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Background‘Non-parametric directionality’ (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015)MethodsThis work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive model to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) and parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording.ResultsWe demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD metric is compared with non-parametric Granger causality (NPG), a well-established methodology for model free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate degrees of instantaneous signal mixing.ConclusionsThe results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging.HighlightsNon-parametric directionality (NPD) is a novel directed connectivity metric.NPD estimates are equivalent to Granger causality but more robust to signal confounds.Multivariate extensions of NPD can correctly identify signal routing.AbbreviationsdFCDirected functional connectivityEEGElectroencephalogramLFPLocal field potentialMEGMagnetoencephalogramMVARMultivariate autoregressive (model)NPDNon-parametric directionalityNPGNon-parametric Granger (causality)SMASupplementary motor areaSNRSignal-to-noise ratioSTNSubthalamic Nucleus

Associative learning is encoded under anesthesia and involves the medial prefrontal cortex (mPFC). Neuronal activity in mPFC increases in response to a conditioned stimulus (CS+) previously paired with an unconditioned stimulus (US) but not during presentation of an unpaired stimulus (CS−) in anesthetized animals. Studies in conscious animals have shown dissociable roles for different mPFC subregions in mediating various memory processes, with the prelimbic (PL) and infralimbic (IL) cortex involved in the retrieval and extinction of conditioned responding, respectively. Therefore PL and IL may also play different roles in mediating the retrieval and extinction of discrimination learning under anesthesia. Here we used in vivo electrophysiology to examine unit and local field potential (LFP) activity in PL and IL before and after auditory discrimination learning and during later retrieval and extinction testing in anesthetized rats. Animals received repeated presentations of two distinct sounds, one of which was paired with footshock (US). In separate control experiments animals received footshocks without sounds. After discrimination learning the paired (CS+) and unpaired (CS−) sounds were repeatedly presented alone. We found increased unit firing and LFP power in PL and, to a lesser extent, IL after discrimination learning but not after footshocks alone. After discrimination learning, unit firing and LFP power increased in PL and IL in response to presentation of the first CS+, compared to the first CS−. However, PL and IL activity increased during the last CS− presentation, such that activity during presentation of the last CS+ and CS− did not differ. These results confirm previous findings and extend them by showing that increased PL and IL activity result from encoding of the CS+/US association rather than US presentation. They also suggest that extinction may occur under anesthesia and might be represented at the neural level in PL and IL.

Publisher Summary The work of Stiles and Randall and that of Elble and Randall established that the spectrum of a tremor signal consists of two components: an inertial load dependent component and a neurogenic component. The former is a property of the structure exhibiting the tremor and shifts toward lower frequencies with increased inertial loading. The neurogenic component is load independent and was first attributed to central descending processes in the frequency range 8–12 Hz. During a maintained postural task, an additional 15–30-Hz neurogenic component of tremor was identified and associated with motor unit synchronization. Farmer et al . inferred, on the basis of indirect evidence, that the common drive to the synchronized motor units was of descending origin. Conway et al. , using magnetoencephalographic (MEG) techniques, and Halliday et al ., with conventional electroencephalographic (EEG) techniques, demonstrated a correlation between localized cortical activity in the 15–30-Hz frequency band and the neurogenic component of tremor associated with motor unit synchronization. These results imply that the localized rhythmic cortical activity in the 15–30-Hz frequency band that occurs during maintained postural tasks accounts for the 15–30-Hz component of neurogenic tremor, which in turn is a consequence of motor unit synchronization. The analysis of the correlation between the 15–30-Hz rhythmic cortical oscillations and the neurogenic component of tremor is equivalent to an investigation of the descending drive that synchronizes motor units during maintained postural tasks. The application of spectral and coherence analyses therefore provides a powerful procedure for characterizing the central drive associated with rhythmic cortical activity and motor unit synchrony in normal and pathological tremors. The application of these analytical methods reveals significant differences in the correlation between cortical drive and tremor in normal subjects with that observed in the cases of essential tremor and Parkinsonian tremor.

There is both direct and indirect evidence that stretch activation occurs in the dynamic bag1 fibres of the mammalian muscle spindle and that it is responsible for maintaining the high sensitivity of primary sensory endings in stretches great enough to break the resting actomyosin bonds responsible for the short-range stiffness of muscle fibres. However the direct observations of dynamic bag1 fibre behaviour during stretching were made on damaged fibres and during very slow stretches. Preliminary results of experiments employing faster stretches of intact muscle spindles are reported here. An image processing system is being developed to automate and facilitate analysis of sarcomere movements during stretch, release and activation of intrafusal fibres. Unequivocal evidence confirming the development of stretch activation has not yet been found. Boyd (1986a) believed that static bag2 and chain fibres are controlled by separate populations of static gamma motoneurones, while accepting that there is some degree of common innervation. His evidence and the functional implications are discussed.

The object of this work is to consider the application of some methods of spike train analysis that are not widely known, and are concerned with the description of the interactions between spike trains and the determination of causal connections between them. The notation and terminology follow conventions established in the statistical literature. The examples given are based on in-continuity recordings of the spontaneous activity of single Ia afferents from the soleus muscle and single motor units from the same muscle. Cumulant densities are shown to be simple extensions of the traditional cross-correlation methods, and are useful in characterizing the pattern of activity in one spike train that influences that in another, and to reveal interactions between spike trains that would not be apparent from the correlation histogram alone. Parameters based on the Fourier transforms of the spike trains are shown to be useful in determining timing relations between them, and in inferring patterns of connectivity not possible by correlation methods alone.