Identifying heart-brain interactions during internally and externally operative attention using conditional entropy

Highlights

• The conditional entropy technique enabled simultaneous analysis of heart-brain rhythms.

• Directional coupling information C_{(heart→brain)} and C_{(brain→heart)} differentiated internally and externally operative attention.

• Attention states were examined using simultaneous recording of EEG and ECG signals.

• A modified Posner’s spatial orienting task used to assess A_I and A_E attention.

• Internal and external attention states investigated using identical stimulus design.

Abstract

Heart and brain interactions mediate human cognition. This investigation identifies heart-brain interactions during internally operative attention (A_I) and externally operative attention (A_E). A_I attention involves short term memory, whereas A_E attention deals with automatic and transient response to objects in the external world. A modified Posner’s spatial orienting task used to differentiate A_I and A_E attention. Heart and brain rhythms recorded in fourteen healthy participants. Functional coupling from heart-to-brain $(C_{heart\to brain})$ and brain-to-heart ${(C}_{brain\to heart})$ time series derived using an information domain approach based on conditional entropy. The experimental results showed that low-frequency power of heart rate variability (HRV-LF) and sympathovagal balance (LF/HF ratio) during A_E significantly increased compared with that for A_I. Furthermore, the information flow from heart-to-brain increased and decreased form brain-to-heart during A_E as compared to A_I. Also, opposite trend in relationship noted between coupling index $(C_{i\to j})$ and HRV-LF during A_I and A_E attention. The conditional entropy technique enabled simultaneous analysis of heart-brain rhythms to identify heart-brain interactions during A_I and A_E attention.

Introduction

Cognitive neuropsychological research has increasingly reported an association between mental health, heart failure and attention deficits [[1], [2], [3]]. Simultaneous analysis of heart and brain rhythms can provide insight into underlying relationships between associated complex physiological systems, i.e., heart and brain. Heart-brain interactions mediate human cognition [[4], [5], [6], [7]]. Human cognition critically employs attention in the process of perception, information processing, and decision-making abilities [8]. Technological advances in non-linear time series analysis may provide comprehensive information about heart-brain interactions [9]. This information might help in designing robust brain-computer interface [10]; accelerating training paradigms with biofeedback for sports and military personnel [11]. Cognitive processing involves spatial attention to an object over other objects in space. Attention may operate internally or externally. Internally operative attention (A_I) deals with short-term memory. Whereas, externally operative attention (A_E) oriented by the appearance of stimuli in the external world. Previous studies differentiate A_I and A_E attention either with the behavioural evidence [[12], [13], [14]] based on reaction time, accuracy and detection rate or with the combining EEG spectral analysis, event-related-potentials [15,16]. However, the results of these studies were inconsistent [17]. These investigations observed only brain cortex or sub-cortex, with imaging or scalp recordings. Although, these investigations does not investigate heart-brain interactions with simultaneous recordings of EEG and ECG signal during the attention tasks to differntiate A_I and A_E attention. In some cases, EEG and ECG recordings were used to take decisions during surgical rehabilitation, but EEG and ECG assessments only used in isolations. Shreds of evidence reported the correlation between EEG frequency band powers to ECG during meditation [4,6] and mental arithmetic task [18].

The standard electrophysiological measures, such as cross-correlation, coherence and spectral analysis have limitations to incorporate direct and indirect influences while estimating the non-linear behavior of time series [19]. The conditional entropy technique is considered as a preferred method to evaluate directional coupling between physiological systems. Conditional entropy describes the directional information of time series by including past values affecting the present values in the time series while taking account of other associated processes either result of direct or indirect influences [19]. Recently, conditional entropy has been used to identify the effects of deep breathing on heart rate, respiration, and arterial blood pressure signals [19,20] as well as a directional coupling in time series of scalp EEG [9]. In recent clinical studies, coupling analysis of heart and brain signals differentiate normal and pathological conditions such as myocardial infarction, heart failure, attention deficits, epilepsy, sepsis, and preeclampsia. Furthermore, the role of A_I and A_E attention in patients with Parkinson’s disease found to describe the presence and magnitude of cognitive deficits [21].

The non-linear analysis of heart and brain activity during the attention task using conditional entropy motivates to investigate the link between heart and brain functions. Heart rate variability (HRV) accepted as the prominent psychophysiological index of cognitive processing [22]. HRV is rapid R-to-R peak variability in heart rate time series [23]. The HRV characterized sympathetic and parasympathetic nervous system activities. The sympathetic activity controls the fight or flight response of the body, while parasympathetic activity controls rest and digest functions of the body [24]. Neurovisceral-integration-model integrates physiological variability, cerebral blood flow, and cognitive information processing as evidence of autonomic control [25]. Previous research indicates that higher parasympathetic influence is associated with greater activation of the prefrontal cortex and better cognitive engagement [26]. Hansen et al., 2003 investigated the effect of HRV on speed and accuracy during cognitive task [27]. Further, low HRV is associated with hypervigilance and inefficient allocation of cognitive resources [3]. Accordingly, non-linear analysis of HRV and EEG time series may provide converging evidence about the effect of the heart-brain interplay of human cognition. Non-linear time series analysis of heart and brain time series may reveal functional coupling as well as underlying driver-response relation between heart and brain [9].

This research aimed to investigate heart and brain interactions during attention tasks using information domain approach to differentiate A_I and A_E attention. The study involve simultaneous recording of ECG and EEG, to interpret the effects of attention conditions on heart and brain in healthy individuals. Also, given previously reported relation between cognitive and physiological variability, it is hypothesized that individuals performing A_I and A_E attention task; would show the distinctions in directional coupling between heart and brain that can be identified using conditional entropy. To the best of our knowlegde, this is the first study that diffentiate A_I and A_E attention using directional coupling analysis of simultaneously recorded EEG and ECG signals.