The Direction of response selectivity between conspecific and heterospecific auditory stimuli varies with response metric.

Species recognition is an essential behavioral outcome of social discrimination, flocking, mobbing, mating, and/or parental care. In songbirds, auditory species recognition cues are processed through specialized forebrain circuits dedicated to acoustic discrimination. Here we addressed the direction of behavioral and neural metrics of zebra finches' (Taeniopygia guttata) responses to acoustic cues of unfamiliar conspecifics vs. heterospecifics. Behaviorally, vocal response rates were greater for conspecific male zebra finch songs over heterospecific Pin-tailed Whydah (Vidua macroura) songs, which paralleled greater multiunit spike rates in the auditory forebrain in response to the same type of conspecific over heterospecific auditory stimuli. In contrast, forebrain activation levels were reversed to species-specific song playbacks during two functional magnetic resonance imaging experiments: we detected consistently greater responses to whydah songs over finch songs and did so independently of whether subjects had been co-housed or not with heterospecifics. These results imply that the directionality of behavioral and neural response selectivity metrics are not always consistent and appear to be experience-independent in this set of stimulus-and-subject experimental paradigms.

Human consciousness is supported by dynamic complex patterns of brain signal coordination.

Adopting the framework of brain dynamics as a cornerstone of human consciousness, we determined whether dynamic signal coordination provides specific and generalizable patterns pertaining to conscious and unconscious states after brain damage. A dynamic pattern of coordinated and anticoordinated functional magnetic resonance imaging signals characterized healthy individuals and minimally conscious patients. The brains of unresponsive patients showed primarily a pattern of low interareal phase coherence mainly mediated by structural connectivity, and had smaller chances to transition between patterns. The complex pattern was further corroborated in patients with covert cognition, who could perform neuroimaging mental imagery tasks, validating this pattern's implication in consciousness. Anesthesia increased the probability of the less complex pattern to equal levels, validating its implication in unconsciousness. Our results establish that consciousness rests on the brain's ability to sustain rich brain dynamics and pave the way for determining specific and generalizable fingerprints of conscious and unconscious states.

[Evaluation of vascular reactivity to overcome limitations of neurovascular uncoupling in BOLD fMRI of malignant brain tumors]. / Otsenka vazoreaktivnosti dlia preodoleniia ogranichenii neirovaskuliarnogo razobshcheniia BOLD fMRT pri zlokachestvennykh opukholiakh golovnogo mozga.

The sensitivity of fMRI in identification of eloquent cortical centers in the case of large infiltrative growing tumors and pronounced peritumoral edema may be reduced or significantly limited in some cases. The main cause is an attenuated Blood-Oxygen-Level-Dependent response (BOLD) caused by pathological vascular reactivity and subsequent neurovascular uncoupling of fMRI. In our study, we attempted to overcome these limitations and increase the sensitivity of this technique in identification of eloquent cortical areas adjacent to brain tumors by using vasoreactivity features of a breath-holding test and including these data in the BOLD analysis. Local vasoreactivity using a breath-holding paradigm with the same block design of both motor and speech tests was determined in 5 healthy volunteers and 3 patients in the preoperative period (two patients with high grade gliomas and one patient with single metastasis). A coherence-based model was developed for analysis of BOLD fMRI, which took into account altered hemodynamics in peritumoral zones. Obtained coherence maps demonstrated clinically more significant activation zones that were not seen with standard methods of fMRI processing. Thus, neurovascular uncoupling that is known to affect the accuracy of the BOLD fMRI response adjacent to brain tumors may be partially overcome by including an independent measurement of vasoreactivity using a breath-holding test in the BOLD analysis.

The strength and spread of the electric field induced by transcranial rotating permanent magnet stimulation in comparison with conventional transcranial magnetic stimulation.

BACKGROUND: Weak or low intensity transcranial stimulation of the brain, such as low field magnetic stimulation and electrical stimulation, can produce significant functional and therapeutic neuromodulatory effects. NEW METHOD: We have recently developed a portable wearable multifocal brain stimulator called transcranial rotating permanent magnet stimulator (TRPMS) that uses rapidly spinning high field strength permanent magnets attached to a cap. It produces oscillatory stimuli of different frequencies and patterns. Here we compared the strengths and spatial profiles of the changing magnetic fields of a figure-of-eight transcranial magnetic stimulator (TMS) coil, a TRPMS prototype, and a scaled-up version of TRPMS. We measured field strengths and directions of voltages induced in a magnetic field sensor oriented along all three orthogonal axes. RESULTS AND COMPARISON WITH EXISTING METHODS: The spatial spread of the TRPMS-induced electric field is more restricted, and its shape and strength vary less with the orientation of the inductance than TMS. The maximum voltage induced by the current prototype is ∼7% of the maximal TMS output at depths corresponding to the human cerebral cortex from the scalp surface. This field strength can be scaled up by a factor ∼8 with a larger diametrically magnetized magnet. These comparative data allow us to estimate that intracortical effects of TRPMS could be stronger than other low intensity stimulation methods. CONCLUSIONS: TRPMS might enable greater uniformity, consistency and focality in stimulation of targeted cortical areas subject to significant anatomical variability. Multiple TRPMS microstimulators can also be combined to produce patterned multifocal spatiotemporal stimulation.

Radioiodinated Capsids Facilitate In Vivo Non-Invasive Tracking of Adeno-Associated Gene Transfer Vectors.

Viral vector mediated gene therapy has become commonplace in clinical trials for a wide range of inherited disorders. Successful gene transfer depends on a number of factors, of which tissue tropism is among the most important. To date, definitive mapping of the spatial and temporal distribution of viral vectors in vivo has generally required postmortem examination of tissue. Here we present two methods for radiolabeling adeno-associated virus (AAV), one of the most commonly used viral vectors for gene therapy trials, and demonstrate their potential usefulness in the development of surrogate markers for vector delivery during the first week after administration. Specifically, we labeled adeno-associated virus serotype 10 expressing the coding sequences for the CLN2 gene implicated in late infantile neuronal ceroid lipofuscinosis with iodine-124. Using direct (Iodogen) and indirect (modified Bolton-Hunter) methods, we observed the vector in the murine brain for up to one week using positron emission tomography. Capsid radioiodination of viral vectors enables non-invasive, whole body, in vivo evaluation of spatial and temporal vector distribution that should inform methods for efficacious gene therapy over a broad range of applications.

The application of a mathematical model linking structural and functional connectomes in severe brain injury.

Following severe injuries that result in disorders of consciousness, recovery can occur over many months or years post-injury. While post-injury synaptogenesis, axonal sprouting and functional reorganization are known to occur, the network-level processes underlying recovery are poorly understood. Here, we test a network-level functional rerouting hypothesis in recovery of patients with disorders of consciousness following severe brain injury. This hypothesis states that the brain recovers from injury by restoring normal functional connections via alternate structural pathways that circumvent impaired white matter connections. The so-called network diffusion model, which relates an individual's structural and functional connectomes by assuming that functional activation diffuses along structural pathways, is used here to capture this functional rerouting. We jointly examined functional and structural connectomes extracted from MRIs of 12 healthy and 16 brain-injured subjects. Connectome properties were quantified via graph theoretic measures and network diffusion model parameters. While a few graph metrics showed groupwise differences, they did not correlate with patients' level of consciousness as measured by the Coma Recovery Scale - Revised. There was, however, a strong and significant partial Pearson's correlation (accounting for age and years post-injury) between level of consciousness and network diffusion model propagation time (r = 0.76, p < 0.05, corrected), i.e. the time functional activation spends traversing the structural network. We concluded that functional rerouting via alternate (and less efficient) pathways leads to increases in network diffusion model propagation time. Simulations of injury and recovery in healthy connectomes confirmed these results. This work establishes the feasibility for using the network diffusion model to capture network-level mechanisms in recovery of consciousness after severe brain injury.

Brain Region-Specific Degeneration with Disease Progression in Late Infantile Neuronal Ceroid Lipofuscinosis (CLN2 Disease).

BACKGROUND AND PURPOSE: Late infantile neuronal ceroid lipofuscinosis (CLN2 disease) is a uniformly fatal lysosomal storage disease resulting from mutations in the CLN2 gene. Our hypothesis was that regional analysis of cortical brain degeneration may identify brain regions that are affected earliest and most severely by the disease. MATERIALS AND METHODS: Fifty-two high-resolution 3T MR imaging datasets were prospectively acquired on 38 subjects with CLN2. A retrospective cohort of 52 disease-free children served as a control population. The FreeSurfer software suite was used for calculation of cortical thickness. RESULTS: An increased rate of global cortical thinning in CLN2 versus control subjects was the primary finding in this study. Three distinct patterns were observed across brain regions. In the first, subjects with CLN2 exhibited differing rates of cortical thinning versus age. This was true in 22 and 26 of 34 regions in the left and right hemispheres, respectively, and was also clearly discernable when considering brain lobes as a whole and Brodmann regions. The second pattern exhibited a difference in thickness from healthy controls but with no discernable change with age (9 left hemispheres, 5 right hemispheres). In the third pattern, there was no difference in either the rate of cortical thinning or the mean cortical thickness between groups (3 left hemispheres, 3 right hemispheres). CONCLUSIONS: This study demonstrates that CLN2 causes differential rates of degeneration across the brain. Anatomic and functional regions that degenerate sooner and more severely than others compared with those in healthy controls may offer targets for directed therapies. The information gained may also provide neurobiologic insights regarding the mechanisms underlying disease progression.

Assessment of disease severity in late infantile neuronal ceroid lipofuscinosis using multiparametric MR imaging.

BACKGROUND AND PURPOSE: LINCL is a uniformly fatal lysosomal storage disease resulting from mutations in the CLN2 gene that encodes for tripeptidyl peptidase 1, a lysosomal enzyme necessary for the degradation of products of cellular metabolism. With the goal of developing quantitative noninvasive imaging biomarkers sensitive to disease progression, we evaluated a 5-component MR imaging metric and tested its correlation with a clinically derived disease-severity score. MATERIALS AND METHODS: MR imaging parameters were measured across the brain, including quantitative measures of the ADC, FA, nuclear spin-spin relaxation times (T2), volume percentage of CSF (%CSF), and NAA/Cr ratios. Thirty MR imaging datasets were prospectively acquired from 23 subjects with LINCL (2.5-8.4 years of age; 8 male/15 female). Whole-brain histograms were created, and the mode and mean values of the histograms were used to characterize disease severity. RESULTS: Correlation of single MR imaging parameters against the clinical disease-severity scale yielded linear regressions with R2 ranging from 0.25 to 0.70. Combinations of the 5 biomarkers were evaluated by using PCA. The best combination included ADC, %CSF, and NAA/Cr (R2=0.76, P<.001). CONCLUSIONS: The multiparametric disease-severity score obtained from the combination of ADC, %CSF, and NAA/Cr whole-brain MR imaging techniques provided a robust measure of disease severity, which may be useful in clinical therapeutic trials of LINCL in which an objective assessment of therapeutic response is desired.

Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS).

We introduce an algorithm for diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both voxel space and diffusion-gradient space. The method, called POAS, does not refer to a specific model for the data, like the diffusion tensor or higher order models. It works by embedding the measurement space into a space with defined metric, in this case the Lie group of three-dimensional Euclidean motion SE(3). Subsequently, pairwise comparisons of the values of the diffusion weighted signal are used for adaptation. POAS preserves the edges of the observed fine and anisotropic structures. It is designed to reduce noise directly in the diffusion weighted images and consequently also to reduce bias and variability of quantities derived from the data for specific models. We evaluate the algorithm on simulated and experimental data and demonstrate that it can be used to reduce the number of applied diffusion gradients and hence acquisition time while achieving a similar quality of data, or to improve the quality of data acquired in a clinically feasible scan time setting.

Modeling the orientation distribution function by mixtures of angular central Gaussian distributions.

In this paper we develop a tensor mixture model for diffusion weighted imaging data using an automatic model order selection criterion for the number of tensor components in a voxel. We show that the weighted orientation distribution function for this model can be expanded into a mixture of angular central Gaussian distributions. We investigate properties of this model in extensive simulations and in a high angular resolution scan of a human brain. The results suggest that the model improves imaging of cerebral fiber tracts. In addition, inference on canonical model parameters could potentially provide novel clinical markers of altered white matter. Software to compute the tensor mixture model from diffusion weighted MRI data is made available in the programming language R.

Arterial spin labeling and altered cerebral blood flow patterns in the minimally conscious state.

OBJECTIVE: To use arterial spin labeling (ASL) to compare cerebral blood flow (CBF) patterns in minimally conscious state (MCS) patients with those in normal controls in an observational study design. METHODS: Subjects meeting MCS criteria and normal controls were identified. A pseudocontinuous ASL sequence was performed with subjects and controls in the resting awake state. Multiple CBF values for 10 predetermined regions of interest were sampled and average CBF was calculated and compared between controls and subjects. RESULTS: Ten normal controls were identified, with ages ranging from 26 to 54 years. Four subjects met the MCS criteria and received an ASL study, with one patient receiving a second study at a later date. Subjects ranged in age from 19 to 58 years and had traumatic brain injury, stroke, or hypoxic-ischemic encephalopathy. Regional CBF for controls ranged from 21.6 to 57.2 mL/100 g/min, with a pattern of relatively increased blood flow posteriorly including the posterior cingulate, parietal, and occipital cortices. CBF patterns for MCS subjects showed greater variability (from 7.7 to 33.1 mL/100 g/min), demonstrating globally decreased CBF in gray matter compared with that in normal controls, especially in the medial prefrontal and midfrontal regions. In the one subject studied longitudinally, global CBF values increased over time, which correlated with clinical improvement. CONCLUSIONS: We identified globally decreased CBF and a selective reduction of CBF within the medial prefrontal and midfrontal cortical regions as well as gray matter in MCS patients. ASL may serve as an adjunctive method to assess functional reserve in patients recovering from severe brain injuries.

Accurate localization of brain activity in presurgical FMRI by structure adaptive smoothing.

An important problem of the analysis of functional magnetic resonance imaging (fMRI) experiments is to achieve some noise reduction of the data without blurring the shape of the activation areas. As a novel solution to this problem, recently the propagation-separation (PS) approach has been proposed. PS is a structure adaptive smoothing method that adapts to different shapes of activation areas. In this paper, we demonstrate how this method results in a more accurate localization of brain activity. First, it is shown in numerical simulations that PS is superior over Gaussian smoothing with respect to the accurate description of the shape of activation clusters and results in less false detections. Second, in a study of 37 presurgical planning cases we found that PS and Gaussian smoothing often yield different results, and we present examples showing aspects of the superiority of PS as applied to presurgical planning.

Eye-target synchronization in mild traumatic brain-injured patients.

Eye-target synchronization is critical for effective smooth pursuit of a moving visual target. We apply the nonlinear dynamical technique of stochastic-phase synchronization to human visual pursuit of a moving target, in both normal and mild traumatic brain-injured (mTBI) patients. We observe significant fatigue effects in all subject populations, in which subjects synchronize better with the target during the first half of the trial than in the second half. The fatigue effect differed, however, between the normal and the mTBI populations and between old and young subpopulations of each group. In some cases, the younger (40 years old) normal subjects. Our results, however, suggest that further studies will be necessary before a standard of "normal" smooth pursuit synchronization can be developed.

Assessing disease severity in late infantile neuronal ceroid lipofuscinosis using quantitative MR diffusion-weighted imaging.

BACKGROUND AND PURPOSE: Late infantile neuronal ceroid lipofuscinosis (LINCL), a form of Batten disease, is a fatal neurodegenerative genetic disorder, diagnosed via DNA testing, that affects approximately 200 children in the United States at any one time. This study was conducted to evaluate whether quantitative data derived by diffusion-weighted MR imaging (DWI) techniques can supplement clinical disability scale information to provide a quantitative estimate of neurodegeneration, as well as disease progression and severity. MATERIALS AND METHODS: This study prospectively analyzed 32 DWI examinations from 18 patients having confirmed LINCL at various stages of disease. A whole-brain apparent diffusion coefficient (ADC) histogram was fitted with a dual Gaussian function combined with a function designed to model voxels containing a partial volume fraction of brain parenchyma versus CSF. Previously published whole-brain ADC values of age-matched control subjects were compared with those of the LINCL patients. Correlations were tested between the peak ADC of the fitted histogram and patient age, disease severity, and a CNS disability scale adapted for LINCL. RESULTS: ADC values assigned to brain parenchyma were higher than published ADC values for age-matched control subjects. ADC values between patients and control subjects began to differ at 5 years of age based on 95% confidence intervals. ADC values had a nearly equal correlation with patient age (R2=0.71) and disease duration (R2=0.68), whereas the correlation with the central nervous system disability scale (R2=0.27) was much weaker. CONCLUSION: This study indicates that brain ADC values acquired using DWI may be used as an independent measure of disease severity and duration in LINCL.

Hybrid grammar-based approach to nonlinear dynamical system identification from biological time series.

We introduce a grammar-based hybrid approach to reverse engineering nonlinear ordinary differential equation models from observed time series. This hybrid approach combines a genetic algorithm to search the space of model architectures with a Kalman filter to estimate the model parameters. Domain-specific knowledge is used in a context-free grammar to restrict the search space for the functional form of the target model. We find that the hybrid approach outperforms a pure evolutionary algorithm method, and we observe features in the evolution of the dynamical models that correspond with the emergence of favorable model components. We apply the hybrid method to both artificially generated time series and experimentally observed protein levels from subjects who received the smallpox vaccine. From the observed data, we infer a cytokine protein interaction network for an individual's response to the smallpox vaccine.

Phase synchronization from noisy univariate signals.

We present methods for detecting phase synchronization of two unidirectionally coupled, self-sustained noisy oscillators from a signal of the driven oscillator alone. One method detects soft phase locking; another hard phase locking. Both are applied to the problem of detecting phase synchronization in von Kármán vortex flow meters.

Aperiodic flow-induced oscillations of collapsible tubes: a critical reappraisal.

The evidence for the aperiodic self-excited oscillations of flow-conveying collapsible tubes being mathematically chaotic is re-examined. Many cases which powerfully suggest nonlinear deterministic behaviour have not been recorded over time-spans which allow their exhaustive examination. The present investigation centred on a previously recorded robust and generic oscillation, but more recent and more discerning tests were applied. Despite hints that a low embedding dimension might suffice, the data appeared on most indices high-dimensional. A U-shaped return map was found and modelled using both radial basis functions and polynomials, but lack of detailed structure in the map prevented effective parameter estimation. On the basis of power-law rather than exponential divergence of nearby trajectories, and of inability to discriminate against behaviour which would also be manifested by a surrogate consisting of a noise-perturbed nonlinear periodic oscillator, it is concluded that the data do not support the idea that the aperiodicity in the particular oscillation examined is caused by deterministic chaos. There was evidence that the distributed nature of the physical system might underlie aspects of the high dimensionality. We advocate equally searching testing of any future candidate chaotic oscillations in the investigation of collapsed-tube flows.

Normalization of DNA-microarray data by nonlinear correlation maximization.

Signal data from DNA-microarray ("chip") technology can be noisy; i.e., the signal variation of one gene on a series of repetitive chips can be substantial. It is becoming more and more recognized that a sufficient number of chip replicates has to be made in order to separate correct from incorrect signals. To reduce the systematic fraction of the noise deriving from pipetting errors, from different treatment of chips during hybridization, and from chip-to-chip manufacturing variability, normalization schemes are employed. We present here an iterative nonparametric nonlinear normalization scheme called simultaneous alternating conditional expectation (sACE), which is designed to maximize correlation between chip repeats in all-chip-against-all space. We tested sACE on 28 experiments with 158 Affymetrix one-color chips. The procedure should be equally applicable to other DNA-microarray technologies, e.g., two-color chips. We show that the reduction of noise compared to a simple normalization scheme like the widely used linear global normalization leads to fewer false-positive calls, i.e., to fewer genes which have to be laboriously confirmed by independent methods such as TaqMan or quantitative PCR.

How well can epileptic seizures be predicted? An evaluation of a nonlinear method.

The unpredictability of the occurrence of epileptic seizures contributes to the burden of the disease to a major degree. Thus, various methods have been proposed to predict the onset of seizures based on EEG recordings. A nonlinear feature motivated by the correlation dimension is a seemingly promising approach. In a previous study this method was reported to identify 'preictal dimension drops' up to 19 min before seizure onset, exceeding the variability of interictal data sets of 30-50 min duration. Here we have investigated the sensitivity and specificity of this method based on invasive long-term recordings from 21 patients with medically intractable partial epilepsies, who underwent invasive pre-surgical monitoring. The evaluation of interictal 24-h recordings comprising the sleep-wake cycle showed that only one out of 88 seizures was preceded by a significant preictal dimension drop. In a second analysis, the relation between dimension drops within time windows of up to 50 min before seizure onset and interictal periods was investigated. For false-prediction rates below 0.1/h, the sensitivity ranged from 8.3 to 38.3% depending on the prediction window length. Overall, the mean length and amplitude of dimension drops showed no significant differences between interictal and preictal data sets.

Identification of nonlinear spatiotemporal systems via partitioned filtering.

The problem of identifying continuous spatiotemporal nonlinear systems from noisy and indirect observations is determined by its computational complexity. We propose a solution by means of nonlinear state space filtering along with a state partition technique. The method is demonstrated to be computationally feasible for spatiotemporal data with properties that occur typically in experimental recordings. It is applied to one component of the simulated chaotic data of a two-component reaction diffusion system, yielding estimates of both the unobserved state component and the diffusion constant.