John R. Terry

Origin of ultradian pulsatility in the hypothalamic–pituitary–adrenal axis

The hypothalamic–pituitary–adrenal (HPA) axis is a neuroendocrine system that regulates the circulating levels of vital glucocorticoid hormones. The activity of the HPA axis is characterized not only by a classic circadian rhythm, but also by an ultradian pattern of discrete pulsatile release of glucocorticoids. A number of psychiatric and metabolic diseases are associated with changes in glucocorticoid pulsatility, and it is now clear that glucocorticoid responsive genes respond to these rapid fluctuations in a biologically meaningful way. Theoretical modelling has enabled us to identify and explore potential mechanisms underlying the ultradian activity in this axis, which to date have not been identified successfully. We demonstrate that the combination of delay with feed-forward and feedback loops in the pituitary–adrenal system is sufficient to give rise to ultradian pulsatility in the absence of an ultradian source from a supra-pituitary site. Moreover, our model enables us to predict the different patterns of glucocorticoid release mediated by changes in hypophysial-portal corticotrophin-releasing hormone levels, with results that parallel our experimental in vivo data.

Conditions for the generation of beta oscillations in the subthalamic nucleus-globus pallidus network

The advance of Parkinsons disease is associated with the existence of abnormal oscillations within the basal ganglia with frequencies in the beta band (13–30 Hz). While the origin of these oscillations remains unknown, there is some evidence suggesting that oscillations observed in the basal ganglia arise due to interactions of two nuclei: the subthalamic nucleus (STN) and the globus pallidus pars externa (GPe). To investigate this hypothesis, we develop a computational model of the STN–GPe network based upon anatomical and electrophysiological studies. Significantly, our study shows that for certain parameter regimes, the model intrinsically oscillates in the beta range. Through an analytical study of the model, we identify a simple set of necessary conditions on model parameters that guarantees the existence of beta oscillations. These conditions for generation of oscillations are described by a set of simple inequalities and can be summarized as follows: (1) The excitatory connections from STN to GPe and the inhibitory connections from GPe to STN need to be sufficiently strong. (2) The time required by neurons to react to their inputs needs to be short relative to synaptic transmission delays. (3) The excitatory input from the cortex to STN needs to be high relative to the inhibition from striatum to GPe. We confirmed the validity of these conditions via numerical simulation. These conditions describe changes in parameters that are consistent with those expected as a result of the development of Parkinsons disease, and predict manipulations that could inhibit the pathological oscillations.

The origin of glucocorticoid hormone oscillations.

Characterization of a peripheral hormonal system identifies the origin and mechanisms of regulation of glucocorticoid hormone oscillations in rats.

Detection and description of non-linear interdependence in normal multichannel human EEG data.

OBJECTIVES This study examines human scalp electroencephalographic (EEG) data for evidence of non-linear interdependence between posterior channels. The spectral and phase properties of those epochs of EEG exhibiting non-linear interdependence are studied. METHODS Scalp EEG data was collected from 40 healthy subjects. A technique for the detection of non-linear interdependence was applied to 2.048 s segments of posterior bipolar electrode data. Amplitude-adjusted phase-randomized surrogate data was used to statistically determine which EEG epochs exhibited non-linear interdependence. RESULTS Statistically significant evidence of non-linear interactions were evident in 2.9% (eyes open) to 4.8% (eyes closed) of the epochs. In the eyes-open recordings, these epochs exhibited a peak in the spectral and cross-spectral density functions at about 10 Hz. Two types of EEG epochs are evident in the eyes-closed recordings; one type exhibits a peak in the spectral density and cross-spectrum at 8 Hz. The other type has increased spectral and cross-spectral power across faster frequencies. Epochs identified as exhibiting non-linear interdependence display a tendency towards phase interdependencies across and between a broad range of frequencies. CONCLUSIONS Non-linear interdependence is detectable in a small number of multichannel EEG epochs, and makes a contribution to the alpha rhythm. Non-linear interdependence produces spatially distributed activity that exhibits phase synchronization between oscillations present at different frequencies. The possible physiological significance of these findings are discussed with reference to the dynamical properties of neural systems and the role of synchronous activity in the neocortex.

HPA Axis-Rhythms

The hypothalamic-pituitary-adrenal (HPA) axis regulates circulating levels of glucocorticoid hormones, and is the major neuroendocrine system in mammals that provides a rapid response and defense against stress. Under basal (i.e., unstressed) conditions, glucocorticoids are released with a pronounced circadian rhythm, characterized by peak levels of glucocorticoids during the active phase, that is daytime in humans and nighttime in nocturnal animals such as mice and rats. When studied in more detail, it becomes clear that the circadian rhythm of the HPA axis is characterized by a pulsatile release of glucocorticoids from the adrenal gland that results in rapid ultradian oscillations of hormone levels both in the blood and within target tissues, including the brain. In this review, we discuss the regulation of these circadian and ultradian HPA rhythms, how these rhythms change in health and disease, and how they affect the physiology and behavior of the organism.

Onset of polyspike complexes in a mean-field model of human electroencephalography and its application to absence epilepsy

In this paper, we introduce a modification of a mean-field model used to describe the brains electrical activity as recorded via electroencephalography (EEG). The focus of the present study is to understand the mechanisms giving rise to the dynamics observed during absence epilepsy, one of the classical generalized syndromes. A systematic study of the data from a number of different subjects with absence epilepsy demonstrates a wide variety of dynamical phenomena in the recorded EEG. In addition to the classical spike and wave activity, there may be polyspike and wave, wave spike or even no discernible spike–wave onset during seizure events. The model we introduce is able to capture all of these different phenomena and we describe the bifurcations giving rise to these different types of seizure activity. We argue that such a model may provide a useful clinical tool for classifying different subclasses of absence epilepsy.

Chaotic communication using generalized synchronization

Abstract We present a new technique for the chaotic communication of a signal using the concept of generalized synchronization . We develop a general approach for implementing our technique and illustrate it using a Rossler system driving a Lorenz system. It is demonstrated that the scheme is robust with respect to noise in the communication channel and to small parameter mismatches in the system. Finally, we discuss the advantages of this technique over existing methods and examine ways of improving the scheme.

Seizure generation: The role of nodes and networks

The longstanding dichotomy between the concepts of “focal” and “primary generalized” epilepsy has become increasingly blurred, raising fundamental questions about the nature of ictal onset in localized brain regions versus large‐scale brain networks. We hypothesize that whether an EEG discharge appears focal or generalized is driven by the pattern of connections in brain networks, irrespective of the presence of focal brain abnormality. Using a computational model of a simple “brain” consisting of four regions and the connections between them, we explored the effects of altering connectivity structure versus the effects of introducing an “abnormal” brain region, and the interactions between these factors. Computer simulations demonstrated that electroencephalography (EEG) discharges representing either generalized or focal seizures arose purely as a consequence of subtle changes in network structure, without the requirement for any localized pathologic brain region. Furthermore we found that introducing a pathologic region gave rise to focal, secondary generalized, or primary generalized seizures depending on the network structure. Counterintuitively, we found that decreasing connectivity between regions of the brain increased the frequency of seizure‐like activity. Our findings may enlighten current controversies surrounding the concepts of focal and generalized epilepsy, and help to explain recent observations in genetic animal models and human epilepsies, where loss of white matter pathways was associated with the occurrence of seizures.

Topographic organization of nonlinear interdependence in multichannel human EEG

This paper investigates the spatial organization of nonlinear interactions between different brain regions in healthy human subjects. This is achieved by studying the topography of nonlinear interdependence in multichannel EEG data, acquired from 40 healthy human subjects at rest. An algorithm for the detection and quantification of nonlinear interdependence is applied to four pairs of bipolar electrode derivations to detect posterior and anterior interhemispheric and left and right intrahemispheric interdependences. Multivariate surrogate data sets are constructed to control for linear coherence and finite sample size. Nonlinear interdependence is shown to occur in a small but statistically robust number of epochs. The occurrence of nonlinear interdependence in any region is correlated with the concurrent presence of nonlinear interdependence in other regions at high levels of significance. The strength, direction and topography of the interdependences are also correlated. For example, posterior interhemispheric interdependence from right-to-left is strongly correlated with right intrahemispheric interdependence from back-to-front. There is a subtle change in these correlations when subjects open their eyes. These results suggest that nonlinear interdependence in the human brain has a specific topographic organization which reflects simple cognitive changes. It sometimes occurs as an isolated phenomenon between two brain regions, but often involves concurrent interdependences between multiple brain regions.

Encoding and Decoding Mechanisms of Pulsatile Hormone Secretion

Ultradian pulsatile hormone secretion underlies the activity of most neuroendocrine systems, including the hypothalamic‐pituitary adrenal (HPA) and gonadal (HPG) axes, and this pulsatile mode of signalling permits the encoding of information through both amplitude and frequency modulation. In the HPA axis, glucocorticoid pulse amplitude increases in anticipation of waking, and, in the HPG axis, changing gonadotrophin‐releasing hormone pulse frequency is the primary means by which the body alters its reproductive status during development (i.e. puberty). The prevalence of hormone pulsatility raises two crucial questions: how are ultradian pulses encoded (or generated) by these systems, and how are these pulses decoded (or interpreted) at their target sites? We have looked at mechanisms within the HPA axis responsible for encoding the pulsatile mode of glucocorticoid signalling that we observe in vivo. We review evidence regarding the ‘hypothalamic pulse generator’ hypothesis, and describe an alternative model for pulse generation, which involves steroid feedback‐dependent endogenous rhythmic activity throughout the HPA axis. We consider the decoding of hormone pulsatility by taking the HPG axis as a model system and focussing on molecular mechanisms of frequency decoding by pituitary gonadotrophs.