529765 research-article2014

EEGXXX10.1177/1550059414529765Clinical EEG and NeuroscienceVecchio et al

Article

Cortical Brain Connectivity and B-Type Natriuretic Peptide in Patients With Congestive Heart Failure

Clinical EEG and Neuroscience 2015, Vol. 46(3) 224­–229 © EEG and Clinical Neuroscience Society (ECNS) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1550059414529765 eeg.sagepub.com

Fabrizio Vecchio1, Francesca Miraglia1, Lavinia Valeriani2, Maria Gabriella Scarpellini3, Placido Bramanti4, Oriano Mecarelli5, and Paolo M. Rossini1,6

Abstract The brain has a high level of complexity and needs continuous oxygen supply. So it is clear that any pathological condition, or physiological (aging) change, in the cardiovascular system affects functioning of the central nervous system. We evaluated linear aspects of the relationship between the slowness of cortical rhythms, as revealed by the modulation of a graph connectivity parameter, and congestive heart failure (CHF), as a reflection of neurodegenerative processes. Eyes-closed resting electroencephalographic (EEG) data of 10 patients with CHF were recorded by 19 electrodes positioned according the international 10-20 system. Graph theory function (normalized characteristic path length λ) was applied to the undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA software, therefore getting rid of volumetric propagation influences. The EEG frequency bands of interest were: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz). The analysis between B-type natriuretic peptide (BNP) values and λ showed positive correlation in delta, associated with a negative correlation in alpha 2 band. Namely, the higher the severity of the disease (as revealed by the BNP vales), the higher the λ in delta, and lower in alpha 2 band. Results suggest that delta and alpha λ indices are good markers of the severity of CHF. Keywords congestive heart failure, electroencephalography, B-type natriuretic peptide, connectivity and graph theory, characteristic path length Received October 26, 2013; accepted February 28, 2014.

Introduction CHF is a common complication of most diseases of the heart. Its prevalence increases exponentially from age 60 years,1 such that CHF is now one of the leading causes of hospitalization, morbidity, and mortality in Western societies.2 Several surveys indicate that physical, social, work and leisure activities are significantly impaired among subjects with CHF.3 Another important, but neglected, aspect of the quality of life of patients with CHF is cognitive functioning. Early reports indicated that up to 80% of patients with severe CHF display deficits in memory and other cognitive abilities.4 The consequences of these deficits are not clear, but it is conceivable that patients with cognitive impairment have even higher morbidity and mortality rates. A brief review of the literature5 indicates that CHF is associated with a pattern of generalized cognitive impairment that includes memory and attention deficits. Our recent study seems to confirm that CHF is associated with cognitive impairment, seen by EEG to be similar to Alzheimer disease.6 These results suggest that cognitive deficits become more prominent with increasing severity of illness, although Schall et al4 found that cardiac transplant fails to reverse the deficits of some cognitive skills, such as memory.

The mechanisms that contribute to the development of cognitive impairment among patients with CHF remain unclear. Zuccalà et al7 observed a linear relationship between MiniMental State Examination (MMSE) scores and left ventricular ejection fraction rates for values lower than 40%. Similarly, Putzke et al8 noted that Trail B, Digit Symbol Substitution and Stroop scores were all significantly associated with cardiac output. Cerebrovascular disease is another likely cause of cognitive impairment, because many patients with CHF have widespread cardiovascular problems and are at increased risk 1

Brain Connectivity laboratory, IRCCS San Raffaele Pisana, Rome, Italy Casa di cura San Raffaele Montecompatri e Rocca di Papa, Rome, Italy 3 Dip. Emergenza DEA, Sapienza University, Rome, Italy 4 IRCCS Centro Neurolesi Bonino–Pulejo, Messina, Italy 5 Dept Neurology and Psichiatry, Sapienza University, Rome, Italy 6 Dept of Neurology, Catholic University “Sacro Cuore” Rome, Italy 2

Corresponding Author: Fabrizio Vecchio, Brain Connectivity Laboratory, IRCCS San Raffaele Pisana, Via Val Cannuta, 247, 00166 Rome, Italy. Email: [email protected] Full-color figures are available online at http://eeg.sagepub.com

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Vecchio et al Table 1.  Clinical Information of Each Participant. Days of BNP CHF Onset Arterial Diabetes Age NYHA (pg/ (Years) Gender Class mL) MMSE Before EEG Hypertension Mellitus Subj_01 Subj_02 Subj_03 Subj_04 Subj_05 Subj_06 Subj_07 Subj_08 Subj_09 Subj_10

88 80 79 87 79 83 47 73 69 76

Male Male Male Male Male Male Female Male Male Female

III III II III IV III II IV II III

139 465 308 960 299 417 226 1140 257 703

13 27 24 28 18 18 12 21 26 21

7 7 7 9 9 3 7 3 4 6

× × × × × × × × × ×

Chronic Obstructive Pulmonary Disease

× ×

Dilated Cardiomyopathy

Chronic Ischemic Renal Heart Atrial Disease Fibrillation Failure ×

× ×

×

×

× ×

× ×

× ×

×

× ×

×

×

        ×     ×    

Abbreviations: NYHA, New York Heart Association; BNP, B-type natriuretic peptide; MMSE, Mini-Mental State Examination; CHF, congestive heart failure.

for strokes. Other cardiovascular problems, such as low9 and high blood pressure,10 both frequent among patients with CHF, are associated with cognitive impairment. Cognitive impairment in CHF may also be due to the abnormal hormonal response that characterizes the disease, although no direct evidence is currently available to support this hypothesis. In conclusion, the results of the present systematic review suggest that CHF is associated with a pattern of cognitive impairment that includes attention and memory deficits. Taking in mind that CHF is associated with an increased risk of cognitive decline and dementia, we should remember that there is a peptide, the B-type natriuretic peptide (BNP), that is associated with the CHF severity. BNP is a serum marker for congestive heart failure, secreted by the cardiac ventricles in response to excessive stretching.11 Two recent studies have revealed that high BNP levels may predict cognitive performance of patients with CHF.12,13 Several studies of dementia and cognitive decline14-17 showed EEG abnormalities that were associated with impaired global cognitive function, as evaluated by MMSE.18 These studies indicate that EEG rhythms reflect cognitive decline. In our previous study,6 we observed that chronic ischemic hypoxia of CHF could affect cortical neural synchronization, generating resting state EEG rhythms, inducing “slowing” of EEG, typical of Alzheimer’s disease, as observed in increase of delta and decrease of alpha, and that these modulations were correlated with BNP values. We wanted to understand whether slowing of the EEG could also affect connectivity values (evaluated by graph theory from eLORETA data), such as normalized characteristic path length (namely, the lambda [λ] value), which measures the average shortest path length of a network and is therefore a global index of how easy it is to travel from one part to another. The study of complex networks has attracted attention in recent years, and has resulted in applications in various fields, including the study of metabolic systems, airport networks, and brain connectivity. This novel approach, applying concepts from graph theory (a branch of the mathematical field of complex network theory) to neurophysiological data, is a promising new way to characterize brain activity.19-21 Recently, we also confirmed (F.

Vecchio et al, unpublished data, 2014)46,47 the utility of a mathematical approach to investigate age-related features in real complex brain networks. In this work we demonstrated modulation of the brain network by non-specific neurological pathology.

Methods Subjects and Diagnostic Criteria In this study, 10 patients with CHF (mean age 76.1 years ±3.7 SE, education 7.2 years ±1.5 SE) from a previous study6 were recruited; they were from II to IV of the New York Heart Association (NYHA) class (clinical information of each participant is reported in Table 1). Local institutional ethics committees approved the study. All experiments were performed with informed consent of each participant, in line with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Inclusion and exclusion criteria for CHF were based on previous reports of the European Society of Cardiology (ESC 2008) and American College of Cardiology Foundation/ American Heart Association (ACC/AHA 2009). Of note, one of the inclusion criteria was a BNP higher than 100 pg/mL; values in the range 139 to 1140 pg/mL. None was diagnosed with dementia before admission to the emergency department; in fact, one exclusion criterion was any sort of overt dementia before hospitalization. Benzodiazepines and/or antidepressant drugs were withdrawn for about 24 hours before EEG recordings, in order to pair the period from the last assumption of the drugs and EEG recording across subjects. A battery of neuropsychological tests was performed to assess cognitive performance in the domains of memory, language, executive function/attention, and visuoconstruction abilities. Tests to assess memory were the immediate and delayed recall measure and the re-evoked measures of the Rey Auditory Verbal Learning Test.22,23 Tests to assess language were the 1-minute verbal fluency for letters,24 the 1-minute verbal fluency for fruits, animals, or cars.24 Tests to assess executive function and attention were the Digit Forward and the

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Clinical EEG and Neuroscience 46(3)

Digit Backward.25 Finally, the test to assess visuoconstruction was Clock Drawing.26

BNP Testing by BIOSITE Method: Triage BNP BNP concentration can be evaluated in plasma or whole blood by fluorescent immunoassay. To determine the concentration of BNP, we used Biosite, the so-called Triage BNP, because it is far simpler and requires no laboratory equipment. In 2000, the Food and Drug Administration approved the test (Biosite, San Diego, CA, 2000),27 a rapid, point-of-care BNP analysis system. The test assesses BNP levels within 5 to 5000 pg/mL; measured values have been found to be linear throughout this range. In the present study, the BNP level ranged between 139 and 1140 pg/mL.

EEG Recordings EEG’s were recorded at rest (eyes-closed); (0.3-70 Hz bandpass; cephalic reference) from 19 electrodes according to the international 10-20 system. To monitor eye movements, horizontal and vertical electro-oculograms (0.3-70 Hz bandpass) were also collected. All data were digitized in continuous recording mode (5 minutes of EEG; 256 Hz sampling rate). Recordings were performed in the late morning. To keep constant the level of vigilance, an experimenter alerted the subject any time there were signs of behavioral and/or EEG drowsiness. The data were processed in Matlab R2011b (MathWorks, Natick, MA) using scripts based on EEGLAB 11.0.5.4b toolbox (http://www.sccn.ucsd.edu/eeglab). The EEG recordings were bandpass filtered from 0.1 to 47 Hz, using a finite impulse response filter. Imported data were divided in 2-second epochs, and artifacts in the EEG (eye movements, cardiac activity, and scalp muscle contraction) were removed using an independent component analysis (ICA) procedure. ICA is a blind source decomposition algorithm, that enables the separation of statistically independent sources from multichannel data. It has been proposed as an effective method for separating ocular movement and blink artifacts from EEG28,29 (F. Vecchio et al, unpublished data, 2014). ICA was performed using the Infomax ICA algorithm30 as implemented in the EEGLAB. Artifact-free EEG signals were used for further analyses.

Functional Connectivity Analysis Brain connectivity was computed by sLORETA/eLORETA software on 84 regions of interest (ROIs), defined according to the 42 available Brodmann areas, for the left and right hemispheres. ROIs are needed for the estimation of electric neuronal activity, that is used to analyze brain functional connectivity. Among the eLORETA current density time series of the 84 ROIs, intracortical lagged linear coherence, extracted by “all nearest voxels” method31,32 (F. Vecchio et al, unpublished data, 2014), was computed between all possible pairs of the

84 ROIs, for each of the 7 independent EEG frequency bands33,34 of delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-45 Hz). The lagged linear coherence between time series x and y in the frequency band ω is reported by the following equation31,32:  Im Cov ( x, y )  = 2 Var ( x) × Var (Y ) − [ Re Cov( x, y ) ] 2

2 xyω

LagR

where Var and Cov are, respectively, the variances and covariance of the signals. It was developed as a measure of true physiological connectivity not affected by volume conduction and low spatial resolution as shown by Pascual-Marqui et al.32 The values of connectivity between all pairs of ROIs, for each frequency band and for each subject, were used as measure of weight of the graph in the following analyses.

Graph Analysis A network is a mathematical representation of a real-world complex system, and is defined by a collection of nodes (vertices) and links (edges) between pairs of nodes. Nodes in largescale brain networks usually represent brain regions, while links represent anatomical, functional, or effective connections,35 depending on the data set. Anatomical connections typically correspond to white matter tracts between pairs of gray matter (cortical areas or subcortical relays) brain regions. Functional connections correspond to magnitudes of temporal correlations in activity, and may occur between pairs of anatomically unconnected regions. The nature of nodes and links in individual brain networks is determined by combinations of brain mapping methods, anatomical parcellation schemes, and measures of connectivity. Many combinations occur in various experimental settings.36 Nodes should ideally represent brain regions with coherent patterns of extrinsic anatomical or functional connections.37 An undirected and weighted network, based on the connectivity between the 84 ROIs, the nodes of the network being ROIs, and the edges of the network being weighted by the lagged linear connectivity, was built.38 The software used for the graph analysis was the Brain Connectivity Toolbox (http://www.brainconnectivity-toolbox.net/), adapted by own Matlab scripts. We computed a core measure of graph theory, the characteristic path length (L), representative of global connectedness, reported in the following39 L=

Σ j ⊂ N , j ≠ i dij 1 1 ∑ Li = ∑ n i⊂ N n i⊂ N n −1

where Li is the average distance between node i and all other nodes. This is the average shortest path connecting any nodes couple of the graph: The length of a path is indicated by the

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Vecchio et al Table 2.  Neuropsychological Data of Subjects With Congestive Heart Failure. Subjects Mini-Mental State Examination Immediate recall of Rey list Delayed recall of Rey list Re-evoked recall of Rey list Clock drawing Verbal fluency for letters Verbal fluency for categories

Congestive Heart Failure 20.8 (±1.9) 15.3 (±2.0) 3.3 (±0.6) 9.1 (±1.5) 3.7 (±0.6) 11.1 (±2.5) 13.1 (±1.9)

number of connections it contains. The characteristic path length L (averaged shortest path length between all node pairs) is an emergent property of the graph, which indicates how well its elements are integrated/interconnected. To obtain scale-free network measures, the values of characteristic path length were divided by the mean values obtained by a set of 1000 random digraphs (obtained by randomization of all actual matrices; F. Vecchio et al, unpublished data, 2014), having the same number of nodes and connections as the actual graphs. The set of 1000 random digraphs was obtained by software taken from the mentioned Web site. These functions randomize the network, while preserving the degree distribution. The distributions of global (L-random) connectedness values were calculated and averaged to obtain a mean value for each core measure. The scale-free values (λ) were evaluated from the normalized L (L/L-random).

Statistical Analysis In line with the working hypothesis, we tested Pearson’s linear correlations between BNP levels and the normalized characteristic path length (λ) for each of the 7 independent EEG frequency bands of interest.

Results Neuropsychological Tests Table 2 reports the values and the neuropsychological data of the patients with CHF.6 The effect of the disease on cognition is evident in the table, for example, MMSE scores were approximately 20, comparable to those of demented patients.

Statistical Correlation Between Network Characteristic and B-Type Natriuretic Peptide Correlation analyses were performed on the normalized characteristic path length (λ) in the delta, theta, alpha 1, alpha 2, beta 1, beta 2, and gamma bands. The analysis between BNP values and λ showed positive correlation in delta (P < .024, r = 0.699) associated with a negative correlation in alpha 2 (P < .038, r = −0.66). Namely, the

higher the BNP values the higher the λ in delta, and lower the λ in alpha 2 (see Figure 1). As a control, we also performed a correlation analysis between age and BNP, but no statistical significance (P = .6) was obtained. Several control analyses were also performed to find correlation between λ indices and neuropsychological tests, and between BNP levels and neuropsychological tests. These control analyses reported that delta λ positively correlates with “Verbal Fluency for Categories” (P < .005, r = 0.805), while BNP did not present significant correlation with any neuropsychological test.

Discussion In this study, a relationship between the individual normalized characteristic path length (λ) and the severity of the disease, as revealed by the BNP values indicating CHF severity,40 was investigated in patients with CHF. The analysis between BNP values and λ showed positive correlation in delta band associated with a negative correlation in the alpha 2 band. Namely, the higher the BNP values (higher the severity of the disease), the higher the λ in delta, and the lower the λ in alpha 2. No significant correlation was found between age and BNP because the subjects had comparable ages, and also because the severity of the disease, as revealed by BNP, could be independent of age. The results showed that CHF could be considered a disease contributing to cognitive decline,6 as revealed by neuropsychological tests, and that the cortical connectivity abnormalities could be correlated with severity of the disease. Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. The present results are also in line with previous evidence (F. Vecchio et al, unpublished data, 2014) of the correlation of age and normalized characteristic path length (λ). In the cited study, the correlation between age and λ showed that the higher the age, the higher the λ in delta and theta bands, and lower the λ in alpha 2 band. The present results indicated that CHF could cause pathological aging on affected brains. In particular, the alpha result extends those of previous clinical EEG and magnetoencephalography studies,41-43 in which it was demonstrated that in demented patients the characteristic path length decreased in alpha with respect to normal elderly subjects. A possible interpretation of the present results is that hypoperfusion, due to CHF, brings about cerebral hypoxia, increasing disconnection among brain areas. The increase of low-frequency characteristic path length (λ), which measures the average shortest path length of a network, indicates a global index of how easy it is to travel from one part of the network to another, as also observed in demented patients. With regard to the low frequencies (delta and theta), it is suggested that in the awake brain, alpha rhythms dominate in the posterior areas and delta rhythms are low in amplitude, thus reflecting a condition of likely alpha–delta “reciprocal inhibition”44; on the other hand it is well known that anatomical, or

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Clinical EEG and Neuroscience 46(3) neuropsychological tests; however, there was no evidence of correlation between BNP and neuropsychological tests. The novelty of the present study is the possibility to follow the severity of the disease (as evaluated by BNP) by a combination of simple EEG markers (delta and alpha λ), representing a sign of disconnection among brain areas, not actually well revealed by neuropsychological tests. Authors’ Note Dr Francesca Miraglia participated to this study in the framework of her PhD program at the Doctoral School in Neuroscience, Department of Neuroscience, Catholic University of Rome, Italy.

Declaration of Conflicting Interests The author(s) declared no conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The article is partially funded by the Italian Ministry of Instruction, University and Research MIUR (“Approccio integrato clinico e sperimentale allo studio dell’invecchiamento cerebrale e delle malattie neurodegenerative: basi molecolari, epidemiologia genetic, neuroimaging multimodale e farmacogenetica” and “Functional connectivity and neuroplasticity in physiological and pathological aging” PRIN project).

References Figure 1.  Scatterplots relative to correlation analyses between B-type natriuretic peptide (BNP) and λ values in delta and alpha 2 bands. In patients with congestive heart failure (CHF) patients, the BNP positively correlated in delta band (P < .024, r = 0.699) and negatively correlated in alpha 2 band (P < .038, r = −0.66).

functional, disconnection from related cortical areas generates spontaneous slow oscillations in virtually all recorded neurons45,46 (F. Vecchio et al, unpublished data, 2014). The low frequency increase of “global” (=average shortest path length of a network therefore being a global index of how easy it is to travel from one part of the network to another) could be seen as the effect of the disease on the connectivity, characterized by shortest length of links in the network edges as a sign of functional disconnection (F. Vecchio et al, unpublished data, 2014).

Conclusion The cardiovascular pathology of CHF leads to cognitive decline, as revealed in the present work by several neuropsychological tests. The present study shows how a simple and noninvasive test, like the EEG, can improve the management of CHF and our understanding of the mechanisms associated with cognitive decline due to cardiologic pathology, so that it can be prevented and cured. In conclusion, the cardiovascular pathology of CHF leads to cognitive decline as underlined by

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Cortical Brain Connectivity and B-Type Natriuretic Peptide in Patients With Congestive Heart Failure.

The brain has a high level of complexity and needs continuous oxygen supply. So it is clear that any pathological condition, or physiological (aging) ...
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