Increased fMRI Sensitivity at Equal Data Burden Using Averaged Shifted Echo Acquisition

Olfactory fMRI: Implications of Stimulation Length and Repetition Time

Studying olfaction with functional magnetic resonance imaging (fMRI) poses various methodological challenges. This study aimed to investigate the effects of stimulation length and repetition time (TR) on the activation pattern of 4 olfactory brain regions: the anterior and the posterior piriform cortex, the orbitofrontal cortex, and the insula. Twenty-two healthy participants with normal olfaction were examined with fMRI, with 2 stimulation lengths (6 s and 15 s) and 2 TRs (0.901 s and 1.34 s). Data were analyzed using General Linear Model (GLM), Tensorial Independent Component Analysis (TICA), and by plotting the event-related time course of brain activation in the 4 olfactory regions of interest. The statistical analysis of the time courses revealed that short TR was associated with more pronounced signal increase and short stimulation was associated with shorter time to peak signal. Additionally, both long stimulation and short TR were associated with oscillatory time courses, whereas both short stimulation and short TR resulted in more typical time courses. GLM analysis showed that the combination of short stimulation and short TR could result in visually larger activation within these olfactory areas. TICA validated that the tested paradigm was spatially and temporally associated with a functionally connected network that included all 4 olfactory regions. In conclusion, the combination of short stimulation and short TR is associated with higher signal increase and shorter time to peak, making it more amenable to standard GLM-type analyses than long stimulation and long TR, and it should, thus, be preferable for olfactory fMRI.

Attention-Related Brain Activation Is Altered in Older Adults With White Matter Hyperintensities Using Multi-Echo fMRI

Cognitive decline is often undetectable in the early stages of accelerated vascular aging. Attentional processes are particularly affected in older adults with white matter hyperintensities (WMH), although specific neurovascular mechanisms have not been elucidated. We aimed to identify differences in attention-related neurofunctional activation and behavior between adults with and without WMH. Older adults with moderate to severe WMH (n = 18, mean age = 70 years), age-matched adults (n = 28, mean age = 72), and healthy younger adults (n = 19, mean age = 25) performed a modified flanker task during multi-echo blood oxygenation level dependent functional magnetic resonance imaging. Task-related activation was assessed using a weighted-echo approach. Healthy older adults had more widespread response and higher amplitude of activation compared to WMH adults in fronto-temporal and parietal cortices. Activation associated with processing speed was absent in the WMH group, suggesting attention-related activation deficits that may be a consequence of cerebral small vessel disease. WMH adults had greater executive contrast activation in the precuneous and posterior cingulate gyrus compared to HYA, despite no performance benefits, reinforcing the network dysfunction theory in WMH.

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.