Our previous studies demonstrated that MEMRI provides a sensitive method to detect accumulated sound-evoked activity in the mouse IC (Yu et al., 2005
; Yu et al., 2007
). In this study, we developed and applied a statistical parametric mapping method for quantitative analysis of MEMRI activity patterns. We first analyzed the spatial distribution of activity patterns induced by 40-kHz pure tones at different sound amplitudes. The resulting statistical maps showed activity localized to the ventral IC regions, in excellent agreement with the tonotopic map established by electrophysiology. The volume of the active IC region increased in proportion to the amplitude of the 40-kHz stimulus. Analysis of the MEMRI data within the active regions demonstrated close to a 10% increase in average signal intensity, independent of sound amplitude, comparing stimulated to control mice. The peak signal intensity in the active IC regions was shown to increase in proportion to the stimulus amplitude, up to 20% at the highest sound amplitude. ANOVA analysis was used to identify and localize peripheral IC regions with significant amplitude-dependent signal intensity changes. Taken together, these results indicate that MEMRI can be used to analyze both the spatial location of neuronal activity in the mouse IC, as well as the magnitude of the activity evoked by sound stimuli carrying different energy.
One potential problem with the statistical mapping approach introduced in this study is that there is no standard method to minimize the number of false positive errors from multiple t-test comparisons, while preserving the biological meaning of the significant voxels. In particular, in statistical maps there is no method that can restrict false positive errors (Type I) without introducing false negative errors (Type II). Strategies have been developed to reduce false positives, such as the Bonferroni correction, but these were too conservative to analyze sound evoked activity signals in this study. Specifically, there were no significant differences detected in sound stimulated images using the p-value threshold derived from Bonferroni correction (for 5245 voxels in the segmented mouse IC, pth
= 0.05/5245 = 0.0000095; data not shown). As an alternative approach, we implemented Gaussian filtering to eliminate a significant number of false positive errors from the pre-filtering statistical image (). After Gaussian filtering, IC voxels with p-value less than 0.05 were consistently located in the ventral IC in mice stimulated with 40-kHz. These results are in good agreement with the known electrophysiological maps, and provide confidence that the defined significance threshold does enable detection of the biologically meaningful results. To further investigate this point, we also implemented the false discovery rate (FDR) method to estimate the number of false positives in the statistical parametric images after Gaussian filtering. Similar to the Bonferroni correction method, FDR was very conservative in estimating upper bounds on the number of false positive errors, but nevertheless produced results in general agreement with the Gaussian filtering approach (Supplementary Material
Previous MEMRI studies have implemented statistical (t-test) mapping methods, based on analyses of difference images (post – pre Mn injection) acquired from individual animals. This is a logical approach for dynamic MEMRI studies, comparing signal differences over a single imaging session (Lu et al., 2007
), or for MEMRI studies of Mn transport along axonal tracts after focal brain injection (Cross et al., 2004
). These previous methods employed Z-statistical mapping, using random field theory to constrain the Type I error rate (Friston et al, 1990
; Worsley et al., 1992
), as opposed to our direct computation of pvalue maps from t-statistics. In future, it would be interesting to investigate using a pre-stimulation MEMRI scan for auditory brain mapping, for normalizing subsequent post sound-stimulation images to the pre-scan. However, several issues would require careful examination, including the potential toxic effects of multiple Mn injections required for pre- and post-stimulation imaging. Moreover, the known developmental stage-dependent uptake of Mn in the mouse brain would likely preclude such an approach for studies of functional brain development (Wadghiri et al., 2004
; Yu et al., 2007
To properly analyze and interpret MEMRI activity patterns, it is necessary to understand the mechanistic basis of MRI signal enhancement related to neuronal activity. Previously, we showed that Mn ions accumulated in highly activated brain regions during prolonged periods of sensory stimulation, enabling assessment of accumulated activity patterns based on MEMRI signal enhancement (Yu et al., 2005
). Two mechanisms are likely to serve as the bases for activity-induced Mn accumulation. First, repetitive sound stimulation leads to calcium channel activation, permitting the influx of extracellular Mn (Narita et al. 1990
), which results in its accumulation within activated brain regions. Secondly, Mn can be transported along neuronal connections (Pautler, 2004
). In our study, Mn ions could have entered auditory pathways close to the vasculature, including the organ of corti and cochlear nucleus (Yu et al., 2005
). Since sound-evoked activity can also accelerate Mn trans-synaptic transport through neuronal projections (Van der Linden et al., 2004
), the MEMRI signal enhancement patterns may represent both the accumulation due to sound-evoked synaptic activity and transport via the afferent projecting populations.
The excellent agreement between single tone MEMRI patterns and published isofrequency contour maps has further validated this approach for activity mapping (; Yu et al., 2007
). Nonetheless, there are essential differences between the current MEMRI results and previous electrophysiological tonotopic maps. In MEMRI auditory brain mapping, a defined sound stimulus is used to evoke activity in a broad ensemble of neurons, while electrical recordings target one or a few auditory neurons to measure their response properties with a range of acoustic stimuli. To highlight the frequency selectivity of auditory neurons, isofrequency contour maps typically represent the spatial distribution of auditory neuron responses at threshold. However, within a population of neurons, the response threshold may vary by 20–40 dB (Romand & Ehert, 1990
; Kiang, 1965
; Tsuchitani et al., 1982
). Thus, previous characterizations of tonotopy do not provide a full understanding of supra-threshold activity patterns.
In the current MEMRI study, we mapped the supra-threshold neuronal activity patterns of a single 40-kHz tone at different sound amplitudes. We showed that distinct neuronal ensembles within the 40-kHz isofrequency region were activated as sound pressure levels (SPL) increased. When the peak stimulus SPL was raised from 65 to 89 dB, the activated region expanded by 1.6 mm along the axis of the isofrequency contour, and by 0.55 mm across the contour width (i.e., dorsal-ventral) (). A similar expansion to suprathreshold tonal stimuli has been described previously in auditory cortex using optical mapping (Uno et al., 1993
). These results highlight the fact that threshold tonotopic maps provide a good correlate to the main afferent projections, but do not reveal the functional representation that will emerge when the full range of projections and intrinsic connections are recruited.
In the current study, statistical (t-test) mapping from 3D MEMRI images demonstrated a direct relationship between sound amplitude and both the active IC volume (), as well as signal intensity changes (). Furthermore, MEMRI mapping revealed the 3D spatial patterns of IC neuronal ensembles activated by each test SPL (), as well as the distribution of IC neurons showing distinct firing properties to different SPLs (). To examine the exact relationship between MEMRI signal changes and SPL in the mouse midbrain, a number of factors need to be considered. In other mammals, the threshold levels in auditory neurons range over close to 40 dB SPL, and the dynamic ranges of the neuronal firing rate vs
. sound level functions are variable (Kiang et al., 1965
; Liberman et al., 1978
; Aitkin, 1991
). Some neurons in the auditory midbrain are known to have non-monotonic (not continuously increasing with sound level) firing characteristics (Ramachandran et al., 1999
), but the spatial distribution and size of the non-monotonic population is currently unknown. Given this level of complexity, it is very difficult to predict the relationship between SPL and IC activity, and clearly more than three SPLs will be required to characterize the relationship between sound energy and MEMRI signal level and distribution.
In summary, the MEMRI statistical mapping approach introduced in this report provides an unbiased method to separate activity induced Mn accumulation in active brain regions from the nonspecific Mn distribution. The signal intensity changes between control and stimulated MEMRI images were analyzed quantitatively, and shown to provide a good representation of the activity patterns resulting from defined sound stimuli. We demonstrated the sensitivity of MEMRI mapping to detect and analyze sound frequency and amplitude dependent signal enhancement in the mouse IC. By altering the sound pressure levels, statistical mapping characterized the amplitude-dependent activity patterns induced by supra-threshold stimuli, providing the first insights in to the organization of amplitude-dependent IC activity. Taken together, these results clearly indicate that MEMRI mapping provides a quantitative and unbiased method to assess activity in mice, enabling future analyses of the altered function in genetically and environmentally manipulated mouse models.