The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB’s ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures were assessed using timeseries (amplitude and spectra), network matrix and spatial map analyses. For timeseries and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.

SUMMARY The adaptive effects of physical training on cardiovascular control mechanisms were studied in 11 subjects with mild hypertension. In these subjects we assessed the gain of the heart periodsystolic arterial pressure relationship in the unfit and the fit state by using 1) an open loop approach, whereby the gain is expressed by the slope of the regression of heart period as a function of systolic arterial pressure, during a phenylephrine-induced pressure rise and 2) a closed loop approach with proper simplification, whereby the gain is expressed by the index a, obtained through simultaneous spectral analysis of the spontaneous variabilities of heart period and systolic arterial pressure. Both methods indicated that training significantly increased the gain of the relationship between heart period and systolk arterial pressure at rest and reduced arterial pressure and increased heart period significantly. This gam was drastically reduced during bicycle exercise both in the unfit and fit state. In a second group of normotensive (u = 7; systolic pressure, 133 ± 3 mm Hg) and hypertensive (n -1; systolic pressure, 180 ± 10 mm Hg) subjects undergoing 24 -hour diagnostic continuous ekctrocardiographic and high fidelity arterial pressure monitoring, the index a was significantly reduced in the hypertensive group at rest. Furthermore, when analyzed continuously over the entire 24-hour period, this index underwent minute-to-minute changes with lower values during the day and higher values during the night. We propose the index a as a quantitative indicator of the changes in the gain of baroreceptor mechanisms occurring with physical training in mild hypertension and during a 24-hour period in ambulatory subjects. This raises the possibility of recommending physical training as part of the nonpharmacological treatment of hypertension.1 -2 The mechanisms underlying the observed cardiovascular changes are not yet understood but are likely to be quite complex; they may include metabolic, cardiovascular, and neural factors in addition to changes in skeletal muscle fiber type. Our study explored the hypothesis that training induces new operating conditions in the neural regulatory mechanisms. We compared

In this study, we tested the hypothesis that the neural control of circulation in humans undergoes continuous but in part predictable changes throughout the day and night. Dynamic 24-hour recordings were obtained in two groups of ambulant subjects. In 18 hospitalized patients free to move, direct high-fidelity arterial pressures and electrocardiograms were recorded, and in an additional 28 nonhospitalized subjects, only electrocardiograms were obtained. Spectral analysis of systolic arterial pressure and of RR interval variabilities provided quantitative markers of sympathetic and vagal control of the sinus node and of sympathetic modulation of vasomotor tone. With this approach, the low-frequency (-0.1 Hz) component of RR interval and systolic arterial pressure variabilities is considered a marker primarily of sympathetic activity, whereas the high-frequency (-0.25 Hz) component of RR interval variability, related to respiration, seems to be a marker primarily of vagal activity. We observed a pronounced and consistent reduction in the markers of sympathetic activity and an increase in those of vagal activity during the night. In the invasive studies, while the subjects were still lying in bed after waking up, the markers of sympathetic activity rose rapidly and concomitantly with a simultaneous vagal withdrawal. Noninvasive studies confirmed the early morning rise of the markers of sympathetic activity and the circadian pattern of sympathovagal balance. These data indicate that the ominously increased rate of cardiovascular events in the morning hours may reflect the sudden rise of sympathetic activity and the reduction of vagal tone. (Circulation 1990;81:537-547

A new method for measuring the regularity of a process over short data sequences is reported. This method is based on the definition of a new function (the corrected conditional entropy) and on the extraction of its minimum. This value is taken as an index in the information domain quantifying the regularity of the process. The corrected conditional entropy is designed to decrease in relation to the regularity of the process (like other estimates of the entropy rate), but it is able to increase when no robust statistic can be performed as a result of a limited amount of available samples. As a consequence of the minimisation procedure, the proposed index is obtained without an a-priori definition of the pattern length (i.e. of the embedding dimension of the reconstructed phase space). The method is validated on simulations and applied to beat-to-beat sequences of the sympathetic discharge obtained from decerebrate artificially ventilated cats. At control, regular, both quasiperiodic and periodic (locked to ventilation) dynamics are observed. During the sympathetic activation induced by inferior vena cava occlusion, the presence of phase-locked patterns and the increase in regularity of the sympathetic discharge evidence an augmented coupling between the sympathetic discharge and ventilation. The reduction of complexity of the neural control obtained by spinalization decreases the regularity in the sympathetic outflow, thus pointing to a weaker coupling between the sympathetic discharge and ventilation.

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