Age-related changes of deep-brain neurophysiological activity.

Cognitive decline with age is associated with brain atrophy and reduced brain activations, but the underlying neurophysiological mechanisms are unclear, especially in deeper brain structures primarily affected by healthy aging or neurodegenerative processes. Here, we characterize time-resolved, resting-state magnetoencephalography activity of the hippocampus and subcortical brain regions in a large cohort of healthy young (20-30 years) and older (70-80 years) volunteers from the Cam-CAN (Cambridge Centre for Ageing and Neuroscience) open repository. The data show age-related changes in both rhythmic and arrhythmic signal strength in multiple deeper brain regions, including the hippocampus, striatum, and thalamus. We observe a slowing of neural activity across deeper brain regions, with increased delta and reduced gamma activity, which echoes previous reports of cortical slowing. We also report reduced occipito-parietal alpha peak associated with increased theta-band activity in the hippocampus, an effect that may reflect compensatory processes as theta activity, and slope of arrhythmic activity were more strongly expressed when short-term memory performances were preserved. Overall, this study advances the understanding of the biological nature of inter-individual variability in aging. The data provide new insight into how hippocampus and subcortical neurophysiological activity evolve with biological age, and highlight frequency-specific effects associated with cognitive decline versus cognitive maintenance.

Interocular interaction of contrast and luminance signals in human primary visual cortex.

Interocular interaction in the visual system occurs under dichoptic conditions when contrast and luminance are imbalanced between the eyes. Human psychophysical investigations suggest that interocular interaction can be explained by a contrast normalization model. However, the neural processes that underlie such interactions are still unresolved. We set out to assess, for the first time, the proposed normalization model of interocular contrast interactions using magnetoencephalography (MEG) and to extend this model to incorporate interactions based on interocular luminance differences. We used MEG to record steady-state visual evoked responses (SSVER), and functional magnetic resonance imaging (fMRI) to obtain individual retinotopic maps that we used in combination with MEG source imaging in healthy participants. Binary noise stimuli were presented in monocular or dichoptic viewing and were frequency-tagged at 4 and 6 Hz. The contrast of the stimuli was modulated in a range between 0 and 32%. Monocularly, we reduced the luminance by placing a 1.5 ND filter over one eye in the maximal contrast condition. This ND filter reduces the mean light level by a factor of 30 without any alteration to the physical contrast. We observed in visual area V1 a monotonic increase in the magnitude of SSVERs with changes in contrast from 0 to 32%. For both eyes, dichoptic masking induced a decrease in SSVER signal power. This power decrease was well explained by the normalization model. Reducing mean luminance delayed monocular processing by approximately 38 ms in V1. The reduced luminance also decreased the masking ability of the eye under the filter. Predictions based on a temporal filtering model for the interocular luminance difference prior to the model's binocular combination stage were incorporated to update the normalization model. Our results demonstrate that the signals resulting from different contrast or luminance stimulation of the two eyes are combined in a way that can be explained by an interocular normalization model.

Hyperglycemia and the fate of apparent diffusion coefficient-defined ischemic penumbra.

BACKGROUND AND PURPOSE: Previous data have shown the feasibility of identifying ischemic penumbra in patients with acute stroke by using a semiautomated analysis of ADC maps. Here, we investigated whether the fate of ADC-defined penumbra was altered by HG. We also examined the interaction between HG and arterial recanalization on infarct growth. MATERIALS AND METHODS: We examined 94 patients by using MR imaging within 6 hours of stroke onset and a follow-up MR imaging within 7 days. The ADC-defined tissue-at-risk was calculated from the early MR imaging. Patients were classified according to high (>7 mmol/L; n = 34/94, HG) or normal (n = 60/94) baseline SGL. The impact of HG status on infarct growth was assessed by using multiple regression models and analysis of the slopes of regression lines for each group. Interaction between HG status and arterial recanalization on infarct growth was investigated by using multiple regression analysis. RESULTS: The slope of the predicted versus observed infarct growth regression line was steeper in HG than non-HG patients (P = .0008), suggesting that infarct growth within ADC-defined tissue-at-risk was increased in HG patients. The effect was 2.8 times more severe in nonrecanalized patients (P = .01) than in patients with recanalization (P = .001). CONCLUSIONS: ADC-defined tissue-at-risk may represent ischemic penumbra because part of this area may be salvaged in normal SGL patients. The toxicity in HG patients seems to be more related to penumbra-infarction transition than reperfusion injury in humans because the effect was larger in nonrecanalized than in recanalized patients.

Early ADC changes in motor structures predict outcome of acute stroke better than lesion volume.

OBJECTIVES: The lesion volume assessed from diffusion-weighted imaging (DWI) within the first six hours to first week following stroke onset has been proposed as a predictor of functional outcome in clinical studies. However, the prediction accuracy decreases when the DWI lesion volume is measured during the earliest stages of patient evaluation. In this study, our hypothesis was that the combination of lesion location (motor-related regions) and diffusivity measures (such as Apparent Diffusion Coefficient [ADC]) at the acute stage of stroke predict clinical outcome. PATIENTS AND METHODS: Seventy-nine consecutive acute carotid territory stroke patients (median age: 62 years) were included in the study and outcome at three months was assessed using the modified Rankin scale (good outcome: mRS 0-2; poor outcome: mRS 3-5). DWI was acquired within the first six hours of stroke onset (H2) and the following day (D1). Apparent Diffusion Coefficient (ADC) values were measured in the corticospinal tract (CST), the primary motor cortex (M1), the supplementary motor area (SMA), the putamen in the affected hemisphere, and in the contralateral cerebellum to predict stroke outcome. RESULTS: Prediction of poor vs. good outcome at the individual level at H2 (D1, respectively) was achieved with 74% accuracy, 95%CI: 53-89% (75%, 95% CI: 61-89%, respectively) when patients were classified from ADC values measured in the putamen and CST. Prediction accuracy from DWI volumes reached only 62% (95%CI: 42-79%) at H2 and 69% (95%CI: 50-85%) at D1. CONCLUSION: We therefore show that measures of ADC at the acute stage in deeper motor structures (putamen and CST) are better predictors of stroke outcome than DWI lesion volume.

Investigations of dipole localization accuracy in MEG using the bootstrap.

We describe the use of the nonparametric bootstrap to investigate the accuracy of current dipole localization from magnetoencephalography (MEG) studies of event-related neural activity. The bootstrap is well suited to the analysis of event-related MEG data since the experiments are repeated tens or even hundreds of times and averaged to achieve acceptable signal-to-noise ratios (SNRs). The set of repetitions or epochs can be viewed as a set of independent realizations of the brain's response to the experiment. Bootstrap resamples can be generated by sampling with replacement from these epochs and averaging. In this study, we applied the bootstrap resampling technique to MEG data from somatotopic experimental and simulated data. Four fingers of the right and left hand of a healthy subject were electrically stimulated, and about 400 trials per stimulation were recorded and averaged in order to measure the somatotopic mapping of the fingers in the S1 area of the brain. Based on single-trial recordings for each finger we performed 5000 bootstrap resamples. We reconstructed dipoles from these resampled averages using the Recursively Applied and Projected (RAP)-MUSIC source localization algorithm. We also performed a simulation for two dipolar sources with overlapping time courses embedded in realistic background brain activity generated using the prestimulus segments of the somatotopic data. To find correspondences between multiple sources in each bootstrap, sample dipoles with similar time series and forward fields were assumed to represent the same source. These dipoles were then clustered by a Gaussian Mixture Model (GMM) clustering algorithm using their combined normalized time series and topographies as feature vectors. The mean and standard deviation of the dipole position and the dipole time series in each cluster were computed to provide estimates of the accuracy of the reconstructed source locations and time series.

Proximal tubular dysfunction in primary Sjögren's syndrome: a clinicopathological study of 2 cases.

Tubulointerstitial nephritis is the most common renal complication in primary Sjögren's syndrome (SS). It is usually associated with symptoms of distal tubular dysfunction, type I (distal) renal tubular acidosis (RTA) and nephrogenic diabetes insipidus. Proximal tubular abnormalities are considered to be less frequent, and Fanconi's syndrome has been only exceptionally reported in patients with SS. We describe 2 patients with primary SS, characterized by xerostomia, dry eyes, extensive lymphocytic infiltrate on salivary gland biopsy, positive tests for anti-SSA/SSB antibodies and/or antinuclear antibodies, who presented in renal failure with proteinuria, microscopic hematuria and type I RTA. Further studies revealed proximal tubular dysfunction, including renal glucosuria, generalized aminoaciduria, phosphaturia, uricosuria, together with proximal (type II) RTA in 1 case. Neither of these patients had Bence Jones proteinuria or monoclonal gammopathy. Kidney biopsy showed focal proximal tubulitis, associated with proximal tubular cell atrophy and dedifferentiation, and diffuse interstitial nephritis with fibrosis. No significant glomerular or peritubular deposits of immunoglobulin light or heavy chain were observed. These findings demonstrate that diffuse, distal and proximal, tubular dysfunction may occur in patients with SS and interstitial nephritis. Lymphocytic infiltration of proximal tubular cells is probably involved in the pathogenesis of Fanconi's syndrome in SS. However, the mechanisms involved in the alteration of sodium-dependent apical transports remain to be elucidated.

Localization of realistic cortical activity in MEG using current multipoles.

We present a novel approach to MEG source estimation based on a regularized first-order multipole solution. The Gaussian regularizing prior is obtained by calculation of the sample mean and covariance matrix for the equivalent moments of realistic simulated cortical activity. We compare the regularized multipole localization framework to the classical dipole and general multipole source estimation methods by evaluating the ability of all three solutions to localize the centroids of physiologically plausible patches of activity simulated on the surface of a human cerebral cortex. The results, obtained with a realistic sensor configuration, a spherical head model, and given in terms of field and localization error, depict the performance of the dipolar and multipolar models as a function of variable source surface area (50-500 mm(2)), noise conditions (20, 10, and 5 dB SNR), source orientation (0-90 degrees ), and source depth (3-11 cm). We show that as the sources increase in size, they become less accurately modeled as current dipoles. The regularized multipole systematically outperforms the single dipole model, increasingly so as the spatial extent of the sources increases. In addition, our simulations demonstrate that as the orientation of the sources becomes more radial, dipole localization accuracy decreases substantially, while the performance of the regularized multipole model is far less sensitive to orientation and even succeeds in localizing quasi-radial source configurations. Furthermore, our results show that the multipole model is able to localize superficial sources with higher accuracy than the current dipole. These results indicate that the regularized multipole solution may be an attractive alternative to current-dipole-based source estimation methods in MEG.

Automated interictal spike detection and source localization in magnetoencephalography using independent components analysis and spatio-temporal clustering.

OBJECTIVE: Magnetoencephalography (MEG) dipole localization of epileptic spikes is useful in epilepsy surgery for mapping the extent of abnormal cortex and to focus intracranial electrodes. Visually analyzing large amounts of data produces fatigue and error. Most automated techniques are based on matching of interictal spike templates or predictive filtering of the data and do not explicitly include source localization as part of the analysis. This leads to poor sensitivity versus specificity characteristics. We describe a fully automated method that combines time-series analysis with source localization to detect clusters of focal neuronal current generators within the brain that produce interictal spike activity. METHODS: We first use an ICA (independent components analysis) method to decompose the multichannel MEG data and identify those components that exhibit spike-like characteristics. From these detected spikes we then find those whose spatial topographies across the array are consistent with focal neural sources, and determine the foci of equivalent current dipoles and their associated time courses. We then perform a clustering of the localized dipoles based on distance metrics that takes into consideration both their locations and time courses. The final step of refinement consists of retaining only those clusters that are statistically significant. The average locations and time series from significant clusters comprise the final output of our method. RESULTS AND SIGNIFICANCE: Data were processed from 4 patients with partial focal epilepsy. In all three subjects for whom surgical resection was performed, clusters were found in the vicinity of the resectioned area. CONCLUSIONS: The presented procedure is promising and likely to be useful to the physician as a more sensitive, automated and objective method to help in the localization of the interictal spike zone of intractable partial seizures. The final output can be visually verified by neurologists in terms of both the location and distribution of the dipole clusters and their associated time series. Due to the clinical relevance and demonstrated promise of this method, further investigation of this approach is warranted.

On MEG forward modelling using multipolar expansions.

Magnetoencephalography (MEG) is a non-invasive functional imaging modality based on the measurement of the external magnetic field produced by neural current sources within the brain. The reconstruction of the underlying sources is a severely ill-posed inverse problem typically tackled using either low-dimensional parametric source models, such as an equivalent current dipole (ECD), or high-dimensional minimum-norm imaging techniques. The inability of the ECD to properly represent non-focal sources and the over-smoothed solutions obtained by minimum-norm methods underline the need for an alternative approach. Multipole expansion methods have the advantages of the parametric approach while at the same time adequately describing sources with significant spatial extent and arbitrary activation patterns. In this paper we first present a comparative review of spherical harmonic and Cartesian multipole expansion methods that can be used in MEG. The equations are given for the general case of arbitrary conductors and realistic sensor configurations and also for the special cases of spherically symmetric conductors and radially oriented sensors. We then report the results of computer simulations used to investigate the ability of a first-order multipole model (dipole and quadrupole) to represent spatially extended sources, which are simulated by 2D and 3D clusters of elemental dipoles. The overall field of a cluster is analysed using singular value decomposition and compared to the unit fields of a multipole, centred in the middle of the cluster, using subspace correlation metrics. Our results demonstrate the superior utility of the multipolar source model over ECD models in providing source representations of extended regions of activity.

A multiresolution framework to MEG/EEG source imaging.

A new method based on a multiresolution approach for solving the ill-posed problem of brain electrical activity reconstruction from electroencephaloram (EEG)/magnetoencephalogram (MEG) signals is proposed in a distributed source model. At each step of the algorithm, a regularized solution to the inverse problem is used to constrain the source space on the cortical surface to be scanned at higher spatial resolution. We present the iterative procedure together with an extension of the ST-maximum a posteriori method [1] that integrates spatial and temporal a priori information in an estimator of the brain electrical activity. Results from EEG in a phantom head experiment with a real human skull and from real MEG data on a healthy human subject are presented. The performances of the multiresolution method combined with a nonquadratic estimator are compared with commonly used dipolar methods, and to minimum-norm method with and without multiresolution. In all cases, the proposed approach proved to be more efficient both in terms of computational load and result quality, for the identification of sparse focal patterns of cortical current density, than the fixed scale imaging approach.

Rapidly recomputable EEG forward models for realistic head shapes.

With the increasing availability of surface extraction techniques for magnetic resonance and x-ray computed tomography images, realistic head models can be readily generated as forward models in the analysis of electroencephalography (EEG) and magnetoencephalography (MEG) data. Inverse analysis of this data, however, requires that the forward model be computationally efficient. We propose two methods for approximating the EEG forward model using realistic head shapes. The 'sensor-fitted sphere' approach fits a multilayer sphere individually to each sensor, and the 'three-dimensional interpolation' scheme interpolates using a grid on which a numerical boundary element method (BEM) solution has been precomputed. We have characterized the performance of each method in terms of magnitude and subspace error metrics, as well as computational and memory requirements. We have also made direct performance comparisons with traditional spherical models. The approximation provided by the interpolative scheme had an accuracy nearly identical to full BEM, even within 3 mm of the inner skull surface. Forward model computation during inverse procedures was approximately 30 times faster than for a traditional three-shell spherical model. Cast in this framework, high-fidelity numerical solutions currently viewed as computationally prohibitive for solving the inverse problem (e.g. linear Galerkin BEM) can be rapidly recomputed in a highly efficient manner. The sensor-fitting method has a similar one-time cost to the BEM method, and while it produces some improvement over a standard three-shell sphere, its performance does not approach that of the interpolation method. In both methods, there is a one-time cost associated with precomputing the forward solution over a set of grid points.

Evaluation of inverse methods and head models for EEG source localization using a human skull phantom.

We used a real-skull phantom head to investigate the performances of representative methods for EEG source localization when considering various head models. We describe several experiments using a montage with current sources located at multiple positions and orientations inside a human skull filled with a conductive medium. The robustness of selected methods based on distributed source models is evaluated as various solutions to the forward problem (from the sphere to the finite element method) are considered. Experimental results indicate that inverse methods using appropriate cortex-based source models are almost always able to locate the active source with excellent precision, with little or no spurious activity in close or distant regions, even when two sources are simultaneously active. Superior regularization schemes for solving the inverse problem can dramatically help the estimation of sparse and focal active zones, despite significant approximation of the head geometry and the conductivity properties of the head tissues. Realistic head models are necessary, though, to fit the data with a reasonable level of residual variance.

Validation of a diagnosis model for differentiating bacterial from viral meningitis in infants and children under 3.5 years of age.

The aim of this study was to validate, in a population of infants and children under 3.5 years of age, a diagnosis model that provides a figure for the probability of bacterial meningitis (pABM), based on four parameters collected at the time of the first lumbar tap: the cerebrospinal fluid (CSF) protein level, CSF polymorphonuclear cell count, blood glucose level, and leucocyte count. The best cut-off value for distinguishing between bacterial and viral meningitis was previously found to be 0.1, since 99% of meningitides associated with pABM<0.1 were viral. The charts of 103 consecutive children aged 0.1-3.5 years who had been hospitalised for acute meningitis were reviewed. Each case was sorted into the following three categories for aetiology: bacterial (positive CSF culture, n=48); viral (negative CSF culture and no other aetiology, and no antibiotic treatment after diagnosis, n=36); and undetermined (fitting neither of the first two definitions, n=19). After computation of pABM values in each case, the predictive values of the model were calculated for different pABM cut-off values. The results confirmed that the best cut-off pABM value was 0.1, for which the positive and negative predictive values in this model were 96% and 97%, respectively. Only one case of bacterial meningitis (lumbar tap performed early in an infant with meningococcal purpura fulminans with negative CSF culture) was associated with a pABM value of <0.1. This model is quite reliable for differentiating between bacterial and viral meningitis in children under 3.5 years of age, and it may enable physicians to withhold antibiotics in cases of meningitis of uncertain aetiology.

Ion-selective electrode for dodecyldimethylamine oxide: a "twice-Nernstian" slope for the determination of a neutral component

An electrode originally sensitive to dodecyltrimethylammonium (DTA+) was proven to be sensitive to dodecyldimethylamine oxide (DDAO), a surfactant with acidobasic properties. The response of the electrode was tested from pH 2 to 9.3. Its slope is Nernstian when the surfactant is entirely protonated. At a pH where the molecule is mainly under the neutral form, the electrode responds with a "twice-Nernstian" slope around 120 mV/decade. The validity of this electrode for measurements was checked by confronting the evolution of the critical micelle concentration of DDAO vs pH with data already published and by determining the complexation constant of DDAO and beta-cyclodextrin. A possible explanation of the "twice-Nernstian" slope, using a dimer of DDAO is proposed.

EEG source localization and imaging using multiple signal classification approaches.

Equivalent current dipoles are a powerful tool for modeling focal sources. The dipole is often sufficient to adequately represent sources of measured scalp potentials, even when the area of activation exceeds 1 cm2 of cortex. Traditional least-squares fitting techniques involve minimization of an error function with respect to the location and orientation of the dipoles. The existence of multiple local minima in this error function can result in gross errors in the computed source locations. The problem is further compounded by the requirement that the model order, i.e. the number of dipoles, be determined before error minimization can be performed. An incorrect model order can produce additional errors in the estimated source parameters. Both of these problems can be avoided using alternative search strategies based on the MUSIC (multiple signal classification) algorithm. Here the authors review the MUSIC approach and demonstrate its application to the localization of multiple current dipoles from EEG data. The authors also show that the number of detectable sources can be determined in a recursive manner from the data. Also, in contrast to least-squares, the method can find dipolar sources in the presence of additional non-dipolar sources. Finally, extensions of the MUSIC approach to allow the modeling of distributed sources are discussed.

Combined MEG and EEG source imaging by minimization of mutual information.

Though very frequently assumed, the necessity to operate a joint processing of simultaneous magnetoencephalography (MEG) and electroencephalography (EEG) recordings for functional brain imaging has never been clearly demonstrated. However, the very last generation of MEG instruments allows the simultaneous recording of brain magnetic fields and electrical potentials on the scalp. But the general fear regarding the fusion between MEG and EEG data is that the drawbacks from one modality will systematically spoil the performances of the other one without any consequent improvement. This is the case for instance for the estimation of deeper or radial sources with MEG. In this paper, we propose a method for a cooperative processing of MEG and EEG in a distributed source model. First, the evaluation of the respective performances of each modality for the estimation of every dipole in the source pattern is made using a conditional entropy criterion. Then, the algorithm operates a preprocessing of the MEG and EEG gain matrices which minimizes the mutual information between these two transfer functions, by a selective weighting of the MEG and EEG lead fields. This new combined EEG/MEG modality brings major improvements to the localization of active sources, together with reduced sensitivity to perturbations on data.

Influence of skull anisotropy for the forward and inverse problem in EEG: simulation studies using FEM on realistic head models.

For the sake of realism in the description of conduction from primary neural currents to scalp potentials, we investigated the influence of skull anisotropy on the forward and inverse problems in brain functional imaging with EEG. At present, all methods available for cortical imaging assume a spherical geometry, or when using realistic head shapes do not consider the anisotropy of head tissues. However, to our knowledge, no study relates the implication of this simplifying hypothesis on the spatial resolution of EEG for source imaging. In this paper, a method using finite elements in a realistic head geometry is implemented and validated. The influence of erroneous conductivity values for the head tissues is presented, and results show that the conductivities of the brain and the skull in the radial orientation are the most critical ones. In the inverse problem, this influence has been evaluated with simulations using a distributed source model with a comparison of two regularization techniques, with the isotropic model working on data sets produced by a nonisotropic model. Regularization with minimum norm priors produces source images with spurious activity, meaning that the errors in the head model totally annihilate any localization ability. But nonlinear regularization allows the accurate recovery of simultaneous spots of activity, while the restoration of very close active regions is profoundly disabled by errors in the head model. We conclude that for robust cortical source imaging with EEG, a realistic head model taking anisotropy of tissues into account should be used.

A phonological representation in the infant brain.

In adults, neural networks for phonological processing distinct from those involved in acoustical processing are located in the left temporal lobe. We now report that by the age of 3 months, infants display phonological processing devices analogous to those found in adults. Within a stream of identical syllables, acoustic deviants were introduced, either crossing a phonetic boundary or remaining within the same category. Event-related potentials were recorded using a 64-electrode net. Although the acoustical change was of similar amplitude in the two deviants, the electrophysiological response was larger for a phonological change and involved a more posterior and dorsal temporal region than for an acoustical change. These results demonstrate that infants, like adults, already possess a dedicated neuronal network for phonetic processing.

A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem.

In this paper, we present a new approach to the recovering of dipole magnitudes in a distributed source model for magnetoencephalographic (MEG) and electroencephalographic (EEG) imaging. This method consists in introducing spatial and temporal a priori information as a cure to this ill-posed inverse problem. A nonlinear spatial regularization scheme allows the preservation of dipole moment discontinuities between some a priori noncorrelated sources, for instance, when considering dipoles located on both sides of a sulcus. Moreover, we introduce temporal smoothness constraints on dipole magnitude evolution, at time scales smaller than those of cognitive processes. These priors are easily integrated into a Bayesian formalism, yielding a maximum a posteriori (MAP) estimator of brain electrical activity. Results from EEG simulations of our method are presented and compared with those of classical quadratic regularization and a now popular generalized minimum-norm technique called low-resolution electromagnetic tomography (LORETA).