Relating neuronal dynamics for auditory object processing to neuroimaging activity: a computational modeling and an fMRI study

We investigated the neural basis of auditory object processing in the cerebral cortex by combining neural modeling and functional neuroimaging. We developed a large-scale, neurobiologically realistic network model of auditory pattern recognition that relates the neuronal dynamics of cortical auditory processing of frequency modulated (FM) sweeps to functional neuroimaging data of the type obtained using PET and fMRI. Areas included in the model extend from primary auditory to prefrontal cortex. The electrical activities of the neuronal units of the model were constrained to agree with data from the neurophysiological literature regarding the perception of FM sweeps. We also conducted an fMRI experiment using stimuli and tasks similar to those used in our simulations. The integrated synaptic activity of the neuronal units in each region of the model, convolved with a hemodynamic response function, was used as a correlate of the simulated fMRI activity, and generally agreed with the experimentally observed fMRI data in the brain areas corresponding to the regions of the model. Our results demonstrate that the model is capable of exhibiting the salient features of both electrophysiological neuronal activities and fMRI values that are in agreement with empirically observed data. These findings provide support for our hypotheses concerning how auditory objects are processed by primate neocortex.     

Frequency flows and the time-frequency dynamics of multivariate phase synchronization in brain signals

The quantification of phase synchrony between brain signals is of crucial importance for the study of large-scale interactions in the brain. Current methods are based on the estimation of the stability of the phase difference between pairs of signals over a time window, within successive frequency bands. This paper introduces a new approach to study the dynamics of brain synchronies, Frequency Flows Analysis (FFA). It allows direct tracking and characterization of the nonstationary time-frequency dynamics of phase synchrony among groups of signals. It is based on the use of the one-to-one relationship between frequency locking and phase synchrony, which applies when the concept of phase synchrony is not taken in an extended 'statistical' sense of a bias in the distribution of phase differences, but in the sense of a continuous phase difference conservation during a short period of time. In such a case, phase synchrony implies identical instantaneous frequencies among synchronized signals, with possible time varying frequencies of synchronization. In this framework, synchronous groups of signals or neural assemblies can be identified as belonging to common frequency flows, and the problem of studying synchronization becomes the problem of tracking frequency flows. We use the ridges of the analytic wavelet transforms of the signals of interest in order to estimate maps of instantaneous frequencies and reveal sustained periods of common instantaneous frequency among groups of signal. FFA is shown to track complex dynamics of synchrony in coupled oscillator models, reveal the time-frequency and spatial dynamics of synchrony convergence and divergence in epileptic seizures, and in MEG data the large-scale ongoing dynamics of synchrony correlated with conscious perception during binocular rivalry.     

Individualized localization and cortical surface-based registration of intracranial electrodes

In addition to its widespread clinical use, the intracranial electroencephalogram (iEEG) is increasingly being employed as a tool to map the neural correlates of normal cognitive function as well as for developing neuroprosthetics. Despite recent advances, and unlike other established brain-mapping modalities (e.g. functional MRI, magneto- and electroencephalography), registering the iEEG with respect to neuroanatomy in individuals—and coregistering functional results across subjects—remains a significant challenge. Here we describe a method which coregisters high-resolution preoperative MRI with postoperative computerized tomography (CT) for the purpose of individualized functional mapping of both normal and pathological (e.g., interictal discharges and seizures) brain activity. Our method accurately (within 3 mm, on average) localizes electrodes with respect to an individual's neuroanatomy. Furthermore, we outline a principled procedure for either volumetric or surface-based group analyses. We demonstrate our method in five patients with medically-intractable epilepsy undergoing invasive monitoring of the seizure focus prior to its surgical removal. The straight-forward application of this procedure to all types of intracranial electrodes, robustness to deformations in both skull and brain, and the ability to compare electrode locations across groups of patients makes this procedure an important tool for basic scientists as well as clinicians.     

Multivariate time-frequency analysis of electromagnetic brain activity during bimanual motor learning

Although the relationship between brain activity and motor performance is reasonably well established, the manner in which this relationship changes with motor learning remains incompletely understood. This paper presents a study of cortical modulations of event-related beta activity when participants learned to perform a complex bimanual motor task: 151 channel MEG data were acquired from nine healthy adults whilst learning a bimanual 3:5 polyrhythm. Sources of MEG activity were determined by means of synthetic aperture magnetometry that yielded locations and time courses of beta activities. The relationship between changes in performance and corresponding changes in event-related power were assessed using partial least squares. Behavioral data revealed that participants successfully learned to perform the 3:5 polyrhythm and that performance improvement was mainly achieved through the proper timing of the finger producing the slow rhythm. We found event-related modulation of beta power in the contralateral motor cortex that was inversely related to force output. The degree of beta modulation increased during the experiment - although the force level remained constant - and was positively correlated with motor performance, in particular for the motor cortex contralateral to the slow hand. These electrophysiological findings support the view that activity in motor cortex co-varies closely with behavioral changes over the course of learning.     

Probing the mind: anesthesia and neuroimaging

In 1947, a second power of anesthesia was described: "With anesthetic agents we seem to have a tool for producing and holding at will, and at little risk, different levels of consciousness--a tool that promises to be of great help in studies of mental phenomena." In 1995, anesthetic manipulation was coupled with neuroimaging, paving the way for detailed assessments of the relationship between the structure and the functioning of the brain. Anesthesia combined with neuroimaging thus provides a unique tool for investigating the neural correlates of human cognition.     

Effects of estrogen on patterns of brain activity at rest and during cognitive activity: a review of neuroimaging studies

Animal and human studies provide evidence of systematic effects of estrogen on cerebral activity and cognitive function. In this article, we review studies of the activational effects of estrogen on cerebral activity during rest and during the performance of cognitive tasks in pre- and postmenopausal women. The goal is twofold--to better understand evidence suggesting that estrogen influences brain functioning and argue for the importance of considering hormone effects when designing neuroimaging studies. Hormone-related increases in blood flow during the resting state have been documented in healthy elderly women, elderly women with cerebrovascular disease, and middle-aged postmenopausal women with early menopause. There is no reliable influence of estrogen on blood flow during the resting state in women with Alzheimer's disease. Hormone therapy has been associated with changes in brain activation patterns in middle-aged and elderly postmenopausal women during performance of verbal and figural memory tasks, providing critical biological support for the view that estrogen might protect against age-associated changes in cognition and lower the risk of Alzheimer's disease. There is a paucity of studies examining changes in brain activation patterns across the menstrual cycle and a need for randomized studies of hormone therapy in postmenopausal women to confirm findings from observational studies. General procedural guidelines for controlling and investigating hormone effects in neuroimaging studies are discussed.     

LOFA: software for individualized localization of functional MRI activity

Although PET, SPECT, and fMRI studies have led to significant advances in functional mapping of the human brain, precise localization and quantification of activity in individual brains require additional procedures. Difficulties to be addressed by a localization strategy are: resolution of individual anatomic differences, differentiation of functional activity in closely juxtaposed brain regions, and management of multiple intricately shaped 3D anatomic structures. In this paper, we describe a localization tool, LOFA, which addresses these problems by forming ROIs with a user-driven interface. Using LOFA, complex 3D anatomy can be defined through open or closed loops and anatomic landmarks. Resulting partitions can be overlaid on top of each other to form multiple regions of interest (ROIs), and functional activity in these ROIs can be extracted individually, one after the other. LOFA introduces important paradigmatic advances over the other ROI analysis methods. The toolbox is interactive, fully compatible with AFNI (MCW), and requires Pv-Wave (VNI Inc.) license to run.     

The multiscale character of evoked cortical activity

Both the architecture and the dynamics of the brain have characteristic features at different spatial scales. However, the existence, nature and function of dynamical interdependencies between such scales have not been investigated. We studied the multiscale properties of functional magnetic resonance imaging (fMRI) data acquired while human subjects viewed a visual image. Traditional "region of interest" analysis of this data set revealed evoked activity in primary and extrastriate visual cortex. Wavelet transform in the spatial domain provides a multiscale representation of this evoked brain activity. Studying the correlation structure of this representation revealed strong and novel interdependencies in these data within and between different spatial scales. We found that such correlations are stronger than those evident in the original data and comparable in magnitude to those obtained after Gaussian smoothing. However, analysis of the data in the wavelet domain revealed additional structure such as positive correlations, strong anti-correlations and phase-lagged interdependencies. Statistical significance of these effects was inferred through nonparametric bootstrap techniques. We conclude that the spatial analysis of functional neuroimaging data in the wavelet domain provides novel information which may reflect complex spatiotemporal neuronal activity and information encoding. It also affords a quantitative means of testing hierarchical and multiscale models of cortical activity.     

Topographic time-frequency decomposition of the EEG

Frequency-transformed EEG resting data has been widely used to describe normal and abnormal brain functional states as function of the spectral power in different frequency bands. This has yielded a series of clinically relevant findings. However, by transforming the EEG into the frequency domain, the initially excellent time resolution of time-domain EEG is lost. The topographic time-frequency decomposition is a novel computerized EEG analysis method that combines previously available techniques from time-domain spatial EEG analysis and time-frequency decomposition of single-channel time series. It yields a new, physiologically and statistically plausible topographic time-frequency representation of human multichannel EEG. The original EEG is accounted by the coefficients of a large set of user defined EEG like time-series, which are optimized for maximal spatial smoothness and minimal norm. These coefficients are then reduced to a small number of model scalp field configurations, which vary in intensity as a function of time and frequency. The result is thus a small number of EEG field configurations, each with a corresponding time-frequency (Wigner) plot. The method has several advantages: It does not assume that the data is composed of orthogonal elements, it does not assume stationarity, it produces topographical maps and it allows to include user-defined, specific EEG elements, such as spike and wave patterns. After a formal introduction of the method, several examples are given, which include artificial data and multichannel EEG during different physiological and pathological conditions.     

The parcellation of cortical areas using replicator dynamics in fMRI

In this paper, we show that replicator dynamics can be used as an exploratory analysis tool to detect subregions of cortical areas on the basis of the similarity between fMRI time series. As similarity measure, we propose to use canonical correlation, a multivariate extension to the typically employed Pearson's correlation coefficient. We applied the replicator process to data obtained from two different experimental paradigms in the search for subregions within the left lateral frontal cortex (LFC). In both cases, the replicator process resulted in a parcellation that corresponds to a recently suggested subdivision of the LFC in anterior-posterior direction. Most notably, these results were very consistent when compared across different measurements of a single subject and across a group of subjects.     

A model of the coupling between brain electrical activity,metabolism, and hemodynamics: application to the interpretation of functional neuroimaging

In order to improve the interpretation of functional neuroimaging data, we implemented a mathematical model of the coupling between membrane ionic currents, energy metabolism (i.e., ATP regeneration via phosphocreatine buffer effect, glycolysis, and mitochondrial respiration), blood-brain barrier exchanges, and hemodynamics. Various hypotheses were tested for the variation of the cerebral metabolic rate of oxygen (CMRO(2)): (H1) the CMRO(2) remains at its baseline level; (H2) the CMRO(2) is enhanced as soon as the cerebral blood flow (CBF) increases; (H3) the CMRO(2) increase depends on intracellular oxygen and pyruvate concentrations, and intracellular ATP/ADP ratio; (H4) in addition to hypothesis H3, the CMRO(2) progressively increases, due to the action of a second messenger. A good agreement with experimental data from magnetic resonance imaging and spectroscopy (MRI and MRS) was obtained when we simulated sustained and repetitive activation protocols using hypotheses (H3) or (H4), rather than hypotheses (H1) or (H2). Furthermore, by studying the effect of the variation of some physiologically important parameters on the time course of the modeled blood-oxygenation-level-dependent (BOLD) signal, we were able to formulate hypotheses about the physiological or biochemical significance of functional magnetic resonance data, especially the poststimulus undershoot and the baseline drift.     

Localization of correlated network activity at the cortical level with MEG

In both hemodynamic and neurophysiological imaging methods, analysis of functionally interconnected networks has typically focused on brain areas that show strong activation in specific tasks. Alternatively, connectivity measures may be used directly to localize network nodes, independent of their level of activation. This approach requires initial cortical reference areas which may be identified based on their high level of activation, their coherence with an external reference signal, or their strong connectivity with other brain areas. Irrespective of how the nodes have been localized the mathematical complexity of the analysis methods precludes verification of the accuracy and completeness of the network structure by direct comparison with the measured data. Therefore, it is critical to understand how the choices of parameters and procedures used in the analysis affect the network identification. Here, using simulated and measured magnetoencephalography (MEG) data, and Dynamic Imaging of Coherent Sources (DICS) for connectivity analysis, we quantify the veracity of network detection at the individual and group level as a function of relevant parameter choices. Using simulations, we demonstrate that coupling measures enable accurate identification of the network structure even without external reference signals, and illustrate the applicability of this approach to real data. We show that a valid estimate of interindividual variability is critical for reliable group-level analysis. Although this study focuses on application of DICS to MEG data, many issues considered here, especially those regarding individual vs. group-level analysis, are likely to be relevant for other neuroimaging methods and analysis approaches as well.     

Reduced cortical activity during maximal bilateral contractions of the index finger

The bilateral deficit refers to the phenomenon in which homologous muscles produce per muscle less force when contracting simultaneously than when contracting individually. The mechanism underlying the bilateral deficit is still unknown, but the most likely cause is a decline in the activation of motor units during bilateral contractions. In the present study, we used functional magnetic resonance imaging (fMRI) to measure the degree of brain activity during unilateral and bilateral maximal contractions in combination with force and EMG measurements. Subjects performed, in a semi-randomized order, maximal isometric contractions (MVC) with the right index finger, the left index finger and with both fingers simultaneously. During the task, brain activation was measured with a 3 T MR scanner, in combination with force and EMG recordings. The most important activated areas in the brain during the contractions were the sensorimotor cortex (precentral and postcentral gyrus), cerebellum, premotor cortex and supplementary motor area. During bilateral contractions, a significant decline in force and EMG values was found and detailed analysis of the brain activation data showed that this decline was accompanied with a significant decline in the activation of the precentral gyrus. This result suggests that the bilateral decline is the resultant of a decline in input to the primary motor area and shows that the main source of the bilateral deficit lies upstream of the primary motor cortex.     

Insight into N-terminal localization and dynamics of engineered virus-like particles

Virus-like particles composed of the cowpea chlorotic mottle virus (CCMV) capsid protein (CP) have been extensively studied as carrier systems in nanoscience. One well-established method to improve their stability under physiological conditions is to fuse a stimulus-responsive elastin-like polypeptide (ELP) to the N-terminus of the CPs. Even though the N-terminus should in principle be localized in the inner cavity of the protein cage, studies on the native CCMV revealed its accessibility on the particle surface. We verified that such phenomenon also applies to ELP-CCMVs, by exploiting the covalent functionalization of the CP N-terminal domain via a sortase A-mediated reaction. Western-blot analysis and Förster resonance energy transfer (FRET) experiments furthermore revealed this to be caused by both the external display of the N-termini and the interchange of CPs among preformed capsids. Our findings demonstrate the tunability of ELP-CCMV stability and dynamics and their potential effect on the exploitation of such protein cages as a drug delivery system.

The N-terminal localization and dynamic intermixing of engineered cowpea chlorotic mottle virus-like particles were studied independently from each other.     

The subcellular localization of peptidase activity in the human jejunum

Abstract. The subcellular localization of peptidase activity in the normal human jejunum has been investigated. Subcellular organelles were fractionated by density gradient centrifugation. The localization of peptidases was determined by comparing the distributions of peptidase activities with those of organelle 'marker' enzymes. The organelles and their markers were: cytosol—lactate dehydrogenase; brush border—neutral α-glucosidase, γ-glutamyl transferase and leucyl-2-naphthylamidase; plasma membrane—5'-nucleotidase; lysosomes—N-acetyl- β -glucosaminidase; mitochrondria—malate dehydrogenase; endoplasmic reticulum—alkaline α-glucosidase; peroxisomes—catalase.
Thirteen dipeptides, seven tripeptides, two tetrapeptides, two pentapeptides and a hexapeptide were used as substrates. The distribution of dipeptidyl peptidase IV was also determined.
Irrespective of whether the NH₂-terminal or COOH-terminal amino acid was neutral, basic or acidic, the major or exclusive locus of dipeptidase activity was cytosolic. All of the activity against a dipeptide with the amino acid proline at the NH₂-terminus was in the cytosol.
The distribution of tripeptidase activity was quite different. Although the cytosol hydrolysed all tripeptides, as much as 50% of tripeptidase activity was particulate. For both tetrapeptides, one of the pentapeptides and the hexapeptide, the major or exclusive locus of activity was the brush border membrane. Pentaphenylalanine, however, was hydrolysed by both the cytosol and the brush border. Dipeptidyl peptidase IV was localized in the brush border.     

Ethanol modulates cortical activity: direct evidence with combined TMS and EEG

The motor cortex of 10 healthy subjects was stimulated by transcranial magnetic stimulation (TMS) before and after ethanol challenge (0.8 g/kg resulting in blood concentration of 0.77 +/- 0.14 ml/liter). The electrical brain activity resulting from the brief electromagnetic pulse was recorded with high-resolution electroencephalography (EEG) and located using inversion algorithms. Focal magnetic pulses to the left motor cortex were delivered with a figure-of-eight coil at the random interstimulus interval of 1.5-2.5 s. The stimulation intensity was adjusted to the motor threshold of abductor digiti minimi. Two conditions before and after ethanol ingestion (30 min) were applied: (1) real TMS, with the coil pressed against the scalp; and (2) control condition, with the coil separated from the scalp by a 2-cm-thick piece of plastic. A separate EMG control recording of one subject during TMS was made with two bipolar platinum needle electrodes inserted to the left temporal muscle. In each condition, 120 pulses were delivered. The EEG was recorded from 60 scalp electrodes. A peak in the EEG signals was observed at 43 ms after the TMS pulse in the real-TMS condition but not in the control condition or in the control scalp EMG. Potential maps before and after ethanol ingestion were significantly different from each other (P = 0.01), but no differences were found in the control condition. Ethanol changed the TMS-evoked potentials over right frontal and left parietal areas, the underlying effect appearing to be largest in the right prefrontal area. Our findings suggest that ethanol may have changed the functional connectivity between prefrontal and motor cortices. This new noninvasive method provides direct evidence about the modulation of cortical connectivity after ethanol challenge.     

Neuromagnetic localization of rhythmic activity in the human brain: a comparison of three methods

Cortical rhythmic activity is increasingly employed for characterizing human brain function. Using MEG, it is possible to localize the generators of these rhythms. Traditionally, the source locations have been estimated using sequential dipole modeling. Recently, two new methods for localizing rhythmic activity have been developed, Dynamic Imaging of Coherent Sources (DICS) and Frequency-Domain Minimum Current Estimation (MCE(FD)). With new analysis methods emerging, the researcher faces the problem of choosing an appropriate strategy. The aim of this study was to compare the performance and reliability of these three methods. The evaluation was performed using measured data from four healthy subjects, as well as with simulations of rhythmic activity. We found that the methods gave comparable results, and that all three approaches localized the principal sources of oscillatory activity very well. Dipole modeling is a very powerful tool once appropriate subsets of sensors have been selected. MCE(FD) provides simultaneous localization of sources and was found to give a good overview of the data. With DICS, it was possible to separate close-by sources that were not retrieved by the other two methods.     

Cluster headache: A review of neuroimaging findings

Classified as a trigeminal autonomic cephalalgia, cluster headache is characterized by recurrent short-lived excruciating pain attacks, which are concurrent with autonomic signs. These clinical features have led to the assumption that cluster headache’s pathophysiology involves central nervous system structures, including the hypothalamus. In the past decade, neuroimaging studies have confirmed such clinically derived theory by uncovering in vivo neuronal changes located in the inferior posterior hypothalamus. Using a variety of neuroimaging techniques (functional [eg, functional MRI], biochemical [eg, magnetic resonance spectroscopy], and structural [eg, morphometry]) in patients with cluster headache, we are making improvements in our understanding of the role of the brain in this disorder. This article summarizes neuroimaging findings in cluster headache patients, describing neuronal changes that occur during attacks and remission, as well as during hypothalamic stimulation.