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Enhanced Effective Connectivity in Mild Occipital Stroke Patients With Hemianopia Xiaoli Guo, Zheng Jin, Xinyang Feng, and Shanbao Tong, Senior Member, IEEE

Abstract—Plasticity-based spontaneous recovery and rehabilitation intervention of stroke-induced hemianopia have drawn great attention in recent years. However, the underlying neural mechanism remains unknown. This study aims to investigate brain network disruption and reorganization in hemianopia patients due to mild occipital stroke. Resting-state networks were constructed from 12 hemianopia patients with right occipital infarct by partial directed coherence analysis of multi-channel electroencephalograms. Compared with control subjects, the patients presented enhanced connectivity owing to newly formed connections. Compensational connections mostly originated from the peri-infarct area and targeted contralesional frontal, central, and parietal cortices. These new ipsilesional-to-contralesional inter-hemispheric connections coordinately presented significant correlation with the extent of vision loss. The enhancement of connectivity might be the neural substrate for brain plasticity in stroke-induced hemianopia and may shed light on plasticity-based recovery or rehabilitation. Index Terms—Hemianopia, enhanced connectivity, mild occipital stroke, newly formed connections, resting-state networks.

I. INTRODUCTION

U

NILATERAL occipital stroke typically leads to visual field defects referred to as hemianopia. Hemianopia has great impacts upon an individual’s reading, scanning and attention, resulting in difficulties in daily living [1], [2]. Spontaneous recovery of stroke-induced hemianopia has been noticed in clinical investigations [3]–[5]. Approximately 60% of patients could experience spontaneous improvement, usually within the first month after injury [3], [5]. Proactive treatment approaches also have been developed, and the most ambitious one, visual field restoration based on the hypothesis of plasticity, has attracted increasing attention in recent years [6].

Manuscript received October 24, 2013; revised January 13, 2014, March 19, 2014, May 12, 2014; accepted May 14, 2014. Date of publication May 22, 2014; date of current version November 13, 2014. This work was supported by National Basic Research Program of China (2011CB013304), National Natural Science Foundation of China (61001015), the Medical-Engineering Joint Research Program of Shanghai Jiao Tong University (YG2011MS68), and the Science-Engineering Joint Research Program of Shanghai Jiao Tong University. X. Guo and Z. Jin contributed equally to this work. X. Guo and X. Feng are with the School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. Z. Jin is with the Department of Neurology, The Fifth People’s Hospital of Shanghai, Shanghai 200240, China. S. Tong is with the School of Biomedical Engineering and the Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200240, China (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TNSRE.2014.2325601

Recovery from stroke, whether “spontaneous” or “learned”, has been demonstrated to be modulated by brain plasticity from molecular to systems to behavior [7]. Many functional deficits and their recovery after stroke, such as motor, neglect, language and cognitive recovery, have been studied from neuroplasticity [8]. Based on stimulating or increasing neuroplasticity, new approaches like behavioral manipulations, adjuvant therapies, or noninvasive brain stimulations, have been proposed to promote the recovery of function [9]. However, the underlying neural plasticity or reorganization following occipital stroke, which is critical to spontaneous recovery or treatment intervention of hemianopia, remains unknown. Considering that brain is a complex network of dynamical system with abundant interactions between different areas [10], connectivity-based methods provide new insight into brain strategies for recovery from a system perspective [11]. Various methods, such as coherence [12], [13], mutual information [14]–[16], partial directed coherence (PDC) [17], [18], and phase synchronization [19]–[21], have been applied to the quantification of neural connectivity between different brain regions using neuroimaging or electrophysiological techniques. Brain plasticity after ischemic stroke has been widely studied from the perspective of connectivity, especially in the motor network [22]–[25]. The findings can be summarized by two major patterns of changes after stroke: 1) reduced brain connectivity related to clinical deficits, especially the inter-hemispheric connections [19], [23], [26]–[28] and the intra-hemispheric connections in the ipsilesional hemisphere [29]–[31]; and 2) compensational enhanced connectivity contributed to functional recovery [13], [19], [32]. However, the detailed patterns depend greatly on the injury (e.g., lesion site and severity). Alteration of functional networks in stroke-induced hemianopia also has been investigated by graph theoretical analysis in our preliminary study of seven patients with lesions in left occipital, parietal, or temporal cortex [33]. We found that hemianopia stroke patients had no significant differences from healthy controls in terms of the global topological metrics (i.e., characteristic path length and clustering coefficient) of brain networks, but had greater node strength in ipsilesional temporopolar and orbitfrontal areas as well as the contralesional associative visual cortex [33]. However, undirected functional brain networks based on phase synchronization ignored the directions of cortical interactions and the neural driving architecture which are important aspects of describing the information interchange between brain regions [34]. In addition, further investigation and more reliable statistical analyses should be performed on more lesion-focused samples.

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TABLE I SUBJECTS DEMOGRAPHY

With these considerations, in this study, we enrolled 12 hemianopia patients due to right occipital stroke, and constructed effective brain networks using PDC analysis of multi-channel electroencephalography (EEG) signals. Connective abnormality of the patients was detected by comparing with control subjects from characteristics of global network or individual connection. Further, we studied the correlation between the patients’ visual deficit and connectivity measures. The current study aims to characterize the disruption and reorganization of cortical networks of hemianopia stroke patients and provide new insights into the neural substrate for brain plasticity in stroke-induced hemianopia. II. MATERIAL AND METHODS A. Subjects years, range from 46 Twelve patients (age to 80 years; male/female ) with hemianopia due to right occipital ischemic stroke were recruited from the Department of Neurology in the Fifth People’s Hospital of Shanghai. The lesions were confirmed by a clinical MRI scanner (GE Signa HDx 1.5T, GE Healthcare, Waukesha, WI, USA). All patients reported hemianopia symptom after mild stroke (NIHSS: 1–3) and eleven of them were diagnosed with an automated perimetry (ZEISS Humphrey 750i field analyzer, Carl Zeiss Medibec Inc., Dubin, CA, USA) using Humphrey 24–2 or 30–2 threshold test. The vision field index (VFI) was provided to evaluate the percentage of the remaining visual field (VF) of an eye. From integrated VF of both eyes, binocular VFI (BVFI) was estimated to assess binocular visual function [35], [36]. The BVFIs of patients were ranging from 16.5% to 90.3%. Twelve age- and gender-matched control subjects (age years, range from 52 to 78 years; male/female ) were recruited from the local community. All control subjects had normal or corrected-to-normal vision, and reported no history of neurological diseases or psychiatric disorders. Each participant signed a written informed consent in compliance with the Declaration of Helsinki. This study was approved by the Ethics Committee of Shanghai Jiao Tong University and the Fifth People’s Hospital of Shanghai. Demographic details of each subject were presented in Table I.

B. EEG Recording and Preprocessing All subjects were seated in relaxation, keeping awake with eye closed during EEG recording. Two minutes of resting EEG signals were recorded continuously with BrainAmp (BrainAmp DC, Brain Products GmbH, Munich, Germany) from 30-channel Ag-AgCl electrodes (EasyCap, Brain Products GmbH, Munich, Germany) at a sampling rate of 1000 Hz. Another two channels of horizontal and vertical electrooculograms (EOGs) were recorded for rejecting undesired eye movements and blinks. An electrode between Fz and Cz (i.e., FCz) served as the reference. All the electrode impedances were kept below 5k . EEG preprocessing was performed offline using Analyzer (Version 2.0, Brain Products GmbH, Munich, Germany). In order to investigate the whole brain network, including the vertex (i.e., Fz and Cz), EEG signals were re-referenced to the average of electrodes TP9 and TP10 [37]. Then there were 28 channels (Fp1, Fp2, F3, F4, F7, F8, Fz, FC1, FC2, FC5, FC6, C3, C4, T7, T8, CP1, CP2, CP5, CP6, Cz, P3, P4, P7, P8, Pz, O1, O2, Oz) left for subsequent network analysis. EEG signals were band-pass filtered into the alpha band (8–13 Hz), which is predominant during wakeful relaxation with closed eyes and plays an active role in network coordination and communication [38]. For each subject, a 4 s artifact-free epoch of EEG signals was selected for further PDC analysis. C. Partial Directed Coherence Partial Directed Coherence (PDC) is a Granger Causality measure in the frequency domain [39]. It has been widely used to investigate information flow in the brain with either fMRI or EEG data in the past decade [17], [40], [41]. Briefly, the -channel ( in this study) EEG signals at time point are defined as a vector (1) where denotes the EEG signal from the th channel. Then the EEG series can be modeled with a -order multivariate autoregressive (MVAR) model (2)

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where are the coefficient matrixes and is a vector of multivariate uncorrelated white Gaussian noise. The model order are determined by the Akaike Information Criterion (AIC) [42] and are estimated by the Levison–Wiggins–Robinson (LWR) algorithm [43]. Once the MVAR coefficients have been adequately estimated, the information flow from channel to at frequency could be calculated as [44] (3)

(4) where is the element of at th row and th column. Usually, the average PDC value in a frequency range (i.e., 8–13 Hz in this study) is used to analyze the strength and direction of the interaction

Fig. 1. Grand averages of brain networks of control subjects and hemianopia . Size stroke patients. Arrows indicate the significant connections of the nodes is proportional to the degree. Connections which are significantly enhanced in patients’ networks are highlighted, and those of newly formed connections and strengthened existing connections are colored in green and blue, respectively.

(5) III. RESULTS In this study, the spectral causality criterion (SCC) introduced by Schnider et al. was adopted to evaluate the significance of estimated PDC values [45]. According to this empirical SCC, no significant causality was considered unless the corresponding PDC value exceeded a threshold of 0.1. D. Network Analyses In this study, 756 PDCs were estimated for all electrodes in pair to construct brain network. The sum of PDCs (sPDC ) of all significant connections (those with ), and the number (N), mean PDC (mPDC), and anatomical length (L) of significant connections were calculated to assess the global connectivity of cortical network. The degree of each node was calculate to investigate regional characteristics. In a directed graph, the degree is usually divided into in-degree and out-degree. The in-degree is the total number of connection incoming (afferent) to the node and the out-degree is the total number of connection outgoing (efferent) from the node . The degree of the node is the sum of its in-degree and out-degree (6) E. Statistical Analyses Statistical analyses were performed using SPSS 17.0 (SPSS Inc., Chicago, IL, USA). Group differences of sPDC , N, mPDC, and L of significant connections, and the PDC value of each connection, were tested with two-tailed t-test. The correlations between PDC measures and BVFI were assessed by Pearson’s correlation. Statistical significance was accepted at . For analyses of each individual connection, the significance threshold was adjusted for multiple comparisons by rough false discovery rate (RFDR) correction (p adjusted to 0.025) [46]. Recently, the RFDR criterion has been applied to the comparison of networks in different conditions [47].

A. Global Network Connectivity The grand averages of brain networks of hemianopia stroke patients and control subjects, respectively, were representatively visualized with the BrainNetViewer (http://www.nitrc.org/projects/bnv/) (Fig. 1). Generally, the network of hemianopia stroke patients presented a global overconnectivity compared with control subjects. Larger degrees of nodes were observed and more significant connections were involved in the patients’ network. Detailed statistical analyses were performed to study the abnormality of brain networks of hemianopia stroke patients. The sPDC , which includes overall strength of the cortical connections, showed a significant difference between hemianopia stroke patients and control subjects (Fig. 2(a), patients: versus control: ), presenting an enhancement of global network connectivity after occipital ischemic stroke. Furthermore, hemianopia stroke patients exhibited more significant connections than control subjects (Fig. 2(b), patients: versus control: ), indicating the formation of new connections after the occipital ischemic stroke. Nevertheless, the mean strength (mPDC) of significant connections were comparable in two groups (Fig. 2(c), patients: versus control: ). B. Hemianopia-Related Changes at Individual Connection Two-tailed t-test of each connection was performed to investigate the detailed differences of cortical networks between hemianopia stroke patients and control subjects. Thirty among 756 connections were significantly stronger for hemianopia stroke patients, including 27 newly formed significant connections (group averaged for patients and for controls) and 3 strengthened existing connections (group averaged for both groups) [Fig. 1(b)]. The

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C. Correlation Between Enhanced Connectivity and BVFI Either global network or individual connection presented enhancement of connectivity after occipital stroke; we further investigated their correlation with the extent of VF loss (indexed as BVFI). Global network measures (sPDC , and N) and each individual new connection, which were significantly enhanced in hemianopia stroke patients, had no correlation with the BVFI. However, those new connections from right to left hemisphere (i.e.,

Fig. 2. Global network connectivities of hemianopia stroke patients and control ) of all significant connections subjects. (a) Sum of PDCs (sPDC ; (b) number (N) of significant connections; (c) mean PDC (mPDC) of sig. nificant connections.

results also supported the dominating role of newly formed connections in enhancement of connectivity in hemianopia stroke patients. Difference network between two groups which only considered the 27 new significant connections was plotted [Fig. 3(a)]. It could be noticed that more new connections directed to the intact (left) hemisphere (i.e., right-to-left inter-hemispheric (RL) and intra-left hemispheric (LL) connections) were activated than those targeting the ischemic (right) hemisphere (left-to-right inter-hemispheric (LR) and intra-right hemispheric (RR) connections) versus , presenting a compensation of the contralesional hemisphere [Fig. 3(b)]. According to the anatomical (Euclidean) distance between electrodes based on the standard Montreal Neurological Institute (MNI) brain [48], connections were classified into local ( mm), mid-distance (80–140 mm), and long-distance ( mm) sub-categories. Most of the new connections were mid-distance or long-distance [Fig. 3(a)], so that patients’ networks preserved a smaller proportion of local connections (patients: % versus control: % ) and a larger proportion of long-distance connections (patients: % versus control: % ) than controls’ [Fig. 3(c)]. Accordingly, the average anatomical length (L) of significant connections in patients’ networks were longer than that in control subjects (Fig. 3(d), patients: mm versus control: mm, ). To investigate regional characteristics of new connections, the in-degree and out-degree of each node in the difference network were computed, respectively. Topographic mappings of the in-degree and out-degree were presented using a revised topoplot algorithm from EEGLAB [49] (Fig. 4). High out-degree at P8 was found, involving five long- or mid-distance inter-hemispheric interactions to Fp1, F3, FC5, CP1, and CP5. Whereas, areas with relatively high in-degree were distributed in almost all left frontal, central and parietal cortices (Fp1, F3, FC1, C3, CP1, CP5, and Pz) and right centro-posterior region (CP2). It was inferred that most compensational information flow should originat from peri-infarct area (i.e., P8) and target the contralesional hemisphere after occipital ischemic stroke.

), coordinately presented a significant negative correlation with the BVFI (mPDC of new RL connections and , Fig. 5). BVFI: IV. DISCUSSION A. Enhanced Connectivity After Occipital Stroke Post-stroke disruption of brain networks has been extensively studied by both neuroimaging [50], [51] and electrophysiological [19], [52] techniques. So far the results are not absolutely consistent because of the high variance in patients, due in part to age, stroke severity, lesion site, and time of recovery. However, the most consistent finding was that stroke may lead to a reduction of brain connectivity, especially the inter-hemispheric interactions [19], [23], [26]–[28] and the intra-hemispheric interactions in the ipsilesional hemisphere [29]–[31]. On the other hand, there were also evidences of some compensational increase of connectivity. Relatively higher connectivity in the contralesional hemisphere was suggested by coherence analyses of EEG data recorded in well-recovered stroke patients [13]. The synchronization of EEG signals increased among intact areas in the ipsilateral hemisphere for unilateral stroke patients [19]. Higher functional connectivity with the ipsilesional frontal and parietal cortices, bilateral thalamus, and cerebellum was also demonstrated in stroke patients by fMRI [32]. However, the increased connectivity might not compensate for the drastic reduction in information propagation [52] and lead to a global hypoconnectivity. In this study, occipital stroke patients presented a global increased connectivity, especially the information flow to the contralesional hemisphere, which could be due to relatively mild severity of the stroke (NIHSS score: range from 1 to 3), so that the compensation overweighed the mild reduction of connectivity caused by the brain damage. In contrast, a hemianopia patient with a ) and lesion site at parietal moderate stroke (NIHSS and occipital lobes, who was excluded in this study, exhibited a significant reduction of overall connectivity compared with control subjects. The enhancement of connectivity, which was also supposed as the reflection of cortical disinhibition induced by lesions [53], was highly involved in functional reorganization and network reconfiguration [54], [55], and presumed as neural mechanism of recovery of stroke-induced hemianopia. Our results showed that the enhanced connectivity of hemianopia stroke patients was a manifestation of the forming of new significant connections. The outgrowth of new connections may compensate for impaired pathways after stroke. Similar evidences, especially in the contralesional hemisphere, have been found in stroke patients using DTI and fMRI [13], [23], [56], [57]. Different from random outgrowth patterns suggested by previous studies [58], this study of occipital stroke patients

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Fig. 3. Properties of difference network between hemianopia stroke patients and control subjects. (a) Overview of difference network with 27 new significant connections. In this representation, the local, mid-distance, and long-distance connections are colored in red, green, and yellow, respectively. (b) Intra-left hemispheric (LL), intra-right hemispheric (RR), left-to-right inter-hemispheric (RL), and right-to-left inter-hemispheric (RL) sub-networks of difference network. (c) Probability distribution of local, mid-distance, and long-distance connections in brain networks of hemianopia stroke patients and control subjects and difference network. . (d) Average anatomical length (L) of significant connections in brain networks of hemianopia stroke patients and control subjects.

found that most of the new connections were mid- or long-distance, and mainly originated from the peri-infarct area and targeted the contralesional hemisphere. Our preliminary study of functional networks using phase synchronization did not find different global topological metrics between hemianopia stroke patients and control subjects [33]. In this study, enhanced connectivity of both global network and individual connections were observed, suggesting the potential usefulness of effective networks in characterization of hemianopia stroke patients. B. Roles of Peri-Infarct Cortex and Contralesional Hemisphere in Compensation After Stroke Peri-infarct cortex and contralesional hemisphere are well acknowledged as main regions for functional compensation

after stroke. However, their roles in compensation remain unclear. One hypothesis is that the recruitment of peri-infarct cortex or contralesional hemisphere is presumably mediated by the severity of stroke [7]. A mild stroke is likely to involve peri-infarct area that has a similar function [59]–[61]; by contrast, a severe stroke would activate long-distant integration for functional rehabilitation, such as the regions in the contralateral hemisphere [62]. However, functional brain imaging studies suggested that peri-infarct cortex and contralesional hemisphere were involved in two sequential phases of remapping, respectively [63], [64]. That is, lost functions would initially shift toward the intact hemisphere and later, as the inflammation subsides and blood flow improves, those functions are taken over by the peri-infarct cortex. In our study from the aspect of causal connectivity, both peri-infarct cortex and contralesional hemisphere were involved in functional compensation after

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loss. Patients with relatively larger vision loss after right occipital stroke presented more prominent enhancement of new ipsilesional-to-contralesional inter-hemispheric connections. D. The Effects of Different Thresholds

Fig. 4. Topographic mappings of the out-degree and in-degree of the difference network. Topography shows the direction of compensational information flow from P8 to left hemisphere.

Fig. 5. Negative correlation between mPDC of new right-to-left (RL) connections and the extent of VF loss.

mild occipital stroke, and served as main origin and destination of compensational information flow, respectively. C. Enhanced Connectivity in Relation to Vision Field Loss Disruption of brain network, especially disturbed inter-hemispheric connectivity, has been found associated with behavioral deficit after stroke. Carter et al. reported that inter-hemispheric functional connectivity in the somatomotor network positively correlated with motor performance at the subacute stage after stroke [26]. Park et al. found that stronger inter-hemispheric connectivity of the ipsilesional M1 with the contralesional thalamus, supplementary motor area, and middle frontal gyrus at onset predicted better motor recovery at six months post-stroke [32]. Similarly, the early inter-hemispheric synchrony of EEG presented a high correlation with NIHSS two months after stroke [19]. Such correlations were not exclusively reported in motor systems, but also in patients with attention deficits [26], [27] or aphasia [28]. In our study of occipital stroke patients, new connections from ipsilesional to contralesional hemisphere coordinately correlated with the extent of vision

Different thresholds have been proposed for defining the existence of the connections, e.g., the fixed threshold of by SCC (e.g., 0.1) [45], and data-dependent thresholds on an empirical distribution using surrogate data [65] or on the rigorous asymptotic distributions of PDC [66]. Accordingly, different thresholding methods would result in different adjacency matrix of a network, and thus affect the graph-theoretic measurements [67]. In statistic aspect, data-dependent thresholds which take into account the spectral information of signals make more sense than SCC method, particularly when the topographic patterns (e.g., by graph theory) were interested. For the data in this study, we also extracted the threshold from the statistical validation with a phase-shuffled surrogate method [68], and got similar conclusions on the global network connectivity. For example, the patients showed more significant connections with longer average anatomical length (L) compared with control subjects (N: patients: versus control: ; L: patients: versus control: ), and the mPDC of significant connections has no significant difference between two groups (mPDC: patients: versus control: ). In many studies including this one, however, we also want to know whether a specific connection is strengthened or weakened. Thus, the absolute values of the connection strength will be statistically compared. In such a situation, those data-dependent thresholds make it difficult to compare connection strength, because the same PDC values from different subjects/groups actually are not in the same scale and represent different significances. Therefore, a fixed threshold was used in this study to compare the connection strength between controls and stoke groups. Although the fixed threshold was considered essentially arbitrarily, it has been supported by numerical simulations [69], [70] and widely used in neuroscience [40], [45], [69], [71]. Nevertheless, we recommend using those data-dependent thresholds if only the topographic patterns of a network are interested. V. CONCLUSION In summary, resting-state networks of mild occipital stroke patients with hemianopia were investigated by partial directed coherence using multi-channel EEGs. The patients presented enhanced connectivity due to newly formed connections. Most of the new connections were mid- or long-distance, and mainly originated from peri-infarct area and targeted contralesional frontal, central, and parietal cortices. The new ipsilesional-to-contralesional inter-hemispheric connections coordinately correlated well with the extent of vision loss. This study suggested the enhancement of connectivity as a neural mechanism of recovery of stroke-induced hemianopia and demonstrated the potential usefulness of effective networks in characterization of stroke patients.

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 22, NO. 6, NOVEMBER 2014

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GUO et al.: ENHANCED EFFECTIVE CONNECTIVITY IN MILD OCCIPITAL STROKE PATIENTS WITH HEMIANOPIA

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[70] L. Astolfi et al., “Assessing cortical functional connectivity by partial directed coherence: Simulations and application to real data,” IEEE Trans. Biomed. Eng., vol. 53, no. 9, pp. 1802–1812, Sep. 2006. [71] C. Zhu et al., “Influences of brain development and ageing on cortical interactive networks,” Clin. Neurophysiol., vol. 122, pp. 278–283, Feb. 2011. Xiaoli Guo received the B.S. degree and the Ph.D. degree in biomedical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2003 and 2008, respectively. Currently, she is an Assistant Professor in the School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. Her research interests include neural signal processing and neural plasticity and rehabilitation after stroke.

Zheng Jin received the B.S. degree of clinical medicine and the M.S. degree of neurology from Shanxi Medical University, Shanxi, China, in 1993 and 2001, respectively. She worked as a Resident Doctor from 1993 to 2001 and as an Attending Doctor from 2002 to 2008 at the Department of Neurology of The Third People’s Hospital of Datong, Shanxi, China. Currently, she is an Associate Chief Physician at the Department of Neurology of Shanghai The Fifth People’s Hospital, Fudan University, Shanghai, China. Her research interests include cerebrovascular disease, headache, and peripheral neuropathy. Dr. Jin is a committee member of Deficiency Syndrome and Geriatric Medicine, Chinese Association of the Integration of Traditional and Western Medicine, and China Association of Traditional Chinese Medicine.

Xinyang Feng was born in Shijiazhuang, China, in 1991. He is an undergraduate student in biomedical engineering at Shanghai Jiao Tong University, Shanghai, China. His research interests include neuroimaging and neural signal processing.

Shanbao Tong (SM’10) received the B.S. degree in radio technology from Xi’an Jiao Tong University, Xi’an, China, in 1995, the M.S. degree in turbine machine engineering, and the Ph.D. degree in biomedical engineering from Shanghai Jiao Tong University, Shanghai, China, in 1998 and 2002, respectively. From 2000 to 2001, he was a Research Trainee in the Biomedical Instrumentation Laboratory, Biomedical Engineering Department, Johns Hopkins School of Medicine, Baltimore, MD, USA. He was a Postdoctoral Research Fellow in the Biomedical Engineering Department, Johns Hopkins School of Medicine from 2002 to 2005. Currently, he is a Professor in the School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China. His research interests include neural signal processing, neurophysiology of brain injury, and laser speckle imaging and instrumentation. Prof. Tong is the founding chairs of the IEEE EMBS Shanghai Chapter and the IEEE EMBS international summer school on neural engineering (ISSNE). He is also an active Associate Editor of the journal IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Associate Editor of Medical and Biological Engineering and Computing, Regional Editor of IEEE Pulse Magazine, and member of the IEEE EMBS Technical Committee on Neuroengineering.