquisition was constructed using variable readout lengths and maximum ramp sampling as well as blipped-CAIPI z-gradient encoding for simultaneous multislice (SMS) and 3D volumetric imaging. A signal equation model with constant and linear phase terms was used to iteratively reconstruct images with low ghosting. Simulation, phantom, and human imaging experiments including audio/visual fMRI were performed at 3T using a 52-channel coil.

RESULTS

Application of CEPI gradients with duration of 27 ms covering a 22-cm FOV at a 64 × 64 pixel resolution in SMS and 3D acquisitions resulted in images with comparable quality to those of standard Cartesian EPI. With parallel imaging techniques robust detection of BOLD fMRI activation with temporal sampling down to 275 ms was possible. The high temporal resolution enabled higher activation statistics at a penalty in increased noise and residual aliasing. The un-accelerated 3D acquisition showed large temporal instability compared with a standard 2D acquisition.

CONCLUSION

Nonuniform sampling and generalized image reconstructions can be applied to EPI acquisitions including those with blipped-CAIPI z gradients. The same gradients can be used for either SMS or 3D acquisitions providing identical coverage.

Optimal repetition time reduction for single subject event-related functional magnetic resonance imaging

PURPOSE

Short TRs are increasingly used for fMRI as fast sequences such as simultaneous multislice excitation become available. These have been associated with apparent sensitivity improvements, although greater temporal autocorrelation at shorter TRs can inflate sensitivity measurements leading to uncertainty regarding the optimal approach.

METHODS

In volunteers (n = 10), the optimal TR was assessed at the single subject level for event-related designs (visual stimulation) with 4 frequencies of presentation at 4 TR values (412-2550 ms). T-values in the visual cortex localized in each individual were obtained and receiver operating characteristics (ROC) analysis was performed by counting voxels within and outside expected task active regions at different thresholds. This analysis was repeated using 4 different autoregressive (AR) models; SPM AR(1) and SPM AR(fast) which globally estimate autocorrelation, and fMRIstat AR(1) and AR(5) that use a local estimate.

RESULTS

The use of modest multiband factors of 2 or 3 with a reduction in TR to 1000 ± 200 ms had greater sensitivity and specificity as shown by higher T-values in visual cortex and ROC analysis. At these TRs, the ROC analysis demonstrated that a local AR model fit improved performance while high order AR models were unnecessary.

CONCLUSIONS

Modest TR reductions (to 1000 ± 200 ms) optimally improved event-related fMRI performance independent of design frequency. Autoregressive models with a local as opposed to global fit performed better, while low order autoregressive models were sufficient at the optimal TR.

Accurate modeling of temporal correlations in rapidly sampled fMRI time series

Rapid imaging techniques are increasingly used in functional MRI studies because they allow a greater number of samples to be acquired per unit time, thereby increasing statistical power. However, temporal correlations limit the increase in functional sensitivity and must be accurately accounted for to control the false‐positive rate. A common approach to accounting for temporal correlations is to whiten the data prior to estimating fMRI model parameters. Models of white noise plus a first‐order autoregressive process have proven sufficient for conventional imaging studies, but more elaborate models are required for rapidly sampled data. Here we show that when the “FAST” model implemented in SPM is used with a well‐controlled number of parameters, it can successfully prewhiten 80% of grey matter voxels even with volume repetition times as short as 0.35 s. We further show that the temporal signal‐to‐noise ratio (tSNR), which has conventionally been used to assess the relative functional sensitivity of competing imaging approaches, can be augmented to account for the temporal correlations in the time series. This amounts to computing the t‐score testing for the mean signal. We show in a visual perception task that unlike the tSNR weighted by the number of samples, the t‐score measure is directly related to the t‐score testing for activation when the temporal correlations are correctly modeled. This score affords a more accurate means of evaluating the functional sensitivity of different data acquisition options.

A comprehensive evaluation of increasing temporal resolution with multiband-accelerated protocols and effects on statistical outcome measures in fMRI

Accelerated functional Magnetic Resonance Imaging (fMRI) with 'multiband' protocols is now relatively widespread. These protocols can be used to dramatically reduce the repetition time (TR) and produce a time-series sampled at a higher temporal resolution, which may produce benefits in the statistical methods typically used to analyse fMRI data. We tested the effects of higher temporal resolutions for fMRI on statistical outcome measures in a comprehensive manner on two different MRI scanner platforms. Spatial resolution was maintained at a constant of 3 mm isotropic voxels, and an in-plane acceleration factor of 2 was used for all experiments. Experiment 1 tested a range of acceleration factors (1-6) against a standard EPI protocol on a single composite task that mapped a number of basic sensory, motor, and cognitive networks. Experiment 2 compared the standard protocol with acceleration factors of 2 and 3 on both resting-state and two task paradigms (an N-back task, and faces/places task), with a number of different analysis approaches. Results from experiment 1 showed modest but relatively inconsistent effects of the higher sampling rate on statistical outcome measures. Experiment 2 showed strong benefits of the multiband protocols on results derived from resting-state data, but more varied effects on results from the task paradigms. Notably, the multiband protocols were superior when Multi-Voxel Pattern Analysis was used to interrogate the faces/places data, but showed less benefit in conventional General Linear Model analyses of the same data. In general, ROI-derived measures of statistical effects benefitted only modestly from higher sampling resolution, with greater effects seen when using a measure of the top range of statistical values. Across both experiments, results from the two scanner platforms were broadly comparable. The statistical benefits of high temporal resolution fMRI with multiband protocols may therefore depend on a number of factors, including the nature of the investigation (resting-state vs. task-based), the experimental design, the particular statistical outcome measure, and the type of analysis used.

Improving the signal detection accuracy of functional Magnetic Resonance Imaging

A major drawback of functional Magnetic Resonance Imaging (fMRI) concerns the lack of detection accuracy of the measured signal. Although this limitation stems in part from the neuro-vascular nature of the fMRI signal, it also reflects particular methodological decisions in the fMRI data analysis pathway. Here we show that the signal detection accuracy of fMRI is affected by the specific way in which whole-brain volumes are created from individually acquired brain slices, and by the method of statistically extracting signals from the sampled data. To address these limitations, we propose a new framework for fMRI data analysis. The new framework creates whole-brain volumes from individual brain slices that are all acquired at the same point in time relative to a presented stimulus. These whole-brain volumes contain minimal temporal distortions, and are available at a high temporal resolution. In addition, statistical signal extraction occurred on the basis of a non-standard time point-by-time point approach. We evaluated the detection accuracy of the extracted signal in the standard and new framework with simulated and real-world fMRI data. The new slice-based data-analytic framework yields greatly improved signal detection accuracy of fMRI signals.

Evaluating the impact of fast-fMRI on dynamic functional connectivity in an event-based paradigm

The human brain is known to contain several functional networks that interact dynamically. Therefore, it is desirable to analyze the temporal features of these networks by dynamic functional connectivity (dFC). A sliding window approach was used in an event-related fMRI (visual stimulation using checkerboards) to assess the impact of repetition time (TR) and window size on the temporal features of BOLD dFC. In addition, we also examined the spatial distribution of dFC and tested the feasibility of this approach for the analysis of interictal epileptiforme discharges. 15 healthy controls (visual stimulation paradigm) and three patients with epilepsy (EEG-fMRI) were measured with EPI-fMRI. We calculated the functional connectivity degree (FCD) by determining the total number of connections of a given voxel above a predefined threshold based on Pearson correlation. FCD could capture hemodynamic changes relative to stimulus onset in controls. A significant effect of TR and window size was observed on FCD estimates. At a conventional TR of 2.6 s, FCD values were marginal compared to FCD values using sub-seconds TRs achievable with multiband (MB) fMRI. Concerning window sizes, a specific maximum of FCD values (inverted u-shape behavior) was found for each TR, indicating a limit to the possible gain in FCD for increasing window size. In patients, a dynamic FCD change was found relative to the onset of epileptiform EEG patterns, which was compatible with their clinical semiology. Our findings indicate that dynamic FCD transients are better detectable with sub-second TR than conventional TR. This approach was capable of capturing neuronal connectivity across various regions of the brain, indicating a potential to study the temporal characteristics of interictal epileptiform discharges and seizures in epilepsy patients or other brain diseases with brief events.

Reducing task-based fMRI scanning time using simultaneous multislice echo planar imaging

PURPOSE

To maintain alertness and to remain motionless during scanning represent a substantial challenge for patients/subjects involved in both clinical and research functional magnetic resonance imaging (fMRI) examinations. Therefore, availability and application of new data acquisition protocols allowing the shortening of scan time without compromising the data quality and statistical power are of major importance.

METHODS

Higher order category-selective visual cortical areas were identified individually, and rapid event-related fMRI design was used to compare three different sampling rates (TR = 2000, 1000, and 410 ms, using state-of-the-art simultaneous multislice imaging) and four different scanning lengths to match the statistical power of the traditional scanning methods to high sampling-rate design.

RESULTS

The results revealed that ~ 4 min of the scan time with 1 Hz (TR = 1000 ms) sampling rate and ~ 2 min scanning at ~ 2.5 Hz (TR = 410 ms) sampling rate provide similar localization sensitivity and selectivity to that obtained with 11-min session at conventional, 0.5 Hz (TR = 2000 ms) sampling rate.

CONCLUSION

Our findings suggest that task-based fMRI examination of clinical population prone to distress such as presurgical mapping experiments might substantially benefit from the reduced (20-40%) scanning time that can be achieved by the application of simultaneous multislice sequences.

Serial correlations in single-subject fMRI with sub-second TR

When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences.