Scanning fast and slow: current limitations of 3 Tesla functional MRI and future potential

Abstract

Functional MRI at 3T has become a workhorse for the neurosciences, e.g., neurology, psychology, and psychiatry, enabling non-invasive investigation of brain function and connectivity. However, BOLD-based fMRI is a rather indirect measure of brain function, confounded by physiology related signals, e.g., head or brain motion, brain pulsation, blood flow, intermixed with susceptibility differences close or distant to the region of neuronal activity. Even though a plethora of preprocessing strategies have been published to address these confounds, their efficiency is still under discussion. In particular, physiological signal fluctuations closely related to brain supply may mask BOLD signal changes related to "true" neuronal activation. Here we explore recent technical and methodological advancements aimed at disentangling the various components, employing fast multiband vs. standard EPI, in combination with fast temporal ICA. Our preliminary results indicate that fast (TR <0.5 s) scanning may help to identify and eliminate physiologic components, increasing tSNR and functional contrast. In addition, biological variability can be studied and task performance better correlated to other measures. This should increase specificity and reliability in fMRI studies. Furthermore, physiological signal changes during scanning may then be recognized as a source of information rather than a nuisance. As we are currently still undersampling the complexity of the brain, even at a rather coarse macroscopic level, we should be very cautious in the interpretation of neuroscientific findings, in particular when comparing different groups (e.g., age, sex, medication, pathology, etc.). From a technical point of view our goal should be to sample brain activity at layer specific resolution with low TR, covering as much of the brain as possible without violating SAR limits. We hope to stimulate discussion toward a better understanding and a more quantitative use of fMRI.

Keywords: fMRI; physiological noise; resting state; sensitivity; specificity; speed.

Figure 1. Comparison of the mean spectral distribution of 12 resting-state networks shown in green (i.e., from medial visual, motor, cerebellum, lateral visual, posterior parietal, left-lateral fronto-parietal, temporal, medial frontal, default-mode, to limbic lobe, basal ganglia, right lateral fronto-parietal and anterior temporal lobe, in descending order of explained variance) and various physiological noise components in red.

Experiments were performed at 3T (n = 26), TR/TE = 1000/28 ms (3.3 × 3.9 × 4 mm³ voxel resolution) during 5 min, and at 4T 3 (n = 15), TR/TE =2200/33 ms (3 × 3× 3 mm³ voxel resolution) during 10 min sessions. For more details see Robinson [31]. Note the high power of noise components between 0.01 and 0.1 Hz, not to be eliminated via bandpass filtering, and limiting the detection of more resting-state networks or subtle differences between networks in group studies.

Figure 2. Illustrates relevant macroscopic components in BOLD-based fMRI of the brain.

(A) Arterial vessels ex vivo (copyright Gunther von Hagens, Körperwelten, Institut für Plastination, Heidelberg, www.koerperwelten.de), (B) MR-venography of venous vessels in vivo at 7 Tesla [courtesy Dr. M. Barth; adapted from Koopmans [36]]. (C) Representative slice across the brain of a young healthy subject. T1-weighted structural image, segmented gray matter mask, segmented deep white matter mask and segmented CSF space (from left to right). Note that in contrast to (A,B), vessels are almost invisible. (D) Mean SWI (n = 3, left), highlighting brain regions with strong susceptibility differences (dark regions) causing artifacts (e.g., frontal lobe/nasal cavities, temporal lobe/ear canals, veins near the brain stem, etc.). MR-venogram (n = 1), visualizing the basal vein of Rosenthal, running next to the amygdalae and brainstem.

Figure 3. Improving image SNR (left) and time-series SNR (right) via faster scanning.

Although SNR per slice or volume is lower at lower TR, SNR per unit time is increasing due to more efficient scanning, as compared to TR = 1800 ms. Furthermore, time series SNR is also improving, depending however on the brain region or ROI chosen (motor, motor cortex; visual, visual cortex; amy, amygdala; wm, white matter; csf, cerebrospinal fluid; bs, brain stem). Note that while tSNR is increased compared to standard EPI when using a multiband factor of 4, it is however, decreased at a multiband factor of 8.

Figure 4. Temporal ICA of low TR, multiband EPI fMRI data from three subjects.

Using a strong task (image matching paradigm, block design shown in red), tICA identifies the activation map in the visual cortex but also adjacent to the amygdalae and at the fronto-basis, corresponding to task related time courses as well as strong pulsations (high frequency noise). Note also the major draining vein (V. parieto-occipitalis interna connecting to V. basalis Rosenthal) following medially the temporal lobe next to the amygdalae.