Ventral striatum dysfunction in children and adolescents with reactive attachment disorder: functional MRI study

Shinichiro Takiguchi; Takashi X Fujisawa; Sakae Mizushima; Daisuke N Saito; Yuko Okamoto; Koji Shimada; Michiko Koizumi; Hirokazu Kumazaki; Minyoung Jung; Hirotaka Kosaka; Michio Hiratani; Yusei Ohshima; Martin H Teicher; Akemi Tomoda

doi:10.1192/bjpo.bp.115.001586

This article has been retracted.

Retraction in: BJPsych Open. 2024 Jan 5;10(1):e24 See also: PMC Retraction Policy

. 2015 Oct 14;1(2):121–128. doi: 10.1192/bjpo.bp.115.001586

Ventral striatum dysfunction in children and adolescents with reactive attachment disorder: functional MRI study

Shinichiro Takiguchi ¹, Takashi X Fujisawa ², Sakae Mizushima ³, Daisuke N Saito ⁴, Yuko Okamoto ⁵, Koji Shimada ⁶, Michiko Koizumi ⁷, Hirokazu Kumazaki ⁸, Minyoung Jung ⁹, Hirotaka Kosaka ¹⁰, Michio Hiratani ¹¹, Yusei Ohshima ¹², Martin H Teicher ¹³, Akemi Tomoda ^14,^✉

¹Shinichiro Takiguchi, MD, Advanced Biomedical Sciences Course, Graduate School of Medical Sciences, and Research Center for Child Mental Development, University of Fukui, Fukui, Japan

²Takashi X. Fujisawa, PhD, Division of Developmental Higher Brain Functions, United Graduate School of Child Development, and Research Center for Child Mental Development, University of Fukui, Fukui, Japan

³Sakae Mizushima, MA, Division of Developmental Higher Brain Functions, United Graduate School of Child Development, and Research Center for Child Mental Development, University of Fukui, Fukui, Japan

⁴Daisuke N. Saito, PhD, Division of Developmental Higher Brain Functions, United Graduate School of Child Development, University of Fukui, Research Center for Child Mental Development, University of Fukui, and Biomedical Imaging Research Center, University of Fukui, Fukui, Japan

⁵Yuko Okamoto, PhD, Division of Developmental Higher Brain Functions, United Graduate School of Child Development, University of Fukui, Fukui, Research Center for Child Mental Development, University of Fukui, Fukui

⁶Koji Shimada, PhD, Division of Developmental Higher Brain Functions, United Graduate School of Child Development, University of Fukui, Research Center for Child Mental Development, University of Fukui, and Biomedical Imaging Research Center, University of Fukui, Fukui, Japan

⁷Michiko Koizumi, PhD, Research Center for Child Mental Development, University of Fukui, Fukui, Japan

⁸Hirokazu Kumazaki, MD, Division of Developmental Higher Brain Functions, United Graduate School of Child Development, and Research Center for Child Mental Development, University of Fukui, Fukui, Japan

⁹Minyoung Jung, PhD, Division of Developmental Higher Brain Functions, United Graduate School of Child Development, and Research Center for Child Mental Development, University of Fukui, Fukui, Japan

¹⁰Hirotaka Kosaka, MD, PhD, Division of Developmental Higher Brain Functions, United Graduate School of Child Development, University of Fukui, Research Center for Child Mental Development, University of Fukui, and Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Fukui, Japan

¹¹Michio Hiratani, MD, PhD, Research Center for Child Mental Development, University of Fukui, Hiratani Clinic for Developmental Disorders of Children, Fukui, Japan

¹²Yusei Ohshima, MD, PhD, Research Center for Child Mental Development, and Department of Pediatrics, Faculty of Medical Sciences, University of Fukui, Fukui, Japan

¹³Martin H. Teicher, MD, PhD, Developmental Biopsychiatry Research Program, McLean Hospital, Belmont, Massachusetts, Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA

¹⁴Akemi Tomoda, MD, PhD, Division of Developmental Higher Brain Functions, United Graduate School of Child Development, and Research Center for Child Mental Development, University of Fukui, Fukui

^✉

Correspondence: Akemi Tomoda, Research Center for Child Mental Development, University of Fukui, 23-3 Matsuoka-Shimoaizuki, Eiheiji-cho, Fukui 910-1193, Japan. Email: atomoda@u-fukui.ac.jp

PMCID: PMC4995568 PMID: 27703736

Abstract

Background

Child maltreatment is a major risk factor for psychopathology, including reactive attachment disorder (RAD).

Aims

To examine whether neural activity during reward processing was altered in children and adolescents with RAD.

Method

Sixteen children and adolescents with RAD and 20 typically developing (TD) individuals performed tasks with high and low monetary rewards while undergoing functional magnetic resonance imaging.

Results

Significantly reduced activity in the caudate and nucleus accumbens was observed during the high monetary reward condition in the RAD group compared with the TD group (P=0.015, family-wise error-corrected cluster level). Significant negative correlations between bilateral striatal activity and avoidant attachment were observed in the RAD and TD groups.

Conclusions

Striatal neural reward activity in the RAD group was markedly decreased. The present results suggest that dopaminergic dysfunction occurs in the striatum of children and adolescents with RAD, leading towards potential future risks for psychopathology.

Declaration of interest

None.

Copyright and usage

Reactive attachment disorder (RAD), a severe social functioning disorder associated with early childhood maltreatment, has a prevalence of 1.4–2.4% in the general population¹ and 19.4–40% among foster children.^2,3 According to DSM-5,⁴ RAD is characterised by emotionally withdrawn/inhibited behaviours towards caregivers, which is different from disinhibited social engagement disorder (disinhibited type of RAD based on DSM-IV criteria), where the child exhibits indiscriminate social/disinhibited behaviours.⁵ Children with RAD display various difficulties with cognition (learning and memory), behaviour, and social and emotional disturbances.^6,7 Complex neurodevelopmental problems, including multiple psychiatric comorbidities,⁸ such as substance use disorders,^9,10 are also prevalent. These individuals are likely to experience disturbed and developmentally inappropriate social interactions throughout adulthood.^8,11

Research on RAD has reported several deficiencies, including the absence of focused attachment behaviour towards a preferred caregiver, failure to seek and respond to comfort when distressed, reduced social and emotional reciprocity and emotion regulation disturbances (e.g. reduced positive affect and unexplained fearfulness or irritability).^12,13 Children with disrupted attachment, who had experienced early maternal withdrawal, developed a substance use disorder in young adulthood,¹⁰ and childhood maltreatment is associated with increased severity of substance use disorder and may also increase the number or frequency of relapse.⁹ A brain imaging study has demonstrated strong links between substance use disorder and dysfunction in reward circuitry, and in particular dopamine function within cortico-striatal circuitry.¹⁴ Given an increased risk for drug and alcohol misuse, dopaminergic dysfunction may underlie RAD pathophysiology. Thus, identifying neural reward system functionality in RAD could help elucidate disease pathogenesis and inform preventive and therapeutic interventions. However, assessments of reward circuitry in RAD using functional magnetic resonance imaging (fMRI) have yet to be conducted (for a structural MRI study of RAD see Shimada et al¹⁵).

Therefore, the present study investigated potential deficiencies in reward-related neural activity in children and adolescents with RAD using fMRI during performance of a monetary reward task.¹⁶ Furthermore, we investigated the association between neural activity in the reward system and clinical symptoms. Given the key role of the basal ganglia, including the striatum (nucleus accumbens (NAc), caudate and putamen), in reward processing,¹⁷ we hypothesised that children and adolescents with RAD would display reduced neural activity in response to reward, and these decrements would be predictive of clinical RAD symptoms.

Method

Participants

Sixteen right-handed, medication-naive children and adolescents with a clinical diagnosis of RAD (DSM-5 criteria), aged 10–15 years (mean 12.6 years), were recruited from the Department of Child and Adolescent Psychological Medicine at the University of Fukui Hospital from August 2013 to March 2015.

A diagnosis of RAD was confirmed during structured interviews by five licensed paediatric-psychiatric clinicians (S.T., S.M., H.K, H.Ko, A.T.) according to DSM-5 criteria. To exclude other psychiatric diseases (e.g. mood-related disorders, anxiety disorders, post-traumatic stress disorder (PTSD) and substance use disorder) and neurodevelopmental disorders, such as autism spectrum disorder and attention-deficit hyperactivity disorder (ADHD), the Mini-International Neuropsychiatric Interview for Children and Adolescents (MINI-KID)¹⁸ and an assessment module of DSM-IV ADHD taken from the Schedule of Affective Disorders and Schizophrenia for School-Age Children, Epidemiologic version (K-SADS-E) were administered to the participants.

All participants with RAD had a history of severe maltreatment when younger, including emotional abuse, neglect, physical abuse and/or sexual abuse. The participants with RAD were living within a stable placement (in a child welfare facility) even though they were not living with biological parents (for information about child welfare services in Japan see Suzuki et al¹⁹). Participants were all medication-naive, and those taking dopaminergic drugs (including treatment with methylphenidate and atomoxetine), selective serotonin reuptake inhibitors for mood and anxiety symptoms, or antipsychotic medications for aggressive behaviour and irritability were excluded from the study.

A group of 20 right-handed age- and gender-matched individuals considered as typically developing (TD; mean age 12.7 years) with no histories of abuse or neglect and meeting DSM-IV²⁰ criteria for any major Axis I or Axis II psychiatric disorder were included as a control group. The control sample was recruited from the general Fukui Prefecture community. Participants were excluded if they had a full scale intelligence quotient (FSIQ) <80 on the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV).²¹

The study protocol, approved by the Ethics Committees from the University of Fukui, Japan (Assurance No. FU25-157), was conducted in accordance with the Declaration of Helsinki. All participants and their parent(s) or director of the respective child welfare facility gave written informed assent and consent for participation. This study is registered with the University Hospital Medical Information Network (UMIN000014655).

Psychiatric symptom measures

We obtained official information regarding participants’ maltreatment during early childhood from Child Protective Services. All psychiatric symptom measures were administered to the participants by five licensed paediatric-psychiatric clinicians (S.T., S.M., H.K, H.Ko, A.T.). To assess participants’ maltreatment in early childhood, the Child Abuse and Trauma Scale (CATS)²² was administered. Attachment-related symptoms were measured using the Internal Working Model Scale (IWMS) questionnaire.²³ The IWMS consists of three subscales: secure attachment (e.g. ‘I can get close to others easily’), avoidant attachment (e.g. ‘I do not like being dependent on others’) and ambivalent attachment (e.g. ‘I get annoyed by others because I always want to be with them’). Each subscale scores can be treated as scale scores or the highest subscale score is useful as a categorical score. This questionnaire has been standardised, and its reliability and validity have been confirmed.¹⁸ Behavioural/emotional problems were assessed using the Child Behaviour Checklist (CBCL).²⁴

Additional instruments used to exclude other psychiatric diseases included the Birleson Depression Self-Rating Scale for Children (DSRSC),²⁵ the Impact of Event Scale-Revised (IES-R),²⁶ the Autism-Spectrum Quotient (AQ)²⁷ and the Japanese version of the ADHD Rating Scale-IV (ADHD-RS-IV-J).²⁸ To assess RAD-related symptom severity, participants completed the self-report version of the Strength and Difficulties Questionnaire (SDQ),²⁹ a 25-item questionnaire assessing children's behaviour problems, as well as prosocial behavioural tendencies.

Experimental task

Participants performed a block-design gambling task used in our previous studies^16,30 (Fig. DS1). Participants were asked to choose one of three cards by pressing a button. Each card was randomly associated with 0, 30 or 60 yen. Three conditions of eight trials were performed (24 s). Unknown to participants, the total reward was predetermined. To investigate the effect of reward levels between groups, two reward conditions with different levels were designed. In the high monetary reward (HMR) condition, participants earned an average of 330 yen (range 270–390), which was consistently higher than the expected value of the 8 reward trials (240 yen). In the low monetary reward (LMR) condition, participants earned an average of 150 yen (range 90–210), which was consistently lower than the expected value. In the no monetary reward (NMR) condition, the outcome presented was always ‘XXX’ to control for effects other than reward level. The NMR condition or a fixation rest condition (24 s) was always inserted between the two reward conditions. During scanning, participants performed four sessions. Each session consisted of four blocks from each of the four conditions (HMR, LMR, NMR and fixation rest). The order of the four sessions was counterbalanced across participants.

fMRI acquisition and analysis

Functional images from each participant were acquired using a 3.0-T MR scanner (Discovery MR 750; General Electric Medical Systems, Milwaukee, Wisconsin, USA). For functional imaging, a series of 528 volumes (132 volumes per session) was acquired using interleaved T2-weighted, gradient echo, echo planar imaging (EPI) sequences. Each volume consisted of 44 transaxial slices with a thickness of 3.0 mm between slices, which included the entire cerebrum and cerebellum (repetition time (TR), 3000 ms; echo time (TE), 25 ms; flip angle (FA), 90°; field of view (FOV), 192 mm; in-plane matrix size, 64 × 64 pixels, voxel dimensions, 3.0 × 3.0 × 3.0 mm; slice gap, 0 mm).

Data were analysed using SPM8 (The Wellcome Trust Centre for Neuroimaging, London, UK) implemented in MATLAB 2014a (Mathworks, Natick, Massachusetts, USA; see supplemental file for more details). At the first level, individual task-related activation was evaluated. Three regressors for each condition (i.e. HMR, LMR and NMR) were modelled at the onset of each block (duration of 24 s), which were convolved with a canonical haemodynamic response function to obtain expected task-related signal change. The weighted sum of the parameters estimated during individual analyses consisted of ‘contrast’ images. Global signal changes were utilised to remove confounding factors such as scanner gain.

At the second level, contrast images corresponding to each condition for each participant were used for group analyses with a random-effects model to obtain population inferences. We utilised one-sample t-tests to depict relevant brain regions for each group and two-sample t-tests to compare activation between the RAD and TD group. To exclude the effects of age, gender, intellectual ability and depression,^31–33 we included these measures as regressors. Significant signal change for each contrast was assessed by means of t-statistics on a voxel-by-voxel basis. The statistical threshold was set at P<0.001 at the voxel level and P<0.05 with a family-wise error (FWE) correction for multiple comparisons at the whole-brain cluster level.

We depicted brain regions that responded to each reward condition, relative to the control condition, with the following contrasts: HMR v. NMR, LMR v. NMR and HMR v. LMR for each group. We then compared activations between the RAD and TD groups. Finally, we conducted a correlation analysis to examine whether brain activation patterns were related to clinical symptoms.

Sensitive period analysis

The presence of a potential ‘sensitive period,’ during which exposure to childhood maltreatment might be more strongly associated with alterations in striatal responses to HMR, was assessed using random forest regression with conditional inference trees (‘cforest’ in R package party³⁴), as used in previous studies²⁵ (see supplemental file for more details). The analysis was conducted to evaluate the importance of potential predictors of left and right striatal responses.

We determined the importance of exposure during specific time periods including in utero exposure in mothers experiencing domestic violence and direct exposure to neglect or physical, emotional or sexual abuse from birth to 15 years of age, annually. Years were scored either 0 or 1 for exposure to any type of maltreatment during the year. Duration of abuse was included as an additional predictor to control for the potential confounding relationship between early exposure and longer duration.

Statistical analyses

Statistical analyses were performed using IBM SPSS 22 software package (SPSS, Chicago, Illinois, USA). Bonferroni adjustments for partial correlations were applied when such analyses were used. Analysis of variance (ANOVA) or analysis of covariance (ANCOVA) was used to compare participant characteristics across groups.

Results

Demographics and assessment outcomes

Clinical characteristics of the RAD and TD groups are shown in Table 1. The two groups were well matched on gender and age. In comparison with the TD group, the RAD group showed lower FSIQ, F(1, 33)=7.30, P<0.05, and WISC subscale scores (Verbal Comprehension Index, F(1, 33)=4.58, P<0.05; Perceptual Reasoning Index, F(1, 33)=1.41, P=0.024; Working Memory Index, F(1, 33)=3.83, P=0.06; and Processing Speed Index, F(1, 33)=5.47, P<0.05). Regarding the IWMS, the RAD group showed significantly lower secure, F(1, 32)=6.24, P<0.05, whereas the RAD group showed significantly higher, avoidant and ambivalent scores, F(1, 32)=9.75, P<0.01 and F(1, 32)=9.14, P<0.01 respectively.

Table 1. Demographic and clinical characteristics of the TD and RAD groups.

			Statistics (ANCOVA)

	TD group n=20	RAD group n=16	F	χ²	d.f.	P
Age, years: mean (s.d.)	12.7 (1.8)	12.6 (2.0)	0.047^a		1, 33	0.83

WISC-IV FSIQ, mean (s.d.)	103.5 (9.5)	94.5 (10.4)	7.30^a		1, 33	<0.05
Verbal Comprehension Index	106.3 (11.2)	97.6 (12.9)	4.58^a		1, 33	<0.05
Perceptual Reasoning Index	98.9 (13.8)	93.8 (11.0)	1.41^a		1, 33	0.24
Working Memory Index	102.7 (13.3)	94.0 (12.7)	3.83^a		1, 33	0.06
Processing Speed Index	102.4 (9.6)	95.4 (9.8)	5.47^a		1, 33	<0.05

SDQ total difficulties score, mean (s.d.)	7.2 (3.8)	16.1 (6.0)	23.5^b		1, 32	<0.001

CBCL total, mean (s.d.)	50.2 (7.8)	61.3 (10.3)	11.13^b		1, 32	<0.01

ADHD-RS total score, mean (s.d.)	1.6 (1.9)	8.3 (8.2)	8.94^b		1, 32	<0.01

AQ total score, mean (s.d.)	10.8 (3.1)	18.3 (8.4)	7.95^b		1, 32	<0.01

DSRSC, mean (s.d.)	6.5 (3.3)	14.6 (7.7)	17.88^b		1, 32	<0.001

IWMS-secure, mean (s.d.)	22.9 (6.6)	17.1 (5.1)	6.24^b		1, 32	<0.05

IWMS-avoidant, mean (s.d.)	13.6 (4.2)	15.6 (4.3)	9.75^b		1, 32	<0.01

IWMS-ambivalent, mean (s.d.)	14.8 (4.8)	19.0 (6.8)	9.14^b		1, 32	<0.01

CATS, mean (s.d.)	15.9 (6.4)	41.6 (27.2)	19.15^b		1, 31	<0.001

IES-R, mean (s.d.)	2.0 (4.6)	23.1 (18.4)	36.38^b		1, 32	<0.001

Male participants, n (%)	10 (50)	8 (50)		1.00	1	1.00

Right handedness, n (%)	20 (100)	16 (100)		1.00	1	1.00

Types of maltreatment, n (%)
Physical abuse	–	7 (44)
Emotional abuse	–	10 (63)
Neglect	–	11 (69)
Sexual abuse	–	1 (6)

Number of types of maltreatment, mean (s.d.)	–	1.8 (0.8)

Duration (years) of maltreatment, mean (s.d.)	–	8.0 (4.5)

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TD, typically developing; RAD, reactive attachment disorder; WISC-IV, Wechsler Intelligence Scale for Children-Fourth Edition; FSIQ, full scale intelligence quotient; SDQ, Strengths and Difficulties Questionnaire; CBCL, Child Behaviour Checklist; ADHD-RS, ADHD Rating Scale-IV; AQ, autism spectrum quotient; DSRSC, Depression Self-Rating Scale for Children; IWMS, Internal Working Models Scale; CATS, Child Abuse and Trauma Scale; IES-R: Impact of Event Scale-Revised.

Adjusted for gender.

Adjusted for gender and FSIQ.

The RAD group showed significantly higher scores on the CATS, F(1, 31)=19.15, P<0.001, total behaviour problems on the SDQ, F(1, 32=23.5, P<0.001, and CBCL, F(1, 32)=11.13, P<0.01, in comparison to the TD group. The RAD group scored higher on certain items of the ADHD-RS-IV-J, F(1, 32)=8.94, P<0.01; AQ, F(1, 32)=7.95, P<0.01; DSRSC, F(1, 32)=17.88, P<0.001; and IES-R, F(1, 32)=36.38, P<0.001, measures. However, no participant met the criteria for PTSD, ADHD or autism spectrum disorder.

Behavioural results

Response rates on the reward task did not differ between the RAD group (mean=99.7%, s.d.=0.4%) and the TD group (mean=99.9%, s.d.=0.1%). Moreover, there were significant differences in terms of reaction times (RTs), with the RAD group responding more slowly as compared with the TD group (Fig. DS2). A two-way ANOVA revealed no significant main effect of reward condition, F(2, 68)=2.37, P=0.101, and no interaction with group and reward condition, F(2, 68)=0.16, P=0.850, whereas the main effect of group was significant, F(1, 34)=4.75, P=0.036. Furthermore, there was no correlation between RTs and FSIQ.

Imaging results

A whole-brain analysis with FWE-cluster correction (P<0.05) was conducted for the HMR and LMR conditions (HMR or LMR minus NMR; Fig. 1). The TD group, regardless of monetary reward level, exhibited significant activation in several brain regions, including the ventral striatum (caudate nucleus and NAc). In contrast, the RAD group did not show significant activation in the ventral striatum in any reward condition (Table DS1). To further specify the brain regions involved in reward level-dependent differences, we contrasted the HMR condition with the LMR condition (HMR minus LMR) in each group. No significant differences were found with this analysis.

To identify potential deficiencies in reward-related activation in RAD, we used the between-group contrast (TD minus RAD) in the HMR (HMR minus NMR) and LMR (LMR minus NMR) conditions (Fig. 1). For the HMR condition, there was a significant decrease in activation in the bilateral ventral striatum (caudate nucleus and NAc) for the RAD group compared with the TD group (FWE corrected P=0.015 at the cluster level). Within the identified cluster of the ventral striatum, we extracted activation intensity (i.e. contrast estimates) at the peak Montreal Neurological Institute (MNI) coordinates (left, x = −8, y = 12, z = −4; right, x = 8, y = 14, z = −4). In the LMR condition, no differences were detected in the activation level of each region between the TD and RAD groups. There was no correlation between intensity and RTs in either sample. No differences were found for either reward condition when the RAD group was contrasted with the TD group (RAD minus TD).

Correlations between fMRI activation and clinical symptom measures

We calculated partial correlation coefficients between activation levels and relevant clinical measures, controlling for age, gender, the FSIQ and DSRSC (Table 2). Results revealed a negative association between the IWMS avoidant attachment score and bilateral ventral striatal activation (right: r=−0.481, P=0.0054; left: r=−0.469, P=0.0068), indicating that children with high avoidant tendencies had less striatal activity than children with low avoidant tendencies.

Table 2. Significant negative partial correlations between fMRI activation and clinical measures for all participants, controlling for age, gender, FSIQ and DSRSC.

	Left ventral striatum		Right ventral striatum

Measures	Correlation	P	Correlation	P
SDQ total	−0.263	0.145	−0.413	0.019

CBCL total	−0.336	0.060	−0.344	0.054

IWMS-secure	0.044	0.813	−0.024	0.898

IWMS-avoidant	−0.469	0.0068^*	−0.481	0.0054^*

IWMS-ambivalent	−0.173	0.343	−0.191	0.295

CATS	−0.314	0.086	−0.379	0.036

IES-R	−0.347	0.052	−0.350	0.049

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fMRI, functional magnetic resonance imaging; FSIQ, full scale intelligence quotient; DSRSC, Depression Self-Rating Scale for Children; SDQ, Strengths and Difficulties Questionnaire; CBCL, Child Behaviour Checklist; IWMS, Internal Working Models Scale; IES-R: Impact of Event Scale-Revised; CATS, Child Abuse and Trauma Scale.

For Bonferroni correction at P<0.05 (individual test, P<0.007 without Bonferroni correction).

Sensitive period analysis: age of exposure

As illustrated in Fig. 2, the most important temporal predictor of right striatal activation to HMR was whether children and adolescents with RAD were exposed to maltreatment at 0–1 year of age. Monte Carlo re-randomisation tests indicated that the probability of obtaining an importance measure of this magnitude at any age was quite low (P=0.0004). The second most important age was 2–3 years, and the probability of obtaining two peaks with this combined degree of importance was P=0.001. Altogether, importance was high from 0 to 4 years of age. Total duration of exposure was the fourth most important predictor of right striatal responses. Predicted responses based only on these temporal factors were correlated with actual responses (r=0.571, P<0.011). Similarly, the most important predictor of left striatal activation to HMR was exposure to maltreatment at 0–1 year of age (P=0.0005). Total duration of exposure was the second most important temporal predictor, and the likelihood of obtaining two peaks of this combined height was low (P=0.003). The sensitive period for left striatal responses appeared to be shorter than for the right striatum. Predicted responses based only on these temporal factors were correlated with actual responses (r=0.462, P<0.05; Fig. DS3).

Discussion

The present fMRI study investigated whether neural activity during reward processing is altered in children and adolescents with RAD. Results showed remarkably decreased activity in the ventral striatum (caudate and NAc) between the RAD and TD groups during the HMR condition. Additionally, activity in the identified regions during reward processing was negatively associated with avoidant attachment scores for both groups. Together, these results suggest that neural reward processing is severely reduced in RAD, and these alterations in neural processing may influence the emergence of an avoidant attachment style.

Our finding of reduced activation in the ventral striatum is largely consistent with previous reports, suggesting decreased striatal activity during reward anticipation in adults and adolescents with a history of childhood adversity.^36–39 Specifically, two notable neuroimaging studies directly assessing reward system deficits during adolescence observed reduced activity to reward cues in the ventral striatum among Romanian adolescent adoptees who had experienced early global deprivation,³⁶ and individuals who had experienced emotional neglect in early childhood.³⁹ Whereas ventral striatal activity during reward processing was previously associated with depressive symptoms in maltreated adolescents,³⁹ the reduced reward-related activity in the ventral striatum was also observed in the RAD group compared with the TD group, despite controlling for related depression scale scores (i.e. DSRSC). That is, reward dysfunction (as well as depressive symptoms) may be associated with attachment problems in maltreated individuals. The present study also differed from these aforementioned reports in that participants were medication-naive and diagnosed with RAD. To the best of our knowledge, the present study is the first to demonstrate neural decrements in the bilateral ventral striatum in children and adolescents with RAD. Our findings suggest that these functional alterations may influence RAD pathogenesis and/or severity.

Children and adolescents with RAD are more likely to be at-risk for multiple psychiatric disorders in adulthood, including substance use disorder and depressive disorders. These disorders have been previously associated with reward circuitry dysfunction. For example, substance use disorder has been strongly linked to dysfunction in reward networks, particularly in dopamine function within cortico-striatal circuitry.¹⁴ Moreover, patients with major depressive disorder also show reduced striatal responses to rewards.³³ Thus, our findings may indicate that alterations in the striatal system represent an early stage in psychosis development.

Regarding the effect of reward level, we also found significant activation in the ventral striatum for LMR conditions for the TD group but not the RAD group; however, differences between groups were only marginally significant based on a between-groups contrast. These findings suggest consistently reduced activation in the ventral striatum, regardless of reward level; conversely, our previous study suggested specific dysfunction within a ‘low reward’ level in ADHD.³⁰ Children and adolescents with RAD exhibited extremely low activity in the ventral striatum during both high- and low-value reward trials. These results suggest that reward sensitivity neural processing is severely impaired in children and adolescents with RAD. The decreased reward sensitivity and severe symptoms observed among children and adolescents with RAD may contribute to decreased neural processing in the ventral striatum, leading to diminished motivation for coping with daily life activities.

Although the neural mechanisms underlying RAD due to early life adversity remain unclear, it is likely that child maltreatment affects both anatomical alterations and behavioural dysfunction. Although anatomical changes associated with reward processing in RAD remain unknown, structural neuroimaging studies suggest that exposure to early life adversity is associated with smaller caudate volumes.^40,41 Hence, for children and adolescents with RAD, there may be a possible association between decreased reward sensitivity and brain structure. Similarly, a previous fMRI study assessing the effects of early-life maltreatment on reward-related learning³⁷ showed significantly decreased left basal ganglia activity during reward anticipation, which was associated with anhedonia symptoms in adulthood. Together, these studies suggest that dopaminergic dysfunction may be a key RAD symptom, which may disrupt the utilisation and modulation of attentional processes typically employed for endogenous attentional control.⁴²

As hypothesised, the present study identified deficiencies in reward-related ventral striatal activity in RAD samples. Conversely, functional alterations associated with reward processing were not observed in other brain regions linked with the reward-related dopaminergic system, such as the prefrontal cortex, the cingulate cortex and the limbic system, which have been implicated as important for reward processing.^14,16 Although there were no significant between-group differences in reward-related activity, only the TD (not RAD) group exhibited significant reward-related activity (i.e. HMR or LMR minus NMR) in the prefrontal and anterior cingulate cortices (Table DS1). Therefore, the RAD group may not only exhibit ventral striatal dysfunction but also a trend towards overall dopaminergic system dysfunction.

We observed negative associations between function in the ventral striatum and avoidant attachment tendencies. A growing body of evidence suggests an association between reward circuits and reinforcing processes in social approach and bonding.⁴³ In securely attached individuals, social interactions seem to be generally associated with more positive emotion experiences and stronger signals of reward. Conversely, these positive responses are much weaker or even absent in individuals with an avoidant attachment style.⁴⁴ Thus, reduced striatal responses to rewards in RAD could be explained by a lack of sensitivity towards social reward (e.g. bonding to a caregiver), leading to deficits in forming secure attachments with significant others.

We also investigated the existence of potential ‘sensitive periods,’ during which exposure to childhood maltreatment might be more strongly associated with alterations in striatal responses to HMR. We observed that a significant temporal predictor of bilateral striatal activation to HMR was whether children and adolescents with RAD were exposed to maltreatment at 0–1 year of age. Identifying sensitive periods when adversity has a particularly harmful impact on striatal activity is pivotal for linking childhood experiences to later psychopathology. Interestingly, we have previously discussed possible evidence for sensitive periods during which childhood maltreatment influences regional brain development, indicating a potential causal mechanism.^45,46 Timing of exposure may be extremely important, and overall exposure levels across childhood may be misleading whether the maltreatment occurred before or after a sensitive period. Thus, the present findings support the assertion that brain regions may have unique windows of vulnerability towards traumatic stress. In fact, the present results (vulnerability from 0 to 1 year of age) also indicate that very early maternal care and post-birth child care system interventions are extremely important for preventing RAD and minimising long-term consequences of abuse.

Limitations

The present study has a few notable limitations. First, the main limitation was the small sample size in both groups. Although the current sample size was similar to those in previous brain imaging studies,^36,47,48 the small sample may preclude relevant analyses on reward activity related to RAD-related changes in brain functioning associated with the experimental task. Hence, further, large-scale longitudinal studies are needed to address these issues for better elucidating whether particular genetic factors or social support could be a protective factor against adversity-induced reward system dysfunction.^49,50 Second, there were substantial IQ differences between the groups, with the RAD group showing a lower FSIQ compared with the TD group, which has been reported elsewhere.^8,51 Although we applied statistical methods that controlled for IQ effects, the present results may not be clearly generalisable to an IQ-matched RAD group. Third, there is a potential limitation regarding RAD diagnostics. As previously suggested,^1,7,52 a child is typically given a ‘suspected’ RAD diagnosis because such diagnostics are not fully clear. A more robust diagnosis needs to be based on ecological observations of the child's interactions, as well as clinical interviews and questionnaire data.¹ Furthermore, it has been suggested that the RAD phenotype in the DSM-5 may be further encompassed by a broader phenotype.⁷ Last, since we did not control for the amount or type of maltreatment, results described herein might identify neurobiological differences that are not only specifically associated with RAD, but might also apply to other forms of maltreatment. However, despite these limitations, the present study sheds light on the neural underpinnings of dysfunctional reward processing as a plausible, core RAD symptom.

Implications

The current study provides novel evidence that children and adolescents with RAD have decreased striatal activity during reward processing. The pattern of neural activity was similar to the profile we have observed in maltreated individuals tested with reward task. Such neurobiological decrements in RAD may lead to an increased risk for later dependency. Given that child maltreatment is a key factor for RAD, these results provide important insights towards better understanding the aetiology of this prevalent and debilitating disorder.

Acknowledgments

We express our sincere appreciation to the participants and their families who participated in this research. We would also like to thank Dr Kei Mizuno for his helpful discussions and excellent technical advice in MRI data analyses, and Ms Reiko Fujisawa, Mr Hiroaki Naruse, Ms Kumi Yasuda and Ms Minaho Nishizato for their assistance in assessing participants and data analyses.

Funding

This work was supported by a Grant-in-Aid for Scientific Research (B), Young Scientists (B) and Challenging Exploratory Research (Houga) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan (KAKENHI: grant number 24300149 and 25560386 to A.T., grant number 25750405 to T.F.). This work was also partially supported by a Grant-in-Aid for Scientific Research from the Japan–US Brain Research Cooperation Program (grant number 230201 to A.T.).

References

1.Minnis H, Macmillan S, Pritchett R, Young D, Wallace B, Butcher J, et al. Prevalence of reactive attachment disorder in a deprived population. Br J Psychiatry 2013; 202: 342–6. [DOI] [PubMed] [Google Scholar]
2.Zeanah CH, Scheeringa M, Boris NW, Heller SS, Smyke AT, Trapani J. Reactive attachment disorder in maltreated toddlers. Child Abuse Negl 2004; 28: 877–88. [DOI] [PubMed] [Google Scholar]
3.Lehmann S, Havik OE, Havik T, Heiervang ER. Mental disorders in foster children: a study of prevalence, comorbidity and risk factors. Child Adolesc Psychiatry Ment Health 2013; 7: 39. [DOI] [PMC free article] [PubMed] [Google Scholar]
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9.Andersen SL, Teicher MH. Desperately driven and no brakes: developmental stress exposure and subsequent risk for substance abuse. Neurosci Biobehav Rev 2009; 33: 516–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Pechtel P, Woodman A, Lyons-Ruth K. Early maternal withdrawal and nonverbal childhood IQ as precursors for substance use disorder in young adulthood: results of a 20-year prospective study. Int J Cogn Ther 2012; 5: 316–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
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12.O’Connor TG, Rutter M. Attachment disorder behavior following early severe deprivation: extension and longitudinal follow-up. English and Romanian Adoptees Study Team. J Am Acad Child Adolesc Psychiatry 2000; 39: 703–12. [DOI] [PubMed] [Google Scholar]
13.Green J, Goldwyn R. Annotation: attachment disorganisation and psychopathology: new findings in attachment research and their potential implications for developmental psychopathology in childhood. J Child Psychol Psychiatry 2002; 43: 835–46. [DOI] [PubMed] [Google Scholar]
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23.Hazan C, Shaver P. Romantic love conceptualized as an attachment process. J Pers Soc Psychol 1987; 52: 511–24. [DOI] [PubMed] [Google Scholar]
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26.Asukai N, Kato H, Kawamura N, Kim Y, Yamamoto K, Kishimoto J, et al. Reliability and validity of the Japanese-language version of the impact of event scale-revised (IES-R-J): four studies of different traumatic events. J Nerv Ment Dis 2002; 190: 175–82. [DOI] [PubMed] [Google Scholar]
27.Wakabayashi A, Baron-Cohen S, Uchiyama T, Yoshida Y, Tojo Y, Kuroda M, et al. The autism-spectrum quotient (AQ) children's version in Japan: a cross-cultural comparison. J Autism Dev Disord 2007; 37: 491–500. [DOI] [PubMed] [Google Scholar]
28.Yamazaki K. ADHD-RS-IV Japanese Versions. Japanese Guideline for the Diagnosis and Treatment of Attention Deficit Hyperactivity Disorder (ADHD). Jiho, 2003; 48–54. [Google Scholar]
29.Goodman R. The strengths and difficulties questionnaire: a research note. J Child Psychol Psychiatry 1997; 38: 581–6. [DOI] [PubMed] [Google Scholar]
30.Mizuno K, Yoneda T, Komi M, Hirai T, Watanabe Y, Tomoda A. Osmotic release oral system-methylphenidate improves neural activity during low reward processing in children and adolescents with attention-deficit/hyperactivity disorder. Neuroimage Clin 2013; 2: 366–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Edmiston EE, Wang F, Mazure CM, Guiney J, Sinha R, Mayes LC, et al. Corticostriatal-limbic gray matter morphology in adolescents with self-reported exposure to childhood maltreatment. Arch Pediatr Adolesc Med 2011; 165: 1069–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Neubauer AC, Fink A. Intelligence and neural efficiency. Neurosci Biobehav Rev 2009; 33: 1004–23. [DOI] [PubMed] [Google Scholar]
33.Pizzagalli DA, Holmes AJ, Dillon DG, Goetz EL, Birk JL, Bogdan R, et al. Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder. Am J Psychiatry 2009; 166: 702–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Strobl C, Boulesteix AL, Zeileis A, Hothorn T. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics 2007; 8: 25. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Tomoda A, Polcari A, Anderson CM, Teicher MH. Reduced visual cortex gray matter volume and thickness in young adults who witnessed domestic violence during childhood. PLoS One 2012; 7: e52528. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Mehta MA, Gore-Langton E, Golembo N, Colvert E, Williams SC, Sonuga-Barke E. Hyporesponsive reward anticipation in the basal ganglia following severe institutional deprivation early in life. J Cogn Neurosci 2010; 22: 2316–25. [DOI] [PubMed] [Google Scholar]
37.Dillon DG, Holmes AJ, Birk JL, Brooks N, Lyons-Ruth K, Pizzagalli DA. Childhood adversity is associated with left basal ganglia dysfunction during reward anticipation in adulthood. Biol Psychiatry 2009; 66: 206–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Boecker R, Holz NE, Buchmann AF, Blomeyer D, Plichta MM, Wolf I, et al. Impact of early life adversity on reward processing in young adults: EEG-fMRI results from a prospective study over 25 years. PLoS One 2014; 9: e104185. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Hanson JL, Hariri AR, Williamson DE. Blunted ventral striatum development in adolescence reflects emotional neglect and predicts depressive symptoms. Biol Psychiatry 2015; 78: 598–605. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Dannlowski U, Stuhrmann A, Beutelmann V, Zwanzger P, Lenzen T, Grotegerd D, et al. Limbic scars: long-term consequences of childhood maltreatment revealed by functional and structural magnetic resonance imaging. Biol Psychiatry 2012; 71: 286–93. [DOI] [PubMed] [Google Scholar]
41.Cohen RA, Grieve S, Hoth KF, Paul RH, Sweet L, Tate D, et al. Early life stress and morphometry of the adult anterior cingulate cortex and caudate nuclei. Biol Psychiatry 2006; 59: 975–82. [DOI] [PubMed] [Google Scholar]
42.Pessoa L, Engelmann JB. Embedding reward signals into perception and cognition. Front Neurosci 2010; 4: 17. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Aron A, Fisher H, Mashek DJ, Strong G, Li H, Brown LL. Reward, motivation, and emotion systems associated with early-stage intense romantic love. J Neurophysiol 2005; 94: 327–37. [DOI] [PubMed] [Google Scholar]
44.Vrticka P, Andersson F, Grandjean D, Sander D, Vuilleumier P. Individual attachment style modulates human amygdala and striatum activation during social appraisal. PLoS One 2008; 3: e2868. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Andersen SL, Tomoda A, Vincow ES, Valente E, Polcari A, Teicher MH. Preliminary evidence for sensitive periods in the effect of childhood sexual abuse on regional brain development. J Neuropsychiatry Clin Neurosci 2008; 20: 292–301. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Pechtel P, Lyons-Ruth K, Anderson CM, Teicher MH. Sensitive periods of amygdala development: the role of maltreatment in preadolescence. Neuroimage 2014; 97: 236–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Bruce J, Fisher PA, Graham AM, Moore WE, Peake SJ, Mannering AM. Patterns of brain activation in foster children and nonmaltreated children during an inhibitory control task. Dev Psychopathol 2013; 25: 931–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Carrion VG, Garrett A, Menon V, Weems CF, Reiss AL. Posttraumatic stress symptoms and brain function during a response-inhibition task: an fMRI study in youth. Depress Anxiety 2008; 25: 514–26. [DOI] [PubMed] [Google Scholar]
49.Charney DS. Psychobiological mechanisms of resilience and vulnerability: implications for successful adaptation to extreme stress. Am J Psychiatry 2004; 161: 195–216. [DOI] [PubMed] [Google Scholar]
50.Kundakovic M, Gudsnuk K, Herbstman JB, Tang D, Perera FP, Champagne FA. DNA methylation of BDNF as a biomarker of early-life adversity. Proc Natl Acad Sci USA 2015; 112: 6807–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Smyke AT, Zeanah CH, Gleason MM, Drury SS, Fox NA, Nelson CA, et al. A randomized controlled trial comparing foster care and institutional care for children with signs of reactive attachment disorder. Am J Psychiatry 2012; 169: 508–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Zeanah CH, Gleason MM. Annual research review: attachment disorders in early childhood – clinical presentation, causes, correlates, and treatment. J Child Psychol Psychiatry 2015; 56: 207–22. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r1] 1.Minnis H, Macmillan S, Pritchett R, Young D, Wallace B, Butcher J, et al. Prevalence of reactive attachment disorder in a deprived population. Br J Psychiatry 2013; 202: 342–6. [DOI] [PubMed] [Google Scholar]

[r2] 2.Zeanah CH, Scheeringa M, Boris NW, Heller SS, Smyke AT, Trapani J. Reactive attachment disorder in maltreated toddlers. Child Abuse Negl 2004; 28: 877–88. [DOI] [PubMed] [Google Scholar]

[r3] 3.Lehmann S, Havik OE, Havik T, Heiervang ER. Mental disorders in foster children: a study of prevalence, comorbidity and risk factors. Child Adolesc Psychiatry Ment Health 2013; 7: 39. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r4] 4.American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) APA, 2013. [Google Scholar]

[r5] 5.Gleason MM, Fox NA, Drury S, Smyke A, Egger HL, Nelson CA, 3rd, et al. Validity of evidence-derived criteria for reactive attachment disorder: indiscriminately social/disinhibited and emotionally withdrawn/inhibited types. J Am Acad Child Adolesc Psychiatry 2011; 50: 216–31.e3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r6] 6.Guttmann-Steinmetz S, Crowell JA. Attachment and externalizing disorders: a developmental psychopathology perspective. J Am Acad Child Adolesc Psychiatry 2006; 45: 440–51. [DOI] [PubMed] [Google Scholar]

[r7] 7.Kay C, Green J. Reactive attachment disorder following early maltreatment: systematic evidence beyond the institution. J Abnorm Child Psychol 2013; 41: 571–81. [DOI] [PubMed] [Google Scholar]

[r8] 8.Kocovska E, Puckering C, Follan M, Smillie M, Gorski C, Barnes J, et al. Neurodevelopmental problems in maltreated children referred with indiscriminate friendliness. Res Dev Disabil 2012; 33: 1560–5. [DOI] [PubMed] [Google Scholar]

[r9] 9.Andersen SL, Teicher MH. Desperately driven and no brakes: developmental stress exposure and subsequent risk for substance abuse. Neurosci Biobehav Rev 2009; 33: 516–24. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r10] 10.Pechtel P, Woodman A, Lyons-Ruth K. Early maternal withdrawal and nonverbal childhood IQ as precursors for substance use disorder in young adulthood: results of a 20-year prospective study. Int J Cogn Ther 2012; 5: 316–29. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r11] 11.van der Vegt EJM, van der Ende J, Ferdinand RF, Verhulst FC, Tiemeier H. Early childhood adversities and trajectories of psychiatric problems in adoptees: evidence for long lasting effects. J Abnorm Child Psychol 2009; 37: 239–49. [DOI] [PubMed] [Google Scholar]

[r12] 12.O’Connor TG, Rutter M. Attachment disorder behavior following early severe deprivation: extension and longitudinal follow-up. English and Romanian Adoptees Study Team. J Am Acad Child Adolesc Psychiatry 2000; 39: 703–12. [DOI] [PubMed] [Google Scholar]

[r13] 13.Green J, Goldwyn R. Annotation: attachment disorganisation and psychopathology: new findings in attachment research and their potential implications for developmental psychopathology in childhood. J Child Psychol Psychiatry 2002; 43: 835–46. [DOI] [PubMed] [Google Scholar]

[r14] 14.Dichter GS, Damiano CA, Allen JA. Reward circuitry dysfunction in psychiatric and neurodevelopmental disorders and genetic syndromes: animal models and clinical findings. J Neurodev Disord 2012; 4: 19. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r15] 15.Shimada K, Takiguchi S, Mizushima S, Fujisawa TX, Saito DN, Kosaka H, et al. Reduced visual cortex grey matter volume in children and adolescents with reactive attachment disorder. Neuroimage Clin 2015; 9: 13–9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r16] 16.Izuma K, Saito DN, Sadato N. Processing of social and monetary rewards in the human striatum. Neuron 2008; 58: 284–94. [DOI] [PubMed] [Google Scholar]

[r17] 17.Haber SN, Knutson B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology 2010; 35: 4–26. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r18] 18.Sheehan DV, Sheehan KH, Shytle RD, Janavs J, Bannon Y, Rogers JE, et al. Reliability and validity of the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID). J Clin Psychiatry 2010; 71: 313–26. [DOI] [PubMed] [Google Scholar]

[r19] 19.Suzuki H, Tomoda A. Roles of attachment and self-esteem: impact of early life stress on depressive symptoms among Japanese institutionalized children. BMC Psychiatry 2015; 15: 8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r20] 20.American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) APA, 1994. [Google Scholar]

[r21] 21.Wechsler D. Wechsler Intelligence Scale for Children-Fourth Edition. Psychological Corporation, 2003. [Google Scholar]

[r22] 22.Sanders B, Becker-Lausen E. The measurement of psychological maltreatment: early data on the Child Abuse and Trauma Scale. Child Abuse Negl 1995; 19: 315–23. [DOI] [PubMed] [Google Scholar]

[r23] 23.Hazan C, Shaver P. Romantic love conceptualized as an attachment process. J Pers Soc Psychol 1987; 52: 511–24. [DOI] [PubMed] [Google Scholar]

[r24] 24.Achenbach TM. Manual for the Child Behavior Checklist/4–18 and 1991 Profile. Department of Psychiatry, University of Vermont, 1991. [Google Scholar]

[r25] 25.Birleson P. The validity of depressive disorder in childhood and the development of a self-rating scale: a research report. J Child Psychol Psychiatry 1981; 22: 73–88. [DOI] [PubMed] [Google Scholar]

[r26] 26.Asukai N, Kato H, Kawamura N, Kim Y, Yamamoto K, Kishimoto J, et al. Reliability and validity of the Japanese-language version of the impact of event scale-revised (IES-R-J): four studies of different traumatic events. J Nerv Ment Dis 2002; 190: 175–82. [DOI] [PubMed] [Google Scholar]

[r27] 27.Wakabayashi A, Baron-Cohen S, Uchiyama T, Yoshida Y, Tojo Y, Kuroda M, et al. The autism-spectrum quotient (AQ) children's version in Japan: a cross-cultural comparison. J Autism Dev Disord 2007; 37: 491–500. [DOI] [PubMed] [Google Scholar]

[r28] 28.Yamazaki K. ADHD-RS-IV Japanese Versions. Japanese Guideline for the Diagnosis and Treatment of Attention Deficit Hyperactivity Disorder (ADHD). Jiho, 2003; 48–54. [Google Scholar]

[r29] 29.Goodman R. The strengths and difficulties questionnaire: a research note. J Child Psychol Psychiatry 1997; 38: 581–6. [DOI] [PubMed] [Google Scholar]

[r30] 30.Mizuno K, Yoneda T, Komi M, Hirai T, Watanabe Y, Tomoda A. Osmotic release oral system-methylphenidate improves neural activity during low reward processing in children and adolescents with attention-deficit/hyperactivity disorder. Neuroimage Clin 2013; 2: 366–76. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r31] 31.Edmiston EE, Wang F, Mazure CM, Guiney J, Sinha R, Mayes LC, et al. Corticostriatal-limbic gray matter morphology in adolescents with self-reported exposure to childhood maltreatment. Arch Pediatr Adolesc Med 2011; 165: 1069–77. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r32] 32.Neubauer AC, Fink A. Intelligence and neural efficiency. Neurosci Biobehav Rev 2009; 33: 1004–23. [DOI] [PubMed] [Google Scholar]

[r33] 33.Pizzagalli DA, Holmes AJ, Dillon DG, Goetz EL, Birk JL, Bogdan R, et al. Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder. Am J Psychiatry 2009; 166: 702–10. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r34] 34.Strobl C, Boulesteix AL, Zeileis A, Hothorn T. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics 2007; 8: 25. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r35] 35.Tomoda A, Polcari A, Anderson CM, Teicher MH. Reduced visual cortex gray matter volume and thickness in young adults who witnessed domestic violence during childhood. PLoS One 2012; 7: e52528. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r36] 36.Mehta MA, Gore-Langton E, Golembo N, Colvert E, Williams SC, Sonuga-Barke E. Hyporesponsive reward anticipation in the basal ganglia following severe institutional deprivation early in life. J Cogn Neurosci 2010; 22: 2316–25. [DOI] [PubMed] [Google Scholar]

[r37] 37.Dillon DG, Holmes AJ, Birk JL, Brooks N, Lyons-Ruth K, Pizzagalli DA. Childhood adversity is associated with left basal ganglia dysfunction during reward anticipation in adulthood. Biol Psychiatry 2009; 66: 206–13. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r38] 38.Boecker R, Holz NE, Buchmann AF, Blomeyer D, Plichta MM, Wolf I, et al. Impact of early life adversity on reward processing in young adults: EEG-fMRI results from a prospective study over 25 years. PLoS One 2014; 9: e104185. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r39] 39.Hanson JL, Hariri AR, Williamson DE. Blunted ventral striatum development in adolescence reflects emotional neglect and predicts depressive symptoms. Biol Psychiatry 2015; 78: 598–605. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r40] 40.Dannlowski U, Stuhrmann A, Beutelmann V, Zwanzger P, Lenzen T, Grotegerd D, et al. Limbic scars: long-term consequences of childhood maltreatment revealed by functional and structural magnetic resonance imaging. Biol Psychiatry 2012; 71: 286–93. [DOI] [PubMed] [Google Scholar]

[r41] 41.Cohen RA, Grieve S, Hoth KF, Paul RH, Sweet L, Tate D, et al. Early life stress and morphometry of the adult anterior cingulate cortex and caudate nuclei. Biol Psychiatry 2006; 59: 975–82. [DOI] [PubMed] [Google Scholar]

[r42] 42.Pessoa L, Engelmann JB. Embedding reward signals into perception and cognition. Front Neurosci 2010; 4: 17. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r43] 43.Aron A, Fisher H, Mashek DJ, Strong G, Li H, Brown LL. Reward, motivation, and emotion systems associated with early-stage intense romantic love. J Neurophysiol 2005; 94: 327–37. [DOI] [PubMed] [Google Scholar]

[r44] 44.Vrticka P, Andersson F, Grandjean D, Sander D, Vuilleumier P. Individual attachment style modulates human amygdala and striatum activation during social appraisal. PLoS One 2008; 3: e2868. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r45] 45.Andersen SL, Tomoda A, Vincow ES, Valente E, Polcari A, Teicher MH. Preliminary evidence for sensitive periods in the effect of childhood sexual abuse on regional brain development. J Neuropsychiatry Clin Neurosci 2008; 20: 292–301. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r46] 46.Pechtel P, Lyons-Ruth K, Anderson CM, Teicher MH. Sensitive periods of amygdala development: the role of maltreatment in preadolescence. Neuroimage 2014; 97: 236–44. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r47] 47.Bruce J, Fisher PA, Graham AM, Moore WE, Peake SJ, Mannering AM. Patterns of brain activation in foster children and nonmaltreated children during an inhibitory control task. Dev Psychopathol 2013; 25: 931–41. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r48] 48.Carrion VG, Garrett A, Menon V, Weems CF, Reiss AL. Posttraumatic stress symptoms and brain function during a response-inhibition task: an fMRI study in youth. Depress Anxiety 2008; 25: 514–26. [DOI] [PubMed] [Google Scholar]

[r49] 49.Charney DS. Psychobiological mechanisms of resilience and vulnerability: implications for successful adaptation to extreme stress. Am J Psychiatry 2004; 161: 195–216. [DOI] [PubMed] [Google Scholar]

[r50] 50.Kundakovic M, Gudsnuk K, Herbstman JB, Tang D, Perera FP, Champagne FA. DNA methylation of BDNF as a biomarker of early-life adversity. Proc Natl Acad Sci USA 2015; 112: 6807–13. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r51] 51.Smyke AT, Zeanah CH, Gleason MM, Drury SS, Fox NA, Nelson CA, et al. A randomized controlled trial comparing foster care and institutional care for children with signs of reactive attachment disorder. Am J Psychiatry 2012; 169: 508–14. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r52] 52.Zeanah CH, Gleason MM. Annual research review: attachment disorders in early childhood – clinical presentation, causes, correlates, and treatment. J Child Psychol Psychiatry 2015; 56: 207–22. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

This article has been retracted.

Ventral striatum dysfunction in children and adolescents with reactive attachment disorder: functional MRI study

Shinichiro Takiguchi

Takashi X Fujisawa

Sakae Mizushima

Daisuke N Saito

Yuko Okamoto

Koji Shimada

Michiko Koizumi

Hirokazu Kumazaki

Minyoung Jung

Hirotaka Kosaka

Michio Hiratani

Yusei Ohshima

Martin H Teicher

Akemi Tomoda

Abstract

Background

Aims

Method

Results

Conclusions

Declaration of interest

Copyright and usage

Method

Participants

Psychiatric symptom measures

Experimental task

fMRI acquisition and analysis

Sensitive period analysis

Statistical analyses

Results

Demographics and assessment outcomes

Table 1. Demographic and clinical characteristics of the TD and RAD groups.

Behavioural results

Imaging results

Correlations between fMRI activation and clinical symptom measures

Table 2. Significant negative partial correlations between fMRI activation and clinical measures for all participants, controlling for age, gender, FSIQ and DSRSC.

Sensitive period analysis: age of exposure

Fig. 2. Maximal sensitivity by age of exposure (maximal importance of age of exposure, regardless of type) in reactive attachment disorder.

Discussion

Limitations

Implications

Acknowledgments

Funding

References

ACTIONS

PERMALINK

RESOURCES

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