Dysconnectivity of the Nucleus Accumbens and Amygdala in Youths with Thought Problems: A Dimensional Approach

Abstract

Background:

Youths with thought problems (TP) are at risk to develop psychosis and obsessive–compulsive disorder (OCD). Yet, the pathophysiological mechanisms underpinning TP are still unclear. Functional magnetic resonance imaging (fMRI) studies have shown that striatal and limbic alterations are associated with psychosis-like and obsessive-like symptoms in individuals at clinical risk for psychosis, schizophrenia, and OCD. More specifically, nucleus accumbens (NAcc) and amygdala are mainly involved in these associations. The current study aims to investigate the neural correlates of TP in youth populations using a dimensional approach and explore potential cognitive functions and neurotransmitters associated with it.

Methods:

Seed-to-voxels functional connectivity analyses using NAcc and amygdala as regions-of-interest were conducted with resting-state fMRI data obtained from 1360 young individuals, and potential confounders related to TP such as anxiety and cognitive functions were included as covariates in multiple regression analyses. Replicability was tested in using an adult cohort. In addition, functional decoding and neurochemical correlation analyses were performed to identify the associated cognitive functions and neurotransmitters.

Results:

The altered functional connectivities between the right NAcc and posterior parahippocampal gyrus, between the right amygdala and lateral prefrontal cortex, and between the left amygdala and the secondary visual area were the best predictors of TP in multiple regression model. These functional connections are mainly involved in social cognition and reward processing.

Conclusions:

The results show that alterations in the functional connectivity of the NAcc and the amygdala in neural pathways involved in social cognition and reward processing are associated with severity of TP in youths.

Impact Statement

Taking thought problems (TP) as a dimension, the multivariate linear regression model showed that the altered functional connectivity of the nucleus accumbens and amygdala is correlated to the severity of TP in youths. The replicability of identified connectivity was tested using an adult cohort. Functional characterization analyses revealed that these functional connectivities are involved in cognition (cognitive control), face detection (social cognition), and motivation/reward. Findings of the altered functional connectivity, related brain regions, and cognitive functions associated with TP can be a stepping stone to understanding the underlying pathophysiological mechanisms.

Introduction

In the past decade, a growing body of research investigated the interindividual differences in severity of mental disorders (First and Wakefield, 2013; Stein et al., 2021; Sumner et al., 2022; Wakefield, 2016; Wendt et al., 2022; Zimmerman et al., 2018), in order to overcome limitations of categorical-based classification systems (American Psychiatric Association, 2013). Many studies applied dimensional approach of constructs for assessments of mental disorders (Deyo and Langdon, 2021; Hart and Lewine, 2017; Mackinley et al., 2021; Stein et al., 2022; Sumner et al., 2022; Wendt et al., 2022). For instance, the Hierarchical Taxonomy of Psychopathology (HiTOP) model was proposed to emphasize the multi-dimensionalities of mental disorders while considering the comorbidity issue, including thought disorder as one of the six major spectra (Kotov et al., 2022). Early signs of thought problems (TPs) are defined as repetitive and negative thoughts about self, others, or situations (Farley et al., 2022; Klimes-Dougan et al., 2013), and typically include illusions/(pseudo-) hallucinations, obsessive-compulsive symptoms, and strange thoughts/behaviors (Abdellaoui et al., 2008). These symptoms are generically evaluated by an empirically derived set of items from the TP subscale of Child Behavior Checklist (CBCL-TP) (Achenbach and Rescorla, 2001) using a dimensional point of view (Ivarsson et al., 2008). TP symptoms characterize various mental disorders (e.g., schizophrenia, bipolar disease, and obsessive-compulsive disorder [OCD]), yet TP alone is not a nosology term in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) (American Psychiatric Association, 2013). Despite the clinical significance of assessing these early manifestations, the pathophysiological mechanism underpinning TP remains not well understood (Abdellaoui et al., 2008; Klimes-Dougan et al., 2013; Salcedo et al., 2018).

Consistently with how they are defined (illusions, obsessive thoughts, etc.), TPs have been regularly associated with psychosis (Salcedo et al., 2018; Simeonova et al., 2014) and OCD (Anagnostopoulos et al., 2016; Ivarsson et al., 2008). In addition, psychotic-like symptoms are relatively common in patients with OCD (14%) (Eisen and Rasmussen, 1993; Okamura et al., 2022). On neurobiological grounds, it has been proposed that psychotic symptoms may arise from striatal dopaminergic dysregulation and the aberrant attribution of motivational/emotional salience to irrelevant stimuli (Griffin and Fletcher, 2017; Kapur, 2003; Winton-Brown et al., 2014). When the salience is incurred by stimulations, phasic dopamine is released subsequently, and the controls of prefrontal cortex toward the limbic system are weakened when there are multiple stimulations (Kapur, 2003; Winton-Brown et al., 2014). The dysregulation of dopaminergic pathway affecting brain regions, including prefrontal cortex and amygdala, could disrupt the priorities of saliences and, in turn, cause the aberrant salience (Howes and Nour, 2016; Winton-Brown et al., 2014). Aligned with this hypothesis, functional magnetic resonance imaging (fMRI) studies have shown that patients with schizophrenia display hyperactivations in the striatum (Ceaser and Barch, 2016), amygdala (Bourque et al., 2017; Dugré et al., 2019; Lukow et al., 2021; Pinkham et al., 2015), hippocampus (Dugré et al., 2019; Kätzel et al., 2020), and insula (Dugré et al., 2019; Walter et al., 2016) in response to neutral/ambiguous stimuli. Moreover, altered functional connectivity of amygdala has been implicated with schizophrenia development (Anticevic et al., 2014; Zhang et al., 2020). Dysfunctions of the amygdala, hippocampus, and striatum have been observed in individuals at clinical high risk for psychosis with altered salience processing (Bourque et al., 2017; Modinos et al., 2020). Higher aberrant salience observed in adolescents with TP is also potentially involved in higher susceptibility to psychosis (Lisi et al., 2021). Previous evidence suggests that evaluation of TP provides promising accuracy in identifying individuals with clinically significant psychosis-spectrum disorders among help-seeking, at-risk youths (Thompson et al., 2022). Taken together, these results show that striatal and limbic alterations are associated with psychotic symptoms in patients with schizophrenia and individuals at clinical risk for psychosis.

Patients with OCD often experience negative emotions such as anxiety in response to obsessive thoughts that may motivate them to engage in compulsive behaviors (Thorsen et al., 2018). Several fMRI studies have examined how these patients respond to stimuli that provoke negative emotions. Such studies found that during emotion processing, patients with OCD have increased activations of the amygdala and putamen, as well as anterior cingulate and frontal regions (Thorsen et al., 2018). Apart from these limbic alterations, there is now growing evidence showing that the brain reward system is involved in the pathophysiology of OCD. The brain reward system comprises the ventral tegmental area, the ventral striatum (nucleus accumbens [NAcc]), and the medial orbitofrontal cortex (mOFC) (Kim and Sul, 2023; Lewis et al., 2021). In theory, the brain reward system is thought to play a key role in OCD through abnormal reinforcement of repetitive behaviors adopted to prevent anticipated catastrophic outcomes (Alves-Pinto et al., 2019; Ferreira et al., 2017). Coherent with this model, the results of a meta-analysis of 47 resting-state functional connectivity studies have shown that the connectivity between the NAcc and fronto-limbic regions (e.g., insula and mOFC) is reduced in OCD (Liu et al., 2022). Adding to the validity of this model, several trials have shown that deep brain stimulation of the NAcc (NAcc-DBS) improves OCD symptoms (Schüller et al., 2022; Shi et al., 2023) in patients with severe and treatment-resistant OCD. The mentioned knowledge is mostly obtained from adult cohorts. To the best of our knowledge, the neural correlates of TP have rarely been directly assessed at the population level in adolescents. Two studies used Adolescent Brain and Cognitive Development (ABCD) dataset to investigate this question. Using the resting-state functional connectivity of this large dataset, Wainberg et al. (2022) showed that several neural networks, including the salience network, and also the cingulo-opercular, cingulo-parietal, default mode, dorsal attention, frontoparietal, and retrosplenial–temporal networks, are linearly associated with the TP dimension in children (Wainberg et al., 2022), whereas another analysis showed that cortico-striatal functional connections are important mediators of TP in children with insufficient sleep (Yang et al., 2022). Overall, these findings indicate that TP may be characterized by deficits in reward and emotion processing, with amygdala and NAcc as potential targets.

In view of the current state of knowledge, the present study aims to investigate the neural correlates of TP in child and adolescent populations using a dimensional approach. In this study, resting-state fMRI data obtained from 1360 children and adolescents were analyzed, and potential confounders related to TP such as anxiety and cognitive functions were included as covariates in multiple regression analyses. Considering that the striatum (NAcc) and amygdala have been implicated in the pathophysiology of both psychotic and obsessive–compulsive symptoms (which are the two main components of TP), we opted to perform seed-to-voxels analyses using the NAcc and amygdala as regions-of-interest. Furthermore, the replicability of the connectivity was tested in adult cohorts. To ease the interpretation of findings, functional decoding of identified altered functional connectivity based on these regions of interest was conducted through meta-analytic coactivation modeling. Exploratory analyses were also performed on the receptor/transporter density maps associated with the functional connectivity findings.

Materials and Methods

Participants: healthy brain network

Data from 2200 participants were obtained from the Healthy Brain Network (HBN), an ongoing initiative in the New York area (USA) that aims to investigate heterogeneity and impairment in developmental psychopathology (5–21 years old) (Alexander et al., 2017). The HBN adopted a community-referred recruitment model in which advertisements were provided to community members, educators, and parents. Exclusion criteria were impairments that prevented full participation in the study (e.g., serious neurological disorders, hearing or visual impairments), neurodegenerative disorder, acute encephalopathy, acute intoxication, and serious psychiatric disorders (recent diagnosis of schizophrenia and/or manic episode). Supplemental information is provided elsewhere (Alexander et al., 2017).

From the 2200 participants included in the Data Release 7.0, functional neuroimaging data were obtained from 1583 participants. Written assent was obtained from participants younger than 18 years old, and written consent was obtained from their legal guardians. Written informed consent was obtained from participants of age ≥18 prior to enrolling in the study. The original HBN study was approved by the Chesapeake Institutional Review Board (now Advarra Inc., see https://www.advarra.com/). The current study was approved by the Local Ethics Committee of Center intégré universitaire de santé et de services sociaux (CIUSSS) de l'Est-de-l'Île-de-Montréal.

Acquisition parameters

MRI acquisition took place at three different sites: mobile 1.5 T Siemens Avanto in Staten Island, 3 T Siemens Tim Trio at Rutgers University Brain Imaging Center (RUBIC), and 3 T Siemens Prisma at the CitiGroup Cornell Brain Imaging Center (CBIC) [acquisition protocols and parameters can be found in Supplementary Table S1 in Alexander et al. (2017), as well as at http://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/]. Data at the CBIC were obtained using the same data acquisition protocol implemented at RUBIC. The acquisition of the two resting-state scans lasted 5 min each, during which participants viewed a fixation cross located at the center of the computer screen. Data for the Siemens Avanto were acquired in a single run lasting 10 min (details in Supplementary Table S1).

Main assessments

TP were evaluated using the Child Behavior Checklist (CBCL) (Achenbach and Rescorla, 2001), which comprised 15 items spanning psychotic-like experiences and obsessive–compulsive problems. Parents rated each item using a 3-point scale (0 = not true to 2 = very true) (α = 0.73).

We also examined whether functional connectivity results may be mediated by clinical and neuropsychological features, such as severity of anxiety [i.e., Parent-report Screen for Child Anxiety Related Disorders (BIRMAHER et al., 1999)] and conduct problems [i.e., CBCL (Achenbach and Rescorla, 2001)]. Executive functions in children were assessed using the NIH Toolbox Cognition Battery, including Dimensional Change Card Sorting and the Flanker tests (Zelazo et al., 2013). For both measures, age-adjusted scale scores were used.

fMRI data preprocessing

Functional images were realigned, corrected for motion artifacts with the Artifact Detection Tool (Power et al., 2014) (ART; setting a threshold of 0.9 mm subject to ART’s composite motion and a global signal threshold of Z = 5) with the implemented in CONN Toolbox (Whitfield-Gabrieli and Nieto-Castanon, 2012), bandpass filtered (0.01 Hz < f < 0.10 Hz), and coregistered to the corresponding anatomical image. The anatomical images were segmented (into gray matter, white matter, and cerebrospinal fluid) and normalized to the Montreal Neurological Institute (MNI) stereotaxic space. Functional images were then normalized based on structural data, spatially smoothed with a 6 mm full-width-at-half-maximum (FWHM) 3D isotropic Gaussian kernel and resampled to 2 mm³ voxels. For the preprocessing, the anatomical component–based noise correction method (aCompCor strategy) (Behzadi et al., 2007) was used to remove confounding effects from the blood-oxygen-level-dependent (BOLD) time series, such as the physiological noise originating from the white matter and cerebrospinal fluid. This method was found to increase the validity and sensitivity of analyses (Chai et al., 2012). As described recently, preprocessed images were manually checked for each of the 1583 participants (Dugré and Potvin, 2023). We found preprocessing issues due to the poor quality of images in 108 participants, which resulted in the software being unable to adequately detect and segment volumes into tissue classes (i.e., gray matter, white matter, and cerebrospinal fluid). In addition, 59 adolescents exhibited high movements (exceeding 3 mm), leaving a remaining sample of 1416 participants. Finally, given that the CBCL measures children and adolescent psychopathologies (<18 years), 56 adult subjects were excluded, leaving a final sample size of 1360 adolescents.

Statistical analyses

Seed-based connectivity analyses

The amygdala and the NAcc were selected as seeds of interest (i.e., FSL Harvard-Oxford Atlas distributed in the CONN toolbox). Physiological noise, realignment parameters, and movement artifacts were regressed as confounding effects from the BOLD time series at each voxel. In the first-level analysis, Pearson’s correlation coefficients were calculated between the residual BOLD time course from each seed and the time course of all other voxels, for each subject. Coefficients were converted to normally distributed z-scores using the Fisher Z-Transformation. Second-level analyses were conducted to identify functional connected voxels that were significantly correlated with severity of thought problems. Linear regression was conducted across the whole sample, while adjusting for age, sex, site, percentage of valid scans, and mean motion. A threshold of p < 0.001 uncorrected with a cluster extent threshold of 20 voxels was applied as a good trade-off (Woo et al., 2014) between a too liberal threshold (Lieberman and Cunningham, 2009) (p = 0.005) that may produce spurious results (Eklund et al., 2016) and a stringent cluster-level FWE, which inherently impact the cluster found in smaller brain regions. We nonetheless report clusters that remained statistically significant after applying cluster family-wise error (cFWE) < 0.05.

Subanalyses

Subanalyses were subsequently conducted using partial correlation, to examine the reliability of the relationship between functional brain connectivity and TP, while adjusting for age, sex, site, valid scans, and framewise displacement. First, we removed items of the CBCL-TP that were clinically specific (Lengua et al., 2001) to Psychotic-Like Experience (“Hears things not there”, “Sees things not there”, “Strange behavior”, and “Strange ideas”) (total of 12 items, α = 0.64) and to Obsessive–Compulsive problems (“Can’t get mind off thoughts”, “Repeats certain acts”, and “Stores up thing”) (total of 11 items, α = 0.66), to test whether results may have been driven by a particular clinical dimension. Differences in brain–behavior correlation between the subscales of the two scales were tested using Fisher r-to-z transformation (p < 0.05, two-tailed). Second, we added clinical (i.e., anxiety and conduct problems) and neuropsychological features (i.e., card sorting and flanker tests), as potential confounders in the partial correlation analyses.

Feature importance across developmental stages

After having identified significantly associated brain connectivity, we further investigated their importance in predicting TP. Feature importance was performed by conducting a multivariate linear regression, which included the resulting brain connectivity measures (independent variables) in association with the severity of TP. Importance of a particular feature is determined through a decrease in model R ² score when a given feature is randomly shuffled (i.e., 5000 times) compared with the baseline model. Cross-validation was determined with Group KFold, using developmental stages (Early Childhood [6 to <8 years], Later Childhood [8 to <10 years], Early Adolescence [10 to <13 years], Middle Adolescence [13 to <16 years], and Late Adolescence [16 to <18 years]) as folds. Moreover, we removed the variance associated with confounds within each fold of the training data (i.e., age, sex, site, valid scans, framewise displacement) rather than on the whole data, as suggested recently (More et al., 2021; Snoek et al., 2019). We extracted changes in R ² scores for each test sets to examine feature importance across developmental stages. Overall performance of the model was evaluated by a Pearson correlation between predicted and true values.

Replicability in adult sample

To assess the replicability of our results, we used the MPI-Leipzig Mind-Brain-Body (MPILMBB) dataset (Mendes et al., 2019). The MPILMBB includes 228 participants, including young- to middle-aged adults (between 20 and 40 years) and older adults (between 55 and 80 years), who performed a resting-state session for 15 min 30 sec (MRI acquisition sequences are detailed elsewhere, http://fcon_1000.projects.nitrc.org/indi/retro/MPI_LEMON.html). The Adult Self-Report (ASR) was used to assess TP (Achenbach and Rescorla, 2003), which significantly overlaps with the CBCL scale in children and adolescents. In this sample, data of the ASR were available only for a subset of the sample (n = 122), in which around 75% were young- to middle-aged adults. We decided to discard data from the older adults’ group to preserve homogeneity of the sample. Preprocessing steps were identical to what is described above using the CONN Toolbox. However, rather than conducting seed-to-voxel analyses, we created a 6 mm sphere around peak coordinates of our results obtained with the HBN dataset and performed region of interest (ROI)-to-ROI functional connectivity analysis. We then examined whether the multivariate model tested in adolescents, using the same functional brain connectivity, was able to predict severity of TP in adulthood. We also tested the association between single brain connections and TP, with partial correlation tests.

Functional decoding

Functional decoding was conducted to better characterize the functional brain connectivity underpinning TP, based on the analysis of external data on task-based functions. The neurochemical correlates of functional connectivity alterations were also examined. To identify the neurocognitive functions characterizing a functional connectivity between two ROIs, we conducted a multi-ROI meta-analytic coactivation modeling (MACM). Briefly, we meta-analyzed studies from the BrainMap repository that reported at least one peak coordinate in both ROIs (i.e., seed and target). The BrainMap environment includes a repository of neuroimaging studies which contain brain coordinates and metadata (e.g., sample size, behavioral categories) for >21,083 experiments. An activation likelihood estimation (ALE) algorithm was then used to calculate spatial convergence across studies (Eickhoff et al., 2012) implemented in NiMaRE (Salo et al., 2022). The resulting ALE meta-analytic map (z-map) would thus reflect a task-based fMRI map in which both ROIs are coactivated.

After having identified the coactivation map of a given functional connectivity associated with TP, we calculated its spatial similarity with 13 data-driven task-based (tb)fMRI maps (see https://neurovault.org/collections/13769/, Dugré et Potvin, Preprint) and 19 receptor/transporter density maps (Hansen et al., 2022), which include serotonin (i.e., 5-HT_1A, 5-HT_1B, 5-HT_2A, 5-HT₄, 5-HT₆, 5-HTT), dopamine (i.e., D₁, D₂, DAT), norepinephrine (i.e., NET), histamine (i.e., H₃), acetylcholine (i.e., α4β2, M₁, VAChT), cannabinoid (i.e., CB₁), opioid (i.e., MOR), glutamate (i.e., NMDA, mGluR₅), and GABA (i.e., GABA_A/BZ). Spatial associations with the tbfMRI maps and the PET density maps were conducted using JuSpace (version 1.4) (Dukart et al., 2021). Briefly, the mean values of 728 brain regions which included 690 parcels of the Yale Brain Atlas (McGrath et al., 2022) and additional 38 subcortical and cerebellar regions from the AAL3 atlas (Rolls et al., 2020) were extracted for the two images. Partial correlation (Spearman’s rank correlation) adjusting for spatial autocorrelation (i.e., local gray matter probabilities) was then performed between the two sets. Exact permutation-based p-values (with 10,000 permutations) were computed and corrected using false discovery rate (FDR).

Results

Description of the HBN sample

The final sample comprised 1360 adolescents with a mean age of 10.79 (SD = 3.16). More than half of the sample was boys (62.1%). About half of the sample was White (47.9%), followed by bi/multiracial (17.5%), Black (16.5%), Hispanic (11.3%), and others. Percentage of valid scans after scrubbing was 86.6%, and mean motion was 0.35 (SD = 0.46). Raw CBCL-TP score was 3.51 (3.49) with an average T-Score of 59 (SD = 8.59).

Seed-based connectivity analyses

In children and adolescents, TP were negatively associated with functional brain connectivity of the left amygdala (seed) with the retrosplenial cortex, the posterior part of the superior temporal gyrus, and the secondary visual area (V2) (Fig. 1, Table 1). Moreover, TPs were positively associated with functional connectivity of the right amygdala (seed) to the right lateral PFC, but negatively with the right amygdala to the left caudate and to the left perigenual anterior cingulate cortex (Fig. 1, Table 1).

FIG. 1.

Seed-to-voxel functional connectivity associated with thought problems. RSC, retrosplenial cortex; pSTG, posterior superior temporal gyrus; V2, visual area 2; lPFC, lateral prefrontal cortex; CAUD, caudate nucleus; pgACC, perigenual anterior cingulate cortex; pPHG, posterior parahippocampal gyrus; aHIP, anterior hippocampus; HATA, hippocampal amygdala transition area; dlPFC, dorsolateral prefrontal cortex; lOFC, lateral orbitofrontal cortex; OFC, orbitofrontal cortex.

Table 1.

Seed-to-Voxel Functional Connectivity Results Associated with Thought Problems

Seed (Output)	MNI coordinates			t	Cluster size (Voxels)
Seed (Output)	x	y	z	t	Cluster size (Voxels)
Left amygdala
Retrosplenial cortex	−4	−54	8	−4.53	124
Posterior STG	−46	−42	14	−4.28	35
Secondary visual area	12	−88	18	−3.62	34
Right amygdala
Lateral PFC	48	48	16	3.39	21
Caudate nucleus	−6	6	8	−3.52	27
Perigenual ACC	−14	46	4	−3.48	23
Left nucleus accumbens
None
Right nucleus accumbens
Posterior PHG	18	−30	−8	4.64	100
Anterior HIP (HATA)	−14	−12	−18	3.74	25
Dorsolateral PFC	42	38	28	−4.60	149*
Lateral OFC	30	48	−14	−3.98	75
FP/Medial OFC	20	64	−6	−3.47	29

p < 0.001 uncorrected; 20 voxels. Clusters surviving a cFWE < 0.05 threshold are marked by an asterisk (*).

STG, superior temporal gyrus; PFC, prefrontal cortex; ACC, anterior cingulate cortex; PHG, parahippocampal gyrus; HIP, hippocampus; HATA, hippocampal amygdala transition area; FP, frontopolar cortex; OFC, orbitofrontal cortex.

Using the NAcc as seeds of interest revealed that TPs were positively associated with functional connectivity of the right NAcc (seed) and the right posterior part of the parahippocampal gyrus and the left Hippocampal–Amygdala Transition Area (HATA)/anterior Hippocampus (Fig. 1, Table 1). TP were negatively correlated with connectivity from the right NAcc (seed) to the right dorsolateral prefrontal cortex, the right lateral orbitofrontal cortex, and frontopolar cortex/orbitofrontal cortex. No significant results were found for the left NAcc (Fig. 1, Table 1).

Subanalyses

We aimed to examine whether functional connectivity associated with the total TP could be driven by a particular clinical subconstruct (i.e., psychotic-like experiences and obsessive–compulsive problems). Comparing correlation coefficients with Fisher’s transformation revealed no significant differences across the 11 pairs of brain regions, suggesting that results are driven by a general TP construct. In addition, there is no significant difference on the relationship between brain connectivity and TP measurement across different age groups (age ≤ 10 years vs. age > 10 years) and scanner types (1.5 T vs 3 T). More specifically, the Z-test between participants with age ≤ 10 years and with age > 10 years of the brain connectivity between Left AMY-RSC, Right NAcc-dlPFC, Right NAcc-lOFC, and Right NAcc-pPHG were all statistically nonsignificant (details of Z-test results are listed in Supplementary Table S3). In addition, the relationship between brain connectivity and TP measurement did not differ between sex except the Right NAcc-FP, which showed greater negative correlation with TP in boys compared to girls (Z = 3.32, p < 0.001; details in Supplementary Table S4). Subanalyses suggested that some functional connectivity associations may be weaker in subjects scanned with a 1.5 T scanner (details in Supplementary Table S3).

Testing the effects of potential clinical confounders revealed a significant relationship between functional brain connectivity and anxiety and conduct problems, but not executive functions. After applying FDR correction for multiple comparisons, anxiety was significantly associated to (A) Left AMY-RSC, (B) Left AMY-pSTG, (C) Left AMY-V2, (D) Right AMY-lPFC, and (E) Right NAcc-lOFC. In turn, conduct problems were related to (A) Left AMY-pSTG, (B) Right NAcc-pPHG, and (C) Right NAcc-lOFC (Supplementary Table S2). Adding the severity of anxiety and conduct problems as confounders weakened some relationships with TP but remained statistically significant (Supplementary Table S2).

Feature importance across developmental stages

As shown in Figure 2, permutation feature importance revealed that across the developmental stages, functional connectivity between the right NAcc-pPHG (R ² changes of 0.029), between the right amygdala–lPFC (R ² changes of 0.022), and between the left amygdala–V2 (R ² changes of 0.017) were the most important features in predicting TP, whereas the left amygdala–pSTG (R ² changes of 0.004) and the right NAcc-lOFC (R ² changes of 0.003) were the least.

FIG. 2.

Importance of functional connectivity across developmental stages. NACC, nucleus accumbens; AMY, amygdala; RSC, retrosplenial cortex; pSTG, posterior superior temporal gyrus; V2, secondary visual area; lPFC, lateral prefrontal cortex; CAUD, caudate nucleus; pgACC, perigenual anterior cingulate cortex; pPHG, posterior parahippocampal gyrus; aHIP, anterior hippocampus; HATA, hippocampal amygdala transition area; dlPFC, dorsolateral prefrontal cortex; lOFC, lateral orbitofrontal cortex; OFC, orbitofrontal cortex.

Across developmental stages with age groups of 6 to <8 years, 8 to <10 years, 10 to <13 years, 13 to <16 years, and 16 to <18yrs, functional connectivity between the right amygdala–lPFC (0.022 ± 0.0077), left amygdala–V2 (0.0166 ± 0.004), and left amygdala–RSC (0.0137 ± 0.0033) showed lowest coefficient of variation of R ² scores indicating stable relationship across developmental stages, whereas the functional connectivity between the left amygdala–pSTG (0.0044 ± 0.0148) highly varies from childhood to adolescence (peak in early adolescence and significantly reduced in mid- and late adolescence, details listed in the Supplementary Table S5).

Replication in adult samples

Partial correlation analyses were then carried out to examine specific relationships. Adjusting for the effect of age, sex, valid scans, and framewise displacement, only the left amygdala–V2 (partial r = −0.256, p = 0.017) and the left amygdala–pSTG (partial r = −0.22, p = 0.04) were associated with severity of TP in adults.

Functional decoding

Functional decoding analyses (Fig. 3) were performed for connectivity between (1) the right NAcc and the pPHG, (2) the right AMY and lateral PFC, and (3) the left AMY-V2, as these were found to be the most strongly correlated features of TP across developmental stages.

FIG. 3.

Functional characterization of seed-to-voxel results significantly associated with thought problems.

Functional decoding analyses for the right NAcc–pPHG connectivity revealed that coactivation pattern (including the left NAcc, anterior midcingulate cortex, bilateral amygdala, and fusiform gyrus) mainly correlated with face detection (z’ = 0.48, pFDR < 0.05), Motivation (z’ = 0.47, pFDR < 0.05), as well as with dopamine transporter (z’ = 0.61, pFDR < 0.05) and serotonin transporter (z’ = 0.61, pFDR < 0.05) density maps.

Coactivation network of the right amygdala and the lateral PFC (i.e., right lateral PFC, left amygdala, thalamus, anterior midcingulate cortex, dorsolateral PFC, anterior insula) was mostly associated with multiple demand (z’ = 0.61, pFDR < 0.05) and cognitive control (z’ = 0.58, pFDR < 0.05), as well as with VAChT (z’ = 0.39, pFDR < 0.05), dopamine transporter (z’ = 0.34, pFDR < 0.05), and D₂ (z’ = 0.33, pFDR < 0.05).

Finally, the coactivation pattern of the left amygdala and the V2 (i.e., right amygdala/anterior hippocampus, dorsomedial PFC, fusiform gyrus, ventrolateral PFC) was significantly associated with face detection (z’ = 0.39, pFDR < 0.05) and both dopamine transporter (z’ = 0.34, pFDR < 0.05) and serotonin transporter (z’ = 0.31, pFDR < 0.05) density maps.

Discussion

The present study investigated the functional connectivity associated with TP in children and adolescents (age under 18 years) using the amygdala and NAcc as seed regions. TPs were positively correlated with the functional connectivity between the right amygdala and right NAcc, whereas TPs were negatively associated with the functional connectivity of left and right amygdala and right NAcc. The best predictors of TP in this study were the functional connectivity between the right NAcc and pPHG, between the right amygdala and lPFC, and between the left amygdala and V2. Two (the right amygdala–lPFC, the left amygdala–V2) out of those three functional connectivities presented high consistency (low variation of R ² coefficients) across the five developmental stages. Moreover, one of those three functional connectivities (the left amygdala–V2) was found to be positively correlated with the severity of TP in adults.

Amygdala is a core structure of limbic system and is well known for its involvement in processing fear (Becker et al., 2012), emotional memories (Richardson et al., 2004), addiction (Ko et al., 2023; Koob, 2009), and social interaction (Becker et al., 2012; Gupta et al., 2011). Intense negative emotions such as irritability or fear can be induced by electric stimulation of the amygdala (da Cunha-Bang et al., 2019; Domínguez-Borràs et al., 2020). Lesions of the amygdala could reduce response of fearful stimulations in humans (Domínguez-Borràs et al., 2020) and potentially contribute to flattening of affect (Bliss-Moreau et al., 2011; Katayama et al., 2009). As for the lateral PFC, a large fMRI literature has shown that it plays a key role in superior executive functions, such as cognitive flexibility and planning skills (Axelsson et al., 2021; Fujino et al., 2017; Jones and Graff-Radford, 2021). Coherently with these findings, functional decoding analyses showed that the functional connectivity between amygdala and lateral PFC was associated with multiple demand and cognitive control. Alteration of functional connectivity between amygdala and lateral PFC has been reported to be associated with childhood adversity (Cheng et al., 2021), bipolar I disorder (Townsend et al., 2013), and it is a candidate endophenotype of schizophrenia (Quarto et al., 2018). The neural connection between the amygdala and lateral PFC is bidirectional with participation of both excitatory and inhibitory neurons and involves hippocampal afferent signals to both amygdala and prefrontal cortex, as well as efferent signal sent from lateral PFC to amygdala and vice versa (Sigurdsson and Duvarci, 2016; Yang and Wang, 2017). In rodents, dopamine, GABA, glutamate, and acetylcholine were neurotransmitters reported to be involved in these neuronal circuits (Sigurdsson and Duvarci, 2016; Tsolias and Medalla, 2022; Wang et al., 2020; Yang and Wang, 2017). Here, we found that the coactivation maps of the functional connectivity between the amygdala and lateral PFC were correlated with the density maps of VAChT, DAT, and D₂ receptors, consistently with the previous preclinical literature (Dixon, 2015; Duncan, 2010).

For the functional connectivity between amygdala and V2, the preclinical literature has shown that both regions are involved in fear conditioning and has highlighted that there is an indirect neuronal pathway from V2 to amygdala, whereby visual information is first processed by V1, then transmitted to V2, and then from V2 to the amygdala (Shi and Davis, 2001). In our study, functional decoding analyses showed that the coactivation maps of the amygdala-V2 functional connectivity were mostly associated with face detection, which is a social cognitive function involved in the early processing of social threat detection (Schellhaas et al., 2022). As such, these results are consistent with the face recognition deficits that have been observed in schizophrenia (Maher et al., 2019; Maher et al., 2016; McBain et al., 2010). Interestingly, one of the symptoms that individuals with TP may experience is consistent, repetitive, and negative thoughts (Farley et al., 2022). These can be triggered by negative emotions (i.e., anxiety, depression, or stress), including fear (Minor et al., 2015). As for the analyses on transporter/receptor density maps, they revealed that the coactivation map of the amygdala–V2 functional connectivity was associated with the DAT and serotonin transporter (SERT). The preclinical literature has clearly shown that both dopamine and serotonin can modulate basic negative emotions through the amygdala (Dannlowski et al., 2013; McDonald, 2023). Moreover, preclinical cytoarchitectonic studies (Rapan et al., 2022) and experimental studies on serotonergic psychedelic drugs (e.g., psilocybin) have shown that serotonin receptors (e.g., 5-HT_1A) are found in significant concentrations in the occipital lobe (including V2) and are involved in visual perception (Castelhano et al., 2021; Rocha et al., 2019). Other than the fact that dopamine receptors do not play a key role in occipital-related functions, our results are generally consistent with the available evidence.

The NAcc is mainly involved in reward processing (Hikida et al., 2016), motivation (Floresco, 2015), and reinforcement learning (Gale et al., 2014), whereas the pPHG is mostly involved in memory encoding and retrieval, spatial navigation, and scene recognition (Bastin et al., 2013; Burgmans et al., 2011). Alteration of NAcc activity is associated with amotivation and anhedonia (Li et al., 2018). The parahippocampal gyrus mediates cortico-hippocampal communication and plays an important role in the regulation of memory, including emotional memories, which are often formed in the context of social interactions (Burgmans et al., 2011; Mikels and Reuter-Lorenz, 2019; Rolls, 2019). The NAcc receives input signals (afferent) and projects output signals (efferent) from and to the hippocampal formation for the integration of reward-related contextual information and memory processes (Makris et al., 2008). Coherently with the known separate roles of the NAcc and parahippocampus, the functional decoding analyses showed that the coactivation maps of the functional connectivity between both regions were mainly associated with face detection (e.g., social cognition) and motivation (reward). Moreover, the coactivation maps of the NAcc-pPHG functional connectivity were correlated to the dopamine and serotonin transporters. These results are consistent with the preclinical literature indicating that both NAcc and parahippocampal gyrus receive dopaminergic and serotonin inputs (Boulenguez et al., 1996; Iritani et al., 2006; Kärkkäinen et al., 2015; Raslau et al., 2015).

In our study, resting-state functional imaging data were collected in >1300 youths (under 18 years). The identified functional connectivity associated with severity of TP remained statistically significant when taking potential confounders such as severity of anxiety, conduct problems, and children’s executive functions (i.e., card sorting and flanker tests) into account. In terms of replicability, the functional connectivity between the left amygdala–V2 was also found to be associated with TP in adults. Moreover, the presented study is one of the first resting-state functional connectivity studies on the TP dimension in young individuals. Yet, there are a few limitations that need to be acknowledged. First, there was a large range of age (6–18 years) across the collected dataset. This could potentially incur the heterogeneity to the data, especially for the cohorts in developmental and adolescent phases. However, in two out of three, the most predictive functional connectivity identified in our study presented the lowest variation across the developmental stages. Second, the CBCL-TP subscale measuring the TP dimension combines OCD-like and psychotic-like symptoms (Lengua et al., 2001). In the dimensional view of mental disorders such as the HiTOP model, OCD-like and psychotic-like symptoms are defined in different spectra (i.e., internalizing and thought disorder spectra) with different dimensions associated with them (Kotov et al., 2022). However, it is noteworthy that stratification of dimension for mental disorders might not be the same between adults and young individuals (age <18 years). On the one hand, there is a continuum between OCD-like and psychotic-like symptoms (Abdellaoui et al., 2008). On the other hand, the developmental periods of childhood and early adolescence may be too early to differentiate between these symptoms. This inconsistency will need to be addressed in future research. In a large sample of youth, this study adopted a dimensional approach to the study of the functional alterations associated with early manifestations of TP. As such, this current study is the first of this kind. Our main results showed that the severity of TP was correlated with the functional connectivity between the right amygdala and lateral PFC, between the left amygdala and occipital cortex, and between the right NAcc and pPHG. Functional characterization analyses revealed that these functional connectivities are involved in cognition (cognitive control), face detection (social cognition), and motivation/reward. Future studies on the neural mechanisms involved in the development of TP will need to adopt longitudinal designs, with follow-ups until late adolescence and early adulthood. In these investigations, attention will need to be paid to the potential commonalities and differences between OCD-like and psychotic-like symptoms.

Footnotes

Acknowledgments

S.P. is holder of the Eli Lilly Canada Chair on schizophrenia research; J.R.D. is holder of a postdoctoral fellowship from the Canadian Institutes of Health Research (MFE-181885).

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This study received no external funding.

Supplementary Material

Supplementary Table S1

Supplementary Table S2

Supplementary Table S3

Supplementary Table S4

Supplementary Table S5

References

Abdellaoui

, Bartels

, Hudziak

, et al. Genetic influences on thought problems in 7-year-olds: A twin-study of genetic, environmental and rater effects. Twin Res Hum Genet, 2008; 11(6):571–578; doi: 10.1375/twin.11.6.571

Achenbach

, Rescorla

. Manual for the ASEBA Adult Forms and Profiles, Burlington: University of Vermont, Research Center for Children, Youth, and Families, 2003.

Achenbach

, Rescorla

. Manual for ASEBA School-Age Forms & Profiles. ASEBA; 2001.

Alexander

, Escalera

, Ai

, et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data, 2017; 4(1):170181; doi: 10.1038/sdata.2017.181

Alves-Pinto

, Rus

, Reess

, et al. Altered reward-related effective connectivity in obsessive-compulsive disorder: An FMRI Study. J Psychiatry Neurosci, 2019; 44(6):395–406; doi: 10.1503/jpn.180195

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th edition. American Psychiatric Association; 2013; doi: 10.1176/appi.books.9780890425596.dsm05

Anagnostopoulos

, Korlou

, Sakellariou

, et al. Comorbid psychopathology and clinical symptomatology in children and adolescents with obsessive-compulsive disorder. Psychiatriki, 2016; 27(1):27–36.

Anticevic

, Tang

, Cho

, et al. Amygdala connectivity differs among chronic, early course, and individuals at risk for developing schizophrenia. Schizophr Bull, 2014; 40(5):1105–1116; doi: 10.1093/schbul/sbt165

Axelsson

SFA

, Horst

, Horiguchi

, et al. Flexible versus fixed spatial self-ordered response sequencing: Effects of inactivation and neurochemical modulation of ventrolateral prefrontal cortex. J Neurosci, 2021; 41(34):7246–7258; doi: 10.1523/JNEUROSCI.0227-21.2021

10.

Bastin

, Committeri

, Kahane

, et al. Timing of posterior parahippocampal gyrus activity reveals multiple scene processing stages. Hum Brain Mapp, 2013; 34(6):1357–1370; doi: 10.1002/hbm.21515

11.

Becker

, Mihov

, Scheele

, et al. Fear processing and social networking in the absence of a functional amygdala. Biol Psychiatry, 2012; 72(1):70–77; doi: 10.1016/j.biopsych.2011.11.024

12.

Behzadi

, Restom

, Liau

, et al. A component based noise correction method (CompCor) for BOLD and perfusion based FMRI. Neuroimage, 2007; 37(1):90–101; doi: 10.1016/j.neuroimage.2007.04.042

13.

Birmaher

, Brent

, Chiappetta

, et al. Psychometric properties of the Screen for Child Anxiety Related Emotional Disorders (SCARED): A replication study. J Am Acad Child Adolesc Psychiatry, 1999; 38(10):1230–1236; doi: 10.1097/00004583-199910000-00011

14.

Bliss-Moreau

, Bauman

, Amaral

. Neonatal amygdala lesions result in globally blunted affect in adult rhesus macaques. Behav Neurosci, 2011; 125(6):848–858; doi: 10.1037/a0025757

15.

Boulenguez

, Rawlins

JNP

, Chauveau

, et al. Modulation of dopamine release in the nucleus accumbens by 5-HT1B agonists: Involvement of the hippocampo-accumbens pathway. Neuropharmacology, 1996; 35(11):1521–1529; doi: 10.1016/S0028-3908(96)00099-8

16.

Bourque

, Spechler

, Potvin

, et al. Functional neuroimaging predictors of self-reported psychotic symptoms in adolescents. Am J Psychiatry, 2017; 174(6):566–575; doi: 10.1176/appi.ajp.2017.16080897

17.

Burgmans

, van Boxtel

MPJ

, van den Berg

KEM

, et al. The posterior parahippocampal gyrus is preferentially affected in age-related memory decline. Neurobiol Aging, 2011; 32(9):1572–1578; doi: 10.1016/j.neurobiolaging.2009.09.008

18.

Castelhano

, Lima

, Teixeira

, et al. The effects of tryptamine psychedelics in the brain: A meta-analysis of functional and review of molecular imaging studies. Front Pharmacol, 2021; 12:739053; doi: 10.3389/fphar.2021.739053

19.

Ceaser

, Barch

. Striatal activity is associated with deficits of cognitive control and aberrant salience for patients with schizophrenia. Front Hum Neurosci, 2016; 9:687; doi: 10.3389/fnhum.2015.00687

20.

Chai

, Castañón

, Öngür

, et al. Anticorrelations in resting state networks without global signal regression. Neuroimage, 2012; 59(2):1420–1428; doi: 10.1016/j.neuroimage.2011.08.048

21.

Cheng

, Mills

, Dominguez

, et al. Characterizing the impact of adversity, abuse, and neglect on adolescent amygdala resting-state functional connectivity. Dev Cogn Neurosci, 2021; 47:100894; doi: 10.1016/j.dcn.2020.100894

22.

da Cunha-Bang

, Fisher

, Hjordt

, et al. Amygdala reactivity to fearful faces correlates positively with impulsive aggression. Soc Neurosci, 2019; 14(2):162–172; doi: 10.1080/17470919.2017.1421262

23.

Dannlowski

, Kugel

, Huber

, et al. Childhood maltreatment is associated with an automatic negative emotion processing bias in the amygdala. Hum Brain Mapp, 2013; 34(11):2899–2909; doi: 10.1002/hbm.22112

24.

Deyo

, Langdon

. Cognitive correlates of ‘formal thought disorder’ in a non-clinical sample with elevated schizotypal traits. Psychiatry Res, 2021; 302:113971; doi: 10.1016/j.psychres.2021.113971

25.

Dixon

. Cognitive control, emotional value, and the lateral prefrontal cortex. Front Psychol, 2015; 6:758; doi: 10.3389/fpsyg.2015.00758

26.

Domínguez-Borràs

, Moyne

, Saj

, et al. Impaired emotional biases in visual attention after bilateral amygdala lesion. Neuropsychologia, 2020; 137:107292; doi: 10.1016/j.neuropsychologia.2019.107292

27.

Dugré

, Bitar

, Dumais

, et al. Limbic hyperactivity in response to emotionally neutral stimuli in schizophrenia: A neuroimaging meta-analysis of the hypervigilant mind. Am J Psychiatry, 2019; 176(12):1021–1029; doi: 10.1176/appi.ajp.2019.19030247

28.

Dugré

, Potvin

. Clarifying the role of cortico-cortical and amygdalo-cortical brain dysconnectivity associated with conduct problems. Neuroimage Clin, 2023; 37:103346; doi: 10.1016/j.nicl.2023.103346

29.

Dukart

, Holiga

, Rullmann

, et al. JuSpace: A tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps. Hum Brain Mapp, 2021; 42(3):555–566; doi: 10.1002/hbm.25244

30.

Duncan

. The Multiple-Demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends Cogn Sci, 2010; 14(4):172–179; doi: 10.1016/j.tics.2010.01.004

31.

Eickhoff

, Bzdok

, Laird

, et al. Activation likelihood estimation meta-analysis revisited. Neuroimage, 2012; 59(3):2349–2361; doi: 10.1016/j.neuroimage.2011.09.017

32.

Eisen

, Rasmussen

. Obsessive compulsive disorder with psychotic features. J Clin Psychiatry, 1993; 54(10):373–379.

33.

Eklund

, Nichols

, Knutsson

. Cluster failure: Why fmri inferences for spatial extent have inflated false-positive rates. Proc Natl Acad Sci U S A, 2016; 113(28):7900–7905; doi: 10.1073/pnas.1602413113

34.

Farley

, McWey

, Ledermann

. Thought problems and aggression over time among youth in foster care. Child Youth Care Forum, 2022; 51(4):795–810; doi: 10.1007/s10566-021-09652-y

35.

Ferreira

, Yücel

, Dawson

, et al. Investigating the role of anticipatory reward and habit strength in obsessive-compulsive disorder. CNS Spectr, 2017; 22(3):295–304; doi: 10.1017/S1092852916000535

36.

First

, Wakefield

. Diagnostic criteria as dysfunction indicators: Bridging the chasm between the definition of mental disorder and diagnostic criteria for specific disorders. Can J Psychiatry, 2013; 58(12):663–669; doi: 10.1177/070674371305801203

37.

Floresco

. The nucleus accumbens: An interface between cognition, emotion, and action. Annu Rev Psychol, 2015; 66(1):25–52; doi: 10.1146/annurev-psych-010213-115159

38.

Fujino

, Tei

, Jankowski

, et al. Role of spontaneous brain activity in explicit and implicit aspects of cognitive flexibility under socially conflicting situations: A resting-state FMRI study using fractional amplitude of low-frequency fluctuations. Neuroscience, 2017; 367:60–71; doi: 10.1016/j.neuroscience.2017.10.025

39.

Gale

, Shields

, Ishizawa

, et al. Reward and reinforcement activity in the nucleus accumbens during learning. Front Behav Neurosci, 2014; 8:114; doi: 10.3389/fnbeh.2014.00114

40.

Griffin

, Fletcher

. Predictive processing, source monitoring, and psychosis. Annu Rev Clin Psychol, 2017; 13(1):265–289; doi: 10.1146/annurev-clinpsy-032816-045145

41.

Gupta

, Duff

, Tranel

. Bilateral amygdala damage impairs the acquisition and use of common ground in social interaction. Neuropsychology, 2011; 25(2):137–146; doi: 10.1037/a0021123

42.

Hansen

, Shafiei

, Markello

, et al. Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nat Neurosci, 2022; 25(11):1569–1581; doi: 10.1038/s41593-022-01186-3

43.

Hart

, Lewine

RRJ

. Rethinking thought disorder. Schizophr Bull, 2017; 43(3):514–522; doi: 10.1093/schbul/sbx003

44.

Hikida

, Morita

, Macpherson

. Neural mechanisms of the nucleus accumbens circuit in reward and aversive learning. Neurosci Res, 2016; 108:1–5; doi: 10.1016/j.neures.2016.01.004

45.

Howes

, Nour

. Dopamine and the aberrant salience hypothesis of schizophrenia. World Psychiatry, 2016; 15(1):3–4; doi: 10.1002/wps.20276

46.

Iritani

, Tohgi

, Arai

, et al. Immunohistochemical study of the serotonergic neuronal system in an animal model of the mood disorder. Exp Neurol, 2006; 201(1):60–65; doi: 10.1016/j.expneurol.2006.03.013

47.

Ivarsson

, Melin

, Wallin

. Categorical and dimensional aspects of co-morbidity in Obsessive-Compulsive Disorder (OCD). Eur Child Adolesc Psychiatry, 2008; 17(1):20–31; doi: 10.1007/s00787-007-0626-z

48.

Jones

, Graff-Radford

. Executive dysfunction and the prefrontal cortex. Continuum, 2021; 27(6):1586–1601; doi: 10.1212/CON.0000000000001009

49.

Kapur

. Psychosis as a state of aberrant salience: A framework linking biology, phenomenology, and pharmacology in schizophrenia. Am J Psychiatry, 2003; 160(1):13–23; doi: 10.1176/appi.ajp.160.1.13

50.

Kärkkäinen

, Laukkanen

, Haukijärvi

, et al. Lower [3H]Citalopram binding in brain areas related to social cognition in alcoholics. Alcohol Alcohol, 2015; 50(1):46–50; doi: 10.1093/alcalc/agu074

51.

Katayama

, Jodo

, Suzuki

, et al. Phencyclidine affects firing activity of basolateral amygdala neurons related to social behavior in rats. Neuroscience, 2009; 159(1):335–343; doi: 10.1016/j.neuroscience.2009.01.002

52.

Kätzel

, Wolff

, Bygrave

, et al. Hippocampal hyperactivity as a druggable circuit-level origin of aberrant salience in schizophrenia. Front Pharmacol, 2020; 11:486811; doi: 10.3389/fphar.2020.486811

53.

Kim

, Sul

. On the relationship between the social brain, social connectedness, and wellbeing. Front Psychiatry, 2023; 14:1112438; doi: 10.3389/fpsyt.2023.1112438

54.

Klimes-Dougan

, Desjardins

, James

, et al. The development of thought problems: A longitudinal family risk study of offspring of bipolar, unipolar, and well parents. Dev Psychopathol, 2013; 25(4 Pt 1):1079–1091; doi: 10.1017/S0954579413000382

55.

, Chi

, Lee

, et al. Altered functional connectivity of the nucleus accumbens and amygdala in cyber addiction: A resting state functional magnetic resonance imaging study. Clin Psychopharmacol Neurosci, 2023; 21(2):304–312; doi: 10.9758/cpn.2023.21.2.304

56.

Koob

. Brain stress systems in the amygdala and addiction. Brain Res, 2009; 1293:61–75; doi: 10.1016/j.brainres.2009.03.038

57.

Kotov

, Cicero

, Conway

, et al. The Hierarchical Taxonomy of Psychopathology (HiTOP) in psychiatric practice and research. Psychol Med, 2022; 52(9):1666–1678; doi: 10.1017/S0033291722001301

58.

Lengua

, Sadowski

, Friedrich

, et al. Rationally and empirically derived dimensions of children’s symptomatology: Expert ratings and confirmatory factor analyses of the CBCL. J Consult Clin Psychol, 2001; 69(4):683–698.

59.

Lewis

, Florio

, Punzo

, et al. The brain’s reward system in health and disease. Adv Exp Med Biol, 2021; 1344:57–69; doi: 10.1007/978-3-030-81147-1_4

60.

, Zhang

, Huang

, et al. Improving motivation through real-time FMRI-based self-regulation of the nucleus accumbens. Neuropsychology, 2018; 32(6):764–776; doi: 10.1037/neu0000425

61.

Lieberman

, Cunningham

. Type I and Type II error concerns in FMRI research: Re-balancing the scale. Soc Cogn Affect Neurosci, 2009; 4(4):423–428; doi: 10.1093/scan/nsp052

62.

Lisi

, Raballo

, Ribolsi

, et al. Aberrant salience in adolescents is related to indicators of psychopathology that are relevant in the prodromal phases of psychosis. Early Interv Psychiatry, 2021; 15(4):856–864; doi: 10.1111/eip.13022

63.

Liu

, Cao

, Li

, et al. Abnormal resting-state functional connectivity in patients with obsessive-compulsive disorder: A systematic review and meta-analysis. Neurosci Biobehav Rev, 2022; 135:104574; doi: 10.1016/j.neubiorev.2022.104574

64.

Lukow

, Kiemes

, Kempton

, et al. Neural correlates of emotional processing in psychosis risk and onset—A systematic review and meta-analysis of FMRI studies. Neurosci Biobehav Rev, 2021; 128:780–788; doi: 10.1016/j.neubiorev.2021.03.010

65.

Mackinley

, Chan

, Ke

, et al. Linguistic determinants of formal thought disorder in first episode psychosis. Early Interv Psychiatry, 2021; 15(2):344–351; doi: 10.1111/eip.12948

66.

Maher

, Ekstrom

, Holt

, et al. The core brain region for face processing in schizophrenia lacks face selectivity. Schizophr Bull, 2016; 42(3):666–674; doi: 10.1093/schbul/sbv140

67.

Maher

, Ekstrom

, Ongur

, et al. Functional disconnection between the visual cortex and right fusiform face area in schizophrenia. Schizophr Res, 2019; 209:72–79; doi: 10.1016/j.schres.2019.05.016

68.

Makris

, Oscar-Berman

, Jaffin

, et al. Decreased volume of the brain reward system in alcoholism. Biol Psychiatry, 2008; 64(3):192–202; doi: 10.1016/j.biopsych.2008.01.018

69.

McBain

, Norton

, Chen

. A female advantage in basic face recognition is absent in schizophrenia. Psychiatry Res, 2010; 177(1–2):12–17; doi: 10.1016/j.psychres.2009.02.005

70.

McDonald

. Functional neuroanatomy of monoaminergic systems in the basolateral nuclear complex of the amygdala: Neuronal targets, receptors, and circuits. J Neurosci Res, 2023; 101(9):1409–1432; doi: 10.1002/jnr.25201

71.

McGrath

, Zaveri

, Collins

, et al. High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter. Sci Rep, 2022; 12(1):18778; doi: 10.1038/s41598-022-21543-3

72.

Mendes

, Oligschläger

, Lauckner

, et al. A functional connectome phenotyping dataset including cognitive state and personality measures. Sci Data, 2019; 6(1):180307; doi: 10.1038/sdata.2018.307

73.

Mikels

, Reuter-Lorenz

. Affective working memory: An integrative psychological construct. Perspect Psychol Sci, 2019; 14(4):543–559; doi: 10.1177/1745691619837597

74.

Minor

, Marggraf

, Davis

, et al. Conceptual disorganization weakens links in cognitive pathways: Disentangling neurocognition, social cognition, and metacognition in schizophrenia. Schizophr Res, 2015; 169(1–3):153–158; doi: 10.1016/j.schres.2015.09.026

75.

Modinos

, Allen

, Zugman

, et al. Neural circuitry of novelty salience processing in psychosis risk: Association with clinical outcome. Schizophr Bull, 2020; 46(3):670–679; doi: 10.1093/schbul/sbz089

76.

, Eickhoff

, Caspers

, et al. Confound removal and normalization in practice: A neuroimaging based sex prediction case study. Mach Learn Knowl Discov Databases, 2021:3–18; doi: 10.1007/978-3-030-67670-4_1

77.

Okamura

, Murahashi

, Umeda

, et al. Obsessive-compulsive disorder with psychotic features: Is it a clinical entity? Healthcare, 2022; 10(10):1910; doi: 10.3390/healthcare10101910

78.

Pinkham

, Liu

, Lu

, et al. Amygdala hyperactivity at rest in paranoid individuals with schizophrenia. Am J Psychiatry, 2015; 172(8):784–792; doi: 10.1176/appi.ajp.2014.14081000

79.

Power

, Mitra

, Laumann

, et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 2014; 84:320–341; doi: 10.1016/j.neuroimage.2013.08.048

80.

Quarto

, Paparella

, De Tullio

, et al. Familial risk and a genome-wide supported DRD2 variant for schizophrenia predict lateral prefrontal-amygdala effective connectivity during emotion processing. Schizophr Bull, 2018; 44(4):834–843; doi: 10.1093/schbul/sbx128

81.

Rapan

, Niu

, Zhao

, et al. Receptor architecture of macaque and human early visual areas: Not equal, but comparable. Brain Struct Funct, 2022; 227(4):1247–1263; doi: 10.1007/s00429-021-02437-y

82.

Raslau

, Mark

, Klein

, et al. Memory part 2: The role of the medial temporal lobe. AJNR Am J Neuroradiol, 2015; 36(5):846–849; doi: 10.3174/ajnr.A4169

83.

Richardson

, Strange

, Dolan

. Encoding of emotional memories depends on amygdala and hippocampus and their interactions. Nat Neurosci, 2004; 7(3):278–285; doi: 10.1038/nn1190

84.

Rocha

, Osório

, Crippa

JAS

, et al. Serotonergic hallucinogens and recognition of facial emotion expressions: A systematic review of the literature. Ther Adv Psychopharmacol, 2019; 9:2045125319845774; doi: 10.1177/2045125319845774

85.

Rolls

. The cingulate cortex and limbic systems for action, emotion, and memory. Handb Clin Neurol, 2019; 166:23–37; doi: 10.1016/B978-0-444-64196-0.00002-9

86.

Rolls

, Huang

C-C

, Lin

C-P

, et al. Automated anatomical labelling atlas 3. Neuroimage, 2020; 206:116189; doi: 10.1016/j.neuroimage.2019.116189

87.

Salcedo

, Rizvi

, Freeman

, et al. Diagnostic efficiency of the CBCL thought problems and DSM-oriented psychotic symptoms scales for pediatric psychotic symptoms. Eur Child Adolesc Psychiatry, 2018; 27(11):1491–1498; doi: 10.1007/s00787-018-1140-1

88.

Salo

, Yarkoni

, Nichols

, et al. NiMARE: Neuroimaging meta-analysis research environment. NeuroLibre, 2022; 1(1):7; doi: 10.5545/8/neurolibre.00007

89.

Schellhaas

, Schmahl

, Bublatzky

. Social threat and safety learning in individuals with adverse childhood experiences: Electrocortical evidence on face processing, recognition, and working memory. Eur J Psychotraumatol, 2022; 13(2):2135195; doi: 10.1080/20008066.2022.2135195

90.

Schüller

, Kohl

, Dembek

, et al. Internal capsule/nucleus accumbens deep brain stimulation increases impulsive decision making in obsessive-compulsive disorder. Biol Psychiatry Cogn Neurosci Neuroimaging, 2022; 8(3):281–289; doi: 10.1016/j.bpsc.2022.10.005

91.

Shi

, Davis

. Visual pathways involved in fear conditioning measured with fear-potentiated startle: Behavioral and anatomic studies. J Neurosci, 2001; 21(24):9844–9855; doi: 10.1523/JNEUROSCI.21-24-09844.2001

92.

Shi

, Wang

, Xiao

, et al. Potential therapeutic mechanism of deep brain stimulation of the nucleus accumbens in obsessive-compulsive disorder. Front Cell Neurosci, 2023; 16:1057887; doi: 10.3389/fncel.2022.1057887

93.

Sigurdsson

, Duvarci

. Hippocampal-prefrontal interactions in cognition, behavior and psychiatric disease. Front Syst Neurosci, 2016; 9:190; doi: 10.3389/fnsys.2015.00190

94.

Simeonova

, Nguyen

, Walker

. Psychosis risk screening in clinical high-risk adolescents: A longitudinal investigation using the child behavior checklist. Schizophr Res, 2014; 159(1):7–13; doi: 10.1016/j.schres.2014.07.046

95.

Snoek

, Miletić

, Scholte

. How to control for confounds in decoding analyses of neuroimaging data. Neuroimage, 2019; 184:741–760; doi: 10.1016/j.neuroimage.2018.09.074

96.

Stein

, Buckenmayer

, Brosch

, et al. Dimensions of formal thought disorder and their relation to gray- and white matter brain structure in affective and psychotic disorders. Schizophr Bull, 2022; 48(4):902–911; doi: 10.1093/schbul/sbac002

97.

Stein

, Palk

, Kendler

. What is a mental disorder? An exemplar-focused approach. Psychol Med, 2021; 51(6):894–901; doi: 10.1017/S0033291721001185

98.

Sumner

, Meyer

, Carruthers

, et al. Assessing the dimensionality of scores derived from the revised formal thought disorder self-report scale in schizotypy. PLoS One, 2022; 17(12):e0278841; doi: 10.1371/journal.pone.0278841

99.

Thompson

, Visser

, Schiffman

, et al. Preliminary evidence supporting the practice of psychosis-risk screening within an inpatient psychiatric setting serving adolescents. Psychiatry Res, 2022; 307:114322; doi: 10.1016/j.psychres.2021.114322

100.

Thorsen

, Hagland

, Radua

, et al. Emotional processing in obsessive-compulsive disorder: A systematic review and meta-analysis of 25 functional neuroimaging studies. Biol Psychiatry Cogn Neurosci Neuroimaging, 2018; 3(6):563–571; doi: 10.1016/j.bpsc.2018.01.009

101.

Townsend

, Torrisi

, Lieberman

, et al. Frontal-amygdala connectivity alterations during emotion downregulation in bipolar I disorder. Biol Psychiatry, 2013; 73(2):127–135; doi: 10.1016/j.biopsych.2012.06.030

102.

Tsolias

, Medalla

. Muscarinic acetylcholine receptor localization on distinct excitatory and inhibitory neurons within the ACC and LPFC of the rhesus monkey. Front Neural Circuits, 2022; 15:795325; doi: 10.3389/fncir.2021.795325

103.

Wainberg

, Jacobs

, Voineskos

, et al. Neurobiological, familial and genetic risk factors for dimensional psychopathology in the adolescent brain cognitive development study. Mol Psychiatry, 2022; 27(6):2731–2741; doi: 10.1038/s41380-022-01522-w

104.

Wakefield

. Diagnostic issues and controversies in DSM-5: Return of the false positives problem. Annu Rev Clin Psychol, 2016; 12(1):105–132; doi: 10.1146/annurev-clinpsy-032814-112800

105.

Walter

, Suenderhauf

, Smieskova

, et al. Altered insular function during aberrant salience processing in relation to the severity of psychotic symptoms. Front Psychiatry, 2016; 7:189; doi: 10.3389/fpsyt.2016.00189

106.

Wang

, Smolker

, Brown

, et al. Association of γ-aminobutyric acid and glutamate/glutamine in the lateral prefrontal cortex with patterns of intrinsic functional connectivity in adults. Brain Struct Funct, 2020; 225(7):1903–1919; doi: 10.1007/s00429-020-02084-9

107.

Wendt

, Jankowsky

, Schroeders

, et al. Mapping established psychopathology scales onto the Hierarchical Taxonomy of Psychopathology (HiTOP). Personal Ment Health, 2022; 17(2):117–134; doi: 10.1002/pmh.1566

108.

Whitfield-Gabrieli

, Nieto-Castanon

. Conn : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks. Brain Connectivity, 2012; 2(3):125–141; doi: 10.1089/brain.2012.0073

109.

Winton-Brown

, Fusar-Poli

, Ungless

, et al. Dopaminergic basis of salience dysregulation in psychosis. Trends Neurosci, 2014; 37(2):85–94; doi: 10.1016/j.tins.2013.11.003

110.

Woo

C-W

, Krishnan

, Wager

. Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations. NeuroImage, 2014; 91:412–419; doi: 10.1016/j.neuroimage.2013.12.058

111.

Yang

, Wang

J-Z

. From structure to behavior in basolateral amygdala-hippocampus circuits. Front Neural Circuits, 2017; 11:86; doi: 10.3389/fncir.2017.00086

112.

Yang

, Xie

, Wang

. Effects of sleep duration on neurocognitive development in early adolescents in the USA: A propensity score matched, longitudinal, observational study. Lancet Child Adolesc Health, 2022; 6(10):705–712; doi: 10.1016/S2352-4642(22)00188-2

113.

Zelazo

, Anderson

, Richler

, et al. II. NIH toolbox cognition battery (CB): Measuring executive function and attention. Monogr Soc Res Child Dev, 2013; 78(4):16–33; doi: 10.1111/mono.12032

114.

Zhang

, Yang

, Fan

, et al. Abnormal amygdala subregional-sensorimotor connectivity correlates with positive symptom in schizophrenia. Neuroimage Clin, 2020; 26:102218; doi: 10.1016/j.nicl.2020.102218

115.

Zimmerman

, Morgan

, Stanton

. The severity of psychiatric disorders. World Psychiatry, 2018; 17(3):258–275; doi: 10.1002/wps.20569

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.03 MB