Resting State Functional Connectivity as a Marker of Internalizing Disorder Onset in High-Risk Youth

doi:10.21203/rs.3.rs-2002564/v1

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Resting State Functional Connectivity as a Marker of Internalizing Disorder Onset in High-Risk Youth

https://doi.org/10.21203/rs.3.rs-2002564/v1

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While research has linked alterations in functional connectivity of resting state brain networks including the default mode (DMN), cognitive control (CCN), and salience networks (SN) to depression and anxiety, little research has examined whether these alterations may be premorbid vulnerabilities. This study examined resting state functional connectivity (RSFC) of the CCN, DMN, and SN as neural markers of risk for developing a first lifetime onset of a depressive or anxiety disorder in adolescents at high familial risk for these disorders. At baseline, 135 participants aged 11–17 completed resting-state functional magnetic resonance imaging (rs-fMRI), measures of internalizing symptoms, and diagnostic interviews to assess history of depressive and anxiety disorders. Diagnostic assessments were completed again at 9- or 18-month follow-up for 112 participants. At baseline, increased CCN connectivity to areas of the visual network, and decreased connectivity between the SN and the precentral gyrus, predicted an increased likelihood of an internalizing disorder onset at follow-up. Altered connectivity between these regions may represent a pre-morbid risk factor for developing a clinically significant onset of an internalizing disorder. Results may have implications for understanding the neural bases of internalizing disorder episodes and for early identification and prevention efforts.

Rates of depression and anxiety increase sharply in adolescence, especially in females where instances of first episode depression or anxiety are approximately double that of males¹. A single episode of depression or anxiety can lead to enduring negative psychosocial consequences, such as suicidality, addiction, and unemployment². In addition, an episode can leave the individual vulnerable to further episodes, at which point depression and anxiety can become chronic, treatment-resistant disorders³.

Recent neuroimaging research in internalizing disorders (i.e., depression and anxiety) has aimed to find neural markers of risk in youth, but there is a lack of longitudinal research predicting first episode onset in high-risk youth^4,5. This study aims to investigate if functional connectivity between core intrinsic networks previously implicated in internalizing psychopathology could act as a neural marker of premorbid risk for internalizing disorder onset in a longitudinal cohort of youth at high risk, by virtue of having a parent with a history of these disorders. Resting state functional connectivity (RSFC) is assessed via the temporal correlation of blood oxygenation level dependent (BOLD) signal in anatomically separate brain regions while the participant rests passively in the scanner ⁶. Several reviews and meta-analyses have found altered RSFC in depression and anxiety, particularly in the DMN, CCN, and SN resting state networks^7–10. Determining potential neural markers of the onset of internalizing disorders in high-risk youth is important for developing and implementing early intervention strategies to reduce illness severity or even prevent onset of the disorder.

Familial risk for depression and anxiety. Anxiety and depression both run in families, with genetic heredity of risk factors, as well as parenting and environmental effects interacting in high familial risk (subsequently “high-risk”) populations to increase risk for developing the disorder to 3–5 times that of low-risk populations^11,12. High-risk adolescents show similar cognitive risk factors as depressed adults, with negative cognitive styles and biases in attention, interpretation, and memory of emotionally-valenced experiences¹¹. Depression and anxiety both have similar hallmark characteristics such as increased rumination and negative self-thought, cognitive biases, and cognitive processing problems, as well as emotional control deficits^8,10. These cognitive features have been linked to alterations in within-network functional connectivity of three intrinsic connectivity networks, the default mode network (DMN), the cognitive control network (CCN), and the salience network (SN)^7–10.

Intrinsic resting-state brain networks and internalizing psychopathology. The DMN is comprised of the posterior cingulate cortex (PCC)/precuneus, middle and lateral parietal and temporal cortices, and medial prefrontal cortex (PFC)¹³. The DMN is de-activated during tasks requiring external attention, relative to passive rest, and is involved in self-referential processing, internal thought, and mental simulation, core processes that are altered in depression and anxiety¹³. In a study of individuals at high-familial risk for major depressive disorder (MDD) it was found that high- versus low-risk participants had increased DMN RSFC¹⁴. Increased RSFC of the DMN in patients with MDD has been shown to reflect increased negative self-thought and maladaptive rumination^15,16. However, some studies have found decreased functional connectivity in individuals with high-trait anxiety, subthreshold depression, and MDD, indicating that while DMN RSFC is related to depression and anxiety the direction of effects are inconsistent^17–19.

The cognitive control network (CCN) is a series of brain regions that are functionally coupled during performance of working memory, inhibitory control, selective attention, cognitive flexibility, and fluid reasoning²⁰. It includes the anterior cingulate cortex (ACC)/pre-supplementary motor area, inferior frontal junction, anterior insular (AI) cortex, dorsolateral prefrontal cortex (DLPFC), dorsal pre-motor cortex, and posterior parietal cortex²¹. Altered functional connectivity of cognitive control regions such as the DLPFC and regions in the posterior parietal cortex are associated with cognitive control impairments and cognitive biases in depression^22,23. The direction of reported RSFC abnormalities in the CCN in depression and anxiety is variable²⁴. Some studies in high-risk populations as well as those with subthreshold depression or elevated trait anxiety have found reduced CCN RSFC^{17,22,25−27}. Others found increased CCN connectivity in those with versus without anxiety disorders²⁸. Still more have found both increased and decreased RSFC within the CCN when looking at connectivity between discrete regions^24,29. Overall, while alterations of CCN RSFC are seen consistently across depression and anxiety, the direction is highly variable.

The salience network (SN) includes the amygdala, dorsal striatum, dorsal and subgenual ACC, and the AI and is involved in detection and processing of salient stimuli both internally and externally as well as recruiting necessary functional networks in response to said stimuli³⁰. One of the primary roles of the SN is to control switching from the DMN to the CCN based on salience of external vs internal stimuli and thus may play a role in inter-network dysfunction in depression and anxiety where there can be a bias towards internal stimuli or excessive attention to perceived threatening stimuli^16,28−30. In adolescent females increased within-SN RSFC was associated with greater subclinical anxiety and depressive symptoms³¹. Individuals with depressive and anxiety disorders, relative to those without, show impairments in the salience processing of emotional and threatening stimuli, often overattributing salience to neutral stimuli³⁰. The SN is one of the least studied RSFC networks in depression and anxiety and as such the direction of alterations in RSFC in depression and anxiety is still to be elucidated.

While FC of these networks has been linked to subclinical depressive and anxiety symptoms and may differentiate those with versus without clinically significant depression or anxiety. However, the direction of association is inconsistent, and it is unknown if alterations in these networks are a result of the disorders or exist prior to the onset of the disorder and confer vulnerability to their development^4,5. As such these networks’ RSFC may act as premorbid markers of the onset of depression and anxiety in at-risk youth. Testing this possibility requires a sample of adolescents at high risk for these disorders but who have yet to develop them prior to neuroimaging.

Overview and objectives. There is limited longitudinal research in adolescents at high familial risk for depression and anxiety predicting episode onset as a function of functional connectivity in the DMN, CCN, and SN resting state networks⁵. This study therefore examined whole brain RSFC of the CCN, DMN, and SN as possible neural markers of risk for first lifetime onsets of internalizing disorders in adolescents at high familial risk. This will aid in determining if the altered RSFC seen in depressed or anxious adults represents a premorbid vulnerability factor for depression and anxiety in high-risk teens. We hypothesize that altered functional connectivity between the CCN, DMN, SN, and the rest of the brain will longitudinally predict first lifetime onset of a diagnosable episode of an internalizing disorder. It is important to understand alterations in RSFC in different stages of illness, particularly in pre-morbid at-risk youth, as associations of these alterations with internalizing disorders may vary depending on disorder stage.

Participants

Participants were 135 adolescents and pre-adolescents ages 11–17. This age range was chosen as it precedes and includes the developmental period in which rates of depression and anxiety in youth increase, and female risk doubles compared to males¹. Participants were recruited based on parental history of either a depressive or anxiety disorder, without having experienced an episode themselves. This risk-enriched design increased the likelihood of participants developing depressive or anxiety disorder episodes during the study period. Additional exclusion criteria included contraindications for MRI, history of traumatic brain injury, and presence of a developmental disorder. This study was approved, and data was collected in accordance with the institutional Research and Ethics Board, the Conjoint Health Research Ethics Board (CHREB) at the University of Calgary. All methods were performed in accordance with the relevant guidelines and regulations, and informed consent was obtained from participants or their legal guardian, minors additionally provided assent.

Demographic characteristics are shown in Table 1. Adolescents were 60% female (n = 81), with a mean age of 13.70 (standard deviation = 1.53), and 66.7% identified as white/Caucasian (n = 90). Parents were 92.6% female (n = 125), with a mean age of 43.15 (standard deviation = 6.07), and 78.5% identified as white/Caucasian (n = 106). Most parents were married or in a common-law relationship (71.9%, n = 97), with 50.4% having a college/ university education or higher (n = 68), and the median annual household income was approximately $87,500. Participant demographics are approximately representative of demographics in the local catchment area.

Table 1

Sociodemographic Characteristics and Key Variables
Characteristic	Parent	Child
Mean Age (SD)	43.15 (6.07)	13.70 (1.53)
Sex Female Male	125 10	81 54
Current Diagnosis Depression Only Anxiety Only Both	48 7 73	11 2 11
Ethnicity Caucasian/White Asian/Pacific Islander Indigenous Hispanic/Latino Arabic Multiracial Other Did not disclose	106 12 4 7 2 3 1 0	90 9 4 3 2 24 2 1
Marital Status Single/Never Married Married/Common Law Divorced/Separated	9 97 29
Annual Household Income (CAD) < $25,000 $25–50,000 $50–75,000 $75–100,000 $100–125,000 $125–150,000 $150–175,000 < $175,000 Did not disclose	8 22 14 24 20 11 9 21 6
Education Some High School High School Diploma Some College/University Trade School College Degree or more Other	6 6 32 22 68 3
Abbreviation: SD, standard deviation; CAD, Canadian dollar.

Baseline rs-fMRI data was available from 135 participants, and 130 completed baseline internalizing symptom measures. Out of that 130, 88 completed diagnostic interviews at 9-month follows-ups to assess for DSM-V criteria for an internalizing disorder³². 77 completed 18-month follow-up diagnostic interviews. 53 have completed both 9-month and 18-month follow-ups. In total, 112 have completed at least one follow-up diagnostic interview as well as baseline MRI scans. Our effective sample size is therefore 112 adolescents. Little’s Missing Completely at Random (MCAR) test including all variables in the current study was non-significant (Chi-Square = 8.534, df = 5, p = .129), indicating data are missing at random and participants with missing data do not differ significantly from those with complete data on any study variable³³. Participants with complete data were therefore included in analyses (n = 112).

Procedure

Parent and participant history of depressive or anxiety disorders was assessed via structured diagnostic interview. Only those youth with a parent with a history of major depressive disorder (MDD), persistent depressive disorder (PDD), bipolar disorder (BD), or general anxiety disorder (GAD) or social anxiety disorder (SAD), but who have not yet met DSM-V criteria for any of these illnesses themselves were included (see Table 1 for parent diagnoses)³². Adolescents completed self-reports of internalizing (i.e., global depressive and anxiety) symptoms at baseline³⁴. Adolescents also completed rs-fMRI resting state scans at baseline. Adolescent participants completed structured diagnostic interviews at 9- and 18-month follow-ups.

Measures

Mini-International Neuropsychiatric Interview (MINI). The MINI was used to assess parents’ lifetime history of either MDD, PDD, BD, GAD or SAD in parents³⁵. The MINI-Kid, validated for ages 6–18, was used to confirm no lifetime history of clinically significant internalizing disorders within the youth participants³⁶. These brief structured diagnostic interviews show good interrater reliability and respectively converge well with the Structured Clinical Interview for the DSM and the Kiddie Schedule for Affective Disorders (KSADS)^32,37. The MINI and MINI-Kid were administered by a single interviewer who underwent training by a licensed clinical psychologist. This training included didactic lessons on diagnostic interviewing for mental health, role play exercises, and observation with feedback of the interviewer by the psychologist of interviews with participants. At follow-up assessments, participants are asked about the period since their last visit to the lab. Youth were categorized as having an internalizing disorder over the follow-up period if they met criteria for MDD, PDD, BD, SAD, panic disorder, GAD, or separation anxiety.

Youth Self-Report. The YSR is a 112-item self-report questionnaire for children and adolescents ages 11–18 to assess internalizing and externalizing symptoms³⁴. It is a widely used, well validated, self-report measure of depression and anxiety in youth³⁴. Behaviors are rated on a 3-point scale: 0-Not true, 1-Somewhat or sometimes true and 2-Very true or often true, based on the preceding 6-months. The current study focuses on the Internalizing Problems subscale which assesses global depressive and anxiety symptoms³². This scale shows good internal consistency and respective convergence with interview-based symptom measures of anxiety and depression in adolescents aged 11 to 18^38,39. Cronbach alpha was.778 for our sample.

Image Acquisition And Preprocessing

Neuroimaging data were acquired on a GE 3T 750 MRI. A 12 channel radiofrequency head coil was used with foam padding to restrain head movement. High-resolution T1-weighted 3D BRAVO anatomical volumes [repetition time (TR) = 7.90 ms, echo time (TE) = 3.06 ms, field of view (FOV) = 24 cm, flip angle = 15°, 180 sagittal slices, 1 mm isotropic voxels], and T2* functional images (2D Gradient Echo Planar Imaging) [TR = 2000 ms, TE = 30 ms, FOV = 25.6 cm, flip angle = 75°, 210 axial slices, 4mm isotropic voxels] were acquired. Rs-fMRI data was acquired in one 7-minute scanning session during which participants were told to relax, keep their eyes open and on the fixation cross, and let their mind wander.

Image preprocessing and denoising was done using CONN, a MATLAB (R2020b; MathWorks) toolbox⁴⁰. Functional images were co-registered and resampled to a reference image (first scan of session) using SPM12’s realign & unwarp procedure followed by slice timing correction using SPM12’s slice-timing correction (STC) procedure. Functional data was normalized to MNI space and resampled to 2mm isotropic voxels using SPM12’s unified segmentation and normalization procedure. Outlier identification flagged outliers with framewise displacement greater than 0.2mm⁴¹. Framewise displacement was computed by estimating the largest displacement of six control points that are placed at the centre of each side of a bounding box (140x180x115mm). Linear regression of confounds included white matter (5 components), cerebrospinal fluid (5 component), motion realignment (12 components; 6 head motion parameters and first order temporal derivatives), scrubbing of any identified outlier scans. Data underwent linear detrending and were bandpass filtered (0.01–0.08) to remove low frequency noise and high frequency physiological noise. Data was smoothed to a Gaussian kernel of 8mm FWHM. All 112 participants had usable baseline rs-fMRI data, based on having a full 7-minute scan (210 slices) and corresponding structural data (180 slices), and a framewise displacement of less than 0.2mm.

Statistical Analysis

Seeds

This study calculated FC as seed to whole-brain temporal correlations using seed regions from the CONN toolbox network regions of interest, which were developed from an independent components analysis of the Human Connectome Project (n = 497)⁴⁰. Center coordinates were chosen, and 5 mm spherical seeds generated around centers. The DMN (Fig. 1A) region was the PCC (center coordinates: 1, -61, 38). CCN (Fig. 1B) regions included the bilateral LPFC (L, center coordinates: -43, 33, 28; R, center coordinates: 41, 38, 30). SN (Fig. 1C) regions included the bilateral AI (L, center coordinates: -44, 13, 1; R, center coordinates: 47, 14, 0).

Functional Connectivity Analyses

First level analysis was done to perform spatial statistical analysis of each participant using CONN. Functional connectivity was determined using a general linear model to determine significant BOLD signal bivariate correlations with respect to time between each seed and each voxel. CONN converted the resulting correlation coefficients to z-scores using Fisher’s Z transformation.

Analyses subsequently consisted of multiple regressions in CONN. The outcome variable was differences in seed to voxel resting state functional connectivity between participants. The predictor variable was whether participants had experienced a DSM-V internalizing disorder (i.e., depressive or anxiety disorder) at either 9-month or 18-month follow-up, based on Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criteria³² Five separate seeds (one DMN; two CCN; two SN) were tested. We report results with baseline global internalizing symptoms and history of internalizing disorders as covariates to determine whether any effects of RSFC on disorder onset are over and above baseline subclinical symptoms and clinical history. This is important given that subthreshold symptoms as well as a history of internalizing disorders are two of the most robust predictors of the onset of future diagnosable illnesses⁴². These analyses are therefore highly conservative. Each seed predictor was analyzed separately with age, sex, baseline internalizing symptoms and history of internalizing disorders as covariates.

All functional connectivity whole brain maps at the group level were thresholded using a cluster defining threshold (i.e., voxel-wise threshold) of p < .001 and a false discovery rate cluster (FDRc) correction of p < .05, as implemented in CONN. Using this correction method based on Gaussian random field theory, the FDR corrected significance of each cluster is obtained and can be further corrected (e.g., with a Bonferroni correction) for the number of seeds tested. We thus performed a Bonferroni correction based on five seed regions, therefore correcting for 5 comparisons in total. This resulted in a cluster size p-threshold of .01 (.05/5 = .01).

Study Participants

Descriptive statistics and bivariate correlations. Table 2 includes descriptive statistics and bivariate correlations between the model covariates. Out of our effective sample size of 112, 11 developed only a depressive disorder (i.e., MDD, PDD, or BD), 2 developed only an anxiety disorder (GAD, SAD, panic disorder, separation anxiety), and 11 developed comorbid depressive and anxiety disorders (see Table 3 for breakdown). Thus, a total of 24 out of 112 youth developed a DSM-V internalizing disorder at follow-up. As seen in Table 2, females showed increased baseline depressive symptoms and an increased likelihood of an internalizing disorder onset at follow-up.

Table 2

Descriptive Statistics and Bivariate Correlations of Covariates in Models
	Mean	SD	1	2	3	4
Sex (1)
Age (2)	13.65	1.53	.168
Baseline YSR Internalizing (3)	13.09	9.71	− .284**	.013
Baseline Diagnosis (4)			− .140	.178*	.414***
Follow-Up Diagnosis (5)			− .197*	.057	.359***	.294**
Abbreviation: SD, standard deviation.
*. Correlation significant at the p ≤ .05 (two-tailed).
**. Correlation significant at the p ≤ .01 (two-tailed).
***. Correlation significant at the p ≤ .001 (two-tailed).

Table 3

Parent and Child Internalizing Disorders Diagnoses
	Parent		Child
	Current	Lifetime	Baseline	9-Month	18-Month
Major Depressive Disorder	17	103	6	8	18
Persistent Depressive Disorder	8	22	0	0	1
Bipolar Disorder	-	15	1	1	1
Generalized Anxiety Disorder	27	38	1	3	6
Social Anxiety Disorder	10	26	4	4	2
Panic Disorder	19	44	3	8	3
Separation Anxiety	-	-	0	0	0
Breakdown of current child diagnoses and parent current and lifetime diagnoses. There is overlap between diagnoses.

Predicting Diagnosable Onset Of Dsm-v Internalizing Disorder

Findings from regression models are reported in Table 4. Out of the 5 seeds examined, 3 had significant clusters when comparing subjects who did develop an internalizing disorder at follow-up to those who did not. Adjusting for age, sex, baseline internalizing symptoms, and internalizing disorder history, youth who experienced an internalizing disorder onset over follow-up showed increased left LPFC (CCN seed) functional connectivity to the right posterior division of the temporal fusiform cortex (Fig. 2a). Youth who experienced an internalizing disorder onset also show increased right LPFC (CCN seed) functional connectivity to 5 clusters centering around the left lingual gyrus, the left occipital fusiform gyrus, the right calcarine cortex (cluster included intracalcarine cortex, and the supracalcarine cortex), the left intracalcarine cortex, and the left supracalcarine cortex (Fig. 2b). Youth who experienced an internalizing disorder onset additionally showed decreased functional connectivity of the left anterior insula (SN seed) with a cluster centered in the right precentral gyrus (Fig. 2c).

Table 4

Regressions Predicting Internalizing Disorder Onset adjusting for Baseline Internalizing Symptoms
Seed Region	MNI Peak Coordinates (x, y, z)	Brain Region	Cluster Size	Increase/Decrease Connectivity	Cluster Level p (FDRc)
Left LPFC	(24, -40, -18)	Right Fusiform Cortex	54	Increase	.0035
Right LPFC	(-10, -82, 00)	Left Lingual Gyrus	104	Increase	.00001
	(-20, -76, -18)	Left Fusiform Gyrus	86	Increase	.000034
	(2, -78, 8)	Right Intracalcarine Cortex	46	Increase	.003102
	(-8, -72, 12)	Left Intracalcarine Cortex	43	Increase	.003538
	(-22, -60, 14)	Left Supracalcarine Cortex	37	Increase	.006745
Left Anterior Insula	(52, 00, 46)	Right Precentral Gyrus	45	Decrease	.008637
Abbreviation: MNI, Montreal Neurological Institute; FDR, False Discovery Rate; LPFC, lateral prefrontal cortex.

Resting state functional connectivity from the PCC and right AI seeds was not significantly different between participants who developed a disorder and those who did not. .

Adolescence is a period of high neuroplasticity and vulnerability to increased onset of mental illness that can become chronic in the developing brain⁴³. Identifying physiological indicators of depression and anxiety before disorder onset in adolescence will inform knowledge of the pathophysiology of these disorders and may be key to early identification and prevention of these disorders that make up a significant portion of economic burden worldwide, particularly in adolescents⁴³.

This study found that the increased functional connectivity between the LPFC and regions of the visual network at baseline, including the fusiform gyrus, lingual gyrus, and areas of the calcarine cortex, differentiated participants who developed an internalizing disorder at follow-up compared to those who did not. The fusiform gyrus has been implicated in task-based fMRI studies, with some finding increased activity in the region during a stress-inducing task in healthy adolescents and others finding decreased connectivity between the fusiform and cognitive control regions in MDD and SAD patients compared to controls^44–46. Structural studies looking at cortical thickness, surface, area and gyrification have also implicated the fusiform cortex, showing an overall decrease in structural metrics in MDD, people with cognitive vulnerability to depression, and high-risk offspring^47–51. In addition to being key areas of the visual network the fusiform, lingual, and calcarine are also involved in attention, emotion processing, social cognition, and memory, often working to facilitate multi-modal integration and inhibitory control^52–56.

While this study initially focused on seed regions representing hubs of the CCN, DMN, and SN, we found more distributed results in line with current research on the role of sensory regions in depression and anxiety. For example, patients with treatment-resistant depression had lower functional connectivity between the left and right fusiform than those with treatment-responsive depression, and lower functional connectivity between the bilateral calcarine cortices than both treatment-responsive and healthy controls⁵². In further analysis the RSFC between the bilateral calcarine cortices discriminated between depressed and non-depressed subjects ⁵². In adults with GAD a study found decreased resting-state connectivity between the right DLPFC, and the left lingual gyrus compared to healthy controls⁵⁷, while another found adolescents with MDD had higher positive functional connectivity between the left DLPFC and left lingual gyrus than controls⁵⁸. Despite ample evidence of both the LPFC and these visual regions in depression and anxiety, few studies have found a behavioural correlation explaining the potential role of functional connectivity between these regions in depression and anxiety. In healthy adults resting state activation of the lingual gyrus was positively correlated with attachment avoidance scores and the study proposed it was related to the role of the lingual gyrus in memory formation and retrieval of representations of others⁵⁹. A study looking at resting state networks in participants who reported experiencing versus high versus low stress found that the high stress participants had increased activity in the visual network at rest, reflecting the hypervigilance and alertness often seen stressed individuals⁶⁰. A graph theory-based study found that patients with SAD exhibited increased connectivity between the frontolimbic and sensory/perceptual processing circuits and argued that it represented top-down control over enhanced emotional perception in SAD⁶¹. Together these studies indicate that even at rest the visual system is involved in depression and anxiety potentially due to its roles in attention and memory. However, the current study cannot delineate whether the LPFC is influencing the visual system or vice versa.

This study also found decreased functional connectivity between the left anterior insula and the right precentral gyrus. The anterior insula is an important resting state network hub and is a key node in the SN³⁰. Functional connectivity studies found that patients with affective disorders and their relatives had decreased activation in the insula at rest^62–67 and during social and emotional tasks^68,69. Gray matter reductions and hypogyrification have also been found in the insula of MDD patients and relatives^70–73.The precentral gyrus is home to the primary motor cortex and is involved in voluntary movement, response inhibition, emotion driven action, working memory, and Theory of Mind^51,74. Structural studies of MDD and high-risk subjects showed cortical thinning of the precentral gyrus and reduced gray matter volume^{49–51, 75,76}. In adult MDD, decreased resting state functional connectivity has been found in the precentral gyrus^{66,71,77−81} as well as in response to reward⁸². MDD patients with a history of suicide attempt and teens with MDD and high suicidal ideation (compared with MDD with low suicidal ideation and healthy controls) had decreased precentral gyrus activation in a verbal fluency and social task respectively^68,83. This association of altered precentral gyrus activity and suicidality is believed to be related to the precentral gyri’s role in response inhibition and impulsivity^68,83,84. Similarly altered precentral gyrus activation in GAD patients in response to emotional distractors during a working memory task could be related to abnormal response inhibition as well as the working memory aspect of the precentral gyrus⁸⁵. Taken together this indicates a deficit in detecting and responding to salient information in internalizing disorders which may related to future onsets of internalizing disorders.

Identifying RSFC anomalies as a potential biomarker for the development of depression and anxiety may facilitate early identification of youth at risk as well as interventions to prevent first lifetime episodes of these disorders. These can include behavioral therapies as there is some evidence that they can influence network connectivity, as well as transcranial magnetic stimulation (TMS) therapies that may increase activity in underactive brain regions or inhibit overactive areas involved in the CCN^16,86.

Our study had several strengths, such as a longitudinal design and a risk-enriched population. Our study also had limitations, such as the lack of a formal control or low-risk group to act as a comparison. While this is not necessary to test our aims as participants who did not develop a diagnosable onset act as a comparison group against those who did develop an internalizing disorder, it is unknown whether results would generalize to youth without a family history of internalizing disorders. Our sample size was also modest, and while a sufficient number of youth developed internalizing disorders for predictive purposes, we may have been underpowered to detect smaller effects. Moreover, the modest number of youth with an onset of a disorder may not generalize to other populations.

This research expands the current literature on longitudinal research in adolescents at high familial risk for developing depressive or anxiety disorders. Despite adolescence being a critical period for the development of anxiety and depression, particularly in youth with a family history of the disorders, there is limited longitudinal research in the area⁵. This study found that increased connectivity between the CCN and the visual network, and decreased connectivity between the SN and the motor cortex may represent a pre-morbid risk factor for developing an onset of a depressive or anxiety disorder. Results overall have implications for identifying those at risk for an internalizing disorder in high-risk populations as well as identifying targets for treatment and prevention.

Data Availability Statement: All data reported in this study are available via contacting the corresponding author.

Acknowledgements: The authors thank the Canadian Institutes for Health Research, National Sciences and Engineering Research Council of Canada, Social Sciences and Humanities Research Council, and the Brain and Behaviour Research Foundation for funding.

Author Contributions: DSK and SB provided conception, design, and methodology of project. MP analyzed data and wrote manuscript. All authors reviewed manuscript.

Hankin, B. L. et al. Development of Depression From Preadolescence to Young Adulthood: Emerging Gender Differences in a 10-Year Longitudinal Study. Journal of Abnormal Psychology Psychological Association, Inc 107, 128–140 (1998).
Fergusson, D. M. & Woodward, L. J. Mental Health, Educational, and Social Role Outcomes of Adolescents With Depression. Arch Gen Psychiatry 59, 225–321 (2002).
Burcusa, S. L. & Iacono, W. G. Risk for recurrence in depression. Clinical Psychology Review vol. 27 959–985 Preprint at https://doi.org/10.1016/j.cpr.2007.02.005 (2007).
Wang, L., Hermens, D. F., Hickie, I. B. & Lagopoulos, J. A systematic review of resting-state functional-MRI studies in major depression. J Affect Disord 142, 6–12 (2012).
Toenders, Y. J. et al. Neuroimaging predictors of onset and course of depression in childhood and adolescence: A systematic review of longitudinal studies. Dev Cogn Neurosci 39, (2019).
Bijsterbosch, J., Smith, S. & Beckmann, C. An Introduction to Resting State fMRI Functional Connectivity. (Oxford University Press, 2017).
Kerestes, R., Davey, C. G., Stephanou, K., Whittle, S. & Harrison, B. J. Functional brain imaging studies of youth depression: A systematic review. Neuroimage Clin 4, 209–231 (2014).
Xu, J. et al. Anxious brain networks: A coordinate-based activation likelihood estimation meta-analysis of resting-state functional connectivity studies in anxiety. Neurosci Biobehav Rev 96, 21–30 (2019).
Hamilton, J. P., Chen, M. C. & Gotlib, I. H. Neural systems approaches to understanding major depressive disorder: An intrinsic functional organization perspective. Neurobiology of Disease vol. 52 4–11 Preprint at https://doi.org/10.1016/j.nbd.2012.01.015 (2013).
Mulders, P. C., van Eijndhoven, P. F., Schene, A. H., Beckmann, C. F. & Tendolkar, I. Resting-state functional connectivity in major depressive disorder: A review. Neuroscience and Biobehavioral Reviews vol. 56 330–344 Preprint at https://doi.org/10.1016/j.neubiorev.2015.07.014 (2015).
Gotlib, I. H., Joormann, J. & Foland-Ross, L. C. Understanding Familial Risk for Depression: A 25-Year Perspective. Perspectives on Psychological Science 9, 94–108 (2014).
Jacobs, R. H., Orr, J. L., Gowins, J. R., Forbes, E. E. & Langenecker, S. A. Biomarkers of intergenerational risk for depression: A review of mechanisms in longitudinal high-risk (LHR) studies. Journal of Affective Disorders vol. 175 494–506 Preprint at https://doi.org/10.1016/j.jad.2015.01.038 (2015).
Buckner, R. L., Andrews-Hanna, J. R. & Schacter, D. L. The brain’s default network: Anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences vol. 1124 1–38 Preprint at https://doi.org/10.1196/annals.1440.011 (2008).
Posner, J. et al. Increased Default Mode Network Connectivity in Individuals at High Familial Risk for Depression. Neuropsychopharmacology 41, 1759–1767 (2016).
Messina, I., Bianco, F., Cusinato, M., Calvo, V. & Sambin, M. Abnormal default system functioning in depression: Implications for emotion regulation. Frontiers in Psychology vol. 7 Preprint at https://doi.org/10.3389/fpsyg.2016.00858 (2016).
de Kwaasteniet, B. P. et al. Decreased resting-state connectivity between neurocognitive networks in treatment resistant depression. Front Psychiatry 6, (2015).
Modi, S., Kumar, M., Kumar, P. & Khushu, S. Aberrant functional connectivity of resting state networks associated with trait anxiety. Psychiatry Res Neuroimaging 234, 25–34 (2015).
Yokoyama, S. et al. Effects of behavioral activation on default mode network connectivity in subthreshold depression: A preliminary resting-state fMRI study. J Affect Disord 227, 156–163 (2018).
Yang, R. et al. Decreased functional connectivity to posterior cingulate cortex in major depressive disorder. Psychiatry Res Neuroimaging 255, 15–23 (2016).
Diamond, A. Executive functions. Annual Review of Psychology vol. 64 135–168 Preprint at https://doi.org/10.1146/annurev-psych-113011-143750 (2013).
Cole, M. W. & Schneider, W. The cognitive control network: Integrated cortical regions with dissociable functions. Neuroimage 37, 343–360 (2007).
Clasen, P. C., Beevers, C. G., Mumford, J. A. & Schnyer, D. M. Cognitive control network connectivity in adolescent women with and without a parental history of depression. Dev Cogn Neurosci 7, 13–22 (2014).
Menon, V. Large-scale brain networks and psychopathology: A unifying triple network model. Trends in Cognitive Sciences vol. 15 483–506 Preprint at https://doi.org/10.1016/j.tics.2011.08.003 (2011).
Manoliu, A. et al. Insular dysfunction within the salience network is associated with severity of symptoms and aberrant inter-network connectivity in major depressive disorder. Front Hum Neurosci 7, (2014).
Hwang, J. W. et al. Subthreshold depression is associated with impaired restingstate functional connectivity of the cognitive control network. Transl Psychiatry 5, (2015).
Chai, X. J. et al. Altered Intrinsic Functional Brain Architecture in Children at Familial Risk of Major Depression. Biol Psychiatry 80, 849–858 (2016).
Shapero, B. G. et al. Neural markers of depression risk predict the onset of depression. Psychiatry Res Neuroimaging 285, 31–39 (2019).
Liao, W. et al. Selective aberrant functional connectivity of resting state networks in social anxiety disorder. Neuroimage 52, 1549–1558 (2010).
Pan, F. et al. Disrupted intrinsic functional connectivity of the cognitive control network underlies disease severity and executive dysfunction in first-episode, treatment-naive adolescent depression. J Affect Disord 264, 455–463 (2020).
Sridharan, D., Levitin, D. J. & Menon, V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci U S A 105, (2008).
Ordaz, S. J. et al. Ruminative brooding is associated with salience network coherence in early pubertal youth. Soc Cogn Affect Neurosci 12, 298–310 (2017).
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. (American Psychiatric Association, 2013). doi:10.1176/appi.books.9780890425596.
Little, R. J. A. A Test of Missing Completely at Random for Multivariate Data with Missing Values. J Am Stat Assoc 83, 1198–1202 (1988).
Achenbach, T. M. & Rescorla, L. Manual for the ASEBA School-Age Forms & Profiles. (University of Vermont, Research Center for Children, Youth, & Families, 2001).
Sheehan, D. v et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): The Development and Validation of a Structured Diagnostic Psychiatric Interview for DSM-IV and ICD-10. J Clin Psychiatry 59, 22–33 (1998).
Sheehan, D. v. et al. Reliability and validity of the mini international neuropsychiatric interview for children and adolescents (MINI-KID). Journal of Clinical Psychiatry 71, 313–326 (2010).
Kaufman, J. et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): Initial Reliability and Validity Data. J Am Acad Child Adolesc Psychiatry 36, 980–988 (1997).
Ebesutani, C., Bernstein, A., Martinez, J. I., Chorpita, B. F. & Weisz, J. R. The Youth Self Report: Applicability and Validity Across Younger and Older Youths. Journal of Clinical Child & Adolescent Psychology 40, 338–346 (2011).
van Lang, N. D. J., Ferdinand, R. F., Oldehinkel, A. J., Ormel, J. & Verhulst, F. C. Concurrent validity of the DSM-IV scales Affective Problems and Anxiety Problems of the Youth Self-Report. Behaviour Research and Therapy 43, 1485–1494 (2005).
Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks. Brain Connect 2, 125–141 (2012).
Yan, C. G. et al. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76, 183–201 (2013).
Cuijpers, P. & Smit, F. Subthreshold depression as a risk indicator for major depressive disorder: a systematic review of prospective studies. Acta Psychiatr Scand 109, 325–331 (2004).
Lee, F. S. et al. Adolescent mental health-Opportunity and obligation: Emerging neuroscience offers hope for treatments. Science (1979) 346, 547–549 (2014).
Sacu, S. et al. Effective connectivity during face processing in major depression – distinguishing markers of pathology, risk, and resilience. Psychol Med 1–13 (2022) doi:10.1017/S0033291722000824.
Duval, E. R., Javanbakht, A. & Liberzon, I. Neural circuits in anxiety and stress disorders: A focused review. Ther Clin Risk Manag 11, 115–126 (2015).
Corr, R. et al. Neural mechanisms of acute stress and trait anxiety in adolescents. Neuroimage Clin 29, 102543 (2021).
Kemp, J. V. A., Bernier, E., Lebel, C. & Kopala-Sibley, D. C. Associations Between Parental Mood and Anxiety Psychopathology and Offspring Brain Structure: A Scoping Review. Clin Child Fam Psychol Rev 25, 222–247 (2022).
Chen, C. et al. Decreased Cortical Folding of the Fusiform Gyrus and Its Hypoconnectivity with Sensorimotor Areas in Major Depressive Disorder. J Affect Disord 295, 657–664 (2021).
Maggioni, E. et al. Common and different neural markers in major depression and anxiety disorders: A pilot structural magnetic resonance imaging study. Psychiatry Res Neuroimaging 290, 42–50 (2019).
Papmeyer, M. et al. Cortical Thickness in Individuals at High Familial Risk of Mood Disorders as They Develop Major Depressive Disorder. Biol Psychiatry 78, 58–66 (2015).
Zhang, X. et al. Gray matter volume abnormalities in individuals with cognitive vulnerability to depression: A voxel-based morphometry study. J Affect Disord 136, 443–452 (2012).
Guo, W. et al. Decreased Interhemispheric Coordination in Treatment-Resistant Depression: A Resting-State fMRI Study. PLoS One 8, e71368 (2013).
Wang, Y., Yang, S., Sun, W., Shi, Y. & Duan, H. Altered functional interaction hub between affective network and cognitive control network in patients with major depressive disorder. Behavioural Brain Research 298, 301–309 (2016).
Ye, T. et al. Altered functional connectivity of the dorsolateral prefrontal cortex in first-episode patients with major depressive disorder. Eur J Radiol 81, 4035–4040 (2012).
Chen, H. et al. More optimal but less regulated dorsal and ventral visual networks in patients with major depressive disorder. J Psychiatr Res 110, 172–178 (2019).
Wang, L. et al. Interhemispheric Functional Connectivity and Its Relationships with Clinical Characteristics in Major Depressive Disorder: A Resting State fMRI Study. PLoS ONE 8, (2013).
Wang, W. et al. Aberrant regional neural fluctuations and functional connectivity in generalized anxiety disorder revealed by resting-state functional magnetic resonance imaging. Neurosci Lett 624, 78–84 (2016).
Chi, S. et al. Prospective study on resting state functional connectivity in adolescents with major depressive disorder after antidepressant treatment. J Psychiatr Res 142, 369–375 (2021).
Deng, M., Zhang, X., Bi, X. & Gao, C. Neural basis underlying the trait of attachment anxiety and avoidance revealed by the amplitude of low-frequency fluctuations and resting-state functional connectivity. BMC Neurosci 22, (2021).
Soares, J. M., Sampaio, A., Ferreira, L. M., Santos, N. C. & Marques, P. Stress Impact on Resting State Brain Networks. PLoS One 8, 66500 (2013).
Yang, X. et al. Network analysis reveals disrupted functional brain circuitry in drug-naive social anxiety disorder. Neuroimage 190, 213–223 (2019).
Qiu, C. et al. Analysis of Altered Baseline Brain Activity in Drug-Naive Adult Patients with Social Anxiety Disorder Using Resting-State Functional MRI. Psychiatry Investig 12, 372–380 (2015).
Wang, L., Yu, L., Wu, F., Wu, H. & Wang, J. Altered whole brain functional connectivity pattern homogeneity in medication-free major depressive disorder. J Affect Disord 253, 18–25 (2019).
Shan, X. et al. Shared and distinct homotopic connectivity changes in melancholic and non-melancholic depression. J Affect Disord 287, 268–275 (2021).
Liu, Z. et al. Decreased regional homogeneity in insula and cerebellum: A resting-state fMRI study in patients with major depression and subjects at high risk for major depression. Psychiatry Res Neuroimaging 182, 211–215 (2010).
Zhuo, C. et al. Common and distinct global functional connectivity density alterations in drug-naïve patients with first-episode major depressive disorder with and without auditory verbal hallucination. Prog Neuropsychopharmacol Biol Psychiatry 96, (2020).
Li, G., Liu, P., Zhang, A., Andari, E. & Zhang, K.-R. A resting state fMRI study of major depressive disorder with and without anxiety. Psychiatry Res 315, 114697 (2022).
Harms, M. B. et al. Adolescent suicide attempts and ideation are linked to brain function during peer interactions. Psychiatry Res Neuroimaging 289, 1–9 (2019).
Watters, A. J. et al. Profiling risk for depressive disorder by circuit, behavior and self-report measures of emotion function. J Affect Disord 227, 595–602 (2018).
Lai, C. H. & Wu, Y. te. Frontal-insula gray matter deficits in first-episode medication-naïve patients with major depressive disorder. J Affect Disord 160, 74–79 (2014).
Zhuo, C. et al. Different spatial patterns of brain atrophy and global functional connectivity impairments in major depressive disorder. Brain Imaging Behav 11, 1678–1689 (2017).
Opel, N. et al. Differing brain structural correlates of familial and environmental risk for major depressive disorder revealed by a combined VBM/pattern recognition approach. Psychol Med 46, 277–290 (2016).
Zhang, Y. et al. Decreased gyrification in major depressive disorder. Neuroreport 20, 378–380 (2009).
Gordon, E. M. et al. Three Distinct Sets of Connector Hubs Integrate Human Brain Function. Cell Rep 24, 1687–1695.e4 (2018).
Li, J. et al. Anatomical brain difference of subthreshold depression in young and middle-aged individuals. Neuroimage Clin 14, 546–551 (2017).
Bos, M. G. N., Peters, S., van de Kamp, F. C., Crone, E. A. & Tamnes, C. K. Emerging depression in adolescence coincides with accelerated frontal cortical thinning. (2018) doi:10.1111/jcpp.12895.
Song, Y., Huang, C., Zhong, Y., Wang, X. & Tao, G. Abnormal Reginal Homogeneity in Left Anterior Cingulum Cortex and Precentral Gyrus as a Potential Neuroimaging Biomarker for First-Episode Major Depressive Disorder. Front Psychiatry 13, (2022).
Yang, F. et al. Decreased Intrinsic Functional Connectivity in First-Episode, Drug-Naive Adolescents With Generalized Anxiety Disorder. Front Hum Neurosci 12, (2019).
Jiang, X. et al. Common and distinct neural activities in frontoparietal network in first-episode bipolar disorder and major depressive disorder: Preliminary findings from a follow-up resting state fMRI study. J Affect Disord 260, 653–659 (2020).
Geng, J. et al. Altered regional homogeneity in patients with somatic depression: A resting-state fMRI study. J Affect Disord 246, 498–505 (2019).
Liu, P. et al. Brain functional alterations in MDD patients with somatic symptoms: A resting-state fMRI study. J Affect Disord 295, 788–796 (2021).
Oh, H., Lee, J., Patriquin, M. A., Oldham, J. & Salas, R. Reward Processing in Psychiatric Inpatients With Depression. Biol Psychiatry Cogn Neurosci Neuroimaging (2021) doi:10.1016/J.BPSC.2021.05.010.
Tsujii, N. et al. Reduced left precentral regional responses in patients with major depressive disorder and history of suicide attempts. (2017) doi:10.1371/journal.pone.0175249.
Song, H. et al. Cortical Volumetric Correlates of Childhood Trauma, Anxiety, and Impulsivity in Bipolar Disorder. Psychiatry Investig 17, 627–635 (2020).
Moon, C. M., Sundaram, T., Choi, N. G. & Jeong, G. W. Working memory dysfunction associated with brain functional deficits and cellular metabolic changes in patients with generalized anxiety disorder. Psychiatry Res Neuroimaging 254, 137–144 (2016).
Fox, M. D., Liu, H. & Pascual-Leone, A. Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity. Neuroimage 66, 151–160 (2013).

No competing interests reported.

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Resting State Functional Connectivity as a Marker of Internalizing Disorder Onset in High-Risk Youth

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Introduction

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Measures

Image Acquisition And Preprocessing

Statistical Analysis

Seeds

Functional Connectivity Analyses

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Study Participants

Predicting Diagnosable Onset Of Dsm-v Internalizing Disorder

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