A high-density EEG and structural MRI source analysis of the frequency following response to missing fundamental stimuli reveals subcortical and cortical activation to low and high frequency stimuli.

Pitch is a perceptual rather than physical phenomenon, important for spoken language use, musical communication, and other aspects of everyday life. Auditory stimuli can be designed to probe the relationship between perception and physiological responses to pitch-evoking stimuli. One technique for measuring physiological responses to pitch-evoking stimuli is the frequency following response (FFR). The FFR is an electroencephalographic (EEG) response to periodic auditory stimuli. The FFR contains nonlinearities not present in the stimuli, including correlates of the amplitude envelope of the stimulus; however, these nonlinearities remain undercharacterized. The FFR is a composite response reflecting multiple neural and peripheral generators, and their contributions to the scalp-recorded FFR vary in ill-understood ways depending on the electrode montage, stimulus, and imaging technique. The FFR is typically assumed to be generated in the auditory brainstem; there is also evidence both for and against a cortical contribution to the FFR. Here a methodology is used to examine the FFR correlates of pitch and the generators of the FFR to stimuli with different pitches. Stimuli were designed to tease apart biological correlates of pitch and amplitude envelope. FFRs were recorded with 256-electrode EEG nets, in contrast to a typical FFR setup which only contains a single active electrode. Structural MRI scans were obtained for each participant to co-register with the electrode locations and constrain a source localization algorithm. The results of this localization shed light on the generating mechanisms of the FFR, including providing evidence for both cortical and subcortical auditory sources.

Testing a computational model for aural detection of aircraft in ambient noise.

Computational models are used to predict the performance of human listeners for carefully specified signal and noise conditions. However, there may be substantial discrepancies between the conditions under which listeners are tested and those used for model predictions. Thus, models may predict better performance than exhibited by the listeners, or they may "fail" to capture the ability of the listener to respond to subtle stimulus conditions. This study tested a computational model devised to predict a listener's ability to detect an aircraft in various soundscapes. The model and listeners processed the same sound recordings under carefully specified testing conditions. Details of signal and masker calibration were carefully matched, and the model was tested using the same adaptive tracking paradigm. Perhaps most importantly, the behavioral results were not available to the modeler before the model predictions were presented. Recordings from three different aircraft were used as the target signals. Maskers were derived from recordings obtained at nine locations ranging from very quiet rural environments to suburban and urban settings. Overall, with a few exceptions, model predictions matched the performance of the listeners very well. Discussion focuses on those differences and possible reasons for their occurrence.

Effects of tDCS dose and electrode montage on regional cerebral blood flow and motor behavior.

We used three dose levels (Sham, 2 mA, and 4 mA) and two different electrode montages (unihemispheric and bihemispheric) to examine DOSE and MONTAGE effects on regional cerebral blood flow (rCBF) as a surrogate marker of neural activity, and on a finger sequence task, as a surrogate behavioral measure drawing on brain regions targeted by transcranial direct current stimulation (tDCS). We placed the anodal electrode over the right motor region (C4) while the cathodal or return electrode was placed either over a left supraorbital region (unihemispheric montage) or over the left motor region (C3 in the bihemispheric montage). Performance changes in the finger sequence task for both hands (left hand: p = 0.0026, and right hand: p = 0.0002) showed a linear tDCS dose response but no montage effect. rCBF in the right hemispheric perirolandic area increased with dose under the anodal electrode (p = 0.027). In contrast, in the perirolandic ROI in the left hemisphere, rCBF showed a trend to increase with dose (p = 0.053) and a significant effect of montage (p = 0.00004). The bihemispheric montage showed additional rCBF increases in frontomesial regions in the 4mA condition but not in the 2 mA condition. Furthermore, we found strong correlations between simulated current density in the left and right perirolandic region and improvements in the finger sequence task performance (FSP) for the contralateral hand. Our data support not only a strong direct tDCS dose effect for rCBF and FSP as surrogate measures of targeted brain regions but also indirect effects on rCBF in functionally connected regions (e.g., frontomesial regions), particularly in the higher dose condition and on FSP of the ipsilateral hand (to the anodal electrode). At a higher dose and irrespective of polarity, a wider network of sensorimotor regions is positively affected by tDCS.

Multielectrode Network Stimulation (ME-NETS) demonstrated by concurrent tDCS and fMRI.

Non-invasive transcranial direct current stimulation (tDCS) can modulate activity of targeted brain regions. Whether tDCS can reliably and repeatedly modulate intrinsic connectivity of entire brain networks is unclear. We used concurrent tDCS-MRI to investigate the effect of high dose anodal tDCS on resting state connectivity within the Arcuate Fasciculus (AF) network, which spans the temporal, parietal, and frontal lobes and is connected via a structural backbone, the Arcuate Fasciculus (AF) white matter tract. Effects of high-dose tDCS (4mA) delivered via a single electrode placed over one of the AF nodes (single electrode stimulation, SE-S) was compared to the same dose split between multiple electrodes placed over AF-network nodes (multielectrode network stimulation, ME-NETS). While both SE-S and ME-NETS significantly modulated connectivity between AF network nodes (increasing connectivity during stimulation epochs), ME-NETS had a significantly larger and more reliable effect than SE-S. Moreover, comparison with a control network, the Inferior Longitudinal Fasciculus (ILF) network suggested that the effect of ME-NETS on connectivity was specific to the targeted AF-network. This finding was further supported by the results of a seed-to-voxel analysis wherein we found ME-NETS primarily modulated connectivity between AF-network nodes. Finally, an exploratory analysis looking at dynamic connectivity using sliding window correlation found strong and immediate modulation of connectivity during three stimulation epochs within the same imaging session.

Modulating short-term auditory memory with focal transcranial direct current stimulation applied to the supramarginal gyrus.

Previous studies have shown that transcranial direct current stimulation (tDCS) can affect performance by decreasing regional excitability in a brain region that contributes to the task of interest. To our knowledge, no research to date has found both enhancing and diminishing effects on performance, depending upon which polarity of the current is applied. The supramarginal gyrus (SMG) is an ideal brain region for testing tDCS effects because it is easy to identify using the 10-20 electroencephalography coordinate system, and results of neuroimaging studies have implicated the left SMG in short-term memory for phonological and nonphonological sounds. In the present study, we found that applying tDCS to the left SMG affected pitch memory in a manner that depended upon the polarity of stimulation: cathodal tDCS had a negative impact on performance whereas anodal tDCS had a positive impact. These effects were significantly different from sham stimulation, which did not influence performance; they were also specific to the left hemisphere - no effect was found when applying cathodal stimulation to the right SMG - and were unique to pitch memory as opposed to memory for visual shapes. Our results provide further evidence that the left SMG is a nodal point for short-term auditory storage and demonstrate the potential of tDCS to influence cognitive performance and to causally examine hypotheses derived from neuroimaging studies.

A canonical oscillator model of cochlear dynamics.

Nonlinear responses to acoustic signals arise through active processes in the cochlea, which has an exquisite sensitivity and wide dynamic range that can be explained by critical nonlinear oscillations of outer hair cells. Here we ask how the interaction of critical nonlinearities with the basilar membrane and other organ of Corti components could determine tuning properties of the mammalian cochlea. We propose a canonical oscillator model that captures the dynamics of the interaction between the basilar membrane and organ of Corti, using a pair of coupled oscillators for each place along the cochlea. We analyze two models in which a linear oscillator, representing basilar membrane dynamics, is coupled to a nonlinear oscillator poised at a Hopf instability. The coupling in the first model is unidirectional, and that of the second is bidirectional. Parameters are determined by fitting 496 auditory-nerve (AN) tuning curves of macaque monkeys. We find that the unidirectionally and bidirectionally coupled models account equally well for threshold tuning. In addition, however, the bidirectionally coupled model exhibits low-amplitude, spontaneous oscillation in the absence of stimulation, predicting that phase locking will occur before a significant increase in firing frequency, in accordance with well known empirical observations. This leads us to a canonical oscillator cochlear model based on the fundamental principles of critical nonlinear oscillation and coupling dynamics. The model is more biologically realistic than widely used linear or nonlinear filter-based models, yet parsimoniously displays key features of nonlinear mechanistic models. It is efficient enough for computational studies of auditory perception and auditory physiology.

Mode-locking neurodynamics predict human auditory brainstem responses to musical intervals.

The auditory nervous system is highly nonlinear. Some nonlinear responses arise through active processes in the cochlea, while others may arise in neural populations of the cochlear nucleus, inferior colliculus and higher auditory areas. In humans, auditory brainstem recordings reveal nonlinear population responses to combinations of pure tones, and to musical intervals composed of complex tones. Yet the biophysical origin of central auditory nonlinearities, their signal processing properties, and their relationship to auditory perception remain largely unknown. Both stimulus components and nonlinear resonances are well represented in auditory brainstem nuclei due to neural phase-locking. Recently mode-locking, a generalization of phase-locking that implies an intrinsically nonlinear processing of sound, has been observed in mammalian auditory brainstem nuclei. Here we show that a canonical model of mode-locked neural oscillation predicts the complex nonlinear population responses to musical intervals that have been observed in the human brainstem. The model makes predictions about auditory signal processing and perception that are different from traditional delay-based models, and may provide insight into the nature of auditory population responses. We anticipate that the application of dynamical systems analysis will provide the starting point for generic models of auditory population dynamics, and lead to a deeper understanding of nonlinear auditory signal processing possibly arising in excitatory-inhibitory networks of the central auditory nervous system. This approach has the potential to link neural dynamics with the perception of pitch, music, and speech, and lead to dynamical models of auditory system development.