Fa-Hsuan Lin

Task-modulated “what” and “where” pathways in human auditory cortex

Human neuroimaging studies suggest that localization and identification of relevant auditory objects are accomplished via parallel parietal-to-lateral-prefrontal “where” and anterior-temporal-to-inferior-frontal “what” pathways, respectively. Using combined hemodynamic (functional MRI) and electromagnetic (magnetoencephalography) measurements, we investigated whether such dual pathways exist already in the human nonprimary auditory cortex, as suggested by animal models, and whether selective attention facilitates sound localization and identification by modulating these pathways in a feature-specific fashion. We found a double dissociation in response adaptation to sound pairs with phonetic vs. spatial sound changes, demonstrating that the human nonprimary auditory cortex indeed processes speech-sound identity and location in parallel anterior “what” (in anterolateral Heschl’s gyrus, anterior superior temporal gyrus, and posterior planum polare) and posterior “where” (in planum temporale and posterior superior temporal gyrus) pathways as early as ≈70–150 ms from stimulus onset. Our data further show that the “where” pathway is activated ≈30 ms earlier than the “what” pathway, possibly enabling the brain to use top-down spatial information in auditory object perception. Notably, selectively attending to phonetic content modulated response adaptation in the “what” pathway, whereas attending to sound location produced analogous effects in the “where” pathway. This finding suggests that selective-attention effects are feature-specific in the human nonprimary auditory cortex and that they arise from enhanced tuning of receptive fields of task-relevant neuronal populations.

Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates.

Cerebral currents responsible for the extra-cranially recorded magnetoencephalography (MEG) data can be estimated by applying a suitable source model. A popular choice is the distributed minimum-norm estimate (MNE) which minimizes the l2-norm of the estimated current. Under the l2-norm constraint, the current estimate is related to the measurements by a linear inverse operator. However, the MNE has a bias towards superficial sources, which can be reduced by applying depth weighting. We studied the effect of depth weighting in MNE using a shift metric. We assessed the localization performance of the depth-weighted MNE as well as depth-weighted noise-normalized MNE solutions under different cortical orientation constraints, source space densities, and signal-to-noise ratios (SNRs) in multiple subjects. We found that MNE with depth weighting parameter between 0.6 and 0.8 showed improved localization accuracy, reducing the mean displacement error from 12 mm to 7 mm. The noise-normalized MNE was insensitive to depth weighting. A similar investigation of EEG data indicated that depth weighting parameter between 2.0 and 5.0 resulted in an improved localization accuracy. The application of depth weighting to auditory and somatosensory experimental data illustrated the beneficial effect of depth weighting on the accuracy of spatiotemporal mapping of neuronal sources.

Parallel imaging reconstruction using automatic regularization.

Increased spatiotemporal resolution in MRI can be achieved by the use of parallel acquisition strategies, which simultaneously sample reduced k‐space data using the information from multiple receivers to reconstruct full‐FOV images. The price for the increased spatiotemporal resolution in parallel MRI is the degradation of the signal‐to‐noise ratio (SNR) in the final reconstructed images. Part of the SNR reduction results when the spatially correlated nature of the information from the multiple receivers destabilizes the matrix inversion used in the reconstruction of the full‐FOV image. In this work, a reconstruction algorithm based on Tikhonov regularization is presented that reduces the SNR loss due to geometric correlations in the spatial information from the array coil elements. Reference scans are utilized as a priori information about the final reconstructed image to provide regularized estimates for the reconstruction using the L‐curve technique. This automatic regularization method reduces the average g‐factors in phantom images from a two‐channel array from 1.47 to 0.80 in twofold sensitivity encoding (SENSE) acceleration. In vivo anatomical images from an eight‐channel system show an averaged g‐factor reduction of 1.22 to 0.84 in 2.67‐fold acceleration. Magn Reson Med 51:559–567, 2004.

Distributed current estimates using cortical orientation constraints

Distributed source models of magnetoencephalographic (MEG) and electroencephalographic (EEG) data employ dense distributions of current sources in a volume or on a surface. Previously, anatomical magnetic resonance imaging (MRI) data have been used to constrain locations and orientations based on cortical geometry extracted from anatomical MRI data. We extended this approach by first calculating cortical patch statistics (CPS), which for each patch corresponding to a current source location on the cortex comprise the area of the patch, the average normal direction, and the average deviation of the surface normal from its average. The patch areas were then incorporated in the forward model to yield estimates of the surface current density instead of dipole amplitudes at the current locations. The surface normal data were employed in a loose orientation constraint (LOC), which allows some variation of the current direction from the average normal. We employed this approach both in the ℓ2 minimum‐norm estimates (MNE) and in the more focal ℓ1 minimum‐norm solutions, the minimum‐current estimate (MCE). Simulations in auditory and somatosensory areas with current dipoles and 10‐ or 20‐mm diameter cortical patches as test sources showed that applying the LOC can increase localization accuracy. We also applied the method to in vivo auditory and somatosensory data. Hum Brain Mapp, 2005.

Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain.

This paper presents a computationally efficient source estimation algorithm that localizes cortical oscillations and their phase relationships. The proposed method employs wavelet-transformed magnetoencephalography (MEG) data and uses anatomical MRI to constrain the current locations to the cortical mantle. In addition, the locations of the sources can be further confined with the help of functional MRI (fMRI) data. As a result, we obtain spatiotemporal maps of spectral power and phase relationships. As an example, we show how the phase locking value (PLV), that is, the trial-by-trial phase relationship between the stimulus and response, can be imaged on the cortex. We apply the method to spontaneous, evoked, and driven cortical oscillations measured with MEG. We test the method of combining MEG, structural MRI, and fMRI using simulated cortical oscillations along Heschls gyrus (HG). We also analyze sustained auditory gamma-band neuromagnetic fields from MEG and fMRI measurements. Our results show that combining the MEG recording with fMRI improves source localization for the non-noise-normalized wavelet power. In contrast, noise-normalized spectral power or PLV localization may not benefit from the fMRI constraint. We show that if the thresholds are not properly chosen, noise-normalized spectral power or PLV estimates may contain false (phantom) sources, independent of the inclusion of the fMRI prior information. The proposed algorithm can be used for evoked MEG/EEG and block-designed or event-related fMRI paradigms, or for spontaneous MEG data sets. Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain can provide further understanding of large-scale neural activity and communication between different brain regions.

Lexical influences on speech perception: a Granger causality analysis of MEG and EEG source estimates.

Behavioral and functional imaging studies have demonstrated that lexical knowledge influences the categorization of perceptually ambiguous speech sounds. However, methodological and inferential constraints have so far been unable to resolve the question of whether this interaction takes the form of direct top-down influences on perceptual processing, or feedforward convergence during a decision process. We examined top-down lexical influences on the categorization of segments in a /s/-/integral/ continuum presented in different lexical contexts to produce a robust Ganong effect. Using integrated MEG/EEG and MRI data we found that, within a network identified by 40 Hz gamma phase locking, activation in the supramarginal gyrus associated with wordform representation influences phonetic processing in the posterior superior temporal gyrus during a period of time associated with lexical processing. This result provides direct evidence that lexical processes influence lower level phonetic perception, and demonstrates the potential value of combining Granger causality analyses and high spatiotemporal resolution multimodal imaging data to explore the functional architecture of cognition.

PROPELLER EPI: An MRI technique suitable for diffusion tensor imaging at high field strength with reduced geometric distortions†

A technique suitable for diffusion tensor imaging (DTI) at high field strengths is presented in this work. The method is based on a periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) k‐space trajectory using EPI as the signal readout module, and hence is dubbed PROPELLER EPI. The implementation of PROPELLER EPI included a series of correction schemes to reduce possible errors associated with the intrinsically higher sensitivity of EPI to off‐resonance effects. Experimental results on a 3.0 Tesla MR system showed that the PROPELLER EPI images exhibit substantially reduced geometric distortions compared with single‐shot EPI, at a much lower RF specific absorption rate (SAR) than the original version of the PROPELLER fast spin‐echo (FSE) technique. For DTI, the self‐navigated phase‐correction capability of the PROPELLER EPI sequence was shown to be effective for in vivo imaging. A higher signal‐to‐noise ratio (SNR) compared to single‐shot EPI at an identical total scan time was achieved, which is advantageous for routine DTI applications in clinical practice. Magn Reson Med, 2005.

Dynamic magnetic resonance inverse imaging of human brain function.

MRI is widely used for noninvasive hemodynamic‐based functional brain imaging. In traditional spatial encoding, however, gradient switching limits the temporal resolution, which makes it difficult to unambiguously identify possible fast nonhemodynamic changes. In this paper we propose a novel reconstruction approach, called dynamic inverse imaging (InI), that is capable of providing millisecond temporal resolution when highly parallel detection is used. To achieve an order‐of‐magnitude speedup in generating time‐resolved contrast estimates and dynamic statistical parametric maps (dSPMs), the spatial information is derived from an array of detectors rather than by time‐consuming gradient‐encoding methods. The InI approach was inspired by electroencephalography (EEG) and magnetoencephalography (MEG) source localization techniques. Dynamic MR InI was evaluated by means of numerical simulations. InI was also applied to measure BOLD hemodynamic time curves at 20‐ms temporal resolution in a visual stimulation experiment using a 90‐channel head array. InI is expected to improve the time resolution of MRI and provide increased flexibility in the trade‐off between spatial and temporal resolution for studies of dynamic activation patterns in the human brain. Magn Reson Med, 2006.

Attention-driven auditory cortex short-term plasticity helps segregate relevant sounds from noise

How can we concentrate on relevant sounds in noisy environments? A “gain model” suggests that auditory attention simply amplifies relevant and suppresses irrelevant afferent inputs. However, it is unclear whether this suffices when attended and ignored features overlap to stimulate the same neuronal receptive fields. A “tuning model” suggests that, in addition to gain, attention modulates feature selectivity of auditory neurons. We recorded magnetoencephalography, EEG, and functional MRI (fMRI) while subjects attended to tones delivered to one ear and ignored opposite-ear inputs. The attended ear was switched every 30 s to quantify how quickly the effects evolve. To produce overlapping inputs, the tones were presented alone vs. during white-noise masking notch-filtered ±1/6 octaves around the tone center frequencies. Amplitude modulation (39 vs. 41 Hz in opposite ears) was applied for “frequency tagging” of attention effects on maskers. Noise masking reduced early (50–150 ms; N1) auditory responses to unattended tones. In support of the tuning model, selective attention canceled out this attenuating effect but did not modulate the gain of 50–150 ms activity to nonmasked tones or steady-state responses to the maskers themselves. These tuning effects originated at nonprimary auditory cortices, purportedly occupied by neurons that, without attention, have wider frequency tuning than ±1/6 octaves. The attentional tuning evolved rapidly, during the first few seconds after attention switching, and correlated with behavioral discrimination performance. In conclusion, a simple gain model alone cannot explain auditory selective attention. In nonprimary auditory cortices, attention-driven short-term plasticity retunes neurons to segregate relevant sounds from noise.

Cancellation of EEG and MEG Signals Generated by Extended and Distributed Sources

Extracranial patterns of scalp potentials and magnetic fields, as measured with electro‐ and magnetoencephalography (EEG, MEG), are spatially widespread even when the underlying source in the brain is focal. Therefore, loss in signal magnitude due to cancellation is expected when multiple brain regions are simultaneously active. We characterized these cancellation effects in EEG and MEG using a forward model with sources constrained on an anatomically accurate reconstruction of the cortical surface. Prominent cancellation was found for both EEG and MEG in the case of multiple randomly distributed source dipoles, even when the number of simultaneous dipoles was small. Substantial cancellation occurred also for locally extended patches of simulated activity, when the patches extended to opposite walls of sulci and gyri. For large patches, a difference between EEG and MEG cancellation was seen, presumably due to selective cancellation of tangentially vs. radially oriented sources. Cancellation effects can be of importance when electrophysiological data are related to hemodynamic measures. Furthermore, the selective cancellation may be used to explain some observed differences between EEG and MEG in terms of focal vs. widespread cortical activity. Hum Brain Mapp, 2010.