Automated cot-side tracking of functional brain age in preterm infants

Objective A major challenge in the care of preterm infants is the early identification of compromised neurological development. While several measures are routinely used to track anatomical growth, there is a striking lack of reliable and objective tools for tracking maturation of early brain function; a cornerstone of lifelong neurological health. We present a cot-side method for measuring the functional maturity of the newborn brain based on routinely available neurological monitoring with electroencephalography (EEG). Methods We used a dataset of 177 EEG recordings from 65 preterm infants to train a multivariable prediction of functional brain age (FBA) from EEG. The FBA was validated on an independent set of 99 EEG recordings from 42 preterm infants. The difference between FBA and postmenstrual age (PMA) was evaluated as a predictor for neurodevelopmental outcome. Results The FBA correlated strongly with the PMA of an infant, with a median prediction error of less than 1 week. Moreover, individual babies follow well-defined individual trajectories. The accuracy of the FBA applied to the validation set was statistically equivalent to the training set accuracy. In a subgroup of infants with repeated EEG recordings, a persistently negative predicted age difference was associated with poor neurodevelopmental outcome. Interpretation The FBA enables the tracking of functional neurodevelopment in preterm infants. This establishes proof of principle for growth charts for brain function, a new tool to assist clinical management and identify infants who will benefit most from early intervention.Peer reviewe

Hemodynamic Traveling Waves in Human Visual Cortex

Functional MRI (fMRI) experiments rely on precise characterization of the blood oxygen level dependent (BOLD) signal. As the spatial resolution of fMRI reaches the sub-millimeter range, the need for quantitative modelling of spatiotemporal properties of this hemodynamic signal has become pressing. Here, we find that a detailed physiologically-based model of spatiotemporal BOLD responses predicts traveling waves with velocities and spatial ranges in empirically observable ranges. Two measurable parameters, related to physiology, characterize these waves: wave velocity and damping rate. To test these predictions, high-resolution fMRI data are acquired from subjects viewing discrete visual stimuli. Predictions and experiment show strong agreement, in particular confirming BOLD waves propagating for at least 5–10 mm across the cortical surface at speeds of 2–12 mm s-1. These observations enable fundamentally new approaches to fMRI analysis, crucial for fMRI data acquired at high spatial resolution

Modeling the Impact of Lesions in the Human Brain

Lesions of anatomical brain networks result in functional disturbances of brain systems and behavior which depend sensitively, often unpredictably, on the lesion site. The availability of whole-brain maps of structural connections within the human cerebrum and our increased understanding of the physiology and large-scale dynamics of cortical networks allow us to investigate the functional consequences of focal brain lesions in a computational model. We simulate the dynamic effects of lesions placed in different regions of the cerebral cortex by recording changes in the pattern of endogenous (“resting-state”) neural activity. We find that lesions produce specific patterns of altered functional connectivity among distant regions of cortex, often affecting both cortical hemispheres. The magnitude of these dynamic effects depends on the lesion location and is partly predicted by structural network properties of the lesion site. In the model, lesions along the cortical midline and in the vicinity of the temporo-parietal junction result in large and widely distributed changes in functional connectivity, while lesions of primary sensory or motor regions remain more localized. The model suggests that dynamic lesion effects can be predicted on the basis of specific network measures of structural brain networks and that these effects may be related to known behavioral and cognitive consequences of brain lesions

Impact of Load-Related Neural Processes on Feature Binding in Visuospatial Working Memory

BACKGROUND: The capacity of visual working memory (WM) is substantially limited and only a fraction of what we see is maintained as a temporary trace. The process of binding visual features has been proposed as an adaptive means of minimising information demands on WM. However the neural mechanisms underlying this process, and its modulation by task and load effects, are not well understood. OBJECTIVE: To investigate the neural correlates of feature binding and its modulation by WM load during the sequential phases of encoding, maintenance and retrieval. METHODS AND FINDINGS: 18 young healthy participants performed a visuospatial WM task with independent factors of load and feature conjunction (object identity and position) in an event-related functional MRI study. During stimulus encoding, load-invariant conjunction-related activity was observed in left prefrontal cortex and left hippocampus. During maintenance, greater activity for task demands of feature conjunction versus single features, and for increased load was observed in left-sided regions of the superior occipital cortex, precuneus and superior frontal cortex. Where these effects were expressed in overlapping cortical regions, their combined effect was additive. During retrieval, however, an interaction of load and feature conjunction was observed. This modulation of feature conjunction activity under increased load was expressed through greater deactivation in medial structures identified as part of the default mode network. CONCLUSIONS AND SIGNIFICANCE: The relationship between memory load and feature binding qualitatively differed through each phase of the WM task. Of particular interest was the interaction of these factors observed within regions of the default mode network during retrieval which we interpret as suggesting that at low loads, binding processes may be 'automatic' but at higher loads it becomes a resource-intensive process leading to disengagement of activity in this network. These findings provide new insights into how feature binding operates within the capacity-limited WM system

Ventilatory Chaos Is Impaired in Carotid Atherosclerosis

Ventilatory chaos is strongly linked to the activity of central pattern generators, alone or influenced by respiratory or cardiovascular afferents. We hypothesized that carotid atherosclerosis should alter ventilatory chaos through baroreflex and autonomic nervous system dysfunctions. Chaotic dynamics of inspiratory flow was prospectively evaluated in 75 subjects undergoing carotid ultrasonography: 27 with severe carotid stenosis (>70%), 23 with moderate stenosis (<70%), and 25 controls. Chaos was characterized by the noise titration method, the correlation dimension and the largest Lyapunov exponent. Baroreflex sensitivity was estimated in the frequency domain. In the control group, 92% of the time series exhibit nonlinear deterministic chaos with positive noise limit, whereas only 68% had a positive noise limit value in the stenoses groups. Ventilatory chaos was impaired in the groups with carotid stenoses, with significant parallel decrease in the noise limit value, correlation dimension and largest Lyapunov exponent, as compared to controls. In multiple regression models, the percentage of carotid stenosis was the best in predicting the correlation dimension (p<0.001, adjusted R2: 0.35) and largest Lyapunov exponent (p<0.001, adjusted R2: 0.6). Baroreflex sensitivity also predicted the correlation dimension values (p = 0.05), and the LLE (p = 0.08). Plaque removal after carotid surgery reversed the loss of ventilatory complexity. To conclude, ventilatory chaos is impaired in carotid atherosclerosis. These findings depend on the severity of the stenosis, its localization, plaque surface and morphology features, and is independently associated with baroreflex sensitivity reduction. These findings should help to understand the determinants of ventilatory complexity and breathing control in pathological conditions

Unexpected Course of Nonlinear Cardiac Interbeat Interval Dynamics during Childhood and Adolescence

The fluctuations of the cardiac interbeat series contain rich information because they reflect variations of other functions on different time scales (e.g., respiration or blood pressure control). Nonlinear measures such as complexity and fractal scaling properties derived from 24 h heart rate dynamics of healthy subjects vary from childhood to old age. In this study, the age-related variations during childhood and adolescence were addressed. In particular, the cardiac interbeat interval series was quantified with respect to complexity and fractal scaling properties. The R-R interval series of 409 healthy children and adolescents (age range: 1 to 22 years, 220 females) was analyzed with respect to complexity (Approximate Entropy, ApEn) and fractal scaling properties on three time scales: long-term (slope β of the power spectrum, log power vs. log frequency, in the frequency range 10−4 to 10−2 Hz) intermediate-term (DFA, detrended fluctuation analysis, α2) and short-term (DFA α1). Unexpectedly, during age 7 to 13 years β and ApEn were higher compared to the age <7 years and age >13 years (β: −1.06 vs. −1.21; ApEn: 0.88 vs. 0.74). Hence, the heart rate dynamics were closer to a 1/f power law and most complex between 7 and 13 years. However, DFA α1 and α2 increased with progressing age similar to measures reflecting linear properties. In conclusion, the course of long-term fractal scaling properties and complexity of heart rate dynamics during childhood and adolescence indicates that these measures reflect complex changes possibly linked to hormonal changes during pre-puberty and puberty

Upregulation of cortico-cerebellar functional connectivity after motor learning

Interactions between the cerebellum and primary motor cortex are crucial for the acquisition of new motor skills. Recent neuroimaging studies indicate that learning motor skills is associated with subsequent modulation of resting-state functional connectivity in the cerebellar and cerebral cortices. The neuronal processes underlying the motor-learning-induced plasticity are not well understood. Here, we investigate changes in functional connectivity in source-reconstructed electroencephalography (EEG) following the performance of a single session of a dynamic force task in twenty young adults. Source activity was reconstructed in 112 regions of interest (ROIs) and the functional connectivity between all ROIs was estimated using the imaginary part of coherence. Significant changes in resting-state connectivity were assessed using partial least squares (PLS). We found that subjects adapted their motor performance during the training session and showed improved accuracy but with slower movement times. A number of connections were significantly upregulated after motor training, principally involving connections within the cerebellum and between the cerebellum and motor cortex. Increased connectivity was confined to specific frequency ranges in the mu- and beta-bands. Post hoc analysis of the phase spectra of these cerebellar and cortico-cerebellar connections revealed an increased phase lag between motor cortical and cerebellar activity following motor practice. These findings show a reorganization of intrinsic cortico-cerebellar connectivity related to motor adaptation and demonstrate the potential of EEG connectivity analysis in source space to reveal the neuronal processes that underpin neural plasticity

Modeling Magnification and Anisotropy in the Primate Foveal Confluence

A basic organizational principle of the primate visual system is that it maps the visual environment repeatedly and retinotopically onto cortex. Simple algebraic models can be used to describe the projection from visual space to cortical space not only for V1, but also for the complex of areas V1, V2 and V3. Typically a conformal (angle-preserving) projection ensuring local isotropy is regarded as ideal and primate visual cortex is often regarded as an approximation of this ideal. However, empirical data show systematic deviations from this ideal that are especially relevant in the foveal projection. The aims of this study were to map the nature of anisotropy predicted by existing models, to investigate the optimization targets faced by different types of retino-cortical maps, and finally to propose a novel map that better models empirical data than other candidates. The retino-cortical map can be optimized towards a space-conserving homogenous representation or a quasi-conformal mapping. The latter would require a significantly enlarged representation of specific parts of the cortical maps. In particular it would require significant enlargement of parafoveal V2 and V3 which is not supported by empirical data. Further, the recently published principal layout of the foveal singularity cannot be explained by existing models. We suggest a new model that accurately describes foveal data, minimizing cortical surface area in the periphery but suggesting that local isotropy dominates the most foveal part at the expense of additional cortical surface. The foveal confluence is an important example of the detailed trade-offs between the compromises required for the mapping of environmental space to a complex of neighboring cortical areas. Our models demonstrate that the organization follows clear morphogenetic principles that are essential for our understanding of foveal vision in daily life

Prior and Present Evidence: How Prior Experience Interacts with Present Information in a Perceptual Decision Making Task

Vibrotactile discrimination tasks have been used to examine decision making processes in the presence of perceptual uncertainty, induced by barely discernible frequency differences between paired stimuli or by the presence of embedded noise. One lesser known property of such tasks is that decisions made on a single trial may be biased by information from prior trials. An example is the time-order effect whereby the presentation order of paired stimuli may introduce differences in accuracy. Subjects perform better when the first stimulus lies between the second stimulus and the global mean of all stimuli on the judged dimension ("preferred" time-orders) compared to the alternative presentation order ("nonpreferred" time-orders). This has been conceptualised as a "drift" of the first stimulus representation towards the global mean of the stimulus-set (an internal standard). We describe the influence of prior information in relation to the more traditionally studied factors of interest in a classic discrimination task.Sixty subjects performed a vibrotactile discrimination task with different levels of uncertainty parametrically induced by increasing task difficulty, aperiodic stimulus noise, and changing the task instructions whilst maintaining identical stimulus properties (the "context").The time-order effect had a greater influence on task performance than two of the explicit factors-task difficulty and noise-but not context. The influence of prior information increased with the distance of the first stimulus from the global mean, suggesting that the "drift" velocity of the first stimulus towards the global mean representation was greater for these trials.Awareness of the time-order effect and prior information in general is essential when studying perceptual decision making tasks. Implicit mechanisms may have a greater influence than the explicit factors under study. It also affords valuable insights into basic mechanisms of information accumulation, storage, sensory weighting, and processing in neural circuits

Scroll-Wave Dynamics in Human Cardiac Tissue: Lessons from a Mathematical Model with Inhomogeneities and Fiber Architecture

Cardiac arrhythmias, such as ventricular tachycardia (VT) and ventricular fibrillation (VF), are among the leading causes of death in the industrialized world. These are associated with the formation of spiral and scroll waves of electrical activation in cardiac tissue; single spiral and scroll waves are believed to be associated with VT whereas their turbulent analogs are associated with VF. Thus, the study of these waves is an important biophysical problem. We present a systematic study of the combined effects of muscle-fiber rotation and inhomogeneities on scroll-wave dynamics in the TNNP (ten Tusscher Noble Noble Panfilov) model for human cardiac tissue. In particular, we use the three-dimensional TNNP model with fiber rotation and consider both conduction and ionic inhomogeneities. We find that, in addition to displaying a sensitive dependence on the positions, sizes, and types of inhomogeneities, scroll-wave dynamics also depends delicately upon the degree of fiber rotation. We find that the tendency of scroll waves to anchor to cylindrical conduction inhomogeneities increases with the radius of the inhomogeneity. Furthermore, the filament of the scroll wave can exhibit drift or meandering, transmural bending, twisting, and break-up. If the scroll-wave filament exhibits weak meandering, then there is a fine balance between the anchoring of this wave at the inhomogeneity and a disruption of wave-pinning by fiber rotation. If this filament displays strong meandering, then again the anchoring is suppressed by fiber rotation; also, the scroll wave can be eliminated from most of the layers only to be regenerated by a seed wave. Ionic inhomogeneities can also lead to an anchoring of the scroll wave; scroll waves can now enter the region inside an ionic inhomogeneity and can display a coexistence of spatiotemporal chaos and quasi-periodic behavior in different parts of the simulation domain. We discuss the experimental implications of our study