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Identifying inter-area communication in terms of the hierarchical organization of functional brain areas is of considerable interest in human neuroimaging. Previous studies have suggested that the direction of magneto- and electroencephalography (MEG, EEG) source currents depends on the layer-specific input patterns into a cortical area. We examined the direction in MEG source currents in a visual object recognition experiment in which there were specific expectations of activation in the fusiform region being driven by either feedforward or feedback inputs. The source for the early non-specific visual evoked response, presumably corresponding to feedforward driven activity, pointed outward, i.e., away from the white matter. In contrast, the source for the later, object-recognition related signals, expected to be driven by feedback inputs, pointed inward, toward the white matter. Associating specific features of the MEG/EEG source waveforms to feedforward and feedback inputs could provide unique information about the activation patterns within hierarchically organized cortical areas.

Our ability to refocus auditory attention is vital for even the most routine day-to-day activities. Shifts in auditory attention can be initiated "voluntarily", or triggered "involuntarily" by unexpected novel sound events. Here, we employed psychophysiological interaction (PPI) analyses of auditory functional MRI data, to compare functional connectivity patterns of distinct frontoparietal cortex regions during cued voluntary versus novelty-driven involuntary auditory attention shifting. Overall, our frontoparietal seed regions exhibited significant PPI increases with auditory cortex (AC) areas during both cued and novelty-driven orienting. However, significant positive PPI patterns associated with voluntary auditory attention (cue > novel task regressor), but mostly absent in analyses emphasizing involuntary orienting (novel > cue task regressor), were observed with seeds within the frontal eye fields (FEF), superior parietal lobule (SPL), and right supramarginal gyri (SMG). In contrast, significant positive PPIs associated selectively with involuntary orienting were observed between ACs and seeds within the bilateral anterior interior frontal gyri (IFG), and left posterior IFG, SMG, and posterior cingulate cortices (PCC). We also found indices of lateralization of different attention networks: PPI increases selective to voluntary attention occurred primarily with right-hemispheric regions, whereas those related to involuntary orienting were more frequent with left-hemisphere seeds. In conclusion, despite certain similarities in PPI patterns across conditions, the more dorsal aspects of right frontoparietal cortex demonstrated wider connectivity during cued/voluntary attention shifting, whereas certain left ventral frontoparietal seeds were more widely connected during novelty-triggered/involuntary orienting. Our findings provide partial support for distinct attention networks for voluntary and involuntary auditory attention.

Based on the infamous left-lateralized neglect syndrome, one might hypothesize that the dominating right parietal cortex has a bilateral representation of space, whereas the left parietal cortex represents only the contralateral right hemispace. Whether this principle applies to human auditory attention is not yet fully clear. Here, we explicitly tested the differences in cross-hemispheric functional coupling between the intraparietal sulcus (IPS) and auditory cortex (AC) using combined magnetoencephalography (MEG), EEG, and functional MRI (fMRI). Inter-regional pairwise phase consistency (PPC) was analyzed from data obtained during dichotic auditory selective attention task, where subjects were in 10-s trials cued to attend to sounds presented to one ear and to ignore sounds presented in the opposite ear. Using MEG/EEG/fMRI source modeling, parietotemporal PPC patterns were (a) mapped between all AC locations vs. IPS seeds and (b) analyzed between four anatomically defined AC regions-of-interest (ROI) vs. IPS seeds. Consistent with our hypothesis, stronger cross-hemispheric PPC was observed between the right IPS and left AC for attended right-ear sounds, as compared to PPC between the left IPS and right AC for attended left-ear sounds. In the mapping analyses, these differences emerged at 7–13 Hz, i.e., at the theta to alpha frequency bands, and peaked in Heschl's gyrus and lateral posterior non-primary ACs. The ROI analysis revealed similarly lateralized differences also in the beta and lower theta bands. Taken together, our results support the view that the right parietal cortex dominates auditory spatial attention.

Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI) is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD) contrast based whole-head inverse imaging (InI). Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.

Neuronal activation sequence information is essential for understanding brain functions. Extracting such timing information from blood oxygenation level dependent (BOLD) fMRI is confounded by interregional neurovascular differences and poorly understood relations between BOLD and electrophysiological response delays. Here, we recorded whole-head BOLD fMRI at 100 ms resolution and magnetoencephalography (MEG) during a visuomotor reaction-time task. Both methods detected the same activation sequence across five regions, from visual towards motor cortices, with linearly correlated interregional BOLD and MEG response delays. The smallest significant interregional BOLD delay was 100 ms; all delays ≥ 400 ms were significant. Switching the order of external events reversed the sequence of BOLD activations, indicating that interregional neurovascular differences did not confound the results. This may open new avenues for using fMRI to follow rapid activation sequences in the brain.

The acquisition time of BOLD contrast functional MRI (fMRI) data with whole-brain coverage typically requires a sampling rate of one volume in 1–3 s. Although the volumetric sampling time of a few seconds is adequate for measuring the sluggish hemodynamic response (HDR) to neuronal activation, faster sampling of fMRI might allow for monitoring of rapid physiological fluctuations and detection of subtle neuronal activation timing information embedded in BOLD signals. Previous studies utilizing a highly accelerated volumetric MR inverse imaging (InI) technique have provided a sampling rate of one volume per 100 ms with 5 mm spatial resolution. Here, we propose a novel modification of this technique, the echo-shifted InI, which allows TE to be longer than TR, to measure BOLD fMRI at an even faster sampling rate of one volume per 25 ms with whole-brain coverage. Compared with conventional EPI, echo-shifted InI provided an 80-fold speedup with similar spatial resolution and less than 2-fold temporal SNR loss. The capability of echo-shifted InI to detect HDR timing differences was tested empirically. At the group level (n = 6), echo-spaced InI was able to detect statistically significant HDR timing differences of as low as 50 ms in visual stimulus presentation. At the level of individual subjects, significant differences in HDR timing were detected for 400 ms stimulus-onset differences. Our results also show that the temporal resolution of 25 ms is necessary for maintaining the temporal detecting capability at this level. With the capabilities of being able to distinguish the timing differences in the millisecond scale, echo-shifted InI could be a useful fMRI tool for obtaining temporal information at a time scale closer to that of neuronal dynamics.

Neuronal mechanisms of auditory distance perception are poorly understood, largely because contributions of intensity and distance processing are difficult to differentiate. Typically, the received intensity increases when sound sources approach us. However, we can also distinguish between soft-but-nearby and loud-but-distant sounds, indicating that distance processing can also be based on intensity-independent cues. Here, we combined behavioral experiments, fMRI measurements, and computational analyses to identify the neural representation of distance independent of intensity. In a virtual reverberant environment, we simulated sound sources at varying distances (15–100 cm) along the right-side interaural axis. Our acoustic analysis suggested that, of the individual intensity-independent depth cues available for these stimuli, direct-to-reverberant ratio (D/R) is more reliable and robust than interaural level difference (ILD). However, on the basis of our behavioral results, subjects’ discrimination performance was more consistent with complex intensity-independent distance representations, combining both available cues, than with representations on the basis of either D/R or ILD individually. fMRI activations to sounds varying in distance (containing all cues, including intensity), compared with activations to sounds varying in intensity only, were significantly increased in the planum temporale and posterior superior temporal gyrus contralateral to the direction of stimulation. This fMRI result suggests that neurons in posterior nonprimary auditory cortices, in or near the areas processing other auditory spatial features, are sensitive to intensity-independent sound properties relevant for auditory distance perception.

Based on behavioral studies, several relatively distinct perceptual and cognitive functions have been defined in cognitive psychology such as sensory memory, short-term memory, and selective attention. Here, we review evidence suggesting that some of these functions may be supported by shared underlying neuronal mechanisms. Specifically, we present, based on an integrative review of the literature, a hypothetical model wherein short-term plasticity, in the form of transient center-excitatory and surround-inhibitory modulations, constitutes a generic processing principle that supports sensory memory, short-term memory, involuntary attention, selective attention, and perceptual learning. In our model, the size and complexity of receptive fields/level of abstraction of neural representations, as well as the length of temporal receptive windows, increases as one steps up the cortical hierarchy. Consequently, the type of input (bottom-up vs. top down) and the level of cortical hierarchy that the inputs target, determine whether short-term plasticity supports purely sensory vs. semantic short-term memory or attentional functions. Furthermore, we suggest that rather than discrete memory systems, there are continuums of memory representations from short-lived sensory ones to more abstract longer-duration representations, such as those tapped by behavioral studies of short-term memory.

Physiological noise arising from a variety of sources can significantly degrade the detection of task-related activity in BOLD-contrast fMRI experiments. If whole head spatial coverage is desired, effec- tive suppression of oscillatory physiological noise from cardiac and respiratory fluctuations is quite diffi- cult without external monitoring, since traditional EPI acquisition methods cannot sample the signal rapidly enough to satisfy the Nyquist sampling theorem, leading to temporal aliasing of noise. Using a combination of high speed magnetic resonance inverse imaging (InI) and digital filtering, we demon- strate that it is possible to suppress cardiac and respiratory noise without auxiliary monitoring, while achieving whole head spatial coverage and reasonable spatial resolution. Our systematic study of the effects of different moving average (MA) digital filters demonstrates that a MA filter with a 2 s window can effectively reduce the variance in the hemodynamic baseline signal, thereby achieving 57%-58% improvements in peak z-statistic values compared to unfiltered InI or spatially smoothed EPI data (FWHM ! 8.6 mm). In conclusion, the high temporal sampling rates achievable with InI permit significant reductions in physiological noise using standard temporal filtering techniques that result in significant improvements in hemodynamic response estimation. Hum Brain Mapp 00:000-000, 2011. V C 2011 Wiley-Liss, Inc.

The current study investigated the neurobiological role of white matter in visuospatial versus linguistic processing abilities in autism using diffusion tensor imaging. We examined differences in white matter integrity between high-functioning children with autism (HFA) and typically developing controls (CTRL), in relation to the groups’ response times (RT) on a pictorial reasoning task under three conditions: visuospatial, V, semantic, S, and V + S, a hybrid condition allowing language use to facilitate visuospatial transformations. Diffusion-weighted images were collected from HFA and CTRL participants, matched on age and IQ, and significance maps were computed for group differences in fractional anisotropy (FA) and in RT–FA association for each condition. Typically developing children showed increased FA within frontal white matter and the superior longitudinal fasciculus (SLF). HFA showed increased FA within peripheral white matter, including the ventral temporal lobe. Additionally, RT–FA relationships in the semantic condition (S) implicated white matter near the STG and in the SLF within the temporal and frontal lobes to a greater extent in CTRL. Performance in visuospatial reasoning (V, V + S), in comparison, was related to peripheral parietal and superior precentral white matter in HFA, but to the SLF, callosal, and frontal white matter in CTRL. Our results appear to support a preferential use of linguistically-mediated pathways in reasoning by typically developing children, whereas autistic cognition may rely more on visuospatial processing networks.

An important difference between magnetoencephalography (MEG) and electroencephalography (EEG) is that MEG is insensitive to radially oriented sources. We quantified computationally the dependency of MEG and EEG on the source orientation using a forward model with realistic tissue boundaries. Similar to the simpler case of a spherical head model, in which MEG cannot see radial sources at all, for most cortical locations there was a source orientation to which MEG was insensitive. The median value for the ratio of the signal magnitude for the source orientation of the lowest and the highest sensitivity was 0.06 for MEG and 0.63 for EEG. The difference in the sensitivity to the source orientation is expected to contribute to systematic differences in the signal-to-noise ratio between MEG and EEG., National Institutes of Health (U.S.) (Grant NS057500), National Institutes of Health (U.S.) (Grant NS037462), National Institutes of Health (U.S.) (Grant HD040712), National Center for Research Resources (U.S.) (P41RR14075), Mind Research Network

Here we report early cross-sensory activations and audiovisual interactions at the visual and auditory cortices using magnetoencephalography (MEG) to obtain accurate timing information. Data from an identical fMRI experiment were employed to support MEG source localization results. Simple auditory and visual stimuli (300-ms noise bursts and checkerboards) were presented to seven healthy humans. MEG source analysis suggested generators in the auditory and visual sensory cortices for both within-modality and cross-sensory activations. fMRI cross-sensory activations were strong in the visual but almost absent in the auditory cortex; this discrepancy with MEG possibly reflects the influence of acoustical scanner noise in fMRI. In the primary auditory cortices (Heschl's gyrus) the onset of activity to auditory stimuli was observed at 23 ms in both hemispheres, and to visual stimuli at 82 ms in the left and at 75 ms in the right hemisphere. In the primary visual cortex (Calcarine fissure) the activations to visual stimuli started at 43 ms and to auditory stimuli at 53 ms. Cross-sensory activations thus started later than sensory-specific activations, by 55 ms in the auditory cortex and by 10 ms in the visual cortex, suggesting that the origins of the cross-sensory activations may be in the primary sensory cortices of the opposite modality, with conduction delays (from one sensory cortex to another) of 30-35 ms. Audiovisual interactions started at 85 ms in the left auditory, 80 ms in the right auditory and 74 ms in the visual cortex, i.e., 3-21 ms after inputs from the two modalities converged.

Magnetic resonance imaging (MRI) on patients with implanted deep brain stimulators (DBSs) can be hazardous because of the antenna-effect of leads exposed to the incident radio-frequency field. This study evaluated electromagnetic field and specific absorption rate (SAR) changes as a function of lead resistivity on an anatomically precise head model in a 3T system. The anatomical accuracy of our head model allowed for detailed modeling of the path of DBS leads between epidermis and the outer table. Our electromagnetic finite difference time domain (FDTD) analysis showed significant changes of 1 g and 10 g averaged SAR for the range of lead resistivity modeled, including highly conductive leads up to highly resistive leads. Antenna performance and whole-head SAR were sensitive to the presence of the DBS leads only within 10%, while changes of over one order of magnitude were observed for the peak 10 g averaged SAR, suggesting that local SAR values should be considered in DBS guidelines. With rho(lead) = rho(copper) , and the MRI coil driven to produce a whole-head SAR without leads of 3.2 W/kg, the 1 g averaged SAR was 1080 W/kg and the 10 g averaged SAR 120 W/kg at the tip of the DBS lead. Conversely, in the control case without leads, the 1 g and 10 g averaged SAR were 0.5 W/kg and 0.6 W/kg, respectively, in the same location. The SAR at the tip of lead was similar with electrically homogeneous and electrically heterogeneous models. Our results show that computational models can support the development of novel lead technology, properly balancing the requirements of SAR deposition at the tip of the lead and power dissipation of the system battery.

The purpose of this study is to assess the accuracy of spatiotemporal source analysis of magnetoencephalography (MEG) and scalp electroencephalography (EEG) for representing the propagation of frontotemporal spikes in patients with partial epilepsy. This study focuses on frontotemporal spikes, which are typically characterized by a preceding anterior temporal peak followed by an ipsilateral inferior frontal peak. Ten patients with frontotemporal spikes on MEG/EEG were studied. We analyzed the propagation of temporal to frontal epileptic spikes on both MEG and EEG independently by using a cortically constrained minimum norm estimate (MNE). Spatiotemporal source distribution of each spike was obtained on the cortical surface derived from the patient's MRI. All patients underwent an extraoperative intracranial EEG (IEEG) recording covering temporal and frontal lobes after presurgical evaluation. We extracted source waveforms of MEG and EEG from the source distribution of interictal spikes at the sites corresponding to the location of intracranial electrodes. The time differences of the ipsilateral temporal and frontal peaks as obtained by MEG, EEG and IEEG were statistically compared in each patient. In all patients, MEG and IEEG showed similar time differences between temporal and frontal peaks. The time differences of EEG spikes were significantly smaller than those of IEEG in nine of ten patients. Spatiotemporal analysis of MEG spikes models the time course of frontotemporal spikes as observed on IEEG more adequately than EEG in our patients. Spatiotemporal source analysis may be useful for planning epilepsy surgery, by predicting the pattern of IEEG spikes.

Using simultaneous measurements from multiple channels of a radio-frequency coil array, magnetic resonance inverse imaging (InI) can achieve ultra-fast dynamic functional imaging of the human with whole-brain coverage and a good spatial resolution. Mathematically, the InI reconstruction is a generalization of parallel MRI (pMRI), which includes image space and k-space reconstructions. Because of the auto-calibration technique, the pMRI k-space reconstruction offers more robust and adaptive reconstructions compared to the image space algorithm. Here we present the k-space InI (K-InI) reconstructions to reconstruct the highly accelerated BOLD-contrast fMRI data of the human brain to achieve 100 ms temporal resolution. Simulations show that K-InI reconstructions can offer 3D image reconstructions at each time frame with reasonable spatial resolution, which cannot be obtained using the previously proposed image space minimum-norm estimates (MNE) or linear constraint minimum variance (LCMV) spatial filtering reconstructions. The InI reconstructions of in vivo BOLD-contrast fMRI data during a visuomotor task show that K-InI offer 3 to 5 fold more sensitive detection of the brain activation than MNE and a comparable detection sensitivity to the LCMV reconstructions. The group average of the high temporal resolution K-InI reconstructions of the hemodynamic response also shows a relative onset timing difference between the visual (first) and somatomotor (second) cortices by 400 ms (600 ms time-to-peak timing difference). This robust and sensitive K-InI reconstruction can be applied to dynamic MRI acquisitions using a large-n coil array to improve the spatiotemporal resolution.

We investigated linguistic and visuospatial processing during pictorial reasoning in high-functioning autism (HFA), Asperger's syndrome (ASP), and age and IQ-matched typically developing participants (CTRL), using three conditions designed to differentially engage linguistic mediation or visuospatial processing (visuospatial, V; semantic, S; visuospatial + semantic, V + S). The three groups did not differ in accuracy, but showed different response time profiles. ASP and CTRL participants were fastest on V + S, amenable to both linguistic and nonlinguistic mediation, whereas HFA participants were equally fast on V and V + S, where visuospatial strategies were available, and slowest on S. HFA participants appeared to favor visuospatial over linguistic mediation. The results support the use of linguistic versus visuospatial tasks for characterizing subtypes on the autism spectrum.

To exploit the high (millisecond) temporal resolution of magnetoencephalography (MEG) and electroencephalography (EEG) for measuring neuronal dynamics within well-defined brain regions, it is important to quantitatively assess their localizing ability. Previous modeling studies and empirical data suggest that a combination of MEG and EEG signals should yield the most accurate localization, due to their complementary sensitivities. However, these two modalities have rarely been explicitly combined for source estimation in studies of recorded brain activity, and a quantitative empirical assessment of their abilities, combined and separate, is currently lacking. Here we studied early visual responses to focal Gabor patches flashed during subject fixation. MEG and EEG data were collected simultaneously and were compared with the functional MRI (fMRI) localization produced by identical stimuli in the same subjects. This allowed direct evaluation of the localization accuracy of separate and combined MEG/EEG inverse solutions. We found that the localization accuracy of the combined MEG+EEG solution was consistently better than that of either modality alone, using three different source estimation approaches. Further analysis suggests that this improved localization is due to the different properties of the two imaging modalities rather than simply due to increased total channel number. Thus, combining MEG and EEG data is important for high-resolution spatiotemporal studies of the human brain.

In this study, we employed neuromagnetic measurements (MEG) to evaluate both (a) the dynamic changes in the spontaneous brain activities and (b) the spatiotemporal distributions of the event-related brain activities related to the perception of third dimension from 2D optic flow. Both the event-related changes in the spontaneous brain activities and the brain responses evoked by the same events showed significant changes in the parietal, parieto-occipital, and occipito-temporal regions between the 2D and 3D perception conditions. The results indicate that these regions play an important role in the perception of 3D structure from 2D optic flow, which are in agreement with the previous studies of 3D structure perception from motion using fMRI in terms of the location of the activation, and adding further insight into the temporal characteristics of the neural activities in those regions.

We introduce a method of combing MEG and fMRI to generate estimates of synchronized oscillations on the cortex. This method suppresses locations without concordant fMRI activity to avoid over estimation of phase-locking values. Simulations suggest 80% fMRI provides optimal detection power. We demonstrate this method in human somatomotor data to characterize beta-band oscillations.

Magnetic resonance spectroscopic imaging (MRSI) provides spatially resolved metabolite information that is invaluable for both neuroscience studies and clinical applications. However, lengthy data acquisition times, which are a result of time-consuming phase encoding, represent a major challenge for MRSI. Fast MRSI pulse sequences that use echo-planar readout gradients, such as proton echo-planar spectroscopic imaging (PEPSI), are capable of fast spectral-spatial encoding and thus enable acceleration of image acquisition times. Combining PEPSI with recent advances in parallel MRI utilizing RF coil arrays can further accelerate MRSI data acquisition. Here we investigate the feasibility of ultrafast spectroscopic imaging at high field (3T and 4T) by combining PEPSI with sensitivity-encoded (SENSE) MRI using eight-channel head coil arrays. We show that the acquisition of single-average SENSE-PEPSI data at a short TE (15 ms) can be accelerated to 32 s or less, depending on the field strength, to obtain metabolic images of choline (Cho), creatine (Cre), N-acetyl-aspartate (NAA), and J-coupled metabolites (e.g., glutamate (Glu) and inositol (Ino)) with acceptable spectral quality and localization. The experimentally measured reductions in signal-to-noise ratio (SNR) and Cramer-Rao lower bounds (CRLBs) of metabolite resonances were well explained by both the g-factor and reduced measurement times. Thus, this technology is a promising means of reducing the scan times of 3D acquisitions and time-resolved 2D measurements.

Multiple receivers can be utilized to enhance the spatiotemporal resolution of MRI by employing the parallel imaging technique. Previously, we have reported the L-curve Tikhonov regularization technique to mitigate noise amplification resulting from the geometrical correlations between channels in a coil array. Nevertheless, one major disadvantage of regularized image reconstruction is lengthy computational time in regularization parameter estimation. At a fixed noise level, L-curve regularization parameter estimation was also found not to be robust across repetitive measurements, particularly for low signal-to-noise ratio (SNR) acquisitions. Here we report a computationally efficient and robust method to estimate the regularization parameter by partitioning the variance of the noise-whitened encoding matrix based on the estimated SNR of the aliased pixel set in parallel MRI data. The proposed Variance Partitioning Regularization (VPR) method can improve computational efficiency by 2-5-fold, depending on image matrix sizes and acceleration rates. Our anatomical and functional MRI results show that the VPR method can be applied to both static and dynamic MRI experiments to suppress noise amplification in parallel MRI reconstructions for improved image quality.

Naturalistic stimuli such as movies are increasingly used to engage cognitive and emotional processes during fMRI of brain hemodynamic activity. However, movies have been little utilized during magnetoencephalography (MEG) and EEG that directly measure population-level neuronal activity at a millisecond resolution. Here, subjects watched a 17-min segment from the movie Crash (Lionsgate Films, 2004) twice during simultaneous MEG/EEG recordings. Physiological noise components, including ocular and cardiac artifacts, were removed using the DRIFTER algorithm. Dynamic estimates of cortical activity were calculated using MRI-informed minimum-norm estimation. To improve the signal-to-noise ratio (SNR), principal component analyses (PCA) were employed to extract the prevailing temporal characteristics within each anatomical parcel of the Freesurfer Desikan-Killiany cortical atlas. A variety of alternative inter-subject correlation (ISC) approaches were then utilized to investigate the reliability of inter-subject synchronization during natural viewing. In the first analysis, the ISCs of the time series of each anatomical region over the full time period across all subject pairs were calculated and averaged. In the second analysis, dynamic ISC (dISC) analysis, the correlation was calculated over a sliding window of 200 ms with 3.3 ms steps. Finally, in a between-run ISC analysis, the between-run correlation was calculated over the dynamic ISCs of the two different runs after the Fisher z-transformation. Overall, the most reliable activations occurred in occipital/inferior temporal visual and superior temporal auditory cortices as well as in the posterior cingulate, precuneus, pre- and post-central gyri, and right inferior and middle frontal gyri. Significant between-run ISCs were observed in superior temporal auditory cortices and inferior temporal visual cortices. Taken together, our results show that movies can be utilized as naturalistic stimuli in MEG/EEG similarly as in fMRI studies.

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.

Parallel MRI techniques reconstruct full-FOV images from undersampled k-space data by using the uncorrelated information from RF array coil elements. One disadvantage of parallel MRI is that the image signal-to-noise ratio (SNR) is degraded because of the reduced data samples and the spatially correlated nature of multiple RF receivers. Regularization has been proposed to mitigate the SNR loss originating due to the latter reason. Since it is necessary to utilize static prior to regularization, the dynamic contrast-to-noise ratio (CNR) in parallel MRI will be affected. In this paper we investigate the CNR of regularized sensitivity encoding (SENSE) acquisitions. We propose to implement regularized parallel MRI acquisitions in functional MRI (fMRI) experiments by incorporating the prior from combined segmented echo-planar imaging (EPI) acquisition into SENSE reconstructions. We investigated the impact of regularization on the CNR by performing parametric simulations at various BOLD contrasts, acceleration rates, and sizes of the active brain areas. As quantified by receiver operating characteristic (ROC) analysis, the simulations suggest that the detection power of SENSE fMRI can be improved by regularized reconstructions, compared to unregularized reconstructions. Human motor and visual fMRI data acquired at different field strengths and array coils also demonstrate that regularized SENSE improves the detection of functionally active brain regions.

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 2 minimum-norm estimates (MNE) and in the more focal 1 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 27:1-13, 2006. © 2005 Wiley-Liss, Inc.

Here, we used both MEG and fMRI to detect dynamic brain responses to 3-D structure perception from random-dot motion in humans. The visual stimuli consisted of 1000 random dots, which started to move 500 ms after the onset of presentation. The coherence of the motion was controlled from 0 to 100%. A stimulus that is fully coherent had all the dots moving as if they belonged to a rotating spherical surface. On the other hand, the 80, 60, 40, 20, and 0% coherence stimuli contain dots having the same speed as the fully coherent stimuli, but the directions of the 20, 40, 60, 80, and 100% of the dots were randomized, respectively. Neuromagnetic signals were measured with a 306-channel MEG system. More than 60 stimulus-related epochs of 2000 ms, including a 1000 ms pre-stimulus baseline, were recorded and averaged for each condition with a sampling rate of 600 Hz. FMRI experiments were conducted using a 3 Tesla scanner covering the entire brain using blocked design, in which 12 s blocks were presented in pseudo-randomized order. The results of the fMRI analysis were used to impose plausible constraints on the MEG inverse calculation using a “weighted” minimum-norm approach to improve spatial resolution of the spatiotemporal activity estimates. The bilateral occipito-temporal and the intra-parietal regions showed increased neural activity in the fully coherent motion condition around the latencies of 180 ms and 240 ms after the onset of motion, respectively. These results indicate that the bilateral occipito-temporal and intra-parietal regions play an important role in the perception of 3-D structure from random-dot motion. Also, the present study adds further insight into the temporal characteristics of the neural activities in these regions.

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.

Identification of spatiotemporal interactions within/between neuron populations is critical for detection and characterization of large-scale neuronal interactions underlying perception, cognition, and behavior. Univariate analysis has been employed successfully in many neuroimaging studies. However, univariate analysis does not explicitly test for interactions between distributed areas of activity and is not sensitive to distributed responses across the brain. Multivariate analysis can explicitly test for multiple statistical models, including the designed paradigm, and allows for spatial and temporal model detection. Here, we investigate multivariate analysis approaches that take into consideration the 4D (time and space) covariance structure of the data. Principal component analysis (PCA) and independent component analysis (ICA) are two popular multivariate approaches with distinct mathematical constraints. Common difficulties in using these two different decompositions include the following: classification of the revealed components (task-related signal versus noise), overall signal-to-noise sensitivity, and the relatively low computational efficiency (multivariate analysis requires the entire raw data set and more time for model identification analysis). Using both Monte Carlo simulations and empirical data, we derived and tested the generalized partial least squares (gPLS) framework, which can incorporate both PCA and ICA decompositions with computational efficiency. The gPLS method explicitly incorporates the experimental design to simplify the identification of characteristic spatiotemporal patterns. We performed parametric modeling studies of a blocked-design experiment under various conditions, including background noise distribution, sampling rate, and hemodynamic response delay. We used a randomized grouping approach to manipulate the degrees of freedom of PCA and ICA in gPLS to characterize both paradigm coherent and transient brain responses. Simulation data suggest that in the gPLS framework, PCA mostly outperforms ICA as measured by the receiver operating curves (ROCs) in SNR from 0.01 to 100, the hemodynamic response delays from 0 to 3 TR in fMRI, background noise models of Guassian, sub-Gaussian, and super-Gaussian distributions and the number of observations from 5, 10, to 20 in each block of a six-block experiment. Further, due to selective averaging, the gPLS method performs robustly in low signal-to-noise ratio (

The influence of gray and white matter tissue anisotropy on the human electroencephalogram (EEG) and magnetoencephalogram (MEG) was examined with a high resolution finite element model of the head of an adult male subject. The conductivity tensor data for gray and white matter were estimated from magnetic resonance diffusion tensor imaging. Simulations were carried out with single dipoles or small extended sources in the cortical gray matter. The inclusion of anisotropic volume conduction in the brain was found to have a minor influence on the topology of EEG and MEG (and hence source localization). We found a major influence on the amplitude of EEG and MEG (and hence source strength estimation) due to the change in conductivity and the inclusion of anisotropy. We expect that inclusion of tissue anisotropy information will improve source estimation procedures.

Each non-invasive neuroimaging technique has its own inherent limitations resulting from temporal and special inaccuracies due to the nature of information that can be measured. While functional magnetic resonance imaging (fMRI) provides excellent spatial localization of the brain activity up to sub-millimeter resolution, its temporal resolution is limited by the hemodynamic time constant. Conversely, magnetoencephalography (MEG), which measure temporal changes in neural current directly, has temporal resolution of a few milliseconds; however, its spatial accuracy is limited by the non-uniqueness of the biomagnetic inverse problem in which the spatial distribution of neural currents is estimated from the MEG field distributions outside of the head. In this paper, recent developments in multimodal neuroimaging are introduced to allow for the reconstruction of human brain dynamics with high spatial accuracy without compromising temporal resolution. Specifically, we describe a technique to combine data from MEG, MRI and fMRI to visualize human higher order visual processing while perceiving a three-dimensional (3-D) shape from two-dimensional (2-D) motion.

Knowledge of the electrical conductivity properties of excitable tissues is essential for relating the electromagnetic fields generated by the tissue to the underlying electrophysiological currents. Efforts to characterize these endogenous currents from measurements of the associated electromagnetic fields would significantly benefit from the ability to measure the electrical conductivity properties of the tissue noninvasively. Here, using an effective medium approach, we show how the electrical conductivity tensor of tissue can be quantitatively inferred from the water self-diffusion tensor as measured by diffusion tensor magnetic resonance imaging. The effective medium model indicates a strong linear relationship between the conductivity and diffusion tensor eigenvalues (respectively, σ and d ) in agreement with theoretical bounds and experimental measurements presented here (σ/ d ≈ 0.844 ± 0.0545 S⋅s/mm 3 , r 2 = 0.945). The extension to other biological transport phenomena is also discussed.

We used whole brain functional MRI to investigate the neural network specifically engaged in the recognition of “biological motion” defined by point-lights attached to the major joints and head of a human walker. To examine the specificity of brain regions responsive to biological motion, brain activations obtained during a “walker vs. non-walker” discrimination task were compared with those elicited by two other tasks: ( i ) non-rigid motion (NRM), involving the discrimination of overall motion direction in the same “point-lights” display, and ( ii ) face-gender discrimination, involving the discrimination of gender in briefly presented photographs of men and women. Brain activity specific to “biological motion” recognition arose in the lateral cerebellum and in a region in the lateral occipital cortex presumably corresponding to the area KO previously shown to be particularly sensitive to kinetic contours. Additional areas significantly activated during the biological motion recognition task involved both, dorsal and ventral extrastriate cortical regions. In the ventral regions both face-gender discrimination and biological motion recognition elicited activation in the lingual and fusiform gyri and in the Brodmann areas 22 and 38 in superior temporal sulcus (STS). Along the dorsal pathway, both biological motion recognition and non-rigid direction discrimination gave rise to strong responses in several known motion sensitive areas. These included Brodmann areas 19/37, the inferior (Brodmann Area 39), and superior parietal lobule (Brodmann Area 7). Thus, we conjecture that, whereas face (and form) stimuli activate primarily the ventral system and motion stimuli primarily the dorsal system, recognition of biological motion stimuli may activate both systems as well as their confluence in STS. This hypothesis is consistent with our findings in stroke patients, with unilateral brain lesions involving at least one of these areas, who, although correctly reporting the direction of the point-light walker, fail on the biological motion task.

Functional magnetic resonance imaging (fMRI) can provide maps of brain activation with millimeter spatial resolution but is limited in its temporal resolution to the order of seconds. Here, we describe a technique that combines structural and functional MRI with magnetoencephalography (MEG) to obtain spatiotemporal maps of human brain activity with millisecond temporal resolution. This new technique was used to obtain dynamic statistical parametric maps of cortical activity during semantic processing of visually presented words. An initial wave of activity was found to spread rapidly from occipital visual cortex to temporal, parietal, and frontal areas within 185 ms, with a high degree of temporal overlap between different areas. Repetition effects were observed in many of the same areas following this initial wave of activation, providing evidence for the involvement of feedback mechanisms in repetition priming.

The ability to trigger functional magnetic resonance imaging (fMRI) acquisitions related to the occurrence of EEG-based physiologic transients has changed the field of fMRI into a more dynamically based technique. By knowing the temporal relationship between focal increases in neuronal firing rates and the provoked focal increase in blood flow, investigators are able to maximize the fMR-linked images that show where the activity originates. Our mastery of recording EEG inside the bore of a MR scanner has also allowed us to develop cognitive paradigms that record not only the fMR BOLD images, but also the evoked potentials (EPs). The EPs can subsequently be subjected to localization paradigms that can be compared to the localization seen on the BOLD images. These two techniques will most probably be complimentary. BOLD responses are dependent on a focal increase in metabolic demand while the EPs may or may not be related to energy demand increases. Additionally, recording EPs require that the source or sources of that potential come from an area that is able to generate far-field potentials. These potentials are related to the laminar organization of the neuronal population generating that potential. As best we know the BOLD response does not depend on any inherent laminar neuronal organization. Therefore, by merging these two recording methods, it is likely that we will gain a more detailed understanding of not only the areas involved in certain physiologic events, e.g. focal epilepsy or cognitive processing, but also on the sequencing of the activation of the various participating regions.

A sudden change in the direction of motion is a particularly salient and relevant feature of visual information. Extensive research has identified cortical areas responsive to visual motion and characterized their sensitivity to different features of motion, such as directional specificity. However, relatively little is known about responses to sudden changes in direction. Electrophysiological data from animals and functional imaging data from humans suggest a number of brain areas responsive to motion, presumably working as a network. Temporal patterns of activity allow the same network to process information in different ways. The present study in humans sought to determine which motion-sensitive areas are involved in processing changes in the direction of motion and to characterize the temporal patterns of processing within this network of brain regions. To accomplish this, we used both magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). The fMRI data were used as supplementary information in the localization of MEG sources. The change in the direction of visual motion was found to activate a number of areas, each displaying a different temporal behavior. The fMRI revealed motion-related activity in areas MT+ (the human homologue of monkey middle temporal area and possibly also other motion sensitive areas next to MT), a region near the posterior end of the superior temporal sulcus (pSTS), V3A, and V1/V2. The MEG data suggested additional frontal sources. An equivalent dipole model for the generators of MEG signals indicated activity in MT+, starting at 130 ms and peaking at 170 ms after the reversal of the direction of motion, and then again at approximately 260 ms. Frontal activity began 0-20 ms later than in MT+, and peaked approximately 180 ms. Both pSTS and FEF+ showed long-duration activity continuing over the latency range of 200-400 ms. MEG responses in the region of V3A and V1/V2 were relatively small, and peaked at longer latencies than the initial peak in MT+. These data revealed characteristic patterns of activity in this cortical network for processing sudden changes in the direction of visual motion.

The goal of our research is to develop an experimental and analytical framework for spatiotemporal imaging of human brain function. Preliminary studies suggest that noninvasive spatiotemporal maps of cerebral activity can be produced by combining the high spatial resolution (millimeters) of functional MRI (fMRI) with the high temporal resolution (milliseconds) of electroencephalography (EEG) and magnetoencephalography (MEG). Although MEG and EEG are sensitive to millisecond changes in mental activity, the ability to resolve source localization and timing is limited by the ill-posed “inverse” problem. We conducted Monte Carlo simulations to evaluate the use of MRI constraints in a linear estimation inverse procedure, where fMRI weighting, cortical location and orientation, and sensor noise statistics were realistically incorporated. An error metric was computed to quantify the effects of fMRI invisible (“missing”) sources, “extra” fMRI sources, and cortical orientation errors. Our simulation results demonstrate that prior anatomical and functional information from MRI can be used to regularize the EEG/MEG inverse problem, giving an improved solution with high spatial and temporal resolution. An fMRI weighting of approximately 90% was determined to provide the best compromise between separation of activity from correctly localized sources and minimization of error caused by missing sources. The accuracy of the estimate was relatively independent of the number and extent of the sources, allowing for incorporation of physiologically realistic multiple distributed sources. This linear estimation method provides an operator-independent approach for combining information from fMRI, MEG, and EEG and represents a significant advance over traditional dipole modeling.

The behavior of cerebral blood flow and oxygenation during prolonged brain activation was studied using magnetic resonance imaging (MRI) sensitized to flow and oxygenation changes, as well as positron emission tomography sensitized to flow. Neuronal habituation effects and hemodynamic changes were evaluated across tasks and cortical regions. Nine types of activation stimuli or tasks, including motor activation, vibrotactile stimulation, and several types of visual stimulation, were used. Both flow and oxygenation were evaluated in separate time course series as well as simultaneously using two different MRI methods. In most cases, the activation-induced increase in flow and oxygenation remained elevated for the entire stimulation duration. These results suggest that both flow rate and oxygenation consumption rate remain constant during the entire time that primary cortical neurons are activated by a task or a stimulus. Hum. Brain Mapping 5:93–109, 1997. © 1997 Wiley-Liss Inc.

Neurophysiological animal models suggest that anterior auditory cortex (AC) areas process sound identity information, whereas posterior ACs specialize in sound location processing. In humans, inconsistent neuroimaging results and insufficient causal evidence have challenged the existence of such parallel AC organization. Here we transiently inhibit bilateral anterior or posterior AC areas using MRI-guided paired-pulse transcranial magnetic stimulation (TMS) while subjects listen to Reference/Probe sound pairs and perform either sound location or identity discrimination tasks. The targeting of TMS pulses, delivered 55–145 ms after Probes, is confirmed with individual-level cortical electric-field estimates. Our data show that TMS to posterior AC regions delays reaction times (RT) significantly more during sound location than identity discrimination, whereas TMS to anterior AC regions delays RTs significantly more during sound identity than location discrimination. This double dissociation provides direct causal support for parallel processing of sound identity features in anterior AC and sound location in posterior AC. Observational imaging studies in humans have suggested that sound identification and localization occur in the same region of the auditory cortex. Ahveninen et al. now show that sound identity and location are processed in anterior and posterior regions, respectively, of the auditory cortex.

Recent neuroimaging studies implicate that both the dorsal and ventral visual pathways, as well as the middle temporal (MT) areas which are critical for the perception of visual motion, are involved in the perception of three-dimensional (3D) structure from two-dimensional (2D) motion (3D-SFM). However, the neural dynamics underlying the reconstruction of a 3D object from 2D optic flow is not known. Here we combined magnetoencephalography (MEG) and functional MRI (fMRI) measurements to investigate the spatiotemporal brain dynamics during 3D-SFM. We manipulated parametrically the coherence of randomly moving groups of dots to create different levels of 3D perception and to study the associated changes in brain activity. At different latencies, the posterior infero-temporal (pIT), the parieto-occipital (PO), and the intraparietal (IP) regions showed increased neural activity during highly coherent motion conditions in which subjects perceived a robust 3D object. Causality analysis between these regions indicated significant causal influence from IP to pIT and from pIT to PO only in conditions where subjects perceived a robust 3D object. Current results suggest that the perception of a 3D object from 2D motion includes integration of global motion and 3D mental image processing, as well as object recognition that are accomplished by interactions between the dorsal and ventral visual pathways.

Using simultaneous acquisition from multiple channels of a radio-frequency (RF) coil array, magnetic resonance inverse imaging (InI) achieves functional MRI acquisitions at a rate of 100 ms per whole-brain volume. InI accelerates the scan by leaving out partition encoding steps and reconstructs images by solving under-determined inverse problems using RF coil sensitivity information. Hence, the correlated spatial information available in the coil array causes spatial blurring in the InI reconstruction. Here, we propose a method that employs gradient blips in the partition encoding direction during the acquisition to provide extra spatial encoding in order to better differentiate signals from different partitions. According to our simulations, this blipped-InI (bInI) method can increase the average spatial resolution by 15.1% (1.3 mm) across the whole brain and from 32.6% (4.2 mm) in subcortical regions, as compared to the InI method. In a visual fMRI experiment, we demonstrate that, compared to InI, the spatial distribution of bInI BOLD response is more consistent with that of a conventional echo-planar imaging (EPI) at the level of individual subjects. With the improved spatial resolution, especially in subcortical regions, bInI can be a useful fMRI tool for obtaining high spatiotemporal information for clinical and cognitive neuroscience studies.

In everyday listening situations, we need to constantly switch between alternative sound sources and engage attention according to cues that match our goals and expectations. The exact neuronal bases of these processes are poorly understood. We investigated oscillatory brain networks controlling auditory attention using cortically constrained fMRI-weighted magnetoencephalography/EEG source estimates. During consecutive trials, participants were instructed to shift attention based on a cue, presented in the ear where a target was likely to follow. To promote audiospatial attention effects, the targets were embedded in streams of dichotically presented standard tones. Occasionally, an unexpected novel sound occurred opposite to the cued ear to trigger involuntary orienting. According to our cortical power correlation analyses, increased frontoparietal/temporal 30–100 Hz gamma activity at 200–1400 msec after cued orienting predicted fast and accurate discrimination of subsequent targets. This sustained correlation effect, possibly reflecting voluntary engagement of attention after the initial cue-driven orienting, spread from the TPJ, anterior insula, and inferior frontal cortices to the right FEFs. Engagement of attention to one ear resulted in a significantly stronger increase of 7.5–15 Hz alpha in the ipsilateral than contralateral parieto-occipital cortices 200–600 msec after the cue onset, possibly reflecting cross-modal modulation of the dorsal visual pathway during audiospatial attention. Comparisons of cortical power patterns also revealed significant increases of sustained right medial frontal cortex theta power, right dorsolateral pFC and anterior insula/inferior frontal cortex beta power, and medial parietal cortex and posterior cingulate cortex gamma activity after cued versus novelty-triggered orienting (600–1400 msec). Our results reveal sustained oscillatory patterns associated with voluntary engagement of auditory spatial attention, with the frontoparietal and temporal gamma increases being best predictors of subsequent behavioral performance.

Summary Mental imagery is an important cognitive method for problem solving, and the mental rotation of complex objects, as originally described by Shepard and Metzler (1971), is among the best studied of mental imagery tasks. Functional MRI was used to observe focal changes in blood flow in the brains of 10 healthy volunteers performing a mental rotation task. On each trial, subjects viewed a pair of perspective drawings of three-dimensional shapes, mentally rotated one into congruence with the other, and then determined whether the two forms were identical or mirror-images. The control task, which we have called the ‘comparison’ condition, was identical except that both members of each pair appeared at the same orientation, and hence the same encoding, comparison and decision processes were used but mental rotation was not required. These tasks were interleaved with a baseline ‘fixation’ condition, in which the subjects viewed a crosshair. Technically adequate studies were obtained in eight of the 10 subjects. Areas of increased signal were identified according to sulcal landmarks and are described in terms of the Brodmann's area (BA) definitions that correspond according to the atlas of Talaraich and Tournoux. When the rotation task was contrasted with the comparison condition, all subjects showed consistent foci of activation in BAs 7a and 7b (sometimes spreading to BA 40); 88% had increased signal in middle frontal gyrus (BA 8) and 75% showed extrastriate activation, including particularly BAs 39 and 19, in a position consistent with area V5/human MT as localized by functional and histological assays. In more than half of the subjects, hand somatosensory cortex (3-1-2) was engaged, and in 50% of subjects there was elevated signal in BA 18. In frontal cortex, activation was above threshold in half the subjects in BAs 9 and/or 46 (dorsolateral prefrontal cortex). Some (four out of eight) subjects also showed signal increases in BAs 44 and/or 46. Premotor cortex (BA 6) was active in half of the subjects during the rotation task. There was little evidence for lateralization of the cortical activity or of engagement of motor cortex. These data are consistent with the hypothesis that mental rotation engages cortical areas involved in tracking moving objects and encoding spatial relations, as well as the more general understanding that mental imagery engages the same, or similar, neural imagery as direct perception.

Neuropsychological, computational, and psycholinguistic data suggest the existence of semantic maps, i.e. localized representations of semantic information in the brain. Using functional magnetic resonance imaging, this hypothesis was directly tested with a picture naming task involving items from four different semantic categories. Small left lateralized fronto-temporal cortical sites of category-specific activation were found when brain activation signals were averaged. Data suggest the existence of multiple maps coding high-level representations of objects, such that meaningful distinctions, at least in part, govern the physical distribution of cortical semantic storage.