in frontal and occipital gyrus, thalamus, putamen, and caudate (**Figure 5**, **Table 4**). For both groups, the PPIs corresponding to putamen included not only some nucleus in basal ganglia, such as putamen, thalamus and caudate, but also some cortical areas, such as frontal and occipital gyrus. Between group PPI difference was detected in cingulate gyrus, angular gyrus, and precuneus (control > PD) and inferior frontal gyrus (PD > control). See **Figure 6**, **Table 5**. Generally the PPI scope in the control group was much larger. Several studies reported the relatively reduced functional connectivity in PD patients (van Eimeren et al., 2009; Skidmore et al., 2011; Hacker et al., 2012). Specifically in accordance with our results, the functional connectivity corresponding to putamen seemed weaker in PD patients (Hacker et al., 2012). Lower striatal correlations with thalamus, midbrain, pons and cerebellum in PD patients (Hacker et al., 2012), decreased activity in the putamen and increased cortical activity in the frontal lobe (Disbrow et al., 2013) have been reported. In our study, the PPI map of the control group was generally symmetric except in the putamen and thalamus, while the PPI map of the PD group was obviously asymmetric, which was similar with former observation in the functional connectivity of PD patients (Barnes et al., 2010; Hacker et al., 2012).

#### Key Regions in the Activation and Network Profiles of Control and PD Groups

In the activation and network profiles of the control and PD groups, two regions, i.e., the putamen and frontal regions seemed to play the specific roles. The putamen exhibited the control > PD activation and left + right > both hand movement activation within the control group. The frontal gyrus exhibited PD > control activation and connectivity with the putamen. These two

TABLE 4 | Anatomical structure, stereotaxic coordinates, and Z score of the peak areas in the PPI profiles in the control or patient groups.

*The height and extent thresholds were set at P* < *0.001, uncorrected, k* > *10. The location is in MNI coordinates. PD, Parkinson's disease; BA, Brodmann's area.*

regions are specifically crucial in the cortico-subcortical network and frontal network of the PD patients (Litvak et al., 2011).

The human brain network of motor function is composed of basal ganglia, cerebral motor cortex and cerebellum, among which the basal ganglia connect dorsal thalamus, ventromedial nucleus, premotor area and prefrontal cortex, and play the critical role in the complex cortical-subcortical circuits, i.e., the basal ganglia-thalamus-cortex circuits (Alexander and Moeller, 1994). Many researchers have emphasized the pathophysiology of PD as degeneration of dopaminergic nigrostriatal neurons with consequent dysfunction of these circuits (Lang and Lozano, 1998b; Jankovic, 2008; Hacker et al., 2012). The basal ganglia serve motor control functions such as scaling or focusing of movements (Alexander and Crutcher, 1990), and sustain the balance between facilitation and suppression of movements (Mink, 1996). Previous finding on functional connectivity indicated that the dorsal striatum (caudate and putamen) preferentially receives inputs from motor, sensory and premotor cortices, the ventral striatum (the nucleus accumbens and the olfactory tubercle) receives afferent inputs from cingulate cortex (Graybiel et al., 1994; Brooks, 1995). Specifically the putamen is the projection site of the cortical inputs into the basal ganglia and its activity is mainly movement related instead of cognition related (Kraft et al., 2007). Histologically, afferent fibers from the dorsal part of the putamen project somatotopically to the lateral parts of the substantia nigra (SN), which relates to the motor circuit system, and fibers from the caudate project to the rostral nigra, which relates to cingulate and association cortical system (Parent and Hazrati, 1994). In accordance with these results, we observed the putamen modulation on the thalamus, cingulate gyrus and the association area in control group (**Table 4**). The putamen activity and its modulation to cingulate gyrus and the association area seemed larger in control group than in PD group (**Tables 3**, **5**).

The functional role of the basal ganglia in bimanual coordination isn't quite clear till now. Putaminal activity was the greatest during the period of motor task initiation and was critical in the neural control of bimanual coordination (Kraft et al., 2007). An animal experiment found that the majority of the 58 recorded neurons in the basal ganglia exhibited a significant modulation of activity in unimanualtrials irrespective of the movement hand and one-third of the neurons exhibited activity reflecting a bimanual synergy, suggesting a possible role for basal ganglia in bimanual co-ordination (Wannier et al., 2002). In PD patients, the disturbed effective connectivity between prefrontal cortex, premotor areas, and putamen were reported (Ceballos-Baumann et al., 1994; Rowe et al., 2002; Wu et al., 2010). As the important node in the direct and indirect efferent pathways in the basal ganglia, the striatum can influence the basal ganglia output and exert either excitatory or inhibitory effect on the movement behavior including action selection as well as execution (Disbrow et al., 2013; Freeze et al., 2013). In PD, the abnormal interconnection among putamen, SN and GP causes excessive inhibition of the thalamus, and results tremors and difficulty in voluntary movements of the patients (DeLong and Wichmann, 2007). Because the putamen had stronger activation in the unimanual movement than bimanual movement in the control group, we speculated that the putamen mainly had the inhibitory effect and its function should be weakened to include more areas in bimanual movement. The control > PD putamen activation is reasonable considering that the weakened putamen inhibition would result in more involuntary movement in PD patients.

The activation of the left superior frontal gyrus and the connectivity between the left inferior frontal gyrus and putamen was stronger in PD group than that in the control group. These results implied the abnormal function of the left frontal gyrus in PD patients. The activity of the frontal area is related with the shift of the attention and the executive control, which are the crucial pre-movement processes (Wu et al., 2010; Disbrow et al., 2013). There is evidence indicating the hemodynamic responses in the mesiofrontal and sensorimotor cortex, putamen/pallidum, thalamus, and cerebellum and the participation of frontal area in the network for motor preparation (Riecker et al., 2005), movement initiation (Toxopeus et al., 2012), amplitude adjustment (Fabbri et al., 2012; Davare et al., 2015), and speed adjustment (Michely et al., 2015). It is noted that the executive functions of the PD patients may be affected even at early stages of the disease which would result in impaired motor planning, response preparation, and inhibition (Obeso et al., 2011; Toxopeus et al., 2012; Michely et al., 2015). While we noted that in our research, the patients reported no obvious difficulty in performing the unimanual or bimanual finger-tothumb movements and the preserved movement performance

TABLE 5 | Anatomical structure, stereotaxic coordinates, and Z score of the different peak areas between the PPI profiles in the control or patient groups.

*The height and extent thresholds were set at P* < *0.001, uncorrected, k* > *10. The location is in MNI coordinates. PD, Parkinson's disease; BA, Brodmann's area.*

was observed. The hyperactivation/connectivity of the prefrontal cortex was suggested to constitute a compensatory mechanism in the patients' hypodopaminergic state to maintain the task performance at normal levels (Rowe et al., 2002; Wu et al., 2010; Michely et al., 2015). We spectulated that the stronger activation and connectivity with putamen of the left frontal gyrus may express the compensatory neuroplasticity during the motor programs.

#### Limitations and Conclusion

The present study is limited principally by the relatively small size of the subject samples. This limitation is partially because of the rigorous quality assurance standards applied to the fMRI data. However, our results need to be replicated in much larger size of samples. Furthermore, because of the small sample size, the patient group can't be subdivided according to their motor symptoms and phenotypes, which have been demonstrated to functionally relate with the brain activities (Rajput et al., 2008; Bunzeck et al., 2013). Further studies might help in establishing which pathological features are common and different between motor phenotypes.

In contrast to cortical and cerebellar activity, the activity of the basal ganglia is more inconsistently reported in fMRI motor studies (Lehéricy et al., 2006), which may be caused by small signal change in the subareas (for example about 0.5% in the putamen; Lehéricy et al., 2006), the movement paradigm (externally or internally generated), the imaging resolution inefficiency to discriminate the substructures (GP vs. putamen) of basal ganglia (Scholz et al., 2000). It's possible that SN degeneration is common to all the PD patients, but the factors in other parts of the basal ganglia distinguish between phenotypes (Rajput et al., 2008; Bunzeck et al., 2013). Several studies have indicated that there existed different grades of connectivity depending on the striatal subdivision, such as posterior putamen > anterior putamen connectivity with the brainstem (Hacker et al., 2012). In our research, the dysfunction of the putamen was observed, but the further exploration within this region was not performed. In addition, when detecting PPI and PPI difference, we applied the uncorrected hypothesis test. These limitations are subject to the current research condition that only the 1.5T MRI scanner with the relatively low signalto-noise ratio (SNR) is available in our hospital, which has the inefficient spatial resolution and can only generate the signal with small changes in these substructures of the basal ganglia. MRI scanner with higher magnetic field strength or the multi-modal neuroimaging is promising to solve these issues.

There exists the formal possibility that the drug discontinuance time (4 h) might be not long enough to eliminate the medication effects. We didn't request the subjects to withdraw the drug for 1 day or to take their usual medication like other research (Hacker et al., 2012) based on the consideration to fulfill a tradeoff between the impact of head motion on imaging and the impact of medication on data analysis. This warrants the further research to include both the medicated and unmedicated states. The fMRI studies of the unmedicated (drugnaive) patients are challenging and some powerful regression techniques might help to reduce the motion-related artifacts (Helmich et al., 2010). Further studies might examine how PPI differences change depending on medication status and types.

In this paper, the brain network profiles in 11 PD patients without dementia were studied and compared with 12 healthy controls. The right putamen exhibited significant control > PD activation difference and weaker activity during the bimanual movement relative to the unimanual movement in control groups. The PPIs corresponding to the putamen (with peak voxel at [26, 18, −4] and [32, 8, 10]) were explored. Between group PPI difference was detected in cingulate gyrus, angular gyrus and precuneus (control > PD) and inferior frontal gyrus (PD > control). PD patients exhibited reduced putamen activation as well

#### References

as modulation to the cingulate gyrus, angular gyrus, and precuneus during the finger-to-thumb movement, which implies the impaired basal ganglia inhibition in movements. The hyperactivation/connectivity of the frontal gyrus is the compensation to maintain the task performance during the motor programs.

#### Acknowledgments

This work was supported by the Natural Science Foundation of China (Grant 30800253, 31200777).

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Yan, Wu, Zeng and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Dysfunctional activation and brain network profiles in youth with obsessive-compulsive disorder: a focus on the dorsal anterior cingulate during working memory

#### **Vaibhav A. Diwadkar 1\*, Ashley Burgess 1 , Ella Hong1 , Carrie Rix 1 , Paul D. Arnold2 , Gregory L. Hanna3 and David R. Rosenberg1**

1 Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA

2 Department of Psychiatry, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada

3 Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA

#### **Edited by:**

Baojuan Li, Fourth Military Medical University, China

#### **Reviewed by:**

Dewen Hu, National University of Defense Technology, China Lirong Yan, Wuhan General Hospital, China

#### **\*Correspondence:**

Vaibhav A. Diwadkar, Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Suite 5B, 3901 Chrysler Drive, Detroit, MI 48201, USA e-mail<EMAIL_ADDRESS>
Brain network dysfunction is emerging as a central biomarker of interest in psychiatry, in large part, because psychiatric conditions are increasingly seen as disconnection syndromes. Understanding dysfunctional brain network profiles in task-active states provides important information on network engagement in an experimental context. This in turn may be predictive of many of the cognitive and behavioral deficits associated with complex behavioral phenotypes. Here we investigated brain network profiles in youth with obsessive-compulsive disorder (OCD), contrasting them with a group of age-comparable controls. Network interactions were assessed during simple working memory: in particular, we focused on the modulation by the dorsal anterior cingulate cortex (dACC) of cortical, striatal, and thalamic regions. The focus on the dACC was motivated by its hypothesized role in the pathophysiology of OCD. However, its task-active network signatures have not been investigated before. Network interactions were modeled using psychophysiological interaction, a simple directional model of seed to target brain interactions. Our results indicate that OCD is characterized by significantly increased dACC modulation of cortical, striatal, and thalamic targets during working memory, and that this aberrant increase in OCD patients is maintained regardless of working memory demand.The results constitute compelling evidence of dysfunctional brain network interactions in OCD and suggest that these interactions may be related to a combination of network inefficiencies and dACC hyper-activity that has been associated with the phenotype.

**Keywords: dorsal anterior cingulate cortex, obsessive-compulsive disorder, network analysis, working memory, fMRI**

#### **INTRODUCTION**

Obsessive-compulsive disorder (OCD) is a commonly occurring childhood and adolescent-onset neuropsychiatric disorder. It is characterized by obsessions (recurrent and persistent thoughts that typically induce marked distress) and compulsions (repetitive behaviors aimed at alleviating distress). OCD represents the upper extreme of an underlying continuous trait distribution encompassing obsessive-compulsive behaviors common in the general population that are heritable and cross traditional diagnostic boundaries. Thus, OCD represents a clinical "end-point" for a commonly observed trait (~45% of adolescents report OCD symptoms) (Berg et al., 1988; Apter et al., 1996). The 1-year incidence of OCD and sub-clinical OCD in adolescents is ~0.7 and 8.4%, respectively (Valleni-Basile et al., 1996). These relatively high rates of incidence and the association with a trait evident in the general population emphasize the importance of characterizing biological mechanisms underlying OCD. In this report, we aim

to characterize these biological mechanisms by investigating brain network interactions in OCD and their differences from typical healthy controls.

Understanding brain network profiles and brain network dysfunction is a central theme of interest in clinical neuroscience. As suggested by the National Institute of Mental Health (Insel et al., 2010), such a focus may lead to an enhanced understanding of specific bio-behavioral impairments that underpin the emergence of complex behavioral phenotypes which are classified as psychiatric disorders.

Obsessive-compulsive disorder, like most neuropsychiatric conditions, often has its origins in childhood and adolescence when brain network function is still maturing (Paus et al., 2008). Ensuing disordered neurodevelopmental trajectories (in the absence of adaptive responses) may in turn mediate the continued expression of symptoms through adolescence and into early adulthood (Tottenham and Sheridan, 2009). Furthermore,

**Abbreviations:** dACC, dorsal anterior cingulate cortex; FSTC, frontal striatal thalamic circuits; OCD, obsessive-compulsive disorder; PPI, psychophysiological interaction.

the complex patterns of OCD symptoms are linked to the inability to disengage behaviors from intrusive thoughts, implying aberrantly increased inhibitory control (Bari and Robbins, 2013).  In this context, the role of the dorsal anterior cingulate cortex (dACC) assumes significance.

The dACC is positioned as a principal control region in the brain (Paus, 2001) that by itself, or through its mediation of cortical-striatal networks, exercises aspects of cognitive and motor control (Bakshi et al., 2011). The region has been of particular interest in OCD: glutamate dysregulation in the anterior cingulate and striatum has been implicated in pediatric OCD patients (Rosenberg et al., 2000, 2004). Altered glutamate concentrations may be linked to dysfunctional fMRI responses during tasks of behavioral engagement and disengagement. For instance, during conflict processing and action monitoring, OCD subjects evince higher activation in regions including the anterior cingulate cortex and the striatum (Fitzgerald et al., 2005; Maltby et al., 2005; Marsh et al., 2014) that may provide functional expressions of dACC dysfunction in the illness. A question of interest is whether these hyper-activations in the dACC are associated with dysfunctional network profiles.

Network models of fMRI have been applied in OCD. However, a principle focus of network-analyses of *in vivo* imaging data has

been on the classification of resting state *functional connectivity* within (and across) cortical, limbic, striatal, and cerebellar networks (Harrison et al., 2009; Peng et al., 2014). These analyses have been notable as they have revealed categorical and developmental distinctions in resting state functional connectivity (rsFC) between OCD and typical controls in frontal, striatal and thalamic (FSTC) circuits (Fitzgerald et al., 2011). rsFC results are not directly informative about dysfunctional dACC-related profiles in a task-active state. For instance, the relationship between resting state functional connectivity (rsFC) and *task-dependent* functional interactions between regions remains uncertain (Stephan, 2004) and experimental analyses of within subject data have been equivocal (Rehme et al., 2013). Thus rsFC and the low-frequency bold signals it correlates between provide a complimentary snapshot of pathology; task-active analyses of functional network interactions are important for assessing a measure of network dynamics. Moreover, a separate question of interest is whether dysfunctional activation and brain network profiles in OCD are observed in tasks *not involving conflict monitoring*. Such evidence will provide strong support for general network based dysfunction in the disorder extending beyond highly circumscribed behavioral domains.

We had two principal aims in this study (summarized in **Figure 1**): (a) to investigate network profiles originating in the dACC in the task-active state using psychophysiological

**FIGURE 1 | A framework for assessing dysfunctional activation and dACC-related network profiles of cortical, striatal, and thalamic networks in OCD**. **(A)** The two panels depictive activation-based and seed-based approaches to identifying function and dysfunction. The equations represent basic linear model formalisms for each class of models. Note the convolution term (y 0 × u) in the PPI based model that accounts for seed (y 0 = dACC)

modulation of targets in the task-oriented (u = working memory > rest) context. The regions of interest are schematically depicted on the mid-sagittal surface. The second figure schematically depicts the modulatory effects of the dACC assessed using psychophysiological interaction. **(B)** The factorial design space used for the study that assessed the effects of task-demand (1Back vs. 2Back) crossed with group.

Groups did not differ in terms of age (t = 0.32, p = 0.75), height (t = 1.22, p = 0.23), or weight (t = 0.08, p = 0.94). Also comparable were gender (χ 2 = 0.15, p = 0.70) and handedness frequencies (χ 2 = 0.76, p = 0.68). Values in parenthesis represent SD. For lifetime CY-BOCS (lifetime/current): T =Total symptoms, O = Obsessive Symptoms, C = Compulsive symptoms.

interaction (PPI) (Friston et al., 1997; O'Reilly et al., 2012), PPI is a simple framework within the general linear model for investigating contextual modulation of targets (e.g., regions within FSTC) by a seed (e.g., dACC) in a task-active context; (b) to investigate these profiles during parametrically manipulated verbal working memory, (Casey et al., 1995; Diwadkar et al., 2011, 2013), a domain that provides a rich window for investigating normal and dysfunctional activation and network profiles in the FSTC.

#### **MATERIALS AND METHODS**

#### **PARTICIPANTS**

Eighteen participants with a diagnosis of OCD and 27 controls participated in the fMRI studies (see **Table 1**). All participants and their parents were interviewed with the Schedule for Schizophrenia and Affective Disorders for School-Aged Children-Present and Lifetime Version and Schedule for Obsessive-Compulsive and Other Behavioral Syndromes (Wolff and Wolff, 1991; Kaufman et al., 1997). The lifetime (maximum) and current severity of OCD were assessed in the patients with a modified version of the Children's Yale-Brown Obsessive Compulsive Disorder Scale (Goodman et al., 1989; Scahill et al., 1997). Lifetime and current axis I diagnoses were made independently by two clinicians (David R. Rosenberg, Gregory L. Hanna) using all sources of information according to DSM-IV criteria. All patients with OCD had a total lifetime CY-BOCS score of at least 20. Exclusion criteria for patients and controls included lifetime history of psychosis, bipolar disorder, substance abuse or dependence, anorexia or bulimia nervosa, epilepsy, head injury with sustained loss of consciousness, Huntington's disease, dyskinesia, chronically disabling medical illness, autism, mental retardation, or a score >15 on the lifetime version of the Social Communication Questionnaire. Controls were free of all psychiatric illness. Legal guardians provided written informed consent prior and children gave written assent prior to participating in the study. The Human Subjects Investigative committee at Wayne State University and the University of Michigan approved the protocol and all methods therein.

#### **fMRI**

Gradient echo EPI fMRI data acquisition was conducted at Vaitkevicius Magnetic Resonance Centre on a 3T Siemens Verio system using a 12-channel volume head coil (TR: 2.6 s, TE: 29 ms, FOV: 256 mm × 256 mm, acquisition matrix: 128 × 128, 36 axial slices, voxel dimensions: 2 mm × 2 mm × 3 mm). In addition, a 3D T1-weighted anatomical MRI image was acquired (TR: 2200 ms, TI: 778 ms, TE: 3 ms, flip-angle = 13°, FOV: 256 mm × 256 mm,

**FIGURE 2 | Within group changes in activation profiles as a function of load are depicted on identical ascending mosaics of axial views**. The significant clusters (p < 0.05, cluster level) show significant increases in activation with increases in working memory related load. As seen, these increases are evident within both **(A)** healthy control and **(B)** OCD groups. These activation profiles establish within group effects of memory load across previously implicated load sensitive working memory related regions. These include dorsolateral prefrontal cortex (dPFC), the dorsal anterior cingulate (dACC), and the parietal cortex.

256 axial slices of thickness = 1.0 mm, matrix = 256 × 256). A neuroradiologist reviewed all scans to rule out clinically significant abnormalities.

During fMRI, subjects were positioned with adjustable padded restraints employed for head stabilization. Stimuli were rearprojected using an IFIS-SA presentation system (MRI Devices), and subjects responded with a button box unit. During fMRI, subjects participated in an established verbal *n*-back paradigm (Casey et al., 1995). Parametric working memory load was varied between maintaining 0, 1, or 2 items in memory (0-, 1-, or 2-Back; see **Figure 1** insets). Runs were blocked by condition. During each block (30 s), letters were projected in sequence (presentation time: 500 ms; ISI: 2500 ms; 10 letters per block) on a screen; subjects signaled with a two-choice optical response box if the presented letter was a target or not. The paradigm cycled between rest (20 s), 0-, 1-, and 2-Back epochs (three blocks each). The experiment was controlled using presentation (Neurobehavioral Systems Inc.).

#### **fMRI PROCESSING**

fMRI data were processed in SPM8 using typical methods. All images were manually oriented to the AC-PC line, realigned to correct for head movement, spatially normalized to the MNI (Montreal Neurological Institute) template brain and resliced (2 mm × 2 mm × 2 mm). Low frequency components were removed using a low-pass filter (128 s) and images were spatially smoothed using a Gaussian filter (8 mm full-width half maximum; FWHM). An autoregressive AR(1) model was used to account for serial correlation, and regressors modeled as a 30 s boxcar vectors (for each of the task-related conditions) were convolved with a canonical hemodynamic reference waveform.

Subjects' head motion was within accepted limits (<4 mm). Furthermore, in all first level models, the effects of motion were modeled by including the six motion parameters as covariates of no interest. First-level contrasts (1Back > 0Back; 2Back > 0Back) were used to assess the effects of memory load on activation.

PPI (implemented in SPM8) was employed to model dACC modulation of FSTC targets during working memory (Friston et al., 1997; Honey et al., 2005). For *each subject*, time series from the effects of interest contrast (*p* < 0.05) were extracted from the dACC peak (including Brodmann areas 32 and the supra-genual aspects of BA24) (Palomero-Gallagher et al., 2008). The extracted time series (the wave form of which provides an estimate of the continuous physiological response of the dACC) was subsequently convolved with the two contrasts of interest reflecting effects of differential memorial load, specifically, 1-Back > 0-Back (low load) and 2-Back > 0-Back (high load). The resultant interaction term was positively weighted to assess the facilitating influence of the dACC on FSTC targets (with the slope of the effect parametrically encoded in the convolution term and reflecting the degree of modulation).

For all activation or network analyses, first level maps (activation or PPI) from each subject were submitted to a second-level random effects analyses of variance with group modeled as an independent factor and memory load as non-independent factor. The factorial design permitted assessment of intra-group loadrelated effects, as well as between-group differences at varying levels of memory load.

All second level analyses were spatially thresholded in the FSTC regions of interest using deterministic anatomical masks defined in stereotactic space (Maldjian et al., 2003). These maps constitute anatomical representations in stereotactic space representing each of the regions of interest and are widely employed to spatially localize activations in neuroimaging research. Images were corrected using cluster level correction (cluster extent thresholds, *p*c < 0.05) derived from 104 Monte Carlo simulations from voxels across the individual regions of interest (Ward, 2000). Individual voxel peaks in significant clusters are reported in terms of Montreal Neurological Institute coordinates.

#### **RESULTS**

Results are organized to sequentially present evidence of (1) dysfunctional activation profiles and (2) dysfunctional brain network profiles in OCD and HC:

(1a) We first show load-related effects on *activation profiles* within both HC and OCD. These results provide evidence

**Table 2 |The table provides information on clusters of significance and peaks within where each of the groups showed increased activation to variations in memory load (Figure 2)**.

of *within-group* effects of parametric increases in working memory load on FSTC.

#### **LOAD-RELATED EFFECTS ON ACTIVATION PROFILES**

**Figure 2** depicts clusters (*p*c < 0.05) in FSTC showing increased within-group activation in response to increases in memory load (cluster relevant information in**Table 2**). In both groups, increased memory load results in increased recruitment of frontal and parietal regions, and the dACC. These results are unsurprising for the HC group. They are highly consistent with previous assessments of activation profiles in this circuit in HC (Braver et al., 1997; Cohen et al., 1997; Diwadkar et al., 2000), showing increased recruitment in brain circuits committed to implementing working memory related functions. The results in OCD are notable as they demonstrate that the memory effect exerts within-group effects consistent with HC. This is important evidence that FSTC in OCD is sensitive to load-related variations in working memory and that the overall implementation of the task generates load-related effects on activation profiles. Notable is an absence of load-related activation effects in the striatum or the thalamus, regions not typically implicated in core memory-related processing. The basal ganglia contribute to cortical-striatal processing loops that sub-serve complex processing, by supplementing prefrontal function (Hazy et al., 2006; Calzavara et al., 2007; Voytek and Knight, 2010). The thalamus forms cortical-thalamic processing units that integrate information from cortical and striatal loops to modulate complex behavior, but has generally not been sensitive to load-related variations in working memory (Haber and Calzavara, 2009).

#### **BETWEEN-GROUP RESULTS SHOWING ABERRANTLY INCREASED ACTIVATION PROFILES IN FSTC IN OCD PATIENTS**

**Figure 3** depicts clusters (*p*c < 0.05) in FSTC showing increased activation in OCD (relative to HC) at each level of memory load (cluster relevant information in **Table 3**). Several effects are evident. Dysfunctional activation profiles are observed in the frontal and parietal cortices and in the dACC at both levels of load. Absent is evidence of dysfunctional activation profiles in the striatum or the thalamus. Moreover, dysfunction in activation profiles scales as a function of memory load: Increased memory demand leads to increased activation in cortical regions. These analyses are consistent with previous studies in FSTC in OCD participants in other behavioral domains such as conflict monitoring that are closely associated with behavioral phenotypes in the illness (Huyser et al.,2011). As one of our study aims was to assess whether hyper-activation in FSTC constitutes a domain-general property of brain regions in OCD, these analyses extend the findings beyond the domain of conflict processing and suggest that multiple tasks engaging FSTC are sensitive for detecting activation-related dysfunction.

**FIGURE 3 | Dysfunctional activation profiles in OCD (relative to controls) are depicted for both (A) the 1Back level of memory and (B) the 2Back level of memory load**. Increased activation in OCD (p < 0.05, cluster level) is depicted on identical ascending mosaics of axial views. These activation profiles indicate increased activation in dorsolateral prefrontal cortex (dPFC), the dorsal anterior cingulate (dACC), and the parietal cortex in OCD. Notably the degree of dysfunctional activation in OCD scales as a function of memory load. We speculate that the parametric demands as expressed in dysfunctional activation profiles load disproportionately in OCD participants. As will be seen, brain network profiles in OCD do not strictly follow activation patterns, evidence that signatures of network interactions may complement psychopathology revealed in activation models.

#### **BETWEEN GROUP RESULTS ASSESSING DYSFUNCTIONAL BRAIN NETWORK PROFILES IN OCD**

**Figure 4** depicts clusters (*p*c < 0.05) in FSTC showing *increased modulation by the dACC in OCD* (relative to HC) at each level of memory load (cluster relevant information in **Table 4**). We

**Table 3 |The table provides information regarding clusters of significant and significant peaks showing dysfunctional activation profiles in OCD (compared to HC) at each level of memory load (Figure 3)**.

highlight several notable effects. First, dysfunctional network profiles in OCD form a pattern that is distinct and complimentary to that observed in activation profiles. OCD is characterized by increased dACC related modulation at the 1Back level of load but not the 2Back level, suggesting that the degree of dACC modulation (and the mechanisms that can be inferred from it) do not scale with load. We speculate (see Discussion) that this effect may be related to aberrantly increased dACC modulation at the 1Back level itself. The hyper-modulation may reflect inefficiencies in control-related network function or hyper-activity of the dACC, or both. Second, dysfunctional modulation of the striatum is evident, with significantly increased dACC modulation of the caudate and putamen observed at the 1Back level (**Figure 4A**). This effect also constitutes a complementary pattern of dysfunction from activation in OCD where profiles in the striatum appeared normal (**Figure 3**).

#### **DISCUSSION**

We conducted a simple investigation of brain activation and network profiles in a group of OCD youth and age-comparable controls. Participants were assessed with fMRI using a simple working memory paradigm with variable demands (**Figure 1**). Three principle results are highlighted across both classes of analyses: *Activation Profiles*: (1a) Activation profiles were highly sensitive to increases in memory load within each group (**Figure 2**). (1b) Youth with OCD were characterized by aberrantly increased recruitment of frontal and parietal regions (but not striatal or thalamic regions) during both levels of working memory. The degree of hyper-activation scaled as a function of working memory demand (**Figure 3**). *Network Profiles*: (2) Compared to HC, youth with OCD were characterized by increased dACC modulation of frontal, parietal, and striatal regions, particular at lower levels of working memory load (**Figure 4**).

Taken together, these results establish that OCD is characterized by dysfunction in core FSTC regions, detectable using both activation- and network-based analyses of fMRI signals. We suggest that the network-based analyses are notable for being the first to demonstrate dysfunctional network signatures of the dACC, a region closely associated with OCD related pathophysiology. Moreover, these profiles observed using a basic working memory

paradigm, suggest that FSTC deficits are a basic pathophysiologic mechanism underlying OCD, are detectable with a multiplicity of tasks, and affect frontal, striatal, and thalamic circuits. In the remainder of the paper, we discuss the putative mechanisms that may underpin these observations and the implications for OCD related pathology and function.

#### **CINGULATE, FRONTAL, STRIATAL, AND THALAMIC REGIONS: A CRITICAL CIRCUIT SUB-SERVING COMPLEX FUNCTION**

The regions targeted in this investigation collectively form core sub-circuits that implement function in a multiplicity of higherorder domains including working memory (Owen et al., 2005; Diwadkar et al., 2011), sustained attention (Fan et al., 2005; Langner and Eickhoff, 2013; Diwadkar et al., 2014), and cognitive control (Carter et al., 1999; Anderson et al., 2008). These functional sub-circuits are also underpinned by dense patterns of anatomical connectivity. The dorsal-prefrontal cortex and the basal ganglia share topographically mapped monosynaptic connections (Calzavara et al., 2007) that may explain co-activation patterns frequently observed in fMRI studies. Descending connections from cortical regions including the prefrontal cortex and sensory, motor, and frontal regions synapse on multiple thalamic nuclei including the ventral and posteromedial complexes (Ray and Price, 1993; Klein et al., 2010; Li et al., 2013) leading to the notion of "cortical-thalamic processing units" (Briggs and Usrey, 2008). The dACC is uniquely positioned from an anatomical standpoint, with connections to frontal and motor regions, to play a mediating influence in control related mechanisms (Paus, 2001). Each of these regions appears to be relatively specialized for highly sophisticated functions.

The dorsal-prefrontal cortex sub-serves working memory in multiple ways. Phasic activity in prefrontal neurons is strongly correlated with the temporary maintenance of memoranda in working memory (Vijayraghavan et al., 2007), suggestive of a direct link between neuronal responses and overt behavior. Moreover, the prefrontal cortex sub-serves goal-directed behavior in multiple domains (including working memory) through direct "command" signals to multiple cortical and sub-cortical targets (Crowe et al., 2013; Funahashi and Andreau, 2013). The anatomical positions of the basal ganglia allow the structure to receive

**FIGURE 4 | Dysfunctional brain network profiles in OCD (relative to controls) are depicted for both (A) the 1Back level of memory and (B) the 2Back level of memory load**. The clusters depict significantly increased dACC-modulation of cortical and striatal targets in OCD compared to typical controls (p < 0.05, cluster level) depicted on identical ascending mosaics of axial views.  Note the implication of the caudate, not implicated in dysfunctional activation. The increased modulation by the dACC may reflect increased control-related inputs demanded in OCD to sub-serve network function associated with this fundamental domain. The lack of a parametric effect may reflect the fact that dACC related network engagement is already aberrantly increased at the 1Back level. Indeed, OCD participants did not show an increase in dACC modulation going from the 1Back to the 2Back level of demand (whereas HC participants did).

inputs from multiple uni- and heteromodal regions (Ragsdale and Graybiel, 1990). Thus the structure serves as a critical node in multiple network pathways, playing executive and supporting roles in several behavioral domains. Along with the prefrontal cortex, the basal ganglia appear to exert attention-related modulation of working memory related function (Herrero et al., 2002; McNab and Klingberg, 2008). The thalamus is considered a principle gateway to the cortex (McAlonan et al., 2008), engaged in filtering of massive sensory inputs, particularly in the visual domain, and sending extensive outputs to cortical and sub-cortical regions (Haber and Calzavara, 2009). The structure also plays essential computational roles by integrating network activity essential for modulating behaviors. Many of the psychological domains that are underpinned by regional function are implicated in OCD. Thus pediatric OCD patients in particular show deficits in sustained attention (Lucke et al., 2014), executive function and working memory (Melloni et al., 2012), and cognitive control and metacognition (Koch and Exner, 2015). It is therefore not surprising that frontal, striatal, and thalamic circuits have been identified as central to potential interventions in OCD (Burguiere et al., 2015).

#### **HYPER-ACTIVATION IN OCD DURING WORKING MEMORY: POSSIBLE MECHANISMS AND RELATIONSHIP WITH OTHER DISORDERS**

Though working memory deficits are generally seen as secondary to the core pathology of OCD (Harkin and Kessler, 2011), our activation results provide good convergence with recent reports. Memory load-related hyper-activation in frontal-parietal regions has been proposed as an intermediate phenotype for OCD, where the hyper-activation has been labeled as compensatory (Nakao et al., 2009; Koch et al., 2012; de Vries et al., 2013). Effects on dACC activation have, however, been equivocal; previous studies have shown a reverse effect of complexity on dACC activation in OCD, with disengagement of the structure following load related effects. Nevertheless, our results provide good conceptual overlap with studies in pathology that have linked hyperactivation under conditions of task compliance with regional efficiency. This concept of inefficiency finds pronounced expression in the schizophrenia spectrum, where disease-related effects (that have been associated with dopamine dysfunction) are presumed to affect the "duty cycle" of task-relevant brain regions including the prefrontal cortex and the striatum (Callicott et al., 2003; Manoach, 2003; Jansma et al., 2004; Meisenzahl et al., 2007; Diwadkar et al., 2012). These inefficiencies might imply that neuronal pools (that form one electrophysiological origin of the fMRI signal) (Logothetis and Wandell, 2004) engage in excess excitatory firing responses when demand is exerted on FSTC. Moreover, inefficiencies provide a window into the "scalability" of brain regions in response to demand. In other words, the functioning limits of FSTC in OCD may be compromised such that excessive cognitive demand may stretch FSTC ability to sub-serve function. In this view, FSTC hyper-activation during working memory far, from being a peripheral correlate of OCD, is a central mechanism underlying the illness, and a primary intermediate phenotype as previously proposed (de Vries et al., 2013).

A parallel explanation for hyper-activity is that it reflects glutamate-related dysfunction that affects how relevant regions are recruited for a task (Wu et al., 2012, 2013; Stewart et al., 2013; Pauls et al., 2014). As a principle excitatory neurotransmitter,

**Table 4 |The table provides information regarding clusters of significant and significant peaks showing dysfunctional network profiles in OCD (compared to HC) at each level of memory load (Figure 4)**.

glutamate exerts substantial effects on brain function, particularly in the excitatory model. Glutamate dysfunction in OCD can alter the neurochemical-electrophysiological relationship that sub-serves BOLD-based activation. A more complete assessment of the Glutamate-fMRI relationship will require assessment of both classes of signals acquired within subjects. This is an ongoing endeavor in our studies that involves multi-modal acquisition of fMRI and MRS data within subjects.

#### **HYPER-MODULATION OF FSTC BY THE dACC: NOVEL EVIDENCE OF DYSFUNCTIONAL NETWORK PROFILES**

Relatively few studies have assessed connectivity in the task-active state in OCD. Psychophysiological interactions provide a straightforward model of directional effects of seed regions on their targets in a task-related context, providing a window into network interactions. This window is considered intermediate between functional and effective connectivity (Friston, 2011). The interpretations of PPI are constrained by the choice of seeds and the hypothesized role(s) ascribed to the seed. Toward that end, our choice of the dACC was motivated by its role in cognitive control of brain networks (Carter et al., 1999; Paus, 2001; Bakshi et al., 2011).

The dACC plays an integral role in tasks of explicit cognitive control including response conflict and response monitoring (Braver et al., 2001; van Veen et al., 2001), and choice selection (Eshel et al., 2007). The dACC may serve to amplify task-relevant signals to heteromodal association regions of the cortex (Egner and Hirsch, 2005; Sohn et al., 2007). Thus, control processes from the dACC may influence the activity of core working memory systems, and the degree of this modulation may reflect the efficiency of interaction between control and working memory systems. Increased control-related modulation in part reflects decreased efficiency. This hyper-modulation by the dACC may strongly suggest inefficient control-related network profiles in OCD. These effects are again consistent with observed evidence in other disorders, for example, in the schizophrenia spectrum where increased dACC related modulation is strongly associated with the illness and risk for the illness (Bakshi et al., 2011). The absence of a parametric effect on dACC modulation appears related to highly increased aberrant dACC modulation at the lower level of demand in OCD: no intra-group increases in dACC modulation were observed in OCD as memory load increases. As such, the network effects constitute complementary signatures of FSTC dysfunction in OCD.

#### **LIMITATIONS AND CONCLUSION**

Brain network profiles will constitute an important frontier in the search for mechanisms and endophenotypes, and their evidence is an important expression of the goals advocated by Research Domain Criteria (RDoC: Insel et al., 2010). While our sample size (though small) is comparable to several other published studies, it nevertheless precludes us from assessing the role of co-morbid diagnoses and medication effects within OCD youth. These are important clinical questions, and an expansion of this sample is ongoing, and may permit more detailed assessment of our observed effects.

The specific neurochemical and molecular bases of these effects are obscured by the interpretational limits of both the fMRI signal (Logothetis, 2008) that cannot distinguish between a multiplicity of neuronal contributions to the hemodynamic response, and by the relatively limited class of inferences that can be drawn from the application of PPI analyses (Stephan, 2004). Moreover, these technical challenges are compounded by the fundamental limitation in understanding the correlates of brain structure and function: the fact that functional characteristics of brain networks exist in a regressive relationship with their structural substrates (Park and Friston, 2013). Thus, the same underlying structural networks can give rise to a multiplicity of functions and dysfunctions. Nevertheless, our results (and other studies we have cited) promise to reveal mechanisms of disease-related dysfunction as expressed in brain profiles. An understanding of putative mechanisms is a necessary precursor of treatment and cure. Therefore we propose that studies such as ours (and future extensions) will provide better elucidation of disease mechanisms than currently exist.

#### **ACKNOWLEDGMENTS**

This work was supported by the National Institute of Mental Health (MH059299), the Children's Hospital of Michigan Foundation, the Prechter World Bipolar Foundation, the Lyckaki-Young Fund from the State of Michigan, the Miriam Hamburger Endowed Chair of Child Psychiatry, the Paul and Anita Strauss Endowment, the Donald and Mary Kosch Foundation, Detroit Wayne County Authority, and Gateway Community Health. The funding agencies played no role in the analyses or reporting of the data.

#### **REFERENCES**

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obsessive-compulsive disorder: a fMRI study with a Flanker task before and after CBT. *J. Child Psychol. Psychiatry* 52, 1251–1260. doi:10.1111/j.1469-7610.2011. 02439.x

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 12 November 2014; accepted: 03 March 2015; published online: 17 March 2015.*

*Citation: Diwadkar VA, Burgess A, Hong E, Rix C, Arnold PD, Hanna GL and Rosenberg DR (2015) Dysfunctional activation and brain network profiles in youth with obsessive-compulsive disorder: a focus on the dorsal anterior cingulate during working memory. Front. Hum. Neurosci. 9:149. doi: 10.3389/fnhum.2015.00149*

*This article was submitted to the journal Frontiers in Human Neuroscience. Copyright © 2015 Diwadkar, Burgess, Hong , Rix, Arnold, Hanna and Rosenberg . This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Altered Effective Connectivity among Core Neurocognitive Networks in Idiopathic Generalized Epilepsy: An fMRI Evidence

#### Huilin Wei 1 , Jie An2 †, Hui Shen1 , Ling-Li Zeng1 , Shijun Qiu2 \* and Dewen Hu1 \*

*1 Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, China, 2 Department of Medical Imaging, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China*

Idiopathic generalized epilepsy (IGE) patients with generalized tonic-clonic seizures (GTCS) suffer long-term cognitive impairments, and present a higher incidence of psychosocial and psychiatric disturbances than healthy people. It is possible that the cognitive dysfunctions and higher psychopathological risk in IGE-GTCS derive from disturbed causal relationship among core neurocognitive brain networks. To test this hypothesis, we examined the effective connectivity across the salience network (SN), default mode network (DMN), and central executive network (CEN) using resting-state functional magnetic resonance imaging (fMRI) data collected from 27 IGE-GTCS patients and 29 healthy controls. In the study, a combination framework of time domain and frequency domain multivariate Granger causality analysis was firstly proposed, and proved to be valid and accurate by simulation experiments. Using this method, we then observed significant differences in the effective connectivity graphs between the patient and control groups. Specifically, between-group statistical analysis revealed that relative to the healthy controls, the patients established significantly enhanced Granger causal influence from the dorsolateral prefrontal cortex to the dorsal anterior cingulate cortex, which is coherent both in the time and frequency domains analyses. Meanwhile, time domain analysis also revealed decreased Granger causal influence from the right fronto-insular cortex to the posterior cingulate cortex in the patients. These findings may provide new evidence for functional brain organization disruption underlying cognitive dysfunctions and psychopathological risk in IGE-GTCS.

Keywords: idiopathic generalized epilepsy, resting-state fMRI, effective connectivity, multivariate Granger causality, core neurocognitive networks

#### INTRODUCTION

Previous studies have revealed that patients with epilepsy suffer a higher incidence of psychosocial and psychiatric disturbances than healthy people (Mignone et al., 1970; Baker et al., 1996; Cutting et al., 2001; Gelisse et al., 2007). Idiopathic generalized epilepsy (IGE) is the most common type of epilepsy, which can be characterized by electroencephalography (EEG) recordings with generalized spike-and-waves or polyspike-waves (Engel, 2001; Hamandi et al., 2006). As one of the IGE subtypes, IGE patients with generalized tonic-clonic seizures

#### Edited by:

*Adeel Razi, Wellcome Trust Centre for Neuroimaging, UK*

#### Reviewed by:

*Hao He, Mind Research Network, USA Yuhui Du, Mind Research Network, USA*

#### \*Correspondence:

*Shijun Qiu<EMAIL_ADDRESS>Dewen Hu<EMAIL_ADDRESS>†Co-first author.*

Received: *29 April 2016* Accepted: *22 August 2016* Published: *07 September 2016*

#### Citation:

*Wei H, An J, Shen H, Zeng L-L, Qiu S and Hu D (2016) Altered Effective Connectivity among Core Neurocognitive Networks in Idiopathic Generalized Epilepsy: An fMRI Evidence. Front. Hum. Neurosci. 10:447. doi: 10.3389/fnhum.2016.00447* (IGE-GTCS) suffer various neuropsychological impairments such as deficits in working memory, sustained attention, language, as well as executive functions (Hommet et al., 2006; Chowdhury et al., 2014). Prior studies have suggested that disruptions to these higher-order control processes may constitute a key aspect of psychopathology (Sridharan et al., 2008; Menon and Uddin, 2010). Therefore, distinguishing dysfunctional brain architecture may provide greater insight into the psychopathology in IGE-GTCS.

In recent years, identifying disturbed dynamic interactions of large-scale brain networks associated with cognitive and affective dysfunctions has shed new lights on the study of psychopathology. Of the many spatially distinct and functionally specialized stable brain networks, three have tended to be particularly crucial for understanding higher-order cognitive and perceptive processes thus described as core neurocognitive networks, they are: (1) the salience network (SN), involved in conflict monitoring, attention, as well as interoceptive and affective processes; (2) the default mode network (DMN), related to self-referential and social cognitive processes; and (3) the central executive network (CEN), associated with working memory, cognitive control implementation, and decision making in goal-directed behavior (Menon, 2011). Moreover, each of the three core neurocognitive networks are anchored in some key nodes that show strong intrinsic functional coupling as well as co-activation across different cognitively demanding tasks (Sridharan et al., 2008; Menon and Uddin, 2010). These key nodes are: (1) the right fronto-insular cortex (rFIC) and the dorsal anterior cingulate cortex (dACC) of the SN; (2) the ventromedial pre-frontal cortex (VMPFC) and the posterior cingulate cortex (PCC) of the DMN; as well as (3) the dorsolateral pre-frontal cortex (DLPFC) and the posterior parietal cortex (PPC) of the CEN. Particularly, the two key nodes of the SN have been highlighted in numerous researches, suggesting that the rFIC is crucial for initiating network switching between the CEN and the DMN (Sridharan et al., 2008; Menon and Uddin, 2010; Uddin et al., 2011), and the dACC most closely associated with conflict monitoring to mediate higher-order cognitive processes (Botvinick et al., 2004; Menon, 2011). Investigating disruptions to functional dynamics among these key nodes is beginning to identify an important aspect of dysfunctions in psychopathology, thus the three core neurocognitive networks represented by the associated key nodes have been concluded as a "triple network" model (Menon, 2011). Aberrant interconnectivity and intrinsic organization of the triple network is characteristic of various neurological and psychiatric disorders, such as schizophrenia, depression, anxiety disorders and autism (Paulus and Stein, 2006; Walter et al., 2009; White et al., 2010; Uddin et al., 2015), and is likely to provide better understanding of fundamental brain mechanisms underlying cognitive dysfunctions and psychopathology in IGE-GTCS.

Measurement of causal influence that a system exerts over one other is called effective connectivity (Friston et al., 1993). Applying effective connectivity to brain network analysis can obtain full understanding of the network interaction structure including the strength and direction of information flow between brain regions. Granger causality analysis, as an important analytical technique of effective connectivity, has been widely applied in cognitive neuroscience studies since it can measure directional dependence between time courses without any prior model specifications. In Granger causality definition, time course X2 causes time course X1 if combined past value of both X1 and X2 can significantly improve the prediction accuracy of current value of X1, rather than using the past value of X1 alone (Granger, 1969; Seth, 2010). Granger causality is often estimated with multivariate autoregressive (MVAR) modeling of the time courses, and has various time domain as well as frequency domain formulations, including conditional Granger causality (Geweke, 1984), partial Granger causality (Guo et al., 2008), directed transfer function (DTF) (Kaminski et al., 2001), and partial directed coherence (PDC) (Baccalá and Sameshima, 2001), etc. The mentioned time domain Granger causality measures are the straightforward generalization of the notion of Granger causality thus easy to comprehend, the introduced frequency domain Granger causality measures could describe the dynamics of causal relationships between time courses by evaluating Granger causality over different frequency portions (Sato et al., 2009). Based on these, the combined performance of Granger causality analysis in two domains is expected to provide more accurate and informative analysis results.

Little is known about the alteration of effective connectivity among core neurocognitive networks underlying cognitive impairments and psychopathology in IGE-GTCS. Additionally, to our knowledge, no IGE study has conducted multivariate Granger causality analysis in both time and frequency domains and presented the combined analysis results. In the current study, we examined IGE-related changes in effective connectivity across core neurocognitive brain networks using resting-state functional magnetic resonance imaging (fMRI), combining time domain and frequency domain multivariate Granger causality analysis. We hypothesized that the altered causal interactions likely occur among the key nodes of the SN, DMN, and CEN in IGE-GTCS, which may underline cognitive dysfunctions, improving our understanding of the psychopathological mechanism of IGE-GTCS.

## MATERIALS AND METHODS

#### Subjects

Twenty-seven right-handed IGE-GTCS patients (age 24.93 ± 5.95 years; education 10.59 ± 2.58 years; eight female; epilepsy duration 7.76 ± 5.62 years; age of onset 17.13 ± 6.11 years) were recruited in the study. The diagnosis was determined by a comprehensive evaluation including detailed history, video-EEG telemetry, and neuroimaging. All patients had IGE with GTCS only according to the International League against Epilepsy (ILAE) classification, and met the following inclusion criteria: (i) presence of typical clinical symptoms of GTCS, including myoclonus, loss of consciousness, and no partial seizures; (ii) presence of generalized spike-and-wave or polyspike-wave discharges in their scalp EEG; (iii) no focal abnormality in routine structural MRI examinations; and (iv) no obvious history of etiology. All patients were treated with antiepileptic drugs (AEDs), but received no medication for at least 48 h prior to the MRI scanning. All patients had been seizure-free for at least 1 month prior to the MRI scanning.

Twenty-nine right-handed healthy subjects (age 26.93 ± 7.54 years; education 11.45 ± 2.40 years; 12 female) were recruited, with gender, age, and education level demographically matched. All participants had no mass lesion (including tumor, vascular malformation or malformations of cortical development), traumatic brain injury or history of neurological or psychiatric disorder. This study was approved by the Ethics Committee of Guangdong 999 Brain hospital, and all participants provided written informed consent.

 For the patients, scans were conducted during interictal without combined EEG confirmation. All fMRI images were collected using a gradientecho echo-planar pulse sequence sensitive to blood-oxygenationlevel-dependent (BOLD) contrast with the following parameters: TR/TE = 3000/30 ms, thickness/gap = 4.5/0 mm, field of view (FOV) = 230 × 230 mm, flip angle (FA) = 90◦ , matrix = 128 × 128, and slices = 31. Each resting-state fMRI run lasted 8 min, obtaining 160 volumes.

For each subject, the first five volumes of the scanned data were discarded to allow for T1-equilibration effects, and then the fMRI data were pre-processed with SPM8 package (Welcome Department of Cognitive Neurology, Institute of Neurology, London, UK, http://www.fil.ion.ucl.ac.uk/spm), included the following steps (Zeng et al., 2012): (1) slice timing correction; (2) rigid body correction for head motion; (3) atlas registration with an EPI template in the Montreal Neurological Institute (MNI) atlas space, resampling to 3-mm isotropic voxels; (4) spatially smoothing using an 8-mm full-width half-maximum (FWHM) Gaussian kernel; and (5) regressing out nine nuisance signals including signals averaged from white matter, cerebrospinal fluid, and the whole brain, and six parameters obtained by head motion correction. Temporal filtering was not conducted as with some prior studies (Hamandi et al., 2006; Wu et al., 2013), so that the whole effective frequency band of the fMRI data could be included in the frequency domain Granger causality analysis (see Section Effective connectivity: time and frequency domains multivariate Granger causality measures below). After calculation, no subjects were removed due to excessive motion (translation > 2 mm and rotation >2 ◦ ); there was no significant difference in mean motion between the two groups (p = 0.32, two-tailed two-sample t-test; Zeng et al., 2014), thus the effective connectivity would be less probably affected by the head motion (Van Dijk et al., 2012).

## Region of Interest Definition and Time Course Extraction

Six functional regions of interest (ROIs) were selected, including the rFIC and the dACC of the SN, the VMPFC and the PCC of the DMN, as well as the rDLPFC and the rPPC of the CEN. The coordinates of the ROIs (**Table 1**) were set according to

#### TABLE 1 | Coordinates of ROIs.

a published study delimiting these regions in an independent dataset (Uddin et al., 2011). In that study, MNI coordinates of peak voxels (voxels with the highest z-scores) of the six regions chosen from ICA maps were defined as the centers of the ROIs. In our study, the final ROIs were defined as 8 mm radius spheres centered on the coordinates, and the mean time course in each ROI was extracted by averaging the time courses of all voxels within the ROI. At last, each mean time course of the ROIs was detrended and its temporal mean was removed for further analysis. All the time courses were covariance stationarity (i.e., unchanging mean and variance) after time course pre-processing.

#### Effective Connectivity: Time and Frequency Domains Multivariate Granger Causality Measures

On the basis of MVAR modeling, we intended to calculate the effective connectivity strength in both time and frequency domains, thus the well-chosen Granger causality measures in two domains were introduced in the current study, they are: partial Granger causality (Guo et al., 2008) in time domain analysis, and PDC (Baccalá and Sameshima, 2001) in frequency domain analysis. The formalism for these Granger causality measures is given in Appendix.

To obtain the time domain and frequency domain Granger causality measures between each pair of the six ROIs for each subject, the following steps were conducted: (1) MVAR model estimation: six time courses were fit to obtain the unrestricted autoregressive model (see Appendix for details), the model order was set to 1 determined by Bayesian information criterion (BIC), and the regression coefficients were estimated using standard least squares optimization. (2) Calculation of time domain Granger causality measures: for each pair of the six time courses in both directions, the partial Granger causality and the DOI were calculated (see Equations A11 and A12 in Appendix); thus, 30 (6×5) individual partial Granger causality values of time domain Granger causal links and 30 DOI terms of time domain Granger causal links were obtained for each subject. (3) Calculation of frequency domain Granger causality measure: for each pair of the six time courses in both directions, the PDC was calculated (see Equation A13 in Appendix) every 0.001 Hz of the interesting frequency range [0, Fs/2], where Fs is the sampling rate of the fMRI data (i.e., 1/TR); thus 30 × 168 individual PDC values of frequency domain Granger causal links were obtained for each subject.

## Constructing Within-Group Effective Connectivity Graph

Having computed the Granger causality measures in both time and frequency domains, we proceeded to construct effective connectivity graph for each group, respectively. Since all the Granger causality measures used in the study lack known statistical distributions (Seth, 2010), the creation of empirical null distributions that hypothesize no causality between ROIs is of great importance. Meanwhile, we hypothesized that the combined performance of time domain and frequency domain analysis of multivariate Granger causality would present more accurate and informative analysis results. Therefore, based on the procedure conducted in Sato et al. (2009) and Havlicek et al. (2010), we proposed a combination framework of time domain and frequency domain multivariate Granger causality analysis to evaluate the direct causal interactions between time courses. An overview of this method (see **Figure 1**) is given below, and each step is described in detail as follows:

For frequency domain analysis, we get a per-interactionper-frequency significance threshold; the significant effective connectivity is thus defined as the connection which has non-null significant frequency interval. Finally, the consistent results of time domain and frequency domain analysis are determined as significant effective connectivity given by the proposed method.

The validity and improvement in resulting accuracy of the proposed method is proved by several toy models in the following subsection (see Section Simulations). For the IGE study, we used the median partial Granger causality and median PDC to determine the significant connectivity in time domain and frequency domain analysis, respectively. Finally, the significant effective connectivity was defined as the connection that was significant in both time domain and frequency domain analysis, and the within-group effective connectivity graph was thus composed of the significant effective connections of each group. In addition, the significant connections identified by DOI terms in time domain analysis were also recorded as a subset of the final results.

## Evaluating between-Group Effective Connectivity Difference

Among the connections that exhibited significant Granger causality in at least one group (obtained in Section Constructing within-group effective connectivity graph), we further assessed the presence of significant group differences in both time domain and frequency domain Granger causality definition. In time domain analysis, Mann-Whitney U-tests (p < 0.05, FDR corrected) were applied across the 30 time domain Granger causal links to assess the presence of significant group differences (Sridharan et al., 2008). In frequency domain analysis, for each link, Mann-Whitney U-tests (p < 0.05, FDR corrected) were applied across the 168 frequency slices to determine the grouplevel significant frequency interval of that link. And finally the links with non-null significant frequency intervals were taken as the interesting results in frequency domain analysis.

## RESULTS

#### Simulations

Two typical and widely used toy models (Baccalá and Sameshima, 2001; Seth, 2010) were presented here to demonstrate the validity and improvement in resulting accuracy of the proposed combination framework described in Section Constructing within-group effective connectivity graph. In the simulation experiments, the same methods of time course pre-processing (including detrend and removal of temporal mean), MVAR model estimation (using standard least squares optimization to calculate the regression coefficients and residuals, and setting the model order as the real model order of each toy model), time and frequency domains Granger causality calculation, and significance testing (1000 times repetition to get the significance thresholds) that described in Section Effective connectivity: time and frequency domains multivariate Granger causality measures

and Constructing within-group effective connectivity graph were conducted to the toy models.

MVAR, multivariate regressive; PGC, partial Granger causality; DOI, difference of influence; PDC, partial directed coherence.

**Model 1**. Suppose that four simultaneously observed time courses were generated by the equations:

$$\begin{aligned} \chi\_1(n) &= 0.95\sqrt{2}\chi\_1(n-1) - 0.9025\chi\_1(n-2) + \omega\_1(n) \\ \chi\_2(n) &= 0.5\chi\_1(n-2) + \omega\_2(n) \\ \chi\_3(n) &= -0.4\chi\_4(n-3) + \omega\_3(n) \\ \chi\_4(n) &= 0.35\chi\_4(n-2) + \omega\_4(n) \end{aligned} \tag{1}$$

The model contains two direct Granger causal influences, i.e., connections from x1 to x2, and from x4 to x3. The model order is three, ω1 ∼ ω4 are zero-mean uncorrelated white processes with identical variances. The signal to noise ratio (SNR) of the generated time courses is 0.01. **Figure 2** illustrates the simulation results. The Granger causal structure and the raw time courses of each variable are shown in **Figures 2A,B**. The time domain Granger causality analysis result is expressed as a colormap in **Figure 2C**. As expected, the partial Granger causality values of the connections from x1 to x2, and from x4 to x3 were significantly larger and exceeded the corresponding thresholds. **Figure 2D** shows the PDC values (black solid line) and significance thresholds (black dotted line) of each connection. The significant frequency intervals were highlighted in red. Using the PDC representation we could observe the dynamics of causal relationships between time courses. It can be seen that, except for two correct causal influences, the connection from x1 to x3 was misjudged in frequency domain analysis. Obviously, when we conducted the proposed combination method, only the corrected causal interactions would be identified.

**Model 2**. A more complicated system that contains indirect causal influence was generated by the equations:

$$\begin{aligned} \chi\_1(n) &= 0.95\sqrt{2}\chi\_1(n-1) - 0.9025\chi\_1(n-2) + \omega\_1(n) \\ \chi\_2(n) &= 0.5\chi\_1(n-2) + \omega\_2(n) \\ \chi\_3(n) &= -0.4\chi\_1(n-3) + \omega\_3(n) \\ \chi\_4(n) &= -0.5\chi\_1(n-2) + 0.25\sqrt{2}\chi\_4(n-1) \\ &+ 0.25\sqrt{2}\chi\_5(n-1) + \omega\_4(n) \\ \chi\_5(n) &= -0.25\sqrt{2}\chi\_4(n-1) + 0.25\sqrt{2}\chi\_5(n-1) + \omega\_5(n) \end{aligned}$$

In this three order system, x1 is a direct source to x2, x3, and x4, bidirectional connectivity exists between x4 and x5. There is no direct coupling from x1 to x5. The SNR of the generated time courses is 0.01. The simulation results are shown in **Figure 3**. **Figures 3C,D** illustrate the time domain and frequency domain analysis results, respectively. In addition, the results given by DTF (Kaminski et al., 2001) are presented in **Figure 3B** as a reference (see Equation A14 in Appendix). It is obvious that both the time domain partial Granger causality and frequency domain PDC could correctly detect all the direct causal influences, while the DTF mistakenly identified the indirect causal influence from x1 to x5. These results indicate that the Granger causality measures we used in the study could avoid the influence of indirect causal relationship.

Based on the above analysis, we can conclude that the analytical methods and Granger causality measures adopted in the study can efficiently detect the direct causal relationships between time courses, and the combined approach takes the consistent results of the two domains' analyses, which can be seen as a double verification process to present more accurate and confident results. The simulation results were stable under different noise condition. Therefore, using the proposed method in Section Constructing within-group effective connectivity graph is considered to present a convincing result for fMRI data analysis.

#### Within-Group Effective Connectivity Graph

The causal connectivity graphs of healthy controls and IGE-GTCS patients are presented in **Figures 4A,B**. The connecting arrows are weighted according to the strengths of the time domain causal influences (partial Granger causality values normalized by the maximum partial Granger causality value). Meanwhile, each significant connection is respectively marked with the frequency interval where the PDC values are higher than the significance thresholds. And finally a subset of the significant connections that showed a dominant direction of influence (significant DOI term) are highlighted in red in the same figure. It was observed that comparing to the healthy

controls (21 influences), the IGE-GTCS patients (16 influences) established less causal connections among the six ROIs.

## Between-Group Effective Connectivity Differences

The group differences of effective connectivity are illustrated in **Figure 5**. In time domain analysis, two connections exhibited significance, i.e., the increased causal influence from the rDLPFC to the dACC (p < 0.05, FDR corrected), and the decreased causal influence from the rFIC to the PCC (p < 0.05, uncorrected) in the IGE-GTCS patients relative to healthy controls. The connections' means and standard errors of partial Granger causality values across subjects within each group were illustrated in the blue box in **Figure 5**. Meanwhile, frequency domain analysis also found the enhanced causal influence from the rDLPFC to the dACC (p < 0.05, FDR corrected) in patients than healthy controls. The mean PDC values across subjects within each group, as well as the p-value spectrum of this significant connection were shown in the pink box. It can be seen that the group difference of this causal influence was significant in a band of frequencies, [0 0.167] Hz, and the minimum p-value (p = 0.028) was obtained at 0.034 Hz.

## DISCUSSION

Human high-level attention and cognitive control processes rely on the well-balanced dynamic interactions between largescale brain networks, and three core neurocognitive networks including the SN, DMN, and CEN have been highlighted in the study of psychopathology. Our prior work used static as well as dynamic measures of functional connectivity, however,

did not evaluate effective connectivity among brain networks for cognitive dysfunctions and psychopathological risk in IGE-GTCS (Wei et al., 2015). In this study, we have proposed a combination framework of time domain and frequency domain multivariate Granger causality analysis, to reveal alterations in direct causal relationship across key nodes of the SN, DMN, and CEN in the IGE-GTCS patients relative to the healthy controls. The key findings of the study include: (1) the establishment of less causal interactions among the key nodes in the patients compared with healthy controls; (2) two SN-involved effective connectivity that exhibited significant group difference, they are: enhanced causal influence from the rDLPFC to the dACC (p < 0.05, FDR corrected) throughout the whole evaluated frequency range ([0 0.167] Hz) in patients than healthy controls revealed by both the time and frequency domains analyses, and decreased causal influence from the rFIC to the PCC (p < 0.05, uncorrected) in

patients than healthy controls given by the time domain analysis. These findings provide new insights into the brain functional architecture of IGE-GTCS.

#### Methodological Considerations

Several methodological considerations in the present study need to be addressed aforehand. First, the basis of multivariate Granger causality and well-chosen time and frequency domains Granger causality measures ensure the indirect causality between ROIs to be eliminated, and this could be certified by the simulation results of the toy model 2 in Section Simulations. Second, the MVAR model order was set to 1 for all subjects according to the BIC criterion, thus we evaluated the Granger causal relationship between ROIs with a maximum time delay of 3 s (since TR is 3 s). The low model order is common and recommended in several Granger causality studies using resting-state fMRI data considering the low time resolution of fMRI data itself (Sato et al., 2010; Hamilton et al., 2011). Third, to our knowledge, no previous study has evaluated the Granger causal connectivity in IGE-GTCS combined the time domain and frequency domain analysis. In the current study, we have revealed aberrant causal interactions among the core neurocognitive networks in IGE-GTCS confirmed by analyses in two domains. Besides, a combination framework of time domain and frequency domain multivariate Granger causality analysis was proposed, and the improvement of accuracy using this method was verified by the simulation experiments. This general combination framework can also be used in other multisubject studies when effective connectivity measured by multivariate Granger causality is needed.

## The Causal Relationship between the rFIC and the PCC

In the current study, the controls group established bidirectional effective connectivity between the rFIC and the PCC (the influence from the rFIC to the PCC also exhibited significant DOI value), while none of these two connections was significant in the patients group (see **Figure 4**). Further, the betweengroup analysis based on time domain partial Granger causality revealed that the effective connectivity from the rFIC to the PCC exhibited significance (p < 0.05, uncorrected, Mann-Whitney U-test), with decreased connectivity strength in the patients relative to the healthy controls (see **Figure 5**). It is well known that the function of the SN is to identify internal and extra-personal stimuli to guide flexible behavior (Corbetta and Shulman, 2002; Seeley et al., 2007), and the DMN is associated with spontaneous activities and internally oriented cognition (Raichle et al., 2001). Previous task-based as well as resting-state fMRI studies using Granger causality analysis have confirmed that there exists effective connectivity between the SN and the DMN (Sridharan et al., 2008; Uddin et al., 2011). Among these researches, one commonly approved conclusion is that the rFIC acts as a critical causal outflow hub in initiating control signals to activate the CEN and deactivate the DMN, thus provides an interpretation of the directionality of signaling from the rFIC to the PCC. Moreover, a relevant neurodevelopmental study reported that the Granger causal influence from the rFIC to the PCC was significant in the adults group while vanished in the children, suggesting the maturation of rFIC-related causal connectivity is crucial for the sophisticated cognitive abilities (Uddin et al., 2011). For the causal influence from the PCC to the rFIC, Uddin et al. (2009) using Granger causality analysis provided evidence that the PCC may negatively regulate activity in the SN. Such an information inflow may be interpreted as a feedback circuit establishment that suppresses the activity of the DMN in a primed state to make better preparation for the rFIC to release cognitive control processes when salient stimuli occur.

For the frequency-domain interpretation of the causal interactions between the rFIC and the PCC, the significant frequency intervals of the two connections in the control group showed that the PCC conducted causal influence on the rFIC for the lower frequencies ([0 0.085] Hz). This is reasonable given the fact that the PCC as a key node of the DMN, is responsible for information integration in the spontaneous low-frequency range (Leech and Sharp, 2014). Meanwhile, the drive from the rFIC to the PCC was significant throughout the evaluated frequency interval, [0 0.167] Hz, probably indicating that the brain responses for cognitive control processes in switching between exogenous and endogenous stimuli are needed for the whole spectrum of signal changing frequencies (see **Figure 4A**).

Based on the above, we inferred that the bidirectional effective connectivity between the rFIC and the PCC may be associated with well-balanced performance in cognitive flexibility, with which one can flexibly switch between mental processes to appropriately react to salient events in the environment (Scott, 1962). Additionally, prior study has suggested that the active dynamic interactions among brain networks are indispensable for adaptive and flexible cognition and behavior (Cole et al., 2013), while the IGE-GTCS patients (16 influences) established less causal connections among the SN, DMN, and CEN relative to the healthy controls (21 influences, see **Figure 4**). For all the aforementioned proofs, we inferred that the hypoconnectivity of the patients group, especially the decreased causal influence from the rFIC to the PCC, may be associated with impaired cognitive abilities as well as mental inflexibility in IGE-GTCS (Hommet et al., 2006; Chowdhury et al., 2014).

## The Causal Relationship between the dACC and the rDLPFC

Both the time domain and frequency domain analysis in our study consistently revealed the significantly enhanced effective connectivity (p < 0.05, FDR corrected, Mann-Whitney U-test) from the rDLPFC to the dACC in the patients relative to the healthy controls (see **Figure 5**). Interestingly, the withingroup connectivity graphs indicated that the direction of the Granger causality between the rDLPFC and the dACC is opposite in the two groups, i.e., the dACC drives the rDLPFC (also with significant DOI value) in the controls while the rDLPFC drives the dACC (also with significant DOI value) in the IGE patients (see **Figure 4**). Since both the ACC and the DLPFC are co-activated in cognitive control processing and tests of sustained attention (Adler et al., 2001; Miller and Cohen, 2001), the dissociation and functional interactions of the two areas have arouse the interests of the researchers (Kondo et al., 2004; Dosenbach et al., 2007; Seeley et al., 2007). In an event-related fMRI study, Macdonald et al. (2000) conducted a task-switching vision of the Stroop task and suggested that the DLPFC (Brodmann's area (BA) 9) supports implementation of control, while the ACC (BA 24 and BA 32) is responsible for performance monitoring. Furthermore, based on the conflict hypothesis of the ACC, Kerns et al. (2004) explored whether ACC activity associated with conflict and error trial predicted pre-frontal cortex activity under Stroop task, and concluded that once the ACC detects conflicts, it modulates the strength of the rDLPFC (BA 9 and BA 8) representations, which then executes appropriate cognitive control and products corresponding behavioral adjustments. Our study revealed the effective connectivity from the dACC (BA 24) to the rDLPFC (BA 9) in the healthy controls, which may underline the existence of neural circuitry in terms of resting-state Granger causality supporting the above cognitive control processes. By contrast, the establishment of the significantly enhanced causal influence from rDLPFC to dACC in the patients may thus indicate a disruption to the well-organized cognitive control processes, and probably associated with cognitive dysfunctions in IGE-GTCS, such as deficits in working memory, sustained attention, as well as executive dysfunction (Mirsky et al., 2002). This altered causal influence as well as the aberrant connection from the rFIC to the PCC demonstrate that the IGE-GTCS patients exhibit inappropriate mapping with the SN. The findings together with various prior studies highlight the critical role of SN in connecting with DMN and CEN (Sridharan et al., 2008; Menon, 2011; Uddin et al., 2011), which provide informative evidence for the understanding of the cognitive dysfunctions and psychopathological mechanism of IGE-GTCS.

In addition, prior study using Granger causality analysis on EEG/fMRI data of IGE patients found the frontal lobe had the maximum net causal strength, suggesting that frontal and parietal areas were the initiation of absence seizures (Szaflarski et al., 2010). Similarly, our study using both time domain and frequency domain multivariate Granger causal analysis revealed the significantly enhanced causal influence directed from the rDLPFC to the dACC throughout the whole evaluated frequency range ([0 0.167] Hz) in the IGE-GTCS patients, which may as well indicate that the pre-frontal cortex is probably the initiation of GTCS.

Evaluating hemodynamic response function (HRF) effects in the Granger causality analysis of BOLD-fMRI data is a controversial topic (Barnett and Seth, 2014). Noticing that BOLD-fMRI is an indirect transformation of underlying neural activity and Granger causality is a purely data-driven method without biological modeling, in the current study, we have carefully considered the effects of HRF on Granger causality analysis on BOLD-fMRI data. The use of DOI terms in the within-group effective connectivity analysis, the main concern of identifying different effective connectivity patterns between the patients and controls rather than revealing canonical causal structure, as well as the group-level strategy for multisubject Granger causality analysis in the current study, have been suggested by recent analyses that are theoretically useful to relieve the HRF effects (Schippers et al., 2011; Barnett and Seth, 2014). Furthermore, considering that the HRF has been reported to be different in epilepsy subjects (David et al., 2008), we adopted the blind-deconvolution technique proposed by Wu et al. (2013) to deconvolve the mean time courses of the six ROIs (obtained in Section Region of interest definition and time course extraction) for each subject separately, and on the basis of the deconvolved BOLD time courses, we repeated the Granger causality analysis described in Section Evaluating between-group effective connectivity difference. In this case, both the time domain partial Granger causality and the frequency domain PDC have revealed only one effective connectivity that showed significant group difference (Mann-Whitney U-tests, p < 0.05, FDR corrected), i.e., the increased causal influence from the rDLPFC to the dACC in the IGE-GTCS patients than controls, which is consistent with our prior result based on the BOLD time courses without deconvolution. We thus infer that, the altered effective connectivity from the rDLPFC to the dACC, which is consistently revealed by the two domains' multivariate Granger causality analyses on both the BOLD and deconvolved BOLD time courses, is probably a key factor associated with cognitive dysfunctions in IGE-GTCS.

#### Limitations and Future Directions

Several limitations in this study should be mentioned. First, due to the absence of neuropsychological tests for both the patients and the controls, we cannot precisely relate the significant Granger causal connectivity to the specific cognitive functions and neuropsychological parameters, the interpretations of the results are simply inferences derived from earlier researches. Second, it is reported that AED toxicity is related to psychopathology and abnormal neuronal function in epilepsy (Schmitz, 1999). In the current study, all patients were treated with AEDs, including 24 patients with monotherapy and 3 patients with polytherapy; the AEDs included sodium valproate (VBA), phenytoin (PHT), carbamazepine (CBZ), lamotrigine (LTG), phenobarbital (PB), and topiramate (TPM). However, we have carefully considered the potential confounding effects of AEDs on ICNs in the study. We ensured that all patients received no medication for at least 48 h prior to the MRI scanning to avoid direct effects of AEDs on the effective connectivity analysis. Nonetheless, the long-term effects of AEDs could not be excluded. Third, a relatively small number of ROIs were used in the current study to investigate the interconnectivity between networks. An extension to a larger set of nodes across different brain networks would be considered in the future. In addition to the above mentioned aspects, future works could also focus on EEG-fMRI multimodal integration for resting-state as well as task-based time-frequency multivariate Granger causality analysis, and evaluate causal relationship between ROIs using dynamic causal modeling (Friston et al., 2003).

#### CONCLUSIONS

In this study, we conducted combined time and frequency domains multivariate Granger causality analyses to investigate effective connectivity among the key nodes of the three core neurocognitive networks in IGE-GTCS patients and matched healthy controls. The results revealed two SN-involved effective connectivity that exhibited significant group difference. One is the decreased Granger causal influence from the rFIC to the PCC in the patients relative to the healthy controls given by time domain analysis, which may underline impaired cognitive abilities as well as mental inflexibility in IGE-GTCS. Another is the significantly increased Granger causal influence from the rDLPFC to the dACC in patients than controls revealed by both the time and frequency domains analyses. This altered effective connectivity may indicate a disruption to the wellorganized cognitive control processes thus probably leading to disorders in working memory, sustained attention, as well as executive dysfunction in IGE-GTCS. The current work proposes a combination framework of time and frequency domains multivariate Granger causality analyses that is suitable for multisubject studies, and demonstrates for the first time that patients with IGE-GTCS exhibited altered Granger causal interactions across the SN, DMN, and CEN, shedding new lights on the psychopathological mechanism of IGE-GTCS.

#### AUTHOR CONTRIBUTIONS

DH, SQ designed research; HW, JA, HS, and LZ performed research; HW analyzed the data; and HW, JA, HS, and LZ wrote the paper.

## ACKNOWLEDGMENTS

We thank the editor and reviewers for their kindly help and constructive suggestions to improve the work. This work is supported by the National Natural Science Foundation of China<PHONE_NUMBER>1, 61375111, 61503397, 81271389, and 81471251).

## REFERENCES

connectivity in fMRI data. Neuroimage 53, 65–77. doi: 10.1016/j.neuroimage. 2010.05.063

in idiopathic generalized epilepsy. Epilepsy Behav. 46, 118–125. doi: 10.1016/ j.yebeh.2015.03.031

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Wei, An, Shen, Zeng, Qiu and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

#### APPENDIX

#### COMPUTATION OF GRANGER CAUSALITY MEASURES

For better understanding of the calculation process and the definition of each Granger causality measures, we took a general system of N (N ≥ 3) variables Xi(t), i = 1, 2, . . . N as an example. The unrestricted MVAR model of the system can be written as:

$$
\begin{bmatrix} X\_1(t) \\ X\_2(t) \\ \vdots \\ X\_N(t) \end{bmatrix} = \sum\_{j=1}^p \begin{bmatrix} A\_{11}(j) & A\_{12}(j) & \dots & A\_{1N}(j) \\ A\_{21}(j) & A\_{22}(j) & \dots & A\_{2N}(j) \\ \vdots & \vdots & \ddots & \vdots \\ A\_{N1}(j) & A\_{N2}(j) & \dots & A\_{NN}(j) \end{bmatrix}.
$$

$$
\begin{bmatrix} X\_1(t-j) \\ X\_2(t-j) \\ \vdots \\ X\_N(t-j) \end{bmatrix} + \begin{bmatrix} E\_1(t) \\ E\_2(t) \\ \vdots \\ E\_N(t) \end{bmatrix} \tag{A3}
$$

where p is the model order, Ei, i = 1, 2, . . . N are the model residuals (prediction errors), the elements of **A**(j) are called regression coefficients. The noise covariance matrix of the unrestricted model can be represented as:

$$
\Sigma = \begin{bmatrix}
\text{var}(E\_1) & \text{cov}(E\_1, E\_2) & \cdots & \text{cov}(E\_1, E\_N) \\
\text{cov}(E\_2, E\_1) & \text{var}(E\_2) & \cdots & \text{cov}(E\_2, E\_N) \\
\vdots & \vdots & \ddots & \vdots \\
\text{cov}(E\_N, E\_1) & \text{cov}(E\_N, E\_2) & \cdots & \text{var}(E\_N)
\end{bmatrix} \tag{A4}
$$

To measure the Granger causality from X2(t) to X1(t), we delete the row 2 and column 2 of the noise covariance matrix of the unrestricted model, then partition the matrix into blocks:

$$\Sigma = \begin{bmatrix} \text{var}(E\_1) & \text{cov}(E\_1, E\_3) & \cdots & \text{cov}(E\_1, E\_N) \\ \text{cov}(E\_3, E\_1) & \text{var}(E\_3) & \cdots & \text{cov}(E\_3, E\_N) \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ \text{cov}(E\_N, E\_1) & \text{cov}(E\_N, E\_3) & \cdots & \text{var}(E\_N) \end{bmatrix}$$

$$= \begin{bmatrix} \sum\_{11} & \sum\_{12} \\ \cdots & \cdots & \cdots \\ \cdots & \cdots & \cdots \end{bmatrix} \tag{A5}$$

and omit the time course X2(t) to obtain a restricted MVAR model as:

$$
\begin{bmatrix} X\_1(t) \\ X\_3(t) \\ \vdots \\ X\_N(t) \end{bmatrix} = \sum\_{j=1}^p \begin{bmatrix} A\_{11}'(j) & A\_{13}'(j) & \dots & A\_{1N}'(j) \\ A\_{31}(j) & A\_{33}(j) & \dots & A\_{3N}'(j) \\ \vdots & \vdots & \ddots & \vdots \\ A\_{N1}'(j) & A\_{N3}'(j) & \dots & A\_{NN}'(j) \end{bmatrix}.
$$

$$
\begin{bmatrix} X\_1(t-j) \\ X\_3(t-j) \\ \vdots \\ X\_N(t-j) \end{bmatrix} + \begin{bmatrix} E\_1'(t) \\ E\_3'(t) \\ \vdots \\ E\_N'(t) \end{bmatrix} \tag{A6}
$$

and the noise covariance matrix of the restricted model is:

$$\rho = \begin{bmatrix} \text{var}(\boldsymbol{E}\_1') & \text{cov}(\boldsymbol{E}\_1', \boldsymbol{E}\_3') & \cdots & \text{cov}(\boldsymbol{E}\_1', \boldsymbol{E}\_N') \\\\ \text{cov}(\boldsymbol{E}\_3', \boldsymbol{E}\_1') & \text{var}(\boldsymbol{E}\_3') & \cdots & \text{cov}(\boldsymbol{E}\_3', \boldsymbol{E}\_N') \\\\ \vdots & \vdots & \ddots & \vdots \\\\ \text{cov}(\boldsymbol{E}\_N', \boldsymbol{E}\_1') & \text{cov}(\boldsymbol{E}\_N', \boldsymbol{E}\_3') & \cdots & \text{var}(\boldsymbol{E}\_N') \end{bmatrix} \tag{A1}$$

$$= \begin{bmatrix} \rho\_{11} & \vdots & \rho\_{12} \\ \cdots & \cdots & \cdots \\\\ \rho\_{21} & \vdots & \rho\_{22} \end{bmatrix} \tag{A2}$$

Next, we explore the frequency domain representation of MVAR model. The Fourier transform of (A3) gives:

$$
\begin{bmatrix} A\_{11}(f) & A\_{12}(f) & \dots & A\_{1N}(f) \\ A\_{21}(f) & A\_{22}(f) & \dots & A\_{2N}(f) \\ \vdots & \vdots & \ddots & \vdots \\ A\_{N1}(f) & A\_{N2}(f) & \dots & A\_{NN}(f) \end{bmatrix} \begin{bmatrix} X\_{1}(f) \\ X\_{2}(f) \\ \vdots \\ X\_{N}(f) \end{bmatrix} = \begin{bmatrix} E\_{1}(f) \\ E\_{2}(f) \\ \vdots \\ E\_{N}(f) \end{bmatrix} \tag{A8}
$$

where the components of the **A**(f) matrix are:

$$A\_{lm}(f) = \delta\_{lm} - \sum\_{j=1}^{p} A\_{lm}(j)e^{-i2\pi fj} \tag{A9}$$

$$\delta\_{lm} = \begin{cases} 1, & l = m \\ 0, & l \neq m \end{cases}$$

and we rewrite the **A**(f) to the following form:

$$\mathbf{A}(f) = \begin{bmatrix} A\_{11}(f) & A\_{12}(f) & \dots & A\_{1N}(f) \\ A\_{21}(f) & A\_{22}(f) & \dots & A\_{2N}(f) \\ \vdots & \vdots & \ddots & \vdots \\ A\_{N1}(f) & A\_{N2}(f) & \dots & A\_{NN}(f) \end{bmatrix}$$

$$= \begin{bmatrix} \mathbf{a}\_1(f) \ \mathbf{a}\_2(f) & \dots \ \mathbf{a}\_N(f) \end{bmatrix} \tag{A10}$$

then the Granger causality measures in two domains can be formalized as follows:

#### TIME DOMAIN PARTIAL GRANGER CAUSALITY

Referring to (A5) and (A7), the partial Granger causality from X2(t) to X1(t), conditioned on all the remaining variables besides X1(t) and X2(t), is given by:

$$F\_{2 \rightarrow 1|\text{others}}^P = \ln \frac{\left| \rho\_{11} - \rho\_{12} \rho\_{22}^{-1} \rho\_{21} \right|}{\left| \sum\_{11} - \sum\_{12} \sum\_{22}^{-1} \sum\_{21} \right|} \tag{A11}$$

which can avoid influence of exogenous inputs and latent variables, and describe the direct causal relationships between time courses (Guo et al., 2008). When there is no direct influence from X2(t) to X1(t), the A12(j), j = 1 . . . p in (A3) are uniformly equal to zero, leading to F P 2→1|others = 0. On the contrary, when a direct influence from X2(t) to X1(t) exists, we will get F P 2→1|others > 0.

In addition, a difference of influence (DOI) term is introduced to describe the dominant direction of causal influence that measured as the difference (Roebroeck et al., 2005):

$$D\_{2 \to 1\text{\textquotedblleft}\text{others}}^{p} = F\_{2 \to 1\text{\textquotedblleft}\text{others}}^{p} - F\_{1 \to 2\text{\textquotedblleft}\text{others}}^{p} \tag{A12}$$

which can further limits potentially spurious links caused by hemodynamic blurring (Seth, 2010).

#### FREQUENCY DOMAIN PARTIAL DIRECTED COHERENCE

To measure the Granger causality from X2(t) to X1(t) in frequency domain, the PDC is defined as (Baccalá and Sameshima, 2001):

$$
\pi\_{12}(f) = \frac{A\_{12}(f)}{\sqrt{\mathbf{a}\_2^H(f)\mathbf{a}\_2(f)}} \tag{A13}
$$

It can be seen that the evaluation of Granger causality from X2(t) to X1(t) is discretized into a set of frequency slices. Therefore, by selecting the interested frequency range, we can use PDC to evaluate direct Granger causality between time courses at each discrete frequency.

Additionally, the non-normalized DTF from X2(t) to X1(t) is given by Kaminski et al. (2001):

$$\left|\theta\_{12}^{2}(f) = \frac{\left|\mathbf{M\_{21}}\right|^{2}}{\left|\mathbf{A(f)}\right|^{2}}\tag{A14}$$

where |**M21**| is a minor of **A(f)** with row 2 and column 1 removed. The DTF in the multivariate condition could not avoid the detection of indirect causal relationship between time courses (Kaminski et al., 2001).

# Aberrant Functional Connectivity between the Amygdala and the Temporal Pole in Drug-Free Generalized Anxiety Disorder

Wei Li 1 , Huiru Cui 1 , Zhipei Zhu1 , Li Kong2 , Qian Guo1 , Yikang Zhu1 , Qiang Hu3 , Lanlan Zhang4 , Hui Li 1 , Qingwei Li 5 , Jiangling Jiang1 , Jordan Meyers 6 , Jianqi Li 7 , Jijun Wang1, 8, 9 \*, Zhi Yang10 \* and Chunbo Li 1, 8, 9 \*

*1 Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 2 College of Education, Shanghai Normal University, Shanghai, China, 3 Department of Psychology, Qiqihar Mental Health Center, Qiqihar, China, 4 Guangji Hospital of Suzhou, Suzhou, China, 5 Department of Psychiatry, Tongji Hospital of Tongji University, Shanghai, China, 6 Nathan S. Kline Institute for Psychiatric Research, New York, NY, USA, 7 Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai, China, 8 Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China, 9 Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China, 10 CAS Key Laboratory of Behavioral Science and MRI Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China*

#### Edited by:

*Baojuan Li, Massachusetts General Hospital, USA*

#### Reviewed by:

*Xin Di, New Jersey Institute of Technology, USA Dawei Li, Duke University, USA*

#### \*Correspondence:

*Jijun Wang<EMAIL_ADDRESS>Zhi Yang<EMAIL_ADDRESS>Chunbo Li<EMAIL_ADDRESS>
Received: *01 June 2016* Accepted: *14 October 2016* Published: *04 November 2016*

#### Citation:

*Li W, Cui H, Zhu Z, Kong L, Guo Q, Zhu Y, Hu Q, Zhang L, Li H, Li Q, Jiang J, Meyers J, Li J, Wang J, Yang Z and Li C (2016) Aberrant Functional Connectivity between the Amygdala and the Temporal Pole in Drug-Free Generalized Anxiety Disorder. Front. Hum. Neurosci. 10:549. doi: 10.3389/fnhum.2016.00549* The amygdala and the dorsolateral prefrontal cortex (DLPFC) play important roles in "emotion dysregulation," which has a profound impact on etiologic research of generalized anxiety disorder (GAD). The present study analyzed both eyes-open and eyes-closed resting state functional MRI (rs-fMRI) of 43 subjects (21 GAD patients with medicine free and 22 matched healthy controls). The amygdala and the DLPFC were defined as regions of interest (ROI) to analyze functional connectivity (FC) in GAD patients compared with healthy controls. The main findings revealed GAD patients had increased FC between the amygdala and the temporal pole compared to healthy controls, which was found in both eyes-open and eyes-closed rs-fMRI. And altered FC between the ROIs and brain regions that mainly belonged to the default mode network (DMN) were found. These findings suggest that the abnormal FC between the amygdala and the temporal pole may contribute to the pathophysiology of GAD, and provide insights into the current understanding of the emotion dysregulation of anxiety disorders.

Keywords: amygdala, DLPFC, temporal pole, DMN, functional connectivity, generalized anxiety disorder

## INTRODUCTION

Anxiety disorders are the most common of all mental disorders with 30% prevalence in the population, and they significantly contribute to the economic burden of disease (Andlin-Sobocki and Wittchen, 2005; Kessler et al., 2005; Bereza et al., 2009). Among anxiety disorders, generalized anxiety disorder (GAD) is the most common type (Roy-Byrne and Wagner, 2004; Lieb et al., 2005; Kroenke et al., 2007). GAD is characterized by excessive and continuous worry, anxiety, and apprehension. It may also produce distress and/or functional impairments.

Previous studies have argued that "emotional dysregulation," the inability to control or regulate emotional responses, may be responsible for the development of GAD. This hypothesis is grounded in the observation that individuals with GAD concentrate their attention on threatening thoughts. This cognitive model has been widely adopted for understanding GAD (Mathews and MacLeod, 1985; Bar-Haim et al., 2007; Amir et al., 2009; Behar et al., 2009).

The dorsolateral prefrontal cortex (DLPFC), an important region for performing cognitive operations during the regulation of emotional responses, has been shown to play a key role in the pathophysiology of GAD (MacDonald et al., 2000; Miller and Cohen, 2001; Blasi et al., 2007; Meyer et al., 2011; Moon et al., 2015). Altered activation of the DLPFC in patients with GAD has been associated with emotional dysregulation and attention deficit. Functional MRI studies have reported increased activity of the DLPFC under affective stroop and emotion reappraisal tasks in patients with GAD (Ball et al., 2012; Blair et al., 2012). Additionally, functional abnormalities of the amygdala, known as the most prominent "fear-circuit" structure in the brain that plays a central role in automatic affective processing, have been found in most anxiety disorders (LeDoux, 2000; Anderson et al., 2003; Ohman, 2005; Etkin and Wager, 2007; Adolphs, 2008; Shin and Liberzon, 2010; Linares et al., 2012). The amygdala has also demonstrated responsibility for facilitating perceptual processing and bottom-up emotional control in individuals with GAD (LeDoux, 2000; Davis and Whalen, 2001; Phelps, 2006). Models of emotional regulation have therefore focused primarily on the DLPFC and the amygdala. Accordingly, we assume that individuals with GAD display aberrant FC seeded from the amygdala and the DLPFC compared to healthy controls.

Studies have shed less light on the relationship between the temporal cortex, especially the temporal pole, and GAD. Although the function of the temporal pole is not well understood, a damaged temporal pole can impair ability to use experiential knowledge and therefore may cause affective symptoms (Funnell, 2001). The temporal cortex binds complex, highly processed perceptual inputs to visceral emotional responses (Olson et al., 2007). The temporal pole is located at the end of the ventral visual stream and is strongly interconnected with the amygdala (Nakamura and Kubota, 1996; Stefanacci and Amaral, 2002). It integrates conceptual knowledge and meaning with semantic, visual and auditory information (Carlson et al., 2014), and influences emotions via top-down modulations (Pehrs et al., 2015). Studies have reported altered functional connectivity (FC) between the temporal pole and the amygdala in anxiety disorders (Aghajani et al., 2014; Modi et al., 2015). This finding has prompted us to explore the FC between the temporal pole and the amygdala in GAD patients. Therefore, one of our hypotheses is that altered FC between these two areas is attributed to the etiology of GAD.

Accordingly, the amygdala and the DLPFC will be defined as regions of interest (ROIs) to explore in GAD patients. Because the number of volumes in the eyes-open resting state fMRI (rs-fMRI) we collected was too small, we also analyzed the eyes-closed rsfMRI using the same protocol to improve the reliability of our results.

## MATERIALS AND METHODS

#### Participants

All participants received the Mini-International Neuropsychiatric Interview (MINI), Chinese version (Si et al., 2009). Twenty two GAD patients who met the criteria for DSM-IV (Association, 2000) and who were not found to have lifetime psychosis, substance dependence or severe somatic diseases were recruited from the psychological outpatient clinic at the Shanghai Mental Health Center. We excluded patients who had comorbid moods or other anxiety disorders. Twenty one healthy controls were recruited from local communities and Shanghai Jiao Tong University. Controls were matched for gender, age, education level, and did not meet DSM-IV criteria for lifetime mood, anxiety, psychotic, or substance dependence disorders. Forty three participants were enrolled in total for this study and according to the Edinburgh Inventory (Oldfield, 1971), all of them are right-handed adults free of psychotropic medications for at least 2 weeks before enrollment. The study was conducted between August 2011 and November 2012.

This study was approved by the Research Ethics Committee of Shanghai Mental Health Center, China (SMHC-IRB 201217). Written informed consent was acquired from every participant.

All participants were informed of the safety and eligibility criteria for fMRI scanning: no neurological conditions and no implanted ferrous metal. The Hamilton Rating Scale for Anxiety (HAMA) (Hamilton, 1959) and Hamilton Rating Scale for Depression (HAMD) (Hamilton, 1967) were administered to all participants on the day of scanning. Demographic and clinical characteristics of the 43 participants are shown in **Table 1**.

#### Image Acquisition

Images were obtained using a Siemens Trio 3.0 Tesla MRI scanner (Siemens, Erlangen, Germany) with a standard 12 channel head coil. Restraining foam pads was used to reduce head motion and earplugs were used to reduce scanner noise. High-resolution T1-weighted anatomical images (repetition time (TR) = 1900 ms, echo time (TE) = 2.46 ms, flip angle = 9 degrees, 32 transverse slices, field of view (FOV) = 240 × 240 mm, matrix = 256 × 256, slice thickness = 1 mm) were acquired using a magnetization prepared rapid gradient-echo sequence. Resting-state functional MRI data were acquired using a singleshot, gradient-recalled echo planar imaging sequence (TR = 2000

*HAMA, Hamilton Anxiety Scale; HAMD, Hamilton Depression Scale; GAD, generalized anxiety disorder; HC: healthy control.*

ms, TE = 25 ms, flip angle = 90 degrees). 32 transverse slices (FOV = 240 × 240 mm, matrix = 64 × 64, slice thickness = 5 mm) resulting in a total of 80/157 volumes and a scan time of 164/314 s, respectively, in eyes-open and eyes-closed rs-fMRI. During the scan, participants were instructed to stay aware. After the scan, the technicians would check the quality of structural images. If any abnormalities were found in the images, participants were re-scanned.

## Data Processing and Analysis

#### Demographic and Clinical Data Analysis

Using Statistical Product and Service Solutions software 17.0 (SPSS, Inc., Chicago, Illinois), we conducted analysis of age, gender, years of education, HAMA, and HAMD. Independent sample t-tests for continuous variables and chi-square tests for categorical variables were used.

#### Resting-State fMRI Analysis

The Data Processing Assistant for Resting-State fMRI 2.0 (DPARSFA2.0, http://restfmri.net/forum/) (Chao-Gan and Yu-Feng, 2010), which works with the Statistical Parametric Mapping Software (SPM8, http://www.fil.ion.ucl.ac.uk/spm) (Friston et al., 1994) was used to analyze the rs-fMRI data. Preprocessing was completed in 7 steps: (1) Convert DICOM data to NIFTI format and remove first 10 time points of the image; (2) Slice timing correction and realignment of image; (3) Parallel movements in any direction >2.5 mm, or rotary movements >2.5 degree were excluded and subjects using a threshold of frame-wise displacement >0.5 mm (Power et al., 2012) were also excluded; (4) Spatial normalization to the standard Montreal Neurological Institute (MNI) echo-planar imaging template and the resampled voxel size was 3 × 3 × 3 mm; (5) Conduct Friston 24-parameter correction (Yan et al., 2013) to minimize the effect of head motion; (6) Smoothing with a Gaussian kernel of 8-mm fullwidth at half-maximum (FWHM); (7) After linear detrending, the functional data was band-pass filtered (pass frequence band: 0.01–0.1 Hz) to reduce the effects of low-frequency drift as well as high-frequency respiratory and cardiac noise (Biswal et al., 1995).

According to previous studies (Cieslik et al., 2013; Comte et al., 2014; Cui et al., 2016), the bilateral amygdala (MNI: 32,−2,−26;−28, 4,−22) and the bilateral DLPFC (MNI: 30, 43, 23;−51, 27, 30) were defined as ROIs. The peak voxel of each ROI and a 6 mm-radius sphere were selected to proceed with the FC analysis. The Pearson correlation coefficients were calculated between the ROI and the other voxels of the whole brain. Fisher's r-to-z transformation was used to convert correlation coefficients into z-scores so that the correlation coefficient would improve the normality of the data (Hampson et al., 2002; Chao-Gan and Yu-Feng, 2010; Song et al., 2011), and generate FC maps. Voxel-wise two-sample t-tests were conducted to compare group differences between GAD patients and controls. Spearman correlation analysis was performed in GAD patients to investigate the correlation between FC and disease severity (HAMA score). Both t-tests and correlation analysis were conducted under the BrainMask\_61∗ 73∗ 61. As correction for multiple comparisons, a corrected threshold of p < 0.05 (twotailed) was derived from a combined threshold of p < 0.005 for individual voxel with a cluster size >53 voxels. These threshold were determined using the 3dFWHM and 3dClustSim program in AFNI software (https://afni.nimh.nih.gov/afni, parameters: single voxel p < 0.005, 2000 Monte Carlo iterations, estimated FWHM = 9.5 mm, the BrainMask\_61∗ 73∗ 61 was used as mask in estimation of smoothness and correction). Additionally, age, years of education, HAMD score and intracranial volume (ICV) were modeled as covariates. The analyses above were conducted in both eyes-open and eyes-closed rs-fMRI.

In order to improve the reliability of these results, we calculated mean frame-wise displacement for each group and conducted t-tests between matched groups in SPSS.

## RESULTS

## Demographic and Clinical Characteristics

Compared with the healthy control group, the HAMA scores and HAMD scores were significantly different in GAD patients (P < 0.05). Except that, there is no significant difference between GAD patients and healthy controls.

## Resting-State fMRI Results

#### Functional Connectivity

Compared with healthy controls, GAD patients showed increased FC between the left amygdala and the temporal pole both in eyes-open and eyes-closed rs-fMRI (P < 0.005 to define cluster, AlphaSim correction, cluster size >53 voxels, overall p < 0.05).

In eyes-open rs-fMRI, there was increased connectivity between the left amygdala and the inferior frontal gyrus in the GAD group compared with the control group (P < 0.005 to define cluster, AlphaSim correction, cluster size >53 voxels, overall p < 0.05). (**Figure 1**, **Table 2**) There was no significant abnormal FC between GAD subjects and controls seeded from the DLPFC.

In eyes-closed rs-fMRI, we found increased FC in the left amygdala with the middle temporal gyrus (P < 0.005 to define cluster, AlphaSim correction, cluster size >53 voxels, overall p < 0.05). Decreased FC between the left DLPFC and the precuneus, the lingual gyrus, the calcarine sulcus, and the cerebellar vermis was detected in GAD subjects compared with healthy controls (P < 0.005 to define cluster, AlphaSim correction, cluster size >53 voxels, overall p < 0.05). There was decreased connectivity between the right DLPFC and the medial prefrontal cortex (mPFC), the dorsal anterior cingulate cortex (dACC), the middle temporal gyrus, the angular gyrus, the precuneus, the calcarine sulcus, and the cerebellar vermis in GAD subjects compared with healthy controls (P < 0.005 to define cluster, AlphaSim correction, cluster size >53 voxels, overall p < 0.05). (**Figure 2**, **Table 2**).

#### Correlation Analysis of FC and Illness Severity

In eyes-open rs-fMRI, the HAMA score had a significant negative correlation with the FC between the left amygdala and the superior frontal gyrus in GAD subjects (P < 0.005 to define cluster, AlphaSim correction, cluster size >53 voxels, overall p < 0.05). The FC between the right amygdala and the

disorder; HC, healthy control; rs-fMRI, resting state fMRI; IFG, inferior frontal gyrus; TP, temporal gyrus; MTG, middle temporal gyrus; L, left; R, right).

*R, right; L, left; BA, Brodmann's area; MNI, Montreal Neurological Institute; GAD, generalized anxiety disorder; HC, healthy control; FC, functional connectivity; DLPFC, dorsolateral prefrontal cortex; ROI, region of interest.*

fusiform gyrus, the superior/middle occipital gyrus, and the cerebellum was positively correlated to the HAMA score in GAD subjects (P < 0.005 to define cluster, AlphaSim correction, cluster size >53 voxels, overall p < 0.05). We found that the HAMA score was positively correlated with the FC between the DLPFC and the inferior frontal gyrus, the supplementary motor area (SMA), and the cerebellum in GAD subjects (P < 0.005 to define cluster, AlphaSim correction, cluster size >53 voxels, overall p < 0.05). (**Table 3**, Supplement Figure 1)

In eyes-closed rs-fMRI, the HAMA score had a significant positive correlation with the FC between the right DLPFC and the lingual gyrus, the cuneus, the fusiform gyrus, the inferior/middle occipital gyrus, and the cerebellum in GAD subjects (P < 0.005 to define cluster, AlphaSim correction, cluster size >53 voxels, overall p < 0.05). (**Table 3**, Supplement Figure 1).

Additionally, both in eyes-open and eyes-closed rs-fMRI, we did not find a significant difference in frame-wise displacement between GAD patients and healthy controls [p = 0.926 (eyesopen), 0.271 (eyes-closed)].

## DISCUSSION

In this study, we conducted analysis to characterize alterations in FC that may show the pathological basis of GAD using both the eyes-open and the eyes-closed rs-fMRI. While exploring FC seeded from the amygdala and the DLPFC, we found: (1) Patients with GAD showed increased FC between the left amygdala and the temporal pole compared with health controls. (2) In both eyes-open and eyes-closed conditions, the brain regions showed altered FC with amygdala/DLPFC were primarily from the default mode network (DMN).

Increased FC between the amygdala and the temporal pole was detected by both eyes-open and eyes-closed rs-fMRI. This finding may illustrate that the altered FC may contribute to the etiology

correction, cluster size >53 voxels, overall p < 0.05). Hot and cold colors indicate increased and decreased FC in GAD compared with HC. (DLPFC, dorsolateral prefrontal cortex; GAD, generalized anxiety disorder; HC, healthy control; rs-fMRI, resting state fMRI; mPFC, medial prefrontal cortex; dACC, dorsal anterior cingulate cortex; MTG, middle temporal gyrus; PCU, precuneus; CAL, calcarine sulcus; CBV, cerebellar vermis; AG, angular gyrus; LgG, lingual gyrus; L, left; R, right).

of GAD. Previous findings indicate that the temporal pole plays a role in both social and emotional processing, including specific recognition, theory of mind (Wong and Gallate, 2012), memory (Damasio et al., 1996), and encodes similarity relations among different concepts (Patterson et al., 2007). It is also thought to be involved in access to knowledge during "mentalizing," which refers to the attribution of intentions and other mental states (Frith and Frith, 2003). The amygdala is one of the most investigated structures of the brain, especially in the context of emotional processing. The amygdala is marked as the most prominent "fear-circuit" structure, and hyperactivation of the amygdala is found in most anxiety disorders (Etkin and Wager, 2007; Shin and Liberzon, 2010; Linares et al., 2012). And it has been studied extensively within the context of fear conditioning and extinction as key processes for the pathophysiology of anxiety disorders. The FC between the amygdala and the temporal pole may reflect integration of emotional regulation with knowledge during stimuli perception and mentalizing in healthy subjects. This neural process is presumably disrupted in GAD, which is consistent with observations of deficits in socio-emotional behaviors (Aghajani et al., 2014). The increased connectivity between the temporal pole and the amygdala in GAD patients may be responsible for why stimuli more easily evoke anxiety in GAD patients.

The default mode network (DMN) is defined as the set of regions in the brain that are consistently more activated during resting condition than other brain networks (Fox and Raichle, 2007). It is often described as a unitary, homogeneous system that is largely involved in the integration of autobiographical memories and in self-monitoring, in the retrieval and manipulation of past events in an effort to solve problems and develop future plans, and in emotion regulation (Greicius et al., 2003). When a task requires attention, however, the activation of such network is suppressed. Deficits in DMN suppression are reported in several mental illnesses, notably anxiety disorders (Anticevic et al., 2012). Our results showed altered FC between the amygdala and several regions of the brain including the temporal pole, the middle temporal gyrus, which belong to the DMN, in the GAD group compared with the control group. Altered FC was also found between the DLPFC and brain regions of the DMN, such as the mPFC, the angular gyrus, and the precuneus in the GAD group.

The middle temporal gyrus is regarded as an important brain structure in the integration of memory, audiovisual association, object-recognition and visual perception (Li et al., 2013; Shao et al., 2013). The middle temporal gyrus was found to have increased FC between the amygdala. This finding may reflect an increased predisposition for inaccurate interpretation of stimuli (Pannekoek et al., 2013). However, the FC between the DLPFC and the middle temporal gyrus, and the mPFC was decreased in GAD patients compared with healthy controls. The DLPFC is involved in the function of working memory, executive functions, emotion regulation, subjective feelings, and self-awareness (Craig, 2002; Critchley et al., 2004). The mPFC is widely known to be crucial for emotion regulation, especially for controlling negative emotional responses (Etkin et al., 2009). The angular gyrus, which is also a part of the DMN, has been implicated in affective regulation associated with empathic response, anxiety, and mood (Leung et al., 2013). The precuneus is implicated in episodic memory, visuospatial processing, selfreflection and aspects of consciousness (Fox et al., 2015; Hannawi et al., 2015; Kwok and Macaluso, 2015). The decreased FC between the DLPFC and the brain regions in the DMN may explain uncontrolled emotional regulation in GAD patients. This finding was consistent with our previous study and the models of "bottom-up" and "top-down" emotional processing (Phillips et al., 2003, 2008; Ochsner and Gross, 2005; Phan et al., 2005; Goldin et al., 2008; Cui et al., 2016).

TABLE 3 | Correlation between altered functional connectivity and HAMA scores for GAD patients.

*R, right; L, left; BA, Brodmann's area; MNI, Montreal Neurological Institute; GAD, generalized anxiety disorder; HC, healthy control; DLPFC, dorsolateral prefrontal cortex; HAMA, Hamilton Anxiety Scale; ROI, region of interest.*

The lingual gyrus, the cuneus, and the fusiform gyrus all belong to the visual network (VN). Functional abnormalities of these regions reflect excessive vigilance as a hallmark of anxiety disorders. The VN is associated with pathological memories and planning a response to potentially threatening stimuli (Bremner et al., 1999). The dACC, a part of the salience network (SN), plays a central role in detecting emotional salience and triggering cognitive control via FC with the DLPFC (Sridharan et al., 2008; Bressler and Menon, 2010). Both the DLPFC and the dorsal ACC are implicated in emotional regulation circuits (Bush et al., 2000; Brühl et al., 2014). Therefore, the decreased FC between the DLPFC and the VN/SN may be related to the loss of emotional regulation from the DLPFC in GAD patients. The cerebellum is linked with the cerebrum, brainstem, and spinal cord through efferent and afferent fibers, and the cerebellar vermis is connected to the amygdala anatomically in animals (De Bellis et al., 2002). Recently, more and more studies have reported the cerebellum is functionally related to expressing fear and processing fear memory (Supple et al., 1987; Sacchetti et al., 2005). Cerebellar cognitive affective syndrome was observed in patients with cerebellar damage (Stoodley, 2012). The DLPFC is involved in executive control (Habas et al., 2009; O'Reilly et al., 2010; Yeo et al., 2011), so the connectivity between the cerebellum and the DLPFC may mediate anxiety (Caulfield et al., 2016). And it supports our finding that the decreased FC between the DLPFC and the cerebellum in GAD patients compared with healthy controls.

Regarding the underlying mechanism, the temporal lobe, especially the temporal pole, may be the emphasis for future treatment of GAD. Although there some studies reported the effect of transcranial direct current stimulation (tDSC) and repetitive transcranial magnetic stimulation (rTMS) in curing GAD, the outcomes are various. According to our findings, we prefer the temporal pole as the stimulated target. By decreasing the related abnormal FC may remit anxiety in GAD patients.

## CONCLUSION AND LIMITATIONS

In conclusion, our study found altered FC seeded from the amygdala and the DLPFC in GAD patients using eyes-open and eyes-closed rs-fMRI. We found that the increased FC between the amygdala and the temporal pole may be underlying the neural pathophysiology of GAD. We hope these findings will shed light on the current understanding of GAD and on advanced therapeutic interventions.

A limitation in this study is that we were only able to acquire 164 s in the eyes-open resting state fMRI data and the reliability of FC analysis under this condition is limited (Shehzad et al., 2009; Thomason et al., 2011; Braun et al., 2012; Li et al., 2012). Nonetheless, the consistency between eyes-open and eyes-closed conditions alleviates this concern and provides support for our conclusion. Additionally, as fMRI data was acquired using the parameters TR = 2s, slices = 30, band pass filtering in the range 0.01 < f < 0.1 Hz, cardiac and respiratory fluctuations may still reduce the specificity of low frequency fluctuations to functional connected regions (Lowe et al., 1998). Future research will focus on MRI follow-up and will explore changes in neuroimaging of GAD.

## AUTHOR CONTRIBUTIONS

WL: manuscript, data gathering and analysis. HC, ZZ, QH, LZ, JM, and HL: manuscript and data gathering. LK, JJ, QG, and YZ: manuscript and analysis. JL and QL: data gathering and quality control. JW and ZY: manuscript, administration, and editing. CL: research plan development, manuscript, administration, and editing.

## FUNDING

Funding for this study was provided by the National Natural Science Foundation of China (81071098, 81270023, 81571756)(to CL and ZY), Shanghai Health System Leadership in Health Research Program (XBR2011005) (to CL), Shanghai Health Bureau Project (2013SY003) (to JW), the Science and Technology Commission of Shanghai Municipality (13dz2260500,<PHONE_NUMBER>1,<PHONE_NUMBER>0, 20154Y0080) (to CL, ZZ, and JW), Shanghai Clinical Center for Mental Disorders (2014), National Key Clinical Disciplines at Shanghai Mental Health Center (Office of Medical Affairs, Ministry of Health, 2011-873; OMA-MH, 2011-873)(to HC), Beijing Nova Program for Science and Technology (XXJH2015B079)(to ZY), Pfizer Investigator Initiation Research Fund (WI173560)(to CL) and Chinese Government Scholarship (CSC NO.201606230115) (to WL).

## REFERENCES

## ACKNOWLEDGMENTS