Journal of Alzheimer’s Disease 65 (2018) 843–854 843 DOI 10.3233/JAD-170475 IOS Press Alterations of Effective Connectivity Patterns in Mild Cognitive Impairment: An MEG Study Carlos Gomez´ a,∗, Celia Juan-Cruzb, Jesus´ Pozaa,c,d,Saul´ J. Ruiz-Gomez´ a, Javier Gomez-Pilara, Pablo Nu´nez˜ a, Mar´ıa Garc´ıaa, Alberto Fernandez´ e,f and Roberto Horneroa,c,d aBiomedical Engineering Group, University of Valladolid, Spain bDepartment of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands cIMUVA, Instituto de Investigaci´on en Matem´aticas, University of Valladolid, Spain dINCYL, Instituto de Neurociencias de Castilla y Le´on, University of Salamanca, Spain eDepartment of Psychiatry, Faculty of Medicine, Complutense University of Madrid, Spain f Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain Accepted 10 August 2017 Abstract. Neuroimaging techniques have demonstrated over the years their ability to characterize the brain abnormalities associated with different neurodegenerative diseases. Among all these techniques, magnetoencephalography (MEG) stands out by its high temporal resolution and noninvasiveness. The aim of the present study is to explore the coupling patterns of resting-state MEG activity in subjects with mild cognitive impairment (MCI). To achieve this goal, five minutes of spontaneous MEG activity were acquired with a 148-channel whole-head magnetometer from 18 MCI patients and 26 healthy controls. Inter-channel relationships were investigated by means of two complementary coupling measures: coherence and Granger causality. Coherence is a classical method of functional connectivity, while Granger causality quantifies effective (or causal) connectivity. Both measures were calculated in the five conventional frequency bands: delta (δ, 1–4 Hz), theta (θ, 4–8 Hz), alpha (␣, 8–13 Hz), beta (␤, 13–30 Hz), and gamma (␥, 30–45 Hz). Our results showed that connectivity values were lower for MCI patients than for controls in all frequency bands. However, only Granger causality revealed statistically significant differences between groups (p-values < 0.05, FDR corrected Mann-Whitney U-test), mainly in the beta band. Our results support the role of MCI as a disconnection syndrome, which elicits early alterations in effective connectivity patterns. These findings can be helpful to identify the neural substrates involved in prodromal stages of dementia. Keywords: Coherence, connectivity, Granger causality, magnetoencephalography (MEG), mild cognitive impairment, neuroimaging INTRODUCTION until 1995 that Petersen defined it as an indepen- dent clinical condition [2, 3]. MCI is described as a The concept of mild cognitive impairment (MCI) disorder characterized by a cognitive decline higher was first mentioned in the literature in the late 1980s than expected by age and education, but insufficient as the result of a growing concern in portraying the to meet the criteria for the diagnosis of dementia [4]. early stages of dementia [1]. However, it was not Even if cognitive activity is impaired, these patients maintain their independence in their functional and ∗Correspondence to: Carlos Gomez,´ Biomedical Engineering Group, E. T. S. Ingenieros de Telecomunicacion,´ Universidad de social skills. Worldwide studies recently estimated Valladolid, Paseo Belen´ 15, 47011 Valladolid, Spain. Tel.: +34 983 the overall prevalence of MCI within a range from 423981; E-mail: [email protected]. 12% to 18% for elderly people over 60 years [5]. ISSN 1387-2877/18/$35.00 © 2018 – IOS Press and the authors. All rights reserved 844 C. G´omez et al. / Alterations of Effective Connectivity Patterns in Mild Cognitive Impairment Some of them remain stable or return to normal over The characterization of AD and MCI using MEG time, but more than 50% progress to dementia [6]. has been addressed in the last decades by apply- The conversion rate to dementia due to Alzheimer’s ing different signal processing techniques. Until disease (henceforth AD) is approximately 15% per the introduction of methods derived from nonlinear year [7], although MCI can also be a prodromal stage dynamics, MEG signals were explored with linear of other dementia subtypes, such as vascular demen- techniques based on spectral analyses [26, 32]. The tia [8], dementia with Lewy bodies [9], or Parkinson’s most common marker in AD and MCI patients is a disease [10]. For this reason, current perspectives on slowing of MEG activity during resting-state. In par- MCI interpret this condition as a risk state and/or ticular, MCI subjects showed intermediate median prodromal stage for various types of dementia and frequency values between AD patients and controls not as an independent clinical condition [11, 12]. [26]. Several non-linear analysis methods suggested Pharmacological intervention is currently unsuccess- that AD and MCI elicit a complexity decrease in ful in the treatment of MCI or dementia. However, spontaneous brain activity as well as an increase an early and conclusive diagnosis is necessary, since of regularity [27, 28]. Using Lempel-Ziv complex- the medication used to delay the symptoms and opti- ity, Fernandez´ et al. [27] showed that AD patients mize the overall clinical and functional condition of and controls exhibit a parallel tendency to dimin- the patient is more effective in the first stages of ished complexity values as a function of age, but dementia [13, 14]. MCI patients did not show such normal tendency. All During the last decades, functional magnetic these methods (both the spectral and the non-linear resonance imaging (fMRI) and positron emission ones) measure local activation patterns in individual tomography (PET) have been used to investigate cere- sensors. However, it has become clear that simple bral changes in AD and MCI. Previous fMRI studies activation studies are no longer sufficient for a full reported impairments in brain functional activity in characterization of brain dynamics [36]. For this the default mode network of MCI subjects [15, 16]. reason, attention has shifted to coupling analyses During associative encoding of picture-word pairs, during the last years. For instance, Escudero et al. MCI subjects exhibited increased fMRI responses [25] found that AD and MCI cause slight alterations in the posterior hippocampal, parahippocampal, and in the MEG connectivity. Another study [29] revealed fusiform regions [17]. On the other hand, PET stud- significant differences between MCI subjects and ies suggested that changes in glucose metabolism controls in the beta frequency band for both coher- might be useful to predict the conversion from ence (COH) and synchronization likelihood (SL). MCI subjects to AD [18]. Other authors demon- A more recent measure, called phase lag index (PLI), strated that the retention of an amyloid-imaging revealed that AD is associated with a synchroniza- PET tracer in MCI subjects is at an intermediate tion decrease in the lower alpha and beta bands [34]. level between healthy controls and AD patients [19]. Bajo et al. [22] analyzed the MEG activity obtained PET and fMRI exhibit a good spatial accuracy, but during a memory task in 22 MCI subjects and 19 both offer a poor temporal resolution to study brain controls by means of SL. Their results revealed dynamics. On the other hand, electroencephalog- an increase in long distance inter-hemispheric con- raphy (EEG) and magnetoencephalography (MEG) nections in MCI, but a decrease in anteroposterior have much higher temporal resolution than fMRI functional connectivity [22]. All these coupling and PET, allowing the real-time recording of neu- methods (COH, SL, PLI, etc.) measure functional ral activity. EEG and MEG have also proved their connectivity (i.e., dependencies between remote usefulness to characterize the brain abnormalities neurophysiological events) [37]. However, although associated with MCI and AD [20–37]. Compared to awareness of the existence of a connection is EEG, MEG offers reference-free recordings. Addi- important, mapping the directional relationships is tionally, the effect of the heterogeneous conductivity essential to fully characterize information flow of the skull and the scalp is significantly lower for dynamics in MCI. For this reason, measures of magnetic fields. Finally, MEG provides better spa- effective or causal connectivity (i.e., the influence that tial resolution than conventional EEG. Due to the one neural system exerts over another) are needed aforementioned advantages of MEG over other neu- to overcome the limitations of the aforementioned roimaging modalities, neural dynamics in MCI were analyses [37]. evaluated in the current study using this non-invasive The aim of this study is to analyze MEG con- technique. nectivity patterns in MCI by means of COH,a C. G´omez et al. / Alterations of Effective Connectivity Patterns in Mild Cognitive Impairment 845 classical measure of functional connectivity, but also The local Ethics Committee of San Carlos Uni- using Granger causality (GC), a measure of effective versity Hospital approved this investigation. Controls connectivity. To our knowledge, this is the first study and patients’ caregivers signed the informed consent that uses an effective connectivity measure to char- for the participation in this study, which was con- acterize the MEG brain dynamics in MCI. We want ducted in accordance with the Declaration of Helsinki to address the following research questions: