Discerning nonlinear brain dynamics from EEG:an application to autistic spectrum disorder in young children

A challenging goal in neuroscience is that of identifying specific brain patterns characterising autistic spectrum disorder (ASD). Genetic studies, together with investigations based on magnetic resonance imaging (MRI) and functional MRI, support the idea that distinctive structural features could exist in the ASD brain. In the developing brains of babies and small children, structural differences could provide the basis for different brain connectivity, giving rise to macroscopic effects detectable by e.g. electroencephalography (EEG). A significant body of research has already been conducted in this direction, mainly computing spectral power and coherence. Perhaps due to methodological limitations, together with high variability within and between the cohorts investigated, results have not been in complete agreement, and it is therefore still the case that the diagnosis of ASD is based on behavioural tests and interviews. This thesis describes a step-by-step characterisation and comparison of brain dynamics from ASD and neurotypical subjects, based on the analysis of multi-probe EEG time-series from male children aged 3-5 years. The methods applied are all ones that take explicit account of the intrinsically non-linear, open, and time-variable nature of the system. Time-frequency representations were first computed from the time-series to evaluate the spectral power and to categorise the ranges encompassing different activities as low-frequency (LF, 0.8-3.5 Hz), mid-range-frequency (MF, 3.5-12 Hz) or high-frequency (HF, 12-48 Hz). The spatial pathways for the propagation of neuronal activity were then investigated by calculation of wavelet phase coherence. Finally, deeper insight into brain connectivity was achieved by computation of the dynamical cross-frequency coupling between triplets of spatially distributed phases. In doing so, dynamical Bayesian inference was used to find the coupling parameters between the oscillators in the spatially-distributed network. The sets of parameters extracted by this means allowed evaluation of the strength of particular coupling components of the triplet LF, MF→HF, and enabled reconstruction of the coupling functions. By investigation of the form of the coupling functions, the thesis goes beyond conventional measures like the directionality and strength of an interaction, and reveals subtler features of the underlying mechanism. The measured power distributions highlight differences between ASD and typically developing children in the preferential frequency range for local synchronisation of neuronal activity: the relative power is generally higher at LF and HF, and lower at MF, in the ASD case. The phase coherence maps from ASD subjects also exhibited differences, with lower connectivity at LF and MF in the frontal and fronto-occipital pairs, and higher coherence at high frequencies for central links. There was higher inter-subject variability in a comparison of the forms of coupling functions in the ASD group; and a weaker coupling in their theta-gamma range, which can be linked with the cognitive features of the disorder. In conclusion, the approach developed in this thesis gave promising preliminary results, suggesting that a biomarker for ASD could be defined in terms of the described patterns of functional and effective connectivity computed from EEG measurements

Coupling functions in networks of oscillators

Networks of interacting oscillators abound in nature, and one of the prevailing challenges in science is how to characterize and reconstruct them from measured data. We present a method of reconstruction based on dynamical Bayesian inference that is capable of detecting the effective phase connectivity within networks of time-evolving coupled phase oscillators subject to noise. It not only reconstructs pairwise, but also encompasses couplings of higher degree, including triplets and quadruplets of interacting oscillators. Thus inference of a multivariate network enables one to reconstruct the coupling functions that specify possible causal interactions, together with the functional mechanisms that underlie them. The characteristic features of the method are illustrated by the analysis of a numerically generated example: a network of noisy phase oscillators with time-dependent coupling parameters. To demonstrate its potential, the method is also applied to neuronal coupling functions from single- and multi-channel electroencephalograph recordings. The crossfrequency δ, α to α coupling function, and the θ, α, γ to γ triplet are computed, and their coupling strengths, forms of coupling function, and predominant coupling components, are analysed. The results demonstrate the applicability of the method to multivariate networks of oscillators, quite generally

TSDF: A simple yet comprehensive, unified data storage and exchange format standard for digital biosensor data in health applications

Digital sensors are increasingly being used to monitor the change over time of physiological processes in biological health and disease, often using wearable devices. This generates very large amounts of digital sensor data, for which, a consensus on a common storage, exchange and archival data format standard, has yet to be reached. To address this gap, we propose Time Series Data Format (TSDF): a unified, standardized format for storing all types of physiological sensor data, across diverse disease areas. We pose a series of format design criteria and review in detail current storage and exchange formats. When judged against these criteria, we find these current formats lacking, and propose a very simple, intuitive standard for both numerical sensor data and metadata, based on raw binary data and JSON-format text files, for sensor measurements/timestamps and metadata, respectively. By focusing on the common characteristics of diverse biosensor data, we define a set of necessary and sufficient metadata fields for storing, processing, exchanging, archiving and reliably interpreting, multi-channel biological time series data. Our aim is for this standardized format to increase the interpretability and exchangeability of data, thereby contributing to scientific reproducibility in studies where digital biosensor data forms a key evidence base

Ageing of the couplings between cardiac, respiratory and myogenic activity in humans

The balance and functionality of the cardiovascular system are maintained by a network of couplings between the different oscillations involved. We study the effect of ageing on these interactions through the application of wavelet analysis, and by the use of dynamical Bayesian inference to compute coupling functions. The method, applied to phases extracted from microvascular flow recorded by laser Doppler flowmetry (LDF), reveals the coupling functions between oscillations propagated to the smallest vessels. Consistent with earlier work based on analysis of cardiac and respiratory phases obtained from direct measurements, our analysis demonstrates an impairment of the propagated cardio-respiratory coupling with ageing. The coupling weakens despite the increased cardiac component in the LDF with ageing. Our results bring new insight to the effect of ageing on cardiovascular regulation that might help improve the diagnostic potential of LDF monitors

Neural Cross-Frequency Coupling Functions

Although neural interactions are usually characterized only by their coupling strength and directionality, there is often a need to go beyond this by establishing the functional mechanisms of the interaction. We introduce the use of dynamical Bayesian inference for estimation of the coupling functions of neural oscillations in the presence of noise. By grouping the partial functional contributions, the coupling is decomposed into its functional components and its most important characteristics-strength and form-are quantified. The method is applied to characterize the δ-to-α phase-to-phase neural coupling functions from electroencephalographic (EEG) data of the human resting state, and the differences that arise when the eyes are either open (EO) or closed (EC) are evaluated. The δ-to-α phase-to-phase coupling functions were reconstructed, quantified, compared, and followed as they evolved in time. Using phase-shuffled surrogates to test for significance, we show how the strength of the direct coupling, and the similarity and variability of the coupling functions, characterize the EO and EC states for different regions of the brain. We confirm an earlier observation that the direct coupling is stronger during EC, and we show for the first time that the coupling function is significantly less variable. Given the current understanding of the effects of e.g., aging and dementia on δ-waves, as well as the effect of cognitive and emotional tasks on α-waves, one may expect that new insights into the neural mechanisms underlying certain diseases will be obtained from studies of coupling functions. In principle, any pair of coupled oscillations could be studied in the same way as those shown here

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Analisi dell'emodinamica del microcircolo cutaneo tramite applicazione della trasformata wavelet

L’analisi dell’emodinamica del microcircolo cutaneo può essere attuata mediante acquisizione del segnale di flussimetria laser Doppler (LDF). Tale segnale è caratterizzato da oscillazioni periodiche chiamate flowmotion la cui analisi spettrale permette di identificare cinque intervalli di frequenze assegnati ad altrettante attività fisiologiche: endoteliale, neurogena, miogena, respiratoria e cardiaca. La presente tesi ha per obiettivo l’applicazione della trasformata wavelet al segnale LDF per identificare e quantificare il contributo delle diverse attività al variare della condizione microcircolatoria cutanea. Si sono pertanto considerati 3 soggetti sani come riferimento e 20 pazienti affetti da due diversi stadi di arteriopatia periferica (PAD): nei pazienti con PAD non critica, si sono confrontate le condizioni prima e dopo l’induzione di ischemia, mentre nei pazienti più gravi si sono confrontati i segnali acquisiti prima e dopo l’intervento di rivascolarizzazion

Preliminary study of laser doppler perfusion signal by wavelet transform in patients with critical limb ischemia before and after revascularization

The haemodynamics of skin microcirculation can be quantitatively evaluated by Laser Doppler Fluxmetry (LDF). LDF signal in human skin shows periodic oscillations. Spectral analysis by wavelet transform displays six characteristic frequency intervals (FI) from 0.005 to 2 Hz, related to distinct vascular structures activities: heart (0.6-2 Hz), sympathetic respiratory (0.145-0.6 Hz), myogenic (0.052-0.145 Hz), local sympathetic nerve (0.021-0.052 Hz) and endothelial cells NO dependent (0.0095-0.021 Hz) and NO independent (0.005-0.0095 Hz). The most advanced stage of peripheral arterial obstructive disease is the critical limb ischemia (CLI), which causes the reduction of blood perfusion threatening limb viability. Besides macrocirculatory alterations, many studies have shown microvascular misdistribution of skin blood flow as the main factor that leads patients to CLI. Revascularization can save limb and patient's life, too. In the present study, LDF signals have been recorded on the skin of the foot dorsum in 15 patients suffering from CLI. LDF signals have been analyzed before and after limb revascularization by means of the wavelet analysis. Significant changes in frequency distribution before and after limb revascularization have been detected: the median normalized values of spectral power increases for 49.8% (p = 0.0341) in the frequency range 0.050328-0.053707 Hz, whereas spectral power decreases for 77.1% (p = 0.0179) in the frequency range 0.018988-0.029284 Hz. We can conclude that changes in the frequency intervals occur after revascularization, shifting from a prevailing endothelial activity toward a prevailing sympathetic activity

Changes of the cutaneous flowmotion pattern after limb revascularization in patient with critical ischemia

The skin flowmotion of 13 patients suffering from critical limb ischemia (CLI) was studied with wavelet analysis (WA) of the laser Doppler signals (LDS). The WA selects six different frequency components (FCs), each relating to a specific cardiovascular system structures activities; FC I 1-2 Hz heart, FC II 0.2 Hz respiratory, FC III 0.1 Hz myogenic, FC IV 0.04 Hz, sympathetic, FC V 0.01 Hz, and FC VI 0.007 Hz endothelial. The aim of the study was to observe which FC changed after the limb revascularization. The LDS was measured at the dorsum of the foot, one week before and no later than 30 days after revascularisation. The absolute and relative amplitude and energy of the flowmotion WA FCs, the ankle brachial pressure index (ABI) and the transcutaneous pressure of oxygen (TcpO2) were assessed before and after revascularization. The results showed that after successful revascularization ABI and TcpO2 increased from 0.34 ± 0.10 to 0.54 ± 0.09 (p 0.0003) and from 20.3 ± 13.4 to 43.8 ± 18.7 mmHg (p 0.0002) whereas only the absolute amplitude and energy of the cardiac FC I increased from 0.57 ± 0.44 to 1.07 ± 0.69 (P 0.002) AU and 1.14 ± 1.78 AU2 to 3.54 ± 3.78 AU2 (p 0.004). In conclusion after limb revascularization the cardiac component of the flowmotion increased maybe because the cardiac stroke volume had more influence over the skin arterioles