Optimal experimental designs for fMRI via circulant biased weighing designs

Functional magnetic resonance imaging (fMRI) technology is popularly used in many fields for studying how the brain reacts to mental stimuli. The identification of optimal fMRI experimental designs is crucial for rendering precise statistical inference on brain functions, but research on this topic is very lacking. We develop a general theory to guide the selection of fMRI designs for estimating a hemodynamic response function (HRF) that models the effect over time of the mental stimulus, and for studying the comparison of two HRFs. We provide a useful connection between fMRI designs and circulant biased weighing designs, establish the statistical optimality of some well-known fMRI designs and identify several new classes of fMRI designs. Construction methods of high-quality fMRI designs are also given.Comment: Published at http://dx.doi.org/10.1214/15-AOS1352 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Appendix A: “The Basic Postulates of Accounting”

Functional Magnetic Resonance Imaging (fMRI) is a relatively new imaging technique, first reported in 1992, which enables mapping of brain functions with high spatial resolution. Functionally active areas are distinguished by a small signal increase mediated by changes in local blood oxygenation in response to neural activity. The ability to non-invasively map brain function and the large number of MRI scanners quickly made the method very popular, and fMRI have had a huge impact on the study of brain function, both in healthy and diseased subjects. The most common clinical application of fMRI is pre-surgical mapping of brain functions in order to optimise surgical interventions. The clinical fMRI examination procedure can be divided into four integrated parts: (1) patient preparation, (2) image acquisition, (3) image analysis and (4) clinical decision. In this thesis, important aspects of all parts of the fMRI examination procedure are explored with the aim to provide recommendations and methods for prosperous clinical usage of the technique. The most important results of the thesis were: (I) administration of low doses of diazepam to reduce anxiety did not invalidate fMRI mapping results of primary motor and language areas, (II) the choice of visual stimuli equipment can have severe impact on the mapping of visual areas, (III) three-dimensional fMRI imaging sequences did not perform better than two-dimensional imaging sequences, (IV) adaptive spatial filtering can improve the fMRI data analysis, (V) clinical decisions should not be based on activation results from a single statistical threshold

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

Comparison of Semantic and Episodic Memory BOLD fMRI Activation in Predicting Cognitive Decline in Older Adults

Previous studies suggest that task-activated functional magnetic resonance imaging (fMRI) can predict future cognitive decline among healthy older adults. The present fMRI study examined the relative sensitivity of semantic memory (SM) versus episodic memory (EM) activation tasks for predicting cognitive decline. Seventy-eight cognitively intact elders underwent neuropsychological testing at entry and after an 18-month interval, with participants classified as cognitively “Stable” or “Declining” based on ≥1.0 SD decline in performance. Baseline fMRI scanning involved SM (famous name discrimination) and EM (name recognition) tasks. SM and EM fMRI activation, along with Apolipoprotein E (APOE) ε4 status, served as predictors of cognitive outcome using a logistic regression analysis. Twenty-seven (34.6%) participants were classified as Declining and 51 (65.4%) as Stable. APOE ε4 status alone significantly predicted cognitive decline (R2 = .106; C index = .642). Addition of SM activation significantly improved prediction accuracy (R2 = .285; C index = .787), whereas the addition of EM did not (R2 = .212; C index = .711). In combination with APOE status, SM task activation predicts future cognitive decline better than EM activation. These results have implications for use of fMRI in prevention clinical trials involving the identification of persons at-risk for age-associated memory loss and Alzheimer\u27s disease. (JINS, 2012, 18, 1–11

Implementation and evaluation of simultaneous video-electroencephalography and functional magnetic resonance imaging

The objective of this study was to demonstrate that the addition of simultaneous and synchronised video to electroencephalography (EEG)-correlated functional magnetic resonance imaging (fMRI) could increase recorded information without data quality reduction. We investigated the effect of placing EEG, video equipment and their required power supplies inside the scanner room, on EEG, video and MRI data quality, and evaluated video-EEG-fMRI by modelling a hand motor task. Gradient-echo, echo-planner images (EPI) were acquired on a 3-T MRI scanner at variable camera positions in a test object [with and without radiofrequency (RF) excitation], and human subjects. EEG was recorded using a commercial MR-compatible 64-channel cap and amplifiers. Video recording was performed using a two-camera custom-made system with EEG synchronization. An in-house script was used to calculate signal to fluctuation noise ratio (SFNR) from EPI in test object with variable camera positions and in human subjects with and without concurrent video recording. Five subjects were investigated with video-EEG-fMRI while performing hand motor task. The fMRI time series data was analysed using statistical parametric mapping, by building block design general linear models which were paradigm prescribed and video based. Introduction of the cameras did not alter the SFNR significantly, nor did it show any signs of spike noise during RF off conditions. Video and EEG quality also did not show any significant artefact. The Statistical Parametric Mapping{T} maps from video based design revealed additional blood oxygen level-dependent responses in the expected locations for non-compliant subjects compared to the paradigm prescribed design. We conclude that video-EEG-fMRI set up can be implemented without affecting the data quality significantly and may provide valuable information on behaviour to enhance the analysis of fMRI data

A Pneumatically Actuated Manipulandum for Neuromotor Control Research

Functional magnetic resonance imaging (fMRI) techniques have great potential for identifying which neural structures are involved in the control of goal-directed reaching movements. However, fMRI techniques alone are not capable of probing the neural mechanisms involved in acquisition of novel motor behaviors because such studies require that the moving limb be perturbed in a controlled fashion. We outline a plan to design and develop a non-metallic, pneumatically actuated tool that, along with systems identification techniques and functional magnetic resonance imaging (fMRI), will characterize and quantify how the human central nervous system uses sensory information during practice-based motor learning

EEG–fMRI of idiopathic and secondarily generalized epilepsies

We used simultaneous EEG and functional MRI (EEG–fMRI) to study generalized spike wave activity (GSW) in idiopathic and secondary generalized epilepsy (SGE). Recent studies have demonstrated thalamic and cortical fMRI signal changes in association with GSW in idiopathic generalized epilepsy (IGE). We report on a large cohort of patients that included both IGE and SGE, and give a functional interpretation of our findings. Forty-six patients with GSW were studied with EEG–fMRI; 30 with IGE and 16 with SGE. GSW-related BOLD signal changes were seen in 25 of 36 individual patients who had GSW during EEG–fMRI. This was seen in thalamus (60%) and symmetrically in frontal cortex (92%), parietal cortex (76%), and posterior cingulate cortex/precuneus (80%). Thalamic BOLD changes were predominantly positive and cortical changes predominantly negative. Group analysis showed a negative BOLD response in the cortex in the IGE group and to a lesser extent a positive response in thalamus. Thalamic activation was consistent with its known role in GSW, and its detection in individual cases with EEG–fMRI may in part be related to the number and duration of GSW epochs recorded. The spatial distribution of the cortical fMRI response to GSW in both IGE and SGE involved areas of association cortex that are most active during conscious rest. Reduction of activity in these regions during GSW is consistent with the clinical manifestation of absence seizures

Propagated infra-slow intrinsic brain activity reorganizes across wake and slow wave sleep

Propagation of slow intrinsic brain activity has been widely observed in electrophysiogical studies of slow wave sleep (SWS). However, in human resting state fMRI (rs-fMRI), intrinsic activity has been understood predominantly in terms of zero-lag temporal synchrony (functional connectivity) within systems known as resting state networks (RSNs). Prior rs-fMRI studies have found that RSNs are generally preserved across wake and sleep. Here, we use a recently developed analysis technique to study propagation of infra-slow intrinsic blood oxygen level dependent (BOLD) signals in normal adults during wake and SWS. This analysis reveals marked changes in propagation patterns in SWS vs. wake. Broadly, ordered propagation is preserved within traditionally defined RSNs but lost between RSNs. Additionally, propagation between cerebral cortex and subcortical structures reverses directions, and intra-cortical propagation becomes reorganized, especially in visual and sensorimotor cortices. These findings show that propagated rs-fMRI activity informs theoretical accounts of the neural functions of sleep

Ready ... Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals