Physiological recordings: basic concepts and implementation during functional magnetic resonance imaging

Abstract

Combining human functional neuroimaging with other forms of psychophysiological measurement, including autonomic monitoring, provides an empirical basis for understanding brain-body interactions. This approach can be applied to characterize unwanted physiological noise, examine the neural control and representation of bodily processes relevant to health and morbidity, and index covert expression of affective and cognitive processes to enhance the interpretation of task-evoked regional brain activity. In recent years, human neuroimaging has been dominated by functional magnetic resonance imaging (fMRI) studies. The spatiotemporal information of fMRI regarding central neural activity is valuably complemented by parallel physiological monitoring, yet such studies still remain in the minority. This review article highlights fMRI studies that employed cardiac, vascular, respiratory, electrodermal, gastrointestinal and pupillary psychophysiological indices to address specific questions regarding interaction between brain and bodily state in the context of experience, cognition, emotion and behaviour. Physiological monitoring within the fMRI environment presents specific technical issues, most importantly related to safety. Mechanical and electrical hazards may present dangers to scanned subjects, operator and/or equipment. Furthermore, physiological monitoring may interfere with the quality of neuroimaging data, or itself be compromised by artefacts induced by the operation of the scanner. We review the sources of these potential problems and the current approaches and advice to enable the combination of fMRI and physiological monitoring in a safe and effective manner.

Fig. 1

(A) Brain activity associated with sympathetic cardiac control: significant changes correlating with increasing power derived from frequency analysis of interbeat interval (top row orthogonalized low-frequency, sympathetic activity, middle row orthogonalized high-frequency, parasympathetic activity). Segregation of activity relating to effortful cognitive (green) and motor (blue) task performance and activity related to increasing low-frequency power (red) is demonstrated in the bottom row. Reproduced from Critchley et al. (2003) by permission of the copyright holder, Guarantors of Brain. (B) Brain activity associated with mean arterial pressure changes during a stressor task: greater changes in arterial pressure are associated with amygdala activation (left column), and a significant correlation between pressure reactivity and BOLD response is confirmed by a region-of-interest analysis (middle and right column). Arterial pressure changes were also linked to the functional connectivity expressed between the amygdala and pre-autonomic pons (right column). Reproduced from Gianaros et al. (2008) by permission of the copyright holder, Society for Neuroscience.(C). HF-HRV indexed with a point process adaptive-filter algorithm co-varies with functional activity within the amygdala, hypothalamus and periaqueductal gray. (D top) Brain activity associated with heart rate variability during a synchronous/delayed shock stimulation task: power spectra density analysis reveals very low-, low- and high-frequency heart rate variability components. (D bottom) Across subjects, greater high-frequency heart rate variability after delayed shocks correlates with increased BOLD response in the right amygdala and in periaqueductal gray. Panel (D) reproduced from Gray et al. (2009) by permission of the copyright holder, Society for Neuroscience.

Fig. 2

(A top) Infrared camera setup to measure skin temperature in the magnet, and (A bottom) example of a resulting color-map. Reproduced from Boss et al. (2007) by permission of the copyright holder, Wiley-Liss Inc. (B) Thermoregulatory profile of a typical endotherm: below a lower critical threshold (LCT), metabolic heat production gradually increases, whereas above an upper critical threshold (UCT), a steady increase in evaporative heat loss is observed. Reproduced from Adair and Black (2003) by permission of the copyright holder, Wiley-Liss Inc. (C) An FMRI study of skin cooling and rewarming: while correlation with skin temperature was absent for the medulla (left), a significant correlation was found for the raphe nuclei (right) (C top) The time-course of the average BOLD signal in the raphe nuclei (middle) closely follows that of skin temperature (bottom) Panel (C) reproduced from McAllen et al. (2006) by permission of the copyright holder, The National Academy of Sciences of the USA.

Fig. 3

(A) Magnetically-induced force attracting a metallic probe towards the magnet. According to standard safety criteria the deflection angle needs to be less than 45°. Reproduced from Schaefers (2008) by permission of the copyright holder, IEEE. (B) Second-to-third degree burns on the thorax where ECG electrodes were applied, following an incident involving high-voltage induction in long leads. Reproduced from Kugel et al. (2003) by permission of the copyright holder, Springer-Verlag. (C) Reducing lead length, running the conductive wires in parallel near the centre of the magnet and using appropriate padding minimizes risks and improves signal quality. Reproduced from Herrmann and Debener (2008) by permission of the copyright holder, Elsevier.

Fig. 4

Examples of EMG, ECG and EEG signals before and after artefact removal with the IAR method. The signals were recorded during echo-planar imaging on a 1.5 T whole-body MR scanner. The EMG signal was recorded from electrodes placed bilaterally 2–3 cm apart over the index flexor muscles during brisk extension of the index. The ECG signal was recorded according to the DI lead set from electrodes placed 3–4 cm apart on the chest. The EEG signal was recorded from the O1 electrode, referenced to Fz; ballistocardiogram removal was performed. Please note the different voltage scales for the raw and filtered signal plots. Prior to filtering, the physiological signals are inaccessible; after filtering, the index flexor muscle activity (EMG), R-wave (ECG) and alpha rhythm (EEG) are clearly recognizable. Courtesy of Elisa Visani, Istituto Neurologico “C. Besta”, Milan, Italy.