Integrity of Amygdala Subregion-Based Functional Networks and Emotional Lability in Drug-Naïve Boys With ADHD

Abstract

Objective: This study evaluated the functional networks of amygdala subregions (basolateral [BLA], centromedial [CMA], and superficial amygdala [SFA]) in ADHD and their association with emotional lability (EL) symptoms. Method: Resting-state functional connectivity (RSFC) of amygdala subregions and their correlations with EL scores were evaluated in 35 drug-naïve boys with ADHD and 30 age-matched healthy controls (HC). Results: Compared with HC, altered RSFC were detected differently for each amygdala subregion in ADHD: altered RSFC of BLA with the thalamus and vermis; aberrant RSFC of CMA with the superior temporal gyrus/pole and insula, precuneus and cerebellum; reduced RSFC of SFA with dorsal frontoparietal cortices. Within ADHD, higher EL scores were associated with reduced negative RSFC of SFA with the dorsolateral prefrontal cortex and inferior parietal lobe. Conclusion: Diffuse alterations of amygdala subregion-based networks are associated with ADHD, and the weaker SFA–frontoparietal networks might be involved in the hypothesized top–down effortful regulation of emotion.

Keywords

ADHD amygdala subregions resting-state functional connectivity emotional lability dorsal prefrontal cortex

Introduction

ADHD is frequently associated with emotional lability (EL) and deficits in emotional self-regulation (Barkley, 2010; Barkley & Fischer, 2010; Sobanski et al., 2010). The risks of emotional dysregulation were highlighted by Barkley and Fischer (2010) as the most impairing aspects of ADHD, predicting poor occupational, social, and financial adult outcomes. These findings were also broadly replicated by Surman et al. (2015) using an analogous construct called Deficient Emotional Self-Regulation (DESR). This particular construct refers to symptoms denoting emotional instability, frustration intolerance, rapid and drastic changes in mood, emotional fragility, and unpredictable and explosive temper outbursts. This conglomerate of symptoms has been represented by different terminologies in the literature, including emotional dysregulation, mood dysregulation, emotional impulsiveness, EL, and DESR. Of note, the descriptive term, EL, as captured by the subscale in the Conners’ Parent Rating Scale, makes no assumptions about the psychobiological processes that underlie such symptoms.

Two recent studies examined the neural mechanisms in the amygdala for emotional dysregulation in patients with ADHD. Posner et al. (2011) identified that adolescents with ADHD showed stronger functional coupling between the amygdala and lateral prefrontal cortex (LPFC) compared with controls when performing a task involving the subliminal presentation of fearful faces, suggesting these networks as possible neural substrates for emotional reactivity in ADHD. Another study specifically focused on EL symptoms and identified its associations with abnormal amygdala–cortical functional connectivity in children with ADHD (Hulvershorn et al., 2014). More specifically, EL scores were associated with increased positive resting-state functional connectivity (RSFC) between the amygdala and medial prefrontal regions, but they were associated with less positive RSFC between the amygdala and superior temporal gyrus (STG)/insula. However, these studies defined the amygdala as a single homogeneous unit and thereby overlooked the anatomical and functional distinctions of its individual subregions.

Indeed, the amygdala is a complex and functionally heterogeneous structure, consisting of different subregions that are involved in different emotional processes, each with separate pathways (Han, Lee, Kim, & Kim, 2014). Based on the cytoarchitectonic assessment of human postmortem brains (Amunts et al., 2005) and the neuroimaging-based modeling on probabilistic anatomical mapping (Bzdok, Laird, Zilles, Fox, & Eickhoff, 2013), the amygdala can be functionally partitioned into three major subregions: the basolateral (BLA), centromedial (CMA), and superficial amygdala (SFA; Amunts et al., 2005). The different functions of these subregions can be extrapolated from both animal and human studies. The BLA, composed of lateral, basolateral, basomedial, and basoventral nuclei, is related to emotional and associative learning processes (Roy et al., 2009). Overall, the BLA is thought to represent an integration center for coordinating inputs from the prefrontal cortex (PFC), thalamus, hippocampus, and visual and auditory cortices (LeDoux, 2003; Phelps & LeDoux, 2005). The CMA (including central and medial nuclei) has been suggested to receive convergent information from other amygdala subregions, thereby acting as the gateway to generate motor, behavioral, and autonomic emotional responses (LeDoux, 2003; Pessoa, 2011). The SFA modulates approach-avoidant behavioral expression and forms specific functional connectivity with areas of the “limbic lobe,” while processing olfactory, socially relevant and reward-related information (Bzdok et al., 2013; Heimer & Van Hoesen, 2006). In human brains, the amygdala subregions can be delineated based on the probabilistic maps of the cytoarchitectonic boundaries in a quantitatively strict way (Amunts et al., 2005; Eickhoff et al., 2005; Qin, Young, Supekar, Uddin, & Menon, 2012). The feasibility and validity of applying this method to functional Magnetic Resonance Imaging (fMRI) data has been validated in previous studies (Etkin, Prater, Schatzberg, Menon, & Greicius, 2009; Qin et al., 2012; Roy et al., 2009). Indeed, the functional heterogeneity of amygdala subregions has been investigated in both healthy human volunteers and patients with other emotion-related disorders (Etkin et al., 2009; Roy et al., 2009, 2013, 2014; Singh, Kelley, Chang, & Gotlib, 2015). However, so far, comparable studies in children and adolescents, particularly those with ADHD, are few.

Apart from the amygdala being involved in “bottom-up” emotional processing, the PFC also plays a central role in “top-down” regulation (Shaw, Stringaris, Nigg, & Leibenluft, 2014; Wessa & Linke, 2009). Current theories suggest two separable emotional regulatory pathways: “automatic” and “effortful” (Roy et al., 2013). The ventromedial PFC (VMPFC) is implicated in automatic emotional processing (Roy et al., 2013), whereas both the dorsomedial PFC (DMPFC) and dorsolateral PFC (DLPFC) are implicated in effortful/cognitive regulation of emotion (Phillips, Drevets, Rauch, & Lane, 2003; Phillips, Ladouceur, & Drevets, 2008; Roy et al., 2013). Accordingly, emotional dysregulation due to a “top-down” control deficits may arise from alterations in automatic or/and effortful pathways.

To elucidate the neural networks involved in EL expression in children with ADHD, the present study utilized RSFC to investigate the integrity of amygdala subregion-based networks and to explore their correlations with EL symptoms. More specifically, the aim was to evaluate the connectivity between amygdala subregions and the hypothesized neural circuits involved in both automatic and effortful emotional processing. This study recruited drug-naïve boys with ADHD with varying levels of EL expression to minimize the confounding effects of pharmacological treatments. Based on prior literature, we expected that (a) boys with ADHD would show alterations in amygdala subregion-based RSFC circuits when compared with healthy controls (HC), and (b) these alterations, in particular those in RSFC between the amygdala subregions and the putative PFC regions involved in automatic or effortful/cognitive emotional processing, would be correlated with EL symptoms in boys with ADHD.

Method

Participants

Thirty-six drug-naïve (stimulants and other psychotropic drugs) right-handed boys with ADHD (aged 8-14 years) and full-scale IQ scores >80 were recruited from child and adolescent psychiatric clinics of Peking University Sixth Hospital. Exclusion criteria were as follows: (a) a diagnosis or history of head trauma with loss of consciousness, (b) a history of neurological illness or other severe disease, and (c) a diagnosis of schizophrenia, affective disorders, anxiety, tic disorders, pervasive developmental disorders, or mental retardation. In addition, one participant was excluded from further analyses due to excessive head movements (>2.5 mm of translation or degrees of rotation in any direction). The clinical diagnosis of ADHD was first made by a qualified child and adolescent psychiatrist, and the research standard caseness was then determined and validated using the Chinese translated version of Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL; Kaufman et al., 1997) based on Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) criteria by one of the senior child and adolescent psychiatrists (L.S. and Q-J.C.). The interrater reliability for K-SADS-PL definite DSM diagnoses were high with kappas ranging from 0.80 to 1.00. The ADHD Rating Scale-IV (ADHD RS-IV) was also completed by the parents for evaluation of ADHD symptom severity. Nine inattention items and nine hyperactivity-impulsivity items were rated on a Likert-type scale from 0 to 3. This scale has previously been translated into Chinese and demonstrated good reliability and validity (Su et al., 2006). Among the 35 boys with ADHD selected for the final analyses, 16 (45.7%) met the criteria for combined subtype (ADHD-C), 18 (51.4%) for inattentive subtype (ADHD-I), and 1 (2.9%) for hyperactive/impulsive subtype (ADHD-HI). In addition, 11 (31.4%) patients had a comorbid diagnosis of oppositional defiant disorder (ODD).

Thirty age- and sex-matched HCs were recruited from local primary schools. In the HC group, individuals with ADHD, ODD, conduct disorder (CD), or other Axis I psychiatric disorders were excluded using the K-SADS-PL interview. The detailed recruitment procedure is given in a flowchart (see Figure S1, available online).

This study was approved by the Research Ethics Review Board of Peking University Sixth Hospital. Written informed consent was obtained from the parents of participants, and all of the children also gave assent to participate.

EL

EL symptoms were quantified using designated subscale items in the Conners’ Parent Rating Scale, which has been used in several previous studies (Hulvershorn et al., 2014; Liu et al., 2019; Merwood et al., 2014; Sobanski et al., 2010). These included the following: (a) “Mood changes quickly and drastically,” (b) “demands must be met immediately—easily frustrated in effort,” (c) “cries often and easily,” and (d) “temper outbursts, explosive and unpredictable behavior” (Liu et al., 2019; Merwood et al., 2014). These items were rated on a 4-point Likert-type scale (i.e., 0 = never, 1 = sometimes, 2 = often, and 3 = always). The unidimensionality of the EL subscale has been reviewed (Conners, 1997) and further validated by recent studies (Liu et al., 2019; Merwood et al., 2014; Westerlund, Ek, Holmerg, Näswall, & Fernell, 2009). The Chinese version was translated by Xu (1999). An “EL score” was calculated by summing across the four items as conducted in recent published studies (Liu et al., 2019; Merwood et al., 2014).

MRI Data Acquisition

MRI data were acquired using a Siemens Trio 3T scanner (Siemens, Erlangen, Germany) at the Imaging Center for Brain Research, Beijing Normal University. During RS-fMRI scanning, participants lay supine and were instructed to remain still and relaxed with their eyes closed and to keep their mind vacant but without falling asleep. A head strap and foam pads were used to minimize head movements. Immediately after the 8-min RS-fMRI scan, we asked the children whether they had fallen asleep. All children reported that they had not fallen asleep. Functional images were acquired using an echo-planar imaging sequence with the following parameters: repetition time (TR) = 2,000 ms, echo time (TE) = 30 ms, flip angle = 90°, thickness/skip = 3.5/0.7 mm, matrix = 64 × 64, field of view (FOV) = 200 × 200 mm, 33 axial slices, 240 volumes. High-resolution T1-weighted anatomical images were acquired with the following parameters: TR = 2,530 ms, TE = 3.39 ms, inversion time = 1,100 ms, flip angle = 7°, 128 slices, slice thickness = 1.33 mm, FOV = 256 × 256 mm, matrix = 256 × 256.

Data Preprocessing

The data analysis was performed using the Data Processing Assist for Resting-State fMRI (DPARSF; Yan & Zang, 2010) and the Resting-State fMRI Data Analysis Toolkit (REST; http://www.restfmri.net; Song et al., 2011). The data preprocessing consisted of excluding the first 10 functional images of each participant, slice timing, head motion correction, co-registration of individual T1 images to functional images, spatial normalization to the Montreal Neurological Institute (MNI) space (resampled voxel size = 3 × 3 × 3 mm³), spatial smoothing (Gaussian kernel full-width at half-maximum = 6 mm), linear detrending, and band-pass filtering (0.01-0.08 Hz). To model and remove the confounding artifacts of head motion and physiological noise (i.e., cardiac and respiratory fluctuations) during the resting state, nuisance signals (including six motion parameters, global mean and signals from white matter and cerebrospinal fluid) were regressed from the data.

RSFC

Mean time series of three subamygdala regions of interest (ROIs; Roy et al., 2009, 2013) were extracted based on probabilistic anatomical maps (Amunts et al., 2005) using Jülich histological atlas in FSL (http://www.fmrib.ox.ac.uk/fsl). Accordingly, the left and right amygdala subregions (BLA, CMA and SFA) were modeled into MNI space. Detailed information about the definition of amygdala subregions is available in the supporting information (see Supplement 2, Figure S2, available online). For each hemisphere, individual RSFC maps of amygdala subregions were generated by calculating Pearson’s correlation coefficients between the mean time series of ROIs and the time series of each voxel in the whole brain in a voxel-wise manner. Subject-level correlation maps were then converted to z-value maps using Fisher’s transformation.

Statistical Analysis

Group-level analyses were performed using REST and SPSS 19.0. First, one-sample t tests were conducted on z-maps of each ROI to generated maps of all voxels showing significant positive or negative RSFC with each amygdala subregion. Second, to compare between-group differences of each subregion-based RSFC pattern, independent sample t tests between ADHD and HC groups were implemented in a whole-brain voxel-wise manner using REST. Although no significant group differences were observed in mean frame-wise displacement (FD; Power, Barnes, Snyder, Schlaggar, & Petersen, 2012) between two groups, the mean FD together with age were entered as covariates in between-group analyses to control for motion artifacts. To correct for multiple statistical comparisons, a threshold of p < .01 with a cluster size ≥ 45 voxels, which corresponds to a whole-brain cluster level corrected to p < .017 (.05/3, three amygdala subregions), was set by AlphaSim to detect significant between-group differences (Ledberg, Akerman, & Roland, 1998). Finally, Pearson’s correlation analyses were performed between RSFC z-values of brain regions showing significant group differences and EL scores in ADHD and HC groups. The statistical level of significance was set at p < .05.

Validation: Controlling for Potential Confounders

Head movements

The artifacts of head micromovements on RSFC have raised particular concern, and recent studies have proposed different strategies to address this issue (Kong et al., 2014; Power et al., 2012; Zeng et al., 2014). Here, we reconducted our data processing using the “scrubbing” technique (Power et al., 2012). Briefly, volumes with a FD > 0.5 mm were scrubbed, and one volume before and two volumes after the target volume were removed for each participant (Power et al., 2012; Wang et al., 2015). Residual images were then used to recompute the RSFC maps for amygdala subregions and subsequently for between-group comparisons.

Covarying IQ

To adjust for the effects of lower IQ scores among ADHD participants than among HC, we tested the reproducibility by reconducting the comparison analyses with IQ as a covariate.

Correlations with EL covarying potential confounders

To account for the effects of potential confounders, we reconducted correlation analyses between EL scores and the RSFC z-values of brain regions (showing significant between-group differences) while covarying age, FD, IQ, ODD diagnosis, and hyperactivity/impulsivity symptoms (as well as additional inattentive symptoms) as covariates. The detailed reasons for including these factors as covariates are given in the supporting information (see Supplement 3(3), available online).

Results

The demographics and clinical characteristics of ADHD and HC groups are given in Table 1. Briefly, boys with ADHD showed lower IQ scores and higher clinical symptoms levels than HC. No differences in age and FD were detected between the two groups.

Table 1.

Demographic and Clinical Information.

Variables	HC (n = 30)	ADHD (n = 35)	t value	p value
Variables	(M ± SD)	(M ± SD)	t value	p value
Age (years)	10.30 ± 1.68	10.31 ± 1.79	−0.03	.974
Mean FD (mm)	0.18 ± 0.08	0.16 ± 0.07	1.08	.286
Full-scale IQ score	121 ± 14	106 ± 16	4.08	<.001
ADHD symptoms
Inattention	16.86 ± 3.89	27.42 ± 4.12	−10.48	<.001
Hyperactivity/impulsivity	15.45 ± 3.85	23.71 ± 9.29	−4.48	<.001
Total scores	32.31 ± 6.71	51.14 ± 9.98	−8.66	<.001
EL scores	1.37 ± 1.16	4.17 ± 2.29	−6.06	<.001

Note. HC = healthy controls; FD = frame-wise displacement; EL = emotional lability.

Amygdala Subregion-Based RSFC

RSFC maps of amygdala subregions for each hemisphere were derived from voxel-wise analyses of our participants (ADHD and HC groups). In the HC group, spontaneous activity in amygdala subregions correlated with activity in the temporal and limbic lobes, pre and postcentral gyrus (PreCG/PoCG) and medial frontal lobe. Significant negative RSFC of amygdala subregions was also found with the cerebellum, precuneus (PCu), occipital cortex, parietal, and dorsal prefrontal cortices (see Figure 1). Compared with HC, boys with ADHD showed an overall pattern of widespread weaker RSFC (positive or negative) between amygdala subregions and other brain regions, especially the cerebellum, thalamus, parahippocampalgyrus (PHG), insula, STG, PCu, inferior parietal lobe (IPL) and dorsal PFC. More specific findings of each amygdala subregion are given in the following subsections.

Figure 1.

Significant RSFC patterns of three amygdala subregions in the ADHD and HC groups.

BLA

Compared with HC, boys with ADHD showed reduced RSFC of the right BLA with the vermis and thalamus bilaterally and reduced RSFC of the left BLA with the vermis and left PoCG. For these circuits, HC showed positive RSFC, whereas patients with ADHD showed no significant connectivity (see Figure 2, Table 2).

Figure 2.

Group differences in RSFC of the BLA bilaterally.

Table 2.

Clusters Showing Significant Group Differences in Amygdala Subregion-Based RSFC Between the Two Groups.

Seed	Area	L/R	Volume (mm³)	MNI			t value
Seed	Area	L/R	Volume (mm³)	x	y	z	t value
Left BLA
ADHD < HC	Vermis		1,296	6	−57	−24	−3.59
	Postcentral gyrus	L	1,458	−48	−30	45	−3.45
Right BLA
ADHD < HC	Vermis		2,538	0	−51	−15	−3.75
	Thalamus	L, R	4,158	−6	−24	−3	−4.3
Left CMA
ADHD < HC	Cerebellum	L	3,024	−27	−75	−33	4.24
	Postcentral gyrus	R	2,160	39	−18	30	−4.74
	Superior temporal gyrus	L	1,350	−63	−18	9	−4.2
	Superior temporal pole, insula	R	1,539	48	9	−9	−4.43
Right CMA
ADHD < HC	Precuneus	R	1,971	12	−57	51	3.98
	Inferior occipital gyrus	L	2,430	−18	−93	−9	−3.78
	Superior temporal pole	L	1,566	−48	3	−15	−3.48
	Superior temporal pole, insula	R	3,186	60	0	−6	−4.09
Left SFA
ADHD < HC	Medial prefrontal cortex, supplementary motor area	L, R	2,241	−6	27	48	4.13
	Superior frontal gyrus, supplementary motor area	R	3,375	21	24	57	4.29
ADHD > HC	Postcentral gyrus, inferior parietal lobe	L	3,402	−45	−24	45	−4.17
Right SFA
ADHD < HC	Lingual gyrus	L	1,620	−12	−57	−6	−3.58
	Lingual gyrus, fusiform gyrus	R	6,561	24	−78	0	−4.83
	Inferior parietal lobe	L	3,699	−54	−45	51	5.92
	Inferior parietal lobe	R	4,860	51	−42	48	4.25
	Superior/middle frontal gyrus, medial prefrontal cortex	L, R	13,257	−6	30	51	5.08
	Middle frontal gyrus	R	1,971	30	33	48	3.7
	Superior frontal gyrus	R	1,242	24	27	60	3.91

Note. MNI = Montreal Neurological Institute; BLA = basolateral amygdala; CMA = centromedial amygdala; SFA = superficial amygdala; L = left; R = right; HC = healthy control.

CMA

Compared with HC, patients with ADHD showed reduced positive RSFC of the CMA bilaterally with the STG bilaterally extending to the superior temporal pole and insula. In addition, there was reduced positive RSFC between the left CMA and right PoCG as well as reduced negative RSFC between the right CMA and right PCu. Furthermore, the two groups displayed different CMA-based connectivity with the cerebellum and inferior occipital gyrus (IOG). Specifically, HC showed negative RSFC between the left CMA and cerebellum, and patients with ADHD showed no significant RSFC between these two regions. In the occipital lobe, HC showed positive RSFC with the right CMA, whereas patients displayed the opposite association (see Figure 3, Table 2).

Figure 3.

Group differences in RSFC of the CMA bilaterally.

SFA

Between-group comparisons indicated that patients with ADHD showed reduced negative RSFC of the right SFA with the IPL bilaterally, left DMPFC and DLPFC bilaterally (including the dorsal superior and middle frontal gyri [SFG and MFG]), as well as reduced negative RSFC of the left SFA with the DMPFC bilaterally and right SFG. Furthermore, positive RSFC between the left SFA and left PoCG was found in HC, whereas higher negative RSFC was found in patients with ADHD. Positive RSFC between the right SFA and lingual gyrus bilaterally was detected in HC, whereas no significant RSFC was found in these regions in patients with ADHD (see Figure 4A, B, Table 2).

Figure 4.

Group differences in RSFC of the SFA bilaterally.

The above-mentioned results from the between-group comparisons were largely preserved after adjusting for head motion (see Supplement 3(1), Figure S3, available online) and IQ (see Supplement 3(2), Figure S4, available online).

Correlation With EL

RSFC z-values of brain regions showing significant group differences were used for further correlation analyses with EL symptoms. Within the ADHD group, EL scores were positively correlated with reduced negative RSFC of the right SFA with the DLPFC and IPL. Specifically, higher EL scores were associated with weaker negative RSFC of right SFA–right dorsal SFG (r = .491, p = .003) and of right SFA–left and right IPL (left: r = .335, p = .049; right: r = .412, p = .014; see Figure 4C). The correlation between EL and RSFC of SFA–DLPFC still existed with a tendency (p = .066) after strict Bonferroni corrections for the 22 regions analyzed, which showed significant group differences. In contrast, no correlations between EL scores and RSFC patterns were found in the HC group with or without post hoc adjustments.

After controlling for age, FD, IQ, hyperactivity/impulsivity symptoms, and ODD diagnosis, the correlations between EL and RSFC between the right SFA and right DLPFC (r = .417, p = .022) and RSFC between the right SFA and right IPL (r = .422, p = .02) within the ADHD group remained significant. These correlations (former correlation: r = .404, p = .033; latter one: r = .403, p = .033) survived further adjustment for the overall ADHD symptoms (instead of hyperactive-impulsive symptoms).

Discussion

To our knowledge, this is the first study on amygdala subregion-based connectivity and EL expression in boys with ADHD. There are two key findings confirming our two predictions. First, when compared with HC, boys with ADHD showed a general pattern of widespread reduced RSFC between amygdala subregions and the dorsal frontoparietal cortical areas, temporal cortex, and limbic regions. More specifically, the BLA network showed altered RSFC with the thalamus and vermis. Altered RSFC of the CMA was found with the STG/STP and insula, PCu, and cerebellum; RSFC between the SFA and dorsal frontoparietal cortices was reduced. Second, within-group analysis in patients with ADHD showed that higher EL scores were associated with weaker connectivity between the SFA and DLPFC and between the SFA and IPL. These findings suggest that EL expression in boys with ADHD is likely associated with alterations in the hypothesized top–down (“effortful”) regulation of emotion.

Abnormal SFA-Based RSFC and EL Symptoms

Reduced negative RSFC between the SFA bilaterally and dorsal PFC (DLPFC and DMPFC) was observed in boys with ADHD when compared with HC. This finding of negative connectivity is consistent with previous reports in children, adolescents, and adults (Gabard-Durnam et al., 2014; Roy et al., 2009, 2013). The DLPFC and DMPFC play major roles in cognitive control, including response inhibition, motor inhibition, distracting attention, and regulation associated with affective states and emotional behavior (Bari & Robbins, 2013; Durston, van Belle, & de Zeeuw, 2011; Phillips et al., 2003). During effortful emotional processing, the dorsal PFC integrates inputs from amygdala and modulates amygdala activity, and it regulates emotional responses using cognitive strategies such as reappraisal and inhibition (Gross, 2007; Ochsner & Gross, 2005). Our findings are consistent with theories that suggest “top-down” control is implicated by an inverse reciprocal relationship between activation of the dorsal PFC and amygdala (Nigg & Casey, 2005; Quirk & Beer, 2006), and thus may reflect a biological mechanism leading to weaker top–down neural control in circuits for emotional regulation in boys with ADHD. Interestingly, a recent fMRI study found greater amygdala–LPFC connectivity during processing of emotional and fearful faces in youths with ADHD compared with HC (Posner et al., 2011). However, this discrepancy could be due to differences in experimental conditions (“resting-state” vs. “emotional task”) or ROI selections (“amygdala subregions” vs. “single amygdala”).

Our findings identified abnormal RSFC between the SFA and frontoparietal cortices as a possible mechanism related to the EL phenotype in boys with ADHD, which is consistent with our second hypothesis. Within the ADHD group, higher EL symptoms were significantly correlated with weaker RSFC of right SFA–right DLPFC and IPL bilaterally. Our findings appear to support the hypothesis that a “top-down” cognitive regulation deficit via the frontoparietal executive network represents an underlying neural substrate for the EL phenotype in ADHD (Barkley, 2010; Barkley & Fischer, 2010; Nigg & Casey, 2005). Intriguingly, the SFA also forms connectivity with areas regarded as the “limbic lobe” (Heimer & Van Hoesen, 2006). To our knowledge, this is the first study reporting alterations in SFA connectivity in relation to the EL phenotype, highlighting the roles of connectivity with both the traditional “limbic lobe” as well as “effortful” top–down networks.

Abnormal BLA-Based RSFC

The reduction in positive RSFC between the right BLA and thalamus bilaterally deserves careful consideration. In particular, the thalamus is involved in arousal, alertness functions, and orientation (Bailey & Joyce, 2015; Morgane, Galler, & Mokler, 2005), and the BLA integrates information received from the thalamus (LeDoux, 2003). Thus, reduced RSFC between the BLA and thalamus is consistent with anomalies in early orienting to emotional stimuli in ADHD (see review by Shaw et al., 2014).

Abnormal CMA-Based RSFC

Our detection of weaker positive RSFC between the CMA bilaterally and temporal lobe (including the STG extending to the STP and insula) in the ADHD group replicates recent findings (Hulvershorn et al., 2014). The STG receives projections from the amygdala and is involved in face perception (Baron-Cohen et al., 1999). Intrinsic functional connectivity between the amygdala and STG is implicated in detecting and decoding the meaning of social and emotional cues (Bickart, Hollenbeck, Barrett, & Dickerson, 2012). The insula, sharing rich connections with other regions of the ventral network (including the VMPFC, ventral anterior cingulated gyrus [ACC], amygdala, and ventral striatum), is involved in encoding emotional stimuli from the amygdala and may be implicated in automatic emotional regulation (Alexander, Crutcher, & DeLong, 1990; Augustine, 1996; Craig, 2009). Hence, the aberrant CMA–STG/insula connectivity may play a decisive role in the disruption of emotional processing in ADHD.

Interestingly, boys with ADHD showed reduced CMA–PCu negative RSFC compared with HC. Negative amygdala–PCu RSFC has been previously reported in healthy participants (Cullen et al., 2014; Roy et al., 2009). This weaker connectivity has been recently linked with the construct of “mind wandering” (a failure to suppress self-thoughts that spontaneously emerge during rest; Cullen et al., 2014), which has been postulated as one cognitive mechanism in patients with ADHD (Shaw et al., 2014). Our findings may suggest exploring the link between attentional lapses and emotional dysregulation as a novel avenue of research (Shaw et al., 2014).

Strengths and Limitations

One key strength of the present study is the selective recruitment of only boys considering the possible significant sex differences in RSFC of amygdala subregions (Alarcon, Cservenka, Rudolph, Fair, & Nagel, 2015). Another strength is the selection of participants naïve to all stimulant and psychotropic medications, as stimulant use has been linked with structural and functional brain change in patients with ADHD (An et al., 2013; Shaw et al., 2009). However, several limitations should be considered here. First, our participants included all three subtypes of ADHD. Separate subgroup analyses for ADHD-C and ADHD-I yielded largely similar results; however, some minor discrepancies are likely attributable to the reduced sample size and statistical power (see Supplement 4, Tables S1 and S2, available online). To explore fully how ADHD subtypes moderate such relationships, a separate study with a much larger sample is needed. Second, though our 8-min scan duration of RS-fMRI data is consistent with current common research practice (length of scan is often approximately 5-8 min), a recent study suggested that the test–retest reliability of resting-state data could be improved by increasing the scan lengths to 13 min (Birn et al., 2013). Further research with longer scan durations is worth considering for future studies. Third, we defined the amygdala subregion boundaries using the adult cytoarchitectonic probabilistic maps created by Amunts et al. (2005) because no relevant pediatric maps are currently available. Recent studies indicate the feasibility and validity of applying the adult maps to pediatric samples (Kim et al., 2010; Qin et al., 2012); however, pediatric maps when available may help to further optimize our findings. Our findings should therefore be interpreted with some caution and needing further replication with regard to the above-mentioned limitations.

In summary, this study evaluated the integrity of amygdala subregion-based functional networks among boys with ADHD in relation to EL symptoms. Reduced amygdala subregion-based RSFC was detected in boys with ADHD, and disruptive SFA–frontoparietal networks may be involved in effortful top–down dysregulation of emotional processes in boys with ADHD. EL often presents a major clinical challenge in the management of young people with ADHD. Our findings have potential translational relevance in terms of providing a better mechanistic understanding of the neural substrates associated with EL.

Footnotes

Acknowledgements

We thank all the participants in our research.

Authors’ Note

Xiaoyan Yu and Lu Liu contributed equally to this work and are joint first authors.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: FDZ was the recipient of an unrestricted award donated by the American Psychiatric Association (APA), the American Psychiatric Institute for Research and Education (APIRE) and AstraZeneca (Young Minds in Psychiatry Award). He has also received research support from the German Federal Ministry for Economics and Technology (related to the present project as well as other research projects), the European Union (EU), the Princess Margaret Hospital Foundation, the Telethon Perth Children’s Hospital Research Fund, the German Society for Social Pediatrics and Adolescent Medicine, the Paul and Ursula Klein Foundation, the Dr. August Scheidel Foundation, the IZKF fund of the University Hospital of RWTH Aachen University, and a travel stipend donated by the GlaxoSmithKline Foundation. He is the recipient of an unrestricted educational grant, travel support and speaker honoraria by Shire Pharmaceuticals, Germany. In addition, he has received support from the Raine Foundation for Medical Research (Raine Visiting Professorship), and editorial fees from Co-Action Publishing (Sweden).

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the National Basic Research Program of China (2014CB846104), the National Key Technology R&D Program (2015BAI13B01), and the National Natural Science Foundation of China (81371496, 81301171, 81471382, 81401400).

Author Biographies

Xiaoyan Yu is currently a master’s degree candidate in Peking University Sixth Hospital/Institute of Mental Health. Her research mainly focuses on neuropsychology and neuroimaging of ADHD.

Lu Liu is currently a research assistant professor in the department of child psychiatry, Peking University Sixth Hospital/Institute of Mental Health. She received her PhD in psychiatry in 2012. Her major interests are in the neuropsychology and genetics of ADHD.

Wai Chen is a professor of child psychiatry in University of Western Australia, and the head of service, statewide Tier 4 Complex Attention and Hyperactivity Disorder Service (CAHDS), Department of Health, Western Australia. His research is in the areas of ADHD, neurodevelopmental disorders, emotional dysregulation, and their treatments. He also served as a peer reviewer for the DSM-5 Clinical and Public Health Committee (CPHC) during the DSM-5 revision.

Qingjiu Cao is currently a child psychiatrist in Peking University Sixth Hospital/Institute of Mental Health. He received his PhD in psychiatry in 2007. His major interests are in the neuropsychology and neuroimaging of ADHD.

Florian Daniel Zepf is the Chair & Winthrop professor of Child and Adolescent Psychiatry at University of Western Australia, and also the clinical director/Head of Department of Specialised Child and Adolescent Mental Health Services (CAHMS) in Western Australia. His research interests are related to aspects of neuropsychology and neurophysiologyin patients with ADHD, as well as other psychiatric disorders.

Gongjun Ji is currently a postdoctoral researcher in Department of Medical Psychology, Anhui Medical University. His major interests are in the neuroimaging of epilepsy.

Zhaomin Wu is currently a PhD candidate in Peking University Sixth Hospital/Institute of Mental Health. Her research mainly focuses on the neuroimaging and genetics of ADHD.

Li An is currently a psychotherapist in Tianjin University. She received her PhD in psychiatry in 2011.

Peng Wang is currently a research assistant in the department of child psychiatry, Peking University Sixth Hospital/Institute of Mental Health. He received his PhD in psychiatry in 2014. His research mainly focuses on the neuropsychology and neuroimaging of ADHD.

Qiujin Qian is a professor and also the director in chief of the department of child psychiatry, Peking University Sixth Hospital/Institute of Mental Health. She received her PhD in 2002. Her major interests are in neuropsychology and neurophysiology of ADHD.

Yufeng Zang is a professor and also the deputy director of Center for Cognition and Brain Disorders, Hangzhou Normal University. His major interests are in neuroimaging algorithm and methodology, and their applications in brain disorders.

Li Sun is a professor in Peking University Sixth Hospital/Institute of Mental Health. She received her PhD in 2003. Her major interests are in neuropsychology and neurophysiology of ADHD.

Yufeng Wang is a professor in Peking University Sixth Hospital/Institute of Mental Health. Her major interests are in therapeutics, neuropsychology, neurophysiology, and genetics of ADHD.

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