Felix Darvas

Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging

For bioluminescence imaging studies in small animals, it is important to be able to accurately localize the three-dimensional (3D) distribution of the underlying bioluminescent source. The spectrum of light produced by the source that escapes the subject varies with the depth of the emission source because of the wavelength-dependence of the optical properties of tissue. Consequently, multispectral or hyperspectral data acquisition should help in the 3D localization of deep sources. In this paper, we describe a framework for fully 3D bioluminescence tomographic image acquisition and reconstruction that exploits spectral information. We describe regularized tomographic reconstruction techniques that use semi-infinite slab or FEM-based diffusion approximations of photon transport through turbid media. Singular value decomposition analysis was used for data dimensionality reduction and to illustrate the advantage of using hyperspectral rather than achromatic data. Simulation studies in an atlas-mouse geometry indicated that sub-millimeter resolution may be attainable given accurate knowledge of the optical properties of the animal. A fixed arrangement of mirrors and a single CCD camera were used for simultaneous acquisition of multispectral imaging data over most of the surface of the animal. Phantom studies conducted using this system demonstrated our ability to accurately localize deep point-like sources and show that a resolution of 1.5 to 2.2 mm for depths up to 6 mm can be achieved. We also include an in vivo study of a mouse with a brain tumour expressing firefly luciferase. Co-registration of the reconstructed 3D bioluminescent image with magnetic resonance images indicated good anatomical localization of the tumour.

Mapping human brain function with MEG and EEG: methods and validation.

We survey the field of magnetoencephalography (MEG) and electroencephalography (EEG) source estimation. These modalities offer the potential for functional brain mapping with temporal resolution in the millisecond range. However, the limited number of spatial measurements and the ill-posedness of the inverse problem present significant limits to our ability to produce accurate spatial maps from these data without imposing major restrictions on the form of the inverse solution. Here we describe approaches to solving the forward problem of computing the mapping from putative inverse solutions into the data space. We then describe the inverse problem in terms of low dimensional solutions, based on the equivalent current dipole (ECD), and high dimensional solutions, in which images of neural activation are constrained to the cerebral cortex. We also address the issue of objective assessment of the relative performance of inverse procedures by the free-response receiver operating characteristic (FROC) curve. We conclude with a discussion of methods for assessing statistical significance of experimental results through use of the bootstrap for determining confidence regions in dipole-fitting methods, and random field (RF) and permutation methods for detecting significant activation in cortically constrained imaging studies.

High gamma mapping using EEG

High gamma (HG) power changes during motor activity, especially at frequencies above 70 Hz, play an important role in functional cortical mapping and as control signals for BCI (brain-computer interface) applications. Most studies of HG activity have used ECoG (electrocorticography) which provides high-quality spatially localized signals, but is an invasive method. Recent studies have shown that non-invasive modalities such as EEG and MEG can also detect task-related HG power changes. We show here that a 27 channel EEG (electroencephalography) montage provides high-quality spatially localized signals non-invasively for HG frequencies ranging from 83 to 101 Hz. We used a generic head model, a weighted minimum norm least squares (MNLS) inverse method, and a self-paced finger movement paradigm. The use of an inverse method enables us to map the EEG onto a generic cortex model. We find the HG activity during the task to be well localized in the contralateral motor area. We find HG power increases prior to finger movement, with average latencies of 462 ms and 82 ms before EMG (electromyogram) onset. We also find significant phase-locking between contra- and ipsilateral motor areas over a similar HG frequency range; here the synchronization onset precedes the EMG by 400 ms. We also compare our results to ECoG data from a similar paradigm and find EEG mapping and ECoG in good agreement. Our findings demonstrate that mapped EEG provides information on two important parameters for functional mapping and BCI which are usually only found in HG of ECoG signals: spatially localized power increases and bihemispheric phase-locking.

Spatio-temporal source imaging reveals subcomponents of the human auditory mismatch negativity in the cingulum and right inferior temporal gyrus.

We investigated the generators of the mismatch negativity by means of spatio-temporal source imaging on the basis of 64-channel electroencephalography data in order to study the time course and localization of proposed frontal sources. Results indicate that there are additional generators located both within the anterior cingulate gyrus and in the right inferior temporal gyrus, clearly separated from the supratemporal generators in space and time course. The cingulate generator is activated later than the temporal ones, which supports the hypothesis of a frontally located mechanism of involuntary switching of attention triggered by the temporal change detection system. Evidence for an additional right inferior temporal generator supports the hypothesis of right hemispheric dominance in early sound discrimination.

Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography

We investigate fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography for applications in small animal imaging. Our forward model uses a diffusion approximation for optically inhomogeneous tissue, which we solve using a finite element method (FEM). We examine two approaches to incorporating the forward model into the solution of the inverse problem. In a conventional direct calculation approach one computes the full forward model by repeated solution of the FEM problem, once for each potential source location. We describe an alternative on-the-fly approach where one does not explicitly solve for the full forward model. Instead, the solution to the forward problem is included implicitly in the formulation of the inverse problem, and the FEM problem is solved at each iteration for the current image estimate. We evaluate the convergence speeds of several representative iterative algorithms. We compare the computation cost of those two approaches, concluding that the on-the-fly approach can lead to substantial reductions in total cost when combined with a rapidly converging iterative algorithm.

Generic head models for atlas-based EEG source analysis.

We describe a method for using a generic head model, in the form of an anatomical atlas, to produce EEG source localizations. The atlas is fitted to the subject by a nonrigid warp using a set of surface landmarks. The warped atlas is used to compute a finite element model (FEM) of the forward mapping or lead‐fields between neural current generators and the EEG electrodes. These lead‐fields are used to localize current sources from the subjects EEG data and the sources are then mapped back to the anatomical atlas. This approach provides a mechanism for comparing source localizations across subjects in an atlas‐based coordinate system, which can be used in the large fraction of EEG studies in which MR images are not available. The Montreal brain atlas was used as the reference anatomical atlas and 10 individual MR volumes were used to evaluate the method. The atlas was fitted to each subjects head by a thin‐plate‐spline (TPS) warp. The spatial locations of a generic 155‐electrode configuration were used to constrain the warp. For the purposes of evaluation, dipolar sources were placed on the inner cortical surface in the atlas geometry and transferred to each subjects brain space using a polynomial warp. The parameters of the warp were computed using an intensity‐based matching of the atlas and subject brains, thus ensuring that the sources were placed at approximately the same anatomical location in each case. Data were simulated in the subject geometry and a dipole fit was performed on these data using an FEM of the TPS warped atlas. The source positions found in the warped atlas were transferred back to the original atlas and compared to the original position. Sources were simulated at 972 locations evenly distributed over the inner cortical surface of the atlas. The mean error over all 10 subjects was 8.1 mm in the subject space and 15.2 mm in the atlas space. In comparison, using an affine transformation of the electrodes into atlas space and an FEM model generated from the atlas produced mean errors of 22.3 mm in subject space and 19.6 mm in atlas space. With a standard three‐shell spherical model the errors were 27.2 mm in the subject space and 34.7 mm when mapped to atlas space. Hum Brain Mapp, 2005.

Quasi-periodic Fluctuations in Default Mode Network Electrophysiology

The study of human brain electrophysiology has extended beyond traditional frequency ranges identified by the classical EEG rhythms, encompassing both higher and lower frequencies. Changes in high-gamma-band (>70 Hz) power have been identified as markers of local cortical activity. Fluctuations at infra-slow (<0.1 Hz) frequencies have been associated with functionally significant cortical networks elucidated using fMRI studies. In this study, we examined infra-slow changes in band-limited power across a range of frequencies (1–120 Hz) in the default mode network (DMN). Measuring the coherence in band-limited power fluctuations between spatially separated electrodes makes it possible to detect small, spatially extended, and temporally coherent fluctuating components in the presence of much larger incoherent fluctuations. We show that the default network is characterized by significant high-gamma-band (65–110 Hz) coherence at infra-slow (<0.1 Hz) frequencies. This coherence occurs over a narrow frequency range, centered at 0.015 Hz, commensurate with the frequency of BOLD signal fluctuations seen by fMRI, suggesting that quasi-periodic, infra-slow changes in local cortical activity form the neurophysiological basis for this network.

Nonlinear Phase–Phase Cross-Frequency Coupling Mediates Communication between Distant Sites in Human Neocortex

Human cognition is thought to be mediated by large-scale interactions between distant sites in the neocortex. Synchronization between different cortical areas has been suggested as one possible mechanism for corticocortical interaction. Here, we report robust, directional cross-frequency synchronization between distant sensorimotor sites in human neocortex during a movement task. In four subjects, electrocorticographic recordings from the cortical surface revealed a low-frequency rhythm (10–13 Hz) that combined with a higher frequency (77–82 Hz) in a ventral region of the premotor cortex to produce a third rhythm at the sum of these two frequencies in a distant motor site. Such cross-frequency coupling implies a nonlinear interaction between these cortical sites. These findings demonstrate that task-specific, phase–phase coupling can support communication between distant areas of the human neocortex.

Mild passive focal cooling prevents epileptic seizures after head injury in rats.

Post‐traumatic epilepsy is prevalent, often difficult to manage, and currently cannot be prevented. Although cooling is broadly neuroprotective, cooling‐induced prevention of chronic spontaneous recurrent seizures has never been demonstrated. We examined the effect of mild passive focal cooling of the perilesional neocortex on the development of neocortical epileptic seizures after head injury in the rat.

Transient and State Modulation of Beta Power in Human Subthalamic Nucleus during Speech Production and Finger Movement

Signs of Parkinsons disease (PD) are augmented by speech and repetitive motor tasks. The neurophysiological basis for this phenomenon is unknown, but may involve augmentation of β (13-30 Hz) oscillations within the subthalamic nucleus (STN). We hypothesized that speech and motor tasks increase β power in STN and propose a mechanism for clinical observations of worsening motor state during such behaviors. Subjects undergoing deep brain stimulation (DBS) surgery performed tasks while STN local field potential (LFP) data were collected. Power in the β frequency range was analyzed across the entire recording to observe slow shifts related to block design and during time epochs synchronized to behavior to evaluate immediate fluctuations related to task execution. Bilaterally symmetric β event related desynchronization was observed in analysis time-locked to subject motor and speech tasks. We also observed slow shifts of β power associated with blocks of tasks. Repetitive combined speech and motor, and isolated motor blocks were associated with the highest bilateral β power state. Overt speech alone and imagined speech were associated with a low bilateral β power state. Thus, changing behavioral tasks is associated with bilateral switching of β power states. This offers a potential neurophysiologic correlate of worsened PD motor signs experienced during clinical examination with provocative tasks: switching into a high β power state may be responsible for worsening motor states in PD patients when performing unilateral repetitive motor tasks and combined speech and motor tasks. Beta state changes could be chronically measured and potentially used to control closed loop neuromodulatory devices in the future.