A maximum likelihood based technique for validating detrended fluctuation analysis (ML-DFA)

Detrended Fluctuation Analysis (DFA) is widely used to assess the presence of long-range temporal correlations in time series. Signals with long-range temporal correlations are typically defined as having a power law decay in their autocorrelation function. The output of DFA is an exponent, which is the slope obtained by linear regression of a log-log fluctuation plot against window size. However, if this fluctuation plot is not linear, then the underlying signal is not self-similar, and the exponent has no meaning. There is currently no method for assessing the linearity of a DFA fluctuation plot. Here we present such a technique, called ML-DFA. We scale the DFA fluctuation plot to construct a likelihood function for a set of alternative models including polynomial, root, exponential, logarithmic and spline functions. We use this likelihood function to determine the maximum likelihood and thus to calculate values of the Akaike and Bayesian information criteria, which identify the best fit model when the number of parameters involved is taken into account and over-fitting is penalised. This ensures that, of the models that fit well, the least complicated is selected as the best fit. We apply ML-DFA to synthetic data from FARIMA processes and sine curves with DFA fluctuation plots whose form has been analytically determined, and to experimentally collected neurophysiological data. ML-DFA assesses whether the hypothesis of a linear fluctuation plot should be rejected, and thus whether the exponent can be considered meaningful. We argue that ML-DFA is essential to obtaining trustworthy results from DFA.Comment: 22 pages, 7 figure

Identification of criticality in neuronal avalanches: II. A theoretical and empirical investigation of the Driven case

The observation of apparent power laws in neuronal systems has led to the suggestion that the brain is at, or close to, a critical state and may be a self-organised critical system. Within the framework of self-organised criticality a separation of timescales is thought to be crucial for the observation of power-law dynamics and computational models are often constructed with this property. However, this is not necessarily a characteristic of physiological neural networks—external input does not only occur when the network is at rest/a steady state. In this paper we study a simple neuronal network model driven by a continuous external input (i.e. the model does not have an explicit separation of timescales from seeding the system only when in the quiescent state) and analytically tuned to operate in the region of a critical state (it reaches the critical regime exactly in the absence of input—the case studied in the companion paper to this article). The system displays avalanche dynamics in the form of cascades of neuronal firing separated by periods of silence. We observe partial scale-free behaviour in the distribution of avalanche size for low levels of external input. We analytically derive the distributions of waiting times and investigate their temporal behaviour in relation to different levels of external input, showing that the system’s dynamics can exhibit partial long-range temporal correlations. We further show that as the system approaches the critical state by two alternative ‘routes’, different markers of criticality (partial scale-free behaviour and long-range temporal correlations) are displayed. This suggests that signatures of criticality exhibited by a particular system in close proximity to a critical state are dependent on the region in parameter space at which the system (currently) resides

Resting state MEG oscillations show long-range temporal correlations of phase synchrony that break down during finger movement

The capacity of the human brain to interpret and respond to multiple temporal scales in its surroundings suggests that its internal interactions must also be able to operate over a broad temporal range. In this paper, we utilize a recently introduced method for characterizing the rate of change of the phase difference between MEG signals and use it to study the temporal structure of the phase interactions between MEG recordings from the left and right motor cortices during rest and during a finger-tapping task. We use the Hilbert transform to estimate moment-to-moment fluctuations of the phase difference between signals. After confirming the presence of scale-invariance we estimate the Hurst exponent using detrended fluctuation analysis (DFA). An exponent of >0.5 is indicative of long-range temporal correlations (LRTCs) in the signal. We find that LRTCs are present in the α/μ and β frequency bands of resting state MEG data. We demonstrate that finger movement disrupts LRTCs correlations, producing a phase relationship with a structure similar to that of Gaussian white noise. The results are validated by applying the same analysis to data with Gaussian white noise phase difference, recordings from an empty scanner and phase-shuffled time series. We interpret the findings through comparison of the results with those we obtained from an earlier study during which we adopted this method to characterize phase relationships within a Kuramoto model of oscillators in its sub-critical, critical, and super-critical synchronization states. We find that the resting state MEG from left and right motor cortices shows moment-to-moment fluctuations of phase difference with a similar temporal structure to that of a system of Kuramoto oscillators just prior to its critical level of coupling, and that finger tapping moves the system away from this pre-critical state toward a more random state

Attention deficits following ADEM ameliorated by guanfacine

The authors report here the case of a patient with severe deficits in arousal and sustained attention, associated with hemispatial neglect. These impairments were secondary to acute disseminated encephalomyelitis, with bilateral involvement of the medial nuclei and pulvinar of the thalamus. Treatment with the noradrenergic agonist guanfacine, previously used for attention deficits in attention deficit/hyperactivity disorder and stroke, was associated with a significant amelioration of both the spatial and sustained attention impairments in neglect. Guanfacine may prove to be a useful tool in the treatment of disorders of attention associated with neurological conditions

When do Bursts Matter in the Primary Motor Cortex? Investigating Changes in the Intermittencies of Beta Rhythms Associated With Movement States

Brain activity exhibits significant temporal structure that is not well captured in the power spectrum. Recently, attention has shifted to characterising the properties of intermittencies in rhythmic neural activity (i.e. bursts), yet the mechanisms regulating them are unknown. Here, we present evidence from electrocorticography recordings made from the motor cortex to show that the statistics of bursts, such as duration or amplitude, in beta frequency (14-30Hz) rhythms significantly aid the classification of motor states such as rest, movement preparation, execution, and imagery. These features reflect nonlinearities not detectable in the power spectrum, with states increasing in nonlinearity from movement execution to preparation to rest. Further, we show using a computational model of the cortical microcircuit, constrained to account for burst features, that modulations of laminar specific inhibitory interneurons are responsible for temporal organization of activity. Finally, we show that temporal characteristics of spontaneous activity can be used to infer the balance of cortical integration between incoming sensory information and endogenous activity. Critically, we contribute to the understanding of how transient brain rhythms may underwrite cortical processing, which in turn, could inform novel approaches for brain state classification, and modulation with novel brain-computer interfaces

The expression of VvMYBPA1 in tobacco remodulates the phenylpropanoid pathway and diverts the synthesis of anthocyanins into condensed tannins in flowers

Patients in Vegetative State (VS), also known as Unresponsive Wakefulness State (UWS) are deemed to be unaware of themselves or their environment. This is different from patients diagnosed with Minimally Conscious state (MCS), who can have intermittent awareness. In both states, there is a severe impairment of consciousness; these disorders are referred to as disorders of consciousness (DOC) and if the state is prolonged, pDOC. There is growing evidence that some patients who are behaviourally in VS/UWS can show neural activation to environmental stimuli and that this response can be detected using functional brain imaging (fMRI/PET) and electroencephalography (EEG). Recently, it has also been suggested that a more reliable detection of brain responsiveness and hence a more reliable differentiation between VS/UWS and MCS requires person-centred and person-specific stimuli, such as the subject's own name stimulus.In this study we obtained event related potential data (ERP) from 12 healthy subjects and 16 patients in pDOC, five of whom were in the VS/UWS and 11 in the Minimally Conscious State (MCS). We used as the ERP stimuli the subjects' own name, others' names and reversed other names. We performed a sensor level analysis using Statistical Parametric Mapping (SPM) software. Using this paradigm in 4 DOC patients (3 in MCS, and 1 in VS/UWS) we detected a statistically significant difference in EEG response to their own name versus other peoples' names with ERP latencies (~300 ms and ~700 ms post stimuli). Some of these differences were similar to those found in a control group of healthy subjects.This study shows the feasibility of using self-relevant stimuli such as a subject's own name for assessment of brain function in pDOC patients. This neurophysiological test is suitable for bed-side/hospital based assessment of pDOC patients. As it does not require sophisticated scanning equipment it can feasibly be used within a hospital or care setting to help professionals tailor medical and psycho-social management for patients

Classical infratentorial superficial siderosis of the central nervous system: pathophysiology, clinical features and management

The term superficial siderosis (SS) is derived from the Greek word 'sideros', meaning iron. It includes two subtypes, distinguished by their anatomical distribution, causes and clinical features: 'classical' infratentorial SS (iSS, which sometimes also affects supratentorial regions) and cortical SS (cSS, which affects only supratentorial regions). This paper considers iSS, a potentially disabling disorder usually associated with very slow persistent or intermittent subarachnoid bleeding from a dural defect, and characterised by progressive hearing and vestibular impairment, ataxia, myelopathy and cognitive dysfunction. The causal dural defect-most often spinal but sometimes in the posterior fossa-typically follows trauma or neurosurgery occurring decades before diagnosis. Increasing recognition of iSS with paramagnetic-sensitive MRI is leading to an unmet clinical need. Given the diagnostic challenges and complex neurological impairments in iSS, we have developed a multidisciplinary approach involving key teams. We discuss pathophysiology, diagnosis and management of iSS, including a proposed clinical care pathway

Propagation of beta/gamma rhythms in the cortico-basal ganglia circuits of the Parkinsonian rat

Much of the motor impairment associated with Parkinson’s disease is thought to arise from pathological activity in the networks formed by the basal ganglia (BG) and motor cortex. To evaluate several hypotheses proposed to explain the emergence of pathological oscillations in Parkinsonism, we investigated changes to the directed connectivity in BG networks following dopamine depletion. We recorded local field potentials (LFPs) in the cortex and basal ganglia of rats rendered Parkinsonian by injection of 6-hydroxydopamine (6-OHDA) and in dopamine-intact controls. We performed systematic analyses of the networks using a novel tool for estimation of directed interactions (Non-Parametric Directionality, NPD). Additionally, we used a ‘conditioned’ version of the NPD analysis which reveals the dependence of the correlation between two signals upon a third reference signal. We find evidence of the dopamine dependency of both low beta (14-20 Hz) and high beta/low gamma (20-40 Hz) directed interactions within the network. Notably, 6-OHDA lesions were associated with enhancement of the cortical “hyper-direct” connection to the subthalamic nucleus (STN) and its feedback to the cortex and striatum. We find that pathological beta synchronization resulting from 6-OHDA lesioning is widely distributed across the network and cannot be located to any individual structure. Further, we provide evidence that high beta/gamma oscillations propagate through the striatum in a pathway that is independent of STN. Rhythms at high beta/gamma show susceptibility to conditioning that indicates a hierarchical organization when compared to low beta. These results further inform our understanding of the substrates for pathological rhythms in salient brain networks in Parkinsonism