28 August - 1 September, 2012 San Diego, CA USAConference Chair: Michael C.K. Khoo Program Co-Chairs: Gert Cauwenberghs and James D. Weiland

Indexed in PubMed® and MEDLINE®, products of the United States National Library of Medicine

U.S. National Library of Medicine

34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

IEEE Catalog Number: CFP12EMB-USB • ISBN: 978-1-4244-4120-4 • ISSN: 1557-170X © 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. CD-ROM support, contact The Printing House, Inc. at +1-608-873-4500. For more information, please see the “Copyright” page.

Keynote Speakers

Help

Copyright

Search

Author Index

Program in Chronological Order

Advertisements

Welcome

EMBS

Committees

Acknowledgements

Tutorial / Workshops

EDITOR’S NOTES: The 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society hosted an electronic paper submission process for the conference. It was the responsibility of the submitting Author to ensure that the document was viewable and without errors that would prevent the Conference from including the paper in the DVD or Print Proceedings. In the event a paper was submitted that could not be viewed or printed properly, the Conference elected to only publish the abstract of the paper in the Proceedings.

All conference papers were peer-reviewed by experts chosen by the Conference Editorial Board for all contributed, invited and minisymposia papers.

AUTHOR NO SHOW POLICy: EMBS enforces a “no show” policy. Any accepted paper included in the final program is expected to have at least one author attend and present the paper at the Conference. Authors of the accepted papers included in the final program who do not attend the Conference will be subscribed to a “No Show List”, compiled by the Society. The “no-show” papers will be removed from the Master DVD and noted as “Author unavailable for presentation” prior to submitting to IEEE for inclusion in Xplore. The “No Show List” will also be made available to all EMBS conference organizers, who can reject submissions from these authors in the following two years, based on their past negative impact on an EMBS conference.

08:30-08:45 SaA01.3 Gaussian Process Regression in Vital-Sign Early Warning Systems ............................................................ 6161-6164

Clifton, Lei* (University of Oxford); Clifton, David (University of Oxford); Pimentel, Marco A.F. (University of Oxford); Watkinson, Peter J. (University of Oxford, Oxford University Hospitals NHS Trust); Tarassenko, Lionel (University of Oxford)

08:45-09:00 SaA01.4 Extraction of Fetal Heart Rate from Maternal Surface ECG with Provisions for Multiple Pregnancies ...... 6165-6168

Fanelli, Andrea (Politecnico di Milano); Signorini, Maria G.* (Politecnico di Milano); Heldt, Thomas (Massachusetts Institute of Technology)

09:00-09:15 SaA01.5 Determination of Glucose Concentration from Near-Infrared Spectra Using Locally Weighted Partial Least Square Regression .................................................................................................................................... 6169-6171

Malik, Bilal* (Sheffied University); Benaissa, Mohammed (Sheffied University)

09:15-09:30 SaA01.6 Multivariate Spectral Analysis for Identifying the Brain Activations During Olfactory Perception ............ 6172-6175

Kroupi, Eleni* (EPFL); Yazdani, Ashkan (EPFL); Vesin, Jean-Marc (EPFL); Ebrahimi, Touradj (EPFL)

SaA02: 08:00-09:30 Sapphire D 1.5.1 Casuality and Connectivity (Oral Session) Chair: Tong, Shanbao (Shanghai Jiao Tong Univ.) Co-Chair: Porta, Alberto (Univ. degli Studi di Milano)

08:00-08:15 SaA02.1 An Association Framework to Analyze Dependence Structure in Time Series ............................................ 6176-6179

Fadlallah, Bilal* (University of Florida); Brockmeier, Austin (University of Florida); Seth, Sohan (University of Florida); Keil, Andreas (University of Florida); Principe, Jose (University of Florida)

08:15-08:30 SaA02.2 On the Improved Correlative Prediction Scheme for Aliased Electrocardiogram (ECG) Data Compression .................................................................................................................................... 6180-6183

Gao, Xin* (The University of Arizona, Tucson)

08:30-08:45 SaA02.3 Comparing Causality Measures of Fmri Data Using PCA, CCA and Vector Autoregressive Modelling ..... 6184-6187

Shah, Adnan* (National ICT Australia, Canberra, The Australian National University Canberra); Khalid, Muhammad Usman (National ICT Australia, Canberra, The Australian National University Canberra); Seghouane, Abd-krim (National ICT Australia)

08:45-09:00 SaA02.4 Synchrony Analysis of Spontaneous MEG Activity in Alzheimer’s Disease Patients .................................. 6188-6191

Gomez, Carlos* (University of Valladolid, CIF: Q4718001C); Martínez-Zarzuela, Mario (Grupo de Telemática Industrial, University of Valladolid); Poza, Jesús (University of Valladolid); Díaz-Pernas, Francisco Javier (Grupo de Telemática Industrial, University of Valladolid); Fernandez, Alberto (Universidad Complutense de Madrid); Hornero, Roberto (University Of Valladolid)

09:00-09:15 SaA02.5 Towards the Time Varying Estimation of Complex Brain Connectivity Networks by Means of a General Linear Kalman Filter Approach ........................................................................................................................... 6192-6195

Astolfi, Laura* (University of Rome Sapienza); Toppi, Jlenia (University of Rome “Sapienza”)

09:15-09:30 SaA02.6 Cortical Functional Connectivity under Different Auditory Attentional Efforts ............................................ 6196-6199

Hong, Xiangfei (Shanghai Jiao Tong University); Tong, Shanbao* (Shanghai Jiao Tong University)

Abstract— The aim of this study was to analyze the

magnetoencephalography (MEG) background activity in

Alzheimer’s disease (AD) patients using cross-approximate

entropy (Cross-ApEn). Cross-ApEn is a nonlinear measure of

asynchrony between time series. Five minutes of recording

were acquired with a 148-channel whole-head magnetometer in

12 AD patients and 12 age-matched control subjects. We found

significantly higher synchrony between MEG signals from AD

patients compared with control subjects. Additionally, we

evaluated the ability of Cross-ApEn to discriminate these two

groups using receiver operating characteristic (ROC) curves

with a leave-one-out cross-validation procedure. We obtained

an accuracy of 70.83% (66.67% sensitivity, 75% specificity)

and a value of area under the ROC curve of 0.83. These results

provide evidence of disconnection problems in AD. Our

findings show the usefulness of Cross-ApEn to detect the brain

dysfunction in AD.

I. INTRODUCTION

LZHEIMER’S DISEASE (AD) is a primary degenerative neurological disorder of unknown etiology that

gradually destroys brain cells. Nowadays, it is considered the main cause of dementia in western countries [1]. AD affects 1% of population aged 60-64 years, but the prevalence increases exponentially with age, so about 30% of people over 85 years suffer from this disease [2]. Additionally, as life expectancy has improved significantly in the last decades, it is expected that the number of people with dementia increase up to 81 millions in 2040 [2]. Clinically, this disease manifests as a slowly progressive impairment of mental functions whose course lasts several years prior to death [2]. Usually, AD starts by destroying neurons in parts of the patient’s brain that are responsible for storing and retrieving information. Then, it affects the brain areas involved in language and reasoning. Eventually, many other brain regions are atrophied. Although a definite AD diagnosis is only possible by necropsy, a differential diagnosis with other types of dementia and with major

Manuscript received March 29, 2012. This research was supported in

part by the “Ministerio de Economía y Competitividad” under project TEC2011-22987, the Proyecto Cero 2011 on Ageing from Fundación General CSIC, and project VA111A11-2 from Consejería de Educación (Junta de Castilla y León). Asterisk indicates corresponding author.

C. Gómez, J. Poza, and R. Hornero are with the Biomedical Engineering Group at Department of Signal Theory and Communications, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Campus Miguel Delibes, 47011 – Valladolid, Spain (e-mail: cargom@tel.uva.es).

M. Martínez-Zarzuela and F. J. Díaz-Pernas are with the Industrial Telematics Group, University of Valladolid, Spain.

A. Fernández is with the Centre for Biomedical Technology, Technical University of Madrid, Spain.

depression should be attempted. The differential diagnosis includes medical history studies, physical and neurological evaluation, mental status tests, and neuroimaging techniques.

Nowadays, magnetoencephalography (MEG) recordings are not used in AD clinical diagnosis, in spite of its potential as aid diagnostic tool. MEG is a non-invasive technique that records the electromagnetic fields produced by brain activity with good temporal resolution. MEG technology offers some advantages over electroencephalography (EEG). For instance, magnetic fields are not distorted by the resistive properties of the skull. Furthermore, EEG signals are influenced by a wide variety of factors, such as distance between sensors, electrode location, reference point or conducting substance between skin and electrode. On the other hand, the magnetic signals generated by the human brain are extremely weak. Thus, SQUID (Superconducting QUantum Interference Device) sensors are necessary to detect them. In addition, MEG signals must be recorded in a magnetically shielded room. Thus, MEG is characterized by limited availability and high equipment cost.

Entropy is a concept addressing randomness and predictability, with greater entropy often associated with more randomness and less system order [3]. Mainly, there are two families of entropy estimators: spectral entropies and embedding entropies [4]. Spectral entropies extract information from the amplitude component of the frequency spectrum. On the other hand, embedding entropies are calculated directly using the time series. This entropies family provides information about how the signal fluctuates with time by comparing the time series with a delayed version of itself [4]. Both spectral and embedding entropies have demonstrated their usefulness in the analysis of EEG/MEG background activity in AD. An increase of entropy values has been found using approximate entropy (ApEn) [5], sample entropy [6], Shannon spectral entropy, Rényi spectral entropy and Tsallis spectral entropy [7]. However, all these measures are applied to each EEG or MEG channel independently. In the current study, we want to go a step ahead, applying cross-approximate entropy (Cross-ApEn) to MEG recordings from 12 AD patients and 12 age-matched control subjects. Cross-ApEn is a nonlinear measure of asynchrony between time series. It has already been used to study some biological signals, as hormone time series dynamics [8], blood oxygen saturation and heart rate [9].

The purpose of this study was to test the hypothesis that

Synchrony analysis of spontaneous MEG activity in

Alzheimer’s disease patients

Carlos Gómez*, Member, IEEE, Mario Martínez-Zarzuela, Jesús Poza, Member, IEEE, Francisco J. Díaz-Pernas, Alberto Fernández, and Roberto Hornero, Senior Member, IEEE

A

34th Annual International Conference of the IEEE EMBSSan Diego, California USA, 28 August - 1 September, 2012

6188978-1-4577-1787-1/12/$26.00 ©2012 IEEE

Cross-ApEn values of the magnetic brain activity would be different in both groups, hence indicating an abnormal type of dynamics associated with AD.

II. MATERIALS AND METHODS

A. MEG recording

MEGs were recorded using a 148-channel whole-head magnetometer (MAGNES 2500 WH, 4D Neuroimaging) placed in a magnetically shielded room. The subjects lay on a patient bed, in a relaxed state and with their eyes closed. They were asked to stay awake and to avoid eye and head movements. For each subject, five minutes of recording were acquired at a sampling frequency of 678.17 Hz. These recordings were down-sampled by a factor of four, obtaining a sampling rate of 169.55 Hz. Data were digitally filtered between 0.5 and 40 Hz. Finally, artifact-free epochs of 5 seconds (848 samples) were selected.

B. Subjects

The MEG data were acquired from 24 subjects. Twelve patients (3 men and 9 women) fulfilling the criteria of probable AD (age = 70.42 ± 9.04 years, mean ± standard deviation SD) have participated in the present study. The patients were diagnosed according to the criteria of the National Institute of Communicative Disorders and Stroke and the AD and Related Disorders Association (NINCDS-ADRDA). The MMSE score was 17.00 ± 3.98 (Mean ± SD). None of the patients used any kind of medication that could have an influence on the MEG.

MEGs were also obtained from 12 age-matched control subjects (5 men and 7 women, age = 70.42 ± 7.75 years, MMSE = 29.50 ± 0.52, mean ± SD). The local ethics committee approved the study. All control subjects and all caregivers of the demented patients gave their informed consent for the participation in the current research.

C. Cross Approximate Entropy (Cross-ApEn)

Cross-ApEn is a two-parameter family of statistics introduced as a measure of asynchrony between two paired time series [10]. It evaluates secondary as well as dominant patterns in data, quantifying changes in underlying episodic behavior that do no reflect in peak occurrences and amplitudes [8]. To compute Cross-ApEn, two input parameters must be specified: a run length m and a tolerance window r. For two time series, u(i) and v(i), Cross-ApEn measures, within tolerance r, the (conditional) regularity or frequency of v-patterns similar to a given u-pattern of window length m. Although m and r are critical in the calculation of ApEn and Cross-ApEn, no guidelines exist to optimize their values. However, values of m equal to 1 or 2, and r between 0.1 and 0.25 has been suggested [10]. In this pilot study, we have chosen m = 1 and r = 0.2 to compute Cross-ApEn.

Given two equally sampled sequences of length N, u = [u(1), u(2),…, u(N)] and v = [v(1), v(2),…, v(N)], the

algorithm to compute Cross-ApEn is the following [9, 10]:

1) Normalize u(i) and v(i). The normalized time series u*(i) and v*(i) are:

u*(i) = u(i) "mean(u)[ ] /SD(u) (1)

v*(i) = v(i) "mean(v)[ ] /SD(v) (2)

2) Form the vector sequences x(i) = [u*(i), u*(i+1),…,

u*(i+m–1)] and y(j) = [v*(j), v

*(j+1),…, v*(j+m–1)].

These vectors represent m consecutive u* and v* values starting with the ith and jth point, respectively.

3) Define the distance between x(i) and y(j), d[x(i), y(j)], as the maximum absolute difference of their corresponding scalar components:

d[ x(i), y( j)] = maxk=1,2,...,m

u(i + k "1) " v( j + k "1) (3)

4) For each x(i), count the number of j (j=1,2,…,N!m+1) so that d[x(i), y(j)]!r, denoted as Ni

m(r). Then, for i=1,2,…,N!m+1, set

Ci

m(r)(v u) =

Ni

m(r)

N "m +1 (4)

5) Compute the natural logarithm of each Cim(r) and

average it over i:

"m(r)(v u) =

1

N #m +1lnC

i

m(r)

i=1

N #m+1

$ (v u) (5)

6) Finally, Cross-ApEn is defined by: Cross - ApEn(m,r,N )(v u) ="m

(r)(v u) #"m+1(r)(v u) (6)

It is important to note that Cross-ApEn is not always defined because Ci

m(r)(v||u) may be equal to 0 in the absence of similar patterns between u and v. To solve this, two correction strategies have been proposed [11]: bias 0 and bias max. In this study, both correction strategies have been applied. Both strategies assign non zero values to Ci

m(r)(v||u) and Ci

m+1(r)(v||u) in the absence of matches, as follows:

1) Bias 0: Cim(r) = Ci

m+1(r) = 1 if originally Cim(r) =

Cim+1(r) = 0, and Ci

m+1(r) = (N–m)–1 if originally Cim(r) "

0 and Cim+1(r) = 0.

2) Bias max: Cim(r) = 1 if originally Ci

m(r) = 0, and Cim+1(r)

= (N–m+1)–1 if originally Cim+1(r) = 0.

III. RESULTS

Cross-ApEn algorithm was applied to the MEG recordings with parameter values of m = 1 and r = 0.2 and both correction strategies bias 0 and biax max. The end result of computing Cross-ApEn for all pair-wise combinations of MEG channels is a B ! B matrix with B = 148 (number of channels), where each entry Bi,j contains the Cross-ApEn value for channels i and j. It is important to note that there is a direction dependence, due to the fact that "m(r)(v||u) will note generally be equal to "m(r)(u||v). This may be considered an advantage over other synchrony methods as coherence or synchronization likelihood. Fig. 1 and 2 summarize the average Cross-ApEn values estimated at both groups for all the pair-wise combinations of MEG

6189

Control group Alzheimer’s disease group

Fig. 1. Average Cross-ApEn values with bias 0 correction for AD and control groups.

channels using bias 0 and bias max corrections, respectively. This figures show that entropy values were lower in the AD group than in the control group for all channels combinations, which suggests that this disorder is accompanied by a MEG asynchrony decrease. Differences between patients and controls were statistically significant (Student’s t-test) in 55.69% of the 148 ! 148 MEG combinations using bias 0 correction, and in 63.66% using bias max correction. As a multiple comparison correction has not been performed, these results should be taken with caution.

Furthermore, we evaluated the ability of Cross-ApEn to discriminate AD patients from elderly control subjects by means of receiver operating characteristic (ROC) curves. A ROC curve is a graphical representation of the trade-offs between sensitivity and specificity. We define sensitivity as the rate of ADHD patients who test positive, whereas specificity represents the fraction of controls correctly recognized. Accuracy quantifies the total number of subjects precisely classified. The area under the ROC curve (AROC) is a single number summarizing the performance. AROC indicates the probability that a randomly selected AD patient has a Cross-ApEn value lower than a randomly chosen control subject. In order to calculate these values, a leave-one-out cross-validation procedure was used. In the leave-one-out method, the data from one subject are excluded from the training set one at a time and then classified on the basis of the threshold calculated from the data of all other subjects. The leave-one-out cross-validation procedure provides a nearly unbiased estimate of the true error rate of the classification procedure. To simplify the analyses, we calculate the mean value of all the 148 ! 148 Cross-ApEn values, for bias 0 and bias max corrections. For both

correction strategies, we obtained the same accuracy (70.83%), sensitivity (66.67%), specificity (75.00%) and AROC (0.83) values.

IV. DISCUSSION AND CONCLUSIONS

We analyzed the MEG background activity from 12 AD patients and 12 control subjects by means of Cross-ApEn. Our purpose was to test the hypothesis that the brain activity recorded in MEG signals can reflect a disconnection syndrome in AD patients.

Cross-ApEn has proven to be effective in discriminating AD patients from controls. Our study revealed that AD subjects have lower connectivity/asynchrony values. Our findings support the notion that AD involves a loss of functional connectivity. Moreover, significant statistical differences were found in several combinations of MEG channels. However, these findings are preliminary and require replication in a larger patient population before any conclusion can be made about the clinical diagnostic value of this measure.

Several studies have shown the loss of brain connectivity in AD using EEG and MEG recordings. Most of these studies were carried out using the well-known coherence [12]. The main finding is a lower synchronization level in alpha and beta frequency bands. Nevertheless, contradictory results have been found in the other frequency bands [2]. More recently, other connectivity methods have been used to analyze the brain activity in AD, as cross mutual information [13], global field synchronization [14], and synchronization likelihood [15]. For instance, Jeong et al. [13] found that cross mutual information in EEGs from AD patients was lower than in normal controls, especially over frontal and

6190

Control group Alzheimer’s disease group

Fig. 2. Average Cross-ApEn values with bias max correction for AD and control groups.

antero-temporal brain regions. Using global field synchronization, similar results were found: patients showed decreased synchronization values in almost all frequency bands [14]. These results may confirm the hypothesized disconnection syndrome. This connectivity loss in AD may be due to the fact that neuritic plaques appears organized in AD patients’ brains, affecting the ends of corticocortical connections [16].

ROC curves were used to assess the ability of Cross-ApEn to classify ADHD patients and control subjects. We reached an accuracy of 70.83% (66.67% sensitivity, 75% specificity) and a value of area under the ROC curve of 0.83. Nevertheless, these values should be taken with caution due to the small sample size.

In sum, our study leads us to conclude that MEG background activity in AD patients is accompanied by a brain asynchrony decrease. The results obtained with Cross-

ApEn showed significant differences between AD patients and controls, indicating an abnormal type of dynamics associated with this disorder.

REFERENCES

[1] T. D. Bird, “Alzheimer’s disease and other primary dementias,” in Harrison’s principles of internal medicine, E. Braunwald, A. S. Fauci, D. L. Kasper, S. L. Hauser, D. L. Longo, and J. L. Jameson, Eds. New York: The McGraw-Hill Companies Inc, 2001, pp. 2391–2399.

[2] J. Jeong, “EEG dynamics in patients with Alzheimer’s disease,” Clin.

Neurophysiol., vol. 115, pp. 1490–1505, 2004. [3] D. Abásolo, R. Hornero, P. Espino, J. Poza, C. I. Sánchez, and R. de la

Rosa, “Analysis of regularity in the EEG background activity of Alzheimer’s disease patients with approximate entropy,” Clin.

Neurophysiol., vol. 116, no. 8, pp. 1826–1834, Aug. 2005. [4] J. W. Sleigh, D. A. Steyn-Ross, C. Grant, and G. Ludbrook, “Cortical

entropy changes with general anaesthesia: theory and experiment,” Physiol. Meas., vol. 25, no. 4, pp. 921–934, Aug. 2004.

[5] R. Hornero, J. Escudero, A. Fernández, J. Poza, and C. Gómez, “Spectral and non-linear analyses of MEG background activity in patients with Alzheimer’s disease,” IEEE Trans. Biomed. Eng., vol.

55, pp. 1658–1665, 2008. [6] C. Gómez, R. Hornero, D. Abásolo, A. Fernández, and J. Escudero,

“Analysis of MEG background activity in Alzheimer’s disease using non-linear methods and ANFIS,” Ann. Biomed. Eng., vol. 37, pp. 586–594, 2009.

[7] J. Poza, R. Hornero, J. Escudero, A. Fernández, and C. I. Sánchez, “Regional analysis of spontaneous MEG rhythms in patients with Alzheimer’s disease using spectral entropies,” Ann. Biomed. Eng., vol. 36, pp. 141–152, 2008.

[8] J. D. Veldhuis, S. M. Pincus, M. C. García-Rudaz, M. G. Ropelato, M. E. Escobar, and M. Barontini, “Disruption of the joint synchrony of luteinizing hormone, testosterone, and androstenedione secretion in adolescents with polycystic ovarian syndrome,” J. Clin. Endocrinol.

Metab., vol. 86, pp. 72–79, 2001. [9] D. Álvarez, R. Hornero, D. Abásolo, F. del Campo, C. Zamarrón, and

M. López, “Nonlinear measure of synchrony between blood oxygen saturation and heart rate from nocturnal pulse oximetry in obstructive sleep apnoea syndrome,” Physiol. Meas., vol. 30, pp. 967–982, 2009.

[10] S. M. Pincus, “Irregularity and asynchrony in biologic network signals,” Methods Enzymol., vol. 321, pp. 149–82, 2000.

[11] J. S. Richman and J. R. Moorman, “Physiological time series analysis using approximate entropy and sample entropy,” Am. J. Physiol.

Heart Circ. Physiol., vol. 278, pp. H2039"2049, 2000. [12] P. L. Nunez, R. Srinivasan, A. F. Westdorp, R. S. Wijesinghe, D. M.

Tucker, R. B. Silberstein, and P. J. Cadusch, “EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales,” Electroencephalogr.

Clin. Neurophysiol., vol. 103, no. 5, pp. 499–515, 1997. [13] J. Jeong, J. C. Gore, and B. S. Peterson, “Mutual information analysis

of the EEG in patients with Alzheimer’s disease,” Clin. Neurophysiol., vol. 112, no. 3, pp. 827–835, 2001.

[14] T. Koenig, L. Prichep, T. Dierks, D. Hubl, L. O. Wahlund, E. R. John, and V. Jelic, “Decreased EEG synchronization in Alzheimer’s disease and mild cognitive impairment,” Neurobiol. Aging, vol. 26, no. 2, pp. 165–171, 2005.

[15] C. J. Stam, B. F. Jones, I. Manshanden, A. M. van Cappellen van Walsum, T. Montez, J. P. A. Verbunt, J. C. de Munck, B. W. van Dijk, H. W. Berendse, and P. Scheltens, “Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer’s disease,” Neuroimage, vol. 32, no. 3, pp. 1335–1344, 2006.

[16] M. C. de LaCoste and C. L. White, “The role of cortical connectivity in Alzheimer's disease pathogenesis: a review and model system,” Neurobiol. Aging, vol. 14, no. 1, pp. 1–16, 1993.

6191

Goldschmidtboeing, Frank ................................................................................................................ WeB07.3 .............. 535 Goldstein, Goldie ............................................................................................................................... ThC04.3 ............. 3163 Goletsis, Yorgos ................................................................................................................................ WeC13.1 ............ 1262 .......................................................................................................................................................... WeD26.4 ............ 2206 Gollmer, Sebastian T. ....................................................................................................................... FrA05.5 .............. 3990 Golub, Matthew D. ............................................................................................................................ WeC15.5 ............ 1327 Golzan, S.Mojtaba ............................................................................................................................. ThD01.5 ............. 3384 .......................................................................................................................................................... FrD03.12 ............ 5278 Gomes, Pedro Tiago ......................................................................................................................... WeD24.4 ............ 2128 .......................................................................................................................................................... SaC14.4 ............. 6695 Gomez Tames, Jose David ............................................................................................................... ThD06.6 ............. 3576 Gomez, Carlos .................................................................................................................................. ThD02.10 ........... 3444 .......................................................................................................................................................... SaA02.4 ............. 6188 Gomez, James .................................................................................................................................. SaC14.1 ............. 6681 Gómez, Juan ..................................................................................................................................... WeE16.3 ............ 2522 Gomez-Clapers, Joan ....................................................................................................................... WeB08.1 .............. 550 .......................................................................................................................................................... FrC09.2 .............. 5034 Gomez-Foix, Anna Mª ....................................................................................................................... WeE18.3 ............ 2571 Gómez-García, Jorge Andrés ........................................................................................................... FrB01.9 .............. 4217 Gomis Gascó, Sergi .......................................................................................................................... WeA13.2 .............. 248 .......................................................................................................................................................... FrC14.5 .............. 5118 Gonçalves da Silva, Chagas, Vitoria ................................................................................................. WeD07.3 ............ 1578 Goncalves, Carlos Alberto ................................................................................................................ ThD06.10 ........... 3592 Gonenc, Berk .................................................................................................................................... WeC19.1 ............ 1401 Gong, Enhao ..................................................................................................................................... ThC15.6 ............. 3292 Gonzalez, Alejandro .......................................................................................................................... FrB20.8 .............. 4843 Gonzalez, Jose ................................................................................................................................. WeD13.2 ............ 1789 .......................................................................................................................................................... ThD06.6 ............. 3576 .......................................................................................................................................................... FrB05.1 .............. 4345 González-Arriaga, Oscar Hugo ......................................................................................................... FrB03.6 .............. 4262 Gonzalez-Camarena, Ramon ........................................................................................................... WeB11.1 .............. 605 Gonzalez-Martinez, Jorge ................................................................................................................. FrC17.3 .............. 5158 González-Suárez, Juan Manuel ........................................................................................................ WeC07.4 ............ 1165 Good, Norm ....................................................................................................................................... FrD24.9 .............. 5903 Goodwin, Antony ............................................................................................................................... FrB16.9 .............. 4631 Goodwin, Matthew ............................................................................................................................ FrA08.3 .............. 4046 Gopalakrishna, Vanishree ................................................................................................................. WeE03.5 ............ 2271 Gopinath, Ajay ................................................................................................................................... FrA06.3 .............. 4006 Gorbet, Rob ....................................................................................................................................... SaC19.1 ............. 6780 Gorgian Mohammadi, Amrollah ........................................................................................................ FrB04.4 .............. 4303 Górriz-Sáez, Juan Manuel ................................................................................................................ SaA06.4 ............. 6255 Goska, Benjamin ............................................................................................................................... WeD20.7 ............ 2017 Gosselin, Benoit ................................................................................................................................ WeD10.1 ............ 1659 .......................................................................................................................................................... FrE08.6 .............. 6015 Gott, Shannon ................................................................................................................................... WeD09.3 ............ 1639 .......................................................................................................................................................... FrD17.3 .............. 5666 Gottshall, Kim .................................................................................................................................... FrE17.4 .............. 6141 Goubran, Rafik A. .............................................................................................................................. WeA14.2 .............. 268 .......................................................................................................................................................... FrD22.3 .............. 5810 Goudas, Theodosis ........................................................................................................................... FrB08.6 .............. 4414 .......................................................................................................................................................... FrB08.7 .............. 4418 Goudey, Benjamin ............................................................................................................................. WeC11.6 ............ 1258 Goulding, Evan .................................................................................................................................. WeE08.5 ............ 2392